last-line.dev /// michel-marie maudet · linagora
I wrote my last
line of code
in December 2025_
How I became a developer again while remaining CEO of a 200-person company.
A personal feedback. No filter.
Directeur Général · LINAGORA · Open Source AI
git log --author=mmaudet --stat
One year. One inflection point. No retouching.
I run a 200-person company. No official dev time allocated.
Yet here is what happened after November 2025.
git tag -a v0.0 -m "before"
November 2025. Claude Code.
Something unlocks.
$ claude "build me an OVH cost dashboard"
● Reading project structure...
● Writing src/components/CostChart.tsx
● Running tests... ✓ 12 passed
● Committing: feat(dashboard): add cost breakdown view
An AI that reads your code, understands your architecture, executes, tests and commits with full memory of where it is.
Vision capabilities too: screenshot your UI and Claude sees and diagnoses the visual bug directly.
ls ~/projects/ — three intentions that became code
Three projects. One proof.
ovh-cost-manager
React · Express · SQLite
An internal dashboard that was missing from our stack. I described what I needed, Claude built it in hours: monthly billing breakdown, service comparisons, trend charts, cost forecasting. In production the next day, open source the day after.
AGPL-3.0
⎋ github.com/mmaudet/ovh-cost-manager
browserlet
Chrome Extension · BSL/YAML · TypeScript
Semantic web automation with a key architectural choice: AI generates the automation script once using semantic selectors and ARIA roles, then execution is fully deterministic with no AI at runtime. A credential vault with AES-256-GCM encryption. Zero-trust in the browser.
Open Source
⎋ github.com/mmaudet/browserlet
visio-mobile
Rust · iOS + Android + Desktop
Native mobile app for La Suite Numérique Meet. Built in Rust, using the LiveKit Rust SDK for real-time video. Adaptive UI that switches layout based on mobility context: full interface when stationary, compact one-hand layout when walking, ultra-minimal with giant mute button when driving. Local Whisper transcription planned.
AGPL-3.0
⎋ github.com/mmaudet/visio-mobile
3× up to 50×!
faster delivery
3 months → 3 days
0
lines written by hand
since December 2025
1 month only
advanced MVP · 3 platforms · 2 native mobile
git log — evolution of AI-assisted development
Four phases. Most stop at 2.
Phase 1
Editor generation
Copilot · Cursor
Phase 2
Vibe coding
Chat-based
Phase 3
Coding agent
Claude Code
Phase 4 ★
Context engineering
& meta-prompting
↑ click a phase to explore ↑
cat CLAUDE.md
The real skill: context, market fit & tests. Not code.
CLAUDE.md — what it contains
Architecture decisions (locked, not to be questioned)
Coding conventions enforced on every file
What NOT to do — explicit prohibitions
Tech stack rationale and version constraints
PR checklist and test expectations
Team vocabulary and domain concepts
Meta-prompting principles
Instruct HOW to work, not just WHAT to do
PRD (Product Requirements Doc) as structured agent input
Persistent project memory across sessions
Explicit error taxonomy defined upfront
Roles and responsibilities stated clearly
The differentiating skill is no longer writing code.
It is writing context, the market fit and the tests — and that is already what good tech leads do.
diff claude-code.log open-code-sovereign.log
Two pipelines. One conviction.
Open Code — Infra souveraine
OpenRouter + LibreChat stack
Open models: Qwen, Mistral, GLM, Kimi
Full confidentiality compliance
LINAGORA-hosted and auditable
Performance gap closing fast
The trajectory of 2026
Claude Code / Anthropic
Best reasoning quality today
Native context engineering via CLAUDE.md
Complex multi-file autonomous edits
Vision: screenshot to visual bug diagnosed
US infrastructure
Data sovereignty concerns
I use both depending on the project. And I observe that the gap narrows every month.
tail -n 6 lessons-learned.log
Objective and measured learnings.
| Finding |
Before |
After |
| Time-to-orbit of a new project |
3 months3 mois |
3 days |
| Output quality driver |
Model skill |
80% context quality |
| Dominant error type |
Syntax bugs |
Architectural drift |
| Required skill shift |
Writing code |
Specifying intent clearly |
| Context engineering learning curve |
— |
Real but short |
| Bottleneck moved to |
Execution |
Specification and (manual) tests |
echo 'unresolved' > questions.txt
Open questions. My own.
?
Augmented teams
In a team of 10 devs: who prompts, who reviews, who architects? The roles have not been reinvented yet.
?
Maintainability
Is generated code maintainable in 2 years by someone without the original context? What about turnover?
?
Complexity ceiling
Current projects are a few thousand lines. What happens at 100k? With 3 years of technical debt?
?
Process
Gitflow, pair-programming with AI, code review of generated code: our processes must evolve.
git log HEAD..future/main
The gap is closing.
12–18 months.
| Model / Stack |
Origin |
Strength |
Sovereignty |
| Qwen 3.x | Alibaba / open weights | Best open coding model | ✓ Self-hostable |
| GLM-4 | Zhipu AI / open weights | Multilingual + code | ✓ Self-hostable |
| Kimi k2 | Moonshot AI / open weights | Long context + reasoning | ✓ Self-hostable |
| Mistral | Mistral AI / European | Good, improving fast | ✓ Self-hostable, EU, auditable |
| Claude Code | Anthropic / US | Reference today | ✗ US infra |
The question is no longer AI or no AI.
It is: which infrastructure? Which governance?
The developer of tomorrow is a context engineer.
git show HEAD:last-commit.txt
I stopped coding.
I changed my abstraction layer_
December 2025 was not my last line.
It was my last line written by hand.
cat OFFER.md
Now: your turn.
We are building an acceleration offer for dev teams who want to make this shift, with sovereignty by design and not as an afterthought.
We are looking for partners to co-build it with us.
If you want to go from 3 months to 3 days in your own team, let us talk.
All projects are open source and publicly available on GitHub.
cat glossary.md
Glossary.
Context Engineering
The practice of building a structured work environment for an AI agent: architecture decisions, conventions, prohibitions, vocabulary, and project memory. Not prompting — engineering the context in which the AI operates.
CLAUDE.md
A project-root file that Claude Code reads before every session. Contains architecture decisions, coding conventions, explicit prohibitions, and domain vocabulary. The persistent memory of the AI within your project.
Meta-prompting
Instructing the AI on how to work, not just what to do. Defining its reasoning approach, review process, error handling, and behavioral constraints before it starts coding.
PRD
Product Requirements Document. A structured specification that serves as operational brief for the coding agent: features, constraints, acceptance criteria, edge cases. Replaces the verbal back-and-forth of vibe coding.
Vibe Coding
Development by conversation: describe what you want in natural language, iterate via chat, copy-paste and adjust. A real productivity gain, but still manual driving — you translate intent one exchange at a time.
Coding Agent
An AI that executes autonomously inside your project: reads files, writes code, runs tests, fixes failures, commits. Not a chat assistant — a collaborator with full project access. Claude Code is the reference implementation.
Architectural Drift
The dominant error type with AI-generated code. The AI produces working code that gradually diverges from the intended architecture. Context engineering is the antidote: explicit constraints prevent drift before it starts.
Sovereign AI
AI infrastructure hosted on self-controlled servers, using open-weight models (Qwen, Mistral, GLM, Kimi). Full data sovereignty, auditability, and no dependency on US cloud providers. The trajectory of 2026.