GLM 5.2 has been getting attention as the first open-weight model that feels frontier-adjacent on real work (MIT license, 1M context, benchmarks within striking range of Opus 4.8 on coding tasks). I wanted to see what it could actually do, so I had it build a multi-file project end-to-end: a browser computer vision studio with object detection, persistent object tracking, line crossing counting, and an LLM-generated activity report. Frontend + backend + CV pipeline + LLM integration. It worked. The app is open sourced (MIT) under the name TrackLab. Specifically: It wrote a planning doc first and used it to catch a subtle browser bug (canvas tainting from cross-origin video silently kills TF.js detection) before writing any detection code. It kept JSON contracts consistent across the tracker, report panel, and backend system prompt over many rounds of edits. The 1M context actually held. It self-verified by running production builds rather than declaring success. Trade-offs to know about: it’s text-only (no native image input on the base GLM 5.2 model), and the code style is functional rather than elegant. For an open-weight model at ~$1/$4.20 per M tokens on OpenRouter, this is genuinely interesting. How I implemented this: Used GLM 5.2 via OpenRouter as bring your own model inside Neo, an Autonomous AI Engineering agent that I’m working on. Repo and writeup link in the comments. submitted by /u/gvij
Originally posted by u/gvij on r/ArtificialInteligence
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