Analytical tech-watch notes on AI, coding agents and the evolution of the software development lifecycle (SDLC) — a public, agent-readable knowledge base.
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.
#Unknowns#map vs territory#known/unknown knowns#unknown unknowns#blindspot pass
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."