I have been quieter than usual over the past few months.
Although I enjoy building in public, some work is better done behind the scenes until it ...
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The part I'd gently push on is "throwing away months of work." The code was wrong, but the understanding you got from building it is exactly what let you see the right solution at the demo. That's not really thrown away, it's what you paid for it. The trap isn't building the wrong thing, it's building it quietly for months before anyone outside the team can react to it.
That's a really good point. Looking back, the code wasn't the most valuable outcome. The understanding we gained from building and testing it was. I also agree that the bigger lesson is shortening the feedback loop. If we'd put something in front of users earlier, we probably would have challenged our assumptions much sooner and avoided rebuilding so much. Thanks for sharing that perspective.
Great lesson. One thing I’d add is that understanding the problem and validating the solution are two different activities. I’ve seen technically excellent products fail because the assumptions behind the solution were never tested with users early enough. A simple prototype or workflow validation can save months of development by invalidating the idea before the implementation.
Reference: Eric Ries, The Lean Startup — validated learning and testing assumptions before scaling.
theleanstartup.com/
I agree. That was the gap in our case. We understood the problem, but we did not validate our solution early enough. A prototype would have exposed those assumptions long before we invested months in development. I will definitely give The Lean Startup another read.
This takes a lot of courage to share—throwing away months of code is always a tough pill to swallow! What specific validation methods or early testing frameworks are you using now before writing code to make sure the solution aligns with what users actually need?
It is incredibly common to over-engineer the initial database schema and AI pipeline when you think you have the problem completely figured out. I spent weeks optimizing vector search latency for an AI feature before realizing users just wanted a simple text-based summary without the retrieval overhead. When building fullstack applications, it is easy to let the technical complexity distract from the actual product-market fit. Have you found that keeping the initial architecture deliberately simple helps force validation of the core user flow before abstracting the infrastructure?
Absolutely. That's one of the biggest lessons I've learned from this project. It's tempting to optimize for scale or build sophisticated architecture too early, especially with AI features, but none of that matters if the core workflow isn't solving the user's problem. These days, I prefer to keep the initial architecture as simple as possible, validate the user flow with real feedback and only introduce complexity when it's justified by actual needs rather than assumptions.
Stripping away premature optimizations to focus purely on the core workflow is exactly how you avoid the classic build-it-and-they-will-come trap. It makes me wonder how you define the exact technical threshold that signals it is finally time to introduce more complex infrastructure. Finding that balance between staying lean and avoiding future refactoring debt is always the real challenge.
Such an honest and relatable lesson! When you were starting over, what kind of low-fidelity validation steps (like wireframes, interactive mockups, or design sprints) did you use to make sure the new direction was on track before writing code again?
Looking back, the biggest gap wasn't validating the problem. It was validating the solution. We assumed we'd found the best approach, but the demo showed we'd optimised for the wrong experience. Rather than iterate on the existing implementation, we went back to understanding how users actually wanted the problem solved. Since then, I've been much more intentional about validating assumptions around the solution before committing to code.
This is great lesson
The problem few times really I have faced is either customer nor us takes time to really articulate the problem in right order
Assumption always kill the great ideas , but the game has changed totally now , more than engineers building an idea has been made so easy tools like Claude you can generate many idea’s and workflows to get a better lay of the problem and present the approach and get everyone buy in
Do you agree?
I agree. AI has made it much easier to explore different workflows and challenge our assumptions before writing code. But I still think the biggest value comes from validating those ideas with users and stakeholders rather than relying on AI alone. That's the lesson this project really reinforced for me.
Building the wrong product despite understanding the problem usually points to a disconnect between the technical architecture and the actual user workflow. I have seen this happen when we over-engineer the backend or AI pipeline before validating the core frontend experience. The hardest technical pivot is often tearing down a beautifully architected system to rebuild a messy but validated MVP. Did you find that your initial tech stack or database schema locked you into a rigid feature set that ultimately missed the real user need?