Guides

I turned an old Android phone that had been gathering dust into a dedicated, offline privacy hub for encrypted messaging and one‑time passwords. I wanted a device that could live air‑gapped (most of the time), hold my PGP keys and TOTP seeds, and participate in ad‑hoc encrypted exchanges with other phones over Bluetooth or Wi‑Fi Direct. If you’ve got a spare handset and the patience to do a bit of rebuilding, you can create something...

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I set out to run a “GPT-4o‑style” assistant on my home server with two concrete goals: keep everything private and reachable on my LAN, and get interactive latencies under 50 ms for token generation while storing models on inexpensive NVMe drives. After testing several toolchains, models and hardware configurations, I ended up with a reproducible approach that balances cost, latency and real-world usefulness. Below I walk through what...

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I’m going to show you how I lock down a midrange Android phone for private messaging without rooting. The goal is practical: make it hard for casual snoops, resilient against common app-level leaks, and preserve strong cryptographic privacy for messages—without tinkering with the bootloader or installing a custom ROM. I’ll walk through settings, app choices and workflows I actually use and test, and explain the tradeoffs so you can decide...

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I’ve spent the last year helping startups move from “it works on my laptop” to “it’s predictable and affordable in production” when deploying real-time LLM inference. One recurring headache is cloud costs that explode unpredictably because inference usage is measured in tokens, not requests—and tokens vary wildly. In this guide I’ll walk through how I measure token-level costs, build token-aware autoscaling, and put practical...

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I’ve been experimenting with local AI stacks for a while, and getting a truly private multimodal assistant running fast enough to be useful on a Mac Mini M2 has become one of my favorite weekend projects. In this piece I’ll walk you through how I built a system that answers image+text queries locally and routinely returns image-aware responses with sub‑100ms image encoding latency on the M2’s GPU, while keeping the whole pipeline private...

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I learned the hard way that not all USB‑C chargers are created equal. A year ago I had a close call: a third‑party GaN brick supplied the wrong voltage during a power negotiation and my laptop rebooted into a firmware recovery loop. I managed to restore it, but the scare stuck with me — and since then I’ve built a checklist I use whenever I buy a replacement or travel with a spare charger. Below I share that checklist and the practical...

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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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