I teach companies and small businesses how to use AI, and their session recordings are full of things they don't want fed to an AI vendor. This app, Observer, reads a session transcript on a model I control, Qwen3-4B on an Akamai RTX 4000, and returns what was taught, where people got stuck, and what to teach next. The interesting part is that the model is not trusted. It proposes moments and quotes; a Python script then checks that every quote appears literally in the transcript and sends the failures back for the real wording. In one block the model paraphrased twenty quotes and seventeen came back correct on the retry. A second pass checks the labels the same way, which dropped claimed "teaching moments" from 60% of the output to 11%. Owning the inference is what makes that affordable. On a metered API you swallow the bad output because retries cost money. On a GPU you control, you retry until it's right.
Built at Build AI agents that own their inference - #SFTechWeek
Observer
I teach companies and small businesses how to use AI, and their session recordings are full of things they don't want fed to an AI vendor. This app, Observer, reads a session transcript on a model I control, Qwen3-4B on an Akamai RTX 4000, and returns what was taught, where people got stuck, and what to teach next. The interesting part is that the model is not trusted. It proposes moments and quotes; a Python script then checks that every quote appears literally in the transcript and sends the failures back for the real wording. In one block the model paraphrased twenty quotes and seventeen came back correct on the retry. A second pass checks the labels the same way, which dropped claimed "teaching moments" from 60% of the output to 11%. Owning the inference is what makes that affordable. On a metered API you swallow the bad output because retries cost money. On a GPU you control, you retry until it's right.
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HackerSquad project
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AgentFight
AgentFight
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