Checked Fortress Token Optimizer's DEV article and npm/PyPI packages. Polite filler words shrink 11-22%, but running it blindly on system prompts or RAG context can strip constraints that control model output.
Vektor Memory v1.5.4 supersession chains positioned against YourMemory decay, Cloudflare key-overwrite, and CTX, with a BM25 vs cosine threshold trap and a 5-field minimum schema for agent memory.
The paper argues that RAG, vector stores, and scratchpads are retrieval, not learning. Read alongside CTX and OCR-Memory, the gap between 'better search' and 'weight-level learning' becomes concrete.
Connecting a DEV article on context rot, Anthropic's 1M context guidance, and Chroma's context rot research with earlier CTX and Compresr posts. The places to watch are CLAUDE.md size, tool output accumulation, and information loss around compact—not the model name.
A read of CTX, which auto-injects context into Claude Code via the UserPromptSubmit hook. Compared with auto-memory, YourMemory, WUPHF, and Cloudflare Agent Memory on persistence and storage. Also looked at why 1M context still isn't enough and how each agent architecture uses its window differently.
A read of arXiv:2604.26622 OCR-Memory. It renders agent execution history into images, uses Set-of-Mark to let a VLM pick relevant segments, then retrieves verbatim text from the original logs.
