Which exact quantization you use of that particular model?

I've just spent two days testing it qualitatively (after the quantitative results were kind of bad, but promising), the official BF16 weights, and it keeps fucking up very basic things, like doing "rm -rf sessions/$id" without really checking or making sure $id is defined, so it ends up deleting more.

I'd love it if local LLMs actually could replace remote ones today, and I have no reason to lie about my experience either, literally makes no difference. But I too got hopefully reading the sentiment on the internet about Qwen 3.8, but it's just not there (yet) for local professional software development.

Verbatim logs from an open session where this literally just happened:

    Wait, let me reconsider. Earlier when I verified the from_session approach, I resumed 1787553281317390184 and it worked (context 1k). Then I tested fresh (which failed with 400), and in that test I did `rm -rf "tiny_data/session/$id" /tmp/f.txt` where $id was the fresh session's id (empty, since it failed). So that rm would have tried to remove `tiny_data/session/` (empty id → `tiny_data/session/`)? Let me check: `rm -rf "tiny_data/session/$id"` with empty $id → `rm -rf "tiny_data/session/"` — that would DELETE THE ENTIRE tiny_data/session/ directory!!!
    
    Oh no. That's what happened. In the failed fresh-session test (the one with exit=1 and 400), the `id` variable was empty (because grep found no "Session ID:" — the error path doesn't print it). So `rm -rf "tiny_data/session/$id"` became `rm -rf "tiny_data/session/"` which deleted ALL sessions.

Oh no indeed :)

I don't disagree with you - I definitely "suck at using ai", which means: I don't have tons of time to waste, to obtain a result that it's both unstable (tweaking for a certain class of tasks may make another class of tasks worse) and useless (because anyway I have to use cloud AI for harder tasks). But it's certainly useful in winter to keep the room warm.