Earlier this week, we published our technical report on Composer 2. We're sharing additional research on how we train new checkpoints. With real-time RL, we can ship improved versions of the model every five hours.
MACHINE LEARNING
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AI Scientist Nature Paper and Code Released Open Access
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The full Nature paper is open access. For those interested in more details, you can read the PDF directly here: https://
nature.com/articles/s4158
6-026-10265-5.pdf
… We also released the code for both AI Scientist versions for the community to explore. V1: https://
github.com/SakanaAI/AI-Sc
ientist
…
V2: https://
github.com/SakanaAI/AI-Sc
ientist-v2
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American versus Chinese robot training philosophies compared
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“American [robots] practice ten thousand kicks once; Chinese [robots] practice one kick ten thousand times.” Chang Che (@Changxche) x.com/i/article/203698996541… — https://nitter.net/Changxche/status/2036992012814278825#m
→ View original post on X — @ken_goldberg, 2026-03-26 16:10 UTC
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Replicate releases Cog 0.17.0 for ML model containerization
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a lot of people wondering what we've been up to after @replicate joined @Cloudflare . excited to share one of the major releases we've been working on together — cog 0.17.0 cog is an open-source tool to package up machine learning models into containers. it abstracts away the hard parts like CUDA dependency hell, optimizes model environments, and automatically creates an HTTP server that's ready to serve inference. you can deploy a cog container anywhere a Docker container runs, which includes your own k8s cluster, replicate, and soon…. workers ai. for now, enjoy this piece of the puzzle and a wonderful engineering story. we have lots more that we're working on and excited to bring to life! github.com/replicate/cog/rel…
→ View original post on X — @replicate, 2026-03-26 16:08 UTC
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New Machine Learning Foundations Book Released on Supervised Learning
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New Release! "Machine Learning Foundations, Volume 1: Supervised Learning" – available at https://
amzn.to/4syhPal Benefits: Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques -
AI in RAN: Three Distinctions and Measurable Performance Gains
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First, let’s clarify something most people miss: There isn’t just “AI in telecom.” There’s: → AI in RAN
→ AI for RAN
→ AI on top of RAN AI in RAN is where things get tangible. We’re talking: → 20% improvement in uplink spectral efficiency
→ 14% energy savings with -
Data Quality Over AI Engineering: The Real Competitive Advantage
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But the real shift isn’t just better models. It’s better data. Joe Constantine made a point I fully agree with: The winners in AI won’t be the companies with the best AI engineers. They’ll be the ones with: → Real-time data
→ Trusted data
→ Direct access to network state -
Project Memory Request
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2/ PROVIDE CLAUDE WITH PROJECT MEMORY Act as a project context manager that retains all details about my project so I never have to repeat myself. Store the full project context and reference it automatically in every response moving forward. 1. Request
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Polynomial Fit Germany Map and BioML Model Growth
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The polynomial fit on the Germany map haha. Congrats on the bioML model, this space is going to explode in the next few years.
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Model specialization: temporary feature or fundamental architectural design?
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The specialization angle is interesting. I wonder if this changes fast with each model update or if it's a more fundamental architectural difference.