pudiklubi

it was an audio recording, transcribed with speech to text models. there's definitely some errors and words lost. I also tried to emphasize this

anything you'd want fixed immediately? happy to do so – or even take this down if you wish. it's your talk.

the way I read it it's more about how fast examples and references become irrelevant. fundamentals of the speech not.

nice catch! the original transcript kept saying dogs instead of docs. thats the only thing i fixed (until your r's find now) after laughing at it for a while

gotcha. def a good idea! i'm only a bit wary of making all of this a bit too put together, as to create an official source before there is one.

that being said, HN is a negative place, and not what I was trying to go for. thank you for your work with the slides!

so you took my transcript and put it behind a newsletter sub? haha. just share them!

haha, love how we have the two pieces of the puzzle. we should merge!

No – for more context you can check out Karpathy's original essay from 2017: https://karpathy.medium.com/software-2-0-a64152b37c35

For context - I was in the audience when Karpathy gave this amazing talk on software 3.0. YC has said the official video will take a few weeks to release, by which Karpathy himself said the talk will be deprecated.

https://x.com/karpathy/status/1935077692258558443

unexpected nice touch that i can immediately check out the price as well. thanks for sharing!

cool idea, i've looked for tools like this.

i'd love to import my own notes or something like that, to also capture the thoughts i had while reading

This is wild. I've been creating my own dataset of trending articles and ironically this is how I came across your post. I'm doing a similar project for my uni thesis.

I set out with similar hypotheses and goals like you (on a slightly different scale though, haha) but I've been completely stuck on the interactive map part. Definitely getting a lot of pointers from how you handled this!

Maybe one key difference in approach is that I've put more emphasis on trying to extract key topics as keywords.

For ex:

article (title): "Useful Uses of cat"

keywords: ['Software design', 'Contraction', 'Code changes', 'Modularity', 'Ease of extension']

My hypothesis is this will be a faster search solution than using the embeddings, but potentially not as accurate. Not that far yet to really prove this though.

Would love to hear what you think! Any other cool ideas on what could be done with the keywords? I explain my process a bit more here if interested: https://hackernews-demo.streamlit.app/#data-aggregation-meth...