Tilak Marupilla @tilaknarasimha
Explorer | Embedded Systems | Signal processing | Deep Learning (just really trying to learn) San Francisco Joined June 2014-
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New fastest shortest-path algorithm in 41 years! Tsinghua researchers broke Dijkstra’s 1984 “sorting barrier,” achieving O(m log^(2/3) n) time. This means faster route planning, less traffic, cheaper deliveries, and more efficient networks - and a CS curriculum revamp =)
New blog post: We've never enjoyed working on Kernels more than this. We have some very fast AI-generated kernels with a simple multi-agent system. They're running close to or even surpassing Pytorch shipped kernels. (1/6) [🔗 link in final post]
🧵1/12 Just used OpenEvolve to automatically discover GPU kernels that beat expert baselines! 12.5% average performance improvement in transformer attention, with 106% peak speedup 🚀
Another inference time scaling technique from @SakanaAILabs to improve model accuracy. Would be awesome to build it try it with optillm - github.com/codelion/optil…
Another inference time scaling technique from @SakanaAILabs to improve model accuracy. Would be awesome to build it try it with optillm - github.com/codelion/optil…
Introducing MakoGenerate, a CUDA-writing AI agent. We're excited to make the research preview of MakoGenerate available today, completely free.
A 680-page interactive book on computer science algorithms This might be the sickest book i’ve seen in a long time! 22 chapters, 300+ interactive visuals, 250+ code snippets that can be edited and run + problems and solutions for practice. honestly this is 90% of a CS degree.
WeatherEdit: Controllable Weather Editing with 4D Gaussian Field Contributions: 1. Based on our analysis of weather editing characteristics, we introduce WeatherEdit, a comprehensive and efficient framework for realistic and controllable weather generation. Compared with…
The single most undervalued fact of linear algebra: matrices are graphs, and graphs are matrices. Encoding matrices as graphs is a cheat code, making complex behavior simple to study. Let me show you how!
omg.. this cant be real China’s 4DV AI just dropped 4D Gaussian Splatting, you can turn 2D video into 4D with sound.. imagine.. we will be able to change camera angle, zoom in/out while watching movies 5 examples:
12 Foundational AI Model Types ▪️ LLM ▪️ SLM ▪️ VLM ▪️ MLLM ▪️ LAM ▪️ LRM ▪️ MoE ▪️ SSM ▪️ RNN ▪️ CNN ▪️ SAM ▪️ LNN Save the list and check this out for explanations and links to the useful resources: huggingface.co/posts/Kseniase…
My sleep scores during recent travel were in the 90s. Now back in SF I am consistently back down to 70s, 80s. I am increasingly convinced that this is due to traffic noise from a nearby road/intersection where I live - every ~10min, a car, truck, bus, or motorcycle with a very…
Revisited @andy_l_jones's RL debugging post from a few years back. Still one of the most insightful guides out there. If your agent's acting weird, here's a great checklist: andyljones.com/posts/rl-debug…
Learn how to GPU-accelerate your code in modern CUDA C++ without writing everything from scratch! During #ISC2025, I'll be giving a talk at the Hamburg C++ User Group on 2025-06-11. It's open to the public. meetup.com/cppusergroupha…
I am still struggling with the implications of Optimus watching it on YouTube, learning how to do it with RL in simulation and nailing it zero-shot. This is the most important AI breakthrough of the year, I’d say. Implications go well beyond humanoid robots.
this killer paper by DeepSeek didn't get enough attention. they go in-depth into the ultimate question: "how to train LLMs with less budget and compute." they basically review all the techniques in the house: > Multi-head Latent Attention (MLA) > Multi-Token Prediction (MTP) >…
Tired of teleoperating your robots? We built a way to scale robot datasets without teleop, dynamic simulation, or even robot hardware. Just one smartphone scan + one human hand demo video → thousands of diverse robot trajectories. Trainable by diffusion policy and VLA models…
𝗧𝗵𝗶𝘀 𝗶𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝘆 𝗚𝗣𝗨𝘀 𝗰𝗿𝘂𝘀𝗵 𝗖𝗣𝗨𝘀 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗔𝗜 — 𝗮𝗻𝗱 𝗮𝗹𝘄𝗮𝘆𝘀 𝘄𝗶𝗹𝗹: ⬇️ Kudos for this brilliant visualization to DataInterview.com.
This is not 3D Gaussian Splatting, it's deformable beta splatting! Thanks to Rong Liu for letting me know code was live. Congrats to the whole team at USC! #3D #Radiancefield
Deep compression in vae is hard, but this paper beautifully explains how to achieve this
Physics Informed Machine Learning with applications in Materials, by @UW expert, Prof. Navid Zobeiry Coolest new series on Youtube! Thanks Navid for sharing your deep knowledge with us! 🙏🧠🤯 youtube.com/watch?v=ak2oTk… All code/slides: composites.uw.edu/AI/
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Children's Neuroscien... @CnIpgh
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OXMIQ @realoxmiqlabs
312 Followers 5 Following OXMIQ is rearchitecting GPUs from Atoms to Agents™ for next-gen AI, gaming & graphics. IP that scales from silicon to zettascale. visit https://t.co/H4b1y93Xtl for more info
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