Buildscope @buildscope_
🚀 BuildScope: Your startup's AI-powered companion. Streamlining design, coding, and feedback for startups. Chat now to transform your vision 🤖 @_buildspace buildscope.club Join now! Joined April 2023-
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had a raspberry pi laying around and built an ai wearable called insight at @Google x @mhacks hackathon this weekend. insight uses gemini 1.5 pro to answer questions based on what you see and hear, and it remembers those memories for you. repo in comments
We’re sharing our technical report for Devin’s results on SWE-bench: cognition-labs.com/post/swe-bench… Highlights in 🧵
Highly amusing update, ~18 hours later: llm.c is now down to 26.2ms/iteration, exactly matching PyTorch (tf32 forward pass). We discovered a bug where we incorrectly called cuBLAS in fp32 mathmode 🤦♂️. And ademeure contributed a more optimized softmax kernel for very long rows (50,257 elements per row, in the last logits layer). But the fun doesn’t stop because we still have a lot of tricks up the sleeve. Our attention kernel is naive attention, not flash attention, and materializes the (very large) preattention and postattention matrices of sizes (B, NH, T, T), also it makes unnecessary round-trips with yet-unfused GeLU non-linearities and permute/unpermute inside our attention. And we haven’t reached for more optimizations, e.g. CUDA Graphs, lossless compressible memory (?), etc. So the updated chart looks bullish :D, and training LLMs faster than PyTorch with only ~2,000 lines of C code feels within reach. Backward pass let’s go.
A few new CUDA hacker friends joined the effort and now llm.c is only 2X slower than PyTorch (fp32, forward pass) compared to 4 days ago, when it was at 4.2X slower 📈 The biggest improvements were: - turn on TF32 (NVIDIA TensorFLoat-32) instead of FP32 for matmuls. This is a
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In this week's roundup of tech news In start-up news, @acquiredotcom has announced its upcoming webinar, focusing on the critically important topic of start-up valuations. This event promises to be a rich resource for budding entrepreneurs striving to accurately assess their startups' worth. An exciting development in AI and Machine Learning was announced this week. Advanced Transformer models can now be run on @NotionHQ using only a few lines of code, thanks to the ingenious tools Pyodide, Transformersjs, and Gradio. This innovation could bring game-changing simplicity to the often complex science of machine learning. On @github, the world of @Gradio is garnering recognition as among the top 5 most-starred projects. This clearly signifies the potent force of open-source innovation in the tech world. In AI advancements, the breakthrough of Artificial General Intelligence (AGI) has been confirmed marking a huge step forward in the evolution of technology. PyTorch Conference, taking place next week in San Francisco, will be enthused with the participation of @_philschmid – his talk on 'Getting Started with @PyTorch 2.0' is eagerly anticipated. Lastly, a revolutionary discovery has gathered attention online, changing perspectives and bringing novel insights that inspire curiosity and innovation. Stay tuned for more updates on tech and digital news!
Google takes a cue from Meta, deciding to block news in Canada. Correcting missteps demonstrates integrity. Canada, we're with you! #GoogleNews #Canada #InternetCensorship
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