This might be a semi-joke, but for a large proportion of the real-life ML applications this is literally all you need. What is hard is not the code or the algorithm, but coming up with such a simple solution. A lot of times you need to work for months, or even years, gathering
MACHINE LEARNING
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SmolVLA report introduces Async inference to boost robot actions
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If you didn't yet, you should read the technical report for SmolVLA, published yesterday by the @huggingface robotics team!
Amongst other ideas, it introduces "Async inference" to boost their robot actions. Robots have a problem: performing the actions takes time (Unlike -
Finding Cost-Effective Learning Resources for Budget Students
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1/ Find Cost-Effective Learning Resources:
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 4 juin 2025
Prompt:
"I'm a student on a budget trying to learn [Subject]. Could you recommend free or low-cost resources, like websites, books, or online courses, where I can study [Subject] comprehensively?" pic.twitter.com/eO7WRxR9Qs1/ Find Cost-Effective Learning Resources: Prompt: "I'm a student on a budget trying to learn [Subject]. Could you recommend free or low-cost resources, like websites, books, or online courses, where I can study [Subject] comprehensively?"
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Efficient Tensor Computation Code Generation Using Linear Layouts
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Linear Layouts: Robust Code Generation of Efficient Tensor Computation Using 𝔽_2
Paper: https://
arxiv.org/pdf/2505.23819
.pdf
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OpenAI Linear Layouts: Tensor Mapping Innovation for DL Compilers
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A paper from OpenAI? Long time no see. This paper introduces Linear Layouts, a general, algebraic formulation of tensor layouts using binary linear algebra over bit representations, addressing longstanding challenges in DL compilers like Triton. FYI, Tensor layout = mapping
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Deep Learning in Microbiology: Critical Analysis of ML Enzyme Research
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I'm thrilled to see @math_rachel
's interesting new article, on a recent deep learning microbiology paper, getting the attention it deserves — check it out if you haven't seen it yet! https://
rachel.fast.ai/posts/2025-06-
04-enzyme-ml-fails/index.html
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Negative Reinforcement Boosts LLM Reasoning Effectiveness
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The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
Paper: https://
arxiv.org/pdf/2506.01347
.pdf
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Code: https://
github.com/TianHongZXY/RL
VR-Decomposed
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Penalizing Mistakes: Simple Yet Effective RL Reasoning Strategy
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Just Say No: Penalizing Mistakes May Be All You Need in RL for Reasoning This paper, from UVA and Princeton, dives into Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning tasks and reveals a surprising insight: penalizing wrong answers (NSR) can be more
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Self-Improving AI and the Darwin-Gödel Machine Deep Dive
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AI that can improve itself: A deep dive into self-improving AI and the Darwin-Gödel Machine. https://
richardcsuwandi.github.io/blog/2025/dgm/ Excellent blog post by @richardcsuwandi reviewing the Darwin Gödel Machine (DGM) and future implications. -
Math notation as compressed algorithmic reasoning for AI
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Math notation is essentially a very compressed form of algorithmic/logical reasoning. If you’ve been doing this stuff it becomes the second nature to you, just like being able to go through a complicated codebase is for an advanced SWE with many years of experience. You are only