Ai

Earlier this year I built an AI and technology news digest. It pulled from newsletters, Hacker News, and Reddit, filtered for AI terms, and sent the results to Telegram every few days. It was useful. It was also a list generator. The system knew that an article matched a keyword and that a link was popular. It did not know whether I had seen the same underlying story three times, whether a consequential claim had a primary source, whether the item connected to anything I was working on, or whether I had rejected the same kind of recommendation last week. ...

In my previous post I built a garden CCTV system using Frigate NVR on the Pi 5. It worked well — but the AI object detection was running on the CPU, consuming about 35% of a core just sitting there waiting for motion. I installed a Hailo-8L AI HAT to move that workload to a dedicated NPU, and the results were exactly what I hoped for. This post documents the migration: what the Hailo AI HAT is, how to install the driver (with the version pitfalls that will catch you), and how to wire it into Frigate. ...

In my previous post I connected a Raspberry Pi 5 to my AI assistant as a remote node. The natural next step: attach a camera and turn it into a proper garden security system — motion-activated, AI-powered, with notifications straight to Telegram. This post documents what I built: Frigate NVR running on the Pi 5, a Freenove 8MP camera module, automated clip retention, and a Python bot that sends snapshots to a private Telegram channel the moment something moves. ...

I recently got a Raspberry Pi 5 (8GB) and decided to connect it to my OpenClaw setup as a remote node. The idea: give my AI assistant direct access to a physical machine sitting on my home network, so it can run commands there, monitor things, and eventually interact with hardware. This post documents how I did it, the pitfalls I ran into, and what I plan to use it for. ...

I just moved my self-hosted OpenClaw from one cloud VM to another. The whole thing — from “let me have a look at what’s running” to “the bot replied to my Telegram message on the new box” — took about 90 minutes, and the bot was offline for less than two of them. The interesting part isn’t that it worked. The interesting part is what shape the plan had to take so that nothing ended up in a half-migrated state, and how OpenClaw itself helped me think through the migration before I touched a single file. ...

There’s a temptation, when you spot a problem in a personal project, to just fix it. Open the file, start editing, ship. It feels productive. It often isn’t. This week I migrated the state layer of my personal AI assistant from a collection of flat JSON files to SQLite. The migration itself took about 20 minutes of actual work, spread across four automated phases. The planning took two hours. And the planning was the most valuable part. ...

I run OpenClaw as my personal AI assistant — it handles my daily GTD rituals, monitors my email, tracks calendar events, and responds to me on Telegram throughout the day. I depend on it for essential, everyday tasks. Which means when it breaks, I feel it immediately. For a long time, updating it felt like defusing a bomb. The Problem: Updates Are Terrifying When You Depend on the System Every time a new version of OpenClaw dropped, I’d put off installing it. Not because I didn’t want the new features, but because I’d been burned before. Here’s a sample of what had gone wrong across previous updates: ...

Last night was one of those rare London evenings: clear skies, no moon, and a forecast that held. My automated sky alert fired at 5pm, so by 7:30 I had the Seestar S50 out and aligned. But this time I did something new — I planned the session in advance, with AI help. The Setup The telescope is the ZWO Seestar S50 — a compact smart scope with a 50mm aperture, built-in tracking, live stacking, and a phone app that handles everything. It’s not a serious instrument by amateur astronomy standards, but it punches well above its weight for a grab-and-go in a light-polluted city. ...

I’ve been running a personal AI assistant for a few months now — handling email triage, GTD rituals, news digests, morning run reminders, and a bunch of other cron-driven automation. It’s genuinely changed how I operate day-to-day. But here’s something I’ve learned the hard way: the model underneath everything matters more than you’d think. The Reasoning Problem At some point I had extended reasoning enabled on my assistant. In theory, this should make responses more thoughtful — the model “thinks before it speaks.” In practice, it made every message reply a slow, verbose rewrite of whatever I’d asked. Inconvenient enough that I just switched it off. ...

Running an AI assistant with shell access, file system permissions, and messaging integrations is powerful. It’s also a meaningful security surface. You’re giving a language model the ability to execute commands, read files, and respond to messages on your behalf. This isn’t theoretical risk. If you configure OpenClaw carelessly, you’re handing strangers the keys to your system. Here’s how to run OpenClaw safely: keep it updated, audit it regularly, and vet plugins before you enable them. ...