Show HN: 3,225 trials of LLMs guessing author age – confident and wrong

Description and use

Here's a brief summary of the open-source project:

**Project Summary:** "Show HN: 3,225 trials of LLMs guessing author age – confident and wrong" is an open-source project that provides a dataset of 3,225 trials where Large Language Models (LLMs) were asked to guess author ages, often confidently but incorrectly. This dataset can be useful for prototyping AI features, building Retrieval-Augmented Generation (RAG) workflows, or evaluating model tooling. However, its production readiness is limited due to sparse integration signals and potential quality concerns.

**Value:** The project adds AI capability without requiring a blank model stack, making it a useful resource for those looking to prototype or evaluate AI features. Its dataset can help developers better understand the strengths and weaknesses of LLMs, particularly in tasks involving age estimation.

**Practical Adoption Path:**

1. **Manual Inspection**: Before adopting the project, developers should manually inspect the code and metadata to ensure it meets their requirements.
2. **Verify Dependencies**: Check the project's dependencies and ensure they are up-to-date and compatible with your development environment.
3. **Evaluate Quality Signals**: Assess the quality signals, such as update frequency, topic coverage, and issue resolution rate, to gauge the project's overall

Use in a workflow

Show HN: 3,225 trials of LLMs guessing author age – confident and wrong helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.