occasional engineer, former wannabe pro-skier @canadaski

Toronto, Ontario
Joined March 2024
Pinned Tweet
We won the @HockeyAnalytics Big Data Cup 2026! Forechecking is usually described as a system label. We tried to show it as a spatial timing problem: who arrives first, which lane is open, and how fast F2/F3 take that lane away.
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7.25m after 900 gens and roughly 34,000 simulated vaults -> next up getting run up and jump included in the sims
got curious how long it'd take ml to beat the pole vault world record in sim. 300 generations of evolutionary search in mujoco later: 5.81m. still 49cm short of mondo with some pretty interesting technique
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got curious how long it'd take ml to beat the pole vault world record in sim. 300 generations of evolutionary search in mujoco later: 5.81m. still 49cm short of mondo with some pretty interesting technique
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Tomo is acquiring Fae. There are hundreds of personal AI companies. But one of them got added to more group chats with Tomo than any other: Fae Instead of battling it out, Fae’s community is coming home to @tomo. Together, we're building AI for everyday people.
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turns out you can just write the kernels yourself
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why do i need at least 3 different subscriptions to watch basketball legally 🤒🤒
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Here's a similar viz but with Harden, his wrist is harder to track Harden is really so in tune that his makes and misses look indistinguishable, only real difference is the arm lift comes a hair later on misses so the timing gets squeezed
been making some progress on figuring out why ft makes vs misses happen, at least for jarrett allen on his misses the hand goes early and the legs come in late while makes are more in sync
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been making some progress on figuring out why ft makes vs misses happen, at least for jarrett allen on his misses the hand goes early and the legs come in late while makes are more in sync
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still working on processing a few hundred more free throws so the sample size is still small atm but I think this is def a step in the right direction!
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"Why is the loss not going down?"
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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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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lblommesteyn.github.io/roste… compiled all my 2d and 3d blobs onto a site + did some analysis over time how certain rosters have changed shape and overall blob trends in the nba
I've really been enjoying @taylormetrics wyman diagrams but wanted to try and formalize his vibes - here's a sneak peak of some of the work I got done this past weekend
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Luke retweeted
I've really been enjoying @taylormetrics wyman diagrams but wanted to try and formalize his vibes - here's a sneak peak of some of the work I got done this past weekend
Heats roster construction
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Lfg no more gws cli shenanigans
We are making Markdown files work natively across Google Drive and Docs. You can now preview .md files in Drive, open them in Docs, edit them, comment on them, and collaborate without converting the file into a Google Doc!
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We are making Markdown files work natively across Google Drive and Docs. You can now preview .md files in Drive, open them in Docs, edit them, comment on them, and collaborate without converting the file into a Google Doc!
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Toronto deserves nice things too gta-atlas.vercel.app/
I've long wished that online mapping services were far richer and higher-density. They elide so much detail today! We have gloriously high-DPI screens... I want to wallow in cartographic filigree. AI of course now makes this possible: bayatlas.vercel.app
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Been wanting to add some personality to my website and recently built a shelf featuring some of my favorite reads pls leave book suggestions (esp fiction!)
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xitter.cf/grizzshutsdown/status/… inspo for the shelf btw
I loved working on this bookshelf interaction for my website
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now that jane street released the solutions to their reverse asic challenge, wanted to share my approach. my solution was one of their favs :) I rebuilt the chip's wiring straight from the layout, then let a sat solver find the answer
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update on the hand contact part: full 3d body now with hands wrapped around the ball fingers are still the model's best guess though (and unfortunately no beard), on video hands are like 10 pixels wide. and spin is a no from this footage, the seams just aren't reliably visible
Can you see a miss coming? Built a live Bayesian model on the 3D Harden free throws to find out. Turns out his body looks basically identical on makes and misses (which you'd expect for a HoF sg) - trying to figure out how to get some hand contact data + ball spin next
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