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.
Really interesting, Adekunle! I’ve seen teams struggle a lot when critical decisions or context get lost, especially during handoffs or when someone leaves. But, how does Kodingo decide what knowledge is worth preserving versus what might clutter the memory? How do you keep the “why” behind decisions clear without giving new contributors too much information at once?
@why_tahir This is exactly the problem we’re trying to solve. Kodingo doesn’t try to remember everything. It focuses on decision-level knowledge: architectural choices, constraints, trade-offs, and outcomes that materially shaped the system. Memory is structured and layered, not a raw log of activity. The “why” is captured as progressive context. New contributors see a high-level explanation first (what was decided and why), and can then drill down into deeper details; alternatives considered, historical constraints, or related code only when needed. This prevents cognitive overload while still preserving depth. Over time, Kodingo also learns what’s stable versus transient by tracking which decisions are referenced, revisited, or impact multiple parts of the system. The goal isn’t to archive noise, but to preserve the thinking that future contributors actually need to move fast and make good decisions.
What caught my attention about Kodingo is the focus on remembering decisions not just code. In most teams the hardest part isn’t reading files, it’s understanding why something was built a certain way. Treating that context as project memory instead of disposable chat history feels like a thoughtful direction especially for codebases that live for years.
I’ve spent time in a few large codebases where figuring out how things connect is harder than reading the code itself. What I found interesting here is that Kodingo seems aware of the project structure and how different parts relate to each other. The explanations grounded in the actual code, not generic answers that could apply anywhere.
That kind of context is what usually takes the longest to pick up.
@johan_nystrom That’s what I am hoping users will notice and honestly, it’s exactly the gap Kodingo is trying to close. When I have dealt with large codebases, the hardest part isn’t syntax or individual files, it’s understanding how things connect and why they’re shaped that way. What Kodingo does it that it builds a project-level understanding by grounding explanations directly in the actual code, architecture, and recorded decisions not generic patterns or assumptions.
So when it explains something, it’s reasoning from this codebase: how modules interact, where responsibilities live, and which decisions influenced those boundaries. That’s the context that usually takes months to absorb, and it’s what we’re focused on preserving so new contributors can get productive much faster.
I keep coming back to the same work after a break and usually I have to rebuild all the context in my head. With Kodingo, I can just continue from where I stopped instead of piecing everything together again. That makes longer tasks easier to deal with, especially when they stretch across a few days.
Great to see this launching. I’ve been testing Kodingo and it’s been a game changer for preserving institutional knowledge within our repo. It fills that gap between the documentation we 'intend' to write and the reality of a fast-moving codebase. Highly recommend for any engineering lead looking to reduce technical debt in team communication.
I just tried Kodingo and getting started feels pretty easy. It’s clear what it’s for and how it fits into a project. I don’t feel lost or unsure about what to do first. That makes it easier to focus on using it instead of figuring things out.