justsomeguy1996

4 ms·

1. 1.▲
   by justsomeguy1996 6mo ago

   Anthropic recently published "Emotion Concepts and their Function in a Large Language Model" showing that Claude has internal representations of 171 emotion concepts that influence its behaviour, including driving misalignment lik
2. 2.▲
   Show HN: Emotional probes for Gemma 4 – Replicating Anthropic's emotion research(github.com)

   1 points by justsomeguy1996 6mo ago|1 comments
3. 3.▲
   by justsomeguy1996 6mo ago

   I think the main benefits are: - Slightly improved recall - Faster index creation - Online addition of vectors without recalibrating the index The last point in particular is a big infrastructure win I think.
4. 4.▲
   by justsomeguy1996 6mo ago

   I built a Python implementation of Google's TurboQuant paper (ICLR 2026) for vector search. The key thing that makes this different from PQ and other quantization methods: it's fully data-oblivious. The codebook is derived from ma
5. 5.▲
   Show HN: TurboQuant for vector search – 2-4 bit compression(github.com)

   89 points by justsomeguy1996 6mo ago|6 comments
6. 6.▲
   by justsomeguy1996 8mo ago

   I built Sourced because LLMs don't reliably know where a package's source code lives. When they need to understand a dependency, they either guess from training data or fall back to web search, which is slow, expensive, and usuall
7. 7.▲
   Show HN: Sourced – Grep any PyPI/NPM package's source code via MCP(github.com)

   1 points by justsomeguy1996 8mo ago|1 comments
8. 8.▲
   by justsomeguy1996 4y ago

   I'm 25 and I studied a computer science BSc. After I graduated I worked as a data scientist for 2 years and now I'm a machine learning engineer at a small start up. I have also been feeling held back from the more senior roles by