Location: Florida on paper (U.S. citizen born & raised); Currently residing in Nicaragua
Remote: Yes
Willing to relocate: No
Technologies: Rust, TypeScript, Python, Docker, some PyTorch / Tensorflow
Résumé/CV: https://www.linkedin.com/in/jeremyharrisconsultant/
Email: [email protected]
I am using ChatGPT to help me build a language that has a domain-specific use case with non-LLM models. The interpreter is written in Rust and the language resembles a mix of Lisp and SQL.
I don't want a Facebook extension to have access to arbitrary data that I copy and paste in my browser, which may be unrelated to the app I'm developing at the time.
It seems to do well for a lot of searches, though some are questionable, but I believe that I know why. I'm training some different autoencoders to give it some different perspectives.
> Likes/dislikes are stored in local storage and compared against all stories using cosine similarity to find the most relevant stories.
You're referring to using the embeddings for cosine similarity?
I am doing something similar with stocks. Taking several decades worth of 10-Q statements for a majority of stocks and weighted ETF holdings and using an autoencoder to generate embeddings that I run cosine and euclidean algorithms on via Rust WASM.
I may have misinterpreted this comment by thinking you meant that as we squeeze more and more transistors into a small amount of space, the resulting waveforms would start to resemble analog more so than digital.
Excellent article. Judging by your graduation year I estimate that I am roughly 8 years older than you. I am hopeful that you still have plenty of time.