3 posts carrying this tag.
At a conservative 1,000 tokens per source file, 10 million files is roughly 10 billion tokens. A 1 million token window holds 0.01% of that, and a 10 million token window holds 0.1%. Closing that by scaling needs something like a 10,000x increase rather than a 10x one, and quality per token moves against you while you try.
An ETH Zurich evaluation found machine generated context files lowered success rates and raised costs by roughly 20%, human written files bought about 4% on the harder benchmark, and Claude Code was the only agent tested where even developer written files failed to beat having no file at all. The most revealing result came from a control: strip the existing READMEs out and context files finally start helping.
Compliance with an agent's own explicit constraints falls from 73% at turn 5 to 33% at turn 16 across 4,416 measured trials, and Claude Code auto compaction reduces 132,000 tokens of session state to roughly 2,300. Shorter sessions and cleaner instruction files treat the symptom. The window fills with low value tokens because the agent rebuilds an understanding of your codebase from raw source on every task.
