I ve been experimenting a lot with AI tools like V0, Lovable, and Bolt.new to build small products and prototypes.

One pattern keeps showing up: most ideas don t fail because the idea is bad. They fail because the prompt is vague, confusing, or incomplete.

AI isn t a mind reader; it does exactly what you ask. If your prompt is fuzzy, your output will be too.

For example, I recently built PublicWall off a single well-structured prompt. Before that, I wasted hours on iterations that were mostly me not clarifying what I actually wanted the AI to do.

Hey!

I want to dive into practical applications of generative AI and have set myself a challenge to develop a useful product in 30 hours of focused work. My goal is not just an experiment but creating something with genuine practical value.

I have basic programming skills and can use any available APIs and tools (GPT-4, Claude, Stable Diffusion, etc.). The ideal project should:

- Solve a real problem

I m curious what you all devs and founders are relying on day-to-day in 2025. With the flood of new ai tools, it feels like every tool looks different depending on industry and workflow.

• What s ai tool working well for you right now?

• Which AI tools actually save you time?

• Which ones did you try but drop?

Would love to see how other folks are stacking their tools this year.

At @UXPin we've just deployed the prompt enhancer for our AI component creator.
From now on, short prompts will be evaluated and refined if the AI considers them too weak. This aims to improve the AI output - with prototypes returned by the AI more detailed and diverse.

Example and output below:

Original prompt: 'news portal'

Refined prompt:

I have been cranking out apps for the past few years and loving it.  Then one morning a week or 2 ago I got a little ambitious and decided to build a desktop email client because outlook was so-so and superhuman was ridiculously expensive.  

Is this a big mistake?   Am I wasting my time ?

Our team pushes code constantly - multiple deploys per hour some days. The problem? Nobody can keep up with what's changing. You check the repo in the morning, grab coffee, come back and suddenly there are 47 new commits.

Good luck understanding what actually matters or how it affects your work. We built Doculearn to solve this with automated flashcards. Here's how it works:

i just draw a box and write make app and boom agent writes perfect code

me and my team:

1. spends 4 hours debugging a webhook that randomly fails in prod

2. fights over whether a boolean flag should be is_active or active

3. merges a PR that touches 182 files because Typescript

4. writes actual logic that vibe agents refuse to touch

I just finished my first cloud project using Claude Code and I ran five different agents:

a PM

a QA

a Designer

I recently switched main agents from Claude Code to Codex, and wow, the code quality feels way higher. But I'm noticing that Codex doesn't explain its decisions or reasoning as clearly as other models. Is it just me, or does Codex skip the 'why' behind the code more often?

Are there tricks to codex out more reasoning? Curious if anyone else has noticed this or found a good workaround.

I ve built two marketplaces before:

• One for chatbot templates in 2018 that was used by 700+ marketing agencies.

• Another in 2020 for job seekers in the U.S., which reached 5.5M users.

So let s suppose I have some experience. You can read about me on Bootstrappers, TechCrunch, Dev to.

Now I m considering building a marketplace where creators can list their vibe-coded projects along with the code, a live demo link, etc. The idea is to target: