Context Warp Drive – Deterministic context folding for AI agents
90% cache hit rates on Claude by folding context deterministically instead of summarizing.
Empty submission with no code, demo, or description to evaluate.
None
90% cache hit rates on Claude by folding context deterministically instead of summarizing.
Deterministic context folding without LLM summarization calls keeps prompt caches hot.
Smart context window solution, but LLM-based summarization has its own failure modes.
AI context switching with 3D star visualization, but memory tools already exist.
Tiered context summarization beats naive token culling for long agent sessions.
The MCP server to give Claude true persistent memory is the clearest win — it tackles the annoying, real-world problem of lost context across sessions. The repo pairs local analysis (Ollama) with import/export for ChatGPT/Claude/Gemini and ships as Node/TypeScript + Docker with a usable web UI, so you can run everything without vendor APIs; I'd like to see wider model/import support and clearer UX around merges/conflicts, but the core idea and execution feel immediately useful.