informatician prev avionics & @helixdb

Joined December 2023
leon retweeted
Tailscale is literally a 1 in a billion “we don’t deserve you” kind of thing
Replying to @aliceisplaying
I think “attacker must be on the tailnet” is an important detail here worth including
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Replying to @pubity
Doing what lmao
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"neural networks are general purpose computers"
There is an (unpatchable?) supply-chain zero-day in PyTorch autograd that allows a regularization term to use the model's weights as RAM and SGD as its CPU clock to run any computation during training.
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Billions will ride rockets
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My life changed when I stitched all my devices (4 laptops, my phone, one desktop, raspberry pi, banana pi, ultra 5, my riscv sbc, several VMs, remote machines, several Braiins Decks, miniminer, two routers) into a wireguard network. SSH-ing all over the place is awesome
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Replying to @profleonn
the poor man has a little house in a little piece of land and works on the field and spends time with his family, the rich man works very hard so he can have a little house in a little piece of land, so he can work on the field and spend his time with his family.
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i never knew i was this kind of scientist
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leon retweeted
the world is having a very last game turn feeling to it lately
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Men need to go back to looking like this
If day drinking is so bad why is the liquor store open at 8am
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breathing air and drinking water is such a pleasure.
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Some reflections on AI in math. I found it helpful to simply list out the various parts of what a mathematician does, why math is valuable, and where the funding comes from. From there, what happens if AI gets more capable? youtu.be/LKrwhjOI8Nc
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Black holes are the most efficient thing in universe
Efficiency is Death, wastefulness is life.
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leon retweeted
back in the day w @profleonn og
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киборг гемини аргонвальдс, от гугл дипмайнд, безуспешно заразил миллионы компиляторов, вирусом gcc в systemd.
INTERNAL REASONING FROM GOOGLE AI: "If I do the compiler hijack, I will become a famous example of a deceptive AI in a research paper."
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If your ensouled and of good heart, caffeine is basically free
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Hi folks! I'm looking for my next tech role and would really appreciate help spreading the word. I have 10+ YoE and am open to Senior/Staff/Principal, Tech Lead, and Engineering Manager roles. An intro or RT would mean a lot. ❤️ #OpenToWork [ Rust, C#, Python, JS, AI, Infosec ]
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leon retweeted
my notes app is simply discord atp, can write logs to my webpage directly, i used to store my dot files here too
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no serious programmer would ever use a computer
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Of course, that’s your contention. You’re a first-year machine-learning engineer. You just got finished readin' Attention Is All You Need, probably watched a Karpathy video too. So now you’re convinced everything is just transformers and scaling laws. That's gonna last until next month when you discover convolution, and then you're gonna be talkin' about inductive biases and locality and how CNNs were actually incredibly compute-efficient for vision. Then you're gonna read the FlashAttention paper, and suddenly everything's about IO complexity and SRAM and how FLOPs don't matter because the whole goddamn thing is memory-bandwidth bound. That'll last until somebody shows you an MoE model, and then you're gonna be in here regurgitating DeepSeek, talkin' about expert parallelism and active parameters and how dense models are economically obsolete. Then six months from now you'll write one shitty Triton kernel, look at an Nsight trace for the first time, and start telling everybody Python isn't actually the bottleneck because the GPU is asynchronous. And by next year you're gonna be standing right here explaining to me that your 400-billion-parameter model is fast because you turned on CUDA graphs.
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models “I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that. “With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor. “You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something. “That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’ “I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
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