Context Engineering: Why Less Context Makes Better Agents
Context engineering explained: why long-running AI agents suffer context rot, what 2026 research shows about pruning and compaction, plus a Python demo.
Context engineering explained: why long-running AI agents suffer context rot, what 2026 research shows about pruning and compaction, plus a Python demo.
Nemotron 3 swaps most attention layers for Mamba-2 and MoE to reach 1M-token context at 3.3x throughput. Here is how the hybrid architecture actually works.
AI agent memory explained: the four memory types, how the MemoryOS paper borrows from operating systems, and a runnable Python example you can extend.
How sparse attention cuts LLM inference cost by 10x on long contexts. Covers DeepSeek NSA, MInference, H2O, and The Sparse Frontier's findings.
Stanford's AI Index 2026: SWE-bench hit 100%, entry-level dev jobs down 20%, $581B invested, US-China gap at 2.7%. 12 findings, broken down.
Microsoft's FastContext shows coding agents burn 56% of tool turns on exploration. A 4B subagent cuts token use 50% and lifts SWE-bench scores 3 points.
Claude Mythos found 10,000+ critical bugs in 8 weeks. Inside Project Glasswing — real numbers, the patching crisis, and why Anthropic won't release the model.
Anthropic's 2026 report claims coding agents will reshape software development. Here's what the 8 trends actually mean after running agents on production code.
Seven papers fix LLM overthinking: Sketch-of-Thought cuts tokens 84%, shorter chains boost accuracy 34.5%, and budget-aware prompting halves costs.
THINC trains a 4B parameter model to reason entirely in code. It scored 78.1% on competition math, beating Qwen3-235B at 75.2%. Here's how the method works.