Context Window

AI agents re-read every tool result on every single turn, rapidly inflating API costs. Learn how to build a zero-dependency, rule-based context compactor in TypeScript to reduce agent token consumption by up to 96%.

A deep dive into why long LLM conversations experience quadratic token cost growth and how to engineer cost savings using prompt caching, context compaction, and memory layers.

A comprehensive guide on feeding entire software projects to Large Language Models like Claude 3.5 Sonnet and GPT-4o without hitting context limits or leaking secrets.

Explore how Claude 4.7's 1 million token context window is redefining AI architecture, potentially making traditional RAG pipelines obsolete for indie developers.

A comprehensive deep-dive into Claude Sonnet 4.6, featuring 1M token context, 300K batch output, and adaptive thinking for advanced developer workflows.

The shift to 1 million token context windows in models like Claude 4.6 changes AI architecture. Learn about the 'lost in the middle' problem, latency issues, and when to choose RAG over context stuffing.

Explore how to overcome context window limitations using recursive summarization and map-reduce patterns for processing millions of tokens with LLMs.

Explore the breakthrough of Recursive Language Models (RLM) that solve context rot in GPT-5, enabling 10M+ token processing with lower costs and higher accuracy.