Joined March 2024
Tried durian Don't understand the hate It doesn't even smell that bad And while I don’t love it, I don’t hate it either. Not that bad
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Jeffrey retweeted
Is Waterloo equal to Harvard, MIT, Stanford? San Francisco understands the power, strength, and potential of Waterloo better than anyone else. In SF, Waterloo is seen as an incubation chamber of engineering gods. A place where people's quality of output matches (or even surpasses) the very best, places like Stanford, Harvard, MIT, and beyond. In September, I attended the @ycombinator Demo Day to see what the world's entrepreneurial and tech elite think of Waterloo. This is Waterloo vs the World, ep. 2 - ft. YC founders @Maki_Potamianos and @shong0504! Shot by me, edited by @august_distef.
What do Waterloo students do after leaving school? I went to San Francisco to find out. This is Waterloo vs the World Episode 1: ft. @Michael42723115 and @vcarung edited by @august_distef
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I don't want a "dot." I don't want a "muse." I want a scheming Chinese eunuch. I want a silver-tongued, unctuous vizier who says "most judicious, sire."
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We’ve secured $75M in financing to build Solcoa One, our first commercial rare earth metallization plant.
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Jeffrey retweeted
Today we're announcing $75M to build Solcoa One, our 500-tonne rare earth metallization plant in Nevada. At Solcoa, we believe rare earths are the foundation of America's next great industrial era. An era we intend to win on the merits, with superior American technology.
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Jeffrey retweeted
I'm running to be the next Mayor of Waterloo, Canada. More information soon.
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1 year ago, @krish_mendapara and I left school and moved to California. At the time, we had a prototype we built in our dorm room and $4000. Today, the Matrix team has built the cheapest and fastest hormone test the world has ever seen.
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Jeffrey retweeted
today's my last day @cloudflare i was given the opportunity to work on the Workers AI team this summer, huge shoutout to @kevin_flansburg and @michellechen for taking me under their wing and letting me work on some very exciting problems around optimizing and improving their entire inference and model serving stack! very grateful for all the flexibility i was given to work on whatever was most interesting to me and the time i had to close some of the gaps in my inexperience i also got the chance to work with many talented engineers and researchers on this small (but very mighty!) team: Alex, Xiang, @KastanDay it was a great run! i'll still be in SF this upcoming fall, but no longer at Cloudflare - more news to come :)
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Today, we're launching @belvedir_ai We're making private AI models cheap and easy so the world can have trillions of them. Private AI models are cheaper, better, faster, and protect your data from frontier labs, but they're still to expensive and difficult to make for startups and consumers. Belvedir makes private AI models autonomously with less than 5 minutes of setup, so that private AI can finally be accessible to everyone
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Japan spent 17 years developing its premium “Red Princess” citrus, priced at ¥500 each. Just months after launch, Chinese sellers flooded Taobao with stolen saplings grown in China. When confronted, one Chinese farmer smirked on camera: “Don’t you also hope that Chinese people can eat delicious fruit? Ehime farmers and officials are left heartbroken and powerless.
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Jeffrey retweeted
I just spent months handwriting a 200 page guide on the entirety of ML foundations and math from scratch. The guide features: - Neural Nets (Backprop, Adam, SGD, Batch Norm) - ML Algorithms (SVM, Grad Boosting, K-means, PCA) - Hardware (Tensor Cores, Systolic Arrays, CUDA) - Transformers (Multi-Head Attn, KV Cache, LoRA) - Vision (ViT, Convolutions, MAE, IoU, NMS, VLM) - Agents (OpenClaw, ReAct, Memory, Orchestration) Everything I wish I had years ago, for free.
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The fallacy of this is that more creates more. More hours, more hiring, more something. And it is true in a sense. If you put in more work, more work will happen. But I think for most startups, the leverage is really in how differently you approach the problem, how well you cultivate your team, and the strategy. Any large company can outspend you on hours. They have thousands or tens of thousands more people, spending more hours. If hours worked were the metric, every large company and government organization would always win and do the best work. More hours, better output. This thinking is often representative of younger founders, where the startup becomes their identity and life. They have a hard time doing anything else, and cannot understand that your work is not the person that is you. But activities outside of work can grow you as a person too and make you do better work. I’ve never worked this way. As a designer, I always saw the need to take a step back, to take a break. At times, I might work 12 hours or 16 hours, or whatever amount was needed, but it wasn’t the norm. You just can't grind design, you need inspiration. But taking that step away from the work, would give me more perspective, inspiration and I could approach the problem differently or I could just see the solution. Grinding is never good for any creative problem, and startups or creating new products are often mostly about creative problem solving. Grinding works ok for email jobs, or where you just executing on very clear playbook. With Linear, we’ve never worked this way. We work reasonable hours, 5 days a week. All of us founders have families. Many of our employees have families. I personally stop every evening, spend time with the family, cook dinner for the family, eat dinner together, and focus on things outside of work. Sometimes I work in the late evenings or weekends, but to me the pride is that I don’t need to. Company should be succesful without it. My goal is to build a company that is sustainable in the long term, and doesn’t require heroics or personal sacrifices every single day. There are times when our team is heroic. Launches, incidents, some other work that just needs to be done. They will work late into the night because they know it is the right thing. But we don’t require that every day or every week, and the more this happens, the more I think it is a failure of our company and leadership. The team and the leaders should always keep a reserve to use when something is needed. Our thinking was also that quality, which we value, doesn’t emerge from working more or stressing people more. It emerges when you create the conditions for it to emerge. Often it is the appreciation, space, time, and how the person feels. A person who is rested will do better work. I wouldn’t attribute much of our success to working a lot. The success came from having clear thinking, ideas, and focus to do the right things. I sometimes wish we could move the culture more toward a Zen master. Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort.
"If you are not working 7 days per week, you are going to lose". Corgi Insurance is the most intense workplace culture in startups. - The company works 7 days per week. - Founder (@nico_laqua) lives and sleeps in the office. - He built a cafe in the office because there was no local cafe that was open 24/7. - 2/3 of the first 30 team members have the Corgi logo as a tattoo. Today I went behind the scenes with Nico, who has used this culture to scale the company to a $2.6BN valuation in just two years. My condensed notes below: 1. If You Are Not Working 7 Days Per Week, You Are Going to Lose: Whatever you can get done in 5 days, you'll get more done in 6 and 7. If you are trying to solve the world’s hardest problems, a standard 5-day workweek will not cut it. 2. Work Trials Repel the Mediocre: Corgi forces candidates into mock work trials over the weekend. If seeing a full office on a Saturday scares them, they don't belong. True intensity acts as a natural filter to attract killers and repel clock-watchers. 3. Lead from the Front Lines You can’t demand 7-day weeks while sitting on a yacht. Nico sleeps 3–4 hours a night on a mattress inside the office. If you want your troops to bleed, you have to be in the trenches with them. 4. Culture Only Means One Thing: Winning Forget superficial jargon like "hackers" or "ex-founders." Strip away the corporate fluff. A great startup culture is aggressively optimized around one single word: Winning. 5. Lifespan vs. Victories Building something world-historic requires radical sacrifice. When asked if he'd rather build a trillion-dollar company and die at 50, or fail and live to 80, the answer was easy. "I would rather measure my lifespan in victories." 6. Reject the Comfort of "Quiet Quitting." If you are operating in a hyper-growth environment and your days off happen to be Saturday and Sunday every single week, you are quiet quitting. To win, you must deliberately bypass the off-ramps of personal comfort and low volatility. Corgi isn't for everyone—and that’s exactly the point.
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Medicine only works if it reaches people That's why we're building agents that navigate the intricacies of healthcare end to end, accelerating medicine for those who need it, prescribe it, and create it Proud to share Forus and $160M raised!
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I added KV caching and INT8 KV quantization to our transformer inference, improving throughput by 35x. All of this was done from scratch in Rust + CUDA, on top of a homemade ML framework. On a 4-token prompt with 252 generated tokens: - Original: 0.76 tok/s - KV cache fp32: 27.21 tok/s - KV cache int8 (quantized): 27.29 tok/s Try it out yourself here: mni-ml.github.io/demos/kv-ca… In practice: - KV caching gave us about a 35x end-to-end speedup - INT8 KV cache kept roughly the same speed as fp32 but cut KV cache memory by 3.78x FP32 cache used 4.5 MB in this run while the INT8 cache used only 1.19 MB This simple change to inference created a huge impact on performance. To learn more about the KV cache and other optimizations like this, check out the blog at mni.ml!
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I built a neural network from scratch without using PyTorch, TensorFlow, or any libraries for that matter. Instead, I implemented the core math myself. I'm working on making my own machine learning framework from scratch with my friend @_reesechong. He previously trained a similar neural network, but using just scalars. The next step was to use tensors instead. The benefit of this is clear: when using scalars each data point is looped through separately creating its own node in a computation graph. With tensors, these separate nodes are stored together, allowing one forward and backward pass for the whole batch, greatly improving the efficiency of training. We often hear the saying "don't reinvent the wheel", but in my experience rebuilding technologies that abstract away a lot of complexity gives you a better and more thorough understanding of how the system works. Results of the training are shown below. Feel free to checkout the repo and read through the code, linked in the replies.
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Cursor Freeform at Waterloo
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Jeffrey retweeted
You can turn your laptop into a trumpet
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Jeffrey retweeted
we just built git for video editing.
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so maybe a long shot but... @MartinGarrix you wanna run this at a concert for real? i will build it for you. @stmpdrcrds
the riskiest live demo i’ve ever pulled off: 2000 phones synchronized with light and audio down to the millisecond. i'll never forget this night.
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