Akash Gupta
@AkashGu30808281
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Don't think too hard, just have fun with it
Joined January 2020
an ai product???? no no no no product why would release a product? if you show a product people will ask about benchmarks and it will never be enough frontier labs that were the 100X becomes the 2X saas dog but if you have no product you can say you building
SSI strategy of not releasing a product is probably a good one. The minute one releases a product, one will be dragged into so fierce competition with OAI, gemini, ... that the original goal will be forgotten. Maybe it would have been wiser for Anthropic to never release Claude
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We beat Nvidia’s cuBLAS kernels on B200s in 170 LOC. Using zero CUDA. Just pure Mojo. Here’s exactly how we went from 1% to 106% of Nvidia benchmark perf from scratch (with code) 👇🧵
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The novice kept trying to restart the diverging training run. Noam Shazeer said, "you can't fix it without understanding what's wrong", and restarted it. It worked.
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We're about to start a Q&A with Brilliant Labs about the Halo AI Glasses that launched today! Join us now and ask them your questions: https://t.co/hUJ2RlKTCX Text only! #smartglasses #aiglasses #augmentedreality
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But there's five issues I see: 1. They used ZERO healthy patients 95% of sore throats are viral and this AI was only tested on incredibly rare diagnostic cases. We don't know if it will order biopsies on every patient with a sore throat "just to rule out rhabdomyosarcoma."
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ok it actually works, uggghhh
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.
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Wrapped up Stanford CS336 (Language Models from Scratch), taught with an amazing team @tatsu_hashimoto @marcelroed @neilbband @rckpudi. Researchers are becoming detached from the technical details of how LMs work. In CS336, we try to fix that by having students build everything:
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🎉 Thrilled @GoogleDeepMind included ZeroBench in the Gemini 2.5 technical report as a benchmark for image understanding. Gemini has made impressive gains—it’s great to see our benchmark is still challenging for frontier models!
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New Paper: Continuous Thought Machines 🧠 Neurons in brains use timing and synchronization in the way that they compute, but this is largely ignored in modern neural nets. We believe neural timing is key for the flexibility and adaptability of biological intelligence. We
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I implemented an LLM end-to-end in hardware, and ran it on an FPGA. Zero Python. Zero CUDA. Just pure SysVerilog. All my progress + everything I learned from 200h of LLM chip design (demo at the end)👇
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This visual reasoning benchmark is tantalising. Together with the coauthors, I curated and reviewed many of the questions in this benchmark and I'd certainly be taken aback if contemporary models can confidently work through these. Have a go yourself!
Is computer vision “solved”? Not yet Current models score 0% on ZeroBench 🧵1/6
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We need you, eagle-eyed folks of X! Help us red team ZeroBench to find errors To recognise effort, we will offer co-authorship to those who find new issues Details below 1/5
Is computer vision “solved”? Not yet Current models score 0% on ZeroBench 🧵1/6
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I'm able to do basic prompt injections with the invisible bytes but I can't get it to work without explicit decoding hints. https://t.co/WAvp6feRZO The thinking models actually feel a bit more susceptible because they love puzzles and they notice the added bytes and get very
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🚀Big news! We’re launching Project Starlight: the first-ever diffusion model for video restoration. Enhance old, low-quality videos to stunning high-resolution. This is our biggest leap since Video AI was first launched. Like & comment Starlight 👇 to get early-access!
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