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aish
the terminal your agent can afford to ead
AI coding agents run terminals built for humans. Every git diff, test run, or build log comes back full of banners, passing-test spam, repeated paths, and stack trace noise aish sits in front of your shell commands and gives agents compact, evidence-preserving observations instead. Same commands, same information that matters just without the noise. What command wastes the most context for your coding agent? Tell us below — that's exactly what we're building toward next.
Hey PH 👋
I built AgentShell because I kept watching my coding agent burn context on stuff no one actually needs to read — full test suite output for one failing test, entire diffs when only two files actually changed, build logs three thousand lines long for one root-cause error.
aish wraps the commands you already run (aish npm test, aish diff, aish npm install) and gives the agent back only what matters: the failing test, the exact hunk, the actual error — nothing else.
It's a CLI, install is one line: npm install -g aish (or however you're distributing it — confirm exact command), then aish init in any repo to get your agent using it automatically.
Would genuinely love to know: what's the single command that wastes the most tokens for your agent right now? That's directly shaping what I build next.
About aish on Product Hunt
“the terminal your agent can afford to ead”
aish was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #123 on the daily leaderboard. AI coding agents run terminals built for humans. Every git diff, test run, or build log comes back full of banners, passing-test spam, repeated paths, and stack trace noise aish sits in front of your shell commands and gives agents compact, evidence-preserving observations instead. Same commands, same information that matters just without the noise. What command wastes the most context for your coding agent? Tell us below — that's exactly what we're building toward next.
On the analytics side, aish competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how aish performed against the three products that launched closest to it on the same day.
Who hunted aish?
aish was hunted by Yakshith Kommineni. 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 aish including community comment highlights and product details, visit the product overview.