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.
In-depth opinion piece published on **sfeir.com** on June 24, 2026, authored by **Didier Girard** (Managing Director, SFEIR). **Central thesis**: in 2024 everyone was betting on **AI4Business** (AI in business processes) as the great reservoir of value; by 2026, the assessment has **flipped** — it is **AI4IT** (AI for producing the information system: code, SDLC, software factory) that creates **measurable** value. The article *grounds* this thesis in the firm's watch: AI4Business disappointment (MIT study "95% of pilots without ROI," contested but revealing; **organizational** blockage / Mollick's Hayekian problem) vs. quantified AI4IT evidence (Salesforce, Intercom, Raiffeisen, AWS/Bedrock, Atlassian, DORA). Mechanistic explanation: **code verifies itself** (compilation, tests, CI) whereas business processes have neither a compiler nor an immediate feedback loop. **2027 budget consequence**: a **CapEx→OpEx** shift, token pricing dynamics (the ceiling rising — Fable 5 at 2× Opus — vs. inference ÷280 and downward pressure from open weights/desktop), and **AI FinOps** driven by **cost per outcome**. Closes with **4 COMEX recommendations**.
#AI4IT#AI4Business#reversal
**Didier Girard** — Managing Director (CTO / DG) de **SFEIR** · ESN française (~1 000 personnes, France · Belgique · Luxembourg · Suisse). Auteur de l'article ; voix éditoriale du cabinet sur la transformation IA des DSI.
Benchmark announcement from **Artificial Analysis** (independent AI model evaluation platform, via X/Twitter + model page): **GLM-5.2** by **Z.ai** (Zhipu AI, @Zai_org) becomes **the leading open weights model** and climbs to **#3 in the overall ranking** of **GDPval-AA**, a real-world benchmark for *economically valuable knowledge work* (long-horizon, multi-turn, agentic tasks). GLM-5.2 scores **1524 Elo**, behind only **Claude Fable 5 (1783)** and **Claude Opus 4.8 (1615)**, and on par with **GPT-5.5 (xhigh, 1509)**. It leads the next open model (**MiniMax-M3, 1408**) by a wide margin, as well as numerous proprietary models: **Gemini 3.5 Flash (1357)**, **Qwen 3.7 Max (1289)**, **Muse Spark (1158)**. The tasks are genuinely agentic: **~31 turns per task** on average across **1,999 matches**. The same hierarchy holds on the **Artificial Analysis Intelligence Index** (1st among open weights), the **Agentic Index** (#3), and **AA-Briefcase** (#3, ahead of GPT-5.5 xhigh, behind Fable 5). Key highlight: an **open weights** model under **MIT license**, **MoE with 753B parameters / 40B active**, **1M token** context, priced at **$1.40/$4.40 per 1M tokens** input/output, rivals the proprietary frontier on agentic work — a real step for open models.
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 »)
LinkedIn post by Fred Plais (CEO of Archie, ex-Platform.sh): AI made engineers so fast that the **bottleneck moved upstream**, to a place nobody is watching. With execution no longer the slow part, the thinking time that used to exist "while the code was being built" has vanished — the right vision now has to be formed and the right decisions made in a fraction of the time. Two rare profiles are emerging: the one who can **articulate a vision precise enough** for an agent to execute without derailing, and the one who knows how to **orchestrate agents** (anticipating their failures, chaining them, catching an error before it propagates). Hiring for "code output" is becoming obsolete: that is precisely what has stopped being rare. Final thesis: "thinking clearly was always the job — speed just made it impossible to fake".
SFEIR article (in French) that formalizes an **AI-driven SDLC in 11 phases (0 to 10)** and argues that the industry is converging on it. Starting observation: in 2025, organizations added AI tools without transforming their operating model — hence a paradox of "everything changes… and nothing changes" (execution speed multiplies without a proportional gain). The real answer is not a choice of tools but a **redesign of the cycle** for machine-led execution. The SFEIR cycle rests on **three immovable human gates** (Define, Plan, Ship), automatic phases between them, and **two compounding moments** (Compound-1 pre-deployment, Compound-2 in production) that turn lessons into reusable rules. Three principles: **AI executes** (complete artifacts + proof of execution, never trusting the agent's claims), **the human retains control of intent**, and **the system learns cumulatively**. Measured results (a redesign from 6 months to 1 day, **−30% of iterations** after ten cycles) and claimed convergence with ADLC, Google, and DORA 2025.
**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.
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.
Educational article by the **MindStudio Team** (blog of the MindStudio platform, multi-model workflow orchestration) explaining **modèles de langage par diffusion** (*Diffusion Language Models*) through the case of **Diffusion Gemma**, Google's first **open weights** implementation (2B parameters, derived from Gemma 2). The thesis: whereas **autoregressive** models (GPT-4, Claude, standard Gemma) generate text **token by token, left to right** (causal attention, each token fixed once produced), **diffusion** models start from a **masked/noised** sequence and **refine it iteratively** (masked diffusion / *absorbing diffusion*), with **bidirectional attention**: the model can **revise any position at any step**. Consequences: high **parallelism** (a 500-token text would require 50-100 denoising steps instead of 500 sequential passes), natural **infilling** and **constrained generation** (template filling, code completion with surrounding context), and built-in **revision** capability. But at the current scale (2B), Diffusion Gemma **does not match** the large autoregressive models (GPT-4o, Gemini 1.5 Pro) on reasoning, instruction-following, and general knowledge: the gap is "closing" without being closed. The inspiration comes from image generation (Stable Diffusion, DALL-E left autoregression behind years ago); whether the same principle holds for text remains an open question. Diffusion Gemma is distributed on Hugging Face (Google DeepMind), AI Studio, and Vertex AI.
#modèles de langage par diffusion#Diffusion Gemma#Google DeepMind
Satya Nadella (Microsoft) theorizes "the future of the firm" in an AI-driven economy: every company will need to build, alongside its human capital (judgment, relationships, pattern recognition), a "token capital" — its proprietary AI capability. The real value lies not in choosing the best model but in a learning loop (private evals, RL environments, base de connaissances) that encodes institutional knowledge and compounds over time. An argument for a "frontier ecosystem," not merely a "frontier model," so that value diffuses rather than being captured by a handful of models.
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.
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.