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kadi

⚖️ Kadı — a calibrated judge in front of your frontier LLM. Routine decisions answered locally with a conformal-prediction guarantee (≤ α disagreement with the oracle); everything else escalated and learned from. Zero-dependency Python.

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This is 1 of 65 launches in llm evaluation and deployment tools — see how it stacks up on momentum and crowding →

17 other launches read as similar to this one →

Details

External ID
1413451466
Source
GITHUB
Company
—
Product
kadi
Website domain
github.com
Launched
Oct. 10, 2026
Cohort
—
Upvotes
18
Upvotes percentile
0.39
Tags
adaptive-conformal-inference, ai-agents, anthropic, claude, conformal-prediction, distribution-shift, gdpr, guardrails, knowledge-distillation, kvkk, llm, llm-cost-optimization, llm-routing, mlops, model-cascade, online-learning, privacy, python, uncertainty-quantification, zero-dependency

Enrichment

Niche
llm evaluation and deployment tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
calibrated decision judge for frontier llms
Manually corrected
False

Could you build this?

Partial While the zero-dependency Python wrapper is concise, implementing valid statistical conformal prediction algorithms with mathematical finite-sample error guarantees requires specialized statistics knowledge.

What it would actually take: The implementation relies on conformal prediction theory to calibrate confidence scores and determine non-conformity measures for small local models vs. frontier oracles. The hard part is the theoretical statistical calibration ensuring bounded error rate (alpha) across arbitrary non-exchangeable real-world LLM query distributions. It requires expertise in statistical machine learning and uncertainty quantification.

Competitors

Other products that read as similar to this one — 17 launches clear the similarity bar, closest 8 shown.

Attention rank: #14 of 18 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 187 days after the earliest competitor.

Other launches for this product