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bfg_damienon String theory inspires a brilliant, baffling new math proof

Posting the paper: https://arxiv.org/abs/2508.05105

bfg_damienon Re-implementing LangChain in 100 lines of code

I am slowly coming around to the same conclusion. It isn't always clear how some agent types are different from others. Sometimes the prompts expect JSON blobs and sometimes they expect something else. I tried it out because I could see the potential, but I dont think it's architect-ed in a way that is suitable for things beyond simple PoCs.

It would probably be much better to start with the basic OpenAI API and then build on top of it.

What I find particularly frustrating is the difficulty in easily interfacing with my existing python tools (not like add two numbers, but somewhat complex analytics on top of structured data). If anybody has any success with interfacing with existing tools/scripts, would love to know how people are going about doing it.

bfg_damienon Ask HN: How to Learn LLM Fundamentals

Here are a few resources that might help get you started:

1. A thread explaining the internal working of transformers: https://twitter.com/hippopedoid/status/1641432291149848576?s...

2. Paper by DeepMind which provides pseudo-code for important algorithms for Transformer models: https://arxiv.org/pdf/2207.09238.pdf

3. Another thread specifically on large language models: https://twitter.com/cwolferesearch/status/164044611134855577...

Once again these are not courses per se, but do provide intuitive explanations for how transformers work. There is also the nanoGPT series of videos by Karpathy on youtube. First video here: https://www.youtube.com/watch?v=kCc8FmEb1nY

bfg_damienon Ask HN: Guide to Understand Equity Compensation

Thank you very much! It does seem exhaustive but I almost prefer that.