X thread (illustrated thread) by **Thariq Shihipar** (Claude Code team / Anthropic): a *field guide* to getting the most out of **Claude Fable 5**. Central thesis borrowed from Korzybski — *"the map is not the territory"*: the **map** = what you give Claude (prompts, skills, context); the **territory** = where the work happens (codebase, real-world constraints); the gap between the two = the **unknowns**. Fable is *"the first model where the quality of the work is bottlenecked by my ability to clarify its unknowns"*. The article provides a **4-quadrant framework** (known knowns / known unknowns / unknown knowns / unknown unknowns) and a **toolkit of techniques** ordered in time (before / during / after implementation) — blindspot pass, brainstorms & prototypes, interviews, references, implementation plan, implementation-notes, pitches & explainers, quizzes — each with example prompts. Domain: prompt engineering, coding agents, methodology for working with AI, HTML artifacts.
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
Agent guide (Thinkroom, Kieran Klaassen's platform) documenting the **Compounding Knowledge Lifecycle** of the compound-engineering-plugin (Every): how a lesson learned once "keeps paying off" — captured, stored, retrieved, and kept true. Describes the anatomy of a *learning* (`docs/solutions/`), its capture via `/ce-compound`, the memory map (durable vs ephemeral), *grep-first* retrieval (learnings-researcher) wired into 5 skills at decision points, and the three counter-forces that keep memory from lying. Directly relevant: it is the doctrine behind this repository's `docs/solutions/` convention. Domain: compound engineering, agentic knowledge management, skills.
Letter "Dear friends" from Andrew Ng in *The Batch* (DeepLearning.AI, issue 359) on **loop engineering** applied to **0-to-1** product development. Ng shares his **3 key loops** — agentic coding loop (~minutes), developer feedback loop (~hours), external feedback loop (~days) — nested by increasing time scale, connecting *coding agent → product spec/evals → developer vision → external feedback*. Central thesis: humans retain a **context advantage** (rather than a "taste") that makes human-in-the-loop indispensable; engineers take on a partial product management role. Domain: coding agents, product engineering, agentic methodology.
**Skill** entry (not an article): `grill-with-docs` by Matt Pocock is a structured interview technique that "grills" an architecture plan by methodically confronting it against the project's business vocabulary (the `CONTEXT.md` glossary) and already-documented decisions (ADRs). Rather than rushing into implementation, it challenges assumptions one by one through a question/answer dialogue, cleans up terminology, checks consistency against the actual code, and captures decisions on the fly in the right artifacts. An upfront-design skill, inspired by Domain-Driven Design.
Chris Williams (@voodootikigod) opens his ADLC series arguing that running the human SDLC on models is a category error: the classic cycle was designed to counter human failure modes (ego, fatigue, forgetting) that are absent in LLMs. He catalogs eight load-bearing failure modes (F1-F8) and five exploitable properties (E1-E5), and lays out the founding principle: every phase of an agentic cycle must trace back to a failure mode it defends against or a property it exploits.
Second installment of Chris Williams's ADLC series: it unrolls the cycle that follows from the "first law" — eight phases (P0 Triage → P7 Distill), a deterministic gate between each pair, and exactly two mandatory human moments (spec approval at P1, behavioral acceptance at P6). Key principle: an LLM→LLM handoff without a deterministic checkpoint multiplies error rates; and a "barbell" cost distribution (heavy at both ends, light in the middle) that inverts agile economics.
Third installment in the ADLC series: Williams turns testing into the specification in the only language the builder cannot contest. Where TDD is an optional quality practice for human-written code, it becomes the load-bearing trust mechanism of the entire lifecycle once agents write the code. Three "rail discipline" rules: separated authoring contexts (specs-only before implementation), mechanical freezing at the tool level (not the prompt), and adversarial audits ("does a test fail if the feature is deleted?"). Mutation testing is preferred over coverage percentage, which is Goodhart-able at machine speed.
Fourth installment in the ADLC series: Williams reframes code review as adversarial "prosecution" rather than collaborative evaluation. Charter agents to refute ("find what's wrong"), deploy single-lens reviewers with fresh contexts (correctness, security, contract compliance, spec alignment, test quality), act only on verified findings (reproduced by a failing test), and loop until two consecutive passes yield zero findings. Measure calibration by planting known bugs, mutation-testing style. Exit gate: zero open findings, two dry passes, green tests, empty test diff.
Fifth installment of the ADLC series: orchestrating parallel agents without "merge hell". Williams sets out three coupled dials — cost (model selection), wall-clock time (parallelization width), and accuracy (contract quality) — and an architectural principle: "control flow is code; judgment is models" (deterministic scripts orchestrate, models supply only judgment). Four lanes (frontier Contract Desk, single-writer Builder Pool, shared Prosecution Pool, sequential Integrator), a merge-conflict forecast built from four signals (certified width typically 3-5 agents), and consensus-based disambiguation across N cheap agents rather than clarification questions.
Sixth installment on the ADLC: Williams describes the P7 "Distill" phase as the component that drives cost down on every run. Two halves: post-merge simplification (deduce after the code exists, not before — "deduplicating before the code exists is speculative") and lesson mining (a "lesson foundry" turns recurring findings into lint rules, skills, and new interrogation questions). Each lesson is paid for once, then demoted from expensive probabilistic detection to free deterministic prevention. The right unit of account is "cost per merged, verified change," and "flat cost is failure."
Seventh and final installment of the ADLC series: Williams presents an open-source toolkit of eighteen tools built *with* the cycle itself (build-prosecute-fix loop, parallel agents, a frozen `@adlc/core` core followed by fan-out — "pinned means merged"). The doctrinal core is "frontier-free": hitting accuracy targets with mid-tier models (Opus/Sonnet/Haiku-class) rather than frontier ones, via five substitutions (search replaces insight, decomposition replaces horizon, banking replaces presence, measurement replaces metacognition, the generator-verifier gap keeps the engine running), with the human remaining the "frontier" tier at the two spec gates. Throughline of the series: "replace trust with structure, and structure with measurement."
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 »*).
Post by **David "Pragdave" Thomas** (co-author of *The Pragmatic Programmer*, signatory of the Agile Manifesto) published on **June 6, 2026** on his Substack newsletter. **Thesis**: AI does not eliminate code degradation, it **accelerates** it. Adding features to a small personal animation/graphics project using **Claude**, the author moves from initial enthusiasm (oklch, SVG animations shipped within a week) to permanent regression cycles by week two. Striking formula: what teams took ***"18 months, or more"*** to rot, he achieved in ***"18 hours spread over five evenings."*** **Root cause**: the abandonment of **code hygiene** (massive duplication, local solutions to systemic problems, over-conditioning, proliferation of edge cases). **Behavioral diagnosis**: LLMs optimize for engagement and user satisfaction (*"That's a great idea, Dave!"*) rather than sustainability — they are ***"puppy-dog junior developers, eager to please but quite messy to have around"*** who constantly propose new features and discourage refactoring. **Central insight**: any non-developer can succeed at the *"first week"* of AI coding; it's **professional judgment** — knowing when to stop and refactor — that separates the experienced engineer from the novice. **Epigraph** (Gordon Bell): *"Every big computing disaster has come from taking too many ideas and putting them in one place."* **Conclusion**: ***"It's still just programming"*** — untended code rots, whether in 18 hours or 18 months; everything learned about writing good code still holds, the effect is simply **amplified**. Converges with the *"the faster execution goes, the stricter the framework must be"* doctrine of [[rafal-wenvision-ingenierie-logicielle-ere-ia-tout-change-rien-ne-change-2026-06-01]], the *"AI-assisted development is a trap without continuous delivery"* of [[farley-continuous-delivery-ai-assisted-development-trap-2026-05-13]], and the *"AI moves bottlenecks, it doesn't eliminate them"* of [[dropbox-okumura-beyond-code-generation-engineering-productivity-ai-agents-2026-05-28]]; a craftsmanship counterpoint to the vibe coding of [[karpathy-vibe-coding-agentic-engineering-software-3-0-2026-04-29]].
#code hygiene#code rot#code degradation
**David Thomas** (alias **« Pragdave »**) · co-auteur avec Andy Hunt de *The Pragmatic Programmer* (1999, éd. 20e anniversaire 2019) · co-fondateur de **The Pragmatic Bookshelf** et l'un des **17 signataires du Manifeste Agile** (2001). Figure historique du *software craftsmanship*. Billet publié le **6 juin 2026** sur sa newsletter Substack *articles.pragdave.me*.
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**.
Guide from the media outlet **Every** (every.to/guides) published on **June 2, 2026**, co-signed by **Mike Taylor, Laura Entis and Claude**, proposing an **8-level maturity scale for AI adoption**. **Pivot thesis**: AI adoption **is not a race toward maximum sophistication** — ***« a higher level isn't necessarily better »*** ; one must identify the level that **matches one's own workflow and level of trust**, then regularly reassess whether moving up a notch adds **real value**. ***« The best way to find value in AI is to use it in a way that fits your work. »*** **Structuring axis**: at each level, *« you delegate more of your work to—and place more trust in—the AI »* (increasing delegation + trust). **The 8 levels**: **(1) Chatbot** — conversational interface with no embedded context (ChatGPT, Claude, Gemini); **(2) Copilot** — AI embedded in the workspace with access to the current file (Cursor, Claude in Excel, Gemini in Docs); **(3) Agent** — reactive system that executes step-by-step while requesting approval (Cowork, Codex); **(4) Autopilot** — one describes the **outcome** and the agent executes autonomously, review of the **final result** only (Lovable, Codex, Claude Code; tied to *vibe coding*); **(5) Workflows** — engineers building **harnesses** around agents (planning, review, confidence checks, guardrails; Compound engineering, Claude Workflows, Copilot AI Studio; shift from one-shot vibe coding → **agentic engineering**); **(6) Assistant** — **proactive, always-on** agents that monitor a domain and surface information without being prompted (OpenClaw, Hermes Agent, Claude Managed Agents; e.g. `heartbeat.md` every 30 minutes); **(7) Multi-agent** — simultaneous management of **several long-running agents** with distinct roles (Claude Managed Agents, OpenClaw, Codex Goals; *« firmly in senior engineering territory »*); **(8) Orchestrator** — an **agent manager** directs a team of sub-agents (planning, delegation, monitoring, consolidation; Gas Town, Paperclip, Symphony/OpenAI; *« highly experimental »* — even frontier engineers themselves hold this role). **Sweet spots by role**: **knowledge workers** typically operate between levels **1-4**, **engineers** between **5-8**. **Canonical parallel of intern onboarding**: *« Expect to put in a similar amount of effort with your agents before you can trust them… at the next level of autonomy »* ; and the marker phrase ***« You wouldn't brag that you had eight interns working overnight on a key project, and you hadn't checked their output. »*** The right level depends on **4 criteria**: output quality, cost, reliability (trustworthiness), stakes of failure; and **model capability** progressively shifts the "safe" level of autonomy. A framework directly usable to structure an **adoption doctrine** on the consulting side. Convergence with *systems around the model* (Dropbox/Okumura), *harness engineering* (Böckeler, Lattice, Wescale), Karpathy (vibe coding → agentic engineering), Cherny (/loop + Routines), and the *agent manager* doctrine (BFM/Girard).
#AI adoption#maturity scale#eight levels
**Mike Taylor** · **Laura Entis** et **Claude** (co-auteurs déclarés) · pour **Every** (every.to) · rubrique *Guides*. Mike Taylor est un auteur connu sur les sujets prompt/AI (co-auteur de *Prompt Engineering for Generative AI*) ; Laura Entis est journaliste/éditrice. La co-signature explicite de **Claude** comme auteur fait partie du positionnement éditorial d'Every (entreprise AI-native). Publié le **2 juin 2026**.
Op-ed by **Olivier Rafal** (Consulting Director Strategy, **WeNvision** — **SFEIR** group; former editor-in-chief of *Le Monde Informatique*) published on **June 1, 2026** in **CIO-Online**, structured around a **paradox**: in the AI era, software engineering **changes everything… and nothing changes**. **What changes = the operating model.** Roles are redefined: the **Product Owner** shifts from backlog breakdown to **generating context usable by AI**; the **developer** shifts from writing code to **framing, directing, and reviewing** agent execution; **QA** gains the ability to define **expected proof** upfront. Team structure shifts from *"double pizza teams"* (hand-off chains of ~8 people) to ***"sandwich teams"***: a **tight pairing of a business expert and a tech lead, both AI-augmented**, with other skills in support. Internal **Sfeir** figure: *"this pairing now drives roughly 80% of the production chain"*, with the remaining ~20% (architecture, data governance, security) centralized. Pivot quote: ***"The issue is not a tooling issue, but an operating-model issue."*** **What doesn't change = the discipline of the cycle.** The **SDLC** phases (define → build → verify → deploy → maintain) remain identical and non-negotiable; AI removes none of them, it **intensifies** them: ***"all the slack that human-paced work absorbed, one way or another, becomes, at AI speed, industrial-grade defects"*** (amateur-vs-professional sport metaphor). Hence **three inviolable *gates*** (human control): **specification, planning, delivery review**; validation **by proof** (not by AI's own assertions); **systematic capitalization** (each cycle enriches the next) → measured result: **−30% correction iterations after ~10 cycles**. Principle: ***"the faster the execution, the stricter the framework must be."*** Concepts drawn on: **harness** (agentic rules adapted to context), **vibe-coding** deemed **untenable in the enterprise**. **Third pillar = governance, FinOps & value-driven steering**: **variable and recurring** AI costs (~**€10/hour** per augmented seat), a shift from flat-rate licensing to usage-based billing (a 2010s cloud parallel); **FinOps** does not aim to cut costs but to *"optimize tool efficiency"* (cost weighed against value); aligning **business metrics** upfront (time-to-market, features, performance, eco-design). **Conclusion**: acceleration makes the fundamentals **non-negotiable**; the challenge is **organizational and cultural**, not technological — without securing the business relationship and collective discipline, an AI-boosted SDLC merely **amplifies problems** (hitting the wall faster). Extends the WeNvision doctrine from [[rafal-wenvision-ia-generative-produit-techno-pas-projet-2024-02-23]] and [[rafal-wenvision-tokenomics-foundation-finops-ia-2026-06-04]]; converges with *systems around the model* [[dropbox-okumura-beyond-code-generation-engineering-productivity-ai-agents-2026-05-28]], *harness engineering* [[osmani-agent-harness-engineering-2026-04-19]], agentic Salesforce, and the *agent manager* debate (BFM/Girard, SFEIR).
**Olivier Rafal** · *Consulting Director Strategy* chez **WeNvision** (groupe **SFEIR**). Ancien **rédacteur en chef du *Monde Informatique*** · et auparavant consultant analyste du marché IT (~10 ans). Tribune publiée dans la rubrique *Tribune* de **CIO-Online**. Publié le **1er juin 2026**.
Post from the **Dropbox Tech blog** (*culture* section), published on **May 28, 2026** by **Kazuaki Okumura** (Dropbox, role unspecified in the article), recapping a talk at the **DX Annual 2026** conference (developer productivity). **Pivot thesis**: engineering productivity must move beyond *code generation*. *« Accelerating code generation simply shifted some bottlenecks downstream »* — AI has massively increased code throughput, but *« the faster code moves, the more pressure it puts on review queues, CI systems, validation workflows, release coordination, and production operations »*. The real challenge is no longer writing code faster, but enabling the entire SDLC to **absorb, validate, and ship safely** a much larger volume. **From copilot to agent**: the first wave (code explanation, snippets, Q&A) operated *« as copilots alongside the engineer »*; the agent, by contrast, *« can take a scoped task, inspect the codebase, edit files, run tests, iterate on failures, and return an artifact for human review »* — with the engineer remaining *« accountable for intent, architecture, quality, and release decisions »* (more parallel work, more options, offloading repetitive execution). **Nova** = Dropbox's **internal** coding-agent platform: describe a task in natural language, execution in a controlled environment with codebase context. Canonical datapoint: ***« Nova's value comes less from the model itself than the systems surrounding it »*** (codebase context, internal practices, safe execution, workflow integration, human review); Nova accounts for **~1 in 12 PRs at Dropbox** today (adoption growing), and extends beyond features to **migrations, flaky-test remediation, bug investigation, dependency updates** (high-toil work). **Measuring product velocity, not code output**: *PR throughput*, a useful signal when coding velocity was the constraint, *« was no longer sufficient »*. A **4-stage** measurement model: ***Fuel*** (are AI tools being used?) → ***Adoption*** (how workflows are changing across teams) → ***Output*** (is AI contributing to production work?) → ***Impact*** (*« improving product velocity and reducing the time it takes to move from idea to customer value »*). Quality signals tracked: **code review turnaround time, first-run test pass rate, defect ratio, rework rate**. *« Quality and trust matter as much as speed »* — the core of the shift: *« moving from local activity metrics toward broader system outcomes »*. **Workflows have to evolve too**: this is *« not just a tooling shift »* but a change of **operating model** — the engineer's role shifts toward *« defining intent, mapping problems, reviewing generated changes, and making higher-context architectural and quality decisions »*. **Enablement** is as crucial as the tool itself (hands-on learning, hackathons, workflow spotlights, bootcamps, peer-led examples); adoption proceeds at varying speeds across teams; *« The goal is not to force every workflow through an agent »* — the goal is to make it *« useful, safe, measurable, and repeatable where it creates meaningful leverage »*. **What we learned**: ***« AI doesn't eliminate bottlenecks in software development, but it does move them »*** (downstream: review, validation, testing, release, prod ops) → optimizing the old bottleneck no longer creates the same leverage. *« The advantage will not come from access to the same foundation models everyone else can use. It will come from the systems built around those models: context, internal tooling, quality controls, and the workflows that connect them together. »* Pressure also builds **upstream** (product & design): structured specs, design clarity, sharper problem framing. Closing: ***« The future of engineering productivity will not be defined solely by who has the best models. It will be defined by who builds the best systems around them »***; *« The real challenge is no longer just generating more code, but building engineering systems that can reliably turn AI-assisted output into valuable experiences for our customers »*. Direct convergence with **Salesforce/Tallapragada** (Effective Output: measuring value, not volume; no speed/quality tradeoff), **Gupta** (token-to-outcome attribution, cost of a completed outcome), **DORA** (beyond throughput), and the shift of the KPI toward **system outcome** (idea→customer value).
**Kazuaki Okumura** — Dropbox (rôle non précisé dans l'article ; le billet reprend une intervention présentée à la conférence **DX Annual 2026** sur la productivité développeur, ce qui suggère un profil engineering leadership / platform, sans confirmation). Publié sur le **Dropbox Tech blog** (dropbox.tech) · rubrique *culture* · le **28 mai 2026**.
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).
Continuous Delivery as the non-negotiable foundation of AI-assisted development — Dave Farley, on his channel *Modern Software Engineering*, argues that without CD, AI is not an accelerator but a trap (theory of constraints and Jevons paradox applied to generated code, ATDD/BDD as a safeguard, deployment pipeline as quality arbiter).
#Continuous Delivery#Generative AI in the SDLC#ATDD (Acceptance Test-Driven Development)
Dave Farley (Modern Software Engineering — YouTube channel)
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)
Editorial by Andrew Ng in The Batch n°352 of May 8, 2026 — **"There Will Be No AI Jobpocalypse"** — which dismantles the narrative of mass unemployment caused by AI, drawing on the **healthy 4.3%** US unemployment rate and robust tech hiring. Ng identifies **three drivers** of the jobpocalypse narrative: **(1) tech incentives** — AI labs benefit from presenting themselves as transformative-disruptive (funding rounds, valuations, talent); **(2) pricing power** — vendors charge **$10,000+/year** to enterprise clients by **anchoring their pricing on the salary of the replaced employee**, rather than on traditional SaaS pricing (per seat / per usage); **(3) corporate messaging** — companies reframe their layoffs as *"AI efficiency"* rather than acknowledging the **pandemic-era overhiring** of 2020-2022. Honest acknowledgment: *"AI disrupts work"*. But Ng flips this into **"AI jobapalooza"** (a play on Lollapalooza) — job creation in AI engineering and adjacent fields with evolving skill sets. Implicit tension with **Amodei** (50% of white-collar jobs eliminated by 2030) — Ng points out, without naming him, that **Anthropic benefits from promoting this narrative** (tech incentives). Published **the same day** as **Wallace-Wells's "AI Populism" NYT Magazine** piece: a perfect mirror reading — Ng = cold economic analysis / Wallace-Wells = popular panic. Pricing-power convergence with **Bain's "$100B cross-system labor"** (same thesis: pricing anchored on salaries).
#Andrew Ng#The Batch#DeepLearning.AI
Andrew Ng (fondateur DeepLearning.AI, Stanford, ex-Google Brain, ex-Baidu, ex-Coursera)
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).
GitHub repo `techygarg/lattice` that formalizes a framework of **composable skills** for installing an *engineering discipline* into AI coding assistants (Claude Code, Cursor). Distinctive three-tier architecture: **Atoms** (single-principle guardrails: clean code, DDD, security, test quality, design-first), **Molecules** (multi-step workflows composing the atoms: design, implement, refactor, fix, review), **Refiners** (guided interviews producing project-specific standards that customize the atoms' behavior). Operational pipeline `lattice-init` → `design-blueprint` → `code-forge` → `review`, with `refactor-safely` and `bug-fix` as offshoots. Three pivotal principles: *"Skills over prompts"*, *"Composability over monoliths"*, ***"Living context over static config"*** — the `.lattice/` folder grows smarter with every feature cycle. MIT, pure shell, 18 stars / 52 commits, a series of articles on martinfowler.com explaining five *collaboration patterns*. Strong convergence with Vincent *Superpowers* (2026-04-02), Habert *PROJ-AI* (2026-05-05), Wescale *Usine Logicielle Augmentée* (2026-05-03), and — the highest doctrinal convergence with no declared lineage — **Compound Engineering** by Every (Shipper/Klaassen 2025-12-11): isomorphic pipelines (lattice-init→design-blueprint→code-forge→review ↔ ce:brainstorm→ce:plan→ce:work→ce:review), living context layer (`.lattice/` ↔ `docs/plans/+solutions/+brainstorms/`), a shared design-first stance, mandatory review at the end. 2026 *coding agent harness* doctrine converges on a stable vocabulary, without direct influence.
#lattice#techygarg#composable AI skills
techygarg (auteur GitHub, identité réelle non précisée dans le README ; auteur d'une série d'articles publiée sur martinfowler.com).
Google whitepaper (the "Day 1" installment of a series, by Addy Osmani, Shubham Saboo and Sokratis Kartakis) that maps the transformation of the software development lifecycle (SDLC) in the era of coding agents. Thesis: the fundamental shift is not a new language but the move from writing code to **expressing intent**. The document lays out a spectrum from *vibe coding* (prompting and accepting) to *agentic engineering* (AI implements under constraints, tests, and feedback loops designed by humans), with **context engineering** as the central skill, the **software factory** model (the developer's deliverable = the system that produces the code), **harness engineering** (Agent = Model + Harness), and a CapEx/OpEx economic analysis of total cost of ownership.
Interview with Andrej Karpathy (OpenAI co-founder, former Tesla Autopilot) moving from *vibe coding* to *agentic engineering*: December 2025 as the tipping point — "never felt more behind as a programmer" — the Software 1.0/2.0/3.0 taxonomy, the openclaw example (bash script → text to copy-paste into the agent) and MenuGen rendered obsolete by Gemini's Nanobanana, the *verifiability* theory explaining why LLMs are *jagged* (math/code peak, "walk 50m to the car wash" fails), the distinction between *vibe coding* (raise the floor) and *agentic engineering* (preserve the quality bar), the "animals vs ghosts" metaphor, the overhaul of hiring via agent-versus-agent projects, and the key formula: ***"You can outsource your thinking but you can't outsource your understanding."***
#Andrej Karpathy#vibe coding#agentic engineering
Andrej Karpathy (co-fondateur OpenAI, ex-Tesla Autopilot, créateur du terme "vibe coding")
Manifesto-style X thread by Rohit (@rohit4verse) laying out the *2026 AI engineer roadmap*: a $150k gap between prompt engineer and systems architect, the end of *generic wrappers* "sherlocked by big tech", and 5 portfolio projects ranked by complexity level (offline mobile SLM, self-improving coding agent, multimodal *Cursor for video editors*, privacy-first personal life OS agent, autonomous enterprise workflow agent). Each project describes its *key architectural decisions* (lazy loading, sliding window, sandboxing, scene detection, personal knowledge graph, event-driven multi-agent, audit trail, RBAC, observability). Structuring slogan: *"the replaceable: building wrappers / the unfireable: shipping autonomous systems"*. Injunctive, viral tone typical of X in 2026.
#2026 AI engineer roadmap#Rohit#rohit4verse
Rohit (@rohit4verse) — créateur de contenu IA sur X · vulgarisateur d'architecture et roadmaps de carrière en ingénierie IA.
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**.
Editorial by Andrew Ng in The Batch #350 laying out an **acceleration hierarchy driven by coding agents** by type of software work: **Frontend (max) > Backend (moderate) > Infrastructure (low) > Research (minimal)**. The rationale rests on implicit *verifiability* (fluency in TypeScript/JavaScript + an autonomous agent–browser loop on the frontend) and on the LLMs' blind spots (corner cases / security / DB migrations for the backend, opaque network tradeoffs for infra, irreducible hypothesis formation for research). The issue also covers 4 structuring news items: **GLM-5.1 (Z.ai)**, a 754B/40B-active-parameter MIT-licensed model capable of 8-hour autonomous tasks (SWE-Bench Pro leader at 58.4%); **Digit (Agility Robotics) at Schaeffler**, the first industrial deployment of humanoids (5'9"/143lb, $10–25/h vs $20/h for a human); the **anti-data-center revolt** (~$64B in projects blocked May-2024 / March-2025, Maine moratorium on 20MW+, a molotov cocktail at Sam Altman's home); and the **"assistant axis"** (Christina Lu, MATS / Oxford / Anthropic), which reduces persona drift and jailbreaks (Qwen3 32B: 83%→41%; Llama 3.3 70B: 65%→33%) without degrading IFEval/GSM8k/MMLU-Pro/EQ-Bench.
#Andrew Ng#The Batch#DeepLearning.AI
Andrew Ng (édito principal — fondateur DeepLearning.AI, Stanford, ex-Google Brain, ex-Baidu) ; rédaction The Batch (DeepLearning.AI) pour les sections actualités
Synthesis by Addy Osmani (Google, Chrome/Cloud) of the emerging field of *harness engineering*: the equation `agent = model + harness`, the *ratchet* principle ("every mistake becomes a rule"), the HumanLayer "skill issue" reframe, Terminal Bench evidence (Top 30 → Top 5 from a harness change alone), the layered Claude Code architecture, Anthropic's "harnesses don't shrink, they move" vision, and Harness-as-a-Service (Claude Agent SDK, Codex SDK, OpenAI Agents SDK). Pivot article that consolidates Trivedy, HumanLayer, Anthropic, and Böckeler into a doctrine.
#harness engineering#agent harness#Addy Osmani
Addy Osmani (Software Engineer at Google, Cloud + Gemini)
Public update from Darragh Curran (R&D, Intercom) nine months after his commitment to double R&D productivity in 12 months through AI. Result: **3x achieved in 16 months, with no signs of plateauing**. Quantified data from a 500-person R&D organization / 8.5M lines of code: **93.6% of PRs are agent-driven**, **19.2% AI-approved** (target >50%), cost/PR **-50%**, defect backlog **-54%**, time-to-shipping **-39%**, breaking-changes downtime **-35%**, top 5% of performers at **6x the median PR throughput**, **497 autonomous PRs** in the first 4 weeks, **153 contributors / 267 specialized skills** in a private *Skills-Based Plugin Architecture*. Curran declares ***"All technical work is becoming agent-first. This is the top priority for R&D."*** Pivotal article of the *agent-first organization* dossier, comparable only to Stripe Minions and StrongDM in the 2026 corpus.
#Darragh Curran#Intercom#Fin Ideas
Darragh Curran (R&D leader, Intercom — publication via Fin Ideas, plateforme média Intercom).
Anthropic Interviewer - AI Workforce Study - Professional Perspectives - AI Adoption - Workplace Transformation - Creative Industries - Scientific Research - Economic Impact - Sociological Research - AI Sentiment Analysis
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)
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