One thing I've noticed after using AI agents across the same project for a while:

The agent usually doesn't forget what the code does.

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!

Has anyone here rebuilt something they use daily because the official product has a quirk / bug that annoys you?
For example - I use Termius to connect to servers over SSH but on my Ubuntu 26 Desktop, Termius is buggy to the point of being unusable.
So - I recreated it using Claude, with one or two improvements. Now I run my version in Docker whenever I need to use it.

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

Hi Product Hunt,

As a product architect working on data-heavy dashboards (like an on-site event management OS and an automated trading bot), I've been heavily exploring vibe coding lately.

The biggest friction point I've hit: AI coding agents almost always default to "modern," heavily padded UI layouts. I strongly prefer a professional "terminal" feel where I can see all critical data points at a single glance. I constantly find myself having to correct the AI because it sacrifices information density for unnecessary empty space.

Has anyone found a vibe coding tool, or a specific prompt framework, that natively understands how to generate dense, data-rich interfaces (especially integrating Dark Mode or Glassmorphism aesthetics) without needing constant manual overrides?

We ve been seeing a consistent pattern across agent systems:

GPT-5 works well as a judge on average cases
but breaks down on edge cases and policy boundaries.

That s exactly where reliability matters.

In our recent work, we took a different approach:

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.

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.

Maybe it s just the corner of the internet I m in,
but every time I open social media
someone is launching a new AI startup.

And somehow they all give the same vibe:
AI agent for X
AI assistant for Y
AI workflow for Z

Different branding, different color palette, same energy.