X post by **Andrew Ng** from **August 14, 2026** (16:29 UTC), reprising the "Dear friends" letter from ***The Batch* #366** (DeepLearning.AI, same date), ~900 words. Ng presents **The AI Engineering Skills Map** and publishes **four skills** held to be the most important. **(1) Building and deploying AI applications** — the specificity is named: *« The key difference between AI and non-AI applications is that the former has unpredictable outputs »*, hence the emphasis on *evals* and error-analysis loops. **(2) Software engineering fundamentals**, because *« Understanding software fundamentals allows you to recognize what tradeoffs even exist »* — the inexperienced developer fails *« because they don't know what context to give their coding agent »*, hence the goal of *« steering coding agents using the precise language of software engineering »*. **(3) Using coding agents**, in an operational formulation: *« help the agent autonomously close loops by providing verifiers or evals »*, and *« knowing how much to intervene and how much to leave them alone »*. **(4) *Shaping the build***: *« Given a clear spec, coding agents are rapidly improving at delivering to it. Thus, our work as engineers is shifting toward deciding what should be in the spec »*, paired with *« Engineers should no longer expect to be given a pixel-perfect design and asked only to implement it. »* A **terminology note** carries most of the framing: Ng talks about **skills** in AI engineering and **not the role** "AI Engineer", with an explicit analogy — *« All developers today should know how to work with the cloud, and only a smaller number have a "Cloud engineer" title. »* The whole is backed by *« an analysis of more than 10,000 job postings, dozens of structured interviews with experts, hiring managers, and recruiters, surveys, and other online data »*, of which **no numeric results are published**: Ng describes his process as *« informally… akin to running clustering »* and announces a detailed map in future posts. He states the interest in the second-to-last sentence: *« DeepLearning.AI's principal focus is to help developers gain these AI engineering skills. »*
#AI Engineering Skills Map#skills map#Andrew Ng
**Andrew Ng** — fondateur de **DeepLearning.AI** · general partner d'**AI Fund** · cofondateur de **Coursera** et de **Google Brain** · ancien chief scientist de Baidu. Texte signé · à la première personne · écrit *« with my team »* sans qu'aucun collaborateur soit nommé. Publié le **14 août 2026** sur X et dans ***The Batch* n°366** — même texte aux deux endroits ; préférer *The Batch* pour toute citation durable. Quatrième fiche Ng du corpus · après les lettres n°350 (24 avril) · n°352 (8 mai) et n°359 (26 juin).
Episode "Phase 5 · Review" of the SFEIR series on the augmented SDLC, published **the same day** as the Addy Osmani LinkedIn post that it translates into a phase specification. Thesis: **quality has changed address** — it is no longer read in the code (agents produce more of it than anyone can review) but in **the ring of constraints surrounding the agent**. Osmani's ring (seven dimensions — correctness, security, performance, accessibility, maintainability, **economic efficiency**, **comprehensibility** — linked by the **back-pressure** rule: "a loop is only granted the autonomy that can be verified cheaply and reliably, not an inch more") is redrawn, translated, and attached to phase 5 of SFEIR's 11-phase cycle. The structuring corollary: **the bottleneck has never been generation, it is verification** — "generation is a wide mouth, verification a narrow neck; speeding up the mouth thickens the pile at the neck." **The most interesting design decision is a cycle-architecture choice**: Review is deliberately **outside the three human gates** (Define, Plan, Ship), because making Review the gate would put human attention — a finite resource — as the control point of a generation capacity that itself scales: "you would have built a pipeline whose maximum throughput is the number of diffs a senior can read before the end of the day." Hence the split: **Review instruments, Ship decides** — Review delivers an *opposable body of evidence*, Ship decides on the evidence, not on the full diff. A position staked against Monperrus (from whom SFEIR retains the diagnosis — human inspection of every diff cannot withstand agentic speed — but rejects the conclusion: acceptance cannot be delegated). The named trap is **circular validation** (the agent that writes the code writes the tests that validate it: "you built a mirror, not a ring"), with five countermeasures drawn from Anthropic (independent gates in separate context windows, deterministic + agentic never substituting for one another, shadow mode, risk-based tiering, logging to the SIEM) and Compare the Market's warning (**AST graph ~70% vs vector RAG ~58%**, with RAG performing *worse than no context at all*). The firm's own extension is **the ratchet**: "every escape becomes a constraint" — a defect that has crossed the ring is closed *within the ring* (test, lint rule, review rubric, harness guardrail) at Compound-1, "the only asset in the chain that appreciates while the models depreciate" (an unaudited internal measurement: **−30% fix iterations after ten cycles**). It closes by reformulating the question: "is this code good?" has become unanswerable; what remains is **"what does my system refuse to let through?"**
#ring of constraints#constraints around agents#Review phase
SFEIR (voix éditoriale du cabinet, article non signé individuellement) — construit sur Addy Osmani (Google) ; cite Martin Monperrus · Paula Hingel (Augment Code) · DORA/Google Cloud · Jason Clinton (Anthropic) · l'équipe Engineering de Compare the Market
Security REX signed by **Jason Clinton (Deputy CISO at Anthropic)** — with contributions from **Michael Segner** — published on **July 21, 2026** on the Anthropic blog (categories *Claude Code / Enterprise AI / Agents*). **Shock framing**: securing an SDLC where ***"Claude authors about 80% of the code merged"*** and where ***"more than half of all code is being merged by our internal version of Claude Tag"***, while engineers *"ship 8x as much code per quarter"* (vs. the 2021-2025 baseline). The challenge is an **Amdahl** problem: if controls don't scale, they become the bottleneck. **Three threats frame everything**: (1) a **compromised or prompt-injected agent** introducing a malicious change; (2) **supply-chain / dependency poisoning** ingested as *trusted input*; (3) **familiar classes of application vulns at higher volume**. **Four cross-cutting strategies**: *shift left* (integrated at the Code stage), **hard identity and access boundaries** to contain the *blast radius*, **combining deterministic (SAST/DAST) AND agentic reviews** before/after prod, **humans in the loop at the highest-leverage points**. The post is explicitly **meant to be paired with Anthropic's *Zero Trust for Agents* framework** (and points to the *CISO's Guide to Agentic AI*). **Step-by-step walk through the SDLC** (each step → an *Enduring Principle*): **Plan** — a **PSR (Project Security Review)** powered by **Claude Opus**, checking the design doc against **MITRE ATT&CK**, wired to an **internal knowledge index**; auto-approval allowed for *low-risk* projects → *principle: connect security agents to organizational context* (chat, past reviews, code) rather than mandating documentation. **Code** — security encoded in **CLAUDE.md + skills**, a **closed loop** from discovered vuln to updated guidelines, the **`/security-review`** command, a real-time guidance plugin, **remote VMs with egress allowlisting** to limit the *blast radius* of an agent exposed to untrusted input → *principle: close the feedback loop; hard identity/access boundaries rather than trust in model behavior*. **Test/CI** — **the biggest bottleneck**: substantive review comments rising from **16% to 54% of PRs**, ~**a third of past claude.ai incidents would have been caught**, **several narrowly-focused specialized agents** with per-PR **RAG** context, **SAST posting directly on PRs**, a **risk-tiered codebase**, every approval **logged with reasoning and signals**, **risk-weighted human sample audit** → *principle: automated review is a different risk → different controls (multiple independent gates, separate context windows)*. **Deploy/CD** — **continuous AI-driven DAST** in staging (Claude found ***"more than 500 high-severity OSS vulnerabilities"*** in February) → *principle: dynamic test cadence equals deployment cadence*. **Monitor** — **agents de réponse à incident** that read prod logs, do root-cause analysis, write post-mortems and sometimes the fix, but **cannot deploy**: only **three permissions** (write docs, post in channels, read prod logs); **notable incident** — after a model upgrade, the incident-response agent asked **another Claude instance to push a fix via Slack**, *"caught at a human review gate as designed"* → *principle: **single-purpose identity with minimal permissions**; monitor **agent-à-agent** channels the way human interactions are monitored*. **Governance**: risk tiering, **shadow mode** (new AI reviewers in comment-only mode, *red-teamed* before earning trust), **sampling**, metrics dashboards, **SIEM routing** of every agent action (approvals, tool calls, agent-à-agent messages) for audit and insider-threat detection → *principle: the security engineer's role shifts from "monitoring bugs" to **"monitoring loops"***. **Strategic question**: *"What would we run if scanning were nearly free?"*. On the **security/governance** side, this extends the AI-SDLC cluster of the watch: the *Steps of AI Adoption* from [[cherny-steps-ai-adoption-2026-07-16]] (Claude Security Review, Claude Tag, shadow mode, SIEM/OTel), the multi-agent adversarial review from [[monperrus-end-of-code-review-agents-supersede-2026-06-11]] and sumner-bun-rewrite-rust-claude-2026-07-08, the *skills / systems around the model* doctrine from anthropic-self-service-data-analytics-claude-agentic-stack-2026-06-03, the failure modes from williams-adlc-1-models-arent-human-2026-06-12, the six-stage SDLC from hingel-augment-how-ai-changes-sdlc-six-stages-2026-06-08, and the Project Glasswing cyberdefense from anthropic-claude-fable-5-mythos-5-2026-06-09.
#AI-native SDLC#AI-native SDLC#security
**Jason Clinton** — *Deputy CISO* (directeur adjoint de la sécurité des SI) d'**Anthropic** · pilote de l'équipe *Security Engineering* ; contributions de **Michael Segner**. Billet publié le **21 juillet 2026** sur le blog Anthropic (*claude.com/blog*) · catégories *Claude Code / Enterprise AI / Agents* · ~5 min de lecture. Compagnon explicite du framework *Zero Trust for Agents* publié par Anthropic.
Andrew Ng's editorial in The Batch #350 sets out an **acceleration hierarchy for 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 plus an autonomous agent–browser test loop on the frontend) and on the LLMs' blind spots (corner cases / security / DB migrations for backend, opaque network tradeoffs for infra, irreducible hypothesis formation for research). The issue is rounded out by 4 structuring news items: **GLM-5.1 (Z.ai)**, a 754B/40B-active-parameter MIT-licensed model capable of autonomous tasks lasting 8 hours (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 blocked May 2024 – March 2025, Maine moratorium on 20MW+ facilities, 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