Build slowly, stay close to the problem

After your first real session with Claude Code or any AI coding tool, there is a feeling that is hard to shake. You built something. It works. It took hours, not weeks. The next feature is already forming in your head before you have closed the chat.

That feeling is accurate. You can build fast now. Genuinely fast. And that is exactly the moment most projects start dying, because speed without direction is just motion.

This post is about the discipline that keeps a project alive after the initial excitement fades: staying close to the problem you set out to solve, even when the tools make it easy to drift.

Why most AI-built projects get abandoned

Browse any AI coding subreddit and you will see the same pattern. Someone shares a prototype built in an afternoon. The comments are enthusiastic. A month later, the project is gone. Not because the tools failed. Not because the code broke. Because nobody came back.

As one builder on r/indiehackers put it after 9 months of building a SaaS with AI tools: "Stripe integration that worked perfectly in test mode but randomly failed with real customers. I thought I was making money while actual payments were bouncing." The prototype worked. The product did not.

The failure modes vary. Sometimes motivation fades because there is no immediate reward, no user, no feedback loop, just an empty product and silence. Sometimes the project grew too complex and the builder lost sight of what they were trying to solve. Sometimes the next step was not another exciting build session but the slow, unglamorous work of figuring out whether anyone actually needed what they had built. Often it is all three: no method, no continuous progress, no external signal that the work matters. When there is no reward for the idea you are working on, it is easy to get distracted by a shiny new one. Most people skip the hard part and start the next project instead.

This is the vibe coding cycle: idea, prototype, dopamine, abandonment, repeat. The tools are not causing it. They are accelerating it. When building is cheap, the cost of starting over feels low. But attention is not cheap. Neither is the compound learning you lose every time you restart from zero.

This applies most clearly when you are building for other people. If you are building for yourself, the calculus is different: your own discipline and knowledge of what is needed to ship can carry a project through the quiet middle. Some builders do succeed that way, through method and persistence rather than external feedback. But if the goal is a product that other people use and rely on, the vibe coding cycle is the default outcome unless you actively work against it.

The projects that survive are not the ones that shipped fastest. They are the ones where someone stayed.

What staying close to the problem actually looks like

Staying close to the problem sounds like obvious advice. In practice, it is a set of small habits that are easy to skip when the tools are fast and the next feature is tempting.

Have a practice that reconnects you with the problem before each session. This might mean writing the problem down in one sentence, re-reading the last piece of user feedback, or opening the product and using it as a real user would for five minutes. The specific practice matters less than the habit. If you sit down and cannot articulate what problem you are solving without hesitation, you have drifted. Go back.

Build what you need next, not just what feels exciting or easy. There is always a feature that is more tempting than the one that actually matters. It might be a new AI model integration, a rich dashboard, or something that would look good in a demo. The one that matters tends to be less glamorous: fixing the form validation, writing the error messages, testing the flow on a phone. The unglamorous work is the product work. That does not mean fun projects are wrong. If you are building for yourself, build whatever you like. But if the goal is a product that other people rely on, the work that matters is the work that solves their problems, not the work that entertains you.

Talk to the people you are building for between build sessions. The validation post covered research before you start. This is different. This is ongoing alignment: checking, as you build, that the thing you are building still matches what the people you are building for actually need. Their needs shift. Your understanding sharpens. If you only talked to users once at the start, your product is drifting on assumptions you made when you knew the least. And when you can, watch people use the thing rather than just asking what they think. Behaviour is more reliable than opinions. Someone saying "I would use this" and someone actually completing the flow without hesitation are two different signals.

Build the thing that is needed, not the thing that is cool

Every builder has a version of this story. You are working on the important thing, and then you think of a feature that would be genuinely clever. It would demonstrate technical ability. It might even be useful, eventually. But nobody has asked for it, nobody is waiting for it, and building it means the thing people actually need ships a week later.

This pattern shows up everywhere. Imagine spending a weekend adding a product recommendation feature when the checkout flow still loses half its users at the payment step. Or adding social login before the onboarding explains what the product actually does. The shiny feature gets built because it is exciting. The essential one waits because it is not.

The filter is simple: "Is this solving a problem someone has right now, or is this solving a problem I am imagining?" If you cannot point to a specific person (including yourself) who is currently experiencing the pain, it can wait. That does not mean you never build speculative features. It means you build the needed ones first, and you are honest with yourself about which category each feature falls into.

Your understanding of the user should keep getting sharper

The validation post talked about defining who you are building for before you start. That definition is a starting point, not a finished product. The more you build, the more you learn about your audience, and your understanding should update with every new piece of evidence.

Imagine you start a project with the audience defined as "small business owners." That is too broad. After a few conversations with early users, you might discover the real audience is narrower: small business owners who already have an online presence but cannot afford a developer to maintain it. They do not need help getting started. They need help with what comes after. That sharper definition changes what you build, how you describe it, and what you prioritise. None of that would happen if you treated the audience definition as a task you completed in week one and never revisited.

Write down who you are building for. Revisit it after every meaningful interaction with a user. Update it when the evidence tells you something new. The document should get more specific over time, not less.

Order your work by what the user needs next

One of the clearest signals of staying close to the problem is how you sequence your work. Are you building in the order that matters to the person using the thing, or in the order that is most convenient for you?

If your users cannot sign up reliably, adding a dashboard is building out of order. If they cannot understand what the product does, adding a feature is building out of order. Sequence by what the user needs, not by what the backlog says.

This is harder than it sounds, because the convenient order and the user-needs order often diverge. Building a settings page before the onboarding flow might feel like progress. But if new users cannot understand what the product does in the first thirty seconds, the settings page is serving nobody. The user-needs order forces you to confront the hard, visible, user-facing problems first. That is uncomfortable, and that is the point.

Speed is the tool, direction is the job

AI tools have made it possible to build a working prototype faster than ever before. That is real. It is not hype. What the tools have not given you, and cannot give you, is the judgement to decide what to build, in what order, and when to stop. That judgement, along with everything else that surrounds the code, is still the work of the person building the product.

There is another advantage to going slowly and deliberately that is easy to miss. AI coding tools improve as they understand your codebase. When you build incrementally, each session starts with a more complete picture: the agent can read your existing code, your configuration, your commit history. A codebase that grew steadily and coherently is one that AI tools can navigate and extend reliably. A codebase that was thrown together in a sprint is harder for AI tools to work with, because there is less clear structure to infer intent from. Going slowly is not just better for you. It is better for the tools.

The projects that survive the first month are not the ones that shipped the most features. They are the ones where someone kept asking a boring question: "Does this still solve the problem I set out to solve?"

Stay close to the problem. Build what is needed. Talk to the people you are building for. Let your understanding sharpen over time. Sequence your work by what matters to them, not what is convenient for you.

The speed is a gift. Do not waste it building fast in the wrong direction.