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codex-jev

Hyper-AI-Lab/codex-jev

Evidence selection and quota-aware recovery for Codex with optional Jev reranking.

Scoring & Ranking1.00Runner-up: Classification & RoutingViewfinderFunnel
Automated README summary

Automatically summarized from the README on 30 Sept 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

Codex Jev adds a retrieval layer to Codex for large repositories and noisy logs. It finds candidate evidence locally, optionally ranks sanitized excerpts with Jev, and returns bounded, source-addressed evidence with exact follow-up reads. It does not replace your coding model.

How it uses Jev

Jev reranks sanitized excerpts for evidence selection. The state is the query and candidate excerpts. Jev returns calibrated probabilities used to select up to 20 candidates, eight returned blocks and 48 KiB outbound. Small packets bypass Jev; cache hits avoid repeated paid calls.

Primitives:choicescore

Technique worth stealing

Local candidate retrieval with optional Jev reranking and bounded evidence return.

Try it

git clone, npm ci, npm run build, then python3 runtime/manage.py install with required arguments.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectnot found in the excerpt0.06
  • Shows a System One patternnot found in the excerpt0.18
  • Describes handling uncertaintynot found in the excerpt0.22
  • Reports measured numbersnot found in the excerpt0.05
  • Is a runnable toolFound in material0.82
  • Top-10 learning shortlist signalnot found in the excerpt0.27
  • Is a replica of the modelnot found in the excerpt0.07
  • Solves a common problem (0–2)score on a 0–2 scale1.33 / 2
  • About JevFound in material0.86
Signals
Jev is core to the project0.06Shows a System One pattern0.18Describes handling uncertainty0.22Reports measured numbers0.05Is a runnable tool0.82Top-10 learning shortlist signal0.27Is a replica of the model0.0700.51Solves a common problem (0–2)1.33 / 2012

no-judgment band (0.40–0.60)

Design patterns
Funnel0.80Viewfinder0.70Menu0.36Ghostwriter0.2500.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 30 Sept 2026.