Viewing profile — HardikVala

hey hn, wrote a python library for myself to visualize tensors. Makes learning and debugging deep learning code so much easier. Works seamlessly with colab/jupyter notebooks, and o…

A minimal implementation of ModernBERT in pure C, inspired by karpathy's llama2.c. The core (tokenizer + inference code) is around 1000 lines of code with no dependencies (except O…

Check out epoch.ai. You won't get a direct answer but it'll give you directional, and useful data points.

I constantly find myself having to print raw tensors (either by hand or dumping them to stdout), especially when reading pytorch / jax code, to understand the transformations, e.g.…

I think the frameworks became complex over time but were initially simpler because of the more managable set of AI workloads.

Considering the server version of llama.cpp for a commercial use case over bulkier options like vllm. But wondering if it's been battle-tested in production environments.

There might be but instead of thinking about it as a binary, think about it as a continuous conviction curve. Ask yourself, if this idea is true, what's the smallest testable predi…

Not the first product category you think of when it comes to AI consumer hardware but how about smart glasses (eg. Meta Raybans). Extrapolating to the future, AR glasses are probab…

Great recommendation. Based on the ToC, its similar to AIMA.

Andrew Ng's course is great for the learning NN's from scratch, but not understanding how NN's fit in the broader discipline of AI.

True true. I should qualify my statement: Most founders who are able to find micro-PMF in a niche generally can expand that niche or grow threw composition. But this is based on my…

I've been your position before and although I can't say I'm a successful entrepreneur, I can share lessons that helped me escape "analysis paralysis": 1. Fear. This is was a huge i…

No, but there is a PDF version, ahem, floating around on Reddit.

Was not aware of these resources. Thanks for sharing!

Will check out Discrete Mathematics, thanks!

AIMA is wide-ranging, a lot of which is not "must-have", but "nice-to-have" knowledge. But I do like its breadth-over-depth approach to get a full scope of the AI landscape.

3blue1brown's content on NN's is awesome -- The explanations are super intuitive. But I'm also looking to understand, as you say, the big picture and understand where NN's fit.

https://aima.cs.berkeley.edu/ has some good material on game AI. Its surface level but it does discuss SOTA methods like AlphaZero.

Exactly. Nailed it.

Thanks for the recommendations. What's your impression of these texts so far?