Building AI agents, exploring cognitive science, unraveling bioinformatics

Why biological systems offer the ideal training ground for reinforcement learning: automated verification through physics, not human judgment. From protein design with AlphaFold to RNA folding with ViennaRNA, biology provides the verifiable inverse problems that RL needs at scale.

Machine learning is illuminating biology's hidden half: intrinsically disordered proteins and RNA structures that traditional methods could never capture. But can we trust what we're seeing?

World models enable AI agents to imagine futures and plan actions, achieving 10-100x better sample efficiency than traditional reinforcement learning. From DreamerV3 collecting diamonds in Minecraft to foundation models like Sora and Genie, world models represent AI's shift from pattern matching to simulating reality itself.

Smart people disagree dramatically about whether AGI is five years away or fifty. The evidence reveals why this definitional crisis matters more than the technology itself.

MIT researchers used generative AI to create novel antibiotics from scratch, not finding them, but designing them. The breakthrough matters less for what it creates than for what it proves.

Learn how DSPy transforms brittle prompt engineering into systematic, testable code with automatic optimization

Nested Learning: The Illusion of Deep Learning Architectures - A comprehensive guide to the arXiv paper revealing how neural networks learn at multiple timescales through hierarchical optimization.

Neural networks catastrophically forget previous knowledge when learning new tasks—not due to capacity limits but fundamental constraints in distributed learning systems.

Google DeepMind's AlphaEvolve broke a 56-year-old matrix multiplication record and matched or beat human solutions on 95% of 67 mathematical problems.

Are LLMs truly exhibiting emergent capabilities, or are we mistaking measurement artifacts for genuine phase transitions?

How gene length, trait specificity, and luck systematically distort which genes we think are important for disease, and what researchers should do about it

An AI research system maintains coherent reasoning across 200+ agent steps over 12 hours, generating 42,000 lines of code while reviewing 1,500 papers. Kosmos achieves 79.4% accuracy through structured world models and parallel agents, but verification remains humanity's bottleneck.

New research reveals a disturbing truth: reasoning models maintain high performance until they hit a complexity threshold, then collapse entirely. They don't degrade gracefully - they fall off a cliff.

Why does it feel like our tools weren't designed by pathologists? Billions poured into AI models that compress whole slide images into tiny vectors, ignoring how pathologists actually examine tissue. The evidence reveals why scaling won't fix this disconnect.

The definitive guide to building AI agents that monitor their own thinking through dual-loop architecture, Reflexion patterns, and production-ready LangGraph implementations.