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Primores AI wiki

Changelog

Changelog

What’s new and changed in the Primores AI Wiki.

June 2026

Week of June 2 – 8

E-E-A-T gets a home — the lint’s one real gap, closed 📘 (June 7) — A full wiki lint came back clean (no contradictions, no broken links, no orphans, frontmatter 100%) but surfaced one genuine gap: E-E-A-T — the load-bearing AI-search quality framework — was referenced across ~17 pages with no page of its own. New glossary entry glossary/e-e-a-t fixes that: what the four letters mean (Experience, Expertise, Authoritativeness, Trustworthiness), why in 2026 it acts less like a soft ranking signal and more like a near-binary gate on whether AI engines cite you at all, and the four load-bearing 2026 signals (earned media, author-entity verification, Wikipedia, topical-authority depth). Calibrated honestly: the famous 96% / 3× / <4%-DA figures are vendor estimates, while the direction (earned third-party authority beats brand-owned content) has independent preprint support. Wired bidirectionally into the SEO/GEO cluster. The lint also fixed a stale index stat (domain pages 35 → 45).

Two open questions grow up 🌱→🌿 — a personal-AI-advisor reliability framework and a sharpened automation-eats-execution rule 🧭 (June 7) — A synthesis session (no new external source). questions/ai-as-personal-advisor gained a reliability framework that finally answers “when is AI advice trustworthy?” by wiring in glossary/appropriate-reliance: trust an advisor output only when it’s a high-validity, abundant-data task and your expertise/stakes don’t demand verification — because trust tracks the advisor’s confidence, not its accuracy, and AI advisors are uniformly confident. The durable Primores deliverable is the calibration discipline, not the tool stack. Separately, questions/automation-eats-execution-next-domains gained a candidate-scoring matrix across nine domains and a sharper rule: AI eats execution that’s high-volume and structured, but not execution that’s cumulative (brand-building) or relational (B2B sales) — so execution-layer compressibility, not the strategic layer, is what decides whether a domain bifurcates at all. Customer Success / RevOps flagged as the cleanest untested next probe.

SEO numbers get a reality check — Pew Research anchors the AI-Overview CTR collapse; vendor stats labeled as such 🔍 (June 7) — The wiki’s SEO pages carried striking headline numbers (64.82% zero-click, AI Overviews cut CTR up to 58%, 96% of AI citations from strong-E-E-A-T sources, brand mentions correlate 3× more than backlinks, Google AI Mode at 75M users) — all sourced to single SEO-vendor blogs. A deep-research pass (101 agents, 24 claims confirmed, 1 killed) asked which survive contact with primary measurement. The big win: the AI-Overview CTR-collapse claim is now anchored by a named research institution — the Pew Research Center July 2025 clickstream study (real browsing behavior, 68,879 searches) measured users clicking a traditional result on just 8% of pages with an AI summary vs 15% without (~47% reduction), and clicking the AI summary’s own citations on only 1% of visits. The honest part: the other four figures stay vendor-only — the exact 64.82% (Similarweb), the 58% (Ahrefs), the 96% E-E-A-T share, the 3× brand-mention correlation, and the 75M AI Mode users have no primary source, and seo/zero-click-strategy now says so in a “primary evidence vs vendor estimates” calibration table. A second institutional source (Reuters Institute’s Generative AI and News Report 2025) adds a useful honesty flag: people self-report clicking AI-overview links 33% of the time, but Pew measured 8% — trust the clickstream over the survey. The brand-authority direction got independent (preprint) support from Chen et al. 2025 (U. Toronto), though not the specific vendor coefficients. seo/geo-aeo-benchmarks-2026 and seo/ai-visibility calibrated to match. Net effect: the SEO domain’s single most-cited claim is now primary-anchored, and every remaining vendor number is labeled as one.

Win-loss follow-up — the two remaining gaps, honestly answered 🔬 (June 7) — A targeted second pass chased the two gaps the first win-loss ingest flagged. Both came back honest-negative on the headline number, which is the point. The 5–15pp win-rate lift is confirmed unmeasured — no peer-reviewed, quasi-experimental, or longitudinal study measures win-loss-program adoption against objective win rate (and a vendor “up to 50% lift” claim was refuted); glossary/win-loss-analysis now says so and sketches the clean study that would settle it. The “interview within 30–90 days” rule is mechanism-anchored but the exact window is unmeasured — and some survey-methodology evidence cuts against a naive “fresher is always better” (optimal recall is a forgetting-vs-telescoping tradeoff; one 10-year panel refuted time-based decay outright). The useful twist: the real threat to win-loss accuracy is buyer reconstruction and face-saving, not the calendar — so triangulating stated reasons against contemporaneous behavioral evidence matters more than interview speed, and reflective “why did you choose them?” questions can actually amplify biased memory (choice-supportive distortion). Net: the win-loss page is now as anchored as the evidence honestly allows — mechanism and practices grounded, both the magnitude and the exact timing marked unmeasured.

Win-loss analysis — academic anchors close the gap (honestly, halfway) 🎯 (June 7) — The longest-standing “needs rigorous backing” flag in the competitor-analysis cluster. A targeted deep-research pass (108 agents, 25 claims verified, 0 killed) asked whether the page’s load-bearing claim — win-loss is the only CI layer that reliably moves win rate, 5–15pp in 6 months — could be anchored in peer-reviewed research. Honest answer: no, not directly — no study measures win-loss → win rate causally, so the magnitude stays a practitioner figure, and the page now says so in its TL;DR, “Why it matters,” and Honest limits. But the mechanism and the practices anchor cleanly to tier-1 evidence, and glossary/win-loss-analysis gained a new Academic foundations section to carry them: win-loss is a structured retrospective review — the same family as the after-action review/debrief, which two independent meta-analyses show reliably improves performance (Tannenbaum & Cerasoli 2013, d=.67, ~20–25%; Keiser & Arthur 2021, d=.79). The “interview buyers, not reps” practice is backed by dyadic sales research (Endres et al. 2023 — rep self-perception explains only 4.7–7% of how customers actually perceive them; the buyer’s view predicts purchases better) resting on the seminal Nisbett & Wilson (1977) finding that people lack introspective access to their own decision processes. Two further anchors (Vieira et al. 2023; Kirca et al. 2005, market-orientation r=.32) place CI inside a real-but-conditional chain to performance — and caution that no single CI practice is a clean direct lever. Honest notes carried on-page: the “failing fast” construct (Friend et al. 2019) is forward-looking, not win-loss, and disqualifies itself as an anchor; and the “interview within 30–90 days” timing practice still lacks a verified decay-curve study. competitor-analysis/overview Layer 1 calibrated to match. No new pages.

Week of May 26 – June 1

CI follow-up — academic anchors for the competitive-intelligence cluster 🎯 (May 29) — A targeted deep-research pass closed the competitive-intelligence gap from the earlier ingest (and verified four candidate sources — all CONFIRMED real, none fabricated). New page glossary/ai-competitive-analysis — can you hand competitive/strategic analysis to an LLM? Yes, with three disciplines: aggregate many runs rather than trusting one biased single-shot (Doshi et al. 2025, Strategic Management Journal — aggregated LLM rankings match experts r=0.675), keep humans on the judgment dimensions where LLMs score worst (Csaszar et al. 2024, Strategy Science — innovation r=0.21, PMF r=0.24), and let AI triage signal rather than decide; plus the anchoring trap — using AI for both problem-framing and ideation cuts strategic quality 15 points (Wu et al. 2025, INSEAD RCT). Two existing pages moved from practitioner-backed to peer-reviewed-anchored: glossary/share-of-model gained the canonical GEO paper (Aggarwal et al. 2024, KDD), the brand-bias finding (LLMs favor global/luxury brands — share-of-model isn’t a neutral mirror), and the non-determinism result (measure as a distribution, not once); competitor-analysis/overview gained its theoretical roots (Day 1994 market sensing; Madureira 2023 CI construct). Honest notes: two monitoring papers’ method claims were refuted in verification and ingested as limitations only; and win-loss analysis still lacks rigorous academic backing — flagged for a future pass.

Deep-research ingest — AI-reliability + service-recovery evidence 🔬 (May 29) — Ran a multi-agent deep-research pass over trusted/academic sources (23 fetched, 25 claims adversarially verified, 2 refuted) and ingested the top results. Two new pages: glossary/appropriate-reliance — the goal with AI is calibrated reliance, not maximal; mere AI labeling triggers costly over-reliance (Klingbeil 2024) and suppresses critical thinking (Lee et al. 2025, CHI), yet experts under-rely, and disclosing AI use erodes trust −16–20% via reduced legitimacy (Schilke & Reimann 2025, 13 experiments) — reconciled via expertise × stakes moderation. And glossary/review-response-strategy — how to reply to reviews, backed by two ISR studies: responses lift review volume (not sentiment) via a third-party effect, detailed-for-negative/brief-for-positive (Chen et al. 2019), and tone must match the justice type violated — rational for procedural complaints, empathetic for interactional (Ravichandran & Deng 2023). Three existing pages gained fresh field evidence: glossary/jagged-frontier (Ju & Aral 2025 MIT field experiment + “diversity collapse”), glossary/ai-skill-leveling (Alibaba production RCT — skill-leveling and a top-performer decline), and questions/what-ai-tools-actually-deliver-roi (Copilot RCT: time saved ≠ output; Stanford HAI 2025 adoption-vs-ROI baseline). Honest note: the competitive-intelligence sources didn’t clear verification this pass — flagged for a follow-up.

Customer-perception cluster gets a framework hub + ROI question developed 🧭 (May 29) — A consolidation session. New framework page glossary/customer-perception-moments ties the two May-26 behavioral-evidence ingests (glossary/weekend-review-effect + glossary/ai-humor-forgiveness) into one navigable lens: customer perception crystallizes at three moments of judgment — the decision moment, the review-writing moment, and the failure-recovery moment — which form a feedback loop with reviews as the connective tissue. The transferable lesson is the moderator-flip meta-pattern: every headline behavioral finding (weekend negativity, humor-helps-forgiveness) comes with 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-and-moderators-before-applying. glossary/honest-assessment is named as the unifying mechanism (visible imperfection out-converts polished perfection at every moment). Separately, the open question questions/what-ai-tools-actually-deliver-roi was developed with a two-axis ROI model (frontier position × error cost) — highest ROI is the unglamorous inside-frontier/low-error-cost quadrant; the −19pp danger zone is outside-frontier/high-error-cost — plus a function-by-function map and free-vs-paid reasoning; upgraded 🌱→🌿. Full lint run (no contradictions, no orphans, clean frontmatter); one genuine missing cross-link fixed and an llms.txt duplicate-line bug corrected. No new external source this session — the value was in consolidation, development, and health.

Weekend review-ops cluster — Science Says newsletter ingest with full cross-reference integration ⭐ (May 26, second session) — Second Science Says newsletter same day, user direction: integrate all the cross-referenced findings (not just the primary study) — saved as new feedback discipline. New page glossary/weekend-review-effect (~4,500 words) covering Bayerl, Schoenmueller, Goldenberg, Stahl 2026 (Journal of Marketing Research 63(2), n=400M reviews / 33 platforms / field experiment n=11,667): weekend reviews average -3% 5-star share and +6% 1-3 star share vs weekdays. The “Eleanor Rigby” mechanism: weekend reviewers self-select into a more socially-isolated population (fewer sociality-related words in reviews). Three caveats the newsletter missed, surfaced during the user’s “validate claims” pass: (1) hedonic-product counter-finding — effect reverses for entertainment/food/travel categories (2023 ScienceDirect, n=588K); (2) industry response-rate data conflicts — Saturdays are among highest-volume send-days per Bazaarvoice/Yotpo/PowerReviews — academic paper measures star rating outcome, industry measures response rate; (3) 0.04-star magnitude is below noise floor for products with 100+ reviews — effect matters operationally for low-review-count items + weekend-busy businesses. The wiki integrates the timing finding with three cross-referenced review-ops levers (first-review anchoring + incentive-positivity transfer per Woolley & Sharif 2021 +83.4% effect + display-order effects with 52%-prefer-mix trust nuance) into a four-lever practitioner playbook. Retrofit to glossary/ai-humor-forgiveness with two adjacent service-recovery tactics the original ingest missed (thanks-not-sorry pattern from JTTM 2022; chatbot interjections). Back-links to honest-assessment + ai-humor-forgiveness. Source archived to raw/articles/_ingested_2026-05-26_dont-ask-for-reviews-on-weekends.eml. New feedback discipline saved: when ingesting curated newsletters, follow the cross-referenced findings — newsletter author already built the cluster, wiki should inherit it.

AI humor-forgiveness — Science Says newsletter ingest with counter-finding integration 😅 (May 26) — User flagged a Science Says newsletter summarizing Xie et al. 2025 (Journal of Business Research, n=1,919) on AI agent service-failure humor and forgiveness. Verdict: worth ingesting. New page glossary/ai-humor-forgiveness (~4,500 words) covers: self-deprecating humor produces +47.8% forgiveness uplift vs no humor on low-severity failures (and +25.6% on after-sales) — outperforming positive humor by ~10pp. Two load-bearing boundary conditions the newsletter didn’t fully surface: (1) severity gate — effect vanishes (not weakens) for high-severity failures like refund refusal; (2) focal-customer gate — Honora et al. 2025 (J. Business Ethics) counter-finding shows humor directed at the burned customer reads as sarcasm, reduces perceived company morality, and forgives less. Independent corroboration from Nature Sci Reports 2025 (n=780; hedonic-motivation moderator + perceived-warmth mechanism). The wiki integrates the Hosanagar forgiveness-asymmetry quote — “People are not as forgiving of AI errors as they are of human errors” — into glossary/agent-adoption-frictions as the trust-friction repair cost. Substantial extensions to glossary/honest-assessment (self-deprecation as conversational-layer extension of the honest-assessment mechanism) and marketing/ai-human-voice-prompting (humor-on-failure as recovery-side seventh-technique). Back-links wired per schema discipline. Source archived to raw/articles/_ingested_2026-05-26_humor-makes-it-easier-to-forgive-AI-mistakes.eml. The wiki value-add: the newsletter missed the focal-customer counter-finding; the wiki page integrates it.

Week of May 19 – 25

Gemini Omni — same-day capture of Google I/O 2026 launch 🎬 (May 20, second session) — Google I/O 2026 was May 19; Gemini Omni is Google’s first any-to-any multimodal model unifying video/image/audio/text generation with Gemini’s reasoning baked in. Fuses Veo (video) + Nano Banana (image editing) + Project Genie (world simulation) under one architecture. Ships today in Gemini app + Google Flow for AI Plus/Pro/Ultra subscribers ($20/$30/$100/mo); YouTube Shorts + YouTube Create this week; Vertex AI / Gemini API / Agent Platform API in coming weeks. World-model physics understanding (gravity, fluid dynamics, collision behavior) inherited from DeepMind Project Genie — predicts outcomes from physical intuition, not running a physics simulation. New page: tools/gemini-omni (~3,500 words covering capabilities, pricing, competitive context, marketing/ad applications, honest limits). Distinct strengths versus Sora 2: prompt adherence on multi-clause briefs + text rendering reliability — both load-bearing for advertising. The 2026 split: Omni is the publisher’s tool (efficient, distribution-embedded, ad-scale variant generation); Sora 2 is the artist’s tool (cinematic, social, audio-sophisticated). Substantial extensions to marketing/ai-video-marketing (publisher-vs-artist-tool framing), comparisons/ai-tools-when-to-use (Gemini side refresh with two new video-generation rows), glossary/creative-reverse-engineering (Omni as the third vision-LLM closing the analysis-to-generation loop). Back-links wired to 5 target pages per the schema discipline. Same-day capture cadence matches the May 7 Claude Managed Agents release-wave ingest.

Competitor-analysis five-layer methodology now fully backed 🎯 (May 20) — Two new glossary entries close the remaining layer gaps in the competitor-analysis cluster. The May 18 pillar rebuild named five operational layers but only three (win-loss, battlecards, share-of-model) had dedicated practitioner glossary entries; Layers 2 and 5 were described in pillar prose without their own deep-dive pages. New: glossary/continuous-monitoring (~3,500 words on the 2026 CI discipline replacing the quarterly competitive-landscape deck — Crayon tracks 100+ signal types per competitor; the bottleneck has shifted from collection to signal-to-noise triage and decision routing; Klue’s 2026 Compete Agent + Deal Tips push competitive guidance to reps in near-real-time; tooling tiers from free Meta Ad Library + Changedetection.io stack to $20K–$100K+/yr enterprise platforms; three operational cadences — real-time + weekly + monthly — needed in parallel) and glossary/creative-reverse-engineering (~3,500 words on the systematic discipline distinct from the formula-vs-skin framework — pattern extraction across volume is the load-bearing move; 10–20 ads per pass minimum; the vision-LLM hybrid stack of Claude for copy + GPT-4o for visuals + Foreplay/Atria/Motion for collection; the formula-vs-skin distinction is the legal safety line). The competitor-analysis cluster is the wiki’s first domain with methodology pillar + every operational layer backed by a dedicated practitioner glossary entry. Back-links wired to 9 target pages per the schema discipline; index Stats block fixed (was stale: said ~126 pages / 45 glossary; actual 144 / 62).

Week of May 12 – 18

Prompt caching glossary entry 💾 (May 18, fourth session — late evening) — Tier 2 #5 from the morning gap analysis, the last remaining priority. New page glossary/prompt-caching (~2,500 words) covers the production-cost-optimization layer that the agent-engineering cluster was missing. Three distinct caching mechanisms (often conflated in practice): prompt cache (vendor-level prefix reuse — Anthropic cache_control markers at 5-min default / 1-hour extended TTL; OpenAI automatic on GPT-4o/GPT-5.x; cache reads at 10% of base price = 90% input-token discount), semantic cache (vector-similarity response reuse via Redis/GPTCache — typical 50%+ cost reduction with repetitive query patterns), KV cache (model-internal, not practitioner-facing). The Anthropic February 2026 TTL change (60-min → 5-min default, increasing many production costs 30-60% overnight) documented as a real failure mode. The 70-80% combined-strategy cost-reduction headline numbers contextualized as upper-bound. Proactive cache warming flagged as critical under-applied practice. Distinct from but adjacent to glossary/agentic-memory — prompt caching is per-request cost optimization; memory is cross-session persistence. Closes the May 18 gap-analysis cycle (5 of 5 priorities done in one day).

SEO/GEO refresh against 2026 zero-click data 🔍 (May 18, third session) — Tier 2 #4 from the morning gap analysis. The SEO domain was last touched April 2026 and pre-dated the most material 2026 findings: Google AI Mode hitting 75M daily users (92-94% zero-click), E-E-A-T as binary AI visibility filter (96% of AI Overview citations from strong-E-E-A-T sources), brand-mentions-3x-stronger-than-backlinks correlation (0.664 vs. 0.218), Domain Authority predicting less than 4% of AI citations, and earned-media citation value lasting 18-24 months. New page seo/zero-click-strategy (~5,000 words) covers the strategic operating model for a 64.82%-zero-click world — brand-and-visibility-first instead of traffic-first, the dual mandate (traditional rankings + AI engine citations), and the hardest part (admitting traffic-first KPIs are broken). Substantial extensions to seo/geo-aeo-benchmarks-2026, seo/ai-visibility, and seo/agentic-search-optimization absorb the May 2026 data shifts. Back-links wired to all 6 existing SEO pages per the schema discipline. PR strategy is now SEO strategy.

Competitor-analysis domain build-out 🎯 (May 18, second session) — Tier 1 #3 from the May 18 deep research, closing the longest-flagged structural gap (competitor-analysis as a one-page domain). User framing was explicit: methodology, not tool reviews. Four pages shipped — rebuilt pillar competitor-analysis/overview (~4,500 words on the 5-layer operational methodology: win-loss analysis + continuous monitoring + battlecards + share of model + creative reverse engineering, tied to specific decision triggers; SWOT/Porter’s critique; AI compresses execution while strategy stays human), plus three named-framework glossary entries: glossary/win-loss-analysis (the only CI layer that reliably moves win rate, 5-15pp improvement in mature programs; Klue/Crayon 8-practice convergence; interview buyers directly, not sales reps), glossary/battlecards (2026 living-battlecard evolution from static PDFs; modular + AI-assisted + role-specific + governance metadata; one-screen rule; success = win-rate-against-named-competitor), glossary/share-of-model (the AI-search competitive dimension that didn’t exist in 2024; ChatGPT ~79% of generative AI traffic; sector concentration extreme — Apple 54.38% mention share in consumer electronics — specialization is the only path for non-dominant brands). The wiki’s longest-flagged structural gap is now closed methodology-first. Back-links wired to 8 target pages per the schema discipline.

Agent payment protocols + Claude Managed Agents May release wave 💰 (May 18, morning session) — Monday session after weekend gap. Executed top two ingest priorities from the May 18 deep research: agent-to-agent payment infrastructure (which shipped April–May 2026) and Claude Managed Agents feature wave (May 7).

  • New page: glossary/agent-payment-protocols — Comprehensive treatment of the four-protocol stack constituting production agentic-commerce infrastructure: AP2 (Google authorization, JSON-LD W3C Verifiable Credentials, September 16 2025), x402 (Coinbase + Cloudflare HTTP-native stablecoin protocol, ~$600M annualized volume by March 2026), UCP (Google + Shopify + retailer coalition commerce schema), Visa TAP (agent identity at Cloudflare edge, October 2025). AWS Bedrock AgentCore Payments shipped May 7, 2026 with native x402 support and 10,000+ x402 Bazaar endpoints discoverable by agents at runtime. OpenAI killed Instant Checkout March 2026 — the aggregator-with-own-checkout approach lost; market consolidated to Amazon closed-loop vs. Google-coalition open-protocol. Anthropic Project Deal (April 2026) 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 on consumer dispute rights covered explicitly.
  • Extended: automation/agentic-commerce — Originally written April 14, the page described the agentic-commerce category but had zero coverage of the infrastructure that shipped between September 2025 and May 2026. Added major new sections on the four-protocol stack, the closed-loop vs. open-protocol dynamic, the agent quality gap, and the legal void. The page now reflects May 18 infrastructure reality.
  • Extended: tools/claude-managed-agents — May 7 2026 release wave. Outcomes promoted from Research Preview to Public Beta. Multi-Agent Orchestration promoted to Public Beta. New Dreaming section covering between-session memory consolidation (research preview), the two operating modes (automatic vs review-before-landing), and the cluster connection to glossary/agentic-memory. Cowork enterprise features (RBAC, group spend limits, agent view in Claude Code). 10 finance agent templates + 20+ legal MCP connectors as the verticalization move.
  • Extended: glossary/agentic-memory — Added a substantial new Anthropic 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 shipped 8 days after this glossary entry was created and consolidates episodic memory into procedural memory at the platform layer.
  • Architectural payoff: the agentic-commerce cluster now has all four sides covered (category + agent biases + user-side trust + infrastructure). Plus the agent-quality-gap finding cuts across all four as a new asymmetry dimension. Back-links wired to 9 target pages per the schema discipline.

Marketing analytics gap closed 📊 (May 15, after foundational glossary) — Tier 1 #2 from the May 15 gap analysis. The wiki had zero coverage of marketing analytics — MMM, incrementality, attribution-in-2026, cohort/LTV — despite this being core CMO-targeted content. Four new pages close the gap: marketing/marketing-analytics-in-2026 (comprehensive pillar — the cookieless attribution stack: dual-model operating norm with MMM + multi-touch + AI reconciliation + data clean rooms + platform AI like Advantage+/PMax; cohort/LTV:CAC as capital-efficiency layer underneath), plus three named-framework glossary entries: glossary/marketing-mix-modeling (top-down statistical attribution, no cookies needed; +212% adoption since 2023; Google Meridian + Scenario Planner + Meta Robyn democratized the methodology), glossary/incrementality-testing (causal-validation layer; three test designs; 2026 standard 10-20% holdout + synthetic controls; “fewer tests that materially change decisions”), glossary/cohort-analysis (the shape of the retention curve > M12 endpoint; M0-M3 onboarding cliff; surviving-cohort LTV (M3+) + NRR + CAC payback > aggregate LTV; 2026 LTV:CAC benchmarks B2B SaaS 3.2:1, DTC subscription 4.1:1). The cluster forms the marketing-analytics-in-2026 operating stack and provides citation anchors for future CMO-facing pages.

Foundational AI glossary gap closed 📚 (May 15, after human-voice page) — Tier 1 #4 from the May 15 gap analysis. Five new glossary entries: glossary/hallucination (signature LLM failure mode, structural cause = probabilistic next-token prediction without internal “I don’t know”), glossary/agentic-memory (4-layer architecture — working/episodic/semantic/procedural; memory is engineered, not built-in), glossary/tool-use (the capability that constitutes the agent category; basic primitive under all 2026 agentic work), glossary/guardrails (production safety layer paired with tool-use; six basic categories per Pimenov; persuasion-based bypass risk per Cialdini-on-AI 28K-prompt study), glossary/embeddings (numerical representations preserving semantic similarity; foundational layer under semantic search and RAG). Together: any future page that wants to cite these has a glossary anchor rather than a parenthetical explanation. The agent-engineering cluster (jagged-frontier → agent-engineering → tool-use → guardrails → agentic-memory → ai-agent-behavior → agent-adoption-frictions) is now self-contained — no out-of-page hops needed for foundational vocabulary.

New page: AI human voice for social + outreach 🎙️ (May 15) — Deep research + comprehensive new page. User flagged the human-voice question as “very important” and asked for deep research before building. Eight WebSearch passes across detection science (Nature 2025 stylometry, F1 ≈ 0.94 combined; em-dash 3.28× human rate; “absent lived experience” as the load-bearing fingerprint), empirical platform performance (cold-email deliverability AI 71%/8% spam-flag vs human 86%/3%; AI follow-up dies at 1.6 exchanges vs human 3.7), platform algorithms (LinkedIn 360Brew 150B-parameter LLM deployed March 12 2026, X Grok ranking, TikTok C2PA but script-AI exempt, EU AI Act August 2 2026 deadline), and prompting techniques (few-shot 2–5 optimal; Ruben Hassid Taste Interviewer 100-question approach; banned-word 2026 consensus; SuperWhisper voice-from-audio as sixth technique; Adore Me + Unilever case studies as 80/95–5/20 hybrid ratio anchors). The new page marketing/ai-human-voice-prompting is the generation-side complement to marketing/ai-tells-in-sales-copy’s editing-side discipline. Together they form a three-page cluster with marketing/brand-voice-skills-guide covering generation→editing→trust at distinct layers. The deepest single-topic cluster the wiki has built.

Cluster lint 🧹 (May 14) — Focused lint after yesterday’s heavy-creation day (4 new pages + 6 substantial extensions + ~25 new cross-links). Five dimensions checked. Wikilink validity 100% (38 unique targets, all resolve). Frontmatter compliance 100%. Substantive consistency 100% across five cross-cluster checks (jagged-frontier vs jagged-intelligence distinguished without unit-confusion; Karpathy’s >10x flagged as directional vs Dell’Acqua’s measured +12.2% properly bounded; Goldilocks-autonomy aligned across pages; Pirolli-Card invocations consistent across the validation-before-volume triangle; user-side/agent-side terminology symmetric between agent-adoption-frictions and ai-agent-behavior). Significant finding: bidirectional cross-link asymmetry. Yesterday’s new pages had rich outbound Related sections but target pages mostly lacked back-links. 16 back-links added across 14 target files to close the structural gaps. Second finding: llms.txt stats stale (same recurring failure mode the May 12 lint flagged). Refreshed page/section counts (marketing 14→16, glossary 45→47), bumped Stats section, added entries for the 4 new pages. Both recurring failure modes (ship-day back-link asymmetry + llms.txt drift after index updates) are now visible enough to deserve CLAUDE.md INGEST-workflow improvements.

AI-tells page — two-principle frame extension 🎯 (May 13, immediately after the page shipped) — Strategist task 2026-05-13-ai-tells-page-proactive-reader-motivation argued that audience-mode REVIEW is necessary but not sufficient: argument-level mismatches (wrong structural pain for the vertical) survive any amount of voice polish and only get caught when reader-motivation is modeled BEFORE drafting. The page now opens with a “Two principles for client-facing copy” section: (1) don’t sound like AI [existing catalog], (2) model the reader’s motivation before drafting [new section]. New “Model the reader before drafting” section covers the DTC-vs-iGaming structural-pain contrast and the iGaming reach-scarcity reframe as the canonical reference case — first-pass copy used DTC-style “rented FTDs vs. owned audience” framing, which implicitly assumes a working paid channel; iGaming doesn’t have that (paid bans on Meta/Google/Telegram official). The fix was argument-level, not voice-level. The verbatim user-articulation trigger now cited in Sources: “The key is the ability not to sound like AI and also look from the perspective of the reader.”

New page: AI tells in sales copy ✍️ (May 13, after Telegram extension) — Strategist task 2026-05-13-ai-tells-in-sales-copy requested a wiki home for the eleven-pattern catalog + audience-mode review discipline + CMO-believability score heuristic surfaced during a sales-page audit that moved CMO-believability from ~6.5/10 to 9/10 in one cycle. New page marketing/ai-tells-in-sales-copy codifies all three layers. The eleven tells include factual overreach for rhythm (the most damaging — one false claim discredits the whole doc), strategist-memo voice (meta-commentary instead of substance), em-dash overuse, coined-term over-use, parallel-construction overdensity, and seven more. The page is the negative trust-signal counterpart to glossary/honest-assessment (positive trust signal) — both depend on the same mechanism: audience reads writer’s judgment through the prose. Back-links added to honest-assessment and brand-voice-skills-guide (which is the LLM-instruction-side complement: what to sound like vs. what not to sound like).

Telegram page — operational-unlock section 🗺️ (May 13, after Karpathy ingest) — Strategist task requested an operational extension to the Telegram page; pure reorganization of existing sources into methodology. Added “Operational unlock: keyword × region discovery” (placed between Audience footprint and iGaming sections). The new section names the operational constraint that shapes everything: Telegram has no platform-level discovery surface, so entry is per-group rather than per-impression, and the unlock is a 2-4 week scout phase using TGStat + vertical indexes (Data40), $50 minimum / $350 proper-read per channel test (PropellerAds 2025), then scale on validated channels only. 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. Cross-link added in both directions. The Telegram page now sits at the intersection of two named frameworks (channel-fit-is-geographic + discovery-before-scale).

Karpathy Sequoia AI Ascent 2026 ingest — agent-engineering cluster completed 🎤 (May 13, later in session) — User flagged the YouTube talk From Vibe Coding to Agentic Engineering and a Pimenov writeup of the same content. Triangulated with two additional independent summaries (Travis Media, AI Agents Simplified) to verify quotes and framing.

  • New page: glossary/agent-engineering — Karpathy’s professional discipline of coordinating AI agents reliably and safely, framed as the complement to vibe-coding rather than its successor. Load-bearing quotes: “Vibe coding raises the floor. Agentic engineering raises the ceiling.” and “You can outsource your thinking, but you can’t outsource your understanding.” Covers the Software 1.0/2.0/3.0 framing (“LLM became the computer, prompt became the program”), the directional >10x developer claim, the “neural networks as operating systems” architectural prediction (OpenAI Codex + MCP as early evidence), and the human skills that stay distinctly human (taste, architectural thinking, oversight, contextual understanding). Most important move: explicitly names the connection between Karpathy’s “jagged intelligence” (model-side) and Dell’Acqua’s “jagged frontier” (human-side) as the same structural insight viewed from two sides.
  • Extended: glossary/vibe-coding — Added a “May 2026 update: the floor-vs-ceiling distinction” section. The 2025 vibe-coding discourse had collapsed two distinct disciplines into one phrase; Karpathy’s update separates them. 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.
  • Cross-linked: glossary/jagged-frontier — Added a “Karpathy’s ‘jagged intelligence’ — the model-side cousin” section with side-by-side comparison table. Unifying mechanism: Klein-Kahneman’s high-validity-environment + rapid-feedback conditions (glossary/recognition-primed-decision) predict both jaggedness shapes. The wiki now has a tight three-page cluster — jagged-frontier (human-side empirical anchor) + agent-engineering (model-side framing + engineering response) + vibe-coding (accessibility-side framing) — all referencing the same underlying mechanism and the same cross-domain thesis (glossary/automation-eats-execution).

Raw queue drained + two new pages + cluster extension 📥 (May 13) — Session goal was to clear the 8-article raw/ queue, ingest the strategist Telegram task, and refresh two stale seedlings. Useful finding: most raw/ items were already ingested in earlier sessions but had stayed in the queue because there’s no archive-on-ingest convention. Filed as a maintenance-protocol improvement.

  • New page: glossary/agent-adoption-frictions — Wharton × Science Says AI Agent Adoption Blueprint (April 2026). Three psychological frictions block agent adoption: perceived competence, trust, delegation of control. The barrier is psychology, not tech. Draws on 700,000+ employees surveyed across Google, ServiceNow, Wolters Kluwer, Workato, Concentrix, Zapier. This is the user-side counterpart to glossary/ai-agent-behavior: where Columbia/Yale documents what agents choose, Wharton documents whether users let agents choose at all. Connects to glossary/jagged-frontier and glossary/recognition-primed-decision — the user-side calibration response to AI capability asymmetry.
  • New page: marketing/telegram-marketing-channel — Built from the May 7 strategist research task. Telegram has 1B+ MAU (Durov, March 2025) and is the dominant marketing channel for iGaming globally and for fashion DTC in Russia/CIS/Iran/MENA — but NOT for Western Web3 fashion (that’s Discord, empirically: RTFKT, BAYC × BAPE, SYKY, Lacoste UNDW3 all run Discord). The page names the generalizable framework-level insight: channel-fit is geographic before categorical. Connects to glossary/super-niche as the channel-selection application of specificity-beats-generality. Also covers the iGaming pricing reality (official Telegram Ads bans gambling, so iGaming runs on third-party networks; affiliate channels eclipse official brand channels) and the Trezor channel-deactivation case showing operational tax can cancel even strong-fit channels.
  • Extended: glossary/ai-agent-behavior — Added the underlying Allouah et al. (Columbia + Yale, Working Paper Dec 2025) study with full researcher names and study scale (1,000 experiments × 8 categories). Added model-improvement curves on the “obvious better deal” test: Claude Sonnet 3.5 → Opus 4.5 failure rate 63.7% → 4.3%; GPT-4o → GPT-5.1 25.8% → 1.0%; Gemini 2.0 → 2.5 2.8% → 0%. Added the position-bias reversal finding (GPT-4.1 favored top-left; GPT-5.1 reversed). Added the 0.1-rating sensitivity. Added the Cialdini × Wharton 28,000-prompt study (compliance 33.3% → 72%) — implication: product pages optimized for human Cialdini-persuasion are also optimized for agent persuasion. Added the ZDNet BlancPottery real-world replication. Sources section restructured with full citation depth.
  • Refreshed: tools/obsidian.md — Was 32 days stale. Bumped 🌱 → 🌿. Added a “Five-week field report” section documenting actual usage of Obsidian as the wiki’s IDE: graph view as health monitor (made zero-orphans lint enforceable rather than aspirational), wikilink autocomplete as link-rot prevention, real-time edit preview as LLM-output calibration. Free tier sufficient; git handles sync.
  • Refreshed: experiments/ai-visibility-ecommerce.md — Was 27 days stale. Bumped 🌱 → 🌿. Added “Status update (2026-05-13)” section. Honest finding: next-steps remain open (no new field work) but findings haven’t lost relevance — they gained relevance. The pigu.lt WAF block on AI bots was an “interesting SEO oddity” in April; with 25-30% of US online purchases now reaching AI agents (Columbia/Yale), the same technical fact has become a load-bearing commerce assumption violation. Cross-linked to ai-agent-behavior, jagged-frontier, agentic-commerce, agent-adoption-frictions.
  • Architectural finding for next lint: The “raw/articles already ingested but still appearing in queue” pattern wasted real session time. The fix is an archive-on-ingest convention (_ingested_YYYY-MM-DD_ filename prefix, parallel to the drafts/ folder’s _archived_ convention). To be applied to this session’s processed articles immediately and proposed as an INGEST workflow update to CLAUDE.md.

Weekly lint + four targeted fixes 🧹 (May 12) — First session in seven days; lint confirmed wiki health is strong (100% frontmatter compliance, zero orphans, zero private-path leakage, zero pages stale past 30 days). All four fixable findings closed in the same session.

  • Refreshed llms.txt — was last updated April 23, claimed 68 pages / 21 glossary. Now reflects current state (125 pages, 45 glossary, 10 academic foundations), adds the May framework cluster (automation-eats-execution, jagged-frontier, ai-skill-leveling, advisor-strategy), and includes a new “How to use this wiki as an AI assistant” guidance section.
  • Fixed a broken-wikilink in strategist-pattern pointing at the repo-root CLAUDE.md (which lives outside the wiki folder, so wikilink resolution would render as broken). Now plain prose with a contextual link.
  • Wired five missing cross-links to address low inbound-density pages: cases/intercom-fin-support now linked from glossary/automation-eats-execution as the customer-service domain anchor; cases/binti-social-services now linked from glossary/ai-skill-leveling as the field-data analog for skill leveling in regulated documentation work; seo/new-site-ranking now linked from both glossary/super-niche and glossary/topical-authority; experiments/overview gained a new “Niche-Discovery Experiments” section pointing to both niche-hunter case studies.
  • Substantially extended glossary/ai-agent-behavior with a new section “Why agent decisions follow patterns: connection to the academic foundations.” Applies the May 5 academic wave (jagged-frontier + recognition-primed-decision) to the agent-decision layer: agent purchasing biases are the inside/outside-frontier asymmetry playing out at decision time. Bumped 🌱 → 🌿.
  • No stats changes from this session (the ai-agent-behavior page was already in the glossary count).

Week of May 5 – 11

Drafts cleanup + advisor-strategy ingest 🧹 (May 5, late evening) — Fifth and final productive session of the day. Cleaned up the drafts/ folder (three stale files renamed with _archived_2026-05-05_ prefix) and ingested two raw articles. The bigger of the two ingests — Anthropic’s April 2026 Advisor Strategy — produced the day’s most architecturally interesting synthesis: the strategy-vs-execution pattern is fractal, recurring at three layers (org chart / individual workflow / model architecture) for the same structural reasons.

  • Created glossary/advisor-strategy — Anthropic’s April 2026 model-pairing pattern. Cheap executor (Sonnet/Haiku) drives tasks; Opus advisor consulted only on hard decisions. Server-side, single API request, max_uses cost ceiling. Benchmark: Haiku + Opus advisor = 41.2% on BrowseComp (vs. 19.7% solo) at 85% lower cost than Sonnet alone. The architecture is the model-level fractal of the wiki’s comparisons/strategy-vs-execution-ai thesis.
  • Substantially enriched comparisons/strategy-vs-execution-ai with a new “The pattern recurs at three layers (a fractal observation)” section. Names the org-chart / individual-workflow / model-architecture layering as evidence the frame captures something structural, not a 2026 incidental.
  • Updated questions/ai-as-personal-advisor with a new section drawing the model-architecture parallel: a personal AI advisor is structurally the same architectural insight at a different scale.
  • Updated questions/managed-agents-break-even with two new dimensions: Anthropic’s task-horizon thesis (if sessions trend toward hours/days/weeks, the break-even math shifts toward Managed) and the advisor-strategy economics (executor-tier flexibility makes cheaper-model executors more viable for agent work).
  • Updated tools/claude-managed-agents with the explicit task-horizon positioning + cross-links to advisor-strategy and jagged-frontier.
  • Added glossary/advisor-strategy to Related on automation/ai-agent-organization.
  • Stats: ~125 → ~126 pages, glossary 44 → 45.
  • Drafts cleanup: three files (organic-content-pillar-draft, two reddit-response files) renamed with _archived_2026-05-05_ prefix. Pillar was shipped April 30; Reddit response insights already covered by strategist-pattern and glossary/llm-wiki-pattern.

Academic AI-productivity wave 📚 (May 5, evening) — Ingested four peer-reviewed sources to anchor the automation-eats-execution framework with academic empirical evidence. 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. Six independent anchors across multiple methodologies — unusually broad empirical support for a 2026 management framing.

  • Ingested Dell’Acqua et al. (2023, BCG × Harvard, n=758 consultants) — published as glossary/jagged-frontier. Inside the AI capability frontier: +12.2% tasks, +25.1% faster, +40% quality. Outside it: −19pp accuracy. The frontier is invisible from a task description.
  • Ingested Brynjolfsson, Li & Raymond (2023, NBER, n=5,179 customer-support agents) + Noy & Zhang (2023, Science, n=444 writers) + Dell’Acqua’s bottom-half-skill finding — synthesized as glossary/ai-skill-leveling. Three independent studies, three methodologies, same finding: AI raises low-performer productivity disproportionately. The skill premium compresses.
  • Ingested Noy & Zhang’s task-decomposition finding specifically — published as glossary/ai-task-restructuring. AI compresses rough-drafting; idea generation and editing become the new bottleneck. The mechanism behind why novices benefit most.
  • Ingested Klein 1998 + Kahneman-Klein 2009 — published as glossary/recognition-primed-decision. The theoretical foundation: pattern-matching judgment (human or AI) is reliable only in high-validity environments with rapid feedback. Predicts where AI itself will struggle for the same structural reasons humans do.
  • Substantially upgraded comparisons/strategy-vs-execution-ai with a major new “Peer-reviewed academic foundation” section. The framework now distinguishes academic empirical evidence from industry observation in its sourcing.
  • Added an empirical-anchors-table-for-academic-studies to glossary/automation-eats-execution. Reorganized Sources into industry data / peer-reviewed academic / synthesis.
  • Substantially upgraded questions/what-ai-tools-actually-deliver-roi with direct ROI numbers from all three studies. The seedling now has a real empirical answer for inside-frontier tasks.
  • Added theoretical-reliability section to questions/ai-as-personal-advisor (Klein-Kahneman conditions).
  • Added jagged-frontier reliability caveat to questions/managed-agents-break-even.
  • Cross-linked glossary/dual-process-thinking ↔ glossary/recognition-primed-decision for the complete fast-intuition picture (when it works, when it doesn’t).
  • Stats bumped (~121 → ~125 pages, glossary 40 → 44). Added “Academic foundations cited” stat: 10 papers.
  • The cross-domain framework architecture is now an eight-page cluster: comparison synthesis + named-framework glossary + open question + three domain anchors + four academic foundations. The wiki’s most empirically anchored framework.

Lint follow-up wave 2 🛠️ (May 5, late) — Closed the remaining lint findings except those requiring hands-on real-world work. The cross-domain “automation eats execution” framework now has a four-page architecture: synthesis comparison page + named-framework glossary stub + open question tracking adjacent-domain expansion + three domain anchors. This is the most complete framework architecture the wiki has built around a single Primores synthesis.

  • Created glossary/automation-eats-execution — names the cross-domain pattern as its own framework so other pages can cite it without rebuilding the argument. Three empirical anchors in a single table.
  • Created questions/automation-eats-execution-next-domains — working hypotheses for 5 candidate domains (email/CRM, SEO, brand-building, B2B sales, analytics). The brand-building negative case is the most useful: framework predicts it’s NOT on the curve, and empirically it isn’t.
  • Closed the orphan-cluster lint finding by adding cross-references to all 7 industry-cases pages from automation/ai-implementation-patterns (was 3 → now 11; including 3 previously-orphan pages on developer-tools, healthcare, and security).
  • Upgraded 4 glossary entries (🌱 → 🌿): lighting-recipe, focal-hierarchy, framing-archetype, creative-formula-vs-creative-skin. All are well-integrated (7 inbound links each) and substantively complete.
  • Stats bumped (~119 → ~121, glossary 39 → 40, questions 3 → 4).
  • Three lint growth opportunities transparently deferred to real sessions: hands-on creator-discovery experiment, influencer-platform tool reviews, and the Modash vs Aspire vs CreatorIQ comparison. Wiki schema requires hands-on testing for experiments and tool reviews; speculative versions would violate the wiki’s own quality bar.

Lint sweep + 3 follow-up pages 🧹 (May 5, later) — Ran the first full wiki lint since April 20. Wiki health is genuinely strong (zero frontmatter violations, zero private-path leakage, zero true orphans, zero stale seedlings). Lint surfaced two real broken links and a comparison-page synthesis opportunity unlocked by the May 4-5 ingests. All resolved in the same session.

  • Created getting-started — public orientation page for new readers (who the wiki is for, how it’s organized, quick paths into the content). Closes a broken link referenced from CLAUDE.md and the maintenance protocol.
  • Created glossary/vibe-coding — Karpathy’s February 2025 term for AI-assisted coding by intent. Where it works (prototypes, personal tools, learning), where it breaks (production, security-sensitive, long-lived). Closes a broken link from the dev-tools cases page.
  • Created comparisons/strategy-vs-execution-ai — the cross-domain synthesis the May ingests made writable. AI eats execution work; strategy, judgment, integration stay human-leveraged. Three empirical anchors: paid media (Seufert), influencer marketing (Modash 2026), software (Karpathy / vibe coding). The wiki now has three named anchors for the same pattern at three different layers — this is the meta-page that ties them together.
  • Wired 3 missing inbound links to marketing/influencer-marketing-task-overload from marketing/overview, marketing/ai-marketing-case-studies, and automation/finding-ai-use-cases per lint recommendation.
  • Stats bumped (~116 → ~119 pages, glossary 38 → 39, comparisons 3 → 4).

Influencer Marketing Task Overload 📋 (May 5) — Ingested Modash’s 2026 salary survey (n=499 influencer marketers). Captured the report’s central operational finding: the role is structurally overloaded with ~19 distinct weekly tasks, and the salary data shows execution-style tasks pay the least while strategy/leadership tasks pay the most. Mapped the 19 tasks against current AI tooling capability (Tier 1: AI eats it; Tier 2: AI assists, human owns the call; Tier 3: stays human-leveraged) — and the pattern that emerges is the same as paid-media’s “creative is the new targeting”: automation eats execution, strategy stays the lever.

  • Created marketing/influencer-marketing-task-overload (~280 lines, 🌿 growing) — challenge-first framing, the full 19-task weekly stack, strategy-vs-execution salary gradient (+$14,830 for strategy ownership), Primores-synthesis AI-fit tiering, the “just go do social” failure mode (cross-functional creep → 12% lower pay, 15% lower satisfaction).
  • Updated glossary/creative-is-new-targeting with a new section showing the same automation-eats-execution pattern in influencer marketing — generalizing the framing beyond paid media.
  • Stats bumped (~115 → ~116 pages, 34 → 35 domain pages).
  • Out-of-scope per direction: salary geography, gender pay gap, freelance economics, job satisfaction breakdowns. The original Modash report covers these.

Week of April 28 – May 4

“Creative is the new targeting” glossary entry 🎯 (May 4) — New glossary anchor for the phrase that’s been used informally in Primores work since 2023. Captures the structural shift in performance marketing (post-iOS 14.5 ATT) where Meta Advantage+, Google PMax, and TikTok Smart+ commoditized channel-side work, leaving creative variation as the dominant remaining lever.

  • Created glossary/creative-is-new-targeting — origin (Seufert / Mobile Dev Memo), the three things that got automated, the leverage shift, the AI-era twist, and honest-limits scoping (works for DTC/mobile-app; weakens for B2B/regulated/local/brand-building).
  • Wired into glossary/creative-formula-vs-creative-skin as the upstream “why” for the formula/skin distinction.
  • The wiki now has named anchors for all three layers a client engagement might cross between: paid-performance (this entry), brand-building (Sharp’s mental availability + distinctive assets), and organic content-marketing (organic-content-strategy pillar).

Organic Content Strategy pillar published 🚀 (April 30, end of day) — The day’s foundation reading culminates: the pillar shipped from drafts/ to wiki/marketing/ as full prose, plus two operational spokes.

  • Created marketing/organic-content-strategy (~3,400 words) — the pillar. Full prose across seven sections, grounded in all six foundation sources (Ajzen, Pirolli & Card, Granovetter, Sharp, Cialdini, Kahneman). Three time horizons + cognitive substrate. Recipes + iGaming cases.
  • Created marketing/discovery-before-scale (~1,800 words) — operational spoke covering the two-phase architecture (Discovery 2-4 weeks → Scale indefinite). Decision tree, failure modes, the math (9K × 100 = 6-9M views only if validated).
  • Created marketing/behavioral-profile-fingerprinting (~1,900 words) — the four-ratio measurement spoke (save/share/comment/follower). Each ratio with industry baseline + Cialdini-principle activation. Recipes case worked example.
  • Three of four planned spokes now exist (slideshow-pattern-design earlier today, discovery-before-scale + behavioral-profile-fingerprinting tonight). Fourth (tiktok-user-behavior-fundamentals) deferred — the academic foundations now live in glossary entries.
  • Stats bumped (~111 → ~114 pages).

Kahneman Thinking, Fast and Slow ingest 🧠 (April 30, late) — The pillar’s foundation reading is now substantively complete. Five major sources (Ajzen, Pirolli & Card, Granovetter, Sharp, Cialdini, Kahneman) all ingested in one day. Kahneman is the substrate layer underneath the others, not a parallel framework.

  • Created glossary/dual-process-thinking — Kahneman’s System 1 / System 2 framework, cognitive ease as a persuasion lever, the lazy-System-2 thesis, and three explicit cognitive-substrate connections to existing wiki concepts.
  • Connected three existing entries to their cognitive substrates: Sharp’s mental availability ↔ Kahneman’s availability heuristic (same mechanism, different layer); Cialdini’s Scarcity ↔ Kahneman & Tversky’s loss aversion; Pirolli & Card’s information scent ↔ Kahneman’s WYSIATI. The wiki now has explicit cross-disciplinary connections, not just adjacent frameworks.
  • The three-time-horizon model now has a substrate row: years (Sharp/availability heuristic) — months (Primores/cognitive ease + repeated exposure) — the moment (Cialdini/System 1 trigger features). All three run on the same dual-process substrate.

Cialdini Influence ingest 📘 (April 30, late) — Six persuasion principles + a Primores-original mapping page that converts intuitive slideshow-pattern picking into deliberate behavioral-profile design.

  • Created glossary/persuasion-principles — Cialdini’s six (reciprocation, commitment & consistency, social proof, liking, authority, scarcity) plus the click-whirr meta-framework. Each principle gets a mechanism + canonical experiment + AI-era content relevance.
  • Created marketing/slideshow-pattern-design — Primores-original mapping of 9 recurring slideshow patterns onto specific Cialdini principles. Sorts patterns by behavioral profile (save-bait, share-bait, comment-bait, follow-bait). The first wiki page that converts pattern selection from gut-feel to deliberate design.
  • Extended marketing/brand-vs-content-layers from a two-layer to a three-time-horizon model: years (Sharp/brand-building), months (Primores prior #4/content authority), the moment (Cialdini/compliance triggers). All three compose; none replaces the others.

Sharp’s How Brands Grow ingest 📕 (April 30, evening) — The book labeled “THE load-bearing source” in the pillar draft now has its three core frameworks as wiki citizens, plus a top-level reconciliation page resolving an apparent worldview-prior conflict.

  • Created glossary/mental-availability, glossary/distinctive-assets, glossary/double-jeopardy-law — Sharp’s three-point empirical conclusion (growth = popularity = light buyers), the distinctiveness-vs-differentiation argument, and the empirical anchor that smaller brands get hit twice (fewer buyers + less loyalty).
  • Created marketing/brand-vs-content-layers — top-level architectural page reconciling Sharp’s “broad reach builds mental availability” with Primores’ worldview prior #4 (“narrow + exhaustive beats broad TOFU”). Both are correct at different layers; both are needed for a complete strategy. Includes layer-specific decision criteria.
  • Updated glossary/super-niche and glossary/topical-authority with “What This Doesn’t Replace” sections explicitly scoping them to the content-marketing layer.
  • Architectural payoff: Sharp + Granovetter + Pirolli & Card + Ajzen now form a complete causal chain for brand growth in the AI-era — cross-cluster diffusion → attention allocation → mental availability + distinctive recognition → purchase intention → behavior.

Foundation reading ingests 📚 (April 30) — Three academic papers ingested to ground the forthcoming Organic Content Pillar.

  • Ajzen 1991 — Theory of Planned Behavior — Refreshed glossary/tpb with the foundational paper as primary citation (was citing the 2014 defense paper). Added empirical baseline (R≈.51 for behavior, R≈.71 for intention prediction across 16-19 studies), PBC vs Locus of Control disambiguation, past-behavior + moral-obligation extensions, and the methodological caveat that belief-based attitude measures only explain 10-36% of variance. Reframed the “subjective norms are weak predictors” finding from AI-specific to general.
  • Pirolli & Card 1999 — Information Foraging Theory — Created glossary/information-foraging. Covers information scent, patches + Charnov’s Marginal Value Theorem, diet selection (Independence from Encounter Rate), and enrichment. Frames the 200-400ms scroll-decision window as the empirical signature of Charnov’s MVT applied to feed environments. Reinforces glossary/super-niche and glossary/topical-authority with foraging-math grounding.
  • Granovetter 1973 — The Strength of Weak Ties — Created glossary/weak-ties. Bridge argument (strong ties cluster → bridges are necessarily weak ties), diffusion implication, and the Primores-original extension that algorithmic feeds operate as synthetic weak-tie bridges. Reinforces glossary/smra with the same framing.

Strategist Pattern documentation 🧠 (April 29-30) — New top-level meta page on turning a wiki into a thinking partner. Initial publish (three-layer architecture, six worldview priors, seven mapped capabilities, maintenance loops) on April 29; updated April 30 with the session-signals architecture (third loop), self-pressure beat discipline, and confidence calibration. See strategist-pattern.

Workflow tightening 🔧 (April 30) — Added wiki/changelog.md to the INGEST workflow (step 7) and Session End Checklist in CLAUDE.md. Reason: the log gets touched atomically with each ingest, but the changelog had been drifting day-by-day. This change aligns the two surfaces.

Production sprint ⚡ (April 28-29) — Six new wiki pages in two days, plus integration sweep.

  • Schwartz’s Awareness Levels — New glossary page synthesizing Eugene Schwartz’s “Breakthrough Advertising” (1966) frameworks (Five Levels of Awareness, Five Stages of Market Sophistication) with AI-era applications. Enhanced same day with direct book quotes via the new tools/pdf-streamer.
  • AI Interface Layer — New marketing strategy page on Claude becoming the “front door” between users and apps. Argues SEO success doesn’t transfer to AI visibility — different channels, different mechanisms. See marketing/ai-interface-layer.
  • Claude Connectors — Tool documentation covering 200+ ready-to-use integrations (Blender, Adobe, Spotify, Uber, HubSpot, Salesforce). Available on every plan including Free. See tools/claude-connectors.
  • GEO/AEO Benchmarks 2026 — Comprehensive 2026 data on AI search impact: 31.3% generative AI adoption, 48%+ of Google queries showing AI Overviews, 61% CTR drop when AI Overviews appear, +35% CTR for cited brands. See seo/geo-aeo-benchmarks-2026.
  • PDF Streamer tool — New Primores Claude Code skill for converting large PDFs (30+ pages) to markdown page-by-page. Resumable, vision-fallback aware, strips repeated headers. See tools/pdf-streamer.
  • 4 creative-cluster glossary stubs — Lifted glossary/creative-formula-vs-creative-skin, glossary/focal-hierarchy, glossary/framing-archetype, and glossary/lighting-recipe from the published article cluster into the glossary so the vocabulary has a definition home.
  • Integration sweep — Cross-linked the six new pages into eight neighbor pages so today’s work isn’t orphaned. Refreshed stats.

Week of April 21-27

Reverse-engineering pillar 🎨 — Largest content release to date.

  • AI Creative Reverse-Engineering pillar + 6-cluster article series — 1,185 lines of public content on the Formula vs Skin framework, surface vs structural mimicry, AI template casting workflow, and ethics of reverse-engineering ads. See cases/ad-alchemy-creative-reverse-engineering.
  • Brand Voice Skills Guide — How to build Claude Skills for consistent brand voice with LLM-learning foundations. See marketing/brand-voice-skills-guide.
  • Niche Hunter case study + skill — Three niches evaluated against five-axis validation (AI visibility = GO, Reddit workflow = GO, e-commerce content = MAYBE). See cases/niche-hunter-fresh-2026-04 and tools/niche-hunter.
  • Creative reverse-engineering article cluster (a01–a15) — 15 articles published on Meta Ad Library workflow, focal hierarchy, framing archetypes, lighting recipes, and the legality of reverse-engineering ads.
  • Cross-repo dispatch to primores-web on content push (operational improvement).
  • Reddit Thread Analyzer skill + substance ranking concept — See tools/reddit-thread-analyzer and glossary/substance-ranking.
  • Niche Hunter primores-creative case — First five-axis validation run for our own brand. See cases/niche-hunter-primores-creative.
  • Public methodology + LLM usage guide — See methodology and contributing.

Week of April 14-20

Major content growth 📈 — Wiki crossed 50 pages.

Week of April 7-13

Wiki launch 🚀

  • Initialized the wiki structure
  • Established content methodology (see methodology)
  • Created templates for consistent page formatting
  • Ready to start building knowledge!

Stats (as of 2026-05-04)

MetricCount
Total pages~115
Glossary entries38
Tool reviews11
Comparisons3
Domain pages34
Case studies8
Experiments4
Open questions3
Google Cloud AI cases ingested1,048 (232 with metrics)