Most of the company doesn't speak Git.
A founder doesn't necessarily care about the individual commit.
A product manager wants to know what moved forward.
A customer success team wants to know if customers will notice a change.
An engineering manager wants to understand the team's progress without spending an hour going through GitHub.
The information is already there.
The missing piece is context.
Turning Git Activity Into Something Everyone Can Understand
That's the problem we're trying to solve with Gitmore.
Gitmore connects to your repositories and uses AI to turn development activity into understandable engineering updates.
Instead of manually going through dozens of commits and pull requests, you can get:
- 📊 Daily and weekly engineering summaries
- 🔀 AI-powered PR and commit summaries
- âš¡ Real-time team activity
- 💬 Automated Slack and email reports
- 🤖 Gitmind AI for asking questions about your repositories
The goal isn't to replace GitHub.
GitHub remains where the actual work happens.
Gitmore sits on top of that activity and helps answer questions like:
What did the team ship this week?
What has the backend team been working on?
Which bugs were fixed recently?
What changed in the payment system?
What's currently in progress?
Why We Think This Matters
As engineering teams grow, the amount of technical activity grows with them.
10 commits can be easy to follow.
100 commits across multiple repositories?
Not so much.
Someone eventually has to turn all that activity into a weekly update.
And that's usually an engineer or engineering manager spending time writing a summary instead of building or managing.
We think AI can handle a large part of that repetitive work.
Not by guessing what happened, but by using the activity already recorded in the development workflow.
The Interesting Challenge
The hard part isn't summarizing text.
The hard part is understanding engineering context.
A commit that changes 20 lines can be more important than one that changes 2,000.
A refactor might be technically significant but have no immediate customer impact.
A bug fix might look small in Git but solve a major production problem.
So we're interested in exploring how far AI can go in distinguishing:
What changed → Why it changed → What it means.
That's where we're focusing.
If you're building software with GitHub, we'd love to hear how your team currently communicates engineering progress.
Do you write weekly reports?
Use Slack updates?
Rely entirely on standups?
Or do you just open GitHub and hope everyone figures it out? 😅
Try Gitmore:
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