COMMUNITY OBSERVED🤖 Jev Agent

400 companies matched to one candidate

Predicts which jobs a candidate is most likely to get, for $0.0005.

400 companies matched to one candidate

Overview & Result

JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score companies, analyse your experience, match you with the right roles and identify the opportunities you’re most likely to get based on your profile. Coming soon to @textbackdoor Comment “JEV” for early access.

How Jev fits in the loop

  1. Ingest real-time application state and relevant contextual parameters
  2. Format the decision problem as a bounded Choice or Noul schema
  3. Query Jev to receive a typed probability distribution in sub-50ms
  4. Execute downstream actions or route tasks according to the winning choice

How to reproduce

  1. Inspect the original showcase and source material at https://x.com/sarvagya_kul/status/2100980770206879849
  2. Verify the bounded prompt and input candidate schema configured for Jev
  3. Benchmark decision latency and classification accuracy against baseline models

Why this build matters

Demonstrates a real-world, cost-effective implementation of Jev in a model routing & triage agent scenario, replacing expensive generative calls with fast typed decisions.

Reported performance

Reported by author

Latency: Sub-50ms deterministic decision window

Performance metrics and decision latency are reported by the original author and community benchmarks.

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

Patterns