I ve been experimenting with a CLI workflow that removes the most annoying part of AI-assisted dev:
re-explaining context every single time.

Instead of prompts like:

Integrate Stripe payment gateway with X, Y, Z

I just run:

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!

I ve been using Pencil.dev for a few days and honestly it s a big paradigm shift for how fast you can explore UI. I'm loving it!!

One thing I bumped into: I still need to move some screens into Figma to polish details, collaborate, and keep everything in the same place as the rest of our design work.

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.

There's a popular narrative on social media right now that AI can build software so quickly and cheaply that SaaS is dead (or will be soon).

Why pay for Linear when AI can build a project tracker in an afternoon? Why pay Stripe $30k/year when you can vibe code your own billing system in a weekend? The cost of building software has collapsed to near zero, therefore the value of selling software has collapsed to near zero. QED, SaaS is dead.

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?

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.

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'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.