Rust Agent Development Kit (ADK-Rust): Build AI agents in Rust with modular components for models, tools, memory, realtime voice, and more. ADK-Rust is a flexible framework for developing AI agents with simplicity and power. Model-agnostic, deployment-agnostic, optimized for frontier AI models. Includes support for real-time voice agents.
Open measurement infrastructure for agent-facing software. Separate operations from attempts, evidence from inference, and usage from value. Reach → Choice → Use → Utility → Value.
Model-agnostic AI filmmaking workstation. 3D storyboard, ComfyUI local + Fal/Replicate cloud, multi-reference continuity, timeline export. Alternative to Storyboarder, LTX Studio.
Krawl is a customizable lightweight cloud native web deception server and anti-crawler that creates fake web applications with low-hanging vulnerabilities and realistic, randomly generated decoy data
LoRA Pilot is an ultimate docker image for all Stable Diffusion LoRA trainers. Includes kohya_ss, diffusion pipes and TensorBoard for trainings and ComfyUI and InvokeAI for validation. Features shared models, modules, custom integrations and automatization scripts.
The Universal Cross-Model Episodic Memory Standard. Local-first, project-scoped SQLite memory engine for Google Antigravity, Claude Code, Cursor, Windsurf, and Codex. Zero cloud lock-in.
The open-source AI coworker for your team: agents with their own cloud computer, your tools and context, handing back finished work websites, decks, spreadsheets, reports, PRs. Runs Claude Code, Codex, OpenCode on your subscription.
Day-0 deployment of Qwen3.8-Flash-Next (NVFP4) on 2x DGX Spark TP2 via SGLang. Includes the SM121 QSA kernel-guard fix, launcher, benchmarks, and full report.
Docker image for a self-hosted Whisper speech-to-text server with speaker diarization and OpenAI-compatible transcription and translation APIs. Powered by faster-whisper. Supports all Whisper models, NVIDIA GPU (CUDA) acceleration, JSON/SRT/VTT output, SSE streaming, offline mode, and multi-arch (amd64, arm64).
aws-bench measures how well AI agents and model combinations perform on real AWS work — diagnosing misconfigurations, provisioning infrastructure, and operating live cloud environments.