Backlog.md - A tool for managing project collaboration between humans and AI Agents in a git ecosystem - MrLesk/Backlog.md
Git AI - Track AI Code all the way to production
Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.
Gitpod is now Ona: your AI software engineer
Launching the mission control for your personal team of autonomous SWE agents.
Gitpod is now Ona: your AI software engineer
Launching the mission control for your personal team of autonomous SWE agents.
On AI and building your own GIS software
Of the thousands of essays over the past two years surrounding AI and its implications are some very thought provoking ones with enormous implications for GIS. Some of the essays are around the the…
Collaborative AI Engineering: One Dev, Two Dozen Agents, Zero Alignment — Maggie Appleton, GitHub
Managing Changes from Multiple AI Agents
In this video, I walk you through managing changes from multiple AI agents in our to-do app, specifically focusing on the Expert to CSV button. We explore design options using ground agents, where one agent proposes a green design and the other a blue one. After some time, both agents complete their tasks, and their changes are synchronized to GitHub and our local machine. I demonstrate the final designs and decide to ship the green version as the main one. I encourage you to apply this approach in your own projects and consider using rebase in GitHub for similar tasks.
OneDev — The Unified and Autonomous Development Platform
Git, issues, pull requests, CI/CD, packages, and workspaces in one place. Assign work to AI users, review with them, and let them resolve merge conflicts and fix CI/CD failures.
Agent Driven Project Management and Issue Refinement with gh-issue-sync
AI-native project management | Plane
Project management for teams and AI agents. Plan, track, and ship with Projects, Wiki, and AI. Available on cloud, self-hosted, and air-gapped.
GitHub - Responsible-Dataset-Sharing/easy-dataset-share: A CLI tool that helps AI researchers share datasets responsibly.
A CLI tool that helps AI researchers share datasets responsibly. - Responsible-Dataset-Sharing/easy-dataset-share
Artifacts: versioned storage that speaks Git
Give your agents, developers, and automations a home for code and data. We’ve just launched Artifacts: Git-compatible versioned storage built for agents. Create tens of millions of repos, fork from any remote, and hand off a URL to any Git client.
We build AI that works for humans
Imbue builds AI to help people think, create, and build. We share our tools openly because we believe progress in AI should be collaborative and developer-driven
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Large Language Model (LLM) agents have been widely adopted in modern software development workflows. SWE-bench [13] and related works [23, 24, 22, 25, 15] establish the task of issue resolution as a de-facto standard for assessing their capability and usefulness. In this setting, an agent is given an entire codebase, a task description (e.g., a bug report or feature request) in natural language and is instructed to produce a code patch that resolves the issue and passes the repository’s test suite. These benchmarks have been instrumental in demonstrating both the substantial potential and the persistent limitations of current models as SWE agents.
One Developer, Two Dozen Agents, Zero Alignment
Why we need collaborative AI engineering and a tour of Ace: the multiplayer coding workspace
The 2025 AI Agent Index Documenting Technical and Safety Features of Deployed Agentic AI Systems
Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers. In addition to documenting information about individual agents, the Index illuminates broader trends in the development of agents, their capabilities, and the level of transparency of developers. Notably, we find different transparency levels among agent developers and observe that most developers share little information about safety, evaluations, and societal impacts. The 2025 AI Agent Index is available online at https://aiagentindex.mit.edu.
Got to talk at @aidotengineer.bsky.social conf last week about the need for collaborative AI engineering. All our current coding agents are single player. We're trying to scale up individual productivity, but creating tons of alignment problems in the process. We have no good tools for...
