Drasen @comput_intelli

The exponential function has two key properties: it sets up a homomorphism between multiplication and addition (eˣ⁺ʸ=eˣeʸ), and it is an eigenfunction of d/dx (d(eˣ)/dx=eˣ). As far as I know the easiest explanation is Lie groups.

banger articles you should read

flowstate @k_flowstate

7 months ago

Day 5 of posting banger articles you should read

7 10 296 230K 223

Forty minutes of whiteboard. The full transformer architecture. Then open vim and write it in C.

AI slop is good, actually. Slop is what enables fast parallel experimentation. The etiquette and skill is understanding the boundaries of where slop exists and the extent to which it should be cleaned up and how. A few examples: I’m working on the internals of some system right now. The API and GUI of this thing is fully zero shame slop. It’s horrible. But it lets me focus on the core quality while shipping a usable piece of alpha quality software to testers (transparent about the slop frontend). Similarly, this system has plugins. We sent agents in Ralph loops overnight to generate dozens of plugins. The plugins are slop. The quality is bad. The plugin API/SDK is absolutely not done. But we can test a full GUI with a full plugin ecosystem. When we change the API, we can regenerate them all. The cost of change is just tokens, the velocity is incomparable to before. I built Terraform. We tested and shipped TF 0.1 with about 3 very weak providers. Because we ran out of time. Building was slow. And when we changed our SDK the cost was immense. Totally different today, 10 years later. Today, I would’ve slop generated 100 providers (again, with transparency and cleanup later, but just to prove it out). As an anti example, I would not PR this (without prior warning) to another project. I would not throw this onto customers without full review or transparency (as I’m already doing). I would not accept first pass slop. It’s almost never right. Slop is a tool. And like anything else it’s not blanket bad or good. The context is everything.

Algorithms by Jeff Erickson - one of the best algorithm books out there. The illustrations make complex concepts surprisingly easy to follow. Highly recommend this. jeffe.cs.illinois.edu/teaching/algor…

keyboard_arrow_left Previous keyboard_arrow_right Next

Vivek Galatage @vivekgalatage

2 months ago

My go-to algorithm visualizer for explaining DSA to students. algorithm-visualizer.org

4 206 1K 349K 1K

Python made AI accessible. Rust can make parts of AI understandable. That’s the bet behind Category Theory for Tiny ML in Rust. We’re building tiny ML systems from first principles using: Rust types typed transformations composition training loops category theory as an engineering tool Not abstraction cosplay. Executable structure. Working draft. Public feedback welcome.

Unpopular opinion: "AI makes everyone a developer" is true the same way "cameras makes everyone a photographer"

i built a magnetostatic solver + field renderer for arbitrary geometries

engineering is the highest form of art

keyboard_arrow_left Previous keyboard_arrow_right Next

the function of your professional life is to find the most natural structure that allows you to turn the things you do as naturally as breathing or walking into compounding capital and joy over decades this, necessarily, requires rotating quickly out of things that aren't it

Stanford @CS153Systems, Week 1 (Full Lecture) AI Scaling, Bottlenecks, and Why Compute Isn't a Commodity Yet 00:00 Compute Coachella 00:29 Simple Life Heuristic 01:08 Uncertainty Creates Opportunity 01:42 Four Bottlenecks Framework 01:51 Empirical Proof Matters 02:05 Cloud Costs Are Shifting 02:15 Verifiable vs Fuzzy Progress 02:48 Scaling Predictability Explained 03:43 CapEx Explosion in Big Tech 04:06 Chips Aren’t Commodities 04:45 Compute Scarcity Conclusion

The more I look at this the more impressed I am and the more I realize how grateful we should be to Tao. 1. He acknowledges ignorance: this is something academics almost never do since their cultural capital is tied up in them knowing things. But he can since, well, he's Terence Tao. 2. He is explicitly acknowledging his use of GenAI to fight the stigma of using AI. If the child prodigy turned UCLA prof who studied with Erdos uses AI, it is legitimate technology. (please start using this sentence with AI skeptics btw) 3. He is also showing how AI is best used: as a kind of syntactic tool that finds connections in possibility space and has access to a larger library of information than our brains can. There's more here but the cool internet thing is a list of three. I often lament Tao has too playful of a mode of operating, feeling like he plays with linear algebra when he should be doing foundations of mathematics. But not only does this moment prove my view wrong, it also proves just how much Silly Business Theory #SBT is right: in the future the best work, the greatest progress, and the most valuable innovations won't come from people laboring under the false consciousness of Protestantism and Marxism that asserts work must be hard and serious to produce value. The best work is going to come from people playing and having fun. We're on the cusp of a near utopian explosion in human potential and quality of life. And you're bearish?!?!?!?!??????!?

BURKOV @burkov

2 months ago

Terence Tao for some reason referenced ChatGPT as the source of some content in his paper. Expect scientists to cite spelling correctors: "By the way, the word 'elucidate' was suggested by Microsoft Word spelling corrector. I wasn't previously aware of 'elucidate'."

39 18 263 116K 109

Flamegraphs are a great way to narrow in on performance issues + broadly to explore program execution. I built a custom viewer for Postgres queries, inspired Jan Nidzwetzki's excellent blog on the subject. Highly recommend analysis tools like this for learning query execution.

This view just hits different 🌍 @Astro_Christina and @astro_reid take a moment to look back at Earth as they continue deep into space toward the Moon.

keyboard_arrow_left Previous keyboard_arrow_right Next

I first tried to read this book in 2018 and couldn't make it through because I thought it was too hard. 8 years later it's the only book I recommend every developer reads, and I had the chance to review the 2nd edition. Join a study group, give it a read.

keyboard_arrow_left Previous keyboard_arrow_right Next

Google’s Gemma 4 E2B running on-device on iPhone 17 Pro Gemma 4 is built from the same research as Gemini 3, has image understanding capabilities and can reason if needed Running at ~40tk/s with MLX optimized for Apple Silicon

Ollama is now updated to run the fastest on Apple silicon, powered by MLX, Apple's machine learning framework. This change unlocks much faster performance to accelerate demanding work on macOS: - Personal assistants like OpenClaw - Coding agents like Claude Code, OpenCode, or Codex

If you have a Thunderbolt or USB4 eGPU and a Mac, today is the day you've been waiting for! Apple finally approved our driver for both AMD and NVIDIA. It's so easy to install now a Qwen could do it, then it can run that Qwen...

ok it actually works, uggghhh

keyboard_arrow_left Previous keyboard_arrow_right Next

Vikram Sekar @vikramskr

12 months ago

Generative AI meets RF circuit design = game changer • Passive networks tailored by diffusion models. • Specify stop-band/pass-band; AI does the rest. • Pixel patterns are not intuitive to electrical response. Designs getting more abstract. Prepare for a cognitive shift.

62 325 2K 470K 1K