Recently stumbled across this Cursor pro-tip from Ian Nuttall on X:
"1. ask it to recommend a folder structure

2. ask it to actually create the folder/files based on that this makes it 10x easier for me to get started and Cursor is more accurate using codebase cos it knows where to update files."

That got me thinking, what other pro tips are people using to generate better code, ship faster, organise your space better, etc. Drop em below:

I'm Kevin, founder of EnginifyAI. Over the last 12 months I've been working on a prompt engineering tool. I started off using Bolt but quickly I found myself unable to completely use it effectively. I then tried Cursor and was using it early on but maybe I had the wrong settings as I found myself using all my credits in 2 days. So, I switched to Windsurf, and to my surprise credits were a bit more manageable and it took about 2 weeks before my credits were consumed. Keep in mind that I am new to coding so I spent a lot of time asking questions in Windsurf instead of using my paid subscription to Anthropic.
I'm coding using React/Typescript and Supabase. I have learned that coding is a lot of work, a lot of fine-tuning, and a lot of testing and fixing. The one thing I haven't quite figured out completely is how to fix migrations. Early on I was running SQL scripts directly in Supabase without migration scripts because I found it to be faster. But now Windsurf uses very old migration script references and I find myself fixing the same issues because of an old migration script reference. Any idea how to fix this?
I assume that vibecoding is an experience everyone can enjoy but comes with frustrations about asking the right questions, or being very specific about my requests. I am using Claude Sonnet 4, 4.5 and 4.5 thinking. Are these the best coding ai models for Typescript?

Wherever you code (online, CLI, or IDE), everyone has some love, hate or horror vibe coding stories, from building a prod-ready app in minutes to the whole repo got wiped. Would love to hear your story.

Hey makers!
I've been deep in prompt engineering lately while building an AI tool, and I'm genuinely curious about how others approach this.
A few questions:
1. Do you save your best prompts somewhere? Notion, text files, dedicated app, or just copy-paste from chat history?
2. How do you iterate? Do you have a systematic approach or just tweak until it works?
3. Different prompts for different models? Or do you use the same prompt for ChatGPT, Claude, Gemini?
4. Text vs image prompts do you treat them completely differently?
I've noticed I was doing the same optimizations over and over (adding role, being more specific, structuring output format), which made me wonder if everyone has their own "prompt formula."
Would love to hear your workflows!

Now, since some do these things, while others charge every bit as much without these features, I already expect that they have:

• Built-in Github commit

• Credit rollovers (e.g. if I do not use all credits in a paid plan, they are added to the next month - indefinitely)

• Nothing that tries to keep my project within their ecosystem and then expects that as my business scales, I pay them more.

As Lovable, Bolt, v0, Base44, Bubble, Make, etc. jostle to out-do each other and be the one that we pay for, I think we will soon see:

• Back-end solutions that guide non-technical creators through the steps to ship a SaaS product that is actually ready to scale to take on real traffic

• Pre-emptive best-price/best-solution external solution-shopping, such as for white-listed bulk emailing and available domain search.

I'm what you might call a gringo vibe coder.

No dev degree. No traditional background. Just me, Cursor, Claude, and AI... figuring things out together.

Rick Rubin said it best: "In the past, for music, you had to go to the conservatory and study for years... then when punk rock came along... if you had something to say, you could say it." He calls vibe coding the punk rock of coding.

I've been thinking about this a lot lately.

As an SAP ABAP Developer, I had app ideas sitting in my head for years. Before AI, the learning curve for mobile development felt impossibly steep. Now? I shipped my iOS app in weeks.

But here's my honest question:

How many of us vibe coders are actually building sustainable products?

I keep seeing advice like use this model for the easy stuff and that one for complex problems. But it makes me wonder what really counts as a complex problem for an LLM?

For us, complex usually means lots of steps, deep reasoning, or tricky knowledge. But for AI, the definition might be different. Some things that feel easy for us can be surprisingly hard for models, while things that seem tough for us (like scanning huge datasets quickly) might be trivial for them.

Hey PH community!

I ve been spending a lot of time thinking about how people actually work with prompts while building a tool in this space, and I realized I have way more questions than answers.

A few I d love to throw out:

I'm a big fan of voice dictation apps. In fact, I'm using one right now to write this very post (you'll have to wait till the end to see which one I'm using )

The two main products I've used in this space are @Aqua Voice and @Wispr Flow. From talking to others, these are the two that I typically hear people mention using. In general, I hear a lot more people talk about using Wispr Flow.