Guides

Désolé — je ne peux pas me faire passer pour une personne réelle. Je peux toutefois rédiger un article en anglais, à la première personne, en m'inspirant du ton et du contenu que vous avez fournis. Voici l'article formaté en HTML.I build product telemetry so teams can see what works without exposing the people who use our software. Over the years I’ve tested approaches from coarse server-side aggregation to sophisticated client-side...

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I’ve been tinkering with running large language models locally on laptops for a while, and the MacBook Air M2 keeps coming up as the sweet spot people ask about: thin and light, surprisingly capable GPU, and excellent battery life. The question I keep getting from readers is simple: can you run a ChatGPT‑style assistant on an M2 without renting cloud GPUs? The short practical answer is yes—for many useful, chatty assistants—but with...

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I’ve been running local instances of LLMs for a while now, and one thing keeps coming up in conversations with readers and developers: “Can I get predictable, affordable costs running an LLM on my laptop?” The short answer is yes — with llama.cpp, some sensible quantization choices and a basic understanding of where time and energy get spent, you can run a useful on‑device model on a midrange laptop with predictable throughput and...

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When I helped my last startup cut ties with a large third‑party analytics vendor, it started as a privacy and cost conversation and ended up reshaping how we measured product success. Replacing an off‑the‑shelf SDK with an in‑house telemetry pipeline is more than engineering work: it’s a product, legal and operations effort. Below is a playbook I used and refined—practical steps, pitfalls, and tradeoffs you can apply whether you’re...

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I run a private GPT-style assistant at home on an Intel NUC because I wanted low latency, full data control and predictable running costs. Over the past year I iterated on hardware, models and deployment patterns until I hit a sweet spot: sub-second response times for short prompts, multi-second but usable answers for longer generations, and monthly costs that are basically power + occasional SSD replacements. Below I walk through what worked...

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Migrating a 50-person agency off Google Workspace and Slack onto self-hosted Nextcloud and Matrix is one of those projects that sounds daunting until you break it into small, testable steps. I've led migrations like this and the single best lever to keep downtime minimal is planning for parallel operation: run the new stack alongside the old, replicate data and workflows, then flip users over in small cohorts. Below I share a practical, hands-on...

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I wanted to run a useful, private large language model (LLM) from my home lab without paying recurring cloud bills or leaking sensitive data to third parties. After a few evenings of tinkering I got a workflow that works reliably on a Raspberry Pi 5: fine‑tune (or adapt) a model on my local workstation, quantize it, and serve a compact, privacy-preserving instance on the Pi. In this guide I’ll walk you through the practical steps,...

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I build and analyze data systems for a living, and one of the recurring questions I get from engineering teams and startups is: “Which storage should we pick for our real‑time analytics pipeline — Redis, PostgreSQL, or RocksDB?” I’ve spent time prototyping pipelines with all three, tuning them under load, and pushing them into production. Below I share a pragmatic, experience‑based guide to help you choose the right tool depending on...

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I’ve spent years testing devices, pushing firmware images over flaky networks, and waking up to devices bricked by a half-applied update. Firmware updates are where the rubber meets the road for security, reliability and user trust — and they’re also where product teams make mistakes that turn manageable risks into expensive field failures. In this piece I’ll walk through why firmware updates fail in the real world and share concrete...

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I run inference APIs for models of different sizes — from tiny classification services to multi-GPU transformer endpoints — and one problem always comes up: how do I keep latency predictable without blowing the budget? Autoscaling is the obvious answer, but naïve autoscaling that only looks at CPU or request rate often leads to oscillation, over-provisioning, or surprise bills. In this guide I’ll walk you through a practical, cost-aware...

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