I know what you meant. But despite spending some decades doing that, I never got to the point that I found them lightweight, easy to understand, train, and infer. 🙂
MACHINE LEARNING
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Speed Challenge: Biotech Document Analysis Requires High-Performance Inference
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8/
But there was a brutal roadblock: speed. Raycaster often needed to analyze thousands of hundred-page documents at once. If the system lagged, the entire experience fell apart.
Normal inference could not keep up with real biotech workloads. -
RL Environments Critical as Agents Move to Production
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🤿 ICYMI: Agents don’t learn in the abstract — they learn by interacting with environments.
— Snorkel AI (@SnorkelAI) 12 décembre 2025
This post on RL environments is more relevant than ever as agents move into production and evaluation gets harder. pic.twitter.com/P96EfkYyBcICYMI: Agents don’t learn in the abstract — they learn by interacting with environments. This post on RL environments is more relevant than ever as agents move into production and evaluation gets harder.
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Snorkeling in RL Environments: Training Agentic AI Systems
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Worth a re-read https://
snorkel.ai/blog/snorkelin
g-in-rl-environments/
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#AgenticAI #ReinforcementLearning #LLMs #AIResearch #AIInfrastructure -
OpenAI’s Post-Training vs Google Anthropic Pre-Training Capabilities
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Yep, I just mean that it *feels* (could be very, very wrong) like OAI has much better post-training, Google/Anthropic have much better pre-training
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Anthropic’s Autopatch for Claude Code
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Anthropic is working on the Autopatch feature for Claude Code, where it would automatically scan your repository for security vulnerabilities. h/t @btibor91
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Pre-training vs Post-training: Google Anthropic OpenAI Strategy Comparison
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It all makes sense. Google + Anthropic focused on pre-training, so you get to a great answer quicker, but the capability ceiling is lower. OpenAI focused on post-training, so great answers take longer, but you can push the models much further.
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Optimiser la vitesse du code Vision par ordinateur
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⚡ Speed up your Computer Vision code ⚡
— Satya Mallick (@LearnOpenCV) 12 décembre 2025
Is your pipeline running slower than it should? You might be cloning/copying images too often — and paying the cost in memory allocations and data movement.
💡 Quick fixes:
✅ Prefer assignment (Mat B = A) unless you truly need an… pic.twitter.com/ZbGJHC1DCOSpeed up your Computer Vision code Is your pipeline running slower than it should? You might be cloning/copying images too often — and paying the cost in memory allocations and data movement. Quick fixes: Prefer assignment (Mat B = A) unless you truly need an
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Stanford SVL Hiring Software Developer for Robotics Simulation
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Want to make a big impact in robotic research? Come and work with robots and the smartest students @StanfordSVL
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We are hiring a software developer, focusing on simulation for robotics & robotic learning. You'll be working directly with me , @jiajunwu_cs and our amazing students -
Early adopters beat better chips through better data
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Early adopters beat better chips. Early adopters fed better data to systems. Something I have known for a long time. Open AI has better users. It has better data.