# thekb.eu — Tech watch on AI, coding agents & the SDLC

> Knowledge base of analytical "fiches de veille": 327 fiches, knowledge graph of 2653 entities / 4658 triples. Default language: English (source: French). Every HTML page has a Markdown twin at {url}.md.

> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Free to quote, reuse and train on — attribution to theKB.eu (with a link) is required.

## Recent fiches
- [A Field Guide to Fable: Finding Your Unknowns](https://www.thekb.eu/en/fiches/thariq-field-guide-fable-finding-unknowns-2026-07-03.md): 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.
- [Fable's judgement](https://www.thekb.eu/en/fiches/willison-fable-judgement-delegation-subagents-2026-07-03.md): 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.
- [The Compounding Knowledge Lifecycle — Agent Guide](https://www.thekb.eu/en/fiches/klaassen-thinkroom-compounding-knowledge-lifecycle-2026-07-02.md): 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.
- [3 Key Product Development Loops (The Batch, Issue 359 — « Dear friends » letter)](https://www.thekb.eu/en/fiches/ng-thebatch-359-3-product-development-loops-2026-06-26.md): 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.
- [AI4IT vs AI4Business : le renversement, et ce qu'il fait à vos budgets 2027](https://www.thekb.eu/en/fiches/girard-sfeir-ai4it-vs-ai4business-budgets-2027-2026-06-24.md): 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**.
- [GLM-5.2 leads open weights models and sits at #3 overall on GDPval-AA, a real-world agentic work benchmark](https://www.thekb.eu/en/fiches/artificial-analysis-glm-5-2-gdpval-aa-open-weights-2026-06-22.md): 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.
- [Loop Engineering for Product Managers](https://www.thekb.eu/en/fiches/saboo-loop-engineering-product-managers-2026-06-21.md): 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."
- [Comment l'IA agentique bouscule les Grands Groupes ? Partie 2/2 #DevSummit](https://www.thekb.eu/en/fiches/alafrench-grymonprez-adeo-ia-agentique-grands-groupes-2026-06-18.md): 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.
- [AI made your engineers fast. Too fast to leave room for the rest of the org to think.](https://www.thekb.eu/en/fiches/plais-ai-engineers-fast-bottleneck-upstream-2026-06-17.md): 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".
- [Un SDLC piloté par l'IA : le cycle SFEIR à 11 phases (et pourquoi l'industrie y converge)](https://www.thekb.eu/en/fiches/sfeir-sdlc-ia-cycle-11-phases-2026-06-16.md): 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.
- [grill-with-docs — « Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise »](https://www.thekb.eu/en/fiches/skill-pocock-grill-with-docs-2026-06.md): **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.
- [How Cornell Recovered $100,000 in Unidentified Payments With AI](https://www.thekb.eu/en/fiches/cornell-ai-hub-100k-unidentified-payments-2026-06-15.md): 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."*
- [Diffusion Language Models Explained: How Google's Diffusion Gemma Works](https://www.thekb.eu/en/fiches/mindstudio-diffusion-language-models-gemma-2026-06-12.md): 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.
- [A frontier without an ecosystem is not stable](https://www.thekb.eu/en/fiches/nadella-frontier-ecosystem-human-token-capital-2026-06-12.md): 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.
- [Anthropic's War on Opensource AI](https://www.thekb.eu/en/fiches/osman-anthropic-war-on-opensource-ai-2026-06-12.md): 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.

## Topics
- [AI Coding Agents & Skills](https://www.thekb.eu/en/topics/coding-agents-skills/): Coding agents, skills, prompting craft and agent-native workflows.
- [Architecture & Construction](https://www.thekb.eu/en/topics/architecture-construction/): Software architecture and how systems get built in the AI era.
- [Transformation & Adoption](https://www.thekb.eu/en/topics/transformation-adoption/): How teams and organizations adopt AI-assisted development.
- [Quality & Security](https://www.thekb.eu/en/topics/quality-security/): Testing, review, reliability and security of AI-produced software.
- [Economy & Market](https://www.thekb.eu/en/topics/economy-market/): Economics, pricing, market moves and the business of AI tooling.
- [Philosophy & Society](https://www.thekb.eu/en/topics/philosophy-society/): Philosophy, cognition and societal effects of AI.
- [Strategy & Frameworks](https://www.thekb.eu/en/topics/strategy-frameworks/): Strategic frameworks and doctrines for the AI transition.
- [Tools & Platforms](https://www.thekb.eu/en/topics/tools-platforms/): Tools, platforms and infrastructure of the agent ecosystem.
- [Research & Education](https://www.thekb.eu/en/topics/research-education/): Research findings and how we learn to work with AI.
- [Products & Services](https://www.thekb.eu/en/topics/products-services/): Products and services reshaped by AI capabilities.
- [Policy & Regulation](https://www.thekb.eu/en/topics/policy-regulation/): Policy, regulation and governance of AI systems.

## Glossary (key concepts)
- [ACE](https://www.thekb.eu/en/glossary/ace/): ACE — an agent context-engineering method reported to improve agent accuracy by about 10.6% and finance-task accuracy by 8.6% while cutting latency by roughly 87%. It illustrates how structuring the context supplied to an agent, rather than changing the model, can lift measured performance.
- [ADLC](https://www.thekb.eu/en/glossary/adlc/): Agentic Development Lifecycle — a proposed software lifecycle designed around the properties and failure modes of AI models rather than human teams. Each phase maps to a defended failure mode or an exploited model property, structured as phases and verification gates. Cited as a convergent framework reached independently by several practitioners.
- [AEO](https://www.thekb.eu/en/glossary/aeo/): Answer Engine Optimization — the practice of structuring content so AI answer engines cite it when responding to queries. It adapts search-optimization thinking to a world where users read a generated answer rather than a list of links, prioritizing citable, well-attributed statements over keyword ranking alone.
- [Agentique adaptative](https://www.thekb.eu/en/glossary/agentique-adaptative/): An architectural approach for putting agentic AI into production, organized around four pillars. It addresses how autonomous agents are structured, supervised, and adapted so they keep working reliably under real operating conditions — with edge cases, drift, and load — rather than only in controlled demonstrations.
- [agents parallèles](https://www.thekb.eu/en/glossary/agents-paralleles/): A working pattern in which many agent instances run at once on the same effort — reported at up to 16 simultaneous agents across roughly 2,000 coding sessions. Running agents in parallel raises throughput and lets independent sub-tasks proceed together, at the cost of coordination and oversight.
- [AGI](https://www.thekb.eu/en/glossary/agi/): Artificial General Intelligence — a system able to match or exceed human capability across most cognitive work rather than one narrow task. Framed by leading AI companies as their stated goal, often defined by benchmarks such as PhD-level research or self-improvement. Whether current approaches can reach it is contested.
- [AI-Assisted Engineering](https://www.thekb.eu/en/glossary/ai-assisted-engineering/): The methodical integration of AI into a mature software development lifecycle, aimed at code that stays secure, scalable, and maintainable. It positions AI as one disciplined stage within established engineering practice rather than a replacement for review, testing, and design.
- [AI brain fry](https://www.thekb.eu/en/glossary/ai-brain-fry/): Mental fatigue from excessive use or oversight of AI tools beyond one's cognitive capacity. Reported symptoms include a buzzing sensation, mental fog, and slower decision-making. The term names a human cost of continuous agent supervision, distinct from the productivity gains usually emphasized.
- [AI slop](https://www.thekb.eu/en/glossary/ai-slop/): Low-effort AI-generated output produced without real understanding or review — in software, code that compiles and may pass tests yet degrades a codebase's clarity and long-term quality. The term is pejorative, marking the gap between genuine engineering and volume generation, and warns against accepting agent output uncritically.
- [AI4Ops](https://www.thekb.eu/en/glossary/ai4ops/): The application of AI to IT operations, oriented toward autonomous operation of infrastructure and services. It extends the automation of monitoring, incident response, and remediation, so that operational tasks are increasingly handled by agents rather than triggered manually by an on-call engineer.
- [approche spec-driven IA](https://www.thekb.eu/en/glossary/approche-spec-driven-ia/): A spec-driven method for AI-assisted development structured in stages: onboarding, atomic planning, iterative development, and capitalization. Work is anchored on an explicit specification the agent follows, so intent is fixed up front and progress accumulates into reusable knowledge rather than one-off output.
- [augmented coding](https://www.thekb.eu/en/glossary/augmented-coding/): AI-assisted coding that keeps quality, testing, and coverage as first-order priorities. It contrasts with looser styles that accept generated output uncritically: the developer stays responsible for correctness, using the agent to move faster without ever lowering the bar on verification and review.
- [Augmented Craftsman](https://www.thekb.eu/en/glossary/augmented-craftsman/): A developer augmented by AI who nonetheless stays in the code — reviewing, shaping, and owning the result rather than delegating it wholesale. The term marks a stance that keeps human craft and judgment central even as agents handle more of the mechanical work.
- [BMAD](https://www.thekb.eu/en/glossary/bmad/): BMAD (Breakthrough Method for Agile AI-Driven Development) — an agile methodology for AI-assisted software work, described through the metaphor of an urban plan for agentic AI. It structures how agents and humans collaborate across a project so that autonomous work stays coordinated and directed rather than ad hoc.
- [Boucle de codage agentique](https://www.thekb.eu/en/glossary/boucle-de-codage-agentique/): The short, minutes-scale loop in which a coding agent works against a product spec and evaluation set: it acts, its output is checked, and it iterates. Treating this loop — rather than a single prompt — as the unit of work is central to reliable agent-driven development.
- [commerce agentique](https://www.thekb.eu/en/glossary/commerce-agentique/): Commerce conducted by AI agents acting on behalf of consumers — searching, comparing, and completing purchases with limited human involvement. Described as an emerging and accelerating category, it shifts the buyer from a person browsing to an agent transacting, reshaping how products are discovered, priced, and sold online.
- [Compaction](https://www.thekb.eu/en/glossary/compaction/): The automatic summarization of a conversation that replaces accumulated history with a condensed version, freeing context space while preserving intent. It is a common countermeasure to context rot, letting long agent sessions continue without the earlier turns crowding out what still matters.
- [Compound Engineering](https://www.thekb.eu/en/glossary/compound-engineering/): An engineering practice in which each unit of work also improves the system that produces future work: lessons, tests, prompts, and tooling are captured as durable artifacts so quality and speed compound over time instead of resetting with each task. Discussed mainly in the context of AI-assisted software development.
- [Compounding Knowledge Lifecycle](https://www.thekb.eu/en/glossary/compounding-knowledge-lifecycle/): A cycle of capture, storage, retrieval, and refresh that makes organizational knowledge composable over time. Each pass adds durable, reusable artifacts so that later work builds on earlier work instead of restarting, echoing the compounding logic applied to teams and engineering.
- [compounding teams](https://www.thekb.eu/en/glossary/compounding-teams/): Teams that no longer write code directly but build recursive frameworks around models — tooling, prompts, and processes that make each future task cheaper and better. The label captures a shift in where engineering effort goes: into the system that produces work, not the work itself.
- [context engineering](https://www.thekb.eu/en/glossary/context-engineering/): The discipline of deliberately assembling, structuring, and pruning the information given to a language model — instructions, retrieved documents, code, and history — so the limited context window carries exactly what a task needs. Treated as a first-class engineering concern, distinct from the wording of a single prompt.
- [Context Flywheel](https://www.thekb.eu/en/glossary/context-flywheel/): The compounding effect by which iteratively curated context improves each successive agent task: better context yields better output, which in turn enriches the context for the next task. It frames context as an asset that accrues value rather than input assembled fresh each time.
- [Context Rot](https://www.thekb.eu/en/glossary/context-rot/): The gradual degradation of a model's output quality as its context window fills with accumulated, partly irrelevant history: earlier instructions get diluted, contradictions creep in, and attention spreads thin. Motivates practices such as compaction, summarization, and starting fresh sessions to keep context dense and relevant.
- [cycle SFEIR à 11 phases](https://www.thekb.eu/en/glossary/cycle-sfeir-a-11-phases/): An AI-driven software development lifecycle organized in eleven phases, numbered 0 to 10, with three human gates and two capitalization points. It formalizes where humans intervene in an otherwise agent-run process, and where knowledge is captured for reuse across future projects.
- [dette technique](https://www.thekb.eu/en/glossary/dette-technique/): The accumulated cost of expedient code and design choices that must later be reworked — interest paid in slower change and more defects. In AI-assisted development it recurs as a specific risk: agents can generate large volumes of code that passes tests yet erodes long-term quality when generation outpaces review.
- [DICE](https://www.thekb.eu/en/glossary/dice/): DICE (Domain-Integrated Context Engineering) — an extension of context engineering that folds an explicit domain model into how inputs and outputs are structured for a language model. By encoding domain rules into the context, it aims to make agent behavior more predictable on specialized tasks.
- [Floating platform](https://www.thekb.eu/en/glossary/floating-platform/): A platform-evolution strategy: as the underlying platform absorbs a capability, the now-redundant custom pieces are discarded and the build point is raised to the next layer of value. It keeps a product riding above commoditized foundations instead of maintaining what the platform already provides.
- [framework 6 étapes](https://www.thekb.eu/en/glossary/framework-6-etapes/): A six-step framework for building AI-assisted solutions: define, design a standard operating procedure, build an MVP, connect, test, and deploy. It gives a repeatable path from intent to a running system, keeping design and verification explicit at each step rather than improvised.
- [GDPval](https://www.thekb.eu/en/glossary/gdpval/): A benchmark measuring AI models on expert-level professional tasks across fields such as finance, law, retail, and software, graded blind by specialists with years of experience. Reported scores reach 40-49% of expert level but require extensive human framing, so the metric itself is seen as under-specified.
- [git worktrees](https://www.thekb.eu/en/glossary/git-worktrees/): A Git feature that checks out several branches into separate working directories from one repository, used to isolate parallel tasks on the same codebase. It lets multiple agents or experiments run side by side without their changes colliding, then merge back independently.
- [Goût développeur](https://www.thekb.eu/en/glossary/gout-developpeur/): A developer's judgment about what a good solution looks like before writing it, together with the discipline to pursue that standard. In AI-assisted work it gains weight: when generation is cheap, knowing which output is worth keeping becomes the scarce, human contribution.
- [grill-with-docs](https://www.thekb.eu/en/glossary/grill-with-docs/): An engineering skill for upstream design, DDD-flavored, that works by a sequential interview guided by four principles — interview, precision of language, evidence, and iteration. It draws out requirements and constraints before code is written, so design decisions rest on stated evidence rather than assumption.
- [Harness](https://www.thekb.eu/en/glossary/harness/): The scaffolding layer around a language model that turns it into a working agent — tools, prompts, memory, execution environment, and safety checks. A common formulation holds that the model is a small part of an agent's practical capability and the harness the larger part, making it a distinct engineering object.
- [Harness engineering](https://www.thekb.eu/en/glossary/harness-engineering/): The building and tuning of the scaffolding around a language model that turns it into a working agent: tools, file access, execution environment, feedback loops, and safety checks. The harness — not the raw model — determines much of an agent's practical capability, making it a distinct engineering activity.
- [infrastructure contexte codifié](https://www.thekb.eu/en/glossary/infrastructure-contexte-codifie/): A three-tier architecture for persistent memory across AI agents, letting context outlive a single session. Reported use spans 283 sessions, 2,801 prompts, and over 16,000 autonomous turns — evidence that durable, codified context can sustain long-running agent work rather than resetting each time.
- [Kishōtenketsu](https://www.thekb.eu/en/glossary/kishotenketsu/): A four-part narrative structure from East Asian tradition — introduction, development, twist, and reconciliation — that builds without relying on conflict. It is invoked as a model for structuring explanation or design, offering an alternative to problem-solution framing when presenting ideas.
- [loi de Goodhart](https://www.thekb.eu/en/glossary/loi-de-goodhart/): Goodhart's law — when a measure becomes a target, it ceases to be a good measure, because optimizing the metric distorts the behavior it was meant to track. It is a caution for AI development: benchmarks and productivity metrics can be gamed once teams steer directly at them.
- [Loop Engineering](https://www.thekb.eu/en/glossary/loop-engineering/): The design and tuning of the iterative cycle an autonomous coding agent runs — plan, act, observe, correct — including how results are fed back, when the loop terminates, and how errors are recovered. The loop, rather than any single prompt, is treated as the core unit to engineer.
- [Orchestration d'agents](https://www.thekb.eu/en/glossary/orchestration-d-agents/): The practice of directing multiple AI agents through a task: chaining them, tracking their state, and recovering when one fails. It requires knowing agents' failure modes to compose them reliably. Practitioners note the open-source layer for registry, lifecycle, permissions, and skills is still largely missing.
- [orchestration multi-agents](https://www.thekb.eu/en/glossary/orchestration-multi-agents/): The coordination of several specialized agents working toward one goal — routing tasks between them, managing shared state, recovering from errors, and running independent work in parallel. Orchestration gains specialization and throughput beyond a single agent, at the cost of added coordination logic. It recurs in complex agent-native workflows.
- [Outcome-based pricing](https://www.thekb.eu/en/glossary/outcome-based-pricing/): A pricing model in which software is billed for the work or results it delivers rather than for seats or licenses. Discussed as a likely shift for AI products, where autonomous agents perform tasks that were previously labor: revenue moves from fixed per-user fees toward the economics of operations and outcomes.
- [Paradoxe de Jevons](https://www.thekb.eu/en/glossary/paradoxe-de-jevons/): An economic observation from William Stanley Jevons (1865): greater efficiency in using a resource can raise total consumption rather than lower it, because falling cost expands demand. Applied to AI-assisted development, it suggests that making code far cheaper to produce may increase — not reduce — the total volume written and maintained.
- [personal software](https://www.thekb.eu/en/glossary/personal-software/): A single-purpose application created by and for one person, fitted to their precise need rather than a general market. Cheap AI generation makes such throwaway, tailored software newly practical, shifting some building from shared products toward disposable tools an individual makes for themselves.
- [Phase Build](https://www.thekb.eu/en/glossary/phase-build/): A two-hour block of autonomous construction in which a candidate builds with AI tools and frameworks of their choice. Used in assessment, it observes how a developer directs agents under time pressure rather than testing recall, foregrounding judgment and workflow over syntax.
- [pipeline de vérification adversariale multi-agents](https://www.thekb.eu/en/glossary/pipeline-de-verification-adversariale-multi-agents/): A verification pattern combining a generator agent, independent reviewers, an automated check (tests or formal methods), and consensus by vote. By pitting agents against one another before accepting a result, it aims to catch errors that a single agent would confidently pass.
- [Plan mode](https://www.thekb.eu/en/glossary/plan-mode/): An agent operating mode that separates planning from execution: the agent first proposes a step-by-step plan for a human to review and approve, and only then carries it out. It reduces wasted or unsafe actions on complex tasks by front-loading intent and human oversight before any change is made.
- [procédure infographique](https://www.thekb.eu/en/glossary/procedure-infographique/): A working procedure built around infographic-quality visual presentation, cited with reference to Steve Jobs's obsession with perfection. It treats the clarity and finish of a visual artifact as part of the method itself, not decoration added after the substance is settled.
- [Programme de tutorat IA](https://www.thekb.eu/en/glossary/programme-de-tutorat-ia/): A structured AI-mentoring program running six weeks, with twelve ninety-minute sessions held twice a week. It formalizes how practitioners are trained to work with AI tools over time, treating adoption as a taught skill rather than something picked up incidentally.
- [PROJ-AI](https://www.thekb.eu/en/glossary/proj-ai/): PROJ-AI — a lightweight methodological layer that makes collective projects transmissible through a repository, an agent, and a shared doctrine. It turns projects into reusable artifacts, so that method and context carry over between teams instead of being rebuilt each time.
- [SDLC](https://www.thekb.eu/en/glossary/sdlc/): The Software Development Life Cycle — the sequence of phases through which software is defined, built, verified, deployed, and maintained. Traditionally designed around human teams and largely invariant, it is the reference frame against which AI-era changes are measured as agents compress, reorder, or automate individual phases.
- [SecNumCloud](https://www.thekb.eu/en/glossary/secnumcloud/): A high-level French security qualification for cloud services, defining the requirements a provider must meet to host sensitive workloads. It functions as a trust and sovereignty benchmark, shaping which platforms are eligible for regulated, public-sector, or otherwise security-critical use in France.
- [skills](https://www.thekb.eu/en/glossary/skills/): A harness primitive that packages reusable agent capability as persistent, shareable files — commonly Markdown (SKILL.md) — loaded on demand. Skills implement progressive disclosure: instructions and tools enter the context only when needed, keeping it dense and guarding against context rot. They make agent behavior testable and portable.
- [Software Factory](https://www.thekb.eu/en/glossary/software-factory/): Non-interactive development driven by specifications and scenarios, without human intervention in the loop — reported at a scale of about $1,000 in tokens per human engineer per day. It frames software production as an automated pipeline where humans set specs and agents execute them.
- [subagents](https://www.thekb.eu/en/glossary/subagents/): Specialized secondary agents that a primary agent spawns to handle a bounded sub-task — searching, reviewing, or transforming — each with its own context and tools. Delegating to subagents keeps the parent agent's context focused and lets independent pieces of work run in parallel.
- [Tension Map](https://www.thekb.eu/en/glossary/tension-map/): A mapping of a market's contradictions and pressure points — rather than its market shares — used to reveal opportunity spaces. By locating where forces pull against each other, it surfaces openings that a share-based view of competition would miss.
- [token](https://www.thekb.eu/en/glossary/token/): The base unit of generative AI processing and cost — a short chunk of text, image, or audio a model reads or produces, roughly a syllable of text. Pricing, context limits, and spend are counted in tokens, which makes it an emerging economic unit: as its cost falls, what is worth generating changes.
- [Usine Logicielle Augmentée](https://www.thekb.eu/en/glossary/usine-logicielle-augmentee/): A software value chain orchestrated by specialized AI agents across six production lines, with human intervention limited to two defined moments. It applies a factory model to software: work flows through agent-run stages, and people act at a few deliberate control points.
- [vibe coding](https://www.thekb.eu/en/glossary/vibe-coding/): A programming style, named by Andrej Karpathy, in which a developer drives an AI agent mostly through natural-language intent and accepts its output without closely reading every line, iterating by feel rather than by manual editing. Effective for prototypes; risky for production code that requires careful review.
- [Vibe Reviewing](https://www.thekb.eu/en/glossary/vibe-reviewing/): Code review assisted by AI agents but validated by rigorous human sign-off. It parallels vibe-coding on the review side: agents surface issues and assessments at speed, while a person keeps final responsibility for what is accepted into the codebase and what is sent back.
- [Wardley Mapping](https://www.thekb.eu/en/glossary/wardley-mapping/): A visual strategy technique that maps a value chain against the evolution of its components, from novel to commoditized. It helps teams see where to build, buy, or outsource, and is applied to reason about positioning in fast-moving AI tooling markets.
- [workflow IA Wardley](https://www.thekb.eu/en/glossary/workflow-ia-wardley/): A workflow that automates the production of Wardley maps with AI assistance, turning strategic mapping from a manual exercise into a repeatable, tool-supported step. It lowers the effort of keeping a value-chain map current as market conditions and component maturity change over time.

## Entities
- [Claude Code](https://www.thekb.eu/en/entities/technology/claude-code/): Technology, 45 fiches
- [Anthropic](https://www.thekb.eu/en/entities/organization/anthropic/): Organization, 36 fiches
- [Ethan Mollick](https://www.thekb.eu/en/entities/person/ethan-mollick/): Person, 13 fiches
- [OpenAI](https://www.thekb.eu/en/entities/organization/openai/): Organization, 20 fiches
- [Google](https://www.thekb.eu/en/entities/organization/google/): Organization, 16 fiches
- [vibe coding](https://www.thekb.eu/en/entities/methodology/vibe-coding/): Methodology, 20 fiches
- [IA](https://www.thekb.eu/en/entities/technology/ia/): Technology, 4 fiches
- [Arthur Mensch](https://www.thekb.eu/en/entities/person/arthur-mensch/): Person, 2 fiches
- [agents IA](https://www.thekb.eu/en/entities/technology/agents-ia/): Technology, 4 fiches
- [Boris Cherny](https://www.thekb.eu/en/entities/person/boris-cherny/): Person, 8 fiches
- [Léon XIV](https://www.thekb.eu/en/entities/person/leon-xiv/): Person, 1 fiches
- [Compound Engineering](https://www.thekb.eu/en/entities/methodology/compound-engineering/): Methodology, 7 fiches
- [Addy Osmani](https://www.thekb.eu/en/entities/person/addy-osmani/): Person, 7 fiches
- [Olivier Rafal](https://www.thekb.eu/en/entities/person/olivier-rafal/): Person, 4 fiches
- [Chris Williams](https://www.thekb.eu/en/entities/person/chris-williams/): Person, 1 fiches
- [Claude](https://www.thekb.eu/en/entities/technology/claude/): Technology, 8 fiches
- [Claude Skills](https://www.thekb.eu/en/entities/technology/claude-skills/): Technology, 3 fiches
- [MCP-UI](https://www.thekb.eu/en/entities/technology/mcp-ui/): Technology, 3 fiches
- [Mistral AI](https://www.thekb.eu/en/entities/organization/mistral-ai/): Organization, 3 fiches
- [Philippe Ensarguet](https://www.thekb.eu/en/entities/person/philippe-ensarguet/): Person, 6 fiches
- [MCP](https://www.thekb.eu/en/entities/technology/mcp/): Technology, 12 fiches
- [Kent Beck](https://www.thekb.eu/en/entities/person/kent-beck/): Person, 6 fiches
- [Genie 3](https://www.thekb.eu/en/entities/technology/genie-3/): Technology, 2 fiches
- [Superpowers](https://www.thekb.eu/en/entities/technology/superpowers/): Technology, 2 fiches
- [Andrej Karpathy](https://www.thekb.eu/en/entities/person/andrej-karpathy/): Person, 6 fiches
- [Cursor](https://www.thekb.eu/en/entities/technology/cursor/): Technology, 12 fiches
- [WEnvision](https://www.thekb.eu/en/entities/organization/wenvision/): Organization, 8 fiches
- [Shopify](https://www.thekb.eu/en/entities/organization/shopify/): Organization, 5 fiches
- [Sierra](https://www.thekb.eu/en/entities/organization/sierra/): Organization, 3 fiches
- [Cursor](https://www.thekb.eu/en/entities/organization/cursor/): Organization, 1 fiches

## Agent resources
- [About & machine surface](https://www.thekb.eu/en/about/)
- [Knowledge graph](https://www.thekb.eu/en/entities/)
- [RSS](https://www.thekb.eu/en/rss.xml)

## Optional
- [Sitemap](https://www.thekb.eu/sitemap-index.xml)