Kodingo

Kodingo

Project memory for AI-assisted development

5.0
1 review

20 followers

Kodingo is a persistent project memory engine for software teams. It preserves code, architectural decisions, and the why behind them across developers, engineers, and project managers — even as teams change. Kodingo helps new contributors understand the codebase faster, reduces onboarding time, and ensures critical knowledge is never lost.
Kodingo gallery image
Free
Launch Team / Built With
Anima - OnBrand Vibe Coding
Design-aware AI for modern product teams.
Promoted

What do you think? …

Brandon Ellis
I spend most of my day inside my editor, so context switching gets annoying fast. Kodingo handles prompts, repo changes and tests right there, which makes the whole flow feel more natural.
Natalie Brooks
On team projects a lot of context ends up spread across PRs, tickets and random conversations. After a while it gets hard to remember why certain choices were made. Having something like Kodingo keep that shared understanding tied to the code feels useful, especially for projects that last a long time. Tbh, if this helps teams stay on the same page and cuts down on repeat explanations that’s a real win.
Annika

Just checked out your site and saw the part about understanding the whole repo. I’m trying to get how that works day to day. Does it follow how data and pieces connect as the code changes or is it more like a one time scan? How this holds up on bigger projects that aren’t clean.

Hannah Cooper
When it comes to tools like this trust matters more than features. If the explanations line up with what’s actually in the code and stay consistent over time that’s what would make me rely on it. A little question for you: how does Kodingo handle edge cases or older parts of a project where context is usually messy? Getting that right would go a long way toward using it day to day.
Carson Hayes
@adekunle_o_ Congratulations on the launch, Quick question about team usage. If multiple people are working in the same project how does shared context work when opinions or assumptions change over time? how this handles situations where different developers remember things differently.
Yulia Kuznetsova
Sometimes the hardest part of working on a system isn’t the code itself it’s figuring out how everything fits together. With Kodingo, being able to see component relationships, APIs and data models in one place look really helpful. Kinda nice to also have the reasoning behind architectural choices right there instead of guessing or digging through old docs. That kind of visibility makes working on complex systems feel a lot less risky.
Trevor Mitchell
I have tried a lot of AI tools that smart at first but fall apart once they don’t understand the project they are working on. What I like about this approach is that the intelligence seems tied to the actual codebase and its history. If Kodingo can really pick up on patterns, conventions and past decisions, that’s where AI becomes useful instead of noisy. Having that context carried forward could cut down a lot of back-and-forth and make decisions more grounded.