More and more people ask me how I work with AI. Instead of repeating myself, I document everything here: my method, my config files, my projects, and the details of what I'm building — session by session.
My method comes down to a handful of .md files I drop into Claude's "Project Instructions." A main CLAUDE.md that defines my tone, my rules, and my context; extension files loaded on demand (strategy, dev…); and a dedicated file for each brand's DNA.
The guiding principle: Compound Engineering. Every time Claude gets my style wrong, I add a rule. The system improves with use — and all future work benefits from it.
Alongside the journal, I publish clear guides on AI and the tools I use: what it is, what it's for, what it changes. Written for busy merchants and decision-makers, but readable at any level — three reading depths on every page.
The first one lays the groundwork: generative AI and "LLMs", jargon-free, covering the major players and — most importantly — what they can't do.
Guide 01 · May 31, 2026What is AI, and what's an "LLM"?Generative AI explained for fast decisions: what it does, who builds it (OpenAI, Anthropic, Google, Mistral…), and its limits.Read →
What I'm building
Projects
It all fits together: one method (Claude + context files) applied to one territory — high-end audio/video retail and the tools that power it. Dev (Cobra) feeds the stores, the stores feed the community (HiFi Lovers), and the personal lab is where I experiment.
We couldn't manually watch hundreds of references against Darty, Boulanger, Fnac or Son-Vidéo. A rules engine in Odoo (cobra_price_rules) reads competitor prices delivered by Wiser several times a day and computes a target price — best valid competitor, bounded by a minimum margin. Two modes: observation (simulates) then live (applies and pushes to Shopify), going live staying a human decision. Guardrails: never sell at a loss, outlier offers (< 60% of our price) ignored, pair/unit normalization. 6 families live, ~300 prices realigned; the rest adjusts on its own at each Wiser update.
Every supplier price update (a PDF or a ‘new product’ email) meant manual work: read it, find the product in Odoo, check EAN/SKU, key in buy and sell prices. An internal app (‘Product Update & Creation’) automates the pipeline: PDF upload or watching a read-only mailbox, EAN/SKU/price extraction via a Claude model, matching against the Odoo base, duplicate detection before creation, systematic human validation and traceability. A deliberate guardrail: no created product is ever published online automatically.
A per-variant pricing-system overhaul had been in preparation for weeks. While testing the price-update app, a gap between the price written in Odoo and the price synced on the merchant site revealed the overhaul was already in production — without formal confirmation. Each variant now has its own sale price, removing the cross-variant contamination risk. The app was fixed to write to the new field, and the matter escalated to the ERP integrator. Lesson: test in the real system rather than trust the docs.
Nothing clearly separated the invoice is out, the customer has paid and the goods are delivered. Three independent badges (Invoicing / Payment / Delivery) replace the merged status: color coding, deposit rate shown, automatic switch from remaining-to-collect to remaining-to-deliver, delivered lines greyed out. Transposed from the existing supplier module; two latent calculation bugs fixed along the way.
A synthesis across 5 sessions: Cobra's internal app hub goes, in a few weeks, from a few isolated critical apps to a genuine small standardized ecosystem — identical header everywhere, shared Odoo connection, refresh on a common cron. Two incidents (an overwritten version, a cron missing its env vars) caught within minutes thanks to the backup/compare discipline before every deploy.
The Odoo pricing model was stacked and fragile: an auto-computed web pricelist, overlapping lists, a hard-coded 20% VAT, margin dropping to 0 the moment a product went out of stock. A single cobra_price field becomes THE source of truth via a _price_compute override — cost and margin recomputed, Omnibus-compliant strikethrough prices, immediate Shopify sync. 6 modules touched, ~7,300 products migrated, with real post-deploy follow-up (a display bug fixed the same day, 492 promo products re-pushed).
After a Cobra change to an Odoo module, the integrator (Irokoo) came back with 6 technical points (security, reliability, consistency). Each evaluated individually: 3 fixed as recommended, 2 with an explicit trade-off (keeping the chatter visibility Hugo cared about, while neutralizing the risk another way), 1 deliberately declined. An unreported bug caught and fixed along the way. Deployed in an isolated Git worktree so as not to interfere with another job in progress.
The most direct build-vs-buy case of the period: a spec for a competitive price-tracking dashboard (ABC scoring, rules engine, price history), estimated at 3-4 weeks of dev, sent to the integrator in March 2026 — left at to-be-validated-before-development, never started. The most technical brick (price import/matching via Wiser) was already running in production; the control layer was missing. Built in-house with Claude Code, in a few days, on our internal app hub.
I document all of this in public, and I love talking about it. If you're building with Claude, if any of these projects intrigues you, or if you just want to compare approaches — reach out, let's chat.
