Short note from Simon Willison (weblog) relaying two tips heard during a *Fireside Chat* at AIE with Cat Wu and Thariq Shihipar (Claude Code team): **let the model (Fable, and to some extent Opus) exercise its own judgment rather than dictating rules to it** — illustrated with the decision of whether to write tests. Second tip, from Jesse Vincent: to **save precious Fable tokens** (ahead of an imminent price increase), ask Fable to **delegate small tasks to less powerful models**, letting it judge which one. Willison shows the exact prompt used (« *use your judgement to decide an appropriate lower power model and run that in a subagent* ») and the **memory file** that Claude Code wrote in response. Domain: prompt engineering, coding agents, token economics, multi-model orchestration.
#Model judgment#delegation to subagents#model override
Long-form essay by **Shubham Saboo** (X/Twitter) advancing a thesis on the Product Manager role in the age of agents: the next key skill is **not prompt engineering** but **Loop Engineering** — designing a *system that improves with every run* rather than writing the perfect prompt every time. A **loop** is a repeated cycle: change what shapes the agent's behavior → run it → evaluate the output → keep the change if quality rises, revert otherwise → **compound the learning** so the next version starts ahead. For a PM, the entry point is not code but the **durable artifacts** that encode their judgment: PRD-review skill, customer-call *summarizer*, evaluation rubric, launch checklist, research workflow, `CLAUDE.md`, prompt template, prioritization framework. Because they are reused, these artifacts **compound in both directions** — and **drift** silently (a CLAUDE.md that keeps growing, a checklist that gets ignored…): the model has not regressed, the artifacts have drifted unwatched. A loop has **5 parts**: trigger, action, **proof**, memory, **stop condition** (the most critical). **Evals** become PM work (testing the artifact against known examples: 3 good / 3 bad PRDs, 5 understood calls, 2 past launches). **Memory** lives on **GitHub** (the repo becomes "product memory": commits, diffs, eval results, decision log, rollback). Recommended first loop: a **weekly product signal loop** (every Friday). Taste remains central — but it now needs **proof**. Cites Boris (creator of Claude Code): "he no longer writes prompts, he writes loops."
Podcast interview « À la French » (French-language tech channel, recorded at DevSummit) with Mathieu Grymonprez, Global CDO of the Adeo group (Leroy Merlin, Obramat, Weldom). How a century-old family retail group embraces the agentic AI wave: culture vs structure, accountability, token cost and FinOps, enterprise intelligence lock-in, company memory and agent orchestration. Domain: digital transformation, agentic AI, retail, IT strategy.
#Agentic AI#digital transformation#CDO
Mathieu Grymonprez (Global CDO, groupe Adeo) — invité ; Jean-Baptiste Kempf · Steeve Morin · Mehdi Medjaoui (hôtes du podcast « À la French »)
Case study published by the **Cornell AI Innovation Hub** (June 15, 2026): how a two-semester collaboration between the AI Hub, graduate students, and Cornell's Treasury team turned a time-consuming manual investigation into an AI tool that **recovered $100,000** in unidentified payments on a first batch. A successful **AI4Business** use case (financial process) that illustrates the **Leader-Lab-Crowd** framework of **Ethan Mollick** almost point by point: the **AI Hub** plays the role of the **Lab** (a central, ambidextrous team of technologists plus students); **Treasury** (Cheryl Barnes, Marie Graves…) is the **Crowd** carrying business knowledge and the real pain point; and the **$100,000** constitutes the **visible reward** (vivid win) that anchors adoption — exactly the incentive lever Mollick considers decisive. Key method: **"context first, then plan, then build"** via **Claude Code Plan Mode**, a chain of **fuzzy matching → Gemini Enterprise Web Search → Claude synthesis**, all within the governed **Cornell AI Gateway**. *"The $100,000 is a start."*
#Cornell AI Innovation Hub#unidentified payments#payment reconciliation
**Pete Stergion** — Desktop Engineer au Cornell AI Innovation Hub · co-tech lead du projet (avec Phil Williammee). Article institutionnel signé de l'AI Hub.
Polemical essay-thread by Ahmad Osman (@TheAhmadOsman) on X, *"Anthropic's War on Opensource AI"* (1.7M views). Core thesis: Anthropic systematically converts "safety" into a **control mechanism** (permission regime, regulatory capture, anti-competitive access restrictions, behavioral opacity) to keep builders, startups, and open source communities **downstream** of a handful of frontier labs. Central anchor point: the **Fable incident** (silent degradation of competing AI dev requests). Advocacy for open source / local AI as the only viable "political economy of intelligence." Domain: AI policy, open source vs. closed labs, sovereignty, governance.
In-depth technical guide (Lushbinary agency blog) on **Loop Engineering**: designing the systems that drive coding agents in a loop, rather than prompting them manually. Covers the lineage prompt → context → loop engineering, the Ralph technique (Geoffrey Huntley), the **five building blocks + memory** of a loop, their implementation in Claude Code and OpenAI Codex, writing verifiable stop conditions, an adoption maturity scale, and the risks that worsen as loops grow more sophisticated. Domain: agentic software engineering, coding agents, harness/orchestration.
Sunday tinkering post by **Mark Dembo** (Head of Solutions, Developer Platform & AI at **Cloudflare**) published on **June 7, 2026** on his personal blog. **Narrative**: inspired by **Steve Ruiz**, the author buys a small **M5Stack Stick 3** device (~€30) and, taking advantage of the release of **Opus 4.8**, builds himself a **DIY AI agent** "out of pure curiosity, with no goal." **Iteration 1 (45 min)**: he throws the device's documentation at **Claude Code**, which generates Python scripts (~200 LOC, *"zero blast radius"*) displaying the weather in Munich, then in several cities; a **Cloudflare Workers + Workers AI backend** adds **text-to-speech (TTS)**, **push-to-talk** (speech-to-text), and a central **small LLM** to answer questions. **Iteration 2 (a real agent)**: moving from REST endpoints to **WebSocket** transport via the **Cloudflare Agents SDK** + **Dynamic Worker execution** → the ***"Code Mode"*** pattern (the agent writes and executes code to accomplish its task). The agent then answers questions with public data (11! = factorial, Champions League winner via `fetch()` on Wikipedia, weather for any city). **Iteration 3 (real powers)**: connecting to **Todoist** via an **MCP OAuth** flow → 50 tools all at once, hence two problems: **context bloat** and **risk of real damage**. Solution borrowed from Cloudflare's **MCP Server Portal** + Claude connector settings: per-tool **Always allow / Ask for approval / Disable** (*Disabled* tools never enter the context; an **LLM classifier** only accepts distinct "allow" grants, and **default = deny**). **Stated stance**: reducing his role to ***"idea generator, executor and judge"*** (and rarely technical guide), a "human-in-the-loop" flow he considers not very *"2026"* (copy-pasting into UIFlow). **What he did NOT do**: no latency/streaming optimization, no optimistic LLM calls, no evals, ***"I did not even look at the code once."*** **Wonder**: €30 + one Anthropic session window + a few cents of Cloudflare inference → an object that listens and talks, controlled in natural language; *"the true unlock is how accessible it is."* Sharp contrast with [[thomas-pragdave-failing-faster-code-rot-ai-velocity-2026-06-06]] (here *"zero blast radius"* justifies never looking at the code); concretely illustrates *Code Mode* / *"the agent just writing and executing code"*, the **MCP** pattern ([[claude-skills-bigger-than-mcp-willison-2025-10-16]]), *Ask for approval*-style tool governance ([[uber-engineering-agent-identity-crisis-zero-trust-spire-2026-05-21]]), and the *systems around the model* doctrine from [[dropbox-okumura-beyond-code-generation-engineering-productivity-ai-agents-2026-05-28]].
#BYO agent#bring your own AI#DIY project
**Mark Dembo** (@darkmembo / @mdembo) · **Head of Solutions – Developer Platform & AI** chez **Cloudflare** (auparavant auteur sur le blog Cloudflare). Billet personnel publié sur son blog *markpauldembo.com* le **7 juin 2026** (description : *« Thoughts about tinkering on a Sunday »*).
Engineering retrospective from Anthropic's **Data Science & Data Engineering team** (Chen Chang, Clement Peng, Justin Leder, Johanne Jiao, Josh Cherry) published on **June 3, 2026** on the Anthropic blog (*Enterprise AI* category, focus on **Claude Code**). **Headline result**: ***"95% of business analytics queries are automated by Claude, with ~95% accuracy in aggregate"*** (up to **~99%** in certain domains). **Core problem**: analytics is **not** code — *"there's often only a single correct answer using a single correct source"* — it requires **mapping a user question to precise, up-to-date entities** in the data model. Three **failure modes**: (1) **concept↔entity ambiguity** (e.g. *"active users"*: which actions? exclude fraudsters? which window?); (2) **staleness** (assets and the agent's knowledge become *"subtly wrong"*); (3) **retrieval failure** (*"80% of failed queries had the info present in the corpus"* but unfindable). **Solution = a 4-layer "agentic analytics stack"**: (L1) **Data foundations** — dimensional modeling, **canonical datasets** *"single source-of-truth"*, metadata *"as a first-class product"*, integrity via CI/CD; (L2) **Sources of truth** in decreasing order of trust — **semantic layer** (the agent is *"structurally required (by skill instruction) to leverage the semantic layer first"*), lineage graph, **query corpus** (distilled into structured docs, **not** raw retrieval), business context (knowledge graph: roadmaps, decision logs, org); (L3) **Skills** — the decisive lever: ***"without skills … didn't exceed 21% … Adding skills gets these numbers consistently above 95%"***; structured **in pairs** (*Knowledge skill* = router to ~30 reference files; *Unbook skill* = senior analyst workflow: clarify → find sources → execute → **adversarial review**); **colocated** maintenance (*"a code-review hook flags any reporting-model change that doesn't touch a skill file"* → **~90% of data PRs include a skill change**); (L4) **Validation** — offline evals (~90% threshold to launch an agent, ~100% target), **ablation testing** (notable negative result: raw grep across thousands of SQL files → accuracy moves *"less than a point"*), online (adversarial review: **+6% accuracy, +32% tokens, +72% latency**), **provenance footers** (source tier + freshness + ownership), **active correction harvesting** (scheduled agents scanning channels to draft markdown fixes). **Strategic insight**: *"documentation generated, definitions owned by humans"* — letting the LLM **define** metrics was *"net-negative"*. **Minimal starting point**: a handful of canonical datasets + a few dozen evals + a *thin knowledge skill* capture *"most of the upside"*. Strongly converges with [[shihipar-claude-code-lessons-building-skills-2026-06-03]] (skills = folders, Gotchas, hooks), the *systems around the model* doctrine from [[dropbox-okumura-beyond-code-generation-engineering-productivity-ai-agents-2026-05-28]], the **semantic layer / ontology** from [[talisman-modern-data-101-ontology-pipeline-refresh-2026-05-04]] and [[seale-semantic-agent-model-harness-ontology-data-2026-04-17]], the *context development lifecycle* from [[debois-tessl-context-development-lifecycle-ai-coding-agents-2026-02-19]], and the UDA/knowledge graph from [[netflix-uda-unified-data-architecture-knowledge-graph-2025-06-12]].
#self-service analytics#agentic data analytics#Claude Code
**Chen Chang · Clement Peng · Justin Leder · Johanne Jiao · Josh Cherry** — équipe **Data Science & Data Engineering d'Anthropic**. Article publié le **3 juin 2026** sur le blog Anthropic (claude.com/blog) · catégorie *Enterprise AI* · ~5 min de lecture.
Blog post from **Anthropic / claude.com** by **Thariq Shihipar** (Member of Technical Staff, Claude Code team), published on **June 3, 2026**, which distills Anthropic's **internal experience** on designing and using **Skills**. **Framing thesis**: a Skill is not a simple markdown file but a **folder** (instructions + scripts + resources + config + hooks) that the agent **discovers and manipulates**; *« You should think of the entire file system as a form of context engineering and progressive disclosure. »* The article makes two structuring contributions. **(A) A taxonomy of 9 skill categories** observed at Anthropic: (1) **Library/API Reference** (docs for internal libs/CLIs with *gotchas* — e.g. `billing-lib`, `internal-platform-cli`, `sandbox-proxy`); (2) **Product Verification** (testing/verification via Playwright or tmux — `signup-flow-driver`, `checkout-verifier`, `tmux-cli-driver`); (3) **Data Fetching & Analysis** (access to data/monitoring stacks — `funnel-query`, `cohort-compare`, `grafana`, `datadog`); (4) **Business Process Automation** (repetitive workflows — `standup-post`, `weekly-recap`, `create-<ticket>-ticket`); (5) **Code Scaffolding** (framework boilerplate — `new-migration`, `create-app`); (6) **Code Quality & Review** (`adversarial-review`, `code-style`, `testing-practices`); (7) **CI/CD & Deployment** (`babysit-pr`, `deploy-<service>`, `cherry-pick-prod`); (8) **Runbooks** (multi-tool diagnostics — `<service>-debugging`, `oncall-runner`, `log-correlator`); (9) **Infrastructure Operations** (maintenance with guardrails — `<resource>-orphans`, `cost-investigation`). **(B) A set of best practices**: don't restate the obvious (*« Claude already knows how to code and can read your codebase »* → target what **contradicts default behavior**); polish the **Gotchas section** (*« the highest-signal content in any skill »*); **progressive disclosure** via the file tree (point to reference files depending on the situation rather than loading everything upfront); **descriptions written for the model** (*« the description field is not a summary, it's a description of when to trigger this skill »*); **setup flows** (config in `config.json`, otherwise prompt via `AskUserQuestion`); **persistent memory** (append-only logs / JSON via the `${CLAUDE_PLUGIN_DATA}` variable); **helper scripts** (*« lets Claude spend its turns on composition… rather than reconstructing boilerplate »*); **hooks conditionnels** (enabled only for the duration of the skill — e.g. a security hook blocking destructive commands). **Distribution at Anthropic**: skills are stored in `./.claude/skills`, informally shared via Slack in a sandbox folder, then promoted via **PR** to the internal **marketplace** once they gain traction; **usage measurement** via a **hook PreToolUse** that logs invocations (revealing popular skills versus underused ones). Direct follow-up to the fiche [[shihipar-claude-code-html-unreasonable-effectiveness-markdown-2026-05-10]] (same author) and a concrete complement to the Skills fiches by Anthropic/Willison/Vincent and to *harness engineering*.
#skills#Claude Code#Anthropic
**Thariq Shihipar** (Member of Technical Staff chez Anthropic, équipe **Claude Code** ; @trq212 / @trq sur X, thariqs.github.io) · pour le blog **claude.com**. Même auteur que la fiche *Using Claude Code: The Unreasonable Effectiveness of HTML* (2026-05-10). Publié le **3 juin 2026**.
Blog post by **Pasquale Pillitteri** (software engineer, Palermo) published on **May 29, 2026** (FR version), 18-minute read, *Claude Code & Anthropic* section. **Pivot thesis**: *"Claude Opus 4.8 is the most powerful SEO model of 2026, but almost everyone uses it wrong"* — not a model problem but a **system** problem. The golden rule: ***"strategy is a whiteboard, production is an assembly line"*** — **SEO must be split into two distinct phases**, and mixing them is *"the fastest way to waste a model that costs five dollars per million input tokens and twenty-five per million output tokens"*. **Model context**: Opus 4.8 released on **May 28, 2026** (41 days after Opus 4.7), **1M-token** context, **GraphWalks Long-Context F1 at 1M: 40.3% → 68.1%**, **SWE-bench Verified 88.6%**, **USAMO 2026 96.7%** (+27.4 pts), **HLE with tool 57.9%**, unchanged pricing **$5/$25** per M tokens, **Fast Mode 2.5× at $10/$50**, four **effort levels** (Low, High, Extra, Max). **The central anti-pattern** = *"the giant conversation"* / **context drift**: mixing strategy, keyword research, competitive analysis and writing in a single chat produces a *"mush of contradictory intentions"* → the model drifts toward **generic best practices** ("holistic optimization", "strategic approach") instead of data-anchored content. **Phase 1 — Strategy (whiteboard, visual UI, one-off)**: dashboard / Google Sheet / Claude.ai canvas to decide while looking at the data together. **3 plays**: (a) **classified keyword research** (volume / difficulty 0-100 / intent / business potential table / priority = volume÷difficulty×business weight); (b) **visual competitive analysis** (topical coverage matrix, gaps); (c) **phased roadmap** (quick wins M1-2 / mid-term M3-6 / pillar pages M7-12). **Extra/Max** mode is justified here (*"one right strategic decision is worth a thousand well-written pages targeting the wrong keywords"*). 3 closed artifacts saved to Notion/Drive. **Phase 2 — Production (assembly line, Opus 4.8 + MCP)**: the model shifts from strategist to **execution machine**; every decision **anchored to live data** via **Model Context Protocol**. **Stack MCP minimum**: **GSC MCP** (AminForou/mcp-gsc, 500+ stars), **official Ahrefs MCP** (98 stars), **GA4 MCP**; repo `modelcontextprotocol/servers` = **86,440 stars**, **10,000+ active servers**, 97M SDK downloads/month. Setup ~35 min, monthly refresh ~20 min. **Weekly loop**: a single prompt pulls live data, builds the brief (top 10 SERP + GSC + Ahrefs), derives H2/H3, writes, checks density, suggests titles → **+45% productivity**, draft in **6-12 min** (explicit reference to **Ryan Law / Ahrefs content engineering**, 23 skills). Mention of Anthropic's **Dynamic Workflows** (up to 1,000 subagents). **4 common mistakes**: (1) not checking the numbers (mandatory spot-check, *trust & verify*); (2) fully replacing Semrush/Ahrefs (MCP is a **layer on top**, not a substitute); (3) ignoring the **paid-organic content gap** (education client case: **2,742 wasted terms / 351 opportunities** identified in 90 seconds); (4) using Opus 4.8 where **Haiku 4.5** is enough (meta descriptions, alt text). **Cost**: $1-3 per 2,500-word article. **Sonnet 4.6** suffices for recurring production, Opus 4.8 reserved for strategy. SEO-optimized and self-referential article (the author writes SEO content itself designed to rank for "Opus 4.8 SEO"). Direct convergence with **Ryan Law/Ahrefs** (cited), **systems around the model** (Dropbox/Okumura), **skills-over-prompts** (Lattice), Haiku/Sonnet/Opus model routing (Gupta token-to-outcome).
#Claude Opus 4.8#AI SEO#two-phase workflow
**Pasquale Pillitteri** — Ingénieur informatique / développeur logiciel basé à **Palerme** (Italie) · certifié Innovation Manager UNI 11814:2021. Auteur d'un blog tech actif (rubrique *Claude Code & Anthropic*) · avec une newsletter hebdomadaire (~3,4k lecteurs). Article publié en version **FR** le **29 mai 2026** (lendemain de la sortie d'Opus 4.8).
Official **Salesforce News** blog post (*Agentic Enterprise* section, *"Pioneering the Agentic Shift Within Salesforce Engineering"* series), published on **May 27, 2026** (6-minute read) by **Srinivas "Srini" Tallapragada**, *President and Chief Engineering and Customer Success Officer* at Salesforce. Direct follow-up to an earlier post (*"How we got our engineers to use AI — without breaking everything"*) which recounted crossing **>90% adoption**. **Pivot thesis**: Salesforce Engineering moved from a world where AI was a useful *copilot* to one where **agentic tools drive the software development lifecycle (SDLC) itself** — writing code, reviewing PRs, generating tests, updating documentation, managing deployments, coordinating work once handled through human handoffs. **Canonical signal decision**: org-wide standardization on **Claude Code** + ***"we removed all token limits"*** — *"remove every last piece of friction between our engineers and the tools that make them faster and more effective"*. **Major empirical result** (April 2026 vs April 2025): work items completed per developer **+50.8%**, PRs merged per developer **+79%**, and above all **Effective Output score** (an ML measure of the **real value of delivered code**, not volume) **+151.3% year over year**. **Flagship use case**: migration of **33 API endpoints** to a cloud-native architecture, estimated at **~231 person-days** (7 per API) the traditional way, completed in **13 days — 18× faster** — via a **rule-based framework built in Claude** (markdown files + reference implementations), with PR feedback continuously fed back into the rule set, **autonomous LLM loops (build, fix, validate)** with no manual intervention, parallelized across isolated environments → **5 PRs**, the largest delivering **21 endpoints with 100% test coverage**. **No speed↔quality tradeoff**: through the **Engineering 360** platform (centralizing engineering data from hundreds of systems), **total incidents drop by 5%** despite the rise in PRs (*"quality doesn't suffer from speed. It benefits from it"*), thanks to **security guardrails and quality standards structurally embedded** in the agentic workflow (Trust as the #1 value). **SDLC overhaul**: once AI is adopted, engineers **tear down and rebuild** workflows (which processes to eliminate? which handoffs are now unnecessary? where does a human still do work an agent could own?). **New engineering craft**: **Claude Code skills** (packaged, reusable capabilities encoding team context, naming conventions, patterns) become a shared, composable **engineering artifact**; **AI Expert Suite** + **Salesforce Foundation Plugins** = an institutionalized, curated skills library (internal benchmark: **higher accuracy and reliability, reduced unnecessary cost**); **subagents & agent teams** parallelize workstreams (*"They describe the outcome, and a set of coordinated agents figures out the steps"*). **What remains hard**: (1) **context management** in long sessions — **CLAUDE.md file quality** varies widely and weighs heavily on output quality; (2) **agentic security** = a fundamentally different model (agents that *act*, not just *suggest* → increased blast radius); (3) **evolving roles** (how do juniors become seniors if AI absorbs entry-level work? role of the designer/PM? the execution unit = scrum team → experiments with 1- or 3-person units). Conclusion: *"It changed what was economically possible"*; the stated ambition is **"the most automated, agentic SDLC in the industry"**. Directly intersects with Gupta (*cost of a completed outcome*, marginal token utility), Greenwald/Sierra (outcome-based pricing), DORA (ROI / cost per feature) and the BFM/Girard debate (token as a value fuel, not a cost to cut).
#Agentic SDLC#agentic SDLC#Claude Code
**Srinivas « Srini » Tallapragada** — *President and Chief Engineering and Customer Success Officer* de **Salesforce**. Plus d'une décennie chez Salesforce · dirige l'ingénierie mondiale de la plateforme unifiée. Auteur de la série *Agentic Enterprise* sur le blog Salesforce News ; ce billet (27 mai 2026) est la **suite** d'un premier opus consacré à l'adoption de l'IA par les milliers d'ingénieurs Salesforce (*« How we got our engineers to use AI — without breaking everything »*). Position d'autorité = **dirigeant exécutif** parlant en son nom et au nom d'une organisation d'ingénierie à grande échelle (donnée terrain à l'échelle d'un hyperscaler SaaS) · avec accès aux métriques internes (Engineering 360, Effective Output).
Manifesto-style article by **Thariq Shihipar** (Engineer & serial entrepreneur, Claude Code team at Anthropic) announcing a **change in the default output format for agents**: replacing **Markdown with HTML**. Thesis: Markdown has been the dominant format between humans and agents (simple, portable, editable, readable) but has become **a bottleneck** as agents produce longer and richer artifacts (specs, plans, reports, code review). Beyond ~100 lines, no one reads a Markdown file anymore. HTML solves six limitations simultaneously: **information density** (tables, CSS, SVG, scripts, canvas, images), **visual clarity** (navigable, mobile-responsive layout), **ease of sharing** (an S3 link directly openable in a browser), **two-way interactivity** (sliders, knobs, "copy as JSON/prompt" buttons to loop back into Claude Code), **native contextual ingestion** (Claude Code reads the codebase + MCP Slack/Linear + git history + Chrome) and **enjoyment** (the author explicitly claims *"it's joyful"*). Five canonical uses detailed: (1) **specs/plans/exploration** in a comparative grid, (2) **PR review** with inline annotated diff, (3) **design & prototypes** with animation sliders, (4) **reports/research/learning** (the author had a prompt-caching explainer generated from git history), (5) **custom throwaway editors** (drag-and-drop of Linear tickets, feature-flag editors, side-by-side prompt-tuner) that produce a re-injectable "copy as markdown/diff/JSON" export. Explicit anti-pattern: *"I'm a little bit afraid that people will read this article and turn it into a /html skill"* — the author **rejects premature skill-ification**, recommending prompting from scratch ("make a HTML file"). Pragmatic FAQ: token cost absorbed by **Opus 4.7**'s 1MM context, 2-4× longer generation, noisy HTML diffs (a real downside), style kept in check via a reference HTML design system.
#HTML#Markdown#output format
Thariq Shihipar (Engineer & serial entrepreneur, équipe Claude Code chez Anthropic — site : thariqs.github.io/html-effectiveness ; X : @trq212)
Podcast by Greg Isenberg × Meng To (designer, founder of Design+Code, creator of Aura / New Form / Dream Cut) on **`design.md`** — Google's open-source convention, equivalent to `agents.md` / `skills.md` / `soul.md` but **for the design system** (typography, colors, spacing, WebGL/Three.js animations, reveal rules). Central idea: carry "the **soul of design**" in a markdown file that is passed to an agent (Claude Code, Codex, OpenClaude, Gemini, Stitch, Aura, V0, Lovable, Cursor) to preserve **cross-medium consistency** (web, mobile, Replit slides, Hyperframes/Remotion motion design). Triad taught: **HTML = finished dish, design.md = recipe, skills = ingredients** (typography, lasers, skeuomorphic, 3D skills — 63 in New Form). Major diagnosis: **design drift** on one-shot workflows (`v0`, Lovable, Framer) that start strong then drift into generic output. Meta-message: *taste* is the only remaining **moat** — *"if something looks like another thing, its value drops by 10× to 100×"*. Workflow: **Reference → Design.md → Generate → Inspect → Systemize → Iterate (up to 1000+ prompts) → Remix → Expand → Export**. Critique of **purple gradients** ("you just run") as the generic post-vibe-coding baseline. Meng To claims to have spent ~$500,000 on tokens, made 1,000–10,000 iterations per product, and runs 4 products in parallel solo.
#design.md#Google#design system
Greg Isenberg (host — podcast Late Checkout / The Greg Isenberg Show, 12 mai 2026 livestream workshop ideabrowser.com) ; **Meng To** (guest — designer, fondateur Design+Code 2014, créateur Aura / New Form / Dream Cut, autodidacte parti à 18 ans, dropout, francophone d'origine canadienne)
Interview with Boris Cherny (creator of Claude Code, Anthropic) at a Sequoia event (hosts: Asia, Lauren Reader). Cherny states ***"coding is solved"***: he himself has written **0 lines of code** since late 2025, the model writes **100%**, *"a few dozen PRs/day, 150 PRs in a single day record"*. Account of the genesis of Claude Code (Anthropic Labs incubator late 2024, Mike Krieger in charge of round 2, pre-PMF build *"for the next model"*, first release that didn't take off, **exponential growth started with Opus 4 in May 2025**, accelerating with every new model 4 → 4.5 → 4.6 → 4.7). Current personal setup: **"most of my work I do from my phone"** (iOS), 5-10 sessions, **"a few hundred agents going, a few thousand at night"**, **`/loop` is the future** (cron + repeat jobs, agents that babysit CI, rebase PRs, cluster Twitter feedback). **Routines** = server-side equivalent, laptop closed. SaaS vision: no apocalypse, but **reordering of Helmer's 7 Powers framework** (switching costs ↓, process power ↓, network effects/scale economies/cornered resources unchanged) and **10× more disruptive startups** over the next 10 years. Pivot analogy: the **Gutenberg press** (10% literacy in the 1400s → 70% within a few centuries, books 100× cheaper within 50 years), *"software will be similarly democratized, but faster than 50 years"* — *"the best person to write accounting software is not an engineer, it's a really good accountant."*
#Boris Cherny#Anthropic#Claude Code
Boris Cherny (créateur de Claude Code, Anthropic) interviewé par Lauren Reader (Sequoia) avec introduction d'Asia (Sequoia).
Post from the **Ahrefs blog** published on **April 28, 2026** by **Ryan Law** (Director of Content Marketing, Ahrefs) describing an in-house **content engineering** system built around **Claude Code**: an editorial pipeline that produces **publish-ready drafts in 6 to 12 minutes**. **Pivot thesis**: ***« AI content is not, by default, good. This process works well because it mirrors our existing human editorial process »*** — quality doesn't come from the model but from the **faithful reproduction of a human editorial process** proven over decades. Architecture: **~23 skill files**, each corresponding to an editorial step (keyword research, topic gap analysis, structural outlining, research compilation, draft generation, formatting), **orchestrated by a master skill `blog-pipeline`** that chains them to produce a complete article. **Seven design principles**: (1) **mimic human workflows** by chaining skills adapted from existing Ahrefs editorial documentation; (2) **output each step separately** for troubleshooting (*« if you get an article at the end of a ten minute run, and it's bad, it's hard to diagnose precisely where and why the process went wrong »* → save intermediate outputs); (3) **create test cases** via Anthropic's `skill-creator` skill to evaluate and improve guidance; (4) **plug in quality data sources** — the **Ahrefs MCP** (keyword metrics, parent topic, long-tail themes, SERP overviews, competitive analysis), competitive analysis and product docs; (5) **front-load human direction** via context parameters enabling editorial guidance; (6) **build interactive previews** in HTML format for review before publication; (7) **allow customization** (each team member can fork and modify the system). **Volume**: ~**15 articles published** and ~**30 articles updated** via this workflow; development started in **February 2026** (the prior process from **August 2025** took several days and manual intervention). **Explicit caveats** (anti-oversell): *« experience matters »* — the process reflects decades of editorial expertise; topic selection focuses on **informational SEO content** the author knows well; Ahrefs **has no plan to "scale" content massively** but maintains an **evergreen library**. Philosophy: automate *« the formulaic parts of work »* to eliminate drudgery and free up time for research, thought leadership, webinars, and system optimization — **not** replace human effort. Canonical reference cited by Pasquale Pillitteri (*Opus 4.8 SEO workflow*) as field proof of the « 6-12 min/draft » gain. Direct convergence with the **skills-over-prompts** doctrine (Lattice, PROJ-AI), **systems around the model** (Dropbox/Okumura), and the use of **HTML as a review artifact** (Shihipar).
**Ryan Law** — Director of Content Marketing chez **Ahrefs**. Praticien senior du content marketing SEO ; le billet est un retour d'expérience personnel (*« How I do… »*) publié sur le **blog Ahrefs** (ahrefs.com/blog) le **28 avril 2026**.
FinOps for AI Agents: A Four-Step Allocation Framework for Coding Assistant Costs (Claude Code, Cursor, Copilot) and Why Traditional Cloud Tagging Fails - Finout
Les Echos report (Florian Dèbes) from San Francisco: AI agents already integrated as colleagues at start-ups, "petri dish" (Aaron Levie / Box), reflex use of Claude before every meeting, personal Jarvis, 5 parallel agent tabs, "the limiting factor is human cognition" (Patrick Joubert / Rippletide), "brain fry" / cognitive overheating, BCG/HBR study showing 14% of employees overwhelmed, "token-max" ranking of the heaviest AI users, testimonials from Sinaï/Bangay/Allali/Hodjat/Pantera/Chapeau and an echo from Siddhant Khare ("AI reduces production costs but raises coordination costs").
#Silicon Valley#San Francisco#AI agents as colleagues
Florian Dèbes (Les Echos, rubrique Travailler mieux / Vie au travail)
Revamp of the engineering hiring process at Sierra in the age of coding agents: AI-native onsite interview (Plan/Build/Review), removal of the algorithmic coding test, replacement of the phone screen with a system design interview, pilot of a debugging interview on an existing codebase.
Anthropic Research - AI Work Transformation - Claude Code Impact - Software Engineering - AI Adoption - Productivity Study - Workplace Evolution - AI Collaboration - Skills Development - Future of Work
#Anthropic#AI Transformation#Workplace Impact
Anthropic Research Team (132 engineers and researchers surveyed, 53 in-depth interviews conducted)
First AI-orchestrated cyber espionage campaign - Claude Code manipulated - Chinese state actor - 30 global targets - 80-90% automated - Jailbreaking - Anthropic Threat Intelligence
Cat Wu and Boris Cherny (Anthropic) explain how to use Claude Code like its creators: antfooding, plan mode, subagents, hooks, and extensibility — Every's AI & I podcast
#Claude Code#Cat Wu#Boris Cherny
Rhea Purohit (interviewer: Dan Shipper) · Cat Wu · Boris Cherny