Firebender, a simple coding agent for Android Engineers
Hey HN, I made a simple coding agent plugin in Android Studio called Firebender. Here’s an unedited 5-minute video where it writes tests for an Android app and iterates against the Gradle task output on its own (https://docs.firebender.com/get-started/agent). You can use the plugin for free, no sign up needed, on the jetbrains marketplace. The agent can edit multiple files, run gradle tasks like tests, and use the output to improve its changes. At the end, it reports a git diff of all changes that can be accepted or rejected. Under the hood, the agent relies on Claude 3.7…
In plain words
Firebender is an Android Studio plugin that automates coding tasks for Android engineers. The agent writes code, edits multiple files, runs Gradle tasks like tests, and uses the output to iteratively improve its changes. Users can review all modifications as a git diff before accepting or rejecting them. The plugin is free, requires no sign-up, and integrates with Android Studio's IDE features to provide the agent access to code structure and project information.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, I made a simple coding agent plugin in Android Studio called Firebender. Here’s an unedited 5-minute video where it writes tests for an Android app and iterates against the Gradle task output on its own (https://docs.firebender.com/get-started/agent). You can use the plugin for free, no sign up needed, on the jetbrains marketplace. The agent can edit multiple files, run gradle tasks like tests, and use the output to improve its changes. At the end, it reports a git diff of all changes that can be accepted or rejected. Under the hood, the agent relies on Claude 3.7 sonnet and a fast code apply model to speed up edits. We built tools to give deeper access throughout the IDE like IntelliJ’s graph representation of kotlin/java code, “everywhere search” for classes, and have more integrations planned. The goal is for the agent to have access to all the IDE goodies that we engineers take for granted, to improve the agent's responses and ability to gather correct context. In order to improve the agent, there are internal evals like “tasks” and simulate the IDE which serves as a gym for the agent. This is heavily inspired by SWE-bench. Whenever tools, prompts, subagents, or models are changed, this gym helps find regressions quickly. Building the UI was surprisingly hard. I had the great pleasure of becoming proficient in Java Swing (released in ‘96 by Netscape) to get this done right. Things like markdown streaming, or streaming git diffs are prone to layout flickering where Swing tries to recalculate where elements should go. We had to write our own markdown parsing and rendering engine that repaints Swing components only when changed portions of the markdown nodes. The UI tends to focus on simplifying reviewing AI changes, something I have a feeling we’ll be doing much more in the coming years. If you’re an Android engineer, please let me know if you run into any bugs or want anything improved in the plugin!
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, March 2025
the whole month →
Mimic Human Research & Save Findings in AI Knowledge Base
AI · 2025 · sider.ai
