Overview & result
Now using @typesafeai Jev in http://aiseotracker.com, http://linkdr.com, http://genppt.com, etc AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast! Also for regular LLM calls, it is around 10x faster, 50% cheaper
From the original post
Now using @typesafeai Jev in https://t.co/c8wAW9titP, https://t.co/bt2EomCJNV, https://t.co/Naf5ujgWB4, etc
AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast!
Also for regular LLM https://t.co/0HB4RjHFRp
How Jev fits in the loop
- Ingest real-time application state and relevant contextual parameters
- Format the decision problem as a bounded Choice or Noul schema
- Query Jev to receive a typed probability distribution over the options
- Execute downstream actions or route tasks according to the winning choice
How to reproduce
- Inspect the original showcase and source material at https://x.com/illyism/status/2100489521557123265
- Verify the bounded prompt and input candidate schema configured for Jev
- Benchmark decision latency and classification accuracy against baseline models
Why this build matters
Demonstrates a real-world, cost-effective implementation of Jev in a coding & developer tool scenario, replacing expensive generative calls with fast typed decisions.
Limitations
- Task accuracy is bounded by the precision of the defined candidate choices
- Third-party external dependencies and network latency may affect total workflow duration