Unifies agentic tasks, reasoning, and coding in a single MoE model with 355B total / 32B active parameters and a switchable thinking mode. A lighter 106B-param Air variant trades scale for efficiency; both ship MIT-licensed.
Closes a learning loop most agents lack: turns experience into reusable skills, refines them mid-task, and full-text searches its own past sessions for recall. Runs from CLI or Telegram/Discord/Slack and schedules unattended cron jobs.
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.
Coordinates specialized AI agents — developer, browser, document, multimodal — running in parallel on your desktop to automate multi-step work. Runs fully local via Ollama, vLLM, or LM Studio, with built-in MCP tools and human-in-the-loop checkpoints.
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.
Collaborates on web tasks in real time: edit its plan before it runs, pause and grab the browser mid-task, and approve irreversible clicks before they happen. A research prototype for studying human-in-the-loop oversight instead of full autonomy.
Indexes any repo into a knowledge graph of dependencies, call chains, and execution flows, then feeds it to AI coding agents via MCP so they stop missing context. Ships as a CLI plus a zero-install browser graph explorer with chat.
Seven-week course that builds a production RAG system from scratch — an arXiv paper assistant that starts with BM25 keyword search, then layers hybrid vector retrieval, local-LLM generation, Langfuse monitoring, and an agentic LangGraph Telegram bot.
Wraps 20+ AI coding CLIs — Claude Code, Codex, Gemini CLI, Cursor Agent — in one cross-platform desktop app so agents run file, document, and data tasks without a terminal. Adds parallel multi-agent runs and cron-scheduled jobs for unattended work.
Deep research agent for complex, long-horizon research and prediction tasks. Pairs a 256K context window with up to 300 tool calls per query for web search, extraction, and code execution. Ships as open 30B and 235B models scoring 82.7% on GAIA.
A ~3.2M-conversation Hugging Face dataset of non-toxic human–ChatGPT interactions for instruction finetuning and evaluation; includes full transcripts plus request headers, hashed IP/geolocation, turn-level moderation scores and usage metadata.