Manuel Andersen @ManuelAndersen9
Joined April 2021-
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Per request, my talk: "Architecting large software projects" is out. youtu.be/sSpULGNHyoI
bored? build your own git: wyag.thb.lt build your own database: build-your-own.org/database/ build your own redis: build-redis-from-scratch.dev/en/introduction build your own neural networks: sausheong.github.io/posts/how-to-b… build your own os: youtube.com/watch?v=ZjwvMc… I can keep going....
Nice writeup by a PufferLib contributor!
Start building RL with puffer on GPU for brookies today: boxingbytes.github.io/2025/08/24/puf…
@ChShersh There's another Dmitri with a class. It may be retired, but this guy is pretty good - ex-quant. pluralsight.com/courses/cpp-hi…
some backstory: when i first started to use numpy (a bit after my 12th grade) i was surprised to see how fast it was and most importantly, how it handled n-dimensional arrays not just two. i remember writing a simple matmul in C and thinking what would it take to go from two to…
I annotated the tinygrad flash attention kernel to make sure I understand it. automatically generating this GENERICALLY is pretty cool!
I annotated the tinygrad flash attention kernel to make sure I understand it. automatically generating this GENERICALLY is pretty cool! https://t.co/efbgvNcppF
rare interview with an IDA dev just dropped 👀
I love @__tinygrad__ because it helps you be a better developer and engineer. Also, its one of the best places to learn how deep learning and gpu's work. I wrote a short article on my first 4 merged PRs to tinygrad and hope it helps someone contribute and learn a lot from this…
I derived a category-theoretic notion of a (CUDA) kernel as a parallelised function that works *shockingly* well, turning fusion into a compositional property. The remaining hurdle is figuring out how to deal with streamable/looped operations.
This is the reason why I left DeepMind and decided to build an AI security company. I've seen first-hand what RL can do for code generation. Once you treat exploit generation as an RL problem, no software is safe.
This is the reason why I left DeepMind and decided to build an AI security company. I've seen first-hand what RL can do for code generation. Once you treat exploit generation as an RL problem, no software is safe.
can any recent cs graduates tell me if they covered linters, something like pre-commits/basic git commands? i sat down a new team of juniors and ran a little workshop on basic pre-commit hooks, linters etc and how to make your own most told me colllege covered none of this
the kids (9 & 8) asked for a computer so I made a quest for them: build an Apple 1 from scratch and get a MacBook Air. kit just arrrived, I’ll keep you posted
the kids (9 & 8) asked for a computer so I made a quest for them: build an Apple 1 from scratch and get a MacBook Air. kit just arrrived, I’ll keep you posted https://t.co/2nJIeKPZiB
“Modern Databases” playlist on my channel is a great resource if you are keen to dive into Databases. There are 47 episodes purely on Databases. 47 hours of Database internals. 100s of interesting concepts and optimisation techniques. Relational, Columnar, Graph, Embedded,…
someone asked me about how to prepare to work for a systems engineering company this is the advice i gave honestly, i think it's useful for almost all software engineers -- effort spent learning things that change slowly is better amortized. and important things change slowly!
Did a @__tinygrad__ bounty together with github:IntendedConsequence and documented (link in the coments) the process. Was fun!
Since placement season is starting soon, here is a good compilation of the minimum knowledge required to clear most software interviews. I have written the word "minimum" so only adding important things & not everything. Written in priority-wise order. Thread🧵 (1/11)
What I talk about when I talk about IRs bernsteinbear.com/blog/irs/
The intro to Terence Tao's analysis book is a good litmus test of whether you'll like real analysis. If you think these "paradoxes" are interesting, you might enjoy analysis. If you think they're extremely dull mostly useless edge cases, like me, you probably won't
The recording for this talk is now available! tl;dr - GPUs scale like bandwidth, quadratic w.r.t latency - the key bandwidth they scale is math bandwidth (FLOP/s) - among math bandwidths, low precision matmuls have scaled fastest and some takeaways for data eng/data science
The recording for this talk is now available! tl;dr - GPUs scale like bandwidth, quadratic w.r.t latency - the key bandwidth they scale is math bandwidth (FLOP/s) - among math bandwidths, low precision matmuls have scaled fastest and some takeaways for data eng/data science https://t.co/u7gsbezKGG

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