#ai

With Thomas Pierrain and Vanessa Gossent, I revisit AI costs, hiring, parallel agents and the work we have to redo after delivery.

My conversation with Bruno Soulez on context engineering, agent harnesses and code review: what I delegate and what I still check.

Three months, 1,011 pull requests and 8,728 inline AI review comments: 4.5% got a visible human response. What the data shows, and what it cannot.

AI can shorten implementation while specification, review, CI, and rework absorb the saved time. Instrument the full flow before adding more generation.

Opus 5.5 is the next reviewer baseline to test: set effort explicitly, update the API adapter, and keep a second reviewer only when it catches verified defects.

When the same accepted correction returns, test whether it belongs in a linter, type, test, example, repository rule, checklist, or architecture decision.

Compare independent review with self-review and deterministic checks. Measure rescued defects, false acceptances, regressions and the cost of another pass.

A review signal matters only when its provider, commit SHA, coverage, execution state, publication, and place in branch policy are explicit.

Build a bounded review loop with frozen inputs, separate execution and outcome fields, retry limits, evidence handoffs and human escalation.

Run local, CLI, and remote reviewers with explicit trust boundaries, least privilege, structured findings, budgets, degraded states, and shutdown controls.

AI can speed up one step while the request waits elsewhere. A practical way to map the full service, protect quality and measure accepted user outcomes.

A verified $214 bill for a production rewrite, checked against the source transcript, sits beside a corpus of about 2,900 tech talks on coding-agent costs.

Four conference talks and outside sources document Doctolib's AI initiatives; two carry the 30-to-600 engineer rollout, without reducing it to one figure.

Cloud FinOps put a cost study first and a named owner for the number second. Three practitioner cases show that sequence applied to token bills.

One gigawatt of AI data-center capacity costs about $50 billion. OpenAI has discussed adding up to 26, behind the series' cost figures.

Twenty days of my own Claude Code and Codex usage, measured three ways. The tools disagree by a factor of three, and dollars are not what binds.

Opus 5.5 changes input, output and cache economics. Price the accepted task, including retries and review, before choosing a cheaper model.

A Stanford research talk, DX estimates and two field studies measure different forms of AI coding output. None supplies a universal budget forecast.

Claims that nobody can measure AI ROI and that 95% of pilots return zero are weaker than their usual retellings, once their sources are checked.

A $589 billion single-day stock rout, an enterprise model platform, and a €25,000 backup server show three scales of vendor dependence.

Flow Lean fused three response skills. Its historical eval beat each comparator and caught a candidate offering a migration against an inferred target.

Share Claude Code and Codex instructions, skills and MCP definitions through versioned releases, host adapters and checks of installed and runtime behavior.

Anthropic measured merged PRs; METR measured task duration in a different setting. What these results and long-context research establish, and what remains a hypothesis.

6 human commit authors plus Claude in our git history (May 2026 snapshot). What the commit patterns actually look like after months of Claude Code, beyond the marketing claims.

Choose between Claude Code and Cowork, then follow the Start, Build, or Scale path that matches your role and current operating problem.

Nine months of AI configuration in one production project: evolving responsibilities, profile-based generation and a measured reduction in always-on lines.

Everyone talks about shipping 10x faster with AI. Nobody talks about accumulating debt 10x faster. 7 months of production data from a real EdTech platform.

A non-developer modified 80 files in production in 10 days with Cursor AI. Exact timeline, AI config setup, and what it means for engineering teams in 2026.

Jeremy Twei coined it. Addy Osmani popularized it. Margaret-Anne Storey extended it to teams. Here's what I built to fight all three.

What roughly 15 years in software, including 8 to 10 in leadership, give me as an AI-assisted builder, what I had to relearn, and why faster code is not enough.