Identa
CLI to calibrate prompts across local LLMs
Details
- External ID
- 47650438
- Source
- HN
- Company
- —
- Product
- —
- Website domain
- —
- Launched
- April 5, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.11182519280205655
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
A prompt tuned for Llama 3 often degrades on Mistral or Qwen — same task, different behavioral surface. Identa automates the recalibration. It implements two things from the PromptBridge paper (arXiv:2512.01420):A transfer engine that learns a mapping between model behaviors using source/target prompt pairs A MAP-RPE evolutionary loop that iteratively improves candidates against a scoring function until behavioral parity is reachedWorks fully local via Ollama. Also supports OpenRouter for cross-hosted runs. No telemetry, no cloud dependency. Built with Python, Typer, Pydantic. Happy to go deep on the calibration algorithm or the tradeoffs in the scoring design.https://github.com/shepax/identa-agent
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- cli to calibrate prompts across local llms
- Manually corrected
- False
Could you build this?
Partial While building a basic CLI wrapper to test multiple LLMs is straightforward, faithfully implementing the PromptBridge algorithm requires understanding and implementing research-grade prompt calibration and behavior mapping algorithms.
What it would actually take: The system requires a Python/Rust CLI that interfaces with local inference engines (Ollama, llama.cpp, vLLM) and implements behavioral mapping from the PromptBridge paper (arXiv:2512.01420). The hard part is implementing the algorithmic behavioral latent space projection, loss computation, and automated prompt transformation to preserve cross-model semantics. This requires machine learning research expertise and familiarity with empirical evaluation metrics for local LLM token distributions.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 21 launches clear the similarity bar, closest 8 shown.
Attention rank: #18 of 22 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 79 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.
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