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  • I made super quick file sorter with it.. Works like a charm: https://github.com/ioma8/jeff-sort
  • For anyone interested, we tried the adapter approach in a coding agent harness: https://jeffhub.ai/notes/jeff-v1-3

    What's interesting is that these are two adapters: Jeff making simple decisions (which tool to call, how to parameterize it) and Jeff routing between different version of Qwen. There are lots of applications for this approach.

    In terms of Jeff-Code specifically, treat it as an experiment. Swift-Qwen is faster, but also - I understand - worse on benchmarks. The interesting bit is the concept here.

  • Sorry, do we have actual clear implementation details for Jev? I keep seeing these "recreations" or "Do Jev at home" but do we have access to their architecture? I haven't even used the product, I just find it strange.
  • For those of a certain age - the fact that Askjev.com is still available astounds me.
  • I compared it to Jev in my current use cases and it's very inaccurate. 70% vs 94% . for classification, it's unacceptable.
  • What proportion of commercial LLM use is classification? I'm just wondering what happens to business AI spending/data centre usage when they realise they don't need full LLMs.
  • Can someone remove the extra LLM and just have an embedder do the classifier work?

    It's turning into pimp my llm...

  • If you're looking for a more impressive doom example, I put together laya-duum which uses open source micropython implementation of doom (duum) and freeware freedoom1.wad. It uses the standard jev api and will play through the first two levels to completion: https://github.com/Hadlock/laya-duum

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