I'm a bit of an outlier in regards to today's HN culture. I'm still holding on to Stallman's idea that information wants to be free. There was a comment on another thread that aptly elucidated what I think is necessary.

Not everyone has the know-how or desire to compile the Linux kernel, or any what-have-you executable in user space. But a good amount of people do, and it's a boon to security/privacy that they do, and cryptography can verify that this work was done. (granted, manufacturing a computer is an international effort with a lot of unknowns, ie proprietary binary blobs in BIOS/UEFI) (and people are lax to validate the crypto themselves if it's not an automated thing; that's a UX problem imo)

I think we'll eventually find sufficiently performant LLM's trained on public domain information[0] that people can validate. Sufficiently performant training algorithms that people can validate[1]. And FOSS plugins to integrate with their FOSS IDE's which they can validate. And like the Linux kernel, volunteers to make sure there's nothing fishy going on end-to-end.

I have no horse in this race though. I'm just happy I can learn how these things work and toy around with getting quantized models to work well enough for my coding side projects. Though I often reach for a frontier model for serious coding issues, local for rubber-ducking, and Kagi's research assistant for initial planning.

[0] Pipe dream, unless international copyright law drastically changes. Since opening the dataset for any competent LLM will violate said laws, whether obtained legally or not themselves.

[1] Knowing the training algorithm and dataset will let it actually be open source models instead of open weights.

edit: These are just shower thoughts; didn't mean to write such a long response. Would love any pushback or any correspondence in general since it'll be on my mind all day.

Which is exactly what a model with it's layer is.

But seriously, have we tried to run neural data through this.