The Cost of Astra 6: 78.6% Less Token Capacity and 52.0% Less Account ValueNEW
Based on API token prices, weekly account value fell from about $1,143 to $548. Each Astra token burned 4.67× as much weekly quota as the matched Sol baseline.
How much of your agent's context window is actually working on the current task?
Production telemetry: what changed, measured from the underlying event logs.
Based on API token prices, weekly account value fell from about $1,143 to $548. Each Astra token burned 4.67× as much weekly quota as the matched Sol baseline.
I'm Kobe, a developer. On June 12 my agents got a shared memory layer. 828 sessions later: 212× less hand-carried context, 63% fewer tokens, 7 → 17 projects.
What actually fills the context window, how Codex and Claude Code manage it, and how it gets polluted turn after turn.
The LLM is the brain, the harness is the body, and the context window is the whiteboard the brain re-reads before every move.
The hidden cost is not generation. It is re-feeding stale context until the model stops seeing the current job.
Low SNR is what you feel every day: slower turns, higher bills, edits that drift. Open a real session and itemize the waste.
A free local tool for Codex and Claude Code users: see which tokens push the task forward, which are noise, and where the money went.
Save everything, recall exactly what the task needs, warm-start every session — and complex tasks stop falling apart.
Long sessions get dumb, memory cannot travel across tools, and intelligence bought with time and dollars disappears. Echo MCP makes it stay.
Run Agent Doctor, sign in, generate memories, then start Claude Code, Codex, Cursor, CLI, and browser-chat sessions with warm context.
A practical MCP workflow for saving everything, recalling only the memories that matter, and keeping noise out of the prompt.
A fresh session should inherit the repo map, decisions, and constraints without a human clipboard tax.
Speed you can feel, bills that drop, complex tasks that land — and proof the memory holds up.
What changes when signal rises: faster starts, smaller prompts, cleaner edits, and less drift.
Benchmark evidence that memory can retrieve, cite, and refuse when the answer is not in the record.