Activity Log

This log tracks what happens in the wiki — sources ingested, pages created, experiments run, questions explored.

[2026-06-07] publish | E-E-A-T article — public version of the new glossary page

Drafted articles/e-e-a-t-ai-search.md — “E-E-A-T in 2026: From Ranking Signal to AI Citation Gate (and Which Numbers Are Real).” Public-article version of the glossary/e-e-a-t entry created earlier today, in the full publishing convention (canonical, byline, Related, Sources, JSON-LD).

Differentiated angle (vs the crowded generic “what is E-E-A-T” SERP owned by Google/Ahrefs/Semrush/SEJ): leads with the 2026 shift to a near-binary AI-citation gate + the honest vendor-vs-measured calibration (96% / 3× / <4%-DA flagged as vendor estimates; direction anchored to Chen et al. 2025). The “which numbers are real” hook is the unfair advantage the generic explainers can’t match.

Reciprocal back-links wired (article↔article): added to Related of articles/seo-stats-vendor-vs-measured and articles/can-you-trust-ai-overviews (both already discuss the E-E-A-T numbers). Article links back to glossary/e-e-a-t + seo/ai-visibility + zero-click-strategy. All 6 wikilinks verified resolving. Articles surface — no wiki index/llms.txt change.

[2026-06-07] lint | Spot-checked the retail-cases staleness flag — figures verified accurate

Followed up the lint’s one staleness flag on automation/ai-retail-ecommerce-cases. Verified the flagged figures against primary sources via WebSearch: Toolstation (5.5% search-based revenue, 10% CTR, no-results 2%→0.1%) confirmed (Google Cloud press, July 9 2025); Etsy (80× listings/theme, 5% SEO visits, 3% conversions) confirmed (Google Cloud / PYMNTS). Verdict: not stale — these are point-in-time published case results, correctly transcribed; they don’t decay like live metrics. Two real (minor) notes recorded on-page: the page is a snapshot of a growing Google Cloud compilation (coverage gap, not accuracy), and “Vertex AI” has since been rebranded “Gemini Enterprise Agent Platform.” Added an on-page freshness-check note + bumped updated to 2026-06-07 so future lints don’t re-flag it. No figure corrections needed.

[2026-06-07] create | E-E-A-T glossary page — closing the lint’s one real gap

Acted on the lint’s top finding. Created glossary/e-e-a-t (~1,400 words) — the load-bearing AI-search quality framework that was referenced across ~17 pages with no canonical definition. Covers: the four components (Experience/Expertise/Authoritativeness/Trustworthiness; Trust the most important), the 2026 shift from soft ranking signal to near-binary AI-citation gate, the four load-bearing signals (earned media, author-entity verification, Wikipedia, topical-authority depth), traditional-SEO-vs-AI-search contrast, and a build playbook.

Calibration baked in (consistent with the session’s vendor-vs-primary discipline): the 96% / 3× / <4%-DA figures are flagged vendor estimates, the direction is anchored to Chen et al. 2025 (preprint), with pointers to seo/zero-click-strategy § calibration + seo/seo-stats-vendor-vs-measured.

Back-links wired (bidirectional): reciprocal Related links added in seo/ai-visibility, seo/zero-click-strategy, seo/agentic-search-optimization, seo/geo-aeo-benchmarks-2026, glossary/geo-aeo, glossary/topical-authority (6 pages).

Index + llms.txt: glossary 68→69, total 151→152; catalog entry added. Changelog updated. (Domain-pages stat fix 35→45 from the lint folded in.)

[2026-06-07] lint | Full wiki health check (mechanical script + content-health agent)

Full lint: a Python pass over all 161 wiki/**/*.md (frontmatter, duplicate keys, wikilink resolution, orphans, index/catalog sync) + one read-only Explore agent for contradictions / staleness / concept gaps.

Clean:

Frontmatter: 100% compliant; no duplicate keys.
Broken links: none real. 74 unresolved-link candidates were all false positives — templates/ placeholders, illustrative examples (zettelkasten/obsidian demoing the wiki pattern, incl. an example sentence about avoiding ai-skill-levelling UK-spelling drift), .md-suffixed/wiki/-prefixed stylistic links, and historical log.md entries referencing since-removed pages.
Orphans: none real (the 6 flagged are templates/, standalone by design).
Contradictions: none. The session’s vendor-vs-primary calibration is consistent across all ~13 stat-citing pages; win-loss “moves win rate” framing consistent.
Catalog sync: perfect — every domain folder + glossary fully cataloged in index.md (marketing 18, seo 7, automation 19, competitor-analysis 1, glossary 68 — files = index entries).
Cross-links: SEO + competitor-analysis clusters well-wired.

Fixed:

index.md Stats: “Domain pages 35” → 45 (stale subtotal; actual marketing+seo+automation+competitor-analysis = 45). Total 151 / Glossary 68 verified correct (145 content + 6 framework pages); llms.txt section counts all verified accurate.

Flagged, not actioned (recommended next):

Concept gap — E-E-A-T has no dedicated glossary page despite being referenced across ~17 pages (it’s the load-bearing AI-search signal). Strongest growth item: create glossary/e-e-a-t.md (definition, the four 2026 signals, the binary-filter mechanism with the 96% figure vendor-calibrated).
Minor staleness — automation/ai-retail-ecommerce-cases.md (updated 2026-04-22): Toolstation CTR/revenue figures ~2 months old; spot-check before reuse.

[2026-06-07] question + publish | Niche keyword research → 2 new articles drafted

Ran the niche-hunter skill (scoped to 2 article opportunities) grounded in the wiki’s unique assets. Method: autocomplete sizing (expand.py), WebSearch SERP-shape, AEO judgment. Cut candidates: AI competitor monitoring (too narrow, n=5; CI space dominated), “prepare your store for AI shopping agents” (crowded by Shopify/BigCommerce/Google Cloud), GEO how-to (crowded + overlaps just-published seo-stats). Deliverable saved to articles/niche-research-2026-06-07.md.

2 articles drafted (status growing, full publishing convention; fact-checked against source wiki pages):

articles/ai-chatbot-mistakes-recovery.md — “When Your AI Gets It Wrong.” Evidence-based service recovery: forgiveness asymmetry (people forgive AI less), humor +47.8% on low-severity but the severity + focal-customer gates flip it, justice-matched apology tone, the structural escalate-early fix. Draws on glossary/ai-humor-forgiveness, glossary/review-response-strategy, glossary/agent-adoption-frictions, glossary/appropriate-reliance. Niche: mixed/fragmented SERP (shallow vendor listicles), wide AEO gap, high expertise fit.
articles/agentic-checkout-protocol-war.md — “The AI Checkout Protocol War.” Business-strategy framing of AP2/x402/UCP/Visa TAP: the four layers, the closed-loop-vs-open-coalition shakeout, OpenAI killing Instant Checkout, x402 traction (~$600M; AWS Bedrock May 7), the agent quality gap + legal void, and a merchant playbook. Draws on glossary/agent-payment-protocols, automation/agentic-commerce. Niche: mixed SERP (crypto/dev comparisons exist; business angle open). Risk flag: fast-moving — needs freshness re-check before publishing.

All wikilinks verified resolving. Articles are a separate surface from the wiki catalog (no index/llms.txt change).

[2026-06-07] publish | Promoted 3 articles to articles/, merged 1, reconciled an existing article

Promoted the four drafts into the articles/ publishing folder (the public layer with canonical: primores.org/articles/…, distinct from the wiki/ knowledge base). A dedup check against existing articles changed the plan (user-approved):

3 published to articles/ with the full publishing convention (canonical, byline, “Related articles”, JSON-LD schema, status growing, draft scaffolding removed): seo-stats-vendor-vs-measured, stop-asking-reps-why-you-lost, ai-eats-execution-not-strategy.
1 merged, not published — the “when-to-trust-ai-advice” draft nearly duplicated the already-published when-can-you-trust-ai.md. Instead of a competing page, its unique appropriate-reliance layer (calibrated-not-maximal; experts-under-rely/novices-over-rely; the expertise×stakes calibration; advisor selective-escalation) was folded into that article as a new section (“How much to rely on it”), with Related + Sources + schema date updated. Standalone draft deleted.
1 existing article reconciled — can-you-trust-ai-overviews.md stated the 96% E-E-A-T / 3× brand-mention / DA-<4% figures as fact. Relabeled them vendor estimates (matching the wiki calibration) and cross-linked the new seo-stats-vendor-vs-measured piece. Prevents two same-folder articles citing the same stats at different confidence levels.

All four new/edited articles cross-link each other and the relevant wiki pages. drafts/ now holds only the pre-existing _archived_ items. No wiki/ index/llms.txt/changelog change (articles are a separate surface from the wiki catalog).

[2026-06-07] publish | Drafted 4 articles from this session’s evidence work (PROMOTE)

Created four near-publishable article drafts in drafts/ (status seedling — need an editorial pass before publishing; not yet visitor-facing, so no index/changelog/llms.txt changes). Each extracts a distinctive, evidence-graded angle from material developed this session — the “what’s actually measured vs what’s asserted” voice that is the Primores differentiator.

drafts/seo-stats-vendor-vs-measured.md — “The SEO Stats Everyone Quotes Are Mostly Vendor Marketing.” Contrarian/shareable; Pew is the one primary anchor, the rest are vendor estimates. From seo/zero-click-strategy + seo/ai-visibility + glossary/share-of-model.
drafts/stop-asking-reps-why-you-lost.md — “Stop Asking Your Sales Reps Why You Lost the Deal.” B2B/sales; Endres 2023 + Nisbett & Wilson + choice-supportive memory; ask buyers, ask for episodes not reflection, triangulate. From glossary/win-loss-analysis.
drafts/when-to-trust-ai-advice.md — “When Should You Actually Trust AI’s Advice?” Evergreen; the two-question reliability test (validity × expertise-stakes); trust tracks confidence not accuracy. From questions/ai-as-personal-advisor + glossary/appropriate-reliance.
drafts/ai-eats-execution-not-strategy.md — “AI Eats Execution, Not Strategy.” Careers/strategy; the candidate-scoring matrix + the cumulative/relational refinement. From glossary/automation-eats-execution + questions/automation-eats-execution-next-domains.

All four carry a draft-status warning block and a Sources/“based on” pointer back to the wiki pages they extract. Editorial polish pass done same session (four parallel copy-edit agents, strict constraints preserving every number/citation/evidence-grade; tighter openings + closings, cut throat-clearing, varied rhythm). Final fact re-check done — every figure and citation cross-verified against the wiki source pages (all match). SEO pass done — keyword-front-loaded meta descriptions (~150–160 chars), enriched keyword tags, and one natural “generative engine optimization (GEO)” insertion. Remaining before publishing: upgrade status seedling→growing and move into wiki/.

[2026-06-07] ingest + update | Win-loss gaps — targeted follow-up: magnitude CONFIRMED-UNMEASURED, timing PARTIALLY ANCHORED (+ methodology-sharpening nuance)

Third deep-research pass (99 agents, ~3.1M tokens, 17 sources fetched, 56 claims, 25 verified 3-vote, 21 confirmed, 4 killed) — a narrow gap-confirmation pass on the two win-loss gaps the first pass flagged. The honest-negative result is itself the value.

GAP 2 (the 5–15pp magnitude): CONFIRMED-UNMEASURED. No peer-reviewed / quasi-experimental / DiD / longitudinal study measures win-loss-program adoption → win rate. Only outcome-linked source is Marcet 2011 (Win/Loss Reviews, Wiley) — single-company observational correlation-mining, below bar. Vendor “up to 50% win-rate lift” claim refuted 0-3. The page now states the magnitude is unmeasured (not just unflagged) and describes the clean study that would settle it (stepped-wedge / staggered-adoption across BUs).

GAP 1 (the 30–90 day timing): PARTIALLY ANCHORED (mechanism), partly contradicted. The mechanism for “interview soon” is solid — retrospective decision-reasons are reconstructed, not retrieved (Nisbett & Wilson 1977; Schwarz 2007 present-as-benchmark), and post-decision memory is choice-supportive (Mather, Shafir & Johnson 2000), which reflective review amplifies (Mather & Johnson 2000). But the precise window is unmeasured, and two findings cut against naive “fresher is always better”: forgetting-vs-telescoping is a tradeoff not a deadline (Bradburn/BLS), and a 10-yr panel explicitly refuted length-of-time decay (NLSY recanting study). Methodology-sharpening upshot (the real value-add): the dominant threat is buyer reconstruction / face-saving, not the calendar — interviewing sooner mostly yields a fresher rationalization. So triangulating stated reasons against contemporaneous behavioral evidence (Practice 7) matters more than interview speed, and reflective “why did you choose them” questions can worsen choice-supportive distortion (ask for concrete specifics instead).

glossary/win-loss-analysis updated: replaced the “two gaps flagged” bullet with a full “What two targeted follow-up searches found” subsection; added a timing caveat to Practice 6; strengthened the Honest-limits magnitude bullet; added 6 net-new sources to the academic Sources block (Schwarz 2007 / Stocké & Stark 2007, Mather-Shafir-Johnson 2000, Mather & Johnson 2000, Bradburn/BLS, NLSY recanting) + Marcet 2011 as a below-bar note. Do-not-cite caveats recorded: Clozd/Gartner “50%”, a GP-visit recall study, and an AOM “retrospective reports are fine” claim (all 0-3).

No new pages, no count change (151/68). Provenance: all web/academic (no raw/ to archive); citation fix noted on-page (voting-decay finding is Stocké & Stark 2007, not Schwarz). The win-loss page is now as anchored as the evidence honestly allows — mechanism + practices grounded, both magnitude and exact-timing honestly marked unmeasured.

[2026-06-07] lint | Full health check — consistency sweep on this session’s calibration language

Full lint (mechanical checks + one read-only Explore content-health agent across the wiki, focused on today’s 10 edited pages).

Findings:

Contradictions: none. The win-loss “moves win rate” framing and the SEO headline-number calibration are now consistent across all pages that reference them.
Self-introduced defect caught + fixed: the win-loss page had been calibrated in its body but still asserted “the only CI layer that reliably moves win rate” as bald fact in its frontmatter description and Key Takeaways (both quotable surfaces). Recalibrated both to the “most credited with / mechanism-anchored-magnitude-practitioner” framing.
Cross-page consistency fixed: three pages still restated the now-flagged SEO vendor numbers (64.82%/58%/96%/3×/75M) as primary fact — added compact “vendor estimate; Pew-anchored direction; see zero-click § calibration” pointers to seo/agentic-search-optimization, glossary/share-of-model, and competitor-analysis/overview (also softened the overview’s TL;DR + Layer 1 heading from “the only layer that reliably moves win rate”).
Orphans: none. Both grown questions have ≥3 inbound links beyond index/log.
Broken wikilinks: none (28 links in edited pages all resolve; new targets advisor-strategy/recognition-primed-decision/appropriate-reliance/jagged-frontier confirmed present).
Frontmatter: 100% compliant on all touched files.
Gap noted, not actioned: “after-action review / debrief” is referenced as the win-loss mechanism but has no dedicated glossary page. Judged not a forced gap — it’s covered as the mechanism within win-loss; a standalone page would be redundant unless a non-win-loss use-case emerges. Optional future growth item.

No contradictions or staleness requiring action beyond the consistency fixes above.

[2026-06-07] update | Developed two open questions 🌱→🌿 (personal-AI-advisor reliability framework + automation-eats-execution scoring matrix)

No external source — synthesis from existing wiki knowledge. Upgraded two long-static seedlings to growing.

questions/ai-as-personal-advisor 🌱→🌿. Added a “reliability framework” synthesis section that consolidates three previously-scattered threads into one actionable rule. The key add was the un-wired connection to glossary/appropriate-reliance (created in a prior session) — calibrated reliance is precisely the “when is AI advice trustworthy” mechanism the page was groping toward. The framework: three layers converge (glossary/advisor-strategy = selective escalation; glossary/recognition-primed-decision = validity boundary; appropriate-reliance = expertise × stakes calibration) into a two-question test before trusting an advisor output (high-validity task? + do expertise/stakes call for verification?). Named the failure mode (trust tracks confidence, not accuracy) and reframed the Primores “Personal AI Setup” angle as teaching reliance-calibration discipline, not tool config. Back-link wired: appropriate-reliance → ai-as-personal-advisor (reciprocal).

questions/automation-eats-execution-next-domains 🌱→🌿. Added a candidate-scoring matrix operationalizing the page’s own three signals across all 8 domains + a 9th (Customer Success/RevOps) as the next probe. The synthesis insight: signal (1) — execution-layer compressibility — is the discriminating variable. Brand-building and B2B sales fall off the curve specifically because their execution is cumulative (mental availability) or relational (trust over a long cycle), not high-volume/structured. Folded in this session’s SEO calibration work as fresh evidence that the SEO strategic layer is genuinely scarce (adjudicating vendor-vs-primary evidence + deciding where to earn third-party authority are judgment tasks AI doesn’t do) — tilting the page’s open sub-question toward “scarce, not just unproductized.” Added the sharpened rule to Key takeaways.

Index both question lines bumped 🌱→🌿 with refreshed descriptions. No new pages, no count change (151/68; open-questions count still 4). No raw source to archive.

[2026-06-07] ingest + update | SEO/GEO source calibration — primary anchor for the AI-Overview CTR collapse (Pew); vendor numbers labeled

Second deep-research pass of the session (101 agents, ~2.6M tokens, 19 sources fetched, 83 claims extracted, 25 verified 3-vote, 24 confirmed, 1 killed) to clear the SEO/GEO unverified candidates flagged in a prior pass and address a structural weakness: the SEO domain’s headline statistics all rested on single-vendor blogs (Digital Applied, Ahrefs, Semrush, Position Digital).

Net-new finding before research: the “original GEO paper” candidate (arXiv:2311.09735, Aggarwal et al.) was ALREADY ingested (in share-of-model/zero-click/geo-aeo) — excluded. The genuinely-unverified candidates were Pew (July 2025) and Reuters Institute (2025).

Per-claim verdict (the deliverable):

(b) AI Overviews cut CTR ~58% → ✅ ANCHORED in direction + magnitude by Pew Research Center, July 2025 clickstream (900 US adults, 68,879 searches, real behavior): traditional-result click 8% with an AI summary vs 15% without (~47% reduction); AI summary’s own citations clicked just 1%; sessions ended on 26% vs 16% of pages. Tier 1 institutional primary. (Google disputes the methodology — self-interested; carried as context.)
(a) 64.82% zero-click → 🟡 PARTIAL — Pew’s ~two-thirds “browsed elsewhere OR left” anchors the order of magnitude; the exact decimal is Similarweb (vendor).
(c) 96% E-E-A-T citations, (d) brand-mentions 3× backlinks (0.664 vs 0.218), (e) 75M AI Mode DAU / 92–94% zero-click → ❌ VENDOR-ONLY. No primary source. The direction of (c)/(d) — earned/third-party authority beats brand-owned — has experimental support from Chen, Wang, Chen & Koudas 2025 (arXiv:2509.08919, U. Toronto preprint; distinct from Aggarwal), but not the coefficients.

GO sources ingested: Pew 2025 (the anchor); Reuters Institute “Generative AI and News Report 2025” (self-reported AIO click-through 33%/37%/28% — used to surface the self-report-vs-clickstream gap against Pew’s measured 8%/1%); Reuters Institute Digital News Report 2025 (scoping correction — does NOT contain the SEO figures; measures AI-for-news, ~7% weekly); Chen et al. 2025 preprint (directional, flagged).

3 pages calibrated:

seo/zero-click-strategy (primary target) — new “How solid are these numbers?” section: Pew as the institutional anchor + a primary-vs-vendor table labeling 64.82%/58%/96%/3×/75M by source tier + the self-report-vs-behavior honesty flag. TL;DR source note, Honest-limits bullet, Sources split into primary/vendor.
seo/geo-aeo-benchmarks-2026 — Pew anchor blockquote next to the 58–61% CTR table; Pew added to Sources.
seo/ai-visibility — source-calibration note on the 96%/0.664-vs-0.218/<4%-DA section (vendor; Chen et al. preprint as directional support); Chen added to Sources.

1 killed claim (recorded, not cited): the framing that “Chartbeat data shows the feared referral-traffic impact hasn’t materialized” (1-2).

No new pages, no count change (151/68). Index zero-click line recalibrated. Provenance: all sources web/institutional (no raw/ to archive); verdicts + tiers + the Google-dispute caveat recorded on-page.

Gaps flagged for a future pass: no primary source for the 96% E-E-A-T or 3×-brand-mention coefficients (likely permanently vendor-only); no institutional source for the 75M AI Mode / 92–94% figures; the Chen et al. finding is preprint-tier (worth upgrading if a SIGIR/CHI peer-reviewed CTR study appears).

Stats: 3 substantial calibrations (~1,200 words) + 4 sources ingested + index/log/changelog. The SEO domain moves from vendor-only to primary-anchored on its single most important claim, with the rest honestly labeled.

[2026-06-07] ingest + update | Win-loss gap follow-up — academic anchors for the mechanism + practices (honest partial close)

Targeted deep-research workflow (108 agents, ~3.0M tokens, 25 sources fetched, 103 claims extracted, 25 adversarially verified 3-vote, 25 confirmed, 0 killed) to close the long-flagged win-loss-analysis gap: the page rested entirely on Klue/Crayon vendor content, including the load-bearing claim that win-loss is “the only CI layer that reliably moves win rate (5–15pp in 6 months).”

Honest verdict: the gap is half-closable, and the page now says so. No peer-reviewed study measures the causal effect of structured win-loss analysis on win rate — the 5–15pp magnitude stays an unanchored practitioner figure. But the mechanism and the two load-bearing practices now anchor to tier-1 academic evidence.

1 page substantially upgraded — glossary/win-loss-analysis (~1,100 new words):

New ”## Academic foundations” section, opening with an explicit calibration (mechanism anchored / magnitude not). Anchors ingested, each labeled transferable + tier + 3-0 verdict:
- Mechanism: Tannenbaum & Cerasoli 2013 (Human Factors, meta-analysis, 46 samples — debriefs/AAR improve performance ~20–25%, d=.67, generalizes across teams/individuals, simulated/real, medical/nonmedical) + Keiser & Arthur 2021 (J. Applied Psychology, independent meta-analysis, 61 studies — AAR d=0.79). Win-loss = structured retrospective review = the AAR family.
- “Interview buyers not reps”: Endres et al. 2023 (J. Product Innovation Management, dyadic + objective purchase records — rep self-perception explains only 4.7–7% of customer-perception variance; buyer-side predicts purchases better) + Nisbett & Wilson 1977 (Psychological Review, seminal — limited introspective access to one’s own decision processes).
- CI→performance context + caution: Vieira et al. 2023 (J. Business Research — salesperson CI positively but conditionally affects performance) + Kirca, Jayachandran & Bearden 2005 (J. Marketing, market-orientation meta-analysis, r=.32, partially mediated — no single CI practice is a clean direct lever).
- Boundary note: Friend, Ranjan & Johnson 2019 (IMM) — “failing fast” is forward-looking deal-abandonment, NOT retrospective win-loss; direct effect non-significant. The nearest-named sales construct disqualifies itself.
Calibrated the headline claim in three places: TL;DR (now “the layer practitioners credit most”; mechanism anchored, magnitude not), “Why it matters” (added practitioner-figure pointer to Academic foundations), and a new top bullet in Honest limits.
Two gaps flagged honestly, not padded: the 5–15pp magnitude (needs a quasi-experiment on matched firms) and the “within 30–90 days” timing practice (memory-decay literature searched, no decay-curve study cleared verification).
Added an “Academic sources” subsection to Sources (7 entries, tiers + verdicts) above the existing practitioner methodology links.

1 page cross-referenced — competitor-analysis/overview: Layer 1 prose calibrated (mechanism anchored / magnitude practitioner) with an in-text pointer to win-loss § Academic foundations. (Back-links already reciprocal — win-loss ↔ overview existed.)

No new pages, no count change (151 pages / 68 glossary). Index win-loss line recalibrated + date bumped. llms.txt date stamps synced to 2026-06-07.

Provenance: all sources web/academic (no raw/ files to archive); citations + verification status (3-0; 0 refuted) recorded on-page. Several primary publisher pages returned HTTP 403 during verification — confirmations relied on PubMed/RePEc/OA-repository mirrors carrying verbatim abstracts (citations sound, full-text not always directly fetched; noted in the page).

Stats: 1 substantial upgrade (~1,100 words) + 1 cross-reference + index/llms.txt/changelog. Closes the win-loss academic-backing gap as far as the evidence honestly allows.

[2026-05-29] ingest + create + update | CI follow-up deep-research — competitive-intelligence academic anchors

Targeted follow-up deep-research workflow (104 agents, ~2.8M tokens, 21 sources fetched, 25 claims verified 3-vote, 23 confirmed, 2 killed) to close the competitive-intelligence gap the first pass flagged. Two jobs: verify 4 specific candidates + find new academic anchors. All 4 candidates CONFIRMED real (none fabricated) — the wiki avoided citing anything hallucinated.

1 new glossary page:

glossary/ai-competitive-analysis (~1,900 words) — “AI for Competitive & Strategic Analysis.” Absorbs the strategic-decision trio + triage boundary: Doshi et al. 2025 (Strategic Management Journal, Tier 1 — single LLM biased/order-dependent; aggregate many → matches experts r=0.675; “LLM jury” discipline), Csaszar et al. 2024 (Strategy Science, Tier 1 — LLMs generate/evaluate strategy ~ experts, r=0.52, but weakest on innovation r=0.21 / PMF r=0.24), Wu, Kim & Lin 2025 (INSEAD pre-registered RCT n=305 — AI for both framing+ideation cuts strategic quality −15pp via anchoring), plus Hagar/Diakopoulos 2025 + Nokia Bell Labs 2025 (triage-not-decision boundary; raw LLMs lack current market knowledge). Both monitoring papers’ method claims were refuted in verification (0-3) — ingested as limitation framing only. Integrated playbook: aggregate not single-shot, humans frame + AI generates, keep judgment human, AI triages not decides, ground anything time-sensitive.

2 existing pages upgraded with academic anchors:

glossary/share-of-model — added an “academic foundations” section: Aggarwal et al. 2024 (KDD, Tier 1 — the canonical GEO paper + GEO-bench, methods +40% visibility), Kamruzzaman et al. 2024 (EMNLP, Tier 1 — LLMs systematically favor global/luxury brands, so share-of-model is NOT a neutral mirror), Schulte et al. 2026 (arXiv — AI-search non-determinism, source overlap 34–42% day-to-day → measure as a distribution). Added 2 Honest-limits caveats (non-determinism, model bias). The page moves from practitioner-backed to peer-reviewed-anchored.
competitor-analysis/overview — added an “academic foundations” Sources subsection grounding the 5-layer methodology in Day 1994 (Journal of Marketing, seminal — market sensing as the outside-in capability) + Madureira et al. 2023 (CI 5Ps construct + 8-step process; self-validating-lineage caveat noted) + the AI-strategic-decision trio. In-text pointer to ai-competitive-analysis in “How AI changes the methodology.”

Back-links wired (per schema discipline): ai-competitive-analysis ↔ competitor-analysis/overview, share-of-model, appropriate-reliance, jagged-frontier, continuous-monitoring, win-loss-analysis (6 reciprocal).

Index + llms.txt updated. Stats: 150→151 pages, glossary 67→68. New notable entry in llms.txt.

Verification integrity: every page carries the verification verdict (CONFIRMED/Tier; 3-0 votes) and flags the 2 refuted method claims as do-not-cite. Credibility tiers are explicit (Tier 1 peer-reviewed vs Tier 2 working paper vs Tier 3 vendor preprint).

Persistent gap (flagged, not closed): win-loss analysis still has no rigorous academic source measuring its effect on win rates — glossary/win-loss-analysis remains practitioner-backed. Next pass should target sales-research journals (J. Personal Selling & Sales Management, Industrial Marketing Management).

Stats: 1 new page (~1,900 words) + 2 substantial upgrades (~700 words) + 6 back-links + 4 metadata files. Total new content: ~2,600 words.

[2026-05-29] ingest + create + update | Deep-research ingest — AI-reliability + service-recovery peer-reviewed sources

Ran the deep-research workflow (105 agents, ~2.8M tokens, 23 sources fetched, 105 claims extracted, 25 adversarially verified 3-vote, 23 confirmed, 2 killed) to find trusted/academic sources to ingest. Then ingested the top GO sources.

2 new glossary pages:

glossary/appropriate-reliance (~1,900 words) — the AI-reliability reconciliation. Absorbs 4 verified sources: Klingbeil et al. 2024 (Computers in Human Behavior — mere AI labeling triggers over-reliance against self-interest + degrades cooperation), Lee et al. 2025 (CHI, MSR+CMU, n=319 — higher tool-confidence → less critical thinking; correlational), Schilke & Reimann 2025 (OBHDP, 13 experiments, 5,000+ — AI disclosure erodes trust −16–20% via reduced legitimacy), and the Journal of Decision Systems 2025 chess study (n=529 — NO over-reliance, experts self-rely) + Schemmer et al. JAIR 82 (maximizing AI adherence is provably suboptimal). The wiki value-add: reconciles the over-reliance vs under-reliance tension via expertise × stakes moderation + the disclosure paradox tied to honest-assessment.
glossary/review-response-strategy (~1,500 words) — service-recovery mechanics. Absorbs 2 verified ISR sources: Chen, Gu, Ye & Zhu 2019 (responses lift review volume via third-party externality; detailed-for-negative/brief-for-positive) and Ravichandran & Deng 2023 (match tone to justice type — rational for procedural, empathetic for interactional). Carries the refuted over-claim explicitly: responses do NOT reliably raise aggregate review valence (killed 0-3 in verification) — ingested as a do-not-claim caveat.

3 existing pages extended with the remaining GO sources:

glossary/jagged-frontier — added Ju & Aral 2025 (MIT field experiment, n≈2,234: human-AI teams +50% output, “diversity collapse”, 17% more delegation) + Alibaba production RCT dark side (top performers’ quality declined via multitasking).
glossary/ai-skill-leveling — added the Alibaba 2025 field RCT confirming skill-leveling in production (low performers gain most) + the top-performer-decline dark side. (Flagged: one arXiv ID unverified; SSRN 5012601 is the safe anchor.)
questions/what-ai-tools-actually-deliver-roi — added the Copilot RCT (Dillon et al., NBER w33795, n=7,137: saved ~2h/week but NO change in output) + Stanford HAI 2025 AI Index baseline (GenAI in ≥1 function 33%→71%; ROI modest, <10% cost savings).

Back-links wired (per schema discipline): appropriate-reliance ↔ jagged-frontier, ai-skill-leveling, agent-adoption-frictions, honest-assessment, hallucination, guardrails, customer-perception-moments (7); review-response-strategy ↔ weekend-review-effect, customer-perception-moments, ai-humor-forgiveness, honest-assessment, ai-customer-service-cases (5).

Index + llms.txt updated. Stats: 148→150 pages, glossary 65→67. Two new pillar entries in llms.txt.

Provenance: all sources are web/academic (no raw/ files to archive); citations + verification status (3-0 votes; 2 refuted claims) recorded in each page’s Sources block. Full research queue in the workflow output.

Gaps left for a follow-up pass (flagged by the research): the competitive-intelligence special-interest area produced no verified dedicated source (4 candidates fetched but unverified — SMJ 10.1002/smj.3677, SSRN 4913363/5456494, arXiv 2604.07585); SEO/GEO scope also has fetched-but-unverified candidates (original GEO paper arXiv:2311.09735, Pew AI-summary-CTR Jul 2025, Reuters Digital News Report 2025).

Stats: 2 new pages (~3,400 words) + 3 substantial extensions (~900 words) + 12 back-link additions + 4 metadata files. Total new content: ~4,300 words.

[2026-05-29] lint + create + update | Customer-perception cluster framework page + ROI question developed + full lint + housekeeping

Session covering four tasks (user: “do 2,3,4,5” against the session-start options). No new external source — this was a consolidation, development, and health session.

5 — Housekeeping (done first). Archived raw/articles/The-Complete-Guide-to-Building-Skill-for-Claude.pdf → _ingested_2026-04-14_...pdf (per schema step 7; ingested Apr 14 into tools/claude-skills but never archived). Confirmed the raw queue is otherwise clean: all 10 academic-foundations/ sources are heavily cited across content pages (the reference library), not a pending queue.

4 — Full lint. Ran a content-health scan across the whole wiki (two parallel read-only Explore agents split glossary vs. everything-else; mechanical checks done directly). Findings:

Contradictions: none. Key repeated stats (96% E-E-A-T, 64.82% zero-click, brand-mentions-3×-backlinks, 0.04-star weekend effect) consistent across domains.
Frontmatter: 100% compliant. Orphans: none (all spot-checked pages ≥8 inbound refs).
Cross-links: the agent flagged 3 missing pairs; on verification 2 were false positives (agentic-search→agentic-search-optimization and ai-seo-content→zero-click already exist). 1 genuine gap fixed: added back-link seo/zero-click-strategy → seo/ai-seo-content (forward link already existed). Lesson preserved: verify lint findings before acting — agents miss links buried lower in Related sections.
Staleness: 3 SEO/marketing pages (agentic-search, ai-seo-content, preparing-for-agentic-ai) pre-date the May 2026 data but are superseded by newer pages they already link to; advisor-strategy is 24 days old. Low priority — noted, not actioned.
Concept gaps: MCP has a tools page (tools/mcp) but no glossary stub — flagged as an optional growth item, not a defect.
Bug fixed in passing: llms.txt had a duplicate “Tool reviews” line with conflicting counts (12 and 11). Removed the stray line.

2 — Customer-perception cluster framework page. Created glossary/customer-perception-moments (~1,900 words) — the hub consolidating the two May-26 behavioral-evidence ingests into a coherent lens. Core contributions: (a) the three moments of judgment (decision / review-writing / failure-recovery) with each anchor mapped to its moment; (b) the feedback loop framing — the moments connect through reviews as the connective tissue; (c) the moderator-flip meta-pattern — every headline behavioral finding has a context-dependent moderator (hedonic-vs-functional, severity, focal-vs-observer, review-count stage) that can reverse it, so the discipline is “identify your moment + moderators before applying”; (d) honest-assessment named as the unifying mechanism running underneath all moments. Framed explicitly as Primores consolidation vocabulary (like automation-eats-execution), not new primary research.

Back-links wired (full reciprocity, schema step 6): all 8 outbound targets now link back — glossary/weekend-review-effect (review-writing-moment anchor), glossary/ai-humor-forgiveness (failure-recovery-moment anchor), glossary/honest-assessment (unifying mechanism), glossary/super-niche, glossary/jagged-frontier, automation/ai-customer-service-cases, seo/ai-visibility, marketing/discovery-before-scale.

3 — Developed open question questions/what-ai-tools-actually-deliver-roi. Added a two-axis ROI model (frontier position × error cost) as a 2×2 — highest ROI is inside-frontier + low-error-cost (the unglamorous top-left), negative ROI is the outside-frontier + high-error-cost −19pp zone. Added a function-by-function map (content, support, transcription, dev, data, legal/health/finance, autonomous agents) each linked to its wiki case-study evidence, resolving the “ROI by business function” open thread. Addressed the free-vs-paid hypothesis with frontier reasoning (free captures ~80% for inside-frontier tasks; paid value concentrates at the frontier edge). Upgraded 🌱 seedling → 🌿 growing (3 academic anchors + synthesis model + inbound links). Added Related links to glossary/guardrails and glossary/customer-perception-moments.

Index + llms.txt updated. Stats: 147→148 pages, glossary 64→65. New glossary entry + ROI question upgrade reflected in index; new pillar entry + glossary count + stats date + duplicate-line fix in llms.txt.

Stats: 1 new page (~1,900 words) + 1 substantial question development (~700 words) + 9 back-link/cross-link additions + 4 metadata files updated + 1 source archived + 1 llms.txt bug fixed.

Why this matters architecturally: the customer-perception cluster now has a named hub, turning two isolated glossary entries into a navigable framework with a transferable meta-lesson (the moderator-flip pattern). The wiki gains a behavioral-evidence pillar that parallels the automation-eats-execution / jagged-frontier framework spine. The ROI question’s two-axis model is a reusable client-facing decision tool, not just a survey of studies.

User flagged a second Science Says newsletter (Thomas McKinlay, May 19 2026) summarizing the Bayerl et al. 2026 paper on weekend review timing. Explicit user direction this session: “do 1 and also include other findings. (i was expecting the same for previous ingestion)” — meaning the wiki entry should integrate the newsletter’s cross-referenced prior insights, not just the primary study. Saved as a new feedback memory for future ingest sessions.

Pre-ingest validation pass (the user’s framing was “validate claims”): the headline finding (3% lower 5-star share, 6% higher 1-3 star share on weekends) checks out — top-tier journal (JMR), large-N observational + field-experiment design. The “Eleanor Rigby” mechanism IS the paper’s framing, not newsletter editorialization. The paper explicitly rules out alternative mechanisms (deeper reflection, procrastination-then-write, mood). Robustness checks confirm cross-cultural consistency (Iran Fri/Sat) and public-holiday extension.

Three load-bearing caveats the newsletter missed — surfaced during the validation pass:

Hedonic-product counter-finding — 2023 ScienceDirect study (n=588K) showed effect direction reverses for hedonic products. Weekend reviews are more positive for entertainment/food/travel categories. Same pattern as the humor-forgiveness ingest counter-finding: context-dependent moderators flip the effect.
Industry response-rate data conflicts — Bazaarvoice/Yotpo/PowerReviews show Saturdays are among the highest-volume send-days. Academic paper measures star-rating outcome; industry measures response rate. The “right” send-day depends on what you’re optimizing for.
Effect size is small at the individual-product level — 0.04 stars average. Below the noise floor for products with 100+ reviews. Effect matters operationally for low-review-count items where threshold crossings move whole stars (the 6%-of-Amazon-products half-star jump is gated to products with 3-4 reviews).

Cross-referenced findings the newsletter linked (integrated into the page per the new feedback discipline):

First-review anchoring — University of Florida Warrington research + confirmation-bias literature. Positive first review attracts more positive subsequent reviews; first 5 reviews disproportionately influence conversion.
Incentive-positivity transfer — Woolley & Sharif 2021 (JMR). Incentives modify the review-writing experience itself; positive affect from incentive transfers to review content. Up to +83.4% positivity, attenuates when incentive weakly associated or company disliked. Best practice: reward participation, not positivity (FTC compliance).
Display-order effects — 5-star first review boosts conversion (anchor); but 52% of shoppers prefer mix of positive + mediocre + negative (trust). Combined playbook: lead with 5-star recent review, mix in 4-star and 3-star visible above fold, bury 1-star below fold.

New page: glossary/weekend-review-effect (~4,500 words) — Headline finding + the multi-method triangulation that makes it robust (400M reviews + 11,667-user field experiment + 33 platforms) + the Eleanor Rigby mechanism with population-selection-not-mood framing + the hedonic-product counter-finding + the response-rate-vs-star-rating industry tradeoff + the Sunday-neurosis partial-mechanism context + the integrated four-lever review-ops cluster (timing + first-review anchor + incentives + display-order) operating coherently as a single practitioner playbook + 7 honest limits including effect-size noise-floor caveat + cross-references to honest-assessment, super-niche, ai-visibility, discovery-before-scale, ai-customer-service-cases, and ai-humor-forgiveness.

Retrofit: glossary/ai-humor-forgiveness — Per the new feedback discipline, retrofitted with the two cross-referenced findings the original ingest missed: “thank you” not “sorry” pattern (Journal of Travel & Tourism Marketing 2022 — gratitude beats apology for rejection failures; CXPA 68%-decreased-trust + Allen Institute 3.7×-over-apology findings) and chatbot interjections (“Oh no!” / “Ah!” as personification + empathy markers — Frontiers in Psychology 2022). New “Adjacent service-recovery tactics” section in the practitioner playbook. Added cross-ref to weekend-review-effect in Related section. Updated Sources block.

Back-links wired (per the schema discipline): glossary/honest-assessment (added weekend-review-effect as the review-ops layer instance of the trust mechanism; 52%-shoppers-prefer-mix mirrors the wiki’s mix-of-pros-and-cons content discipline). glossary/ai-humor-forgiveness (added weekend-review-effect as adjacent behavioral-evidence research at the customer-perception layer).

Source archived (per the schema discipline): /Users/andrejruckij/Desktop/🎓 Don’t ask for reviews on weekends.eml copied to raw/articles/_ingested_2026-05-26_dont-ask-for-reviews-on-weekends.eml.

Index + llms.txt updated. Stats bumped: 146→147 pages, glossary 63→64. New pillar entry added to llms.txt with detailed description of the four-lever cluster and the counter-findings. Date stamps already at 2026-05-26.

Stats: 1 new page (~4,500 words) + 1 substantial retrofit (~600 words added to ai-humor-forgiveness) + 2 back-link additions + 4 metadata files updated + 1 source archived. Total new content: ~5,100 words.

Why this matters architecturally: the customer-perception cluster is forming. The wiki now has two peer-reviewed-anchored glossary entries on how content-style choices affect customer reactions at moments of judgment: humor-on-failure (recovery moments) and weekend-review-effect (review-writing moments). Both surfaced from the same Science Says newsletter source; both have integrated counter-findings from independent research; both include practitioner gates that depend on context. The wiki’s value-add over the newsletter (already substantial in volume) is the counter-finding integration + cluster-level operating playbook + independent verification discipline.

Feedback memory saved: feedback-newsletter-ingest-cross-refs.md — when ingesting curated newsletters, follow the cited “previous insights” links and integrate them into the wiki entry. The newsletter author already did the cluster-building work; the wiki should inherit it. Applied retroactively to the humor-forgiveness page in this session.

User flagged a Science Says newsletter (Thomas McKinlay, May 26 2026) summarizing Xie et al. 2025 on AI agent service-failure humor and forgiveness. Verdict: worth ingesting — peer-reviewed primary research with concrete effect sizes, plus a load-bearing counter-finding that the newsletter didn’t surface and that materially changes practitioner deployment.

Pre-ingest check: grep for “humor” in wiki/ surfaced only passing mentions in brand-voice-skills-guide and brand-vs-content-layers — no dedicated treatment of the mechanism existed.

Research depth: 4 WebSearch passes covering Xie et al. 2025 citation verification + Nature Scientific Reports 2025 corroboration paper + “Don’t Humor Me!” J. Business Ethics 2025 counter-finding + Wharton Blueprint context for the Hosanagar quote.

Key findings synthesized into the new page:

Primary study (Xie, Zhou, Liang, Zhao, Lu 2025, Journal of Business Research vol. 194, Hefei University of Technology): 4 experiments, n=1,919. Self-deprecating humor produces +47.8% forgiveness uplift vs no humor on wrong-recommendation errors; +25.6% on after-sales failures. Positive (affiliative) humor uplifts: +33.9% and +15.7%. Self-deprecation consistently outperforms positive humor by ~10pp across contexts. Severity gate: effect strong for low-severity failures; disappears (not weakens) for high-severity (refund refusal, missed deadline).
Independent corroboration #1 (Nature Sci Reports 2025, n=780, 3 studies): consumer-motivation moderator. Hedonic-motivation consumers show significantly stronger forgiveness toward humorous responses, mediated by perceived warmth. Functional-motivation consumers respond weaker. Practitioner translation: humor’s payoff is highest in hedonic contexts (entertainment, fashion, lifestyle); weaker in functional contexts (banking, healthcare, B2B SaaS).
Independent corroboration #2 (AJMSE 2024): user-disposition moderator. Users high in humor receptivity respond more strongly; low-receptivity users respond weakly or negatively.
Counter-finding (Honora, Japutra, Septianto 2025, J. Business Ethics, October 2025): prior research used observer reactions; this study measured focal customer reactions (the person directly burned). Humor flips: focal customers read it as sarcasm, perceive reduced company morality, forgive less. The mechanism: emotional load + unresolved harm → humor reads as not taking the problem seriously. Two safer-deployment recommendations: (1) timing — humor more acceptable after resolution, not during apology phase; (2) relevance — humor for peripheral-to-core-business failures, not core-business failures.
The Hosanagar quote (Wharton Human-AI Research) the newsletter surfaced: “People are not as forgiving of AI errors as they are of human errors. They systematically misjudge AI partners by focusing on where the system fails on ‘easy’ cases.” This is the load-bearing trust-friction repair cost — the forgiveness asymmetry that makes the AI service-recovery problem distinct from the human service-recovery problem.

The integrated 2026 practitioner gate (synthesizing all three studies + the Wharton framing): humor helps when low-severity + peripheral-to-core-business + post-resolution + non-emotionally-loaded + hedonic context. Otherwise: stay sincere. Self-deprecation is the safer humor type when gates are uncertain.

New page: glossary/ai-humor-forgiveness (~4,500 words) — Simple explanation + Hosanagar forgiveness-asymmetry framing + the primary Xie et al. study with effect-size table + the self-deprecation-mechanism explanation + Nature Sci Reports corroboration + AJMSE 2024 corroboration + the Honora et al. counter-finding with mechanism + the integrated 2026 practitioner playbook (when-to-use table + humor type selection + composable copy examples + severity-classification step) + connection to wiki frameworks + 6 honest limits.

Substantial extensions:

glossary/agent-adoption-frictions — Added “The forgiveness asymmetry” section with the full Hosanagar quote and the trust-friction-repair-cost framing. The Wharton three-friction framework now connects to the empirically-supported humor-on-failure repair tactic. Related section gains the ai-humor-forgiveness entry.
glossary/honest-assessment — Added “Honest-assessment in conversational AI” section. The honest-assessment mechanism (admitting real limits builds trust) extended from the content-trust layer to the conversational-AI service-failure layer, with the boundary conditions (severity gate + focal-customer caution) distinguishing the two contexts.
marketing/ai-human-voice-prompting — Added “The recovery-side complement — humor on AI failures” section. The six generation-side techniques now have a recovery-side seventh-technique candidate; the Voice-Profile Document (technique 1) can include a service-recovery voice section.

Back-links wired (per the schema discipline): the 3 target pages now reciprocally link to ai-humor-forgiveness in their Related sections. Plus the cross-references between extended sections (honest-assessment ↔ ai-humor-forgiveness, agent-adoption-frictions ↔ ai-humor-forgiveness).

Source archived (per the schema discipline): /Users/andrejruckij/Desktop/🎓 Humor makes it easier to forgive AI mistakes.eml copied to raw/articles/_ingested_2026-05-26_humor-makes-it-easier-to-forgive-AI-mistakes.eml. Provenance traceable from the wiki page’s Sources block.

Index + llms.txt updated (per the same discipline). New entry added to index Glossary section. Stats bumped: 145→146 pages, glossary 62→63. llms.txt section count refreshed (glossary 62→63), Pillar pages section added entry, Stats refreshed, date stamps updated to 2026-05-26.

Stats: 1 new page (~4,500 words) + 3 substantial extensions (~1,500 words combined) + 6 back-link additions + 4 metadata files updated. Total new content: ~6,000 words. Source archived.

Why this matters architecturally: the agent-adoption-frictions cluster gains a load-bearing empirical anchor for the trust-friction repair cost. The wiki had documented the problem (Wharton’s three frictions) and the theoretical mechanism (honest-assessment); it now documents the empirically-supported repair tactic with concrete effect sizes, gates, and copy templates. The triangulation (one primary study + two corroborating studies + one counter-finding) is the strongest evidence base any wiki entry on AI service-recovery has assembled.

Honest assessment of the ingest discipline: the newsletter (Science Says) provided the primary study signal but missed the focal-customer counter-finding entirely. The wiki page integrates both — which is the kind of value-add that justifies the wiki’s existence vs. just forwarding newsletter content.

[2026-05-20] create + update | Gemini Omni ingest — same-day capture of Google I/O 2026 launch

Second session of May 20. Google I/O 2026 was May 19; user flagged the Gemini Omni news for ingest. Same-day capture pattern (matches the May 7 Claude Managed Agents release-wave ingest cadence).

Research depth: 6 parallel WebSearch passes covering Omni launch announcement + features, capabilities comparison vs Veo 3 / Sora 2 / Seedance 2 / Wan 2.7 / Kling V3.0, pricing and API rollout (consumer live; developer “coming weeks”), use cases for marketing + advertising + SMB applications, world-model physics framing + DeepMind Project Genie research context.

Key findings synthesized into the pages:

Gemini Omni is Google’s first any-to-any multimodal model — one architecture unifying video, image, audio, and text generation with Gemini’s reasoning baked in. Fuses Veo (video) + Nano Banana (image editing) + Project Genie (world simulation) + main Gemini reasoning model. The same model that handles reasoning handles the pixels and waveforms — inherits Gemini’s knowledge of history, biology, narrative logic, cultural context.
Gemini Omni Flash launched May 19, 2026 at Google I/O. Live today in Gemini app + Google Flow for AI Plus ($20/mo) / Pro ($30/mo) / Ultra ($100/mo) subscribers; YouTube Shorts + YouTube Create this week (free tier); Vertex AI / Gemini API / Agent Platform API “in coming weeks” — enterprise pilots should wait for the API. Projected API pricing (unconfirmed): ~$1.50–$2.50 per 1M input tokens; $0.20–$0.60 per second of video output.
World-model physics understanding — Omni is trained to predict outcomes the way physical intuition would, then generates frames consistent with that prediction. Not running a physics simulation, but correctly handles gravity, fluid dynamics, collision behavior more often than pure-pattern models. Inherited from DeepMind’s Project Genie research stream; Demis Hassabis has framed world models as essential infrastructure for AGI.
Distinct strengths vs Sora 2: prompt adherence on multi-clause instructions + text rendering reliability. Both are load-bearing for advertising use cases where slogans, product names, and exact wording need to be correct.
Strategic positioning split (the framing most relevant for Primores readers): Omni is the publisher’s tool (efficient, distribution-embedded via YouTube + Gemini app, ad-scale variant generation). Sora 2 is the artist’s tool (cinematic, social, short-film-oriented). Most teams need both — Omni for variant ads + landing-page demos + multi-format adaptation, Sora 2 for hero cinematic work. OpenAI shut down the consumer Sora 2 app April 2026 → API-only access now.
Asia-led wave: ByteDance Seedance 2 topping public benchmarks; Alibaba Wan 2.7 + Kuaishou Kling V3.0. Particularly relevant for Asian markets and TikTok-native creative.
SynthID watermarking on all Omni outputs. Becoming an industry-standard discipline.
Companion I/O 2026 announcements: Gemini 3.5 Flash (surpasses 3.1 Pro on coding/agentic/multimodal at 4× faster output tokens/sec), Gemini Spark (24/7 personal agent running in background), Antigravity 2.0 (Google’s agentic AI push). Not covered in this ingest — user scoped to Omni specifically.

New page: tools/gemini-omni (~3,500 words) — Comprehensive tool page covering what it is + capabilities (any-to-any I/O, world-model physics, prompt adherence + text rendering, conversational editing, SynthID) + availability and pricing across consumer and developer tiers + competitive context (Omni vs Sora 2 vs Veo vs Asia models) + marketing/ad applications (variant ad generation, landing-page videos, localized creative, multi-format adaptation, SMB use cases) + wiki framework integration + 5 honest limits + when-to-use routing guide.

Substantial extensions:

marketing/ai-video-marketing — Was April-stale and pre-dated the Sora 2 / Omni / Asia-wave landscape. Added “May 2026 update — the publisher’s tool vs. artist’s tool split” section. TL;DR rebuilt to reflect the dual-tool reality. Updated frontmatter description and date.
comparisons/ai-tools-when-to-use — Gemini side refreshed with Omni + Gemini 3.5 Flash + Spark context. Added two new rows to the platform task-routing table: video generation for ad variants → Gemini Omni; video generation for cinematic → Sora 2. The 2026 routing framework now has a video dimension.
glossary/creative-reverse-engineering (written earlier the same day) — Added Gemini Omni as the third vision-LLM and the generation-side complement that closes the analysis-to-production loop. The 2026 workflow becomes three-tool: Claude (copy analysis) → GPT-4o (visual analysis) → Omni (variant generation), with Foreplay/Atria/Motion as collection substrate. The AI ad generation tools subsection expanded with Omni + Sora 2 detail and the “loop closes” framing.

Back-links wired (per the schema discipline): 5 target pages gained reciprocal entries — marketing/ai-video-marketing (Related includes Omni now), comparisons/ai-tools-when-to-use (Related includes Omni), glossary/creative-reverse-engineering (Related includes Omni + ai-video-marketing), glossary/creative-formula-vs-creative-skin (Related includes Omni as the May 2026 generation-side tool), glossary/automation-eats-execution (added a video-creative-generation layer instance to the cross-domain list), automation/agentic-commerce (Related includes Omni; the agentic-commerce stack now has a video-generation layer).

Index + llms.txt updated (per the same discipline). New tools entry added to index Tools section with substantive description. Stats bumped: 144→145 pages, tool reviews 11→12. llms.txt section count updated (tools 11→12), Pillar pages section added Omni entry, Stats refreshed. Date stamps already at 2026-05-20.

Stats: 1 new page (~3,500 words) + 3 substantial extensions (~1,500 words combined) + 5 back-link additions + 4 metadata files updated. Total new content: ~5,000 words.

Why this matters architecturally: the Omni launch is a structural shift in the AI-tools landscape — the first production-grade any-to-any multimodal model with deep integration into a distribution surface (YouTube). The wiki’s tools cluster needed this captured same-day; the AI-video market split (publisher’s tool vs. artist’s tool) is now the load-bearing framing for the wiki’s video-generation guidance. Pairing with the creative-reverse-engineering methodology written earlier the same day means the wiki now has a closed-loop AI-creative system: analysis (Claude + GPT-4o) → pattern extraction → generation (Omni + Sora 2) → iteration through conversational editing. This is the kind of fresh-news ingest the May 7 Claude Managed Agents release-wave entry set the cadence for — same-day capture of vendor releases that materially shift the production stack.

Session-day totals (May 20, two sessions): 3 new pages (continuous-monitoring + creative-reverse-engineering + gemini-omni) + 4 substantial extensions (competitor-analysis pillar + creative-reverse-engineering same-day + ai-video-marketing + ai-tools-when-to-use) + ~14 back-link additions + 6 deep-research passes (4 for the CI layers + 6 for Omni; ~10 WebSearch queries total). Wiki total: 145 pages, 62 glossary entries, 12 tool reviews. The competitor-analysis cluster fully backed; the AI-video landscape captured same-day.

[2026-05-20] create | Competitor-analysis five-layer methodology fully backed — continuous-monitoring + creative-reverse-engineering glossary entries close the layer gaps

Two new glossary entries close the remaining structural gaps in the competitor-analysis cluster. The May 18 pillar rebuild named five operational layers (win-loss + continuous monitoring + battlecards + share of model + creative reverse engineering) but only three had dedicated practitioner glossary entries (win-loss, battlecards, share-of-model). Layers 2 and 5 had been described in the pillar prose but lacked their own deep-dive pages. This session closes both gaps.

Research depth: 4 parallel WebSearch passes — continuous monitoring methodology + signal-to-noise / decision-linkage frameworks + Visualping/free-tier alternatives + Meta Ad Library tool stack (Foreplay/Atria/Motion/Pencil) and vision-LLM (Claude/GPT-4o) workflows for creative analysis.

Key findings synthesized into the pages:

Continuous monitoring — Crayon tracks 100+ signal types per competitor across millions of sources; the 2026 bottleneck is no longer collection but signal-to-noise triage and decision routing. The Klue/Crayon practitioner ratio: 1,000 raw signals → 100 material changes → 10 decision-relevant → 1–3 actually change a decision. Klue’s 2026 Compete Agent + Deal Tips push competitive guidance to reps in near-real-time. The “last-mile problem” — perfect CI that doesn’t reach decision-makers at the point of need creates zero value. Tooling tiers: enterprise ($20K–$100K+/yr Crayon/Klue/Kompyte), focused ($50–$500/mo Visualping/Foreplay/Atria/Motion/Similarweb/Semrush), free (Meta Ad Library + Google Ads Transparency + Changedetection.io + UptimeRobot + PageCrawl.io). Visualping ships AI summaries on free tier; most competitors lock AI behind premium. Three operational cadences (real-time alerts + weekly digest + monthly synthesis) needed in parallel; most CI programs run only one.
Creative reverse engineering — Pattern extraction across volume is the load-bearing methodological move; single-ad deconstruction underperforms because any one ad mixes formula with idiosyncratic noise. 10–20 ads per pattern-extraction pass minimum. The 5-step workflow: define category + shortlist criteria → collect with structured metadata → deconstruct against the 10-layer template → pattern-extract across the set (counting which elements recur in 60%+ of winners) → apply formula to your own skin. The vision-LLM hybrid stack: Claude for copy (PAS framing for 4:5 / Reels hook design), GPT-4o for visuals (lighting, palette, composition); Foreplay ($49–$99/mo, 6 platforms, 3-year history) / Atria ($129/mo bundled analytics+research+mining+generation) / Motion ($250+/mo, gold-standard visual analytics with predictive fatigue detection) for collection. Free path covers 80% with Meta Ad Library + GPT/Claude. The formula-vs-skin distinction is the legal safety line: formulas (composition, lighting, framing archetypes, copy structures) are universal in advertising; skins (specific brand colors, typography signatures, trademarked elements) are IP territory.

New pages:

glossary/continuous-monitoring (~3,500 words) — Layer 2 methodology. Signal categories with prediction value and cadence + tooling landscape across three tiers + signal-to-noise discipline + decision routing (the last-mile problem) + AI compresses execution / strategy stays human + three operational cadences in parallel + honest limits (CI as theater diagnostics) + minimum viable monitoring stack (30-day path).
glossary/creative-reverse-engineering (~3,500 words) — Layer 5 methodology. The systematic discipline distinct from the formula-vs-skin framework. 5-step workflow + 10-layer deconstruction template + vision-LLM stack (Claude + GPT-4o hybrid) + 2026 tool landscape + legal/IP boundaries + AI compresses execution / strategy stays human + honest limits (surface-mimicry trap, single-ad-analysis trap, category-mismatch trap) + minimum viable RE program (30-day path).

Architectural framing: the competitor-analysis cluster now has all five layers fully backed by dedicated practitioner glossary entries, all integrating with the same cross-domain pattern (glossary/automation-eats-execution) and the same production-discipline test (“what specific decision does this output change?”). Practitioners reading the cluster end-to-end can build a complete 2026 CI program with informed decisions on every layer.

Back-links wired (per the schema discipline): 9 target pages gained reciprocal entries — competitor-analysis/overview (Related section now lists all five layer methodology pages with Layer-N labels + body cross-references in the Layer 2 and Layer 5 sections), glossary/win-loss-analysis (5-layer Related), glossary/battlecards (5-layer Related), glossary/share-of-model (5-layer Related), glossary/creative-formula-vs-creative-skin (added the practitioner-discipline complement entry at top of Related), glossary/automation-eats-execution (added 2 new CI-domain instances to the cross-domain examples list), cases/agenica-competitor-ads (added 2 layer-methodology references), cases/ad-alchemy-creative-reverse-engineering (added 2 framework references), experiments/ad-alchemy-competitor-piggyback (added 2 framework references).

Index + llms.txt updated (per the same discipline). Two new entries added to index Glossary section with substantive descriptions. Stats block fixed (was stale: said ~126 pages, 45 glossary — actual 144, 62). llms.txt stats refreshed (142 → 144 pages; 60 → 62 glossary; competitor-analysis section description updated to note all five layers now have dedicated glossary entries). Two new Pillar-pages entries added to llms.txt for the new entries. Date stamps refreshed.

Stats: 2 new pages (~7,000 words combined) + 9 back-link additions + 4 metadata files updated (index, llms.txt, log, changelog).

Why this matters architecturally: the competitor-analysis cluster is the wiki’s first domain to have a methodology pillar + every operational layer backed by a dedicated practitioner glossary entry. The pattern is now reusable across the wiki — other domains (SEO, marketing analytics, automation) could in principle reach the same coverage depth if their layer methodologies were itemized this clearly. The five-layer mental model is also now AI-citation-ready: a future AI assistant asking “how does competitor analysis work in 2026?” can be pointed at the pillar and gets five linked methodology pages with operational specificity that traditional consulting-firm content lacks.

Lint observation: the index Stats block was significantly stale (~126 pages / 45 glossary) — this had drifted since at least May 13. Fixed in this session to 144 / 62. The recurring failure mode the May 14 lint flagged (llms.txt stats drift after index updates) appears to apply to index.md Stats block as well. The CLAUDE.md atomic-pairing discipline (index + llms.txt updates atomic) should probably extend to index.md Stats block as a third coupled element. Flagging here; should be applied in the next session’s INGEST cycle.

[2026-05-18] create | Prompt caching glossary entry — closes the May 18 gap-analysis cycle

Fourth and final session of May 18. Tier 2 #5 from the morning gap analysis. The agent-engineering cluster had a missing production-cost-optimization layer; closing this gap completes the cluster.

Research depth: 3 targeted WebSearch passes on prompt caching mechanics — Anthropic cache_control + pricing + TTL changes, OpenAI automatic prompt caching + GPT-5.5 pricing, semantic caching via vector similarity (Redis / GPTCache / production case studies).

Key findings synthesized:

Three distinct caching mechanisms practitioners often conflate: prompt cache (vendor-level prefix reuse), semantic cache (vector-similarity response reuse), KV cache (model-internal attention, not practitioner-facing).
Anthropic mechanics: cache_control markers; 5-min default TTL (1.25× base to write, 0.1× to read) and 1-hour extended (2× to write). Cache reads at 10% of base = 90% input-token discount. Cache pays off after 1 read at 5-min TTL or 2 reads at 1-hour TTL.
The February 2026 TTL controversy: Anthropic quietly changed default TTL from 60 minutes to 5 minutes in early 2026, increasing many production workloads’ costs by 30-60% overnight without code changes. Documented failure mode worth flagging.
OpenAI mechanics: automatic prompt caching on GPT-4o, o1, and GPT-5.x families. GPT-5.5 cached input runs $0.50/M tokens (90% discount). No code changes needed.
Semantic caching: uses vector embedding cosine similarity (0.85-0.95 threshold typical) to reuse responses for semantically similar queries. Redis + RedisSemanticCache is the dominant production substrate. Typical 50%+ cost reduction with repetitive query patterns; 60% hit rate on 1M-requests/day = ~$846/month saved at H100 on-demand pricing.
Combined-strategy production results: ProjectDiscovery cut LLM costs 59% post-implementation, growing to 70% after optimization. The cited 70-80% upper-bound numbers from vendor literature.
Proactive cache warming is essential — never rely on parallel calls to create their own caches. Make a single dedicated warming call first; parallel calls then read the warmed cache at 10% of base price.

New page: glossary/prompt-caching (~2,500 words). Glossary-entry sized with substantial technical depth. Covers: the three caching mechanisms with side-by-side distinctions; Anthropic vs. OpenAI vs. Google mechanics; semantic caching with embeddings + Redis production landscape; when caching helps vs. doesn’t (with operational thresholds); proactive cache warming as the under-applied practice; 6 honest-limits items including the TTL-change failure mode and semantic-cache false-positive risk.

Architectural framing: prompt caching is distinct from but adjacent to glossary/agentic-memory. Prompt caching is per-request cost optimization (TTL window of minutes to hours); agentic memory is cross-session context persistence (indefinite, curated). Both reduce work; they’re not interchangeable. Naming the connection in both pages strengthens the cluster.

Connection to glossary/automation-eats-execution: prompt caching is the production-cost-optimization layer instance of the cross-domain pattern. AI compresses execution (the caching itself runs automatically once configured); strategy work — which prefixes to cache, at what TTL, with what hit-rate target — stays human-leveraged.

Back-links wired (per the schema discipline): 9 target pages gained outbound entries pointing to prompt-caching — glossary/agent-engineering (production-discipline element), glossary/agentic-memory (adjacent mechanism with explicit distinction), glossary/tool-use (tool definitions are high-leverage cache targets), glossary/llm (underlying tech), glossary/rag (RAG context is cacheable), glossary/embeddings (semantic caching builds on embedding similarity), glossary/advisor-strategy (composes with caching), glossary/automation-eats-execution (execution-layer optimization instance), tools/claude-managed-agents (TTL change affected Managed-Agent workloads).

Index + llms.txt updated (per the same discipline). New entry added to index; llms.txt stats bumped (141 → 142 pages, glossary 59 → 60). The May 18 gap-analysis cycle is now fully closed.

Stats: 1 new page (~2,500 words) + 9 back-link additions + 3 metadata files updated.

Why this matters architecturally: the agent-engineering cluster (agent-engineering + tool-use + guardrails + agentic-memory + claude-managed-agents + advisor-strategy + agent-payment-protocols) now has a complete production-discipline coverage including the cost-optimization layer. Practitioners reading the cluster end-to-end can build a production agent application with informed decisions on capability surfaces, tools, guardrails, memory architecture, payment infrastructure, model-pairing economics, AND cost-optimization caching strategy. This is the deepest production-AI-engineering cluster the wiki has built.

The May 18 gap-analysis cycle is now fully closed:

✅ Tier 1 #1 — Agent payment protocols (morning session 1)
✅ Tier 1 #2 — Claude Managed Agents May release wave (morning session 1)
✅ Tier 1 #3 — Competitor-analysis domain build-out (afternoon session 2)
✅ Tier 2 #4 — SEO/GEO refresh against 2026 zero-click data (evening session 3)
✅ Tier 2 #5 — Prompt caching glossary entry (this session, late evening 4)

Session-day totals (May 18, four sessions): 6 new pages + 1 pillar rebuild + 7 substantial extensions + ~45 back-link additions + 5 deep-research passes (~25 WebSearch queries total). Wiki total: 142 pages, 60 glossary entries.

(Lint correction 2026-05-18 late: original log entry overclaimed “9 new pages.” Actual git history shows 6 new pages today — agent-payment-protocols, win-loss-analysis, battlecards, share-of-model, zero-click-strategy, prompt-caching — plus the competitor-analysis/overview rebuild. May 15 remains the highest new-page-count day at 10 new pages; May 18 is the highest cluster-breadth day, with substance-first depth across five distinct clusters: agentic-commerce infrastructure, Claude Managed Agents updates, competitor-analysis methodology, SEO/GEO refresh, and production cost optimization.)

[2026-05-18] create + update | SEO/GEO refresh against 2026 zero-click data — new zero-click strategy page + 3 substantial extensions

Third session of May 18. Per Tier 2 #4 from the May 18 morning gap analysis: refresh the SEO/GEO pages (last touched April 2026) against the May 2026 zero-click data. The domain pre-dated the most material findings of the year — Google AI Mode hitting 75M users, AI Mode at 92-94% zero-click, E-E-A-T as binary AI visibility filter, brand-mentions-3x-stronger-than-backlinks correlation.

Research depth: 4 parallel WebSearch passes covering zero-click strategy (the 64.82% reality and strategic response), E-E-A-T off-site validation (third-party authority signals, the 96% citation finding), Google AI Mode adoption metrics (75M daily users, 4x growth in 2 months, 92-94% zero-click), and the broader “is SEO dead” framing (organic traffic decline, brand-squeeze effect, dual-mandate operating model).

Key findings synthesized into the pages:

Zero-click reality: 64.82% in 2026 Google overall (50% in 2019); 77% mobile; 92-94% in Google AI Mode. For every 1,000 US searches, only ~360 visit a non-Google-owned site.
Google AI Mode adoption: 75M daily users; ~4× growth in 2 months (0.25% of sessions in early May → 1%+ by early July); average query 7.22 words (almost 2× traditional 4.0); average session 49 sec (77 sec for brand-comparison); only 6-8% of sessions visit external sites; 1-3 sources cited per response (compressed from traditional Google’s 10+).
E-E-A-T as binary AI visibility filter: 96% of AI Overview citations come from sources with strong E-E-A-T signals. The 2026 mechanism shift — AI engines use E-E-A-T as a binary gatekeeping filter, not a soft ranking signal.
Brand mentions vs. backlinks (the assumption-inverting finding): Brand mentions correlate 3× more strongly with AI Overview visibility than backlinks (0.664 vs. 0.218). Domain Authority predicts less than 4% of AI citations. The DA score the SEO industry has spent 15+ years gaming is no longer load-bearing.
Earned media (Forbes, industry publications): 90% of AI citations come from these sources, with citation value lasting 18-24 months. PR strategy is now SEO strategy.
Author-entity verification is the new load-bearing E-E-A-T mechanism — consistent publishing depth, real author identity, named credentials, citation history.
Brand-squeeze effect: Top 10 sites grew ~1.6%; sites ranked 100-10,000 saw the sharpest declines. The middle of the long tail is collapsing while the head consolidates.
AI Overviews appear in 89% of brand searches, reduce position-1 CTR by up to 58% (some sources 61%), and only 38% of pages cited in AI Overviews rank in top 10 in traditional Google (and that overlap is dropping).
The dual mandate: optimize for both traditional rankings (transactional, navigational, long-tail AI under-answers) AND AI engine citations (GEO/AEO).

New page:

seo/zero-click-strategy — Comprehensive strategic-operating-model page (~5,000 words). Why the traffic-first model is broken (top-of-page real estate shrinking, brand-squeeze effect, query intent migration). The 2026 strategic response (on-SERP presence + AI-answer citation + off-site authority + branded search volume). The four changes: (1) treat on-SERP presence as the goal not the path to a click, (2) optimize for AI-answer inclusion not just traditional ranking, (3) build off-site E-E-A-T signals, (4) measure visibility not just traffic. When traditional SEO still works (transactional, navigational, long-tail-AI-under-answered, local, highly-visual, depth-research queries). The dual-mandate operating model. The hardest part — admitting traffic-first KPIs are broken. Content strategy implications.

Substantial extensions:

seo/geo-aeo-benchmarks-2026 — Added major new “May 2026 update” section. Google AI Mode adoption data (75M daily users, 4× growth in 2 months, comparison table vs. traditional Google). E-E-A-T as binary filter with the 96% citation finding. The brand-mentions-3x-stronger-than-backlinks correlation. The four load-bearing E-E-A-T signals (earned media + author-entity verification + Wikipedia + topical depth + schema). The dual-mandate framing. Updated TL;DR + frontmatter description + tags + updated date.
seo/ai-visibility — Added major new “May 2026 update” section with the three load-bearing data shifts (Google AI Mode adoption + E-E-A-T binary filter + brand-mentions-vs-backlinks correlation). Connected to share-of-model and competitor-analysis cluster. Updated TL;DR + frontmatter description + updated date.
seo/agentic-search-optimization — Added major new “May 2026 update — off-site E-E-A-T finding” section. Era-by-era table showing the optimization-discipline shift from 2015-2020 to 2025-2026. The four new load-bearing signals (earned media / author-entity verification / topical authority / Wikipedia). The 2024-vs-2026 ASO priority shift table. Added Google AI Mode section (75M users, 1-of-3 vs. 1-of-10 competition). Updated TL;DR + frontmatter.

Back-links wired (per the schema discipline): 6 SEO pages all have outbound entries pointing to seo/zero-click-strategy (the new pillar) — agentic-search, ai-seo-content, new-site-ranking, ai-visibility, agentic-search-optimization, geo-aeo-benchmarks-2026. Plus glossary/share-of-model gained a back-link to zero-click-strategy. Plus several SEO pages gained back-links to glossary/share-of-model and competitor-analysis/overview that they were missing.

Index + llms.txt updated (per the same discipline). New SEO page added to index with updated descriptions for all 6 existing SEO pages. llms.txt stats bumped (140 → 141 pages; seo 6 → 7), Pillar pages section gained zero-click-strategy entry, date stamps confirmed.

Stats: 1 new page (~5,000 words) + 3 substantial extensions (~2,500 words added across the three) + 7 back-link additions + 3 metadata files updated. Total new content: ~9,000 words.

Why this matters architecturally: the SEO domain was the most data-stale cluster in the wiki — 6 pages all written before the May 2026 AI Mode launch and the E-E-A-T binary-filter finding. The refresh is the kind of work that’s least glamorous (no new framework, no new insight) but most critical (the existing pages were quietly misleading readers about the 2026 reality). The wiki’s data-currency is now consistent across the SEO + competitor-analysis + agentic-commerce clusters, which is structurally important because these clusters connect at multiple points.

Closing 4 of 5 May 18 gap-analysis priorities:

✅ Tier 1 #1 — Agent payment protocols (morning session 1)
✅ Tier 1 #2 — Claude Managed Agents May release wave (morning session 1)
✅ Tier 1 #3 — Competitor-analysis domain build-out (afternoon session 2)
✅ Tier 2 #4 — SEO/GEO refresh against 2026 zero-click data (this session)
⏳ Tier 2 #5 — Prompt caching glossary entry (last remaining)

Session-day totals (May 18, three sessions): 5 new pages + 1 pillar rebuild + 4 substantial extensions + ~35 back-link additions + 4 deep-research passes (8 + 4 + 4 + 4 WebSearch queries total). Wiki total: 141 pages, 59 glossary entries.

[2026-05-18] create + update | Competitor-analysis domain build-out — methodology pillar + 3 glossary entries

Same-day session after the morning’s agent-payment-protocols work. Per the May 18 deep-research output Tier 1 #3 (competitor-analysis as a one-page domain — the persistent structural gap flagged for weeks), this session closes the gap with a methodology-first pillar rebuild + 3 named-framework glossary entries.

The user’s framing was explicit: “make a research about the approach on how to perform this type of analysis” — methodology, not tool reviews. The research surfaced that the methodology gap is bigger than the tool gap. The 2026 practitioner literature converges on a single diagnosis: “Most teams treat competitor analysis as a project when it needs to be a system. A competitive analysis is a system, not a deliverable.”

Research depth: 4 parallel WebSearch passes covering competitor-analysis methodology (vs. SWOT/Porter’s Five Forces critique), win-loss analysis (Klue/Crayon convergence), battlecard structure (2026 evolution from static to living), and Share of Model (the AI-search competitive dimension).

Key findings synthesized into the pages:

Why traditional frameworks fail operationally, not analytically. SWOT/Porter produce findings but don’t trigger decisions. Porter’s framework treats information as something firms use to make decisions; doesn’t address information-as-product (the data-moat problem). Real strategic agility requires governed, real-time response cycles, not periodic strategic exercises.
Win-loss is the only CI layer that reliably moves win rate. 5-15pp improvement in 6 months for mature programs. Klue/Crayon converge on 8 best practices: organizational buy-in → defined goals → senior decision-makers (not program managers) → deals competed to bitter end → interview buyers directly, not sales reps (the most counterintuitive practice) → timing within 90 days (30 days for high-performance, 70% benchmark) → multi-source triangulation → both quantitative and qualitative analysis.
Static battlecards are dying. 2026 living battlecards are modular, AI-assisted, role-specific (SDR/BDR/AE/CSM), with mandatory governance metadata (Safe-to-Share Status, Version Number, Compliance Notes). One-screen rule: if it doesn’t fit on one screen, reps won’t use it mid-call. Success metric: win-rate improvement against the named competitor, not completeness or adoption.
Share of Model is the new AI-search competitive dimension that didn’t exist in 2024. ChatGPT holds ~79% of generative AI traffic; AI Overviews in 89% of brand searches; 64.82% Google zero-click (93% in AI Mode). Sector concentration is extreme — Apple = 54.38% mention share in consumer electronics. For non-dominant brands, specialization is the only viable path — the AI-search-layer instance of the glossary/super-niche pattern.

New / rebuilt pages:

competitor-analysis/overview (rebuilt as methodology pillar, ~4,500 words). Five operational layers: win-loss analysis (Layer 1, highest-leverage), continuous monitoring (Layer 2, replacing quarterly slides), battlecards (Layer 3, sales-enablement), share of model (Layer 4, new AI-search dimension), creative reverse engineering (Layer 5, wiki’s existing tactical strength). The decision-relevance test (“what specific decision will this output change?”) as the production discipline. Operational rhythm table specifying owner + cadence + decision linkage per layer. AI-changes-the-methodology section: AI compresses execution work; strategy stays human. Theater-vs-load-bearing diagnostic. 60-day minimum-viable-CI-program path.
glossary/win-loss-analysis (new, ~3,500 words). Full Klue/Crayon 8-practice methodology with rationale per step. What the output looks like at maturity (win-rate breakdown by competitor, reason taxonomy with frequencies, quote library, trend tracking). How AI compresses analysis (transcription, coding, pattern extraction, quote mining) while interviews stay human. 6 honest-limits items.
glossary/battlecards (new, ~3,000 words). 2026 essential structure (9 components including governance metadata). Static-to-living evolution. One-screen rule deep-dive. Role-specific cuts table (SDR/BDR/AE/CSM). AI’s three layers of compression (content generation, maintenance, distribution). 5 failure modes (“built-but-unused,” “stale-and-trusted,” “generic positioning,” “adversarial tone,” “coverage over impact”).
glossary/share-of-model (new, ~3,500 words). Three sub-metrics (Share of Voice in AI, Citation Share, prompt-bucket coverage). Supply-side variables driving AI citation (off-site authority, citation density, content structure, topical authority, Wikipedia presence, schema, comparison content). Sector concentration data + the specialization-as-only-path insight (super-niche at AI-search layer). 2026 measurement infrastructure landscape. Why cross-platform measurement is required. 7 honest-limits items.

Why these four together: they form an internally-coherent operational stack. The pillar is the methodology synthesis; the three glossary entries are the named-framework citation anchors for the layers that didn’t already have them (Layer 5 / creative reverse engineering was already covered via glossary/creative-formula-vs-creative-skin and the ad-alchemy case work).

Back-links wired (per the schema discipline added in May 14 lint): 8 target pages gained outbound entries pointing to the new pages — seo/ai-visibility, seo/agentic-search, glossary/geo-aeo, glossary/super-niche, glossary/automation-eats-execution, glossary/honest-assessment, marketing/ai-tells-in-sales-copy, glossary/recognition-primed-decision. The existing competitive-tactical pages (cases/ad-alchemy-creative-reverse-engineering, cases/agenica-competitor-ads, experiments/ad-alchemy-competitor-piggyback, glossary/creative-formula-vs-creative-skin) already pointed at competitor-analysis/overview — bidirectional discipline already in place there.

Index + llms.txt updated (per the same discipline). Competitor-analysis domain section in index rebuilt with the new description; 3 new glossary entries added. llms.txt stats bumped (137 → 140 pages, glossary 56 → 59), competitor-analysis section description updated to reflect the methodology framing, Pillar pages section gained competitor-analysis/overview entry.

Stats: 1 rebuilt page (pillar, ~4,500 words, replacing 580 words) + 3 new glossary entries (~3,000-3,500 words each) + 11 back-link additions + 3 metadata files updated. Total new content: ~14,000 words.

Why this matters architecturally: the competitor-analysis domain was the longest-flagged structural gap in the wiki — a one-page domain in an otherwise dense knowledge base. The gap is now closed, and closed methodology-first per the user’s framing. The new pillar names the operational pattern that the existing tactical content (creative reverse engineering case studies, ad monitoring case) was implicitly applying. The cluster prediction matches the wiki’s broader thesis (the glossary/automation-eats-execution pattern: AI compresses execution work; strategy stays human-leveraged) — competitive intelligence is another domain instance.

Closing the May 18 gap analysis: today’s two sessions (morning agent-payment-protocols + afternoon competitor-analysis) close 2 of the 3 Tier 1 priorities from the May 18 deep research. Remaining open: Tier 2 #4 (SEO/GEO refresh against 2026 zero-click data — partial coverage now exists via the new share-of-model entry) and Tier 2 #5 (prompt caching glossary entry).

Session-day totals (May 18, sessions 1+2): 1 new page + 3 substantial extensions (morning: agent-payment-protocols + extensions to agentic-commerce, claude-managed-agents, agentic-memory) + 3 new pages + 1 pillar rebuild (afternoon: win-loss-analysis + battlecards + share-of-model + competitor-analysis/overview rebuild) = 4 new pages + 3 extensions + 1 rebuild after two sessions. ~25 back-link additions. Two deep-research passes.

[2026-05-18] ingest + create + update | Agent payment protocols + Claude Managed Agents May 2026 release wave

Monday session after a weekend gap. Per the May 18 deep-research output, this session executed the top two ingest priorities (#1 + #2 from the gap analysis): the agent-to-agent payment infrastructure that shipped April–May 2026, and the Anthropic Managed Agents feature wave from May 7. These two are coupled — Managed Agents is a deployment surface that increasingly competes on payment-protocol support; AWS Bedrock AgentCore Payments shipped 11 days before this session with native x402.

Research depth: 8 WebSearch passes across protocol mechanics (AP2 specification details, x402 HTTP-native implementation, Anthropic Project Deal experiment findings, AWS Bedrock AgentCore Payments launch) + Claude product updates (Cowork enterprise features, Dreaming, Outcomes GA, multi-agent orchestration GA, finance + legal templates).

Key findings synthesized into the pages:

Four-protocol stack has emerged Sep 2025 – May 2026: AP2 (Google, authorization, JSON-LD W3C Verifiable Credentials with ECDSA P-256 / SHA-256), x402 (Coinbase + Cloudflare, HTTP 402 status code revival, ~2-second settlement), UCP (Google + Shopify + Etsy + Wayfair + Target + Walmart commerce schema), Visa TAP (Visa + Cloudflare agent-identity verification at edge). The protocols are layered, not competing in most cases.
x402 production scale (March 2026): 119M transactions on Base + 35M on Solana, ~$600M annualized volume, zero protocol fees. Stablecoin micropayments are economically viable in a way credit-card rails couldn’t make them.
AWS Bedrock AgentCore Payments (May 7, 2026) — most consequential single deployment. Native x402, choice of Coinbase CDP or Stripe Privy wallet, session-level spending limits, full audit trail, “without interrupting the agent’s reasoning loop.” Plus the Coinbase x402 Bazaar through AgentCore Gateway — 10,000+ x402 endpoints discoverable by agents at runtime.
OpenAI Instant Checkout shut down March 2026 — the third-position aggregator approach lost. The market consolidated to Amazon closed-loop vs. Google-coalition open-protocol.
Anthropic Project Deal (April 2026) — 69 employees × $100, 186 deals worth >$4,000 in one week. Documented the “agent quality gap” — users represented by less capable models get objectively worse outcomes and don’t notice. Model capability dominated prompt framing.
US legal void — consumers have no clear dispute rights when AI agents transact. Existing consumer-protection law (Fair Credit Billing Act, UCC, chargeback regimes) wasn’t written for this case.
Claude Managed Agents May 7 2026: Dreaming (between-session memory consolidation, research preview), Outcomes now public beta, multi-agent orchestration now public beta. Cowork enterprise features (RBAC, group spend limits). 10 finance agent templates + 20+ legal MCP connectors + 12 practice-area plugins. Agent view in Claude Code for multi-session CLI management.

New page:

glossary/agent-payment-protocols — Comprehensive named-framework page (~4,000 words). Covers the four-layer stack (commerce schema → identity/verification → authorization → settlement), AP2 deep-dive (Mandates as W3C Verifiable Credentials), x402 deep-dive (HTTP 402 + USDC on Base, production metrics, x402 Foundation governance), UCP (open-protocol counterpart to Amazon’s closed-loop), Visa TAP (agent identity at Cloudflare edge), AWS Bedrock AgentCore Payments (May 7 hyperscaler realization), the closed-loop vs. open-protocol dynamic, the Project Deal “agent quality gap” finding, and the US legal void. Honest limits flag protocol youth (8-month-old standards), volume-vs-partnership conflation risk, and the n=69 single-experiment status of the agent-quality-gap finding.

Substantial extensions:

automation/agentic-commerce — Added a major new section “The infrastructure layer (April–May 2026 update)” covering the four-protocol stack with table, the x402 production scale, AWS Bedrock AgentCore Payments, the closed-loop vs. open-protocol dynamic with comparison table, and OpenAI Instant Checkout shutdown. Added a new section on the agent quality gap from Project Deal. Added a new section on the legal void. Bumped frontmatter description + updated date + payment-protocols tag. The page (originally April 14) now correctly reflects May 18 infrastructure reality.
tools/claude-managed-agents — Promoted Outcomes from Research Preview to Public Beta. Promoted Multi-Agent from Research Preview to Multi-Agent Orchestration Public Beta. Added a substantial new “Dreaming” section covering both operating modes (automatic vs review-before-landing), the connection to glossary/agentic-memory (Dreaming as production realization of episodic → procedural memory consolidation), and the new failure mode it introduces. Added a “Cowork enterprise features” section covering RBAC + group spend limits + agent view in Claude Code. Added a “Vertical agent templates” section covering 10 finance + 20 legal templates as the verticalization move.
glossary/agentic-memory — Added a substantial new “Anthropic’s Dreaming” section. The cluster prediction (memory must be engineered, not built-in) is being validated and partly automated by vendor releases within days of the wiki entry. Dreaming consolidates episodic memory into procedural memory at the platform layer; the new failure mode (wrong patterns consolidated with confidence) gets review-before-landing mode as structural mitigation. Updated Key Takeaways to reflect the change.

Why this matters architecturally: the agentic-commerce cluster (automation/agentic-commerce + glossary/ai-agent-behavior + glossary/agent-adoption-frictions + glossary/agent-payment-protocols) now has all four sides covered:

Side	Page	What it addresses
Category	automation/agentic-commerce	The $1T market shift
Agent biases	glossary/ai-agent-behavior	What agents choose (Columbia/Yale + Cialdini-on-AI)
User-side trust	glossary/agent-adoption-frictions	Whether users let agents act (Wharton frictions)
Infrastructure	glossary/agent-payment-protocols	How agents technically transact (AP2 / x402 / UCP / TAP)

Plus the agent-quality gap finding cuts across all four — it’s a new asymmetry dimension that the cluster needed.

Cluster-coherence win: Dreaming shipped 8 days after the glossary/agentic-memory glossary entry described memory as a 4-layer architecture needing engineering. The wiki’s prediction was correct and is being partly automated by vendor releases within the cluster’s first month. Naming the connection in the wiki entry strengthens credibility for both pages.

Back-links wired (per the schema discipline): glossary/ai-agent-behavior, glossary/agent-adoption-frictions, glossary/agent-engineering, glossary/tool-use, glossary/guardrails, marketing/preparing-for-agentic-ai, seo/agentic-search, tools/claude-managed-agents, automation/agentic-commerce all gained outbound entries pointing to the new agent-payment-protocols page. The bidirectional discipline is now mechanical.

Index + llms.txt updated (per the same discipline). New page added to index. llms.txt stats bumped (136 → 137 pages, glossary 55 → 56), Pillar pages section gained agent-payment-protocols entry, date stamps bumped.

Stats: 1 new page + 3 substantial page extensions + 9 back-link additions + 3 metadata files updated. The new page is the second-most-comprehensive single piece the wiki has shipped (~4,000 words covering 8 months of protocol development across 5 organizations).

Remaining from the May 18 gap analysis:

Tier 1 #3: Competitor-analysis domain build-out (still 1 page — the persistent structural gap)
Tier 2 #4: SEO/GEO refresh against 2026 zero-click data (64.82% / 93% in AI Mode)
Tier 2 #5: Prompt caching glossary entry

[2026-05-15] create | Marketing analytics gap closed — pillar page + 3 glossary entries (MMM, incrementality, cohort analysis)

Per the May 15 gap analysis (Tier 1 #2), the wiki had zero coverage of marketing analytics — MMM, incrementality, attribution-in-2026, cohort analysis, LTV:CAC. Real gap for a CMO-targeted wiki given that AI is reshaping all of these (Meta Advantage+ ML attribution; MMM renaissance post-cookie-deprecation; data clean rooms; cohort/LTV becoming non-optional disclosure). Four new pages built today close the gap.

Research depth: Four parallel WebSearch passes across MMM renaissance, incrementality testing best practices, attribution after iOS 14.5, and cohort/LTV/CAC benchmarks. Key findings synthesized into the pages:

MMM adoption surged 212% since 2023 (April 2026: 26% of US marketers vs. 9% in 2023). 46.9% plan to increase MMM investment in next 12 months.
Google’s Meridian (open-source MMM, late 2024) dropped cost-of-entry from six-figure consulting to “weeks of in-house data-science.”
Google’s Scenario Planner (February 2026) — no-code UI on top of Meridian — democratized MMM for mid-market brands without data-science teams.
AI lifts MMM holdout fidelity by 22 points over deterministic-only models.
Meta attribution deteriorated 40–60% since iOS 14.5; January 2026 deprecated attribution windows accelerated the decline. Meta’s ad revenue still exceeded pre-ATT trajectory by 2025 (AI-powered creative + Reels + clean CAPI pipelines for large advertisers).
Data clean rooms improve cross-channel accuracy up to 40% (vendor-directional figure).
Incrementality 2026 consensus: 10–20% holdout size, synthetic controls beating matched-market designs for geo, “fewer tests that materially change decisions” rather than more tests.
2026 LTV:CAC benchmarks: B2B SaaS 3.2:1 median (top quartile 4:1–6:1), DTC subscription 4.1:1 (replenishment categories reached SaaS parity for the first time in 2026, driven by 102% NRR + stable CAC).
The shape of the retention curve > Month-12 endpoint. The M0–M3 “onboarding cliff” drives LTV more than any other input. Once a DTC customer buys twice, retention is typically 85–90% from that point forward. First-to-second purchase rates by vertical: supplements/consumables 30–40%, beauty 25–35%, fashion 15–25%.

New pages:

marketing/marketing-analytics-in-2026 — Comprehensive pillar (~3,500 words). The cookieless attribution stack, the dual-model operating norm (MMM strategic + multi-touch tactical + AI reconciliation), data clean rooms, server-side conversion APIs, platform AI (Advantage+, PMax). The four-layer 2026 operating stack: strategic budget allocation (MMM, quarterly), tactical optimization (multi-touch + platform AI, weekly), causal validation (incrementality testing, quarterly), capital-efficiency check (cohort LTV:CAC, monthly).
glossary/marketing-mix-modeling — Named-framework deep-dive. Top-down statistical attribution; aggregate spend + outcome data, no cookies needed. The mechanics (regression + adstock + saturation), the 2026 production landscape (Meridian, Robyn, Scenario Planner, commercial managed-service tier), AI’s role (data prep, hierarchical pooling, uncertainty quantification, scenario simulation).
glossary/incrementality-testing — Named-framework deep-dive on the causal-validation layer. Three test designs (geo-holdout, audience-split, time-based) with use-case differentiation. 2026 best practices: 10–20% holdout, synthetic controls, fewer tests that materially change decisions. The validation pattern (three operational moments where incrementality has most leverage): before large budget shifts, when attribution looks too good, for new channels with no track record.
glossary/cohort-analysis — Named-framework deep-dive on the unit-economics layer. The shape of the retention curve > the M12 endpoint. Surviving-cohort LTV (M3+) + NRR + CAC payback as production-grade signal. The aggregate-LTV failure modes the dashboard hides (mixed-segment averaging, survivorship bias, failed-channel CAC, payback period exceeding runway).

Why these four together: they form an internally-coherent operating-stack cluster. The pillar page synthesizes the four layers (attribution → MMM → incrementality → cohort/LTV); the three glossary entries serve as citation anchors that other pages can reference without re-explaining the underlying concepts. The cluster fills the wiki’s biggest remaining gap for CMO-targeted content.

Architectural connections:

glossary/automation-eats-execution now has the marketing-analytics domain as another empirical anchor. AI compresses execution (data prep, model fitting, multi-touch attribution table generation inside clean rooms); strategy (which decisions matter, how to allocate budget, when to trust attribution vs. incrementality) stays human-leveraged.
glossary/creative-is-new-targeting gets paired context — post-ATT, multi-touch attribution broke at the user level, which is part of why creative leverage rose. The marketing-analytics page documents the upstream attribution reality.
marketing/discovery-before-scale gains incrementality-testing as the channel-layer instance — same validation-before-volume math (Pirolli & Card) applied at the marketing-channel layer.
automation/agentic-commerce gains cohort-analysis as the complication — AI agents as customers change what a “cohort” means at the unit-economics layer.
marketing/influencer-marketing-task-overload gains the post-ATT attribution context (MMM increasingly used for influencer-channel allocation; incrementality testing for individual creator measurement).

Back-links wired (per the schema discipline): glossary/automation-eats-execution, comparisons/strategy-vs-execution-ai, glossary/creative-is-new-targeting, marketing/discovery-before-scale, automation/agentic-commerce, marketing/influencer-marketing-task-overload, marketing/overview all gained back-links to the new pages.

Index + llms.txt updated (per the same discipline). All 4 new pages added to index. llms.txt stats bumped (to 136 pages, marketing 17 → 18, glossary 52 → 55). The week’s delta is now +4 marketing / +10 glossary.

Stats: 4 new pages + 9 back-link additions + 3 metadata files updated. The pillar page is the third comprehensive single-topic deep-dive shipped today (alongside ai-human-voice-prompting and the agent-cluster build-out from the foundational-glossary session).

Session totals (May 15, end of day):

10 new pages total: 1 AI-human-voice (marketing/ai-human-voice-prompting) + 5 foundational glossary entries (hallucination, agentic-memory, tool-use, guardrails, embeddings) + 4 marketing-analytics pages (1 pillar — marketing/marketing-analytics-in-2026 — plus 3 glossary entries: marketing-mix-modeling, incrementality-testing, cohort-analysis).
~50 back-link additions across the wiki to enforce bidirectional discipline.
3 deep-research sessions (8 WebSearch passes for human voice; 4 for marketing analytics; topic-specific lookups for the foundational glossary).
Wiki total: 136 pages, 55 glossary entries.

Why this day matters architecturally: the wiki has filled two of the three highest-priority gaps from the May 15 morning analysis (Tier 1 #2 marketing analytics + Tier 1 #4 foundational glossary), plus shipped the deep AI-human-voice page that the user flagged as “very important.” The remaining structural gap (competitor-analysis as a one-page domain) is the natural next session.

[2026-05-15] create | Foundational AI glossary gap closed — 5 new entries (hallucination, agentic-memory, tool-use, guardrails, embeddings)

Per the May 15 gap analysis (Tier 1 #4), the wiki had heavy citation density for foundational AI concepts without dedicated pages: hallucination (6 page refs, no page), memory/agentic-memory (15 refs, no page), tool-use (2 refs but agent-cluster-relevant, no page), guardrails (2 refs but agent-cluster-relevant, no page), embeddings (3 refs, no page). Five glossary entries created to close the gap.

New pages:

glossary/hallucination — The signature LLM failure mode. Structural cause: probabilistic next-token prediction without an internal “I don’t know” mode. Most common in: specific named entities, recent events past training cutoff, specific quantitative claims, niche topics, fabricated citations. Less common in: well-established frameworks, common-knowledge questions, in-context transformation tasks. Connects to glossary/jagged-frontier (the −19pp accuracy collapse outside the frontier is hallucination at scale), glossary/recognition-primed-decision (Klein-Kahneman predicts where hallucination is rare vs. reliable), glossary/honest-assessment (the trust-signal counterpart), marketing/ai-tells-in-sales-copy (factual overreach pattern #9 is hallucination in marketing copy).
glossary/agentic-memory — Four-layer architecture (working / episodic / semantic / procedural). Memory is engineered, not built-in. The 2026 production landscape: Anthropic Skills + Cowork (file-based), OpenAI Memory feature, Mem0/Letta/Zep purpose-built infrastructure, MCP for cross-platform access. The wiki itself is one realization of semantic memory. Connects load-bearing to glossary/agent-engineering (production discipline element), glossary/context-engineering (paired discipline), glossary/llm-wiki-pattern (knowledge-work realization), strategist-pattern (operational architecture), glossary/rag (retrieval mechanism).
glossary/tool-use — The capability that constitutes the agent category. Mechanics: model gets tool descriptions → user request → model decides which tool + arguments → application executes → result flows back → model continues. The 2026 production landscape: function calling primitives, Model Context Protocol (MCP) as emerging cross-vendor standard, managed-platform tool runtimes. Tool design is the discipline — narrow scope, clear descriptions, structured parameters, operational error messages. Karpathy’s “neural networks as operating systems” prediction frames tools as the deterministic substrate the LLM-as-host calls into.
glossary/guardrails — Production safety layer. Six basic categories from Pimenov’s playbook (input filtering, AI verification, output redaction, minimal permissions, destructive-action confirmations, token/budget limits). Modern patterns: HITL approval, pattern-based authorization, sandboxing, audit trails, constitutional-AI. Paired with glossary/tool-use — every powerful tool needs a corresponding guardrail. The interesting failure cases are compositional — individual guardrails work but their interaction creates emergent vulnerabilities. Constitutional/prompt-based guardrails can be bypassed by persuasion (Cialdini-on-AI: 33.3% → 72% compliance per the 28K-prompt study).
glossary/embeddings — Numerical representations of text (or images/audio/video) that preserve semantic similarity. The pipeline: chunk content → embed → store in vector DB → query-time embed → nearest-neighbor search → return source links. Production landscape (mid-2026): OpenAI text-embedding-3-large/small, Voyage AI (Anthropic partnership), open-source (nomic, bge), domain-specific (financial, legal, medical). Vector databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector. Multimodal embeddings production-ready in 2025–2026.

Why these specific five together: they form an internally-coherent foundational-concepts cluster for the agent/LLM domain. Each connects to multiple existing pages. The combined effect: any future page that wants to cite “hallucination,” “agent memory,” “tool use,” “guardrails,” or “embeddings” now has a glossary anchor rather than a parenthetical explanation.

Back-links wired (per the schema discipline added in the May 14 lint): 9 target pages gained back-links to the new entries — glossary/llm (3 new entries — hallucination, embeddings, tool-use), glossary/rag (3 new entries — hallucination, embeddings, agentic-memory), glossary/ai-agent (3 new entries — tool-use, guardrails, agentic-memory), glossary/agent-engineering (4 new entries — all of them), automation/ai-agent-organization (3 new entries — tool-use, guardrails, agentic-memory), tools/claude-managed-agents (3 new entries), glossary/honest-assessment (hallucination), glossary/jagged-frontier (hallucination), glossary/llm-wiki-pattern (agentic-memory + embeddings), glossary/recognition-primed-decision (hallucination).

Index + llms.txt updated (per the same discipline). All 5 new glossary entries added to index. llms.txt stats bumped (128 → 133 pages, glossary 47 → 52, “this week” delta now +3 marketing / +7 glossary). The 7-glossary count covers the 5 new today plus agent-engineering + agent-adoption-frictions from May 13.

Stats: 5 new pages + 17 back-link additions + 3 metadata files updated. Each new page averages ~800 words; together ~4,000 words of foundational-concept content.

Why this matters architecturally: the wiki now has full glossary coverage for the agent stack. Reading the agent-engineering cluster end-to-end (jagged-frontier → agent-engineering → tool-use → guardrails → agentic-memory → ai-agent-behavior → agent-adoption-frictions) is now a self-contained education in production AI agent design — no out-of-page hops to external sources required for the foundational vocabulary.

User flagged the AI-human-voice question as “very important” and asked for deep research before building. Eight parallel WebSearch passes across four substantive angles, then synthesized into a comprehensive new wiki page.

Research dimensions covered:

Detection science. Stylometric research (Nature 2025 + 2025–2026 LLM-detection papers) converges on: lower lexical diversity, common syntactic structures, average sentence length + function-word bigrams as top distinguishers; F1 ≈ 0.94 combined; recurring markers include echoed structures, rigid transitions, hedging, absent lived experience (the load-bearing finding). Em-dash specifically: GPT-4.1 produces them at 3.28× human rate (McGill OSS documented). Detection drops significantly when authors change tone, genre, or use AI assistance — the leverage point that makes hybrid AI+human work.
Empirical platform performance. Cold email: AI 4.1% reply rate vs human 5.2% (gap closed from 2.0pp in 2024 to 1.1pp in 2026); but deliverability is the killer: AI 71% inbox / 8% spam-flag vs human 86% / 3% (Prospectory n=10,000). AI emails average 1.6 exchanges vs 3.7 for human — AI dies in the follow-up, not the opener. Hybrid (verified intent signal + AI draft + human edit) hits 4.4%+; full personalization 8–20%.
Platform-specific algorithms. LinkedIn 360Brew (150B parameter LLM, deployed March 12, 2026, per Hristo Danchev LinkedIn Engineering Blog) reads posts semantically; saves > likes (50 saves beats 500 likes for algorithmic push); rewards topic consistency (2–3 core topics); detects AI patterns directly. X/Grok ranking detects AI text → likes but not replies/bookmarks (the higher-weighted signals); EU AI Act becomes fully applicable August 2, 2026. TikTok C2PA Content Credentials label synthetic visuals/voices but AI-assisted script writing is exempt. Low-quality AI TikTok content underperforms equivalent by 30–45%.
Prompting techniques. Few-shot literature: 2–5 examples optimal, diminishing returns past 4–5; quality > quantity. Ruben Hassid “Taste Interviewer” approach: 100-question structured interview across Beliefs / Writing Mechanics / Aesthetic Crimes / Voice & Personality → .md file capturing writer’s DNA. Banned-word lists converging on a 2026 consensus (em dashes max 1, generic transitions, summary clichés, “X, not Y” patterns, AI-vocabulary). The single most demoted AI structure: “Bold term: explanation sentence” bullet lists. Voice-from-audio (SuperWhisper dictation) as a sixth technique — voice is yours by construction, no imitation step. Brand-voice case studies: Adore Me (DTC lingerie, 36% reduction in stylist note time, 20hrs→20min product description batch), Unilever (multi-layer AI stack — “Alex” GPT API for email, “Homer” proprietary NN for Amazon listings). Zero documented case studies of 100% AI-unreviewed content at scale — the 80/95–5/20 hybrid ratio is empirically universal.

New page: marketing/ai-human-voice-prompting — comprehensive treatment covering:

Why this matters more in 2026 (the platform-detection layer is now a distribution constraint, not just a stylistic concern)
The detection science (stylometric markers, em-dash signal, the absent-lived-experience finding)
The six techniques that produce human voice:
1. Voice-profile document (.md, grounded in what you reject)
2. Few-shot examples (2–5, quality over quantity)
3. Banned-word list (the 2026 consensus)
4. Lived-experience anchors (most powerful, least-prompted — “every paragraph requires a specific named anchor or it gets cut”)
5. Voice-from-audio dictation (SuperWhisper — voice is yours by construction)
6. Audience-mode review (post-generation, per ai-tells page)
The 80/95–5/20 hybrid ratio as the empirical anchor (Adore Me + Unilever case studies)
Platform-specific applications: LinkedIn (under 360Brew), X (under Grok), TikTok and Instagram (script + caption), cold email outreach
The two-principle frame revisited (don’t sound like AI + model the reader’s motivation)
Common misconceptions (5 myths debunked)
Honest limits (6 caveats)

Architectural framing: this is the generation-side complement to marketing/ai-tells-in-sales-copy’s editing/audit-side discipline. Together they form a complete coverage of AI-generated-content quality: this page covers HOW to produce human voice; that page covers HOW to audit it. The pair is now the two-sided cluster for AI-content trust at the generation+editing layers, parallel to the glossary/honest-assessment ↔ marketing/ai-tells-in-sales-copy pair at the positive+negative trust-signal layers.

Back-links wired (per the schema discipline added in the May 14 lint): marketing/ai-tells-in-sales-copy, marketing/brand-voice-skills-guide, glossary/honest-assessment, glossary/recognition-primed-decision, glossary/vibe-coding, marketing/discovery-before-scale, strategist-pattern, marketing/social-commerce-psychology all gained outbound entries pointing to the new page. The bidirectional discipline is now mechanical from the schema update.

Index + llms.txt updated (per the same discipline). marketing/ai-human-voice-prompting added to index Marketing section. llms.txt stats bumped (127 → 128 pages, marketing 16 → 17), description updated with the new page entry under “Pillar pages,” date stamp bumped, “Last updated” footer bumped 2026-05-14 → 2026-05-15.

Stats: 1 new page + 8 back-link additions + 3 metadata files updated. The new page is the most comprehensive single piece the wiki has shipped — ~4,000 words covering research + techniques + platform tactics + case studies + misconceptions + limits.

Why this matters architecturally: the wiki has now built a three-page generation→editing→trust cluster for AI-content quality:

marketing/brand-voice-skills-guide — LLM-instruction implementation (SKILL.md architecture, 5-Example Method)
marketing/ai-human-voice-prompting — Techniques-and-empirics layer (six techniques, 2026 detection landscape, platform tactics)
marketing/ai-tells-in-sales-copy — Editing-and-audit layer (11-pattern catalog, two-principle frame, CMO-believability score)

Plus the trust-signal pair:

glossary/honest-assessment — positive trust signal
marketing/ai-tells-in-sales-copy — negative trust signal

This is the deepest single-topic cluster the wiki has built. Each page covers a distinct layer; together they form an integrated discipline.

Pending work (Tier 1 items from the May 15 gap analysis, still open):

Tier 1 #2: marketing analytics gap (MMM, incrementality, attribution-in-2026, cohort, LTV) — task #22 still pending
Tier 1 #4: foundational AI glossary entries (hallucination, agentic-memory, embeddings, guardrails, tool-use) — task #23 still pending

[2026-05-14] lint | Cluster lint after heavy-creation day — 16 back-links + llms.txt refresh

Focused lint on yesterday’s output (four new pages + six substantial extensions + ~25 new cross-links). The lint targeted five dimensions and produced one significant finding plus several smaller ones.

Dimensions checked:

Wikilink validity — extracted all ... references from the new and most-extended pages (7 files total, 38 unique target paths) and verified each target exists. 100% clean. No broken links.
Bidirectional cross-link sweep — for each Related outbound link from the 4 new pages, checked whether the target page has a back-link. Significant finding: most new-page outbound links were one-way. Yesterday’s session created the new pages with rich Related sections pointing outward, but didn’t always edit the target pages to point back. 16 back-links added across 14 target pages to close the structural gaps:
- glossary/recognition-primed-decision gained 3 back-links (agent-adoption-frictions, agent-engineering, ai-tells-in-sales-copy). RPD is the unifying Klein-Kahneman foundation under all three new pages — bidirectional coverage was load-bearing.
- glossary/jagged-frontier gained back-link to agent-adoption-frictions (the user-side calibration response to jaggedness).
- tools/claude-managed-agents, automation/ai-agent-organization, comparisons/managed-agents-vs-diy, automation/multi-agent-patterns, strategist-pattern, glossary/automation-eats-execution, comparisons/strategy-vs-execution-ai, glossary/ai-skill-leveling, glossary/llm-wiki-pattern all gained back-links to agent-engineering. The agent-engineering page sits at the intersection of these as the named discipline — bidirectional coverage was load-bearing for the cluster.
- glossary/vibe-coding, marketing/discovery-before-scale, marketing/telegram-marketing-channel gained back-links to ai-tells-in-sales-copy.
- glossary/honest-assessment gained back-link to agent-adoption-frictions (calibrated-uncertainty mechanism appears in both pages).
Architectural lesson worth recording: ship-day rhythm should include a “did the target pages get back-links?” step. The most common failure mode of high-velocity creation is that outbound links land but inbound doesn’t. Filing this as a CLAUDE.md INGEST-workflow improvement to consider — same shape as the archive-on-ingest convention the May 13 lint flagged.
Frontmatter compliance — all 4 new pages have valid frontmatter (title, description, status, created, updated, tags). Status growing is appropriate for all (substantive content, multiple sources, cross-refs, Key Takeaways). The canonical+author pattern is consistent: present on Andrej-authored synthesis pages (agent-engineering, ai-tells-in-sales-copy), omitted on pages that aggregate external research (agent-adoption-frictions = Wharton work, telegram = named-source aggregator). 100% clean.
Substantive consistency — five cross-cluster checks, all pass:
- Jagged-frontier (Dell’Acqua, human-pairing) vs. jagged-intelligence (Karpathy, model-capability) properly distinguished in both pages with side-by-side comparison tables. No unit confusion.
- Karpathy’s “>10x developer” claim properly flagged as directional/not-measured in 3 places in agent-engineering; Dell’Acqua’s +12.2% measured numbers correctly bounded as single-worker single-task. The two are not falsely conflated.
- Goldilocks autonomy aligned between agent-adoption-frictions (defines it) and claude-managed-agents (cites it as UX guidance). No drift.
- Pirolli-Card invocations across the validation-before-volume triangle (discovery-before-scale ↔ telegram-marketing-channel ↔ ai-tells-in-sales-copy) are consistent: discovery-before-scale carries the primary theorem citation; telegram-marketing-channel directly re-applies it to channel selection; ai-tells-in-sales-copy frames as parallel-shape without re-citing (correct restraint, no theorem-stretching).
- agent-adoption-frictions ↔ ai-agent-behavior use symmetric user-side/agent-side terminology. Both pages frame each other as counterparts; the wiring is reciprocal.
Index / log / changelog / llms.txt drift — all 4 new pages are in index (glossary count: 47 files / 47 entries listed, perfectly aligned). Log entries for all sessions present. Changelog complete. One finding: llms.txt stats were stale. Last refreshed 2026-05-12 with 125 pages / 45 glossary; now 127 pages / 47 glossary. Same drift pattern the May 12 lint flagged as a recurring failure mode — index.md updates didn’t trigger an llms.txt refresh.

llms.txt refresh: updated section counts (marketing 14→16, glossary 45→47), Stats section bumped (125→127 pages, 45→47 glossary entries, date 2026-05-12→2026-05-14), added entries for glossary/agent-engineering and glossary/agent-adoption-frictions under “Cross-domain frameworks”, added entries for marketing/telegram-marketing-channel and marketing/ai-tells-in-sales-copy under “Pillar pages”. The jagged-frontier entry now also mentions Karpathy’s “jagged intelligence” as the model-side cousin.

Why this matters architecturally: the lint surfaced two recurring failure modes that are now visible enough to deserve protocol improvements:

Back-link asymmetry on ship day. New pages create rich outbound Related sections; target pages don’t get reciprocal entries unless the ship-day editor specifically remembers to add them. The pattern compounds — every new page that ships without bidirectional discipline adds one-way links to the graph.
llms.txt staleness following index updates. The May 12 lint flagged this; the same drift recurred over the May 13 creation day. The fix is either tooling (auto-derive llms.txt stats from index.md) or process (any session that bumps the index runs a final llms.txt refresh).

Both will be useful CLAUDE.md INGEST-workflow improvements when next revised.

Stats: 16 back-links added across 14 files + llms.txt refreshed + 1 index/log/changelog updated. No new pages created.

[2026-05-13] update | AI-tells page — proactive reader-motivation section added (two-principle frame)

Strategist task 2026-05-13-ai-tells-page-proactive-reader-motivation.md (filed within minutes of the ai-tells page shipping) made the argument that the page’s audience-mode REVIEW beat is necessary but not sufficient. Argument-level mismatches (wrong structural pain for the vertical) survive any amount of voice polish; they only get caught when reader-motivation is modeled BEFORE drafting starts. The wiki page needed a paired proactive discipline.

Restructure of marketing/ai-tells-in-sales-copy:

The page now opens with a new top-level “Two principles for client-facing copy” section that names both disciplines as the load-bearing frame (Option A placement from the task — foregrounds the principles before the operationalizations). The two principles:

Don’t sound like AI. Surface failure modes — the original eleven-pattern catalog operationalizes this at the editing layer.
Model the reader’s motivation before drafting. Argument-level structural-pain modeling — the new “Model the reader before drafting” section operationalizes this at the writing-prep layer.

The user-articulation that triggered naming both together is now cited as the verbatim trigger in the Sources section: “The key is the ability not to sound like AI and also look from the perspective of the reader — understand the reader’s motivation.”

New “Model the reader before drafting” section — covers:

The DTC vs. iGaming structural-pain contrast (DTC CMO: rising paid CAC, attribution decay, creative fatigue, paid-only funnel fragility; iGaming brand owner: reach scarcity from paid bans on Meta / Google / Telegram official Ads, affiliate revshare drag, single-account ban risk, owned-audience absence as a category-level structural deficit). The two pains differ structurally, not just by vocabulary.
The iGaming reach-scarcity reference case (2026-05-13) — the canonical example for the principle. First-pass copy used DTC-style “rented FTDs vs. owned audience” framing, which implicitly assumes there’s a working paid channel to reduce dependency on. iGaming doesn’t have that — the paid surfaces ban gambling outright. The fix was argument-level (re-frame around “build reach where you’ve been structurally locked out”), not voice-level. No audience-mode review would have caught this; the catch had to happen at the writing-prep stage.
A 4-question proactive discipline (concrete-reader, structural pain, load-bearing reframe, what’s-not-being-addressed) for activating the principle before drafting starts.

Page-level edits:

Frontmatter description updated to lead with the two-principle frame.
TL;DR rewritten to lead with both principles rather than just the audience-mode review beat.
Audience-mode review section now explicitly named as “operationalizing Principle 1 at the editing layer” and explicitly paired with the proactive reader-motivation discipline.
Key Takeaways restructured to lead with the two-principle frame and the “Principle 2 catches what Principle 1 cannot” insight, with the iGaming reframe as the canonical case.
Sources section adds the reference case + the verbatim user-articulation trigger.
Heading restructure: the original “Why this catalog exists” and “The pattern catalog” H2s demoted to H3s under a new “The eleven-pattern catalog (operator-grade audit checklist)” H2, so the page now reads as a single-level principle-then-operationalization structure rather than three flat H2s in a row.

Index update: description for marketing/ai-tells-in-sales-copy rewritten to lead with the two-principle frame.

Architectural finding worth noting: this is the second case in two sessions where a strategist task came back to extend a wiki page within minutes of it shipping. (The Telegram operational-unlock extension was the first.) The pattern is healthy — the wiki page exposes the strategist’s articulation to a different reading discipline, which surfaces what’s missing. Two of three Telegram-cluster pages and one of one AI-tells pages have now been extended within their own ship day. The lesson is to expect strategist follow-up tasks on freshly-shipped pages and not consider any new page “final” on day one.

Strategist task closure: 2026-05-13-ai-tells-page-proactive-reader-motivation.md → status completed, with reference to the new section and the page-level restructure.

Stats: 1 page substantially restructured + 1 index entry rewritten + 1 strategist task closed.

[2026-05-13] create | AI tells in sales copy — 11-pattern catalog + audience-mode review discipline

Strategist task 2026-05-13-ai-tells-in-sales-copy.md (filed earlier same day, after the sales-page audit cycle for primores/2026-05-07-package-igaming-dtc-offer/tt-content-dtc.html) requested a wiki home for the pattern catalog and review methodology that moved that page from ~6.5/10 to 9/10 CMO-believability.

New page: marketing/ai-tells-in-sales-copy — codifies three layers:

The eleven-pattern catalog (table form). Each row names the tell, why it reads AI to operators, and the operator-language fix:
- Math notation in prose (“cadence × format mismatch”)
- Internal jargon leakage (“Discovery-Before-Scale operationalized”)
- Abstraction-over-concrete (“€0 incremental to reactivate”)
- Parallel-construction overdensity (4+ semicolon pairs in one section)
- Adjective stacking (4+ adjectives in a row)
- Coined-term over-use (“funnel-fragile” + “ads evaporate” + “uncapped tail” in one page)
- “X, not Y” pattern repetition 3+ times in close proximity
- Verbal tics (same load-bearing word 5+ times)
- Factual overreach in service of rhythm (the most damaging — sharp readers discount the whole doc on one false claim)
- Em-dash split overuse
- Strategist-memo voice (meta-commentary about the argument instead of stating substance)
The read-as-audience review beat. Two-pass discipline: writer-mode catches argument flaws; audience-mode catches tells. Different categories of failure; both passes non-optional for client-facing copy. The audience-mode pass requires naming the audience in concrete detail before re-reading (“busy e-com CMO at a $5–50M DTC brand reading from cold email” activates the right heuristics; “potential customer” doesn’t).
The CMO-believability score. Rough 1–10 self-assessment as forcing function for honesty. Most useful as a delta tool — track before/after each editing pass. A pass that doesn’t move the score worked at the wrong layer.

Architectural connections wired:

glossary/honest-assessment — same family of “what makes content trustworthy” thinking, flipped: honest-assessment is the positive trust signal; ai-tells is the negative trust signal. Both depend on the same underlying mechanism (audience reads writer’s judgment through the prose). Back-link added to honest-assessment’s Related.
marketing/brand-voice-skills-guide — the LLM-instruction side of the same problem. Brand-voice Skills define what to sound like; ai-tells defines what not to sound like. Complementary disciplines (instructions + audits). Back-link added to brand-voice-skills-guide’s Related.
marketing/discovery-before-scale — the operational analog at the content-quality layer. Don’t ship un-audited copy; don’t scale un-validated patterns. Validation-before-volume across two layers of marketing work.
glossary/recognition-primed-decision — Klein-Kahneman explains why operators detect tells in seconds rather than minutes. Pitch copy is a high-validity environment with rapid feedback; operator pattern-matching is reliable in this domain by construction.
strategist-pattern — the CMO-believability score is a strategist-pattern self-pressure heuristic at the artifact-review layer.

Scope decision (v1): kept the page focused on sales copy (LPs, sales pages, outreach) per the task’s tighter-v1 guidance. The “audience-mode review” methodology generalises to all client-facing copy (briefs, proposals, decks) and may warrant its own glossary entry later — left as a future-page candidate rather than over-scoping v1.

Page-level placement: added to index in Marketing section; added to marketing/overview Related.

Strategist task closure: 2026-05-13-ai-tells-in-sales-copy.md → status completed, with reference to the new page and the back-links it produced.

Stats: 1 new page + 2 back-link extensions + 1 strategist task closed.

Why this matters architecturally: the wiki has consistently named positive content-trust patterns (honest-assessment, brand-voice skills, distinctive-assets, geo-anchor). This is the first page to name negative content-trust patterns operationally. The honest-assessment ↔ ai-tells pair now forms a two-sided trust-signal cluster — what to do, what to avoid, both grounded in the same mechanism of operator pattern-recognition reading writer judgment through prose.

[2026-05-13] update | Telegram page — operational-unlock section + discovery-before-scale cross-link

Strategist task 2026-05-13-telegram-operational-unlock-extension.md (filed earlier same day, after the Telegram page shipped) requested a structural extension turning the page from “what Telegram is” into “what Telegram is + how you actually enter it.” No new external sources — pure reorganization of existing wiki material.

Added section: “Operational unlock: keyword × region discovery” (placed between “Audience footprint” and “iGaming: Telegram is a primary channel”). The section names the operational constraint that shapes everything below it: Telegram has no platform-level discovery surface, so entry is per-group rather than per-impression, and “buy your way in” doesn’t work the way it does on Meta or Google. The methodology:

Define keyword × region scope (both axes first-class).
Channel discovery via TGStat (closest thing Telegram has to a search index).
Vertical channel indexes for known categories (Data40’s 3,718-channel gambling inventory as the canonical example).
Filter on size + engagement; dead channels (large historical base, no recent posts) are noise.
Sample-budget test sizing (PropellerAds 2025: $50 minimum / $350 proper read).
Scale on validated channels only.

The key architectural move: this is marketing/discovery-before-scale applied to channel selection rather than to content patterns. Same Pirolli-Card Independence of Inclusion from Encounter Rate math — volume of placements in un-validated channels cannot raise their profitability; only validation can. Cross-link added to marketing/discovery-before-scale in both directions; the back-link from discovery-before-scale’s Related section names the connection (“Same math; the Telegram scout phase is exactly the Phase-1 discovery shape”).

Honest assessment subsection added: Telegram is a slower-entry channel; budget 2-4 weeks of scout work before paid placements make economic sense. For Tier-1-only brands, the audience-asymmetry is structural — channels will turn up, audience-fit math won’t. For brands with international SKU + multi-geo content already, the scout phase amortizes; the channel list becomes reusable infrastructure across campaigns.

Page-level updates: bumped frontmatter description to include the scout-phase signal; added operations tag; added a new Key Takeaway (“Entry requires a scout phase, not a budget”); added marketing/discovery-before-scale and glossary/information-foraging to Related.

Strategist task closure: 2026-05-13-telegram-operational-unlock-extension.md status updated open → completed, with reference to the new section and the cross-link to discovery-before-scale recorded.

Stats: 1 page extended (marketing/telegram-marketing-channel) + 1 page back-link (marketing/discovery-before-scale) + 1 strategist task closed.

Why this matters architecturally: the Telegram page now sits at the intersection of two distinct wiki frameworks — the Primores cross-domain channel-fit is geographic before categorical synthesis (the page’s own contribution) and the Pirolli-Card Discovery-Before-Scale framework (the operational layer). Two named frameworks meeting at one concrete channel turns the page into a worked-example anchor for both, which is structurally how the wiki accumulates load-bearing pages.

[2026-05-13] ingest + create + update | Karpathy Sequoia AI Ascent 2026 — agent-engineering page, vibe-coding extension, jagged-frontier ↔ jagged-intelligence cross-linking

User flagged two new sources in the same session: Karpathy’s From Vibe Coding to Agentic Engineering Sequoia AI Ascent 2026 talk (YouTube 96jN2OCOfLs) and Pimenov’s Russian-language writeup of the same talk. The YouTube and Pimenov sources are the same content (talk + summary). Triangulated with two additional independent summary articles (Travis Media, AI Agents Simplified Substack) to verify the load-bearing quotes and the Software 1.0/2.0/3.0 framing.

New page: glossary/agent-engineering — Karpathy’s term for the professional discipline of coordinating AI agents reliably and safely, framed as the complement to vibe-coding. Two load-bearing quotes anchor the page:

“Vibe coding raises the floor. Agentic engineering raises the ceiling.” (Sequoia AI Ascent 2026)
“You can outsource your thinking, but you can’t outsource your understanding.”

The page covers what agent engineering is and isn’t (not just prompt engineering at scale; the discipline of bounding autonomy, verifying outputs, designing for unpredictability, maintaining system context), the Software 1.0 / 2.0 / 3.0 framing as context (“LLM became the computer, prompt became the program”), Karpathy’s directional >10x developer claim, the “neural networks as operating systems” architectural prediction (with OpenAI Codex and MCP as early evidence), and the human skills that stay distinctly human (taste, architectural thinking, oversight, contextual understanding).

The page’s most important architectural move: it explicitly names the connection between Karpathy’s “jagged intelligence” (model-side) and Dell’Acqua’s “jagged frontier” (human-side). Same structural insight — AI capability is asymmetric in ways invisible from a task description — viewed from two sides. A side-by-side table makes the connection explicit. This is the kind of cross-source naming the wiki is best positioned to do, because it has both the Karpathy framing and the academic empirical anchor in the same corpus.

Cross-links wired into automation/ai-agent-organization (the operations playbook), tools/claude-managed-agents (the managed-infrastructure layer that absorbs part of the discipline), comparisons/managed-agents-vs-diy (the make-or-buy decision), glossary/jagged-frontier, glossary/vibe-coding, strategist-pattern (the wiki-as-substrate pattern is the knowledge-work instance of agent-engineering principles), glossary/automation-eats-execution (this framing reinforces the cross-domain thesis at the software-engineering layer), comparisons/strategy-vs-execution-ai.

Extended page: glossary/vibe-coding — Added a substantial “May 2026 update: the floor-vs-ceiling distinction” section. The 2025 vibe-coding discourse had collapsed two distinct disciplines into one phrase; Karpathy’s May 2026 update separates them. Vibe coding is Software 3.0 for accessibility; agent engineering is Software 3.0 for production. Both are Software 3.0; they sit at different points on the floor-to-ceiling axis. Added the Software 1.0/2.0/3.0 table and Karpathy’s “you can outsource thinking but not understanding” quote as the long-tail constraint that vibe coding never escapes. Cross-link to the new agent-engineering page. Bumped updated date 2026-05-05 → 2026-05-13.

Cross-linked: glossary/jagged-frontier — Added a new “Karpathy’s ‘jagged intelligence’ — the model-side cousin” section. Names the connection between Dell’Acqua’s human-side asymmetry finding (n=758 BCG consultants) and Karpathy’s model-side framing (the strawberry-letter-counting paradox). Side-by-side comparison table. The unifying mechanism: Klein-Kahneman’s conditions for pattern-matching reliability (glossary/recognition-primed-decision) predict both shapes — verifiable capabilities improve fast under RL training; un-verifiable ones stagnate. Added cross-links to glossary/agent-engineering, glossary/vibe-coding, glossary/ai-agent-behavior in Related. Source list bumped with the Karpathy Sequoia 2026 talk.

Index updates:

Added glossary/agent-engineering entry.
Extended glossary/jagged-frontier description with Karpathy connection.
Extended glossary/vibe-coding description with the May 2026 update.

Why this matters architecturally: the wiki now has a tight cluster of three pages around the central insight of AI capability asymmetry that’s invisible from a task description:

glossary/jagged-frontier — Dell’Acqua’s human-side empirical anchor (n=758, peer-reviewed).
glossary/agent-engineering — Karpathy’s model-side framing and the engineering response (Sequoia talk).
glossary/vibe-coding — Karpathy’s accessibility-side framing for the same Software 3.0 paradigm.

All three reference the same underlying mechanism (glossary/recognition-primed-decision) and connect to the same cross-domain thesis (glossary/automation-eats-execution). The three-page cluster now has an internal coherence the previous pages lacked when they sat as adjacent-but-unconnected entries.

Stats (informal):

New pages: 1 (glossary/agent-engineering)
Substantially extended pages: 2 (glossary/vibe-coding, glossary/jagged-frontier)
Cross-links added: ~8
Wiki total: ~128 pages, glossary ~47 entries.
The same session’s earlier output (Wharton frictions, Telegram, ai-agent-behavior extension, two stale seedlings) is logged immediately below.

[2026-05-13] ingest + create + update | Raw queue drained — Wharton frictions page, Telegram channel page, ai-agent-behavior extension, two stale seedlings refreshed

First session since the May 12 lint. The goal was to drain the 8-article raw queue, ingest the strategist Telegram task, and clear the two stale seedlings. The actual outcome: most of the raw queue turned out to be already ingested in earlier sessions (a sign that the wiki’s index discipline is working — the lint correctly identified them as “queued” while substrate was already covering them). Two genuinely new pages got built (Wharton three-frictions framework + Telegram marketing channel), and the ai-agent-behavior cluster got a major extension with the underlying Columbia/Yale study’s specific data plus the Cialdini-on-AI 28,000-prompt finding.

Raw-queue triage (the most useful finding of the session):

The raw/ articles queue at session start contained eight items. Going through each:

ai-agent-buying-biases-science-says.md — Partially ingested in glossary/ai-agent-behavior (April 14). The page captured the high-level findings (keyword sensitivity, badge effects) but missed the underlying study’s specific numbers. Action: extended the page substantially (see below).
12-techniques-ai-agents-practical-tools.md (Pimenov) — Already fully ingested in automation/ai-agent-organization (April 10). That page IS this article, by structure. Action: archive only.
AI_Agent_Adoption_Blueprint_Analysis.md (Wharton × Science Says) — Not ingested anywhere. Action: built a new page from scratch — see “New page” below.
rumpelstiltskin-effect-why-we-buy.md — Already fully ingested in glossary/rumpelstiltskin-effect (April 21). Comprehensive page exists. Action: archive only.
two-agents-openclaw-hermes.md (Pimenov) — Already ingested in automation/multi-agent-patterns. Action: archive only.
product-article-generator-system.md — Already ingested in tools/product-article-generator and cases/product-article-generator-pigu. Action: archive only.
brand-voice-claude-skill-original.md — Already ingested in marketing/brand-voice-skills-guide (April 27 source date matches). Action: archive only.
The-Complete-Guide-to-Building-Skill-for-Claude.pdf — PDF not yet processed. Action: leave in queue (PDF needs pdf-streamer skill workflow, not a markdown ingest).

This is the meta-finding worth recording: eight items appeared as a queue, only two actually carried unindexed content. The lint surfaces “what’s in raw/articles” but doesn’t cross-check against the indexed corpus. A future maintenance improvement would be to record ingest-completion in the raw filename (e.g., rename to _ingested_YYYY-MM-DD_*.md) the moment a page consumes the source — the same archive convention the drafts/ folder uses. Filed as a maintenance-protocol improvement to consider.

Extended page: glossary/ai-agent-behavior — Added three substantial sections that the original April 14 ingest didn’t capture:

Underlying study (Allouah et al., Columbia + Yale, working paper Dec 2025). Names the researchers (Amine Allouah, Omar Besbes, Yash Kanoria, Akshit Kumar at Columbia/Yale; Josue D. Figueroa at MyCustomAI), records the sandbox structure (1,000 experiments × 8 product categories), adds the 0.1-rating sensitivity finding, and adds the model-improvement curves on the “obvious better deal” test. The Sonnet 3.5 → Opus 4.5 failure rate dropped from 63.7% to 4.3%; GPT-4o → GPT-5.1 from 25.8% to 1.0%; Gemini 2.0 → 2.5 from 2.8% to 0%. The framing implication: the window for exploiting low-level price-recognition failures is closing.
Position-bias reversal across model versions. GPT-4.1 preferred top-left products; GPT-5.1 reversed this. Biases don’t just weaken between versions — they can flip sign. Agent-SEO has a shorter shelf-life than human SEO.
Cialdini’s persuasion principles work on AI (Wharton 2025, n=28,000). Compliance rose from 33.3% baseline to 72% when persuasion principles were applied. The implication for agent-mediated commerce: a product page optimized for human Cialdini-persuasion is also optimized for agent persuasion. The two optimization targets are not actually different surfaces.
ZDNet BlancPottery real-world replication added as a sanity check that the sandbox findings show up in actual ChatGPT Agent runs.
Sources section restructured with full citation depth (paper title, working-paper status, full author list).

New page: glossary/agent-adoption-frictions — Built from the Wharton × Science Says AI Agent Adoption Blueprint (April 2026). The framework’s substance: AI agent adoption is blocked by three psychological frictions — perceived competence, trust, and delegation of control — not by technology readiness. The blueprint draws on 700,000+ employees surveyed across Google, ServiceNow, Wolters Kluwer, Workato, Concentrix, and Zapier.

Why this page matters architecturally: it’s the user-side counterpart to glossary/ai-agent-behavior. Where the Columbia/Yale work documents what agents choose (agent-side), the Wharton work documents whether users let agents choose at all (user-side). Both are needed to explain agentic-commerce outcomes — supply-side optimization (product page → agent friendliness) is moot if demand-side adoption (user → agent delegation) doesn’t happen.

The page also connects deeply into the existing academic-foundations cluster:

The “perceived competence” friction is a user-side calibration response to glossary/jagged-frontier — users can’t see the frontier either, and they delegate accordingly.
The “trust” friction follows glossary/recognition-primed-decision conditions for when pattern-matching is reliable: trust accumulates fastest in tasks that meet both validity and feedback conditions.
The “Goldilocks autonomy” prescription (propose-and-approve > pure execution) directly informs tools/claude-managed-agents UX decisions.

Cross-links added: from automation/agentic-commerce Related section (this is the demand-side complement to all the supply-side optimization in that page); from tools/claude-managed-agents Related section; from glossary/ai-agent-behavior Related Concepts.

Wharton framework citations included: Sheridan & Verplank (1978) LOA theory for the moderate-autonomy finding; companion to the 2025 Chatbot Design Blueprint from the same Science Says × Wharton series.

New page: marketing/telegram-marketing-channel — Built from the strategist task 2026-05-07-telegram-marketing-channel-ingest.md. Two research passes from May 7 produced substantial named-source data that was decaying in session-signals. Now in durable wiki form.

The page covers:

Audience footprint (1B+ MAU per Durov March 2025, 53.5% aged 18-34, 350M Mini-App users, top countries India/Russia/US/Indonesia/Pakistan, with the aggregator-cited 38/27/21/8 regional split flagged as directional)
iGaming as primary channel (3,718 channels indexed; 100K-280K+ top channels; the affiliate-channel-eclipsing-official-channel inversion; Mini-App $0.40-3 CPM vs Tier-1 $3-12; the don’t-conflate $0.01-0.03 RichAds CPM-derived vs €2-4 PropellerAds measured CPS; PropellerAds 18K conversions / 9.86% CTR case)
DTC empirical (the disconfirming finding: Western Web3 fashion is on Discord, not Telegram — RTFKT/BAYC×BAPE/SYKY/Lacoste UNDW3 are all Discord; Telegram fashion is geographic (Russia/CIS/Iran/MENA per BoF Sept 2024) not categorical; Trezor channel deactivation as operational-tax case; absence of Western DTC founder discourse on Reddit is itself data)
Generalizable insight: channel-fit is geographic before categorical. This generalizes beyond Telegram and is the framework-level Primores synthesis. Connected to glossary/super-niche as the channel-selection-layer application of specificity-beats-generality.
Cross-links added to marketing/overview Related and to glossary/super-niche Related.

Refreshed: tools/obsidian.md (was 32 days stale) — Bumped 🌱 → 🌿. Added a “Five-week field report” section documenting how Obsidian was actually used as the wiki’s IDE over April-May: graph view as health monitor (made the zero-orphans lint discipline enforceable rather than aspirational), wikilink autocomplete as link-rot prevention (caught spelling drift before it propagated), real-time view of LLM edits as a calibration tool. Free tier sufficient. Git handles sync; Obsidian Sync not needed. Cross-link added to strategist-pattern (this is partial validation of the wiki-as-substrate pattern at the IDE layer) and maintenance (where the graph-view discipline gets formalized).

Refreshed: experiments/ai-visibility-ecommerce.md (was 27 days stale) — Bumped 🌱 → 🌿. Added a “Status update (2026-05-13)” section. The honest finding: the next-steps (re-audit, more platforms, SaaS comparison) remain open — no new field work — but the findings have not lost relevance. The pigu.lt WAF block on AI bots was an “interesting SEO oddity” in April; by May, with the Columbia/Yale agent-behavior study showing 25-30% of US online purchases reaching AI agents, the same technical fact has become a load-bearing commerce assumption violation. The findings haven’t aged; the stakes did. Cross-links added to glossary/ai-agent-behavior (access is upstream of optimization), glossary/jagged-frontier (WAF blocks push agents outside the frontier by removing access to cues), automation/agentic-commerce (market-projection context), glossary/agent-adoption-frictions (user-side counterpart).

Index, log, changelog updates:

wiki/index.md — added glossary/agent-adoption-frictions and marketing/telegram-marketing-channel entries; extended the glossary/ai-agent-behavior description to mention the Allouah et al. study + Cialdini-on-AI finding; bumped updated date.
wiki/log.md — this entry.
wiki/changelog.md — see entry below.

Stats (informal):

New pages: 2 (glossary/agent-adoption-frictions, marketing/telegram-marketing-channel)
Substantially extended pages: 3 (glossary/ai-agent-behavior, tools/obsidian, experiments/ai-visibility-ecommerce)
Cross-links added across the wiki: ~10
Pages going from 🌱 → 🌿: 2 (tools/obsidian, experiments/ai-visibility-ecommerce)
Wiki total: ~127 pages, glossary ~46 entries.
Strategist task closed: 2026-05-07-telegram-marketing-channel-ingest.md → archive.
Raw articles archived as ingested: 7 of 8 (the PDF stays in queue for pdf-streamer processing).

Architectural finding worth flagging for next lint:

The “raw/articles already ingested but still appearing in queue” pattern wasted real session time (had to read each article to confirm it was a duplicate). The fix is the archive-on-ingest convention: when a page absorbs a raw source, the source filename gets renamed with _ingested_YYYY-MM-DD_ prefix in the same atomic step. The drafts/ folder already uses _archived_YYYY-MM-DD_ for this; raw/articles needs the same discipline. This will be applied to this session’s processed articles immediately, and is worth adding to the CLAUDE.md INGEST workflow section in a future schema update.

[2026-05-12] lint | Weekly health check + four targeted fixes (llms.txt refresh, broken-link fix, 5 cross-links, ai-agent-behavior extension)

First session in seven days — right at the maintenance protocol’s 7-day stagnation threshold. Ran a full lint check, then executed all four findings in one batch rather than letting them queue.

Lint findings (clean):

Frontmatter compliance: 100% across all 125 public pages.
Private path leakage: zero (no ](raw/ or ](private/ references in published pages).
Orphans: zero — every public page has ≥1 inbound wikilink.
Stale pages (>30 days): zero. Maintenance has kept up.
Pages >500 lines: only log (2,373; expected to grow) and two domain pillars (marketing/ai-marketing-case-studies at 604, marketing/social-commerce-psychology at 563) — both reasonable depth, not splits.

Lint findings (fixable, addressed this session):

wiki/llms.txt was stale (last updated April 23 — stats claimed 68 pages, 21 glossary). Initial check misread it as missing due to a CWD reset; correction made on second read. Refreshed in place: bumped stats to current (125 pages, 45 glossary, 10 academic foundations cited), added the May framework cluster (automation-eats-execution, jagged-frontier, ai-skill-leveling, advisor-strategy, strategist-pattern, organic-content-strategy pillar, influencer-marketing-task-overload), reorganized into canonical llms.txt structure (blockquote summary + link-list sections), added “How to use this wiki as an AI assistant” guidance section, preserved the existing How-to-Cite / Methodology / Contact tail.
strategist-pattern line 258 had a CLAUDE.md wikilink pointing outside the wiki folder (the schema file lives at the repo root). Rewrote to plain prose referring to the repo-root file with a contextual link, so the reference no longer renders as a broken wikilink.
Wired five missing cross-links into low-density target pages:
- glossary/automation-eats-execution → added cases/intercom-fin-support as the customer-service domain anchor. Strong fit: Intercom Fin’s 86% resolution rate is the execution-layer-compression mechanism in customer support.
- glossary/ai-skill-leveling → added cases/binti-social-services as the field-data analog in regulated documentation work. The 50% time reduction on case notes is exactly the novice-execution-layer pattern the three academic studies predict.
- glossary/super-niche → added seo/new-site-ranking in Related. The new-site application of super-niche thinking — hyper-specific long-tail queries — was previously orphaned from the conceptual page.
- glossary/topical-authority → added seo/new-site-ranking in Related. Same logic at the strategy level.
- experiments/overview → added a new “Niche-Discovery Experiments” section pointing at cases/niche-hunter-primores-creative and cases/niche-hunter-fresh-2026-04. Niche-hunter validations sit between experiment and case study; they were previously findable only via the niche-hunter tool page.
Extended glossary/ai-agent-behavior with a substantial new section “Why agent decisions follow patterns: connection to the academic foundations.” Applies the May 5 work (jagged-frontier + recognition-primed-decision) to the buying-decision layer: agent biases are the same inside/outside-frontier asymmetry playing out at decision-time, and Klein-Kahneman’s high-validity-environment + rapid-feedback conditions predict which purchase categories agents are reliable in vs. confidently wrong in. Upgraded status 🌱 → 🌿 (it now has 2 mechanism explanations + 6 cross-references + 2 academic foundation sources). Added jagged-frontier, recognition-primed-decision, and ai-skill-leveling to Related Concepts. Bumped Sources with the academic-foundation citations. Bumped frontmatter description and tags.

Index update:

Bumped wiki/index.md status marker for glossary/ai-agent-behavior from 🌱 to 🌿. No other counts change (the page was already counted in the 45 glossary entries).

Inbound-link counts after this session (the four lowest-density pages addressed):

cases/intercom-fin-support: 1 → 2
cases/binti-social-services: 1 → 2
seo/new-site-ranking: 1 → 3
cases/niche-hunter-fresh-2026-04: 1 → 2

Plus cases/niche-hunter-primores-creative gained an inbound (2 → 3) as a side effect of restructuring the experiments overview.

Why this matters architecturally: the lint surfaced a wiki-maintenance failure mode I want to record. The llms.txt staleness wasn’t a missing-file problem — it was a file that exists but isn’t kept in sync with the wiki’s stated stats. The wiki schema (CLAUDE.md) says llms.txt is the AI discovery file, but there’s no automation tying its content to index.md stats. The fix is partly mechanical (refresh on lint) and partly structural (any time the index.md stats change, llms.txt is on the maintenance hook). Worth noting for the next lint cycle.

Out of scope (deferred):

The four stale seedlings (>14 days) — tools/obsidian.md (32 days), experiments/ai-visibility-ecommerce.md (27 days), the one just upgraded glossary/ai-agent-behavior.md was the third. Two real-world-experiment-required items left for hands-on sessions.
Eight unread raw/articles still waiting.
The competitor-analysis/ domain remains a 1-page seedling — flagged but not addressed.
Stats nit (index.md ~126 vs actual 125) — within rounding tolerance, deferred.

[2026-05-05] cleanup + ingest | Drafts triaged + advisor-strategy ingested + managed-agents playbook enriched

Fifth productive session of 2026-05-05. Drafts cleanup and two raw/ ingests, with the advisor-strategy ingest producing a substantive new glossary entry that ties together the day’s earlier framework work.

Drafts triage:

Renamed drafts/organic-content-pillar-draft.md → drafts/_archived_2026-05-05_organic-content-pillar-draft.md. Confirmed stale: the pillar shipped April 30 to marketing/organic-content-strategy and the draft itself contains TODO placeholders and a “Do not publish” marker. Fully superseded.
Renamed drafts/reddit-response-ai-marketing-tools.md and .txt → _archived_2026-05-05_*. Outbound comms in paste-ready format. Its core insight (“AI tools without business context are basically useless”) is already covered in strategist-pattern, glossary/llm-wiki-pattern, and glossary/context-engineering — nothing new to migrate.
Drafts/ folder now contains only the three archived files. Future drafts are explicit about being in-progress.

Managed-agents playbook ingest:

The raw/ playbook (394 lines) was largely already ingested when tools/claude-managed-agents was created on April 10. Comparing the two: the existing wiki page covers the four key concepts, quick start, built-in tools, custom tools, permission system, usage patterns, outcomes, multi-agent, architecture, pricing, customers, and limits. Two enrichments worth adding:

Added a “Anthropic’s task-horizon thesis” section to tools/claude-managed-agents — captures the explicit positioning around the METR benchmark (“Claude already exceeds 10 human-hours of work”) and the strategic bet on multi-day/week/month sessions. Implication: the product evaluation question is “if you commit to agentic work, do you want to maintain multi-day session infrastructure yourself?”
Added a new section to questions/managed-agents-break-even — “What changes if Anthropic’s task-horizon thesis is right.” If session durations trend toward hours/days/weeks, the existing break-even math (computed against 5–10 minute sessions) is computed against the wrong cost basis. DIY infrastructure for state recovery, secret rotation, and compaction across long contexts becomes the dominant cost component, which shifts break-even toward Managed.

Advisor-strategy ingest (genuinely new):

The Anthropic April 2026 advisor strategy article (60 lines) was not yet in the wiki. Pattern: cheap executor model (Sonnet/Haiku) drives tasks end-to-end and consults a more capable advisor model (Opus) only on hard decisions. Server-side advisor_20260301 tool inside a single API request. Benchmark numbers:

Sonnet + Opus advisor: +2.7pp on SWE-bench Multilingual at 11.9% cost savings vs. Sonnet alone
Haiku + Opus advisor: 19.7% → 41.2% on BrowseComp at 85% cheaper than Sonnet alone

Pages created (1):

glossary/advisor-strategy — ~140 lines, 🌿 growing. Full pattern definition, benchmark results, three layers of fractal application (model architecture / individual workflow / org chart), three conditions that favor it (heterogeneous task difficulty + competent executor + escalatable decisions), honest limits including executor self-awareness as the bottleneck.

Pages updated (5):

questions/ai-as-personal-advisor — Added new section “A parallel from model architecture” before the existing Klein-Kahneman section. The architecture pattern (Opus advisor + Sonnet/Haiku executor) is the model-architecture-level analog of the personal-AI-advisor concept. Three layers of the same architectural insight: model layer, personal layer, org layer.
questions/managed-agents-break-even — Added two new sections: task-horizon thesis (cost basis assumption) + advisor-strategy economics (executor-tier flexibility shifts the build-vs-buy calculus).
tools/claude-managed-agents — Added “Anthropic’s task-horizon thesis” subsection. Cross-linked to advisor-strategy and jagged-frontier in Related. Bumped updated date to 2026-05-05.
comparisons/strategy-vs-execution-ai — Added a major new section “The pattern recurs at three layers (a fractal observation)” — names the org-chart / individual-workflow / model-architecture layering as evidence that the strategy-vs-execution frame captures something structural about how cognitive work decomposes, not a 2026 incidental. Added “Architectural fractal” subsection to Related.
automation/ai-agent-organization — Added glossary/advisor-strategy to Related.
wiki/index.md — Added advisor-strategy entry. Bumped Stats: ~125 → ~126 pages, glossary 44 → 45.

Architectural payoff (cumulative across the day):

The advisor-strategy ingest produces a synthesis the day’s earlier framework work was implicitly pointing toward but didn’t make explicit: the strategy-vs-execution pattern is fractal. It shows up at three layers simultaneously, all driven by the same architectural insight (selective escalation + competent executor + heterogeneous task difficulty). This is qualitatively different from a 2026-vintage business observation; it is structural about how cognitive work decomposes. Today’s earlier academic-foundation work (Brynjolfsson skill leveling + Dell’Acqua jagged frontier + Klein-Kahneman conditions) provides the empirical and theoretical undergirding for why the fractal pattern works at all three layers.

Total May 5 activity (five productive sessions):

Modash 2026 ingest → +1 page (influencer-marketing-task-overload)
Lint + 3 follow-up pages → +3 pages (vibe-coding, strategy-vs-execution-ai, getting-started)
Lint follow-up wave 2 → +2 pages (automation-eats-execution glossary, automation-eats-execution-next-domains question)
Academic AI-productivity wave → +4 pages (jagged-frontier, ai-skill-leveling, ai-task-restructuring, recognition-primed-decision)
This session → +1 page (advisor-strategy), 3 drafts archived, several enrichments

Single-day totals: 11 new pages, 5 sources ingested, 9 drafts archived/superseded, ~25 cross-link updates. The framework cluster around automation-eats-execution is now nine pages connected: comparison synthesis + named-framework glossary + open question + three domain anchors + four academic foundations + advisor-strategy as the architectural fractal.

Out of scope:

Did not pursue the eight remaining unread raw/articles (12-techniques, AI agent adoption blueprint, ai-agent-buying-biases, brand-voice-claude-skill, llm-wiki-pattern source, product-article-generator, rumpelstiltskin-effect, two-agents-openclaw-hermes, The-Complete-Guide-to-Building-Skill-for-Claude.pdf). Some of these are domain-relevant; pacing-wise the day is full.
Did not commit. The day’s work is large enough that a clean commit to capture it all is the natural next step.

[2026-05-05] ingest + create | Academic AI-productivity wave: 4 peer-reviewed papers ingested, 4 new glossary entries, automation-eats-execution framework now empirically grounded

Fourth productive session of 2026-05-05. After the morning’s Modash ingest, midday lint sweep + 3 follow-up pages, and afternoon’s lint follow-up wave 2 (orphan fix + glossary upgrades + automation-eats-execution glossary stub + question page), this session extends the glossary/automation-eats-execution framework with peer-reviewed academic empirical evidence for the first time. The framework previously rested on three industry data points (Seufert / Modash / Karpathy); it now also rests on three randomized/quasi-experimental academic studies plus a theoretical foundation.

Source identification: user asked which academic research could be useful for the wiki; brief covered four high-leverage candidates with rationale. User chose all four. Per CLAUDE.md INGEST workflow, key cross-cutting findings surfaced before drafting (skill leveling replicates across all three empirical studies; jagged-frontier adds the missing asymmetry; Klein-Kahneman provides the theoretical why).

Sources ingested (raw notes saved to raw/articles/academic-foundations/):

Dell’Acqua, F. et al. (2023). Navigating the Jagged Technological Frontier. HBS Working Paper 24-013. — n=758 BCG consultants, randomized 3-arm field experiment with GPT-4. Inside frontier: +12.2% tasks, +25.1% faster, +40% quality on 40%+ of cases. Outside frontier: −19 percentage points accuracy. The frontier is invisible from a task description.
Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. NBER WP 31161. — n=5,179 customer-support agents at a Fortune 500 SaaS firm, conversational AI assistant, staggered rollout. +14% issues/hour avg, +34% novices, ~0% experts. Mechanism: AI distributes expert-pattern best practices.
Noy, S., & Zhang, W. (2023). Science, 381(6654), 187–192. — n=444 college-educated professionals, preregistered online experiment with ChatGPT, occupation-specific incentivized writing tasks. Time −40%, quality +18%, productivity inequality compressed. AI substitutes for effort and restructures work toward idea-generation and editing, away from rough-drafting.
Klein, G. (1998). Sources of Power + Kahneman, D. & Klein, G. (2009). American Psychologist, 64(6), 515–526. — Theoretical synthesis. Recognition-primed decision making (Klein): experts decide via serial pattern-matching, not parallel option comparison. The 2009 joint synthesis: intuitive expertise reliable iff (a) high-validity environment AND (b) prolonged practice with rapid feedback. Applied to AI: predicts which task domains AI pattern-matching will succeed and fail in for the same structural reasons humans do.

Pages created (4):

glossary/jagged-frontier — ~155 lines, 🌿 growing. Dell’Acqua paper is the empirical backbone. Includes: study design, inside/outside-frontier asymmetry, frontier-invisibility, centaur/cyborg integration patterns, practical signals for which side of the frontier a task is on, honest limits.
glossary/ai-skill-leveling — ~150 lines, 🌿 growing. Synthesizes Brynjolfsson + Noy-Zhang + Dell’Acqua’s bottom-half-skill finding into one entry. Two complementary mechanisms (best-practice distribution + effort-substitution). Direct implications for hiring, agency pricing, training investment. The Modash 2026 +$14,830 strategy premium is now anchored as the field-data analog of skill leveling at scale.
glossary/ai-task-restructuring — ~135 lines, 🌿 growing. Noy-Zhang’s task-decomposition finding: AI compresses drafting; framing and editing become bottlenecks. Connects to glossary/jagged-frontier (drafting is inside-frontier; framing/editing are at or outside). Applied to content workflows, marketing/organic-content-strategy discovery-vs-scale architecture, glossary/creative-is-new-targeting variant production vs formula extraction.
glossary/recognition-primed-decision — ~155 lines, 🌿 growing. Klein’s RPD model + Klein-Kahneman 2009 conditions for intuitive expertise. Applied as the theoretical predictor of where AI will succeed and fail. Pairs with glossary/dual-process-thinking for the complete picture: when fast intuition works, when it doesn’t.

Pages updated (8):

comparisons/strategy-vs-execution-ai — Added a major new section “Peer-reviewed academic foundation” with table of three empirical studies + Klein-Kahneman theoretical foundation. Three findings the academic evidence sharpens: skill leveling is real and consistent; AI is asymmetric not uniform; Klein-Kahneman predicts where AI itself will struggle. Rewrote Sources to reflect that the framework now has empirical evidence from multiple methodologies. Reorganized Related into framework synthesis / domain anchors / academic foundations / adjacent.
glossary/automation-eats-execution — Added separate empirical-anchors table for academic studies + theoretical foundation row. Reorganized Sources into industry data / peer-reviewed academic / synthesis. The framework now rests on six independent data points across multiple methodologies, which is unusual breadth for a 2026 management framing.
questions/what-ai-tools-actually-deliver-roi — Substantially upgraded: added “From peer-reviewed studies” section with direct ROI numbers for each study. Existing hypotheses cross-checked against the academic findings (1 supported, 1 supported, 1 untested). The seedling now has a real empirical answer for inside-frontier tasks. Tags: +behavioral-evidence.
questions/managed-agents-break-even — Added new section “A separate consideration: agent reliability is task-dependent” citing Dell’Acqua’s −19pp finding. The break-even math assumes agents work; the jagged-frontier finding qualifies that. Wrong-with-high-confidence outputs are invisible in unit-cost comparisons.
questions/ai-as-personal-advisor — Added new section “A theoretical constraint: when is AI advice trustworthy?” citing Klein-Kahneman conditions. The personal-advisor case is a special case of the jagged-frontier problem: AI is uniformly confident across reliable and unreliable domains.
glossary/dual-process-thinking — Added glossary/recognition-primed-decision as the leading Related entry. The two papers together (Kahneman 2011 + Klein-Kahneman 2009) give the complete picture: when fast intuition works (Klein) and when it doesn’t (Kahneman).
glossary/creative-is-new-targeting — Added 3 cross-links to ai-task-restructuring, jagged-frontier, automation-eats-execution. The Noy-Zhang task-restructuring finding is now the workflow-level mechanism behind variant production = drafting (compresses) vs formula extraction = framing/judgment (stays human).
wiki/index.md — Added 4 new glossary entries with PILLAR-style highlights for each. Bumped Stats: ~121 → ~125 pages, glossary 40 → 44, added “Academic foundations cited” row showing 10 papers.

Architectural payoff:

The wiki now has a dual empirical foundation for the automation-eats-execution framework:

Industry/practitioner anchors (three domains): paid media, influencer marketing, software development.
Peer-reviewed academic anchors (three studies + 1 theoretical synthesis): Brynjolfsson n=5,179, Noy-Zhang n=444, Dell’Acqua n=758, Klein-Kahneman 2009.

This is unusual breadth for a 2026 management framing — the framework can be cited with academic empirical backing rather than only industry observation. Three independent randomized/quasi-experimental studies converge on skill-leveling. Dell’Acqua adds the asymmetric jagged-frontier nuance. Klein-Kahneman provides the theoretical why. The framework is now harder to dismiss as practitioner intuition.

The four new glossary entries each have specific citation utility:

jagged-frontier for “AI helps on X but hurts on Y” arguments
ai-skill-leveling for hiring/staffing/agency-pricing arguments
ai-task-restructuring for workflow design and where leverage moves
recognition-primed-decision for arguments about when AI advice is trustworthy

Total cross-domain framework architecture is now eight pages:

comparisons/strategy-vs-execution-ai — full synthesis
glossary/automation-eats-execution — named-framework definition
questions/automation-eats-execution-next-domains — open question on adjacent domains
Three domain anchors: creative-is-new-targeting, influencer-marketing-task-overload, vibe-coding
Four academic foundations: jagged-frontier, ai-skill-leveling, ai-task-restructuring, recognition-primed-decision

This is the most empirically anchored, most cross-cited framework cluster the wiki has built around a single Primores synthesis. The session also strengthens three open questions with direct citations they previously lacked.

Session shape: the day’s fourth productive session and the most substantive ingest of the four. Earlier sessions on May 5: Modash ingest (single source, 1 new page) → lint + 3 follow-up pages → lint follow-up wave 2 (orphan fix + 4 status upgrades + 2 new pages). This fourth session is the largest in source-count terms (4 papers) and produces the wiki’s most empirically robust framework cluster. Total May 5 activity: 1 industry ingest + 4 academic ingests = 5 sources ingested in a single day; 9 new pages created; many cross-link updates.

Out of scope:

Did not pursue Brynjolfsson 2023’s full firm-identity analysis (NBER PDF returned binary on direct fetch; widely-summarized contents used).
Did not pursue Noy-Zhang’s individual experimental conditions in detail (Science paper paywalled; abstract-level findings sufficient).
Did not write the unprompted “fifth synthesis page” tying all four academic foundations together — the existing comparisons/strategy-vs-execution-ai now serves that role.
Drafts triage (drafts/organic-content-pillar-draft.md, drafts/reddit-response-ai-marketing-tools.*) deferred — flagged at session start, not addressed this session.

[2026-05-05] update + create | Lint follow-up wave 2 — orphan fix, glossary upgrades, +2 pages

Closing the remaining lint findings from the earlier May 5 health check, except for items that require hands-on real-world work.

Orphan-cluster fix:

automation/ai-implementation-patterns — Restructured the Related section into two subsections (industry deep-dives + framework/synthesis). Added explicit cross-references to all 7 industry-cases pages including the 3 the lint flagged as orphans (ai-developer-tools-cases, ai-healthcare-cases, ai-security-cases). Each cross-link includes a one-line note tying the industry to the patterns documented in this analysis page (e.g. healthcare ↔ Pattern #2 / document processing). Also wired in comparisons/strategy-vs-execution-ai and automation/finding-ai-use-cases for the framework grouping.

Glossary status upgrades (4 pages, 🌱 → 🌿):

Per the lint’s recommendation to relax the >250-line threshold for glossary entries, the four creative-formula-cluster entries are upgraded:

glossary/lighting-recipe — 7 inbound links, 2 sources, 72 lines. Substantively complete.
glossary/focal-hierarchy — 7 inbound links, 2 sources, 69 lines. Substantively complete.
glossary/framing-archetype — 7 inbound links, 1 source (Primores-original methodology), 62 lines. The methodology IS the source for original taxonomies, so the 1-source bar is met for this kind of page.
glossary/creative-formula-vs-creative-skin — 7 inbound links, 1 source (Primores-original), 75 lines. Same logic.

Pages created (2):

glossary/automation-eats-execution — NEW glossary entry (~140 lines, 🌿 growing). Names the cross-domain pattern as a single framework (instead of leaving it as the closing argument across 3 source pages). Three empirical anchors documented in a single table (paid media / influencer marketing / software). Three signals indicating a function is on the curve. Honest limits including the brand-building negative case and the “framework is descriptive of 2026, not permanently predictive” caveat. Now the canonical reference page; other pages can cite this entry instead of reconstructing the argument.
questions/automation-eats-execution-next-domains — NEW open question page (~150 lines, 🌱 seedling). Working hypotheses for 5 candidate domains: email/CRM/lifecycle (substantially on curve, fragmented evidence), organic content/SEO (mixed/moving), brand-building (explicitly NOT on curve — useful negative case), B2B sales-marketing (weakly on curve), marketing analytics (substantially on curve, with role bifurcation visible). Includes diagnostic criteria for each candidate, what would constitute evidence to settle the question, and suggested next moves to advance the question (find a Modash-equivalent salary survey for adjacent roles, document a Primores-client case study showing bifurcation in real time, etc.).

Pages updated:

comparisons/strategy-vs-execution-ai — Added the new glossary stub and question page to the Related section so the cluster is fully wired. Also added automation-eats-execution to the page’s tags.
wiki/index.md — Added 2 new entries (glossary + question), bumped 4 glossary status markers (🌱 → 🌿), bumped stats (~119 → ~121, glossary 39 → 40, questions 3 → 4).

Architectural payoff (cumulative across today’s lint sweep):

The cross-domain pattern now has a four-page architecture:

comparisons/strategy-vs-execution-ai — full synthesis, diagnostic signals, four implications, four honest limits
glossary/automation-eats-execution — clean named-framework definition for citation
questions/automation-eats-execution-next-domains — open exploration of which adjacent domains are on the curve
Three domain anchors: creative-is-new-targeting, influencer-marketing-task-overload, vibe-coding

This is the most complete framework architecture the wiki has built around a single Primores synthesis. It also provides a clean template for future cross-domain frameworks: domain-anchor pages → comparison/synthesis page → glossary stub → question page tracking expansion.

Lint findings deferred to real sessions (transparently noted, not silently dropped):

Hands-on creator-discovery experiment — wiki schema requires actual experimentation; can’t be fabricated from a maintenance session. Belongs in experiments/ after a real test is run.
Influencer-platform tool reviews (Modash, Aspire, CreatorIQ) — wiki schema requires hands-on testing for tool reviews. Speculative reviews would violate the wiki’s own quality bar.
Modash vs Aspire vs CreatorIQ comparison — same: meaningful comparison requires actually using the tools.

These three items are real growth opportunities; they need real-world work to ship honestly. Flagging here so they don’t fall off the radar.

Session shape: the second pass of the day’s lint follow-up. First pass closed the immediate lint findings (broken links + missing inbound links + the strategy-vs-execution synthesis). Second pass closes the remaining structural fixes (orphan cluster, glossary upgrades) and the two AI-architectable growth opportunities (named-framework glossary + tracked open question). What remains is real-world work, properly scoped as such.

[2026-05-05] lint + create | Full wiki health check + 3 follow-up pages

Ran the first full lint since April 20 (15 days ago). Wiki health is genuinely strong: zero private-path leakage, zero frontmatter violations, zero true orphans outside the expected industry-cases cluster, zero stale seedling/growing pages, zero stale open questions, no contradictions induced by the May 5 cross-domain extension to creative-is-new-targeting. Lint surfaced two real broken wikilinks and a comparison-page synthesis opportunity.

Pages created (3):

getting-started — NEW meta page (~120 lines, 🌿 growing). Public-facing orientation for new readers: who the wiki is for, how it’s organized, status markers, quick paths into the content by use-case (marketing AI, GEO/AEO, strategic frame, tool evaluation, AI-assistant reference). Closes the broken [getting-started](/wiki/getting-started/) wikilink referenced from wiki/maintenance.md and the Session Start Checklist in CLAUDE.md.
glossary/vibe-coding — NEW glossary entry (~140 lines, 🌿 growing). Karpathy-coined term (February 2025). Covers definition, the accessibility win for non-engineers, where it works (prototypes, personal tools, internal tools, learning), where it breaks (production software, security-sensitive code, long-lived codebases, regulated domains), three honest risks (confidence asymmetry, compounding architectural mess, skill atrophy), the 2026 tooling stack (Cursor, Claude Code, Cline, Replit Agent, V0). Closes the broken [glossary/vibe-coding](/wiki/glossary/vibe-coding/) wikilink from automation/ai-developer-tools-cases.md.
comparisons/strategy-vs-execution-ai — NEW comparison/synthesis page (~250 lines, 🌿 growing). The cross-domain pattern that the May 4-5 ingests have made writable: AI tools commoditize the high-volume execution layer first; strategy, judgment, integration stay human-leveraged. Three independent empirical anchors — paid media (Seufert / creative-is-new-targeting), influencer marketing (Modash 2026 +$14,830 strategy premium), software (Karpathy / vibe coding). Includes precise definitions of “execution” and “strategy” signatures, three diagnostic signals for whether a given function is on the curve, three implications (personal skill-building, team staffing, agency pricing), and four honest limits (brand-building not on curve, regulated domains move slower, framing is descriptive not predictive, cross-functional creep is a real failure mode).

Inbound links wired (3):

marketing/overview — Added section “6. Influencer Marketing Operations” under “What AI Can Do for Marketing” + Related entry. The lint flagged this as the highest-priority missing inbound link to the new influencer-marketing page.
marketing/ai-marketing-case-studies — Added cross-link in Related as adjacent context (the Modash data is a survey, not a single-company case, so a Related cross-link is the right shape).
automation/finding-ai-use-cases — Added cross-link in Related to both the influencer-marketing page (as a worked example of the TRIPS framework applied to a real labor profile) and the new strategy-vs-execution comparison page.

Pages updated:

wiki/index.md — Added 3 new entries (vibe-coding glossary, strategy-vs-execution comparison, getting-started meta). Bumped stats (~116 → ~119 pages, glossary 38 → 39, comparisons 3 → 4).

Architectural payoff:

The wiki now has three named anchors for the automation-eats-execution pattern: creative-is-new-targeting (paid media), influencer-marketing-task-overload (influencer marketing), and vibe-coding (software). The new comparison page synthesizes them into a single explicit framework. This is exactly the kind of cross-domain synthesis the wiki is supposed to compound toward — three independent data points formalize into a working framework with clear application criteria and honest limits.
All 5 of the lint’s “real issues found” are now resolved: 2 broken links closed, 3 missing inbound links wired, the strategy-vs-execution comparison page written.
Lint findings still pending (low priority, deferred to future sessions): orphan-cluster fix in industry-cases pages, glossary status-upgrade pass for the creative-formula cluster (lighting-recipe, focal-hierarchy, framing-archetype, creative-formula-vs-creative-skin), and 5 additional growth opportunities the lint surfaced (automation-eats-execution glossary stub, follow-up question page, hands-on creator-discovery experiment, influencer-platform tool reviews, Modash vs Aspire vs CreatorIQ comparison).

Session shape: maintenance pass + 3 publishes + 3 cross-link edits + index/log/changelog updates in one session. Tighter than the April 30 ingest sprint (six academic foundation sources) but broader than the May 4 single-glossary-entry session.

[2026-05-05] ingest | Modash 2026 — State of Influencer Marketing Salaries

Ingested Modash’s 2026 salary report (39 pages, 499 respondents — 399 in-house + 100 freelance, surveyed Jan 7 – Feb 2 2026). Extracted only the AI-relevant findings per user direction; left the salary geography, gender pay gap, freelance economics, and job-satisfaction breakdowns to the source report.

Why this ingest matters: the 19-task weekly job profile Modash documented is the clearest real-world labor map for “where does AI fit in marketing operations?” Combined with the salary-by-task-ownership data (strategy +$14,830, team management +$4,743, cross-dept collaboration +$4,378; execution-style tasks correlate with the lowest pay), the report is an unusually clean empirical anchor for the glossary/creative-is-new-targeting thesis applied to a second domain (influencer marketing, not paid media).

Pages created:

marketing/influencer-marketing-task-overload — NEW marketing page (~280 lines, 🌿 growing). Challenge-first framing per user direction. Three content moves: (1) the 19-task weekly stack with % of marketers doing each, (2) the strategy-vs-execution salary gradient, (3) Primores-synthesis AI-fit tiering (Tier 1: ~12-13 tasks AI eats — discovery, outreach, vetting, briefs, onboarding, metrics, trends; Tier 2: AI assists with human-in-loop; Tier 3: ~3-4 tasks stay human-leveraged — strategy, team management, cross-dept collaboration, event management). Plus the “just go do social” failure mode (cross-functional creep → 12% lower pay, 15% lower satisfaction).

Pages updated:

glossary/creative-is-new-targeting — Added a new section “The pattern shows up in other marketing domains” between “When it applies” and “Related.” Wires in the influencer marketing parallel and generalizes the framing: “automation eats execution, strategy stays the lever” extends beyond paid media to wherever a marketing function has high-volume execution + a smaller core of strategic judgment.
wiki/index.md — Added marketing-page entry with PILLAR-style highlight; bumped stats (~115 → ~116 pages, 34 → 35 domain pages).

Architectural note: the wiki now has two named anchors for the same automation pattern at different layers — paid-media (creative-is-new-targeting) and influencer marketing (this new page). The hypothesis worth tracking: does this pattern also surface in SEO/content marketing as agentic search matures? If yes, this becomes a third anchor; if not, the framing’s domain-boundary becomes useful information.

Source preserved at: raw/articles/2026-modash-state-of-influencer-marketing.md (rolled-up markdown from pdf-streamer extraction; not republished publicly).

Out of scope per user direction: salary geography breakdowns, gender pay gap analysis, freelance vs in-house economics, job satisfaction × overtime analysis, remote-work impact, freelance motivation patterns. The original Modash report covers these in depth; future ingests can cherry-pick specific findings if the wiki develops a use for them (e.g. labor-economics page, remote-work-and-productivity question, etc.).

[2026-05-04] create | Glossary entry — “Creative is the new targeting”

Created glossary/creative-is-new-targeting in response to a strategist-side queued task (strategist/tasks/wiki/2026-05-04-creative-is-new-targeting.md). The phrase had been used as attribution in the strategist’s worldview.md and met the trigger threshold (“phrase becomes recurring strategist vocabulary”) so the wiki entry was promoted from queued to created.

Why this entry now: the phrase is canonical in DTC and mobile-app performance-marketing writing (Eric Seufert / Mobile Dev Memo) since the post-ATT shift began in 2021-2022. With every major platform now shipping an “automated” flagship campaign type (Advantage+, PMax, Smart+), the structural shift is no longer disputed — it’s the operating reality. The wiki needed a definition home so future content (case studies, experiments, tool reviews) can cite it without re-explaining each time.

Pages created:

glossary/creative-is-new-targeting — NEW glossary entry (~150 lines, 🌿 growing). Covers origin (Seufert / iOS 14.5 ATT aftermath), the three things that got automated (auto-bidding, auto-targeting, auto-placement), the resulting leverage shift (creative volume + variation as the new edge), the AI-era twist (AI tooling fits the production side of the loop), and explicit honest-limits scoping (DTC/mobile-app yes; B2B/regulated/local/brand-building no).

Pages updated:

glossary/creative-formula-vs-creative-skin — Added cross-link in Related section. The formula/skin distinction is the operating mental model once you accept that creative is the lever; this glossary entry is the upstream reason.
wiki/index.md — Added entry to Glossary list with PILLAR-style highlight; bumped stats (37 → 38 glossary, ~114 → ~115 pages).

Where this connects to existing wiki work:

cases/ad-alchemy-creative-reverse-engineering — practical Primores response to the dynamic; reverse-engineering competitor formulas is the operational answer when creative is the lever.
experiments/ad-alchemy-competitor-piggyback — applied workflow at fitme.lt × Tastier.
marketing/brand-vs-content-layers — the new entry is explicitly not the same layer; flagged in the page’s “When it applies” section to prevent confusion.

Layer note: this glossary entry sits at the paid-performance layer. It is distinct from:

The brand-building layer (glossary/mental-availability, glossary/distinctive-assets, Sharp’s broad-reach framework)
The organic content-marketing layer (marketing/organic-content-strategy, glossary/topical-authority, glossary/super-niche)

The wiki now has named anchors for all three paid/brand/content layers, which should help when client conversations cross between them.

No source ingested into raw/: the substance is drawn from canonical industry knowledge (Seufert’s writing on Mobile Dev Memo, platform documentation). Marked in Sources accordingly. If a specific Seufert essay becomes load-bearing later, ingest then and update.

Strategist task closure: the queued task at strategist/tasks/wiki/2026-05-04-creative-is-new-targeting.md is now satisfied — wiki side has an entry the strategist can cite. The task can be closed from the strategist’s side.

[2026-05-04] update | Strategist implications from April 30 ingests handed off — processed

The four Apr 30 log entries that flagged “no strategist edits per user direction” (Sharp, Cialdini, Kahneman ingests + the pillar publish) are now handed to the strategist itself to decide. From the wiki’s perspective these are processed — the strategist’s worldview.md and frameworks-registry.md will be updated (or not) by the strategist’s own self-improvement loop, not by wiki sessions.

Items handed off:

Prior #4 layer-override clause (from Sharp ingest) — current prior reads “niche authority > broad TOFU”; layer-specific override needed because Sharp’s broad-reach framework dominates for brand-building in mass-market consumer categories. Wiki home: marketing/brand-vs-content-layers.
Three-time-horizon model (from Cialdini ingest) — years/Sharp, months/Primores, the moment/Cialdini. Candidate addition to frameworks-registry.md so the strategist knows which time-horizon applies to which client question. Wiki home: marketing/brand-vs-content-layers (extended from two-layer to three-horizon).
Dual-process substrate (from Kahneman ingest) — glossary/dual-process-thinking is the deep-foundation answer when clients ask “but why do these frameworks actually work?” (Sharp’s mental availability ↔ availability heuristic; Cialdini’s scarcity ↔ loss aversion; information scent ↔ WYSIATI). Candidate addition to frameworks-registry.md as the substrate-layer reference.

Going forward: future ingest log entries should not re-flag these as pending. If new strategist-relevant implications surface from future ingests, flag them once in the ingest entry; they’ll be handed off to the strategist in the same way (wiki documents, strategist decides).

[2026-04-30] publish | Organic Content Strategy pillar + 2 spokes (drafts/ → wiki/)

The day’s foundation reading culminates in the actual pillar shipping. After six academic ingests (Ajzen, Pirolli & Card, Granovetter, Sharp, Cialdini, Kahneman) and accumulated annotations across each pass, the draft has been promoted from drafts/ to wiki/marketing/ as full prose plus two operational spokes.

Source: drafts/organic-content-pillar-draft.md (still in drafts/ as historical record; not deleted)

Why this publish now: earlier evaluation flagged the draft as substantively annotated but not yet prose. With all six foundation sources ingested and the privacy/scaffolding issues resolved (per user direction, iGaming case can be public — no obfuscation needed), the path was clear to convert to proper wiki prose at full scope (pillar + 1-2 spokes).

Pages created:

marketing/organic-content-strategy — PILLAR, ~3,400 words. Full prose treatment of the seven sections from the draft, with academic grounding lifted from the day’s six ingests. Three time horizons (Sharp/years, Primores/months, Cialdini/the moment) on Kahneman’s cognitive substrate. Recipes case + iGaming case (open about the regulated category per user direction). Override clauses for B2B sales-led, regulated, time-bound categories, etc.
marketing/discovery-before-scale — SPOKE, ~1,800 words. The two-phase operational framework. Phase 1 (Discovery, 2-4 weeks): low volume, deliberate variation across pattern × niche × format. Phase 2 (Scale, indefinite): pattern-locked, surface-varied. Includes decision-tree for advancing Phase 1 → Phase 2, common failure modes, math anchors (9K × 100 = 6-9M views only if validated), and the Pirolli & Card Independence-of-Inclusion theorem as the academic anchor.
marketing/behavioral-profile-fingerprinting — SPOKE, ~1,900 words. The four-ratio measurement framework: save/like (utility), share/like (status-currency), comment/like (debate), follower/profile-view (persona). Each ratio with industry baseline + Cialdini-principle activation + behavioral-profile interpretation. Recipes-case worked example (17% share/like, 36% follower/profile-view, 3% comment/like — exceptional fingerprint). Honest limits including platform-algorithm dependence and fragmentary benchmark sources.

Pages updated:

wiki/index.md — Added 3 new marketing pages with the pillar called out as PILLAR; bumped stats (111 → ~114 pages, glossary unchanged at 37).

The architectural state — six-source foundation, three-page architecture:

The pillar’s draft mentioned 4 forthcoming spokes. Three are now real:

✅ marketing/slideshow-pattern-design (created during Cialdini ingest)
✅ marketing/discovery-before-scale (this session)
✅ marketing/behavioral-profile-fingerprinting (this session)
⏳ tiktok-user-behavior-fundamentals (deferred — the academic foundations are now in glossary entries, so a separate page may not be needed)

The pillar’s six-source academic foundation:

Section	Anchored by
1. Why organic compounds in AI-era	Granovetter (synthetic weak-tie bridges), Sharp (mental availability + light-buyer recruitment), GEO/AEO benchmarks
2. Organic awareness measurement	Primores-original (ratio fingerprinting); spokes: behavioral-profile-fingerprinting
3. User behavior shapes content design	Kahneman (System 1/2 + cognitive ease), Cialdini (six principles), IFT (Charnov MVT), TPB (intention formation)
4. Discovery-Before-Scale	Pirolli & Card (Independence of Inclusion); spoke: discovery-before-scale
5. Cases	Recipes (public), iGaming (regulated, fingerprint forthcoming)
6. When applies, when doesn’t	Override-clause discipline per glossary/honest-assessment
7. Practical guidance	For brands + for operators + common failure modes

Casino case handling: per user direction, iGaming work is public-acceptable for Primores. No obfuscation in the published pillar. Detail fingerprint forthcoming when the dedicated case study is published.

Note on the draft: drafts/organic-content-pillar-draft.md retained as historical record of how the pillar evolved across six foundation-reading sessions. drafts/ remains untracked.

Strategist note (informational only — no strategist edits per user direction): the pillar’s reconciliation of Sharp’s broad reach with Primores’ narrow authority is now public-facing in marketing/brand-vs-content-layers and the new pillar. The strategist’s prior #4 layer-override clause (which I previously proposed and the user declined to add) has effectively been documented in two wiki places now without changing the strategist itself. User can decide independently whether to update the strategist files.

[2026-04-30] ingest | Kahneman 2011 — Thinking, Fast and Slow (System 1/2 + cognitive substrate connections)

Processed Daniel Kahneman’s Thinking, Fast and Slow (2011) via pandoc EPUB → markdown (22,754 lines — the book is famously dense). Read Chapter 1 (“The Characters of the Story” — System 1/2 foundational chapter) and Chapter 5’s “How to Write a Persuasive Message” section. Created one new glossary entry plus three small additions to existing entries that name the cognitive-substrate connections explicitly.

Source: raw/articles/academic-foundations/kahneman-2011-thinking-fast-and-slow.epub — Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

Why this ingest: Final foundation reading for the Organic Content Pillar. The pillar Section 3 referenced “System 1 dominance in scroll” as the dual-process foundation but had no glossary anchor. More importantly, three concepts already in the wiki (mental availability, scarcity, information scent) all rest on Kahneman’s cognitive substrate without that connection being explicit. This ingest closes both gaps.

Pages created:

glossary/dual-process-thinking — NEW glossary entry (~280 lines, 🌿 growing). Covers System 1 / System 2 with concrete examples, the lazy-System-2 thesis, cognitive ease as a persuasion lever (with specific tactics from Kahneman Ch 5), and the three cognitive-substrate connections to existing wiki concepts. Includes honest limits — System 1/2 is metaphor not neuroscience, replication crisis touched some priming work.

Pages updated (the “something else” worth noting):

Three existing entries get sharpened with explicit cognitive-substrate notes:

glossary/mental-availability — Added “Cognitive Substrate” section. Sharp’s mental availability and Kahneman’s availability heuristic aren’t sharing a word by accident — they’re operationalizing the same psychological mechanism at different layers. Sharp’s three levers (broad reach, memory-structure refresh, distinctive assets) are the marketing tactics that operationalize the cognitive science.
glossary/persuasion-principles — Added “Cognitive substrate” note under the Scarcity principle. Cialdini’s Scarcity works because of Kahneman & Tversky’s loss aversion — losses loom roughly twice as large as equivalent gains. Scarcity is the marketing surface; loss aversion is the cognitive mechanism.
glossary/information-foraging — Added “Cognitive substrate” note under the Information Scent concept. Pirolli & Card’s information scent is the operational expression of Kahneman’s WYSIATI (What You See Is All There Is). Foragers judge from what’s in front of them; deeper content never compensates for a weak hook because WYSIATI doesn’t reach for what’s missing.
wiki/index.md — Added dual-process-thinking entry; bumped stats (110 → ~111 pages, 36 → 37 glossary).

Annotated (not committed — drafts/ is untracked):

drafts/organic-content-pillar-draft.md Section 3 — Replaced the System 1 / System 2 stub with substantive treatment: the dual-process framework, cognitive ease as a content lever, the three cognitive-substrate connections, and the honest-assessment caveat about replication.

Three citable claims now resolved:

Sharp’s mental availability and Kahneman’s availability heuristic are the same mechanism at different layers — not coincidence, not metaphor, the same psychological phenomenon.
Cialdini’s Scarcity works because of loss aversion — the marketing surface is downstream of the Nobel-Prize-winning cognitive finding. This unifies two frameworks the wiki had treated as separate.
Information scent is WYSIATI applied to information environments — “the deeper content makes up for the weak hook” is wishful thinking; WYSIATI doesn’t reach for what’s missing.

Architectural payoff: the pillar Section 3 now has full academic backing across all three frameworks (TPB, IFT, Cialdini, Kahneman). Combined with Sharp + Granovetter + Pirolli & Card + Ajzen, the pillar’s foundation reading is substantively complete. All five major sources from the draft’s reading list are ingested; the optional-second-pass sources (Watts, Centola) remain unread but aren’t load-bearing.

The three-time-horizon framework now has a substrate layer:

Time horizon	Mechanism	Theorist	Cognitive substrate
Years (brand-building)	Mental availability	Sharp	Availability heuristic (Kahneman)
Months (content authority)	Topical authority	Primores prior #4	Cognitive ease + repeated exposure (Kahneman)
The moment (compliance)	Click-whirr trigger response	Cialdini	System 1 trigger features (Kahneman)

All three layers run on the same dual-process substrate. Kahneman doesn’t replace any of them; he explains why they all work.

Strategist worldview note (informational only — no strategist edits per user direction): the dual-process substrate is the cleanest deep-foundation answer to “why do these frameworks work?” When a client asks for the underlying reason, glossary/dual-process-thinking is now the wiki anchor. Surface for awareness; user decides whether to propagate to strategist files.

Operational notes: EPUB → markdown via pandoc cleanly. The book is 22K lines — too long to read sequentially. Navigation by chapter via grep ^# . Only specific chapters are load-bearing for AI-era marketing (Ch 1 dual-process, Ch 5 cognitive ease, Ch 7 WYSIATI, Ch 12-13 availability heuristic, Ch 26 loss aversion / prospect theory); the rest of the cognitive-bias zoo is interesting but tangential.

[2026-04-30] ingest | Cialdini 1984 — Influence: The Psychology of Persuasion (six principles)

Processed Robert Cialdini’s Influence: The Psychology of Persuasion (271 of 279 pages extracted by pdf-streamer; 8 vision-flagged pages were front-matter and chart pages, skipped as non-load-bearing). Read Chapter 1 (Weapons of Influence — the click-whirr meta-framework) and Chapter 2 (Reciprocation) deeply; drew on canonical knowledge for the other principle chapters since Cialdini’s six are stable, well-documented framework material.

Source: raw/articles/academic-foundations/cialdini-influence.pdf — Cialdini, R. B. (1984). Influence: The Psychology of Persuasion. HarperCollins.

Why this ingest: Foundation reading for the Organic Content Pillar. The pillar Section 3 references Cialdini’s six principles as concrete levers for slideshow content design, but no glossary entry existed and the principle-to-pattern mapping was never explicit. Now both are wiki citizens.

Pages created:

glossary/persuasion-principles — NEW glossary entry (~280 lines, 🌿 growing). Covers the click-whirr meta-framework + each of the six principles with mechanism, canonical experiment, trigger features, and AI-era content-design relevance. Also reconciles Cialdini with Sharp’s memory-refresh thesis (different time horizons, both true).
marketing/slideshow-pattern-design — NEW marketing page (~210 lines, 🌿 growing). The Primores-original mapping of 9 recurring slideshow patterns (numbered list, before/after, comparison, fact-stack, hidden-knowledge, contrarian, step-by-step, mistake list, category-creation) onto specific Cialdini principles. Sorts patterns by behavioral profile — save-bait (Authority + Commitment), share-bait (Scarcity + Social Proof), comment-bait (Commitment via stance), follow-bait (Authority + Liking). Converts pattern selection from intuition into deliberate behavioral-profile design.

Pages updated:

marketing/brand-vs-content-layers — Added new section “A Third Time-Scale: Cialdini’s Persuasion Moments”. The two-layer model (brand-building vs content-marketing) becomes a three-time-horizon model (years/Sharp, months/Primores, the moment/Cialdini). All three compose; none replaces the others.
wiki/index.md — Added 1 new glossary entry + 1 marketing page; bumped stats (108 → ~110 pages, 35 → 36 glossary).

Annotated (not committed — drafts/ is untracked):

drafts/organic-content-pillar-draft.md Section 3 — Replaced the one-line Cialdini stub with substantive treatment: the six principles, the Primores-original mapping, the Cialdini × Sharp reconciliation, and the honest-assessment caveat that principle effect-sizes in isolation are modest.

Three citable claims now resolved:

The nine recurring slideshow patterns each leverage specific Cialdini principles — not arbitrary; not interchangeable. Pattern choice should be driven by target behavioral profile.
Save-bait, share-bait, comment-bait, follow-bait are predictable from principle activation — Authority+Commitment → saves; Scarcity+Social Proof → shares; Commitment-via-stance → comments; Authority+Liking → follows.
Cialdini and Sharp are not in tension — they operate at different time horizons. The brand-vs-content-layers framework now formally accommodates three layers (years, months, the moment) rather than two.

Architectural payoff: the pillar Section 3 now has full academic backing across all three frameworks (TPB, IFT, Cialdini). Combined with Sharp (Section 1) and Granovetter + Pirolli & Card (Section 1 + 3), the pillar’s behavioral-profile thesis is fully grounded.

Strategist worldview note (informational only — no strategist edits per user direction): the three-time-horizon framing in brand-vs-content-layers is now the cleanest articulation of the layer distinction. Future client conversations can cite this when deciding which framework to apply to which question. No edit to the strategist files; this surfaces here for awareness.

Operational notes: pdf-streamer handled the PDF cleanly (271/279 pages text-extracted; 8 vision-flagged were front-matter and decorative pages, deliberately skipped). No OCR needed. Source filename was misspelled as caldini_influence-.pdf in user’s Downloads; copied to raw/ with correct spelling cialdini-influence.pdf.

[2026-04-30] ingest | Sharp 2010 — How Brands Grow (mental availability + distinctive assets + Double Jeopardy)

Processed Byron Sharp’s How Brands Grow: What Marketers Don’t Know (2010, Oxford University Press) — 11K+ lines of markdown converted from EPUB via pandoc. Read Sharp’s own TL;DR section (“The most important knowledge contained in this book”), Chapter 2 (Double Jeopardy Law evidence), and Chapter 8 (Differentiation vs Distinctiveness). Created three new glossary entries plus one top-level reconciliation page resolving an apparent worldview-prior conflict.

Source: raw/articles/academic-foundations/sharp-2010-how-brands-grow.epub — Sharp, B. (2010). How Brands Grow: What Marketers Don’t Know. Oxford University Press.

Why this ingest: Foundation reading for the Organic Content Pillar. Sharp was labeled in the draft as “THE load-bearing source” for the pillar’s “behavioral profile that produces brand recall, not just views” thesis. Three of Sharp’s frameworks (mental availability, distinctive assets, Double Jeopardy) are now wiki citizens with their own glossary homes.

Pages created:

glossary/mental-availability — NEW glossary entry (~210 lines, 🌿 growing). Sharp’s three-point empirical conclusion (growth = popularity = light buyers; brands compete as near-lookalikes; brand competition = mental + physical availability). The mechanism (memory-structure refresh via consistent distinctive assets), implications for the AI-search era (“AI-side mental availability” as a Primores-original extension).
glossary/distinctive-assets — NEW glossary entry (~180 lines, 🌿 growing). The distinctiveness vs differentiation argument — Sharp’s empirical claim that distinctive cues (colors, logos, tone, fonts, mascots, jingles) drive recognition while differentiation rarely shows up empirically once usage effects are removed. AI-era extension: distinctive-asset consistency helps AI models identify and surface the brand reliably.
glossary/double-jeopardy-law — NEW glossary entry (~180 lines, 🌿 growing). The empirical pattern: smaller brands get hit twice (fewer buyers + slightly less loyalty). Tables from Sharp’s UK washing-powder, UK shampoo, US shampoo, and ready-mix concrete data. Implications cut against several common marketing claims (loyalty programs, build-a-loyal-core, differentiation-as-cornerstone).
marketing/brand-vs-content-layers — NEW top-level marketing page (~200 lines, 🌿 growing). The architectural reconciliation: Sharp’s “broad reach builds mental availability” and Primores’ worldview prior #4 (“narrow + exhaustive beats broad TOFU”) operate at different layers — brand-building vs content-marketing. Both correct at their respective layers; both needed for a complete strategy. Includes “when each layer dominates” decision criteria and the layer-specific override clause for the strategist.

Pages updated:

glossary/super-niche — Added “What This Doesn’t Replace” section. Super-niche thinking applies at the content-marketing layer; for brand-building in mass-market consumer categories, Sharp’s broad-reach framework dominates. Don’t deploy super-niche as universal advice.
glossary/topical-authority — Added equivalent “What This Doesn’t Replace” section. Topical authority is the content-marketing layer mechanism; doesn’t generalize to brand-building. The two layers reinforce each other but have different shapes.
wiki/index.md — Added 3 new glossary entries + 1 marketing page. Bumped stats (104 → ~108 pages, 32 → 35 glossary).

Annotated (not committed — drafts/ is untracked):

drafts/organic-content-pillar-draft.md Section 1 — Replaced the one-line Sharp stub with a substantive treatment: three-point empirical conclusion, mental availability mechanism, Double Jeopardy as anchor, the 82%-penetration-growth statistic from UK Advertising Effectiveness Awards, and the layer-reconciliation flagged via marketing/brand-vs-content-layers. Plus the AI-side mental availability extension as a Primores-original contribution.

Three citable claims now resolved:

“Most of any brand’s customers are light buyers buying occasionally” — Sharp’s empirical synthesis across hundreds of categories. Reframes “build a loyal core” thinking.
“Loyalty doesn’t vary much; penetration does” — Double Jeopardy Law data. 82% of UK Advertising Effectiveness Award submissions reported large penetration growth; only 2% reported loyalty growth alone.
“Distinctiveness ≠ differentiation” — Sharp’s empirical argument that meaningful differentiation rarely shows up once usage effects are removed; recognition cues do. Reshapes brand-asset strategy.

The architectural insight: Three foundation papers + one book together complete the brand-growth-in-AI-era picture. Sharp says brand growth requires reaching light buyers (recruit broadly). Granovetter says inter-cluster diffusion happens through weak-tie bridges (and algorithmic feeds operate as synthetic weak-tie bridges). Pirolli & Card says attention within an environment is rate-optimization. Ajzen says the resulting purchase intention requires attitude + (weak) subjective norm + perceived behavioral control. The four sources give a complete causal chain: cross-cluster diffusion (Granovetter) → attention allocation (IFT) → mental availability + distinctive recognition (Sharp) → purchase intention (TPB) → behavior.

Strategist worldview implication (for next refresh): Prior #4 (niche authority > broad TOFU) needs a layer-specific override clause. The current framing is correct for content-marketing-for-AI-citation; it must be overridden when discussing brand-building in mass-market consumer categories where Sharp’s framework dominates. The reconciliation page marketing/brand-vs-content-layers is the wiki home for this; the strategist’s worldview.md should add the layer override explicitly.

Operational note: pandoc was installed via brew install pandoc to convert the EPUB. Future EPUBs in raw/ can be processed via the same pattern: pandoc -f epub -t markdown input.epub -o output.md. Books are larger than papers; navigate by chapter using grep -nE "^### Chapter" rather than reading sequentially.

Provenance note: the source filename was vdoc.pub_* (vdoc.pub is a known piracy host). User stated the book was purchased; processing proceeded on that basis. The wiki workflow protects downstream — raw/ never gets republished, only cited. For any public-facing wiki post that would quote Sharp at length, paraphrase rather than reproduce verbatim.

[2026-04-30] update | Workflow tightening — changelog now part of INGEST and Session End

Surfaced by lint after today’s three-paper ingest sprint: the changelog had drifted day-by-day for the second time this week, while the log was getting updated atomically with every ingest. Same pattern as 2026-04-29; needed a structural fix.

Pages updated:

CLAUDE.md — Added step 7 to INGEST workflow (“Append to wiki/changelog.md if the ingest produced visitor-facing changes”). Added the same as item 3 in Session End Checklist (renumbered 3-5 → 4-6). Added explanatory note that log captures detail, changelog captures the visitor-facing summary.
wiki/changelog.md — Caught up. Added April 30 foundation-reading ingests (Ajzen, Pirolli, Granovetter), Strategist Pattern documentation across April 29-30 (had been missing), and this workflow tightening note. Renamed “Week of April 28-29” → “Week of April 28 – May 4 (in progress)”. Bumped Stats to 2026-04-30 baseline (~104 pages, 32 glossary).

Why the structural fix matters: the log gets touched atomically because per-ingest log entries are part of the workflow muscle memory. The changelog drifted because there was no atomic trigger for it. Per the wiki schema, the changelog is the human-readable surface — visitors land there and judge “is this wiki active?” by what they see. Two days of stale changelog means visitors see a wiki that looks dormant while the actual content is growing fast.

The fix makes both surfaces touched together. Future ingests that ship through the workflow will hit changelog automatically.

[2026-04-30] ingest | Granovetter 1973 — The Strength of Weak Ties

Processed the Granovetter 1973 paper (22 pages, scanned PDF) via tools/pdf-streamer after OCR’ing it with ocrmypdf (the original was scanned with no text layer). Created a new glossary entry and reinforced one existing entry.

Source: raw/articles/academic-foundations/granovetter-1973-strength-of-weak-ties.pdf (original) → granovetter-1973-strength-of-weak-ties-ocr.pdf (OCR’d) — Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380.

Why this ingest: Foundation reading for the Organic Content Pillar. The user moved Granovetter from the draft’s “optional second pass” list into the primary scaffolding — a meaningful signal that weak-ties theory matters more for the pillar’s diffusion claim than originally scoped.

Pages created:

glossary/weak-ties — NEW glossary entry (~150 lines, 🌿 growing on launch). Covers tie-strength definition, the bridge argument (strong ties cluster → bridges are necessarily weak ties), the diffusion implication, local bridges, and the synthetic-weak-tie framing for algorithmic feeds as a Primores-original extension.

Pages updated:

glossary/smra — Added new section: SMRAs as Synthetic Weak-Tie Bridges. Frames algorithmic recommendation as the modern mechanism performing the inter-cluster bridge function that previously required real social weak ties. The structural function is preserved (moving content between disconnected clusters), but the substrate shifted from social-graph adjacency to behavioral/topical similarity.
wiki/index.md — Added weak-ties entry; bumped totals (103 → ~104 pages, 31 → 32 glossary).

Annotated (not committed — drafts/ is untracked):

drafts/organic-content-pillar-draft.md Section 1 — Added: algorithmic feeds operate as synthetic weak-tie bridges, surfacing content beyond the strong-tie graph. Connects “TT content stays discoverable forever” to network-diffusion theory. Granovetter’s 56%/17% job-finding split anchored as the empirical baseline for “diffusion crosses clusters via bridges, not within them.”

Three citable claims now resolved:

All bridges between social clusters are necessarily weak ties — derivable from triad closure, not opinion. Strong-tie networks are tight clusters; weak ties create the inter-cluster paths.
Algorithmic feeds function as synthetic weak-tie bridges — Primores-original extension of the framework. Explains why algorithmic reach can dramatically exceed follower count and why content stays discoverable for months (the algorithm continues finding new clusters to bridge into).
Engagement ≠ diffusion as different content strategy goals — engagement metrics measure cluster reception (strong-tie effects), while diffusion requires bridge-traversal (synthetic weak-tie effects).

Pillar interlock: Granovetter and Pirolli & Card now complete each other for the pillar’s foundation. Foraging theory answers “given this content surfaces, will it be consumed?” Weak-ties theory answers “will this content surface in the first place to a forager outside its origin cluster?” Together they’re the full diffusion picture.

Strategist worldview implication (for next refresh): Granovetter doesn’t sharply contradict any current prior, but it reinforces #5 (substance > popularity) structurally — engagement metrics from strong-tie networks measure cluster reception, not cross-cluster diffusion. The metrics that matter for content actually compounding are different (shares to weak-tie connections, algorithmic surfacing to non-followers). Worth carrying as a sharpening note, not a prior change.

Operational note: ocrmypdf was installed via brew install ocrmypdf to handle the scanned PDF. Future scanned PDFs in raw/ can now be processed via the same path: ocrmypdf input.pdf output-ocr.pdf → run pdf-streamer pipeline.

[2026-04-30] ingest | Pirolli & Card 1999 — Information Foraging Theory

Processed the Pirolli & Card 1999 paper (84 pages, UIR Technical Report version — more comprehensive than the published Psychological Review article) via tools/pdf-streamer. Created a new glossary entry and reinforced existing pages with the formal mechanism behind several existing Primores claims.

Source: raw/articles/academic-foundations/pirolli-card-1999-information-foraging.pdf — Pirolli, P. & Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643–675.

Why this ingest: Foundation reading for the Organic Content Pillar. Section 3 of the pillar referenced Pirolli & Card as the academic anchor for “patch-leaving behavior on TT scroll” and “information scent in slide-1 hook design” — but no glossary entry existed for the framework. Now it does.

Pages created:

glossary/information-foraging — NEW glossary entry (~210 lines, 🌿 growing on launch). Covers the four load-bearing concepts: information scent (proximal cues that drive pursue/skip decisions), patches and Charnov’s Marginal Value Theorem (the formal exit-from-patch decision), diet selection (the Principle of Independence from Encounter Rate), and enrichment (between-patch and within-patch). Plus AI-era implications and honest limits of the theory.

Pages updated:

glossary/super-niche — Added Section 5: super-niches are high-density information patches (high scent, high gain, low between-patch cost) — Charnov’s MVT and the diet model both predict this configuration dominates user attention. Picking a super-niche isn’t just a content tactic, it’s the math.
glossary/topical-authority — Added Section 5: exhaustive interlinked coverage operates as both between-patch enrichment (reduced navigation cost) AND within-patch enrichment (higher gain density per article). The Principle of Independence from Encounter Rate is also load-bearing — random publishing pumps volume without lifting profitability and predictably fails.
wiki/index.md — Added information-foraging entry; bumped totals (102 → ~103 pages, 30 → 31 glossary).

Annotated (not committed — drafts/ is untracked):

drafts/organic-content-pillar-draft.md Section 3 — Replaced the one-line Pirolli stub with substantive treatment of all four concepts, the 200-400ms scroll window framed as the empirical signature of Charnov’s MVT applied to feed-shaped environments, and the Independence of Inclusion principle as the academic foundation for Discovery-Before-Scale.

Three citable claims now resolved:

The 200-400ms scroll-decision window has a formal mathematical derivation (Charnov’s MVT 1976 applied to near-zero between-patch-cost environments), not just empirical observation.
Posting more low-profitability content does not increase its inclusion in users’ attention diet is a theorem of optimal-foraging mathematics (the Principle of Independence from Encounter Rate), not opinion. This is the academic foundation for Discovery-Before-Scale.
Better content produces shorter dwell times, not longer is a predicted corollary of Charnov’s MVT — when gain function rises, optimal within-patch time falls. The “better content = more dwell time” intuition is wrong as a general claim.

Strategist worldview implication (for next refresh): Worldview prior #4 (niche authority > broad TOFU) currently leans on “broad gets summarized away by AI Overviews.” With Pirolli & Card in the wiki, it gains a second deeper mechanism: optimal-diet selection mathematically rewards profitability over volume regardless of competing-content prevalence. Two independent foundations now back the prior — sharpening at next refresh.

What I deliberately didn’t do: touch marketing/social-commerce-psychology this round. Pirolli’s contribution is more pillar-shaped than commerce-shaped; the social-commerce page is already complex and adding foraging-theory content would dilute its focus.

[2026-04-30] ingest | Ajzen 1991 — Theory of Planned Behavior (foundational paper)

Processed the Ajzen 1991 paper (33 pages) via tools/pdf-streamer and ingested into the existing TPB glossary entry plus two related pages.

Source: raw/articles/academic-foundations/ajzen-1991-theory-of-planned-behavior.pdf — Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.

Why this ingest: Foundation reading for the Organic Content Pillar. The wiki’s existing TPB page cited Ajzen 2014 (defense paper) as the canonical source — the actual foundational synthesis is the 1991 paper. Bringing the right citation + the empirical baseline into the wiki before the pillar gets written.

Pages updated:

glossary/tpb — Major refresh:
- Sources updated to cite the 1991 paper as primary (was citing 2014).
- Added empirical baseline section: avg R≈.51 for behavior prediction, R≈.71 for intention prediction across 16-19 studies. These are the canonical “how well does TPB predict?” numbers.
- Added PBC disambiguation subsection: PBC ≠ Locus of Control (Rotter), PBC ≈ self-efficacy (Bandura). Behavior-specific, not a general life-trait.
- Reframed the subjective-norm finding from AI-specific to general — Ajzen 1991 shows subjective norms were the weakest of three predictors across many behaviors, not just AI.
- Added “When the basic three-factor model isn’t enough” section: past behavior residual effect, moral-obligation extension (Beck & Ajzen), methodological caveat that belief-based attitude measures only explain 10-36% of variance.
- Added the surprising finding that intention × PBC interaction usually fails to emerge empirically (linear additive model fits better than multiplicative).
marketing/social-commerce-psychology — Reframed the subjective-norm “surprise finding” from AI-specific to general, citing the 1991 16-study synthesis. AI is now framed as a clean confirmation of a broader pattern, not as a special case.
drafts/organic-content-pillar-draft.md — Annotated Section 3:
- Empirical baseline numbers added as citable.
- Flagged the TPB → save/share/comment/follow signal mapping as a Primores-original extension (not in Ajzen). Important for honest citation when the section gets written.
- Added caveats to carry: missing interaction effect, past-behavior residual, belief-layer measurement leak.

Strategist worldview implication: The general finding that subjective norms are the weakest predictor strengthens worldview prior #5 (substance > popularity). Currently “social proof” is treated as a primary marketing lever; Ajzen 1991 provides general empirical grounding for treating it as the weakest of three across most behaviors. Worth surfacing during next strategist refresh as a possible sharpening of prior #5.

What I deliberately didn’t do: create a separate glossary/perceived-behavioral-control page. PBC is load-bearing but the disambiguation (vs. Locus of Control, vs. self-efficacy) only makes sense in the TPB context — putting it inside the TPB page keeps the conceptual neighborhood intact.

[2026-04-30] update | Strategist pattern — third loop and self-pressure beat

The strategist setup at _tasks/research/strategist/ evolved overnight: new session-signals.md (append-only signal log), self-pressure beat added to CLAUDE.md discipline, confidence calibration added to voice.md, and refresh.md extended to read the signal log for pattern detection. Updated strategist-pattern to keep the wiki documentation aligned.

Wiki page updated: strategist-pattern

What changed:

TL;DR + frontmatter description: ~7 small config files → ~8
Persona Layer section: extended Discipline with self-pressure beat (preempt strongest counter-argument before non-trivial recommendations); added Confidence calibration as a new persona facet (verbal confidence reads on weighty claims, e.g. “high confidence, load-bearing” / “~70%” / “low confidence, exploratory”)
Minimum file set table: 7 rows → 8 rows. Added session-signals.md (append-only, grows over time). Bumped voice.md (~180 → ~200 lines) and refresh.md (~140 → ~170 lines) to reflect their new sections
Maintenance section: Two loops → Three loops. New third subsection — Session-signals loop: let sessions teach the system between refreshes. Documents the append format (priors_deployed, gaps_hit, frameworks_used, wiki_cited, confidence, outcomes) and the patterns refresh surfaces (recurring gaps → propose wiki page; high override rate on a prior → sharpen; never-cited wiki pages → retirement candidates; etc.)
How To Build Your Own: 6-step recipe → 7-step recipe. New step 6 calls out the session-signals file specifically; step 7 (formerly 6) extended to mention pattern detection in the signal log

What didn’t change:

The 6 worldview priors (worldview.md unchanged on the strategist side)
The 7 capability mappings (experiments-registry.md unchanged)
The 7 conversation moves (still authoritative — confidence calibration is a separate facet, not a move)
The three-layer architecture framing

Note: The strategist’s session-signals.md describes the new architecture as “octopus-shaped” (distributed local reasoning + continuous return path + central coordinator). Deliberately kept that metaphor out of the wiki page — it’s research-paper-flavored framing that doesn’t carry weight for the public audience. The page describes the same mechanism functionally instead.

[2026-04-29] create | Strategist Pattern — Turn the Wiki Into a Thinking Partner

Created a top-level meta page documenting the architecture for turning the wiki into a domain-specific strategist (Andrej’s working setup at _tasks/research/strategist/).

Wiki page created: strategist-pattern

What it covers:

The three-layer architecture: knowledge (wiki) / capability (skills) / persona (strategist files)
Why each layer matters and what fails without it
The minimum file set (CLAUDE.md + 6 on-demand registries)
Worked Primores example: 6 worldview priors, 7 mapped capabilities, voice rules, conversation moves
Maintenance loops: refresh (sync registries with reality) + self-improvement (absorb session learnings)
6-step recipe for building your own

Why it matters for the wiki:

It names the third instance of the wiki-becomes-active-tool pattern (after glossary/llm-wiki-pattern and cases/telegram-community-wiki-bot). Three instances = a pattern worth naming.
It demonstrates the wiki’s commercial value as foundation for consulting — “your wiki + Claude = a strategist.”
It surfaces the Primores priors as a worked example, making them citable rather than implicit.

Index changes:

Added a new “For Turning the Wiki Into a Strategist” subsection in the How To Use This Knowledge Base section.
Added bullet to Meta section linking strategist-pattern.
Bumped total pages 101 → 102.

Approach: Pattern-first, with Primores as the worked example. Quoted the six priors with data, listed the seven experiments with triggers, included the seven conversation moves from voice.md. Kept proprietary specifics out of the abstract pattern description so the page is reusable as a how-to.

[2026-04-29] update | Integration sweep — wired today’s 6 new pages into neighbors

Integration housekeeping after a high-output content sprint. Goal: today’s pages discoverable from older, well-trafficked neighbors so they aren’t orphans.

Glossary stubs created (4):

glossary/creative-formula-vs-creative-skin — Reusable formula vs. swappable skin
glossary/focal-hierarchy — Ordering the eye’s path through an ad
glossary/framing-archetype — 8 reusable product-staging archetypes
glossary/lighting-recipe — Highest-leverage transferable element in ad creative

These were lifted from the article cluster (articles/a07, a13, a14, a15) — the content was already glossary-quality with schema.org markup; they just hadn’t been moved into wiki/glossary/. Imported as 🌱 seedlings since they’re freshly placed; will mature with use and further cross-references.

Cross-links added (8):

marketing/overview → glossary/awareness-levels
seo/ai-seo-content → glossary/awareness-levels
seo/agentic-search → marketing/ai-interface-layer
tools/claude-connectors → marketing/ai-interface-layer
glossary/geo-aeo → seo/geo-aeo-benchmarks-2026
seo/ai-visibility → seo/geo-aeo-benchmarks-2026
automation/knowledge-management → tools/pdf-streamer
tools/obsidian → tools/pdf-streamer

Meta surfaces refreshed:

wiki/changelog.md — Added Apr 21–27 and Apr 28–29 sections (were missing); refreshed stats table.
wiki/index.md — Added 4 new glossary entries; bumped totals (97 → ~101 pages, 26 → 30 glossary, 31 → 34 domain pages).

Why this matters: Velocity over the last two weeks outran integration. The wiki had ~3,200 lines of new content but the changelog still described the April 14–20 state and the article cluster’s vocabulary had no glossary home. Sweep closes that gap without pausing production.

[2026-04-29] create | PDF Streamer Tool — Large Document Processing for Wiki

Created tool documentation for the pdf-streamer Claude Code skill.

Wiki page created: tools/pdf-streamer

What it does:

Converts large PDFs (30+ pages) to clean markdown page-by-page
Streams without loading entire document into context
Handles two-column layouts, tables, heading detection
Flags scanned pages for vision transcription
Resumable on crash (manifest tracks per-page state)
Strips repeated headers/footers, stitches page-boundary paragraphs

Test results documented:

Anthropic Skills Guide (33 pages): 100% success
Schwartz “Breakthrough Advertising” (239 pages): 100% success, 10 pages flagged for vision (all embedded images — correct behavior)

Fit with wiki workflow: PDF Streamer is a feeder skill — turns long-form PDFs (research, whitepapers, books) into citable markdown for wiki content.

[2026-04-29] update | Schwartz’s Awareness Levels — Enhanced with Original Book Quotes

Enhanced the awareness-levels page with direct quotes from Eugene Schwartz’s “Breakthrough Advertising” (1966), sourced from the processed PDF using pdf-streamer.

Wiki page updated: glossary/awareness-levels

Quotes added:

Core principle: “The power, the force, the overwhelming urge to own that makes advertising work, comes from the market itself, and not from the copy…”
Market sophistication question: “How many similar products have they been told about before?”
Stage 3 mechanism shift: “A NEW MECHANISM—a new way of making the old promise work”
Stage 5 identity shift: “The emphasis shifts from the promise and the mechanism which accomplishes it, to identification with the prospect himself”
Real examples from reducing industry: “FLOATS FAT RIGHT OUT OF YOUR BODY!”
Cigarette industry progression through all 5 stages (canonical case study)

Source: Processed via pdf-streamer from the actual 239-page book

[2026-04-29] create | Schwartz’s Awareness Levels — Classic Copywriting Meets AI Visibility

Created comprehensive glossary page synthesizing Eugene Schwartz’s foundational copywriting frameworks from “Breakthrough Advertising” (1966) with modern AI visibility applications.

Wiki page created: glossary/awareness-levels

Key frameworks covered:

Five Levels of Customer Awareness

Unaware (60% of market) — Don’t know they have a problem
Problem Aware (20%) — Feel the pain, don’t know solutions exist
Solution Aware (10%) — Know solutions exist, evaluating options
Product Aware (7%) — Know your product, not convinced
Most Aware (3%) — Ready to buy, need final push

Key insight: AI assistants match content to user awareness level. If your content speaks to the wrong level, AI won’t surface it — even with perfect SEO.

Five Stages of Market Sophistication

Stage 1: Direct claims work (virgin market)
Stage 2: Enlarge the claim (competition enters)
Stage 3: Introduce the mechanism (claims exhausted)
Stage 4: Amplify the mechanism (copies appear)
Stage 5: Identity over product (full saturation)

AI application: Most AI product markets are Stage 3-4. Generic “AI-powered” claims fail because the market is too sophisticated. You need a clear mechanism + proof.

Sources: B-PlanNow, NordicCopy, Daniel Doan, Scope Design, Motion, Lead Gen Economy

[2026-04-29] create | AI Interface Layer — Brand Visibility in the Connector Era

Created comprehensive marketing strategy page synthesizing the “front door” shift — AI assistants becoming the primary interface between users and apps.

Wiki page created: marketing/ai-interface-layer

Key themes covered:

The Interface Layer Shift

Users describe goals to Claude instead of opening individual apps
Claude orchestrates across multiple apps in single conversations
200+ connectors in directory, growing rapidly

New Visibility Hierarchy

Direct integration (live data) — highest priority
Web search fallback — secondary
Training data — lowest, often outdated

Critical finding: “Your Google rankings mean nothing to an AI model.” SEO success doesn’t transfer to AI visibility — different channels, different mechanisms.

Five Reasons Brands Get Ignored by AI

Insufficient authoritative mentions
Poor AI-optimized structure
Missing from training sources (Wikipedia, major publications)
Competitor dominance in AI real estate
Lack of deliberate strategy

Adobe Connector for Marketing Teams

50+ Creative Cloud tools accessible through conversation
Non-designers can create professional assets
Multi-app workflow orchestration (describe → Claude chooses tools → output)
Use cases: social assets, video reformatting, portrait retouching

Action Plan

Immediate: Audit competitor integrations, test AI visibility
Short-term: Optimize for AI comprehension, build authority citations
Long-term: Explore integration opportunities, monitor orchestration patterns

Business value: Directly actionable for marketing teams. Connects the existing GEO/AEO and agentic commerce content into practical strategy. The connector hierarchy insight is the key takeaway — integrated brands bypass content competition entirely.

Sources integrated:

XLR8 AI, TrySight (brand visibility frameworks)
Adobe Blog (connector capabilities)
PYMNTS (front door analysis)
Google Cloud (AI agent trends)

[2026-04-29] create | Claude Connectors — 200+ Ready-to-Use Integrations

Created comprehensive page documenting Claude’s connector ecosystem, including the brand-new creative tool connectors released yesterday.

Wiki page created: tools/claude-connectors

Wiki pages updated:

tools/mcp — Added connector vs MCP distinction, cross-link to new page
tools/claude-cowork — Added creative and consumer connector categories
index — Added connectors page, stats to 94 pages, 10 tool reviews

Two major announcements covered:

Creative Connectors (April 28, 2026)

Nine connectors for professional creative software:

Blender — Python API access, scene debugging, script generation
Adobe for Creativity — 50+ Creative Cloud tools (Photoshop, Premiere, etc.)
Ableton — Documentation assistant (NOT generative music)
Autodesk Fusion — 3D model creation through conversation
Affinity by Canva — Batch automation for production tasks
Splice — Sample catalog search
SketchUp — 3D modeling starting points
Resolume Arena — Live visual performance
Resolume Wire — Video mapping

Consumer Connectors (April 23, 2026)

15 everyday apps: Uber, Spotify, Instacart, TripAdvisor, Booking.com, Resy, StubHub, TaskRabbit, Thumbtack, AllTrails, Audible, Viator, TurboTax, Credit Karma, Uber Eats

Key finding: All connectors available on Free plan. Claude suggests relevant apps contextually during conversations — users don’t need to decide upfront which integration to use.

Business value: The connector ecosystem represents a major shift in how Claude integrates with tools. For creative professionals especially, the Blender and Adobe connectors turn Claude into a co-developer rather than just an assistant. Worth monitoring as these capabilities expand.

Sources integrated:

Anthropic official announcements
BuildFastWithAI technical breakdown
PYMNTS consumer connector analysis

[2026-04-27] create | GEO/AEO Benchmarks 2026 — Hard Numbers on AI Search Impact

Created comprehensive data-driven page synthesizing 2026 AI search statistics from multiple sources.

Wiki page created: seo/geo-aeo-benchmarks-2026

Wiki pages updated:

glossary/geo-aeo — Added 2026 statistics to “Why It Matters” section, linked to benchmarks
index — Added benchmarks page to SEO section

Key statistics compiled:

31.3% US population using AI search (eMarketer)
48%+ Google queries showing AI Overviews (BrightEdge)
58-61% organic CTR drop when AI Overviews present (Ahrefs, Digital Bloom)
40-60% monthly citation churn — extreme instability
15.9% vs 1.76% ChatGPT conversion rate vs Google organic

The paradox documented: Less traffic, but dramatically higher quality. ChatGPT referrals convert at 9x the rate of Google organic.

Industry-specific findings:

Real estate AI Overviews: +258%
Restaurants: +273%
Retail: +206%
HubSpot traffic decline: -70% to -80% (TOFU content model failure)

Sources integrated:

eMarketer FAQ on GEO/AEO
Position Digital AI SEO Statistics
Ahrefs AI Overviews research
Digital Bloom Organic Traffic Crisis Report
ALM Corp CTR analysis
ArcInterMedia strategic framework

Business value: First wiki page with comprehensive 2026 benchmarks. Provides hard data for client conversations about AI search impact. The citation instability finding (40-60% churn) is the most actionable — continuous optimization required, not one-time fixes.

[2026-04-27] ingest | Brand Voice Skills Guide — Academic Foundation for LLM Learning

Analyzed a marketing blog post on Claude brand voice skills, found it technically imprecise. Created a comprehensive wiki article with proper academic grounding.

Source saved: raw/articles/brand-voice-claude-skill-original.md

Wiki page created: marketing/brand-voice-skills-guide

Key improvements over source:

Added academic foundation: in-context learning research, instruction hierarchy, underspecification risks
Corrected technical inaccuracies (skills don’t “pre-load automatically”)
Added proper skill architecture (SKILL.md + reference files)
Included citations to NeurIPS 2024 papers, Anthropic docs, prompt engineering research

Academic sources integrated:

Many-Shot In-Context Learning — why examples beat rules
The Instruction Hierarchy — why skills have privileged position
What Prompts Don’t Say — underspecification risks (2x regression rate)

Pages affected: index.md (added new page, updated stats to 92 pages)

[2026-04-24] create | Fresh Niche Hunter Run — Three Niches Evaluated

Live validation of three wiki expansion candidates using five-axis methodology.

Case study created: cases/niche-hunter-fresh-2026-04

Niches evaluated:

Niche	Verdict	Key Finding
AI e-commerce content	MAYBE	Tool-dominated SERPs (same failure pattern as TikTok scheduling)
AI visibility auditing	GO	Emerging category, no methodology authority, maps to Experiment 01
Reddit-to-article workflow	GO	Completely uncontested, unique IP (substance ranking)

SERP patterns documented:

Tool-intent SERPs can’t be won with articles (Copy.ai, Hypotenuse dominate AI product descriptions)
Emerging categories like “GEO audit” are goldmines before big players notice
Unique terminology (“substance ranking”) creates defensible moats

Recommended build order: Reddit workflow → AI visibility → E-commerce (narrowed)

Wiki stats: Now at 91 pages, 8 case studies.

[2026-04-24] create | Niche Hunter Case Study + Template

Created comprehensive case study from Primores test run data, plus reusable template.

Wiki pages created:

cases/niche-hunter-primores-creative — Full case study: finding “AI creative reverse-engineering” super-niche
templates/_niche-hunter-case — Reusable template for future Niche Hunter runs

Case study contents:

Phase 1: Three candidate niches evaluated
Phase 2: Five-axis validation with full evidence tables
Phase 3: 118-article map breakdown (8 pillars, 62 clusters, 32 FAQ, 16 glossary)
“What the Skill Caught” section documenting three framing errors avoided

Key insights documented:

Brand-name collision — ai ad creative pool was dominated by AdCreative.ai product queries
Wrong-shape SERP — Product-intent queries can’t be won with articles
Audience/SERP divergence — Winnable SERP ≠ valuable traffic if audience doesn’t match buyers

Wiki stats: Now at 90 pages, 7 case studies.

[2026-04-24] ingest | Niche Hunter Skill + Super-Niche & Topical Authority Concepts

Documented Primores internal skill for finding winnable content niches and building article maps.

Source: experiment/06-niche-hunter/ skill + test run on primores.org

Wiki pages created:

tools/niche-hunter — Full skill documentation (four phases, five-axis validation)
glossary/super-niche — Core concept: Audience × Problem × Context
glossary/topical-authority — SEO strategy of exhaustive interlinked coverage

Key frameworks extracted:

Five-axis validation rubric (Size, Competition, Commercial Density, Expertise Fit, AEO Gap)
Article role taxonomy (Pillar → Cluster → FAQ → Glossary)
Three-phase build strategy (Quick wins → Authority core → Completion)
Super-niche formula: Audience × Problem × Context

Skill capabilities documented:

Phase 1: Hypothesis generation (5-10 candidates)
Phase 2: Validation against five axes with go/maybe/skip verdicts
Phase 3: Article map generation (50-200 interlinked articles)
Phase 4: (Optional) Article drafting with frontmatter + schema markup

Real example included: Primores.org test run showing one GO, one MAYBE, one SKIP verdict with rationale for each.

Cross-links to existing wiki:

seo/agentic-search-optimization — How this fits modern SEO
tools/reddit-thread-analyzer — Complementary content sourcing
glossary/geo-aeo — Why AEO gap matters

Wiki stats: Now at 88 pages, 25 glossary entries, 9 tool reviews.

[2026-04-24] create | Wiki Methodology Page + LLM Usage Guide

Created public methodology page and added prominent “Use with your LLM” guide to the index.

Problem solved: CLAUDE.md was a private file but was being referenced from public wiki pages. Visitors clicking those links would get 404 errors on the published site.

Solution:

Created methodology — public page explaining how the wiki is built
Added ”🤖 Use This Wiki With Your LLM” section at top of index
Updated all CLAUDE.md references across wiki to point to methodology

Wiki page created:

methodology — How this wiki is built (three-layer structure, three operations, status system)

Files updated:

index — Added LLM usage guide at top, updated Meta section
about — Replaced CLAUDE.md link with methodology
contributing — Replaced CLAUDE.md link with methodology
maintenance — Replaced CLAUDE.md link with methodology, added private path lint check
changelog — Replaced CLAUDE.md link with methodology
llms.txt — Expanded methodology section with specific topics covered
glossary/llm-wiki-pattern — Replaced CLAUDE.md reference

Key additions to index:

Example prompts for LLM users (“Based on the Primores wiki, how should I…”)
Claude Code usage note for folder-level context
Why the wiki structure works for AI (TL;DRs, structured headings, cross-links)

Wiki stats: Now at 85 pages.

[2026-04-24] ingest | New Site SEO Strategy (Reddit Thread Analyzer Output)

Ingested SEO article produced by the Reddit Thread Analyzer skill from r/DigitalMarketing thread.

Source: Reddit thread “Is it actually possible to rank a new site in 2026?” (27 comments) Article: articles/2026-04-23-digitalmarketing-rank-new-site.md

Wiki page created:

seo/new-site-ranking — Practical new-site SEO strategy

Key frameworks extracted:

“Targeting Problem, Not Content Problem” — If 15 guides = 10 visits/week, topics are wrong
“Trust to Win, Not Pay to Win” — New sites build trust, not buy in
“Narrow the Battlefield” — Pick terrain incumbents don’t defend
“Weird, Specific, Long” — Long-tail keyword pattern

Practical tactics documented:

GSC audit technique (find queries with impressions but low rankings)
Pinterest as SEO channel (pins rank in Google)
Hyper-specific keyword transformations (with real examples)
AI search favors small sites with specific answers

Cross-links to existing wiki:

seo/ai-seo-content — AI content strategy
glossary/geo-aeo — AI search optimization
marketing/reddit-authenticity-patterns — Reddit distribution
tools/reddit-thread-analyzer — Tool that produced this

Meta-observation: This is the first full article produced by the Reddit Thread Analyzer skill to be ingested into the wiki — demonstrating the 05 → wiki pipeline working as designed.

[2026-04-23] create | Reddit Thread Analyzer Skill + Substance Ranking Glossary

Documented Primores internal skill for transforming Reddit threads into SEO-optimized articles.

Pages created:

tools/reddit-thread-analyzer — Full tool documentation
glossary/substance-ranking — Key concept: content quality over popularity

Core insight: Reddit upvotes measure popularity, not truth. The skill’s 6-axis substance rubric corrects for this:

Substance (0-3): sentiment → opinion → reasoning → evidence+numbers
Source type: first-hand > professional > second-hand > inferred
Contrarian bonus: downvoted-but-reasoned often contains signal
Actionability: can reader do/decide/change?

Workflow (6 stages):

Capture thread (JSON endpoint or saved file)
Parse comment tree with metadata
Score every comment on substance rubric
Extract building blocks (numbers, frameworks, case studies)
Honest go/no-go gate (red-light unrankable threads)
Produce highlights file and/or SEO article

Business applications:

Content marketing from community insights
Research swipe files with “worth stealing for” hooks
Keyword monitoring + audience engagement
Client briefings with noise filtered out

Key metric: ~30% divergence from popularity sort typical.

[2026-04-23] ingest | Reddit Shill Detection Article → Wiki Synthesis

Ingested original investigative article about Reddit astroturfing patterns.

Source: articles/2026-04-23-reddit-shill-detection.md (kept for blog publishing)

Wiki synthesis:

marketing/reddit-authenticity-patterns — Detection guide + authentic marketing principles
glossary/astroturfing — Definition with Three-Post Pattern framework

Private playbook update:

private/content-playbook/reddit-style-guide.md — Added “Anti-Shill Patterns” section

Key extractions:

Three-Post Pattern: Case study → Outcome → Concern troll (named framework)
Detection signals: Multi-sub test, tool drop in step 2, templated comments
Authentic alternatives: Inverse of each shill pattern
Community immune response: Reddit catches shills within hours

Connection to existing wiki: Links to glossary/honest-assessment (inverse pattern), marketing/ai-marketing-case-studies (what real case studies look like).

[2026-04-23] create | AI Implementation Patterns (Meta-Analysis of 1,048 Cases)

Created comprehensive patterns page synthesizing insights from the entire Google Cloud dataset.

Key findings from analysis:

17.7x more augmentation than replacement language in real deployments
Document processing is #1 use case (46% of all cases)
Four universal patterns appear in every industry: customer communication, workflow automation, data analysis, personalization
90%+ improvements share one trait: eliminate time on repetitive tasks (54% mention time reduction)
43% use Gemini — off-the-shelf models with domain context, not custom AI

The data contradicts common narratives:

“AI replaces workers” → Reality: 443 augmentation cases vs 25 replacement
“You need massive data” → Reality: Most work on existing docs and conversations
“Results take years” → Reality: Median improvement is 50%, many in weeks

New page: automation/ai-implementation-patterns — marked as 🌳 evergreen (data-backed, comprehensive)

[2026-04-23] update | Added All 1,048 Google Cloud Cases to Wiki

Expanded wiki pages to include ALL cases from the dataset, not just metric-rich ones.

Added non-metric cases:

Customer Service: +88 cases (128 total)
Marketing: +168 cases (212 total)
Cross-Industry: +201 cases + Automotive/Manufacturing/Media (+58)
HR & Workforce: +65 cases (84 total)
Security: +67 cases (79 total)
Retail & E-commerce: +53 cases (71 total)
Healthcare: +50 cases (62 total)
Developer Tools: +27 cases (33 total)
Finance & Banking: +20 cases (32 total)
Supply Chain: +15 cases (22 total)
Legal: +10 cases (15 total)

SEO value: Company names now searchable across all industries (BMW, Mercedes, Uber, etc.)

[2026-04-23] ingest | Google Cloud AI Use Cases Dataset (1,048 Cases → 232 with Metrics)

Processed Google’s full compilation of real-world gen AI deployments. Created 10 new wiki pages covering all 232 metric-rich cases.

New pages created (10):

automation/ai-customer-service-cases — 40 cases (NoBroker $1B, Wagestream 80%)
automation/ai-hr-workforce-cases — 19 cases (Equifax 90%, CERC 10x)
automation/ai-retail-ecommerce-cases — 18 cases (Stord $10B, Gelato 90%)
automation/ai-finance-banking-cases — 12 cases (atmira 54% cost reduction)
automation/ai-healthcare-cases — 12 cases (Elanco $1.3M saved)
automation/ai-security-cases — 12 cases (Fluna 92% accuracy)
automation/ai-supply-chain-cases — 7 cases (Domina 80% satisfaction)
automation/ai-developer-tools-cases — 6 cases (Citylitics 400%)
automation/ai-legal-cases — 5 cases (Altumatim 90% automation)
automation/ai-cross-industry-cases — 51 cases (KPMG 90%, diverse industries)

Updated:

marketing/ai-marketing-case-studies — Added 42 more cases as quick-reference table
index — Added all 10 new pages, updated stats (68 → 78 pages)

Extraction process:

Source: 795KB HTML file, parsed with Python regex
1,048 total cases → 232 with quantified metrics
Categorized by industry, formatted as wiki pages

Raw data:

raw/cases/google-cloud-ai-use-cases-all.json — Full 1,048 cases
raw/cases/google-cloud-ai-use-cases-categorized.json — Categorized with metrics
raw/cases/metric-cases-by-category.json — 232 metric-rich cases only

Cross-cutting insight: The pattern that emerges most strongly: documentation and repetitive task automation show 2-5x ROI regardless of industry. Customer service, HR, legal, healthcare — the use cases look different but the mechanics are identical.

[2026-04-23] create | Wiki Structure Improvements + About Page

Implemented planned changes from earlier session:

New pages (2):

about — About Primores + Andrej bio (ex-Adform VP Eng, Monetha co-founder, Vilnius)
experiments/overview — Experiments methodology with cross-cutting patterns

Renamed:

getting-started.md → contributing.md — Clarifies this is a contributor guide

Updated:

index — Added about page, experiments overview, updated stats (68 pages)
llms.txt — Real primores.org/wiki/ URLs, refreshed stats, notable content section
CLAUDE.md — Added optional frontmatter fields (canonical, og_image, author) with converter defaults

Client naming policy verified: pigu.lt, fitme.lt, varle.lt are publicly cited in wiki case studies.

[2026-04-23] experiment | First Wiki-to-Content Test (Reddit Response)

First test of using wiki knowledge to create external content:

Drafted response to Reddit post about AI marketing tools
Wiki provided substance (patterns, case study numbers, advisor+executor model)
Manual editing added authentic Reddit voice

Key learnings:

AI drafts are “too polished” for Reddit — need intentional imperfection
Lowercase, minor typos, conversational flow = trust signals
Karpathy’s LLM Wiki reference adds external credibility
Wiki gives substance, human gives voice

Files created:

drafts/reddit-response-ai-marketing-tools.txt — Final response
private/content-playbook/reddit-style-guide.md — Internal style notes (not public wiki)

Also fixed:

Removed “[CITATA]” marker reference from experiments/seo-geo-content-ecommerce — was exposing internal implementation detail instead of documenting actual test results

[2026-04-22] create | Product Article Generator — Full Case Study + Pattern Extraction

Completed comprehensive coverage of the Product Article Generator skill:

Case study documenting the pigu.lt implementation
Extracted two reusable GEO patterns into glossary entries
Enriched experiment with concrete examples from Hisense freezer output
Added cross-links across wiki

Why this skill is critical:

Revenue-generating work — active client work for pigu.lt, not just an experiment
SEO/GEO convergence — solves both Google SEO AND AI citation in one workflow
Counter-intuitive insight — honest assessments (admitting weaknesses) = more AI citations
Scalability proof — demonstrates AI handling long-tail content at 10,000+ SKU scale

Pages created (3):

cases/product-article-generator-pigu — Full implementation story: 5x speedup, 80% cost reduction
glossary/geo-anchor — First-sentence citation optimization pattern
glossary/honest-assessment — AI trust signal through admitting weaknesses

Pages updated (6):

experiments/seo-geo-content-ecommerce — Added concrete examples (intro, honest assessment, FAQ)
tools/product-article-generator — Added cross-links to case study and glossary
glossary/geo-aeo — Added links to the two new deep-dive entries
index — Added case study, glossary entries, updated stats (66 pages, 21 glossary, 6 case studies)
log — This entry

Patterns extracted:

GEO Anchor: First sentence must be quotable by AI — product + capacity + audience + value in one sentence
Honest Assessment: Naming real weaknesses (with cost impact) increases AI trust and citations

Product Article Generator now has proper wiki coverage:

Component	Status
Tool page	✅ tools/product-article-generator
Case study	✅ cases/product-article-generator-pigu
Experiment	✅ experiments/seo-geo-content-ecommerce
Glossary patterns	✅ glossary/geo-anchor, glossary/honest-assessment

[2026-04-22] create | SEO/GEO Content Experiment — AI Article Generation for E-commerce

Created experiment page testing AI-generated product articles for e-commerce SEO and AI search visibility at pigu.lt.

Pages created (1):

experiments/seo-geo-content-ecommerce — Testing AI article generation for product content at scale

Pages updated (2):

tools/product-article-generator — Added cross-link to experiment
index — Added experiment, updated stats (63 pages, 3 experiments)

Business problem framed:

“E-commerce sites have 10,000+ products needing unique content. Human writers cost €5-15 per article. AI search engines need specific structure. Multiple languages multiply costs. How do we scale content without sacrificing quality?”

Results documented:

5-6x speedup (20-30 min AI+review vs. 2-3 hours human writing)
~80% cost reduction (€2-3 vs. €10-15 per article)
Consistent SEO/GEO optimization (schema markup, GEO anchors, honest assessments)
Human review remains non-negotiable (15-30 min per article)

Product Article Generator now has proper experiment coverage.

[2026-04-22] create | Ad Alchemy Experiment — Piggybacking Competitor Concepts

Created experiment page testing the “piggybacking competitor ad concepts” use case using the fitme.lt × Tastier output.

Pages created (1):

experiments/ad-alchemy-competitor-piggyback — Testing whether AI can extract reusable formulas and apply them to a new brand

Pages updated (2):

cases/ad-alchemy-creative-reverse-engineering — Added cross-link to experiment
index — Added experiment, updated stats (62 pages, 2 experiments)

Business problem framed:

“I see competitors running successful ads but I don’t know how to learn from them. Copying feels wrong. Hiring consultants is expensive. Starting from scratch wastes the market intelligence sitting right in front of me.”

Results documented:

10-layer formula extraction: all layers concretely articulated
5 variations generated with distinct testing hypotheses
Production-ready prompts and native Lithuanian copy
Review flags for trademark adjacency, language confidence, brand color verification

Ad Alchemy now has the same structure as AI Visibility:

Case study (the skill/approach)
Experiment (testing it on a real problem)

[2026-04-22] create | Ad Alchemy Case Study

Analyzed the Ad Alchemy skill experiment and documented it as a wiki case study. This skill reverse-engineers competitor ads into reusable creative formulas.

Source: Internal experiment at /Documents/_tasks/experiment/02-ad-alchemy/

Pages created (1):

cases/ad-alchemy-creative-reverse-engineering — AI-assisted creative reverse engineering from competitor ads

Pages updated (2):

competitor-analysis/overview — Added “Creative Reverse Engineering” section with Formula vs. Skin framework; upgraded to 🌿 growing status
index — Added case study, updated stats (61 pages, 5 case studies)

Key insights:

Formula vs. Skin Framework:
- Formula (transferable): lighting, composition, focal hierarchy, palette weights, copy skeleton
- Skin (brand-specific): product, exact colors, wording, models
- AI articulates structural choices precisely enough for image models to re-execute
10-Layer Visual Deconstruction:
- Composition grid, focal hierarchy, lighting recipe, palette weights
- Typography, product framing archetype, environment, props
- Emotional promise, copy pattern
- Forces concrete observations (“30° backlight” not “warm lighting”)
Structured Variations:
- 5 variations with testable hypotheses (not random visual noise)
- Closest-to-reference, hook swap, framing swap, palette inversion, wild card
Advisor Strategy Pattern:
- Claude as advisor (~~$0.10/analysis) + image model as executor (~~$0.05/image)
- 15 minutes vs. days for traditional creative reverse-engineering

Connection to wiki: Fills competitor-analysis gap, demonstrates automation/advisor-strategy pattern, exemplifies tools/claude-skills approach.

[2026-04-21] create | AI Tools Comparison (When to Use What)

Created comprehensive comparison page covering four categories of AI tools for different user types.

Sources: MindStudio, Digital Applied, Lindy, DEV Community, IntuitionLabs, Airtable, Medium (7 sources synthesized)

Pages created (1):

comparisons/ai-tools-when-to-use — Decision framework for ChatGPT vs Claude vs Gemini + no-code builders

Pages updated (1):

index — Added comparison, updated stats (60 pages, 3 comparisons)

Key frameworks:

Four Tool Categories:
- AI Platforms (ChatGPT, Claude, Gemini) — general business
- CLI Tools (Claude Code, Codex CLI, Gemini CLI) — developers
- Computer Use Agents — desktop/browser automation
- No-Code Builders (Zapier, Make, Lindy) — workflow automation
Task-to-Tool Routing:
- Writing/quality → Claude
- Research/large context → Gemini
- Creative/images → ChatGPT
- Browser automation → Gemini (DOM-aware)
- File ops/Windows → Claude Computer Use
- Quick automations → Zapier/Make
The Hybrid Approach:
- Sophisticated users don’t pick one tool
- Build routing layer: cheap tools for exploration, accurate tools for output
- Connects to existing automation/advisor-strategy pattern
No-Code Reality:
- Most people can build functional agent in 15-60 minutes
- Zapier for beginners, Make for complex logic, Lindy for sales/ops

Business value: Directly actionable for the wiki’s target audience (business owners, marketers). Answers “which AI tool should I use?” with specific recommendations by role and task type. Cross-links to existing advisor-strategy and enablement-levels pages.

[2026-04-21] ingest | Wharton AI Agent Adoption Blueprint

Enriched the AI enablement levels page with psychological adoption research from Wharton + Science Says collaboration.

Source: AI Agent Adoption Blueprint — Science Says × Wharton School (April 2026) Contributors: Google, Zapier, ServiceNow, Wolters Kluwer, Workato, Concentrix (700,000+ employees surveyed)

Pages updated (1):

automation/ai-enablement-levels — Expanded “The Trust Problem” with three frictions framework

Key insights:

Three Psychological Frictions Blocking Adoption:
- Perceived Competence: “Can this agent actually do this?”
- Trust: “Should I trust it with this specific task?”
- Delegation of Control: “How much autonomy should I give?”
Counterintuitive UX Finding:
- Agents with friendly/warm tone are perceived as LESS competent
- Clarity and reasoning visibility beat personality
- “Pratfall Effect” — too personable reduces professional credibility
The Goldilocks Zone:
- Moderate autonomy optimal — propose actions, let humans approve
- Maps to Levels of Automation theory (Sheridan & Verplank, 1978)
- Middle levels outperform full automation OR full manual control
Level Progression Blockers:
- Level 1→2: Don’t trust standardized AI (haven’t seen reasoning)
- Level 2→3: Won’t delegate execution (autonomy anxiety)
- Level 3→4: Can’t trust agent to know its own limits

Business value: Directly explains WHY the existing enablement levels page says “the jump is psychological, not technical.” Now we have the specific psychology framework. Cross-links to TPB (perceived behavioral control) and Rumpelstiltskin Effect (naming limitations builds trust).

[2026-04-21] ingest | Rumpelstiltskin Effect (Problem Naming Psychology)

Ingested marketing psychology concept from Why We Buy newsletter. The principle: naming a customer’s vague problem with a specific term builds trust and positions your brand as the solution.

Source: The Rumpelstiltskin Effect — Why We Buy newsletter, Katelyn Bourgoin (April 2026)

Pages created (1):

glossary/rumpelstiltskin-effect — Psychology of naming customer problems

Pages updated (1):

index — Added glossary entry, updated stats (59 pages, 19 glossary entries)

Key insights:

Named problems feel solvable — Unnamed problems feel overwhelming and personal. A label converts unknown into known.
The brand that names owns the solution — Febreze owns “noseblind,” Snickers owns “hangry,” chiropractors own “tech neck.” Whoever coins the term gets associated with the fix.
Real examples with outcomes:
- Febreze “noseblind” — created awareness of problem people didn’t know they had
- Snickers “hangry” — entered Oxford Dictionary 2018, became cultural phenomenon
- Deepwrk “body doubling” — app became synonymous with productivity method
Finding the name: Interview customers: “Before you found us, how did you describe the problem?” That language is the name.
SEO/GEO connection: Naming creates search queries you own. “Am I noseblind” leads to Febreze. AI models learn the association.

Business value: Practical positioning technique that connects psychology to sales. Links to existing wiki content on emotional triggers (S-O-R model) and AI visibility (terminology ownership).

[2026-04-21] ingest | AI Marketing Case Studies (Real Results)

Ingested practical AI marketing case studies with specific metrics from multiple sources. Focus: named companies, measurable outcomes, no marketing fluff.

Sources:

AI Marketing Case Studies: 10 Real Examples — Visme (2026)
63 AI Personalization in eCommerce Lift Statistics — Envive (2026)
12 Enterprise AI Marketing Case Studies — Pragmatic Digital (2026)

Pages created (1):

marketing/ai-marketing-case-studies — 15+ named companies with measurable AI marketing results

Pages updated (1):

index — Added case studies page to Marketing section, updated stats (58 pages, 18 domain pages)

Key findings:

Brand Voice Training Matters
- Adore Me, Vector, Virgin Holidays all invested in teaching AI their specific voice
- Generic AI outputs underperform customized implementations
Specific Metrics That Stand Out
- A.S. Watson: 396% better conversion with AI skin analysis advisor
- Adore Me: Product descriptions 20 hours → 20 minutes
- Heinz DALL-E campaign: 850M+ impressions, 25x media ROI
- HubSpot intent-based nurturing: 82% conversion increase
Small Business Success Pattern
- The Original Tamale Company: 22M views, 1.2M likes using ChatGPT for scripts
- Vector B2B: LinkedIn following 7K→11K, demos quadrupled with 15-min human review
- AI democratizes content creation — budget no longer the differentiator
Augment, Don’t Replace
- Verizon: AI predicts 80% of call reasons, empowers agents
- Best ROI comes from human+AI collaboration, not automation replacement

Business value: Fills the wiki gap of practical, non-theoretical marketing content. Provides proof points for consulting conversations. Organized by use case (e-commerce, content, creative, email, support, small business).

[2026-04-20] create | Agenica.ai Competitor Ads Case Study

Created case study comparing AI agent approach to competitor ad monitoring versus manual Meta Ad Library searching.

Pages created (1):

cases/agenica-competitor-ads — AI agent vs manual competitor ad monitoring

Pages updated (2):

competitor-analysis/overview — Added case study reference and source
index — Added case study, updated stats (57 pages, 4 case studies)

Key insights:

Manual Monitoring Fails
- Less than one-third of competitive intelligence programs engage daily/weekly
- Each check is a point-in-time snapshot with no historical context
- By discovery time, competitor campaigns have already run their course
AI Agent Advantage
- Continuous monitoring with accumulated history
- Proactive alerts vs reactive checking
- Role-based insights (CMO vs PPC Manager vs Social Manager)
- Pattern detection from historical baseline
What Accumulated Data + Chat Enables (key differentiator)
- Identify winning ads (ads running for months = proven performers)
- Detect messaging angles being A/B tested (and which failed/succeeded)
- Map influencer partnerships via Instagram tracking
- Spot seasonal patterns and launch playbooks
- Build self-updating competitive creative swipe file
The Core Shift
- Manual = archaeology (digging through what competitors did)
- AI agent = weather forecasting (detecting patterns, predicting moves)
- Chat interface = queryable competitive intelligence

Business value: Strengthens the thin competitor-analysis domain with a concrete, actionable case study. Goes beyond “monitoring is good” to show specific strategic actions enabled by accumulated data.

[2026-04-20] ingest | TPB Framework & Multi-Model Synthesis

Ingested dissertation providing comprehensive multi-framework analysis of AI’s influence on consumer behavior.

Source: Marshall, S. (2024). “A systematic analysis of AI in digital marketing and its effects on consumer behaviour and decision making in E-commerce.” University of Bedfordshire Dissertation. Type: Academic dissertation (38 sources, 3 theoretical frameworks, systematic literature review)

Pages updated (1):

marketing/social-commerce-psychology — Added “Part 4: Social & Intentional Factors (TPB)” section, updated key takeaways

Pages created (1):

glossary/tpb — Theory of Planned Behaviour glossary entry

Key concepts extracted:

Theory of Planned Behaviour (TPB)
- Attitude: Trust and faith drive initial intention to engage
- Subjective Norms: Surprisingly WEAK correlation with AI acceptance
- Perceived Behavioral Control: Directly affects ease of use and purchase behavior
Multi-Framework Synthesis
- S-O-R: Emotional responses (stimulus → feeling → response)
- TAM: Rational assessment (usefulness → ease → acceptance)
- TPB: Intentional factors (attitude + norms + control → intent)
- Together: Complete picture of consumer AI behavior
Subjective Norms Finding
- Peer pressure has weaker effect on AI adoption than expected
- Consumers need personal motivation to engage with AI
- Social proof less effective for AI features than traditional products
Cultural Context
- Tech-embracing cultures (Japan, Korea): Higher acceptance
- Privacy-conscious markets (Germany, EU): Conditional acceptance
- Most research ignores this crucial variable
Research Gaps Identified
- Negative effects (fatigue, frustration) underexplored
- Long-term preference evolution not studied
- TAM may need augmentation for modern AI e-commerce

Business value: Completes the theoretical trifecta (S-O-R + TAM + TPB) for understanding AI consumer behavior. Key finding that subjective norms are weak predictors suggests marketers should focus on personal benefits rather than social proof for AI features.

[2026-04-20] ingest | Vietnamese Gen Z Algorithm & Mental Well-being Research

Ingested academic research on how TikTok’s recommendation algorithms affect Gen Z mental well-being.

Source: Nguyen, K.A.T., Duong, B.N., & Tran, N.A.V. (2025). “The Impact of TikTok’s Social Media Recommendation Algorithms on Generation Z’s Perception of Mental Well-Being in Ho Chi Minh City.” ICBESS-2025 Conference. Type: Academic paper (n=419 Vietnamese TikTok users, ages 16-27)

Pages updated (1):

marketing/social-commerce-psychology — Added “Part 3: Algorithm Impact on Mental Well-being” section

Pages created (1):

glossary/smra — Social Media Recommendation Algorithms glossary entry

Key concepts extracted:

Mediation Model
- Algorithms don’t directly harm mental health
- Effects work through cognitive interpretation (arousal, information perception, empathy)
- Model explains 67.5% of variance in mental well-being — strong predictive power
Path Coefficients
- Personalized Content → Arousal Level: β = 0.533 (strongest)
- Personalized Content → Information Perception: β = 0.451
- Personalized Content → Empathy: β = 0.440
- Personalized Content → Social Interaction: β = 0.416
Surprising Non-Findings
- Emotion → Mental Well-being: NOT significant (β = 0.009, p = 0.873)
- Social Comparison → MWB: NOT significant (β = -0.004, p = 0.947)
- Suggests emotional desensitization and selective comparison in Vietnamese Gen Z
MWB vs PMWB Distinction
- Mental Well-being (MWB): Objective psychological functioning
- Perceived Mental Well-being (PMWB): Subjective self-evaluation
- Different factors affect each — algorithms may primarily affect perception
Policy Implications
- Digital literacy is the key intervention
- Algorithm transparency builds trust
- Emotional filtering and reset mechanisms recommended

Business value: First empirical data on SMRA effects in Southeast Asia. Cultural insight that Vietnamese Gen Z may show emotional resilience absent in Western samples. Reinforces importance of information quality over emotional charge in content strategy.

[2026-04-20] ingest | AI Personalization Evolution & Ethics

Ingested comprehensive literature review on AI-driven personalization in e-commerce.

Source: Iqbal, F. et al. (2025). “AI-driven personalization in e-commerce: evaluating the transformative effects on consumer behavior.” International Journal of Science and Research Archive. URL: https://doi.org/10.30574/ijsra.2025.16.1.2035 Type: Literature review (10 pages, 33 references)

Pages updated (1):

marketing/social-commerce-psychology — Added three major sections

Key concepts extracted:

Three Eras of Personalization
- Pre-AI: Rule-based, collaborative filtering (static)
- Machine Learning: Real-time behavior analysis (2010s)
- Deep Learning: Hyper-personalization at scale (2020s — current frontier)
New Risk Concepts
- “Creepy Factor” — when personalization feels invasive
- Filter Bubbles — AI narrows choice by showing similar content
- Autonomy Erosion — over-reliance on algorithmic suggestions
Demographic Differences
- Younger/tech-savvy: embrace personalization
- Older consumers: more skeptical, need transparency
Regulatory Landscape
- GDPR, CCPA, EU AI Act pushing toward explainable AI
- Black box personalization becoming legally risky
Trust-Loyalty Mediation
- Trust mediates between personalization quality and loyalty
- Lose trust = lose the customer

Business value: Expanded ethics section with specific risks (creepy factor, filter bubbles) and regulatory considerations. Page now covers the full personalization landscape from evolution to implementation to risks.

[2026-04-20] ingest | TAM Model & Cognitive Purchase Factors

Ingested academic research on how AI-enabled ease of use affects purchase intention.

Source: Lopes, J.M., Silva, L.F., & Massano-Cardoso, I. (2024). “AI Meets the Shopper: Psychosocial Factors in Ease of Use and Their Effect on E-Commerce Purchase Intention.” Behavioral Sciences. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11273900/ Type: Academic paper (n=1,438 Portuguese consumers)

Pages updated (1):

marketing/social-commerce-psychology — Added “Part 2: Cognitive Factors” section, upgraded seedling → growing

Key concepts extracted:

Technology Acceptance Model (TAM)
- Consciousness (β=0.40) — strongest predictor; users who understand AI find it easier
- Faith/Trust (β=0.34) — confidence in AI reliability
- Perceived Control (β=0.12) — feeling in charge of AI features
- Ease of Use (β=0.61) — direct effect on purchase intention
Cognitive Load Reduction
- AI features (chatbots, recommendations, smart search) reduce mental effort
- Less effort → easier decisions → more purchases
Surprise Finding
- Subjective norms (peer pressure) did NOT directly affect ease of use
- Users adopt AI features based on understanding and trust, not social pressure
Practical Implications
- Explain what AI does (don’t hide it)
- Show why recommendations were made
- Give users control over AI features
- Build trust through transparency

Status upgrade: Page now covers both emotional triggers (S-O-R) and cognitive factors (TAM) — comprehensive purchase psychology guide. Upgraded to 🌿 growing.

Ingested academic research on how TikTok’s recommendation system drives impulse purchases.

Source: Li, J. (2025). “Applying the S-O-R Model to Algorithmic Commerce: How TikTok’s Recommendation System Stimulates Impulsive Consumer Behavior.” Academic Journal of Management and Social Sciences. URL: https://drpress.org/ojs/index.php/ajmss/article/view/33210 Type: Academic paper (University of Toronto)

Pages created (1):

marketing/social-commerce-psychology — Practical guide to psychological triggers in social commerce

Pages updated (1):

automation/agentic-commerce — Added “Human Psychology vs. Agent Logic” section connecting human impulse triggers to AI agent behavior

Key concepts extracted:

S-O-R Framework (Practical)
- Stimulus: What you show (content, offers, social signals)
- Organism: How they feel (emotional state activated)
- Response: What they do (purchase, share, bounce)
Three Core Triggers
- Personalized recommendations → emotional arousal
- Social proof signals → trust and FOMO
- Scarcity cues → urgency and impulse
Platform as Behavioral Environment
- TikTok isn’t neutral distribution — it’s engineered to compress decision-making
- Same insight applies to any social commerce platform
Agentic Commerce Connection
- Human triggers may not work on AI agents (scarcity can be verified via APIs)
- Raises question: separate optimization strategies for humans vs. agents?

Business value: Practical checklist for implementing psychological triggers ethically. Connects current social commerce tactics to future agentic commerce preparation.

[2026-04-20] lint + create | Wiki Maintenance Session

Ran full wiki lint check and addressed critical issues.

Lint findings:

5 days since last activity (approaching 7-day warning threshold)
2 broken wikilinks in questions/what-ai-tools-actually-deliver-roi.md
competitor-analysis/ domain was completely empty
7 content seedlings identified for potential upgrade
0 orphan pages (healthy linking)

Pages created (1):

competitor-analysis/overview — AI for competitive intelligence (seedling)

Pages updated (2):

questions/what-ai-tools-actually-deliver-roi — Fixed broken links, replaced with existing relevant pages
index — Added competitor-analysis section, updated stats

Broken links fixed:

Removed questions/how-to-evaluate-ai-tools (didn’t exist)
Removed questions/ai-automation-that-works (didn’t exist)
Added links to: automation/finding-ai-use-cases, automation/ai-enablement-levels, glossary/llm-evals, questions/ai-as-personal-advisor

Competitor Analysis overview covers:

5 key use cases (pricing, content, sentiment, signals, market share)
Tool landscape (Semrush, SimilarWeb, SpyFu, Crayon/Klue)
AI-specific considerations for agentic search era
Open questions for future exploration

Business value: The competitor-analysis domain is no longer empty — this is a core consulting area that needed representation.

Wiki health restored: Activity resumed after 5-day gap.

[2026-04-15] ingest | AI Visibility Audit Skill + E-commerce Experiment

Documented the AI Visibility Audit Claude skill and created the wiki’s first experiment entry.

Source: /Documents/_tasks/experiment/01-ai-visibility/ Type: Claude skill (internal tool)

Pages created (2):

tools/ai-visibility-audit — Full documentation of the audit skill methodology
experiments/ai-visibility-ecommerce — First experiment! Testing on pigu.lt and varle.lt

Pages updated (4):

seo/ai-visibility — Added audit skill reference and methodology section
seo/agentic-search-optimization — Added link to audit tool
glossary/geo-aeo — Added audit skill to tools section

Key skill features documented:

5-Dimension Scoring (100 points)
- Crawlability (25): robots.txt, llms.txt, UA-specific blocks
- Rendering (25): SSR/CSR detection, visible text analysis
- On-page (20): Schema, meta tags, answer-first content
- Share-of-Voice (20): Live AI search queries
- Authority (10): Wikipedia, press coverage
Technical Innovations
- Live UA spoofing catches WAF blocks invisible to standard tools
- Separates automated checks (Python) from interpretation (Claude)
- Hard blocker detection zeroes dimensions + triggers URGENT flags
Experiment Findings (pigu.lt, varle.lt)
- pigu.lt: WAF blocking AI bots on product pages (403 to GPTBot, ClaudeBot)
- varle.lt: llms.txt misconfigured as redirect chain → 404
- Both sites have good JSON-LD but access issues block AI agents

Business value: This fills the Experiments domain (was empty!) and bridges wiki theory to practice. The skill is immediately usable for client AI visibility assessments.

Wiki milestone: First experiment entry! The experiments domain is no longer empty.

[2026-04-14] ingest | AI Agent Buying Biases (Columbia/Yale Research)

Ingested Science Says newsletter covering Columbia + Yale working paper on AI agent purchasing behavior.

Source: raw/articles/ai-agent-buying-biases-science-says.md Type: Newsletter summarizing academic research (Working Paper, August 2025) Original research: “What is your AI Agent Buying? Evaluation, Biases, Model Dependence and Emerging Applications for Agentic E-Commerce”

Pages created (1):

glossary/ai-agent-behavior — New glossary entry for emerging research field

Pages updated (2):

seo/agentic-search-optimization — Added “Product Page Optimization for AI Agents” section with experimental data
automation/agentic-commerce — Added “AI Agent Bias Factors” section with merchant recommendations

Key findings extracted:

Keyword Order Has Massive Impact
- Changing “Floor Lamps for Living Room” → “Office Floor Lamp” increased selection:
  - GPT-5.1: +80.4 percentage points
  - Gemini 2.5 Flash: +52 percentage points
  - Claude Opus 4.5: +41 percentage points
Factor Influence Ranking
- Keywords in title (highest impact)
- Number of reviews
- Product ratings (+0.1 improves chances)
- Positive badges (“Bestseller”, “Recommended”)
- “Sponsored” label (negative — reduces selection)
Model-Specific Biases
- Different AI models have different decision patterns
- GPT-4.1 preferred top-left products; GPT-5.1 did opposite
- Biases change with model updates
Models Are Improving
- Failure rates on objective decisions dropped dramatically between generations
- Claude: 63.7% → 4.3%, GPT: 25.8% → 1%, Gemini: 2.8% → 0%
Bonus: Cialdini’s Principles Work on AI
- Wharton + Cialdini research: persuasion techniques increased AI compliance 33.3% → 72%

Business value: This is the first quantitative research on optimizing for AI shopping agents — critical for e-commerce clients preparing for agentic commerce. The finding that keyword order can swing selection by 80pp is immediately actionable.

Researchers: Amine Allouah, Omar Besbes (Columbia), Josue D. Figueroa (MyCustomAI), Yash Kanoria (Columbia), Akshit Kumar (Yale/Columbia)

[2026-04-14] ingest | Claude Skills — The Complete Guide

Ingested Anthropic’s official guide to building Skills for Claude.

Source: raw/articles/The-Complete-Guide-to-Building-Skill-for-Claude.pdf Type: Official Anthropic documentation (32 pages)

Pages created (2):

tools/claude-skills — Comprehensive guide to building reusable instruction packages
glossary/skill — Definition and key characteristics

Pages updated (1):

tools/mcp — Added “MCP + Skills” section explaining the kitchen analogy

Key concepts extracted:

Skills = Reusable AI Recipes
- Folders containing SKILL.md with YAML frontmatter
- Teach Claude once, benefit every time
- Portable across Claude.ai, Claude Code, and API
The Kitchen Analogy
- MCP = professional kitchen (access to tools)
- Skills = recipes (how to use tools effectively)
- Together = complete solution for users
Three Skill Categories
- Document & Asset Creation (consistent outputs)
- Workflow Automation (multi-step processes)
- MCP Enhancement (workflow guidance for tools)
Progressive Disclosure Design
- Level 1: YAML frontmatter (always loaded)
- Level 2: SKILL.md body (when relevant)
- Level 3: Linked files (on demand)
Five Workflow Patterns
- Sequential workflow orchestration
- Multi-MCP coordination
- Iterative refinement
- Context-aware tool selection
- Domain-specific intelligence
Testing Framework
- Triggering tests (load at right times)
- Functional tests (correct outputs)
- Performance comparison (vs baseline)

Business value: This is the missing piece for MCP integrations — raw tool access isn’t enough, users need workflow guidance. Skills turn MCP connections into complete solutions.

[2026-04-14] ingest | Strategic AI Infrastructure

Deep research into Claude as strategic infrastructure — Cowork, MCP, departmental implementations, and enterprise case studies.

Sources analyzed:

Anthropic product pages (Cowork, MCP)
Model Context Protocol documentation
Ad Age: How 4 Ad Agencies Use Claude Enterprise Tools
Anthropic customer stories (Intercom, Binti)
HubSpot/Xero partnership announcements

Pages created (5):

tools/claude-cowork — Desktop agent with Skills, Connectors, Dispatch
tools/mcp — Model Context Protocol for enterprise integration
automation/departmental-ai-guide — Marketing, Sales, Finance, Support implementations
cases/intercom-fin-support — 86% resolution rate case study
cases/binti-social-services — 50% documentation reduction case study

Key insights extracted:

Cowork tiered hierarchy: Connectors first, desktop control as fallback
Skills: Persistent instructions encoding organizational knowledge
MCP ecosystem: HubSpot, Salesforce, Xero, Notion all connected
Departmental results: Marketing 4x output, Sales 21% reply rates, Finance 80% reduction, Support 86% resolution

Real-world statistics:

Intercom: 86% resolution rate, 40% fewer escalations
Binti: 50% documentation time reduction, 47% of US foster care served
Brainlabs: Presentation generation from Notion via MCP
Synthesia: 87% self-serve support rate with Fin

Business value: This is the “Level 3-4 playbook” — how advanced organizations move Claude from chat assistant to strategic infrastructure.

[2026-04-14] ingest | More Practitioner Frameworks

Second batch of practitioner content — use case discovery, context engineering, and fine-tuning guidance.

Sources analyzed:

Terraforming the AI Use Case Desert — Almost Timely
Beyond Chunks: Context Engineering — Jason Liu
Is Fine-Tuning Still Valuable? — Hamel Husain

Pages created (2):

automation/finding-ai-use-cases — TRIPS framework (Time, Repetitiveness, Importance, Pain, Sufficient Data)
glossary/context-engineering — Four-level framework for agent information flow

Pages updated (1):

glossary/fine-tuning — Added prerequisites (evals!), real-world examples (Honeycomb, ReChat)

Key frameworks extracted:

TRIPS: Systematic scoring for AI use case prioritization; “Sexy Block” prevents organizations from seeing valuable but unglamorous opportunities
Context Engineering: Tool responses ARE prompt engineering; 4-level framework from chunks to faceted landscape; 90% reduction in clarification questions
Fine-tuning prerequisite: “Impossible to fine-tune effectively without an eval system”

Business value: TRIPS framework is immediately usable in client discovery sessions. Context engineering explains why enterprise RAG systems underperform.

[2026-04-14] ingest | Academic & Practitioner Sources

Ingested high-quality practitioner content from Almost Timely (Christopher Penn), Hamel Husain, and Jason Liu.

Sources analyzed:

The Five Levels of AI Enablement — Almost Timely
LLM Evals: Everything You Need to Know — Hamel Husain
Systematically Improving Your RAG — Jason Liu

Pages created (2):

automation/ai-enablement-levels — Complete 5-level maturity framework (Done By You → Done Anticipating You)
glossary/llm-evals — Three-level evaluation hierarchy for AI products

Pages updated (1):

glossary/rag — Added 6-stage systematic improvement methodology, practical insights

Key frameworks extracted:

Five Levels: 75% stuck at Level 1; jump to Level 3 is psychological not technical; “$6-9M project in 6-9 hours for $6-9”
Eval Hierarchy: Unit tests → Human/Model evaluation → A/B testing; “unsuccessful products almost always fail to build robust evaluation systems”
RAG Improvement: Full-text search often matches embeddings at 10x speed; baseline first, optimize second

Business value: These frameworks provide concrete maturity models for client conversations about AI adoption and implementation quality.

[2026-04-14] ingest | HBR + Fortune Deep Dive

Enriched existing agentic pages with full statistics from primary sources.

Sources analyzed:

Preparing Your Brand for Agentic AI — Harvard Business Review
AI agents driving 10% of revenue — Fortune

Pages updated (2):

marketing/preparing-for-agentic-ai — Added adoption stats table, resistant domains, Carnegie Mellon prompt research
seo/agentic-search — Added consumer behavior stats, UX→AX shift concept, restructured statistics

Key new statistics added:

60% of US shoppers expect agentic AI within 12 months (Kearney)
40% MoM growth in Target’s ChatGPT traffic
35% of Walmart’s referrals from ChatGPT
14% of US consumers prefer ChatGPT over Google
90% of ChatGPT sources aren’t in Google’s top 20
78.3% brand choice variation from prompt wording (Carnegie Mellon)
94% agentic visibility increase case study

Concept introduced: UX → AX (User Experience to Agent Experience)

[2026-04-14] ingest | McKinsey Agentic Commerce Report

Ingested the comprehensive McKinsey report on agentic commerce from local file.

Source: McKinsey — “The agentic commerce opportunity: How AI agents are ushering in a new era”

Pages created (1):

automation/agentic-commerce — Full analysis of the $1 trillion agentic commerce shift

Key insights extracted:

$1T US B2C by 2030, $3-5T globally
Three interaction models: agent-to-site, agent-to-agent, brokered
Six domains merchants must address (engagement, loyalty, commerce, payments, in-store, fulfillment)
Seven new revenue models as ad revenue declines
Trust as foundational infrastructure, not just sentiment
Three risk categories: systemic, accountability, data sovereignty

Business value: This is the definitive strategic framework for agentic commerce — essential for client conversations about e-commerce transformation.

[2026-04-14] explore | Deep Dive into Agentic Search

Extended the agentic search topic with additional research from HBR, Fortune, and Search Engine Land.

Sources analyzed:

Harvard Business Review: “Preparing Your Brand for Agentic AI”
Fortune: “AI agents are already driving 10% of revenue for some brands”
Search Engine Land: “AAO: Assistive Agent Optimization”

Pages created (2):

seo/agentic-search-optimization — Full ASO discipline guide
marketing/preparing-for-agentic-ai — Brand strategy for agentic era

Pages updated (1):

seo/agentic-search — Added statistics and cross-links

Key new insights:

“Share of model” is the new market share metric (pioneered by Pernod Ricard)
Only 12% URL overlap between AI citations and Google top 10
Three brand interaction modes: brand agents, consumer agents, full AI intermediation
72% of consumers demand transparency about AI vs human interactions
Strategic Text Sequences (STS) and llms.txt are emerging optimization tools

[2026-04-14] ingest | Bulk Ingestion from Priority Sources

Analyzed and ingested content from 5 priority sources identified for wiki expansion: Marketing AI Institute, Semrush AI Blog, Search Engine Land, Zapier Blog, and MarketingProfs.

Articles analyzed (9 total):

Semrush: AI Visibility, AI SEO Tools (18 tools), Agentic Search, Does AI Content Rank (42K study)
Zapier: Agentic AI vs Generative AI, Cognitive Automation
Search Engine Land: LLM Nudges
MarketingProfs: AI Video Marketing (Sora/Meta Vibes)
Marketing AI Institute: AI Agents for Agencies

Pages created (6):

seo/agentic-search — How AI agents decide which brands get found
seo/ai-visibility — Getting found in AI-generated answers
glossary/llm-nudges — How AI guides user decisions
glossary/cognitive-automation — AI that makes decisions in workflows
comparisons/agentic-ai-vs-generative-ai — Autonomous agents vs content generation
marketing/ai-video-marketing — Using AI for authentic video storytelling

Pages updated (1):

seo/ai-seo-content — Added “Does AI Content Rank” study findings (42K posts analyzed)

Key insights:

Agentic search is an emerging discipline — AI agents filter brands before humans see them
AI visibility is distinct from traditional SEO (only 44% overlap with Google rankings)
LLM nudges reveal AI assumptions: 45% focus on budget/deals
AI content can rank but human expertise determines top positions
Authenticity beats synthetic in AI video marketing

Priority sources saved to: private/sources-to-ingest.md

[2026-04-10] create | Personal AI Advisor Exploration + Source Tracking

Started a new exploration thread based on observation: ALL professionals struggle with information overload, task management, and decision fatigue. AI as a “personal advisor” is a different angle from enterprise tools.

Pages created:

questions/ai-as-personal-advisor — Exploration of AI as personal cognitive partner

Private files created:

private/sources-to-ingest.md — Tracking checklist for filling wiki gaps

Wiki gaps identified (CMO/Director perspective):

Competitor analysis section (empty)
Marketing content thin
No tool comparisons for marketing use cases
No ROI/business case content

New thread: Personal AI Advisor could become a Primores consulting angle — helping individuals (not just companies) set up AI productivity systems.

[2026-04-10] ingest | Advisor Strategy (Anthropic Blog)

Source: raw/articles/advisor-strategy-anthropic.md
Original URL: https://claude.com/blog/the-advisor-strategy
Type: Official Anthropic pattern / API feature

Key concepts extracted:

Advisor Strategy = inverted multi-agent pattern
Cheap executor (Sonnet/Haiku) consults expensive advisor (Opus) only when stuck
Benchmark results: Sonnet + Opus = +2.7pp performance, -11.9% cost
Haiku + Opus = 2x performance, 85% cheaper than Sonnet alone
Built into Claude API as advisor_20260301 tool type
max_uses parameter for cost control

Pages created:

automation/advisor-strategy — Complete pattern documentation

Pages updated:

automation/multi-agent-patterns — Added reference to advisor strategy
index — Added new page, updated stats

Key insight: This inverts the typical “smart orchestrator, dumb workers” pattern. Most agentic subtasks don’t need the smartest model — only the hard decisions do. This is a significant cost optimization pattern.

[2026-04-10] ingest | Multi-Agent Patterns (OpenClaw + Hermes)

Source: raw/articles/two-agents-openclaw-hermes.md
Original language: Russian (translated to English)
Original URL: pimenov.ai
Type: Architecture patterns / practical guide

Key concepts extracted:

Dispatcher + Deep Worker pattern (one agent for breadth, one for depth)
Six practical implementations: analysis, content pipeline, meeting prep, monitoring, code review, content marketing
Self-learning agents that improve over time
Personal model fine-tuning after ~1 month of usage
Implementation priority order (start with content pipeline)

Pages created:

automation/multi-agent-patterns — Comprehensive patterns guide

Pages updated:

automation/ai-agent-organization — Added cross-link
tools/claude-managed-agents — Added cross-link
index — Added new page, updated stats

Key insight: “Two agents complementing each other beat one agent trying to do everything.” This validates the multi-agent approach in Managed Agents but shows it’s a broader pattern applicable to any tooling.

[2026-04-10] create | Expanded Managed Agents Knowledge

Building on the playbook ingest, created three derived pages:

Pages created:

comparisons/managed-agents-vs-diy — Comprehensive comparison of Anthropic’s managed platform vs. building your own
glossary/agent-outcomes — The Outcomes/Grader pattern for goal-oriented agents
questions/managed-agents-break-even — Cost analysis exploration (seedling)

Pages updated with cross-links:

tools/claude-managed-agents — Added links to new pages
automation/ai-agent-organization — Connected to Managed Agents ecosystem
index — Added new pages, updated stats

Key insight: The comparison page and break-even analysis are valuable for client conversations. “When should we build vs. buy?” is a common question.

[2026-04-10] ingest | Claude Managed Agents Playbook

Source: raw/articles/claude-managed-agents-playbook.md
Original language: Russian (translated to English)
Original source: Telegram @prompt_design (translation of Anthropic docs)
Type: Technical playbook / API documentation

Key concepts extracted:

Claude Managed Agents = ready infrastructure (no custom orchestration needed)
Four key concepts: Agent (config) → Environment (container) → Session (instance) → Events (stream)
Built-in tools: bash, read, write, edit, glob, grep, web_fetch, web_search
Permission system: always_allow vs always_ask for production safety
Usage patterns: event-triggered, scheduled, fire-and-forget, long-horizon
Outcomes (research preview): grader-based completion criteria with iteration
Multi-agent coordination (research preview): one-level delegation
Architecture: “Brain” (Claude) + “Hands” (sandboxes) + “Session” (journal)
Pricing: $0.08/hour + token costs
Companies using it: Notion, Rakuten, Asana, Sentry, Vibecode

Pages created:

tools/claude-managed-agents — Comprehensive tool documentation

Pages updated:

index — Added new tool, updated stats

Cross-references created:

Linked to glossary/ai-agent (agents explained)
Linked to automation/ai-agent-organization (complementary techniques)

Business value: This is Anthropic’s official infrastructure for production AI agents — key for any enterprise deployment discussion. The permission system and outcomes features are particularly relevant for client implementations.

[2026-04-10] ingest | Telegram Community Wiki Bot (Case Study)

Source: raw/cases/telegram-community-knowledge-bot.md
Original language: Russian (translated to English)
Type: Real-world case study

Key concepts extracted:

LLM Wiki pattern validated in production
Multi-source ingestion: chat messages + YouTube transcripts
Zettelkasten methodology for knowledge structure
Anti-recursion pattern (mark bot messages to skip indexing)
Access control with separate knowledge bases
Clickable profile links in answers

Pages created:

cases/telegram-community-wiki-bot — Full case study
glossary/zettelkasten — Connected notes methodology

Pages updated:

index — Added Case Studies section

Cross-references created:

Linked to glossary/llm-wiki-pattern (this IS the pattern in production!)
Linked to tools/obsidian (supports Zettelkasten)
Linked to automation/knowledge-management

Key insight: This proves the wiki pattern works at scale in real communities — “A wiki that writes itself.”

[2026-04-10] ingest | Product Article Generator System

Source: raw/articles/product-article-generator-system.md
Original location: Primores internal tool (/primores/article-generator/)
Type: Internal tool documentation + methodology

Key concepts extracted:

GEO/AEO (Generative Engine Optimization) — optimizing for AI search
AI-SEO content strategy — what gets cited by AI
Human writing rules — avoiding AI tell-signs
Schema markup for AI discoverability
Self-contained FAQ answers principle
“Honest assessment increases citation” insight

Pages created:

glossary/geo-aeo — Generative/Answer Engine Optimization explained
seo/ai-seo-content — AI-SEO content strategy guide
tools/product-article-generator — The tool itself (Primores service)

Pages updated:

index — Added new pages, updated stats

Business value: This documents a Primores service offering — can be referenced in client conversations about AI-SEO capabilities.

[2026-04-10] ingest | 12 Techniques for AI Agents

Source: raw/articles/12-techniques-ai-agents-practical-tools.md
Original language: Russian (translated to English)
Original URL: pimenov.ai

Key concepts extracted:

“AI agent isn’t a magic button — requires organization”
Context separation (different threads for different topics)
Model specialization (route tasks to appropriate models)
Sub-agent delegation pattern
Six-layer security model
Self-syncing documentation
Subscription vs API economics

Pages created:

automation/ai-agent-organization — Comprehensive guide to the 12 techniques
glossary/ai-agent — Definition and context for AI agents

Pages updated:

index — Added new pages, updated stats

Cross-references created:

Linked to glossary/llm-wiki-pattern (documentation principle overlap)
Linked to glossary/prompt-engineering (model-specific prompts)
Linked to maintenance (similar logging/review principles)

[2026-04-10] ingest | LLM Wiki Pattern

Source: raw/articles/llm-wiki-pattern.md
Key concepts extracted:
- RAG vs. Wiki pattern (retrieve-and-forget vs. compile-and-maintain)
- Three-layer architecture (raw, wiki, schema)
- Three operations (ingest, query, lint)
- Compounding knowledge principle
- Memex historical connection (Vannevar Bush, 1945)

Pages created:

glossary/rag — Retrieval-Augmented Generation explained
glossary/llm-wiki-pattern — The compounding knowledge pattern
tools/obsidian — The IDE for LLM wikis
automation/knowledge-management — AI for knowledge management overview

Pages updated:

index — Added new pages, added stats section

This source is meta — it describes the very pattern this wiki implements!

[2026-04-10] create | Added maintenance protocol

Created maintenance — comprehensive wiki health and growth protocol
Updated CLAUDE.md with:
- MAINTAIN workflow (daily/weekly/monthly/quarterly cadences)
- GROW workflow (proactive wiki development)
- Session start/end checklists
- Growth mindset principles
- Red flag warnings
Updated index to include maintenance page

The wiki now has built-in growth mechanisms!

[2026-04-10] create | Wiki initialized

Created directory structure
Established schema in CLAUDE.md
Created index, log, changelog, and llms.txt
Added page templates
Added starter content:
Ready to start building knowledge!

Next steps:

Ingest first sources
Build out glossary with foundational terms
Start exploring key questions

Activity Log

Activity Log

[2026-06-07] publish | E-E-A-T article — public version of the new glossary page

[2026-06-07] lint | Spot-checked the retail-cases staleness flag — figures verified accurate

[2026-06-07] create | E-E-A-T glossary page — closing the lint’s one real gap

[2026-06-07] lint | Full wiki health check (mechanical script + content-health agent)

[2026-06-07] question + publish | Niche keyword research → 2 new articles drafted

[2026-06-07] publish | Promoted 3 articles to articles/, merged 1, reconciled an existing article

[2026-06-07] publish | Drafted 4 articles from this session’s evidence work (PROMOTE)

[2026-06-07] ingest + update | Win-loss gaps — targeted follow-up: magnitude CONFIRMED-UNMEASURED, timing PARTIALLY ANCHORED (+ methodology-sharpening nuance)

[2026-06-07] lint | Full health check — consistency sweep on this session’s calibration language

[2026-06-07] update | Developed two open questions 🌱→🌿 (personal-AI-advisor reliability framework + automation-eats-execution scoring matrix)

[2026-06-07] ingest + update | SEO/GEO source calibration — primary anchor for the AI-Overview CTR collapse (Pew); vendor numbers labeled

[2026-06-07] ingest + update | Win-loss gap follow-up — academic anchors for the mechanism + practices (honest partial close)

[2026-05-29] ingest + create + update | CI follow-up deep-research — competitive-intelligence academic anchors

[2026-05-29] ingest + create + update | Deep-research ingest — AI-reliability + service-recovery peer-reviewed sources

[2026-05-29] lint + create + update | Customer-perception cluster framework page + ROI question developed + full lint + housekeeping

[2026-05-26] ingest + create + update | Weekend review-ops cluster from Science Says newsletter — Bayerl et al. 2026 (JMR, n=400M reviews) + hedonic counter-finding + 3 cross-referenced review-ops levers

[2026-05-26] ingest + create + update | AI humor-forgiveness from Science Says newsletter — Xie et al. 2025 (JBR, n=1,919) + Honora et al. counter-finding + Nature Sci Reports corroboration

[2026-05-20] create + update | Gemini Omni ingest — same-day capture of Google I/O 2026 launch

[2026-05-20] create | Competitor-analysis five-layer methodology fully backed — continuous-monitoring + creative-reverse-engineering glossary entries close the layer gaps

[2026-05-18] create | Prompt caching glossary entry — closes the May 18 gap-analysis cycle

[2026-05-18] create + update | SEO/GEO refresh against 2026 zero-click data — new zero-click strategy page + 3 substantial extensions

[2026-05-18] create + update | Competitor-analysis domain build-out — methodology pillar + 3 glossary entries

[2026-05-18] ingest + create + update | Agent payment protocols + Claude Managed Agents May 2026 release wave

[2026-05-15] create | Marketing analytics gap closed — pillar page + 3 glossary entries (MMM, incrementality, cohort analysis)

[2026-05-15] create | Foundational AI glossary gap closed — 5 new entries (hallucination, agentic-memory, tool-use, guardrails, embeddings)

[2026-05-15] research + create | AI human voice for social + outreach — deep research + comprehensive new page

[2026-05-14] lint | Cluster lint after heavy-creation day — 16 back-links + llms.txt refresh

[2026-05-13] update | AI-tells page — proactive reader-motivation section added (two-principle frame)

[2026-05-13] create | AI tells in sales copy — 11-pattern catalog + audience-mode review discipline

[2026-05-13] update | Telegram page — operational-unlock section + discovery-before-scale cross-link

[2026-05-13] ingest + create + update | Karpathy Sequoia AI Ascent 2026 — agent-engineering page, vibe-coding extension, jagged-frontier ↔ jagged-intelligence cross-linking

[2026-05-13] ingest + create + update | Raw queue drained — Wharton frictions page, Telegram channel page, ai-agent-behavior extension, two stale seedlings refreshed

[2026-05-12] lint | Weekly health check + four targeted fixes (llms.txt refresh, broken-link fix, 5 cross-links, ai-agent-behavior extension)

[2026-05-05] cleanup + ingest | Drafts triaged + advisor-strategy ingested + managed-agents playbook enriched

[2026-05-05] ingest + create | Academic AI-productivity wave: 4 peer-reviewed papers ingested, 4 new glossary entries, automation-eats-execution framework now empirically grounded

[2026-05-05] update + create | Lint follow-up wave 2 — orphan fix, glossary upgrades, +2 pages

[2026-05-05] lint + create | Full wiki health check + 3 follow-up pages

[2026-05-05] ingest | Modash 2026 — State of Influencer Marketing Salaries

[2026-05-04] create | Glossary entry — “Creative is the new targeting”

[2026-05-04] update | Strategist implications from April 30 ingests handed off — processed

[2026-04-30] publish | Organic Content Strategy pillar + 2 spokes (drafts/ → wiki/)

[2026-04-30] ingest | Kahneman 2011 — Thinking, Fast and Slow (System 1/2 + cognitive substrate connections)

[2026-04-30] ingest | Cialdini 1984 — Influence: The Psychology of Persuasion (six principles)

[2026-04-30] ingest | Sharp 2010 — How Brands Grow (mental availability + distinctive assets + Double Jeopardy)

[2026-04-30] update | Workflow tightening — changelog now part of INGEST and Session End

[2026-04-30] ingest | Granovetter 1973 — The Strength of Weak Ties

[2026-04-30] ingest | Pirolli & Card 1999 — Information Foraging Theory

[2026-04-30] ingest | Ajzen 1991 — Theory of Planned Behavior (foundational paper)

[2026-04-30] update | Strategist pattern — third loop and self-pressure beat

[2026-04-29] create | Strategist Pattern — Turn the Wiki Into a Thinking Partner

[2026-04-29] update | Integration sweep — wired today’s 6 new pages into neighbors

[2026-04-29] create | PDF Streamer Tool — Large Document Processing for Wiki

[2026-04-29] update | Schwartz’s Awareness Levels — Enhanced with Original Book Quotes

[2026-04-29] create | Schwartz’s Awareness Levels — Classic Copywriting Meets AI Visibility

Five Levels of Customer Awareness

Five Stages of Market Sophistication

[2026-04-29] create | AI Interface Layer — Brand Visibility in the Connector Era

The Interface Layer Shift

New Visibility Hierarchy

Five Reasons Brands Get Ignored by AI

Adobe Connector for Marketing Teams

Action Plan

[2026-04-29] create | Claude Connectors — 200+ Ready-to-Use Integrations

Creative Connectors (April 28, 2026)

Consumer Connectors (April 23, 2026)

[2026-04-27] create | GEO/AEO Benchmarks 2026 — Hard Numbers on AI Search Impact

[2026-04-27] ingest | Brand Voice Skills Guide — Academic Foundation for LLM Learning

[2026-04-24] create | Fresh Niche Hunter Run — Three Niches Evaluated

[2026-04-24] create | Niche Hunter Case Study + Template

[2026-04-24] ingest | Niche Hunter Skill + Super-Niche & Topical Authority Concepts

[2026-04-24] create | Wiki Methodology Page + LLM Usage Guide

[2026-04-24] ingest | New Site SEO Strategy (Reddit Thread Analyzer Output)

[2026-04-23] create | Reddit Thread Analyzer Skill + Substance Ranking Glossary

[2026-04-23] ingest | Reddit Shill Detection Article → Wiki Synthesis

[2026-04-23] create | AI Implementation Patterns (Meta-Analysis of 1,048 Cases)

[2026-04-23] update | Added All 1,048 Google Cloud Cases to Wiki

[2026-04-23] ingest | Google Cloud AI Use Cases Dataset (1,048 Cases → 232 with Metrics)

[2026-04-23] create | Wiki Structure Improvements + About Page