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JFrog Boost
Save AI tokens & sharpen your coding agents
Boost is a free, local-first CLI that compresses noisy tool output before it reaches Cursor, Claude Code, Codex, or GitHub Copilot. Save tokens without changing workflows. Instead of blind truncation that breaks agents, Boost uses a shift-right, retrieval-backed approach. If your agent truly needs the raw logs, it can fetch them instantly. Standout features include: 1. Context-Aware Noise Compaction 2. BoostGraph 3. File Optimization 4. Agent Observability & Telemetry 5. Enterprise-Grade Privacy
We built Boost after hitting $700+ Cursor bills and $1,700+ Claude Code sessions in our R&D org at JFrog. Tracing those soaring token costs led us to one huge culprit: our AI agents were swallowing massive amounts of irrelevant context on every single prompt turn - test logs, progress bars, and verbose CI outputs, all charged at premium token rates.
We realized ~90% of what we were paying for was pure noise. Boost uses multiple techniques to strip out the fluff before it hits the API, making your agents faster, cheaper, and smarter. Best of all, it eliminates "agent amnesia." If the model hits a roadblock and actually needs full output to debug, our retrieve feature instantly fetches the raw, unfiltered logs from local history.Everything runs locally to keep your code private, with only minimal, anonymous telemetry to help us crush bugs.
Boost already saves over 1 trillion tokens every month for tens of thousands of developers and vibe coders worldwide, without compromising on the agent response quality.
Today, we’re opening it up completely free to the community!
It’s backed by real benchmarks, with every feature battle-tested on 1,500+ developers at JFrog before rolling out.
We’d love your honest feedback! How are you currently keeping your agent context lean without breaking your dev workflows?
About JFrog Boost on Product Hunt
“Save AI tokens & sharpen your coding agents”
JFrog Boost was submitted on Product Hunt and earned 59 upvotes and 19 comments, placing #31 on the daily leaderboard. Boost is a free, local-first CLI that compresses noisy tool output before it reaches Cursor, Claude Code, Codex, or GitHub Copilot. Save tokens without changing workflows. Instead of blind truncation that breaks agents, Boost uses a shift-right, retrieval-backed approach. If your agent truly needs the raw logs, it can fetch them instantly. Standout features include: 1. Context-Aware Noise Compaction 2. BoostGraph 3. File Optimization 4. Agent Observability & Telemetry 5. Enterprise-Grade Privacy
On the analytics side, JFrog Boost competes within Software Engineering, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how JFrog Boost performed against the three products that launched closest to it on the same day.
Who hunted JFrog Boost?
JFrog Boost was hunted by Yahav Ohana. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of JFrog Boost including community comment highlights and product details, visit the product overview.
Yahav Ohana
Hey Product Hunt! @yahav_ohana and @shay_dahan here, the builders behind Boost.
We built Boost after hitting $700+ Cursor bills and $1,700+ Claude Code sessions in our R&D org at JFrog. Tracing those soaring token costs led us to one huge culprit: our AI agents were swallowing massive amounts of irrelevant context on every single prompt turn - test logs, progress bars, and verbose CI outputs, all charged at premium token rates.
We realized ~90% of what we were paying for was pure noise. Boost uses multiple techniques to strip out the fluff before it hits the API, making your agents faster, cheaper, and smarter. Best of all, it eliminates "agent amnesia." If the model hits a roadblock and actually needs full output to debug, our retrieve feature instantly fetches the raw, unfiltered logs from local history. Everything runs locally to keep your code private, with only minimal, anonymous telemetry to help us crush bugs.
Boost already saves over 1 trillion tokens every month for tens of thousands of developers and vibe coders worldwide, without compromising on the agent response quality.
Today, we’re opening it up completely free to the community!
It’s backed by real benchmarks, with every feature battle-tested on 1,500+ developers at JFrog before rolling out.
We’d love your honest feedback! How are you currently keeping your agent context lean without breaking your dev workflows?