Ordia – standup from GitHub and Jira, no human input
Passive standups from Git data, but Geekbot and Standuply already do this.

AI adoption dashboard for GitHub, but detection accuracy fully depends on commit message patterns.
Open-source researchers, developers tracking AI adoption trends
GitHub Insights · OpenAI Codex adoption trackers
So I created a small project on https://aivshuman.thedaviddias.com/ which parses commits and try to find trace of AI tools. Not 100% accurate, it depends on people commiting using AI.
That's only when you can potentially determine that AI tools were used. It's interesting and triggers some questions about if we should have a better or stricter way to measure AI vs Human produced code.
Feel free to share any thoughts or improvements! The project is open source (https://github.com/thedaviddias/ai-vs-human)
Passive standups from Git data, but Geekbot and Standuply already do this.
Summarizes Git activity for stakeholders, but GitHub's own PR dashboards and Slack notifications already do this.
AI wrapper for GitHub Issues when Linear and Copilot already handle this.
Git-based context switching tracker, but time estimates rely on unvalidated research assumptions.
Instant architecture overviews, tech-stack breakdowns, and entry-point maps from a pasted GitHub URL are the pitch — perfect for quick onboarding or audits. The site nails low friction (no login for public repos, indexed repo browser) but the space is crowded and the product needs clarity on accuracy, model limits, and private-repo support to feel essential.
Video truth detection in a space already crowded by deepfake scanners.