Cross‑platform AI client for web, desktop, and mobile that lets teams pick model providers, run local or on‑prem inference, and keep data self‑hosted — aimed at enterprise self‑deployment to avoid vendor lock‑in.
Parses PDF resumes into structured JSON using LLMs, enriches profiles with GitHub signals, and outputs explainable category scores, evidence, bonuses and deductions. Runs fully local with Ollama or via Google Gemini; designed for reproducible, fairness-constrained resume scoring in hiring workflows.
Audits source code for security flaws using LLM agents, then auto-generates and runs proof-of-concept exploits in Docker sandboxes to confirm which findings are real. Retrieves CWE/CVE knowledge via RAG; runs on hosted or local Ollama models.
Provides a systematic, project-driven tutorial and runnable codebase for building AI agents, RAG pipelines, and multi-agent systems—focused on Python, LangChain/LangGraph, tooling, deployment, and an interview question bank for engineers aiming to ship production agent applications.
Turns a PC, Mac, or Linux machine into a private AI server with one-command installers: local LLM inference, a ChatGPT-style web UI, voice, agents, RAG, workflows, image generation, hardware-aware model selection, and optional cloud/hybrid modes.
Lets any LLM operate a ComfyUI instance: generate and iterate images/video/audio, manage models and custom nodes, and edit the live graph in natural language. Local-first control plane with a sidebar agent, multi-provider LLM support, and installer packs for ready workflows.
Desktop-first personal agent that compresses your connected accounts into a local memory tree and runs agentic workflows. Key features include 118+ one‑click integrations, TokenJuice token compression into an Obsidian‑style vault, model routing with optional local models (Ollama).
Provides a deployable personal AI assistant that runs locally or in the cloud, supports multi-channel chat, extensible Skills/Plugins, and local-model runtimes. Key features include three-layer memory, kernel-level sandboxing and tool/file guards, and bundled QwenPaw-Flash local models for zero-API deployments.
Runs locally to learn your tastes and proactively discover content across Bilibili, Xiaohongshu, Douyin, YouTube, X, Zhihu, Reddit and the open web. Local-first agent storing data in a local SQLite, with a browser extension, optional desktop backend bundling embeddings (bge-m3/Ollama), and conversational feedback to refine recommendations.