VQV Signal
According to a post on X by @Marktechpost, Microsoft Research and UC Berkeley have open-sourced TokenEconomizer, a plugin for coding agents. It learns from past sessions in a repository, adds one-line facts the agent keeps rediscovering, drops unused tool schemas, and redirects the agent when it loops. Across 277 SWE-Bench tasks, the post says it cut average cost by 18.4% to 30.9% for Claude Code, Codex and Copilot CLI, with a small accuracy dip.
According to a post on X by @Marktechpost, Microsoft Research and UC Berkeley have open-sourced TokenEconomizer, a plugin for coding agents. It learns from past sessions in a repository, adds one-line facts the agent keeps rediscovering, drops unused tool schemas, and redirects the agent when it loops. Across 277 SWE-Bench tasks, the post says it cut average cost by 18.4% to 30.9% for Claude Code, Codex and Copilot CLI, with a small accuracy dip.
AI-assisted summary based on the listed source.
Microsoft Research and UC Berkeley open-sourced TokenEconomizer, a plugin that learns from past sessions in a repository, adds one-line facts the agent keeps rediscovering, drops unused tool schemas and redirects the agent when it loops. Across 277 SWE-Bench tasks it cut average cost by 18.4% to 30.9% for Claude...
Teams running AI coding agents on large codebases could see lower token costs, though the savings are reported from benchmark tasks rather than production use. The small accuracy dip is a trade-off that users would need to measure on their own code. Watch for independent tests on real projects and whether the open-source plugin is adopted across these three tools.
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VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to Scobleizer News Pulse (X).
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