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First background agents in Jetbrains IDEs [video]

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First background agents in Jetbrains IDEs [video]

TLDR: made the first background coding agent that has an isolated workspace and runs locally Howdy - I’m Kevin, co-founder of Firebender, and we built the first background coding agent in android studio / Jetbrains! Why not just use Cursor background agents or OpenAI Codex? Both of these require setting up a cloud container and cloning your existing developer environment, and maintaining it. Then when you want to iterate on changes as AI inevitably makes a mistake, you either throw away the work, or have to pull down the branch and clean it up. This feels really clunky. With Firebender, background agents run locally in a lightweight git worktree/IDE tab. This means when the agent is done, you can easily clean up the changes and run code with a few clicks. Under the hood, the agent behaves similarly to claude code (didn’t want to reinvent the wheel), but also leverages all of the hooks into IntelliJ sdk like go-to-definition, find usages, auto-imports for accuracy, and it gives a cleaner visual UI for reviewing changes and merging them. You can use any frontier model like gpt-5/sonnet-4 as the base. We’ve had to do quite a bit of reverse engineering of the IntelliJ codebase to cleanly set up and manage the isolated environment, and I think you’ll appreciate the simple UX of hitting cmd+enter to run a background agent anywhere. get started docs: https://docs.firebender.com/get-started/background-agents download the plugin: https://firebender.com Would love to get your feedback to help us improve the tool for you! Thanks!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, apple
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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