Hi Product Hunt! We built TokenSaver after seeing AI coding agents repeatedly fill their context with long logs, searches, file reads, and test output instead of the information needed to finish the task. Context reduction already exists in tools such as RTK and Headroom, as well as built-in client compaction. TokenSaver is different in its combination of one cross-client local proxy, a native Rust engine, bounded fail-open processing, adjustable profiles, and evidence generated from your own workflow. Instead of showing only a savings percentage, TokenSaver reports measured tokens before and after optimization, local processing overhead, failures, and per-client results. You can connect Claude Code, Codex, Gemini, Grok, or another supported client, work normally, and generate a self-contained QA report to judge the tradeoff yourself. In our published 32-scenario benchmark, VIC-E reduced 282,323 benchmark tokens to 60,917 while passing all 32 fidelity checks. We publish the methodology and limitations because this is maintainer-run evidence and should be open to scrutiny. TokenSaver runs on Windows, macOS, and Linux. Personal, non-commercial home use is free. I’d especially value difficult feedback: output that should remain exact, missing context, integration problems, or workloads where the overhead is not worthwhile. Thanks for trying it, I’ll be here answering questions throughout the launch.

VIC-E TokenSaver

Local, cross-client token savings with built-in evidence

Local, cross-client token savings with built-in evidence