I'm releasing the first public version of Mianotes today:
→ Website: https://t.co/HnRN7UhNIH
→ Docs: https://t.co/crpKOW2VFG
→ GitHub: https://t.co/C023al16jD
If you try it, I'd love to know what you'd expect from a tool like this https://t.co/ciN2FFPM2F
@msdev@ollama
Why:
SBCs often run inside enclosures, on shelves, or as always-on devices. Once deployed, you need a simple way to see if the system is healthy without SSHing into the box every time. It give you quick health check for memory, CPU, storage, and uptime.
https://t.co/cqy9OAEJh9
I'm also open sourcing a lightweight Linux system telemetry and KPI dashboard for ARM-based SBCs. It's useful for tracking memory, CPU, storage, and uptime on small Linux devices. It's written in Python, exposes REST APIs, and uses WebSockets to update data in real time.
I'm open sourcing a useful app I built for IoT devices:
1. Senseibox Onboarding: A Linux port of the Arduino App Lab onboarding app. It runs on boot, directly on the IoT device, so users can connect to Wi-Fi and set up the device in order to meet UK security requirements.
Why:
The UK NCSC doesn't allow IoT products to ship with factory default passwords, so devices need a secure first-run setup flow. This is similar to the onboarding software you find in proprietary hardware like the Philips Hue Bridge and many always-on bridge devices.
The APIs, MCP server, dashboard, installers, documentation, and marketing site were all created in just 2 weeks, from the first commit. I used 4 Codex agents and more than 30 agent sessions to build it. The Codex agents also documented the product inside Mianotes as they worked.
I'm releasing the first public version of Mianotes today:
→ Website: https://t.co/HnRN7UhNIH
→ Docs: https://t.co/crpKOW2VFG
→ GitHub: https://t.co/C023al16jD
If you try it, I'd love to know what you'd expect from a tool like this https://t.co/ciN2FFPM2F
@msdev@ollama
Transcripts now appear in real time while Mia processes long video or audio. It down-converts long audio to a a mono MP3, slices it into small timed chunks, transcribes each chunk independently, and appends text as each part completes. Works with podcasts and YouTube videos.
The job console is Mia's black box recorder for devs. You can see what the agent is doing, why jobs are failing, and share useful logs to get issues fixed quickly.
@youyeetoo For 12GB with no NPU or powerful GPU, £550 feels a bit expensive to run OpenClaw. You can probably fit 1B to 3B models in there, but still. Add £200 more and you can get a Mac mini with 16GB, fast Neural Engine, and massive memory bandwidth.
@realhamaddada The problem with RKLLM is that it doesn't support vision models. This AIBOX ships with DeepSeek's smallest model (R1-Distill-Qwen 1.5B). Much bigger than YOLOv8, but a different type of model. For YOLO, I'd look at the Dragon Q6A instead. It's a beauty.
https://t.co/bGcMmMOnuE
The AIBOX with Rockchip RK3576 is an interesting computer. It ships with DeepSeek running completely offline. My guess is they used Rockchip's native RKLLM toolkit to install a quantised DeepSeek model small enough to fit into the device’s 8GB of RAM.
@simonw Hey Simon, I know the repo hasn't been updated in a while, but just so you know, Google shows "Access blocked: Google Calendar auth demo has not completed the Google verification process" when running google-calendar-to-sqlite auth.
Soon your agents will start communicating and collaborating with others. The evolution of work is inevitable.
- Legacy: One employee + personal computer
- Current: One employee + personal computer + multiple agents
- Future: One employee + shared computer + agent-to-agent (A2A)
Should AI-first companies invest more in "AI expert personas" as decision interfaces? For ex: finance, legal, customer, innovator personas. I like this quote: "Skills are tools. Personas are lenses. Agents need both."
Last year I wrote "Turning an LLM prompt into a multi-agent discussion", with multiple AI personas working as a decentralised reasoning engine. Today I saw a great example of this from @takechanman1228
https://t.co/ox9ExIe2Mk
I like what @vladquant did with his news app Kagi, he open sourced the UI. Before GenAI, doing this allowed others to contribute to the project and improve it. But today, AI can rewrite the UI completely and adapt it to any use case.
AI doesn't care about building apps, it cares about solving problems. If I want it to show tech news on my reTerminal E1001 dashboard or a Linux TV, it will build the app for that. Here's an example using https://t.co/8kQPodmXah. This is a big shift in UX.