In previous posts we explored what happens if we treat LLMs as processors that run Markdown as their machine code: a user-space IP stack written in Markdown and a BASIC interpreter written in Markdown. Today we are going to look at an emulator for a 6502 microprocessor written in Markdown and executed with OpenCode and the GLM 5.1 model running on Grunden.ai.
How AI coding agents built a city-scale IoT asset tracking simulator in weeks, testing 100k+ Bluetooth assets across Birmingham before hardware arrived.
We all know people use AI to write their texts. And we all know they tend to have a similar feel to them. There is something with those texts that makes them look like other texts. We instinctively feel that there is something about them that just smells AI. But what is it?
Most product ideas fail because nobody needed them in the first place. And by the time we figured that out, it may be too late. But there is a simple question that can cut through that particular fog: Whose problem are we solving?
We built a testbed with 100+ Bluetooth beacons on nRF52840 dongles to stress-test an IoT system at scale, catching firmware and OTA bugs before customers do.
This was a ridiculous idea I had one day: wouldn’t it be fun to have a personal, portable laugh track – you know, like in those old sitcoms where there would be a canned laughter after every joke. So I figured it would be a fun project to. This is the result: Online demo How it works The principle is simple: sample the microphone. If there is sound, wait until it gets quiet. Then play a laughter.…