Local-first AI project OS with run records and workflow automation
A local-first operating system for AI projects that manages run records, automates workflows, and provides a visual interface for tracking experiments. It uses LLMs to help organize and execute project tasks, solving the problem of scattered AI project management for developers and researchers.
Related
Luna OS: AI-powered desktop assistant for local use
Luna OS is an AI-powered desktop application that acts as a local assistant. It uses LLMs to provide useful assistance directly on the user's machine, aiming to be a practical alternative to cloud-based assistants. Built for a hackathon, it targets developers and users who want an AI assistant that runs locally.
Local-first coding agent for long autonomous runs
Grinta is a local-first coding agent designed for long autonomous runs. It uses LLMs to autonomously plan and execute coding tasks, solving the problem of needing constant human supervision for extended development sessions.
ContextOS: AI session notes to resume interrupted work
ContextOS is a tool that logs session notes and decisions when you pause work, then generates a resume briefing with blockers and suggested next steps when you return. It uses AI to summarize context and suggest the highest-impact next action, helping developers and project jugglers avoid losing time reorienting across multiple projects.
Collaborative context-sharing memory platform for AI agents and teams
A platform that enables AI agents and human teams to share and persist context across sessions. It uses LLMs to manage memory, allowing agents to recall past interactions and collaborate on tasks. Solves the problem of fragmented context in multi-agent systems and team workflows.
Local desktop orchestrator that plans and delegates coding tasks to cloud AI
Forge is a desktop app that uses a local LLM to plan and decompose a high-level coding intent into small, specific tasks, then delegates each task to a cloud AI (Google Antigravity) for execution. It solves the problem of autonomous agents over-engineering or breaking existing code by keeping the AI on a tight leash, giving developers more control and reliability.
