# The 'Astra can read and write weights to improve Qwen' post is misleading

- HackerByte page: <https://hackerbyte.net/en/stories/a-post-clarifying-misconceptions-about-weight-transfer-between-machine-learning-50351111>
- Original source: <https://honnibal.dev/blog/astra-read-write-misleading>
- Hacker News discussion: <https://news.ycombinator.com/item?id=50351111>
- Submitted to Hacker News: 2026-11-03T12:21:17.000Z

A post clarifying misconceptions about weight transfer between machine learning models and Astra's capabilities in this regard.

## AI-generated summary

This article critiques a recent post claiming that Astra can improve Qwen models by directly manipulating their weights. The author argues that the original post misrepresents the experiments, conflating simple weight reading with complex tasks involving Python scripting and model execution. While Astra demonstrates capabilities in generating code to modify weights, the claim of 'just looking at raw weights' is only applicable to smaller, less complex models and not the large language models like Qwen.

## Key points

- The original post misleadingly suggests Astra can improve Qwen by only reading raw weights.
- Experiments involving large models like Qwen actually use Python to write programs that modify weights.
- Directly reading raw weights is only performed on smaller networks \(17K\-172K weights\).
- The Qwen experiment shows Astra can devise tasks similar to human ML engineers, including writing code.
- Conflating simple weight reading with complex code generation tasks misrepresents Astra's capabilities.