Anuj Dutt @anujdutt92
GenAI @Adobe | Previously Edge AI @Jabra_US | Program Advisor @UCIrvine | Ex ML Engineer @VideaHealth | Ex AI Researcher @Bose | Mentor @TFUGChandigarh anujdutt9.github.io Cupertino, CA Joined July 2012-
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Thanks for sharing our work @arankomatsuzaki ! Really excited about how reasoning-based memory drives and scales for self-evolving agents 💫 : 🏬ReasoningBank stores insights from both successful and failure trajectories; 🛠️ MaTTS builds on this powerful experience learner, and…
Thanks for sharing our work @arankomatsuzaki ! Really excited about how reasoning-based memory drives and scales for self-evolving agents 💫 : 🏬ReasoningBank stores insights from both successful and failure trajectories; 🛠️ MaTTS builds on this powerful experience learner, and…
This open-source NotebookLM alternative demonstrates a complete architecture for document-powered AI apps: 🏗️ Event-driven workflows orchestrate complex multi-step processes like document parsing, summary generation, and podcast creation ☁️ LlamaCloud handles the heavy lifting…
If you're learning math for AI/ML, try out Deep ML. It's a leetcode style platform for ML, focusing on code implementations for a variety of problems > Lin Alg / Calc / stats / prob > ML / DL / RL > Optimization / CV / NLP Solve a qs as you learn it! deep-ml.com
Today on Connectionism: establishing the conditions under which LoRA matches full fine-tuning performance, with new experimental results and a grounding in information theory
Today on Connectionism: establishing the conditions under which LoRA matches full fine-tuning performance, with new experimental results and a grounding in information theory
The sparse attention in the new DeepSeek v3.2 is quite simple. Here's a little sketch. - You have a full attention layer (or MLA as in DSV3). - You also have a lite-attention layer which only computes query-key scores. - From the lite layer you get the top-k indices for the each…
New in-depth blog post time: "Inside NVIDIA GPUs: Anatomy of high performance matmul kernels". If you want to deeply understand how one writes state of the art matmul kernels in CUDA read along. (Remember matmul is the single most important operation that transformers execute…
Deepseek is using TileLang instead of Triton. TileLang is a rlly elegant language! Also reminds me of this surface-level blog I wrote when first learning about it. It only takes less than 100 lines of code to achieve 630 TFLOPS for softmax attn fwd in TileLang (1.3x of FA2)
Deepseek is using TileLang instead of Triton. TileLang is a rlly elegant language! Also reminds me of this surface-level blog I wrote when first learning about it. It only takes less than 100 lines of code to achieve 630 TFLOPS for softmax attn fwd in TileLang (1.3x of FA2) https://t.co/hCLu73npSW
How does @deepseek_ai Sparse Attention (DSA) work? It has 2 components: the Lightning Indexer and Sparse Multi-Latent Attention (MLA). The indexer keeps a small key cache of 128 per token (vs. 512 for MLA). It scores incoming queries. The top-2048 tokens to pass to Sparse MLA.
How does @deepseek_ai Sparse Attention (DSA) work? It has 2 components: the Lightning Indexer and Sparse Multi-Latent Attention (MLA). The indexer keeps a small key cache of 128 per token (vs. 512 for MLA). It scores incoming queries. The top-2048 tokens to pass to Sparse MLA. https://t.co/QzzPRvAaNa
You're in a Research Scientist interview at OpenAI. The interviewer asks: "Our investors want us to contribute to open-source. o3 crushed benchmarks. But we can lose a competitive edge by open-sourcing it. What do we do?" You: "Release the research paper." Interview over.…
Two papers we submitted got accepted to NeuRIPS: Vision Transformers Don't Need Trained Registers (spotlight, x.com/nickhjiang/sta…) Same Task, Different Circuits (x.com/YNikankin/stat…) Understanding emergent computation in vision (and language) models enables so much!
Two papers we submitted got accepted to NeuRIPS: Vision Transformers Don't Need Trained Registers (spotlight, x.com/nickhjiang/sta…) Same Task, Different Circuits (x.com/YNikankin/stat…) Understanding emergent computation in vision (and language) models enables so much!
📚 Practical Guide to Bare Metal C++ by Alex Robenko arobenko.github.io/bare_metal_cpp/
Evaluating reasoning models is non-trivial. But you can use a verifier to check if answers are indeed correct. Just finished a 35-page chapter on building one from scratch. Lots of symbolic parsing, math equivalence, edge cases… quite the project. Sneak peek on GitHub below 🔗
more on this when you launch a cuda kernel, you are not running a function per say like we do in c++, you are handing an abstract specification of a parallelism, often in an intermediate form called ptx, to the nvidia driver, the driver acts as a final stage, just in time…
more on this when you launch a cuda kernel, you are not running a function per say like we do in c++, you are handing an abstract specification of a parallelism, often in an intermediate form called ptx, to the nvidia driver, the driver acts as a final stage, just in time…
Mem0 is now part of Microsoft’s AI Agents for Beginners course 🎉 If you want to dive deeper into AI Agent Memory and how it powers self-improving agents, this lesson is a great place to start. This lesson covers: → What AI Agent memory is and why it’s essential → Short-term…
You're in middle of a MLE interview at Spotify. "Our real-time recommendation model for the Discover Weekly playlist has high inference costs. You propose adding a cache to reduce latency and cost. What is the single biggest risk of this approach?" You: "Cache invalidation is…
Hardware-Aware Coding: CPU Architecture Concepts Every Developer Should Know by @abhi9u Abhinav has been writing some of the best articles on CPU architectures, and this particular one is an excellent read in the quoted context. blog.codingconfessions.com/p/hardware-awa…
Hardware-Aware Coding: CPU Architecture Concepts Every Developer Should Know by @abhi9u Abhinav has been writing some of the best articles on CPU architectures, and this particular one is an excellent read in the quoted context. blog.codingconfessions.com/p/hardware-awa… https://t.co/Gw2YFIfrOO
You can now train OpenAI gpt-oss with Reinforcement Learning in our free notebook! This notebook automatically creates faster kernels via RL. Unsloth RL achieves the fastest inference & lowest VRAM vs. any setup - 0 accuracy loss gpt-oss-20b GRPO Colab: colab.research.google.com/github/unsloth…
by the way. recently wrote a paper on this! for transformers, the number is about 3.6 bits-per-parameter so you would need 25GB ÷ 3.6 bits ≈ 56.9B parameters to exactly memorize Wikipedia that’s a pretty big model actually
by the way. recently wrote a paper on this! for transformers, the number is about 3.6 bits-per-parameter so you would need 25GB ÷ 3.6 bits ≈ 56.9B parameters to exactly memorize Wikipedia that’s a pretty big model actually https://t.co/CJXFMAOieC

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2048 Ventures 🧬 �... @2048vc
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LMSYS Org @lmsysorg
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Thiel Fellowship @thielfellowship
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