I ve been building my app mostly with AI coding tools, and the experience has been pretty weird.

At first, it feels almost like cheating.

You describe a feature AI writes it you test it repeat.

But once the app gets bigger, the problems change.

Everyone talks about which tool they're using right now. Fewer people talk about the tool they left and why.

I switched from Bolt to Cursor halfway through a project a few months ago, not because of a big feature gap, but because of something small: Bolt kept losing context after a few file edits and I'd have to re-explain the whole architecture every session. It wasn't a benchmark loss, it was a patience loss.

That's the kind of thing that never shows up in comparison posts. Nobody writes "Tool A has 12% worse long-context memory," they just quietly open a new tab and start using Tool B.

So, curious about real switches, not hypotheticals:

Hi friends, recently started vibe coding as a way to explore a product that I've been wanting to build for myself and others. I started in the world of V0 and Lovable then into Cursor, but moved away when I felt like I was wasting more credits on debugging and re-providing context. I feel like I've finally hit my stride with Claude Code I would appreciate any tips that you would have given yourself at this point in my journey. Thanks all!

A workflow question I keep going back and forth on, and how I landed.

I ship a lot of AI-written code as a solo founder, and I hit the wall you probably know: if a human has to review every agent PR, you didn't buy leverage, you hired yourself as a full-time code reviewer. So I stopped asking "should I approve this PR" and started asking two things instead: how confident am I it works, and how bad is it if I'm wrong.

I work in AI text detection, so I spend most of my time on the content side of this question: whether a paragraph can be traced back to a model. Vibecoding asks the same thing one layer down, about code, and the answers people give are completely different.

Nobody labels a commit. There's no convention for it, nothing in a README, and the honest reason might be that a label wouldn't survive anyway. Once generated code has been reviewed, renamed and refactored into the rest of the codebase, whatever was distinctive about it is gone. One editing pass does the same thing to a watermark in text.

So does provenance matter for code, or only for content?

Where I've got to so far, and I'd change my mind easily on this:

With vibe coding, it s incredibly easy to get a feature working for the happy path.

But I m more curious about the flows that aren t obvious from the code you re currently working on.

A change might look perfectly safe within one repo, but what if it affects:

• An edge case handled by another service or repository

• An older flow that isn t documented in the current codebase

• A downstream API or integration

• A workflow that the AI simply has no context about

Might anyone have some open URLs of their Vibe coded prototypes on Lovable / V0 / Bolt / Figma Make or Claude Code? I'm looking for some prototypes without logins to test a new version of our product which we'll be announcing next week. I'd like to check it on some existing Vibes.

Bonus points if you have a task for me to user test for you, I'd gladly share feedback.

It was just today that I realized that if you continuously use base44 you have to pay for a plan. An app that I've worked on for ages has now been put on hold because of something like this. I want to ask if I should start on a new platform since I don't have the funds or should I wait for 14days to continue for free points? Please tell me which is wiser

I've been building with AI for a while now, and I've noticed something: the projects that work best aren't the ones where I let AI do the most. They're the ones where I've done the hard thinking first.

Here's what I mean. Last week I was mapping out a feature set, and I realized I'd spent two hours prompting Claude to help me decide what to build. It was... fine. But it felt hollow. When I stepped back, predefined exactly what problem we were solving and what constraints mattered, then asked AI to help execute against that? Completely different quality.

We ran a benchmark to see how well @Claude Code actually refactors legacy code alone and then redid the same test, but this time with code-health guidance via MCP server.

• To limit any vendor bias, we used a public data set of 25,000 source code files from competitive programming, including carefully crafted unit tests. 

• We assessed agent correctness by running those tests. 

• We measured the Code Health impact using CodeScene.

• (See our research Code for Machines, Not just Humans for more details on the methodology and data)

Claude Code that was MCP-guided achieved 2 5x more more improvements in Code Health compared to unguided refactoring.