IdeaWell

Project ideas from Hacker News discussions.

How accurately calibrated is Jev?

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Theme 1 – Calibration and reliability of Jev’s probability outputs
- “On actual NLP problems (including ones with uncertainty under human review) it does appear to be well calibrated.” – cannedbread
- “Jev should ideally respond in a non‑random way, it should just list out the probabilities.” – jezzamon
- “Claims like this needs to be deeply analyzed… it’s hard to exactly say where there’s some internal mechanism generating a true answer and where we’re just getting lucky with some distribution.” – dvt
- “If I feed that into a model, the answer I want is: ‘the combination of the model and the provided state has nothing useful to add to your prior’.” – amluto

Theme 2 – Model’s inherent capabilities and limits (size, emergent learning, vs luck)
- “It’s a very small and dumb model. That’s the only reason why it’s so fast.” – singularity2001
- “Models do have some emergent capabilities… semantics is actually learned … some math seems like it also might be learned.” – dvt
- “Unless the numbers are all made up, this didn’t read like slop to me… maybe it’s your slopatron that’s miscalibrated!” – exe34

Theme 3 – Human analogy and expectations for model behavior (priors, uncertainty, pedantic answers)
- “Wouldn't asking humans have this same kind of problem too? … we also have our own biases.” – clarle
- “The author is expecting Jev to output the equivalent of 'H: 50%, T:50%', not the equivalent of 'TTHTTHTHH...'” – unholiness
- “The point of the theoretical problems is that they should be the easiest cases to handle. How can you trust the probabilities from real world classifiers if it can't even handle well defined problems.” – charcircuit
- “What I want out of a system like Jev is to tell me how the probabilities change as a result of the per‑sample data I provide.” – amluto

🚀 Project Ideas

Generating project ideas…

CalibrateLLM: Post-hoc Calibration Toolkit for LLMs

Summary

  • Provides automated temperature/vector scaling calibration for any LLM using a small validation set of factual or probabilistic queries.
  • Core value proposition: converts raw model logits into well‑calibrated probability estimates, enabling reliable uncertainty‑aware decisions.

Details

Key Value
Target Audience ML engineers, researchers, product teams using LLMs for classification, risk scoring, or any task requiring trustworthy probabilities
Core Feature Calibration pipeline that computes expected calibration error, reliability diagrams, and applies scaling to improve probability outputs
Tech Stack Python, PyTorch/HuggingFace Transformers, scikit‑learn, FastAPI for serving
Difficulty Medium
Monetization Revenue-ready: Subscription tier based on API calls or monthly active users

Notes

  • HN users lamented that models like Jev act as random number generators instead of giving calibrated probabilities (e.g., “Jev should ideally respond in a non‑random way, it should just list out the probabilities”). CalibrateLLM directly addresses this by turning model outputs into proper probability distributions.
  • Enables discussion around calibration techniques on HN and provides a practical utility for teams that need trustworthy uncertainty estimates without retraining large models.

BeliefUpdate: Bayesian Updating API for LLMs

Summary

  • Accepts a prior distribution and observed per‑sample data, then returns the updated posterior and predictive probabilities via a simple API.
  • Core value proposition: gives models the ability to explain how evidence changes beliefs, fulfilling the desire for dynamic probability updates.

Details

Key Value
Target Audience Data scientists, analysts, developers building decision‑support or advisory systems
Core Feature Bayesian updating engine (Dirichlet‑categorical, Gaussian‑Gaussian, etc.) that takes priors and counts, outputs posterior parameters and predictive distributions
Tech Stack Python, NumPy/SciPy, optional JAX for acceleration, FastAPI
Difficulty Medium
Monetization Revenue-ready: Pay‑per‑request or tiered usage plans

Notes

  • Directly mirrors amluto’s request: “What I want out of a system like Jev is to tell me how the probabilities change as a result of the per‑sample data I provide.” BeliefUpdate provides that mechanistic update.
  • Sparks practical discussion on HN about integrating Bayesian reasoning with LLMs and offers a ready‑to‑use tool for applications like spam detection, medical diagnosis, or any scenario where evidence accumulates over time.

Probabilistic Prompt Wrapper: Constrained Decoding for Deterministic Probability Outputs

Summary

  • Wraps any LLM with grammar‑guided (constrained) decoding to force structured JSON probability outputs instead of random samples.
  • Core value proposition: delivers reliable, deterministic probability estimates for tasks like coin flips, classification, or risk scoring directly from LLMs.

Details

Key Value
Target Audience Developers integrating LLMs into products that require explicit uncertainty quantification
Core Feature Uses outlines/lm-format-enforcer or similar to constrain generation to a probability schema (e.g., {“heads”:0.5,”tails”:0.5})
Tech Stack Python, HuggingFace Transformers, outlines or lm-format-enforcer, FastAPI
Difficulty Medium
Monetization Hobby (can be extended to usage‑based SaaS if demand grows)

Notes

  • Addresses jezzamon’s comment that “Jev should ideally respond in a non‑random way, it should just list out the probabilities.” The wrapper guarantees the model lists probabilities rather than sampling.
  • Provides a concrete tool for HN debate about whether LLMs can be trusted for probabilistic reasoning and offers a practical way to obtain repeatable, interpretable outputs from any model.

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