#AI

GPT-4's 128K context window? It only uses about 10% effectively. Google's TITANS architecture introduces test-time memory learning that outperforms GPT-4 on long-context tasks with 70x fewer parameters.

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.

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.

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

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.

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

An AI system at Google DeepMind discovered how bacteria share genes across species barriers using 7 days of computational reasoning. When tested, it matched unpublished experimental observations exactly.

Foundation models trained on biological data are transforming protein structure prediction, genomics, drug discovery, and pathology. Learn how machine learning benchmarks in 2024 are revealing biology's dark matter through RNA analysis and metagenomic discovery.

DeepAgent bridges the gap between research and production AI agents with autonomous memory folding and reinforcement learning that scales from tens to thousands of tools.

When you provide GPT-4 with examples, it adapts without training. This post reveals the mechanism: ghost fine-tuning through implicit weight updates.

Why does it feel like our tools weren't designed by pathologists or researchers? Anthropic's Claude for Life Sciences attempts to bridge this gap by embedding AI directly into existing scientific workflows through Connectors and Agent Skills, building an operating system for R&D that actually respects how scientists work.

Google's ReasoningBank framework solves agent amnesia by distilling experience into reusable strategies, enabling AI agents to learn from both successes and failures.

Discover why monolithic embeddings fail for RAG systems and learn how chunking strategies can transform your retrieval performance.

Comparing two AI titans battling for your terminal, Claude's agentic coding specialist vs. Gemini's extensible ecosystem powerhouse

A technical guide to custom Claude Code slash commands for automated content review, AI image generation, and quality assurance

How graph-based representations are transforming language models beyond sequential text to unlock deeper reasoning capabilities

Learn the RATS pattern: Retrieval-Augmented Tool Selection for building scalable AI agents that dynamically select the right tools for each task.

Language models can write poetry but struggle with basic spelling. Discover why current tokenization breaks language, and how morphology-aware approaches fix it.

AI assistants make starting projects effortless but finishing harder than ever. Explore the psychology and practical strategies to break the cycle.

A look at how I built an open-source acoustic horn design pipeline using Nextflow, FEniCSx, and AI development tools to overcome the limitations of legacy software.

Discover Meta MCP: LLMs that generate both UI and backend servers to interact persistently beyond simple request-response cycles.

Exploring how Reinforcement Learning (RL) combined with Physics-Informed Machine Learning (PIML) can teach AI to design structurally sound and resilient buildings by learning from simulated physical environments.

Exploring a new approach to AI language training that grounds language in physical interactions and leverages abstraction for deeper understanding.

Words aren't just symbols anymore, they're coordinates in high-dimensional space. Explore how vector embeddings revolutionized AI's understanding of language.