Maven is an open-source personal AI agent harness that runs locally or on personal hardware, featuring persistent memory, modular extensions, and proactive task management for a JARVIS-like experience.
With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware. The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting. **What stood out in practice** • One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups. • Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system. • Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings. The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven I’m curious how others are thinking about personal agent setups in 2026. • Do you prefer fully local models, cloud APIs, or a mix? • What capabilities feel most missing from today’s consumer AI assistants? • How important is “owning” your agent data and runtime vs. using polished third-party services? Would love to hear experiences or concerns from both technical and non-technical users.
Deno 2.9 introduces `deno desktop` for building native desktop applications using web technologies, along with improved Node.js compatibility, CSS module imports, and faster startup.
Proposes OpenAgent, a spec for defining AI agent identity (face, voice, writing style) in a single signed YAML file, enabling portability across different harnesses.
DeepReinforce releases Ornith-1.0, an MIT-licensed open-source family of agentic coding LLMs including a 397B MoE model that surpasses Claude Opus 4.7 on SWE-Bench and Terminal-Bench, using a novel self-improving training strategy.
A curated GitHub list called Awesome AI Agents 2026 that organizes 340+ AI agent tools and frameworks into 20+ categories to help developers navigate the rapidly evolving agent ecosystem.