Can‘t recommend the fast.ai course by Jeremy Howard highly enough, it walks you through building your own deep learning stack from scratch. What I really appreciate about it is that it demystifies a lot of jargon to what really are quite simple ideas at their core (e.g. „rectified linear unit“ sounding scary even though its literally only a line with a floor).
The 2022 edition isn’t so much about LLMs as about image generation with stable diffusion, but the underlying techniques are still foundational enough to be generally useful. YMMV, but for me building things from scratch, even if results don‘t reach SOTA, is the single most effective way to learn what‘s really going on.
If you enjoyed Permutation City, you‘d probably also like „The Metamorphosis of Prime Intellect“ by Roger Williams!
It explores a similar premise of a post-singularity future (though the mechanism is superintelligence rather than cellular automata/mind uploading), but rather than imagining exactly how we‘d get there, it tries to imagine what human flourishing would look like in a world of perfect abundance!
This is cool, basically the first 3D codebase visualization I've seen that doesn't immediately give me a headache, so good job! :)
Always interesting to see different ways of visualising the same thing. A while ago my friend and I also made a codebase visualisation tool ([https://www.codeatlas.dev/gallery](https://www.codeatlas.dev...), but instead of taking the graph route, we opted for Voronoi treemaps in 2D! It's a tradeoff between form and function for sure, modelling code as a DAG is definitely more powerful for static analysis. However, in most graph-based visualizations (this, gource) I just find myself getting lost super quickly, because the shapes are just not very recognisable.
Really impressed by how polished this already is, nice docs, on-the-fly rendering, congrats!
If I ever find time to work on codebase visualisation again, I might have to steal the idea of codebase entropy to better layout which files to place close to which others!
My friend and I made a codebase visualisation tool (https://www.codeatlas.dev/gallery) that's based on Voronoi treemaps, maybe of interest as an illustration of the aesthetics with a non-rectangular layout!
We've opted for zooming through double-clicks as the main method of navigating the map, because in deep codebases, the individual cells quickly get too small to accurately target with the cursor as shown in the key-path label approach!
The 2022 edition isn’t so much about LLMs as about image generation with stable diffusion, but the underlying techniques are still foundational enough to be generally useful. YMMV, but for me building things from scratch, even if results don‘t reach SOTA, is the single most effective way to learn what‘s really going on.