HardikVala

7 ms·

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Show HN: Easily visualize torch, Jax, tf, NumPy, etc. tensors(github.com)
4 points by HardikVala 11mo ago|0 comments
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Show HN: ModernBERT in Pure C(github.com)
3 points by HardikVala 11mo ago|1 comments
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by HardikVala 11mo ago
Check out epoch.ai. You won't get a direct answer but it'll give you directional, and useful data points.
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Ask HN: How do you visualize tensors?
2 points by HardikVala 11mo ago|0 comments
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Prompt injection as a solution to make agents reliable(controlpla.in)
1 points by HardikVala 1y ago|0 comments
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by HardikVala 1y ago
I think the frameworks became complex over time but were initially simpler because of the more managable set of AI workloads.
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The Unstoppable Rise of Disposable ML Frameworks(petewarden.com)
1 points by HardikVala 1y ago|2 comments
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Ask HN: Is anybody using llama.cpp for production?
11 points by HardikVala 1y ago|1 comments
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by HardikVala 2y ago
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 prediction you can make? It can mean running a small experim
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by HardikVala 2y ago
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 probably going to be heavily dependent on AI.
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by HardikVala 2y ago
Great recommendation. Based on the ToC, its similar to AIMA.
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by HardikVala 2y ago
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.
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by HardikVala 2y ago
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 own anecdotal evidence, with a slant towards optimism.
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by HardikVala 2y ago
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 inhibitor of action. I was afraid
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by HardikVala 2y ago
No, but there is a PDF version, ahem, floating around on Reddit.
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by HardikVala 2y ago
Was not aware of these resources. Thanks for sharing!
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by HardikVala 2y ago
Will check out Discrete Mathematics, thanks!
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by HardikVala 2y ago
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.
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by HardikVala 2y ago
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.
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by HardikVala 2y ago
https://aima.cs.berkeley.edu/ has some good material on game AI. Its surface level but it does discuss SOTA methods like AlphaZero.
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by HardikVala 2y ago
Exactly. Nailed it.
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by HardikVala 2y ago
Thanks for the recommendations. What's your impression of these texts so far?
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by HardikVala 2y ago
There are so many attributes that its impossible to list them all, just like there are countless attributes that can distinguish a person, with new ones being discovered everyday. You mention "personality" and that's a good a
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by HardikVala 2y ago
Ya, the phrase "first principles" is vague...I meant starting from an axiomatic and actionable definition of AI and learning from there. The first chapter of AIMA does a swell job of enumerating different definitions of and then e
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by HardikVala 2y ago
Interesting idea. I like it.
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by HardikVala 2y ago
+1, I'm a few chapters in and its highly instructive. Gives me a deeper appreciation for the modern deep learning regime. Also, as we enter the agent supercycle, I think many of the basic algorithms for search, planning, etc. will make
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Ask HN: How to learn AI from first principles?
145 points by HardikVala 2y ago|56 comments
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by HardikVala 2y ago
An interesting coincidence is that the first fabless semiconductor company, Chips and Technologies, was also founded in 1985, by Dado Banatao and Gordon Campbell. They didn't partner with TSMC but contracted with companies like Hitachi
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by HardikVala 2y ago
This lines up well with Google's definition of "quality" when it comes to search engine content, which is it must be non-trivial to reproduce. Content generated by prompting LLMs is likely easy to reproduce, and hence lower q
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List of judging opportunities to help with O-1 visa / EB-1 green card(github.com)
2 points by HardikVala 2y ago|1 comments

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