lalassu

The obvious question would be how does it compare to dedicated vector databases (qdrant, pinecone, chroma, etc..). What are the tradeoffs?

Can someone explains why does that work?

I mean you can't social engineer a human using poetry? Why does it work for LLMs? Is it an artefact of their architecture or how these guardrails are implemented?

I think many people are discovering again that the cost of software maintenance is much higher than the cost of building it the first time.

> 180 million-plus developers now work and build on GitHub

I did not realise the market for devtools is this huge. That explains why Cursor succeeded so much. That's 2% of humanity, no?

It's sad that they only compare to open weight models. I feel most users don't care much about OSS/not OSS. The value proposition is the quality of the generation for some use case.

I guess it says a bit about the state of European AI

I think the future of videos as a source of truth is bleak.

You can't trust anything that you can see in a video. And you can deny any video.

I think this will make an interesting post-truth world. What will happen when we can't almost prove anything. Eye witnesses are untrust worthy. Videos ditto. Cryptographic proof? Will politician sign their videos and messages with their public keys?

Disclaimer: I did not test this yet.

I don't want to make big generalizations. But one thing I noticed with chinese models, especially Kimi, is that it does very well on benchmarks, but fails on vibe testing. It feels a little bit over-fitting to the benchmark and less to the use cases.

I hope it's not the same here.

There is a very active account in twitter claiming to be her: https://x.com/ElizabethHolmes

I wonder what is the story behind it?