droidperf
AI that reads your Gradle build log and fixes it
What it does
droidperf 2.0 adds AI-powered build analysis on top of the existing rule-based auditor. Point it at your Gradle build log and it finds bottlenecks, explains why they're slow, and applies fixes directly to gradle.properties with a backup and dry-run preview before touching anything. New in 2.0: - npx droidperf analyze — AI reads your actual build output - --apply — writes fixes automatically - --dry-run — preview before committing Works on any gradle projects. One command. No config needed.
Does the same job
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- FAFirebender, a simple coding agent for Android Engineers2025 · docs.firebender.com · ▲53
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…
- NSNexa SDK – Build powerful and efficient AI apps on edge devices2024 · github.com · ▲27
Hey HN! Alex and Zack here from Nexa AI. We're excited to share something we've been working on. Our journey began with the Octopus series --- action models for mobile AI agents (https://huggingface.co/NexaAIDev/Octopus-v2). We focused on making sub-billion parameter models excel at function calling, making high accurate and fast function-calling possible on mobile and edge devices. But as we delved into developing full-fledged on-device applications, we hit a roadblock. We realized that optimizing for function calling (tool-use) alone wasn't enough. Building powerful…
More ai this month
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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 · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com