AI Agent2025
Provides ready-to-use sample agents for Google’s Agent Development Kit across Python, TypeScript, Go, Java, Kotlin, and Android, from simple assistants to multi-agent workflows.
AI Coding2025
Orchestrates AI coding agents around tasks, sessions, artifacts, reviews, and parallel Claude Code workflows so teams can manage complex codebase work with more visibility.
AI Coding2025
Combines static code analysis with LLM reasoning to produce interactive architecture diagrams, component-level documentation, and navigable outputs for IDEs, CI, and docs. Emits Mermaid diagrams and incremental updates with CLI and editor integrations.
MCP Server2025
Exposes a local MCP server that lets LLMs (e.g., Claude Desktop) query decompiled Android app context from a modified JADX GUI—supporting class/method retrieval, resources, xrefs, and debugger hooks for interactive reverse engineering workflows.
AI Agent2025
Autonomously proposes hypotheses, runs experiments, analyzes results, and drafts workshop-level papers via an agentic tree-search pipeline. Unlike template-driven predecessors, it explores open-ended ML research paths but requires GPU/PyTorch and careful sandboxing due to execution of LLM-written code.
AI Dataset2025
Provides 5 million instruction–response pairs for supervised fine-tuning of code LLMs, with inputs, outputs, unit tests, and automated LLM judgments. Uses hybrid automated/synthetic generation and is released under CC BY 4.0 for large-scale SFT workflows.
MCP Server2025
Turns natural-language requirements into a dependency-aware graph of atomic, testable dev tasks for AI coding agents. Adds cross-session memory and a plan-reflect loop that forces the agent to think through each step before writing code.
AI Agent2025
Lets LLM agents drive real Android and iOS devices from natural-language commands by turning each screen's accessibility tree into structured text the model reads directly, not just screenshots. LLM-agnostic; runs via CLI, Python, or Docker.
AI Video2025
Clean-room, modular implementations of multi-object tracking algorithms — SORT, ByteTrack, OC-SORT, BoT-SORT, C-BIoU — behind one interface. Detector-agnostic: works with YOLO, DETR, or any bounding-box model via supervision.Detections.
AI Deploy2025
Run large-language and multimodal models locally on edge devices (Android, iOS, desktop, web, Raspberry Pi) with hardware acceleration, function-calling, and multi-language SDKs—designed for low-latency, privacy-sensitive on-device inference.
AI Client2025
Brings Gemini models into the terminal as an agent that reads files, runs shell commands, and edits code in place. Includes Google Search grounding, MCP server support, and a free OAuth tier (60 req/min, 1,000 req/day) with a 1M-token context window.