
transcript
show notes
In this episode:
how you fingerprint a model you know nothing about,
why the evidence points at Z.ai's unreleased multimodal GLM,
what the 113-task benchmark runs really show versus the viral 80 percent,
the three contradictory data policies governing your prompts,
and the thought I keep coming back to – that the platform a model launches on is becoming as decisive as the lab that trained it.
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Sources and further reading
Ox Alpha vs GLM-5.3 on OpenRouter: https://openrouter.ai/compare/stealth/ox-alpha/z-ai/glm-5.3
Ox Alpha on OpenCode https://opencode.ai/data/unknown/ox-alpha
OpenCode Zen documentation https://dev.opencode.ai/docs/zen
OpenRouter Stealth Model Terms https://openrouter.ai/terms/stealth
The Tokenizer Is a Fingerprint by Joseph Elstner https://isimplifyme.com/whitepapers/the-tokenizer-is-a-fingerprint
DeepSWE result https://x.com/winkey_h/status/2090814178810306874/photo/1
58.4% run, MatchaOnMuffins/oxalpha https://github.com/MatchaOnMuffins/oxalpha/blob/main/README.md
64.6% run, jyeric/ox-alpha-deepswe https://github.com/jyeric/ox-alpha-deepswe/blob/main/README.md
Community fingerprinting summary: https://cellcog.ai/blog/what-is-ox-alpha/
Prediction market on the reveal: https://manifold.markets/Sketchy/who-is-behind-ox-alpha-the-mysterio
#OxAlpha #OpenRouter #OpenCode #GLM #AIcoding #stealthmodel