There’s a classical intuition in computer science that verifying a solution is easier than finding one. For NP-complete problems, this
Continue readingWhy Verification Is Harder Than Generation for AI Coding Agents
The dominant scaling narrative in large language models has been straightforward: more parameters, more data, more compute. But there’s a
Continue readingLoopCoder-v2: Why Two Loops Beat Four in Test-Time Compute Scaling
The attention mechanism is the backbone of every transformer model, but it carries a brutal cost: quadratic complexity with respect
Continue readingHow MiniMax Sparse Attention Achieves 28x Compute Reduction at 1M Context Length
AI agents have a skill problem. You give a language model a system prompt — or “skill” — and it
Continue readingSkillOpt: Training AI Agent Skills Like Neural Networks
AI coding agents are getting scary good at writing functional code. Give them a loose description and they’ll spin up
Continue readingConstraint Decay: Why Your AI Agent Forgets the Rules (and What to Do About It)
The first version of any RAG pipeline usually looks the same: embed a query, search a vector store, stuff the
Continue readingBeyond Naive RAG: 4 Advanced Patterns That Actually Work in Production
Entering the AI space feels like learning a new language. Everyone throws around RAG, RLHF, GGUF, MoE, MCP like you’re
Continue readingThe AI Glossary: Every Term You Need to Know in 2025