Came across the recent Claude Code leak from Anthropic, and what stood out wasn t the leak itself, but what it revealed about how these systems actually work.

• A source map file accidentally exposed ~500k lines of TypeScript

• Turns out Claude Code is basically a multi-step prompt orchestration system, not some mysterious black box

• Includes things like:

  • layered prompt pipelines ( prompt sandwich )

  • fake tools to prevent model distillation

  • simple frustration detection (regex for rage prompts )

• Even hints at future features like background agents and persistent memory systems

What s interesting is this:

It kind of confirms that the real product layer in AI isn t just the model it s everything wrapped around it.

Which raises a few questions:

Hi ProductHunt community,

This term has been coined by someone and there are already more than 80 products that you could put in this category. Looking at the numbers, it's growing pretty fast.

For the past couple weeks I've been on my #indiehacking coding grind

All the tools out there to go from just vibecoding to actually launching can be quite overwhelming when just starting out

I'm a UX designer and Product strategist. Not a fullstack developer.
And I just shipped a full SaaS product.
Here's how I did it.
I work as a UX and Product SaaS strategist and also run a remote digital agency Koylabs.
I always had product ideas but never the engineering background to build them myself. That changed this year when I started seriously experimenting with AI tools and vibe coding.
I picked a real problem I kept seeing with clients they wanted Google reviews on their website and every existing solution was clunky, complex and a bit dated. Simple problem. Clear market. So I decided to build the solution myself.
The stack:
Next.js + Supabase for the backend
Stripe for payments and subscriptions
Cursor as my AI coding environment
Vercel for hosting
What surprised me:
AI is incredible at boilerplate and repetitive logic
The hard parts were still hard embed systems, iframe height communication, Stripe webhook edge cases
Product thinking was my biggest advantage.
Knowing exactly what to build and what to leave out saved weeks
Vibe coding is real but you still need to understand what you're building
What I actually shipped:
Full auth and multi-tenant architecture
Stripe subscriptions with trials, webhooks and billing portal
A custom iframe embed system that works on Framer, Webflow, WordPress and any platform
Automated review sync
Four widget layouts
Start to launch: a few weeks.
The result is Review OS a simple clean way to show your Google reviews on your website. One embed code, done in under a minute. Only $9/month.
If you or someone you know needs this try it free for 7 days at www.reviewos.co 7 day free trial
And if you're thinking about building your own project follow along.
Give this tool a try and share your thoughts I'm looking forward to your feedback.
My next post will be a full playbook on how I did it, tools, process, mistakes and all.
What are you currently working on? Drop a comment would love to know.
Launching on Product Hunt this Wednesday would love your support

I'm a freelance consultant. Tried Folk, Attio, HubSpot free, Google Sheets. Never stuck with any of them. The problem wasn't the features, it was that I never went back to the tool.

So I built a CRM inside my AI assistant (Claude + MCP server + Supabase). Six contact lists, email drafting, a Chrome extension that scrapes LinkedIn profiles at $0.001 each. Total cost: $10.

The whole thing lives where I already work. That's why I actually use it.

Planning to open source Voiden (https://voiden.md/) soon.

This isn t a maybe someday idea anymore, it s a deliberate step we want to take in the coming weeks.

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

Do any of the more experienced coders ever feel like closing the loop in vibecoding is a bit too much control to let go off? I'm not sure if I can categorize myself as an experienced coder. I'm been mainly self-taught and been doing it for almost a decade now, so I have strong opinions about what I consider best practices. (I actually love refactoring, because it feels like order is once again restored in the codebase).

However, with AI becoming so good at so many parts of the product development, I'm starting to feel like a project manager, or god forbid, scrum master... On the one hand, it's nice that I get to spend most of my time thinking about the problems themselves rather than the implementation of the solution. But on the other hand, I think my coding style is a bit like trying to find a way out of a dark room by running into the wall repeatedly as opposed to planning a way out. And I kind of missing running into the wall to be honest. Now, I'm enviously looking at AI hitting the wall.

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.

Two days ago I saw this thread about how we are having more launches in post-GPT era.
And a question was born in my head: what quantity is optimal now? Of course, you can often see a trend among builders on X, where they launch a project per month, then roughly 4 months later 1 project takes off and we don't see new projects for the next 6 months because the person is busy scaling (and that's ok, testing a hypothesis shouldn't take much time)
But still, what pace should be considered right? 12 in 12 months slow in modern reality. Launch a product in a day? Unrealistic (SEO, ads, app approvals, various settings and optimizations). Theeeeen...48 products a year?
Or should we look at this from another angle, where LLMs allow us to create 12 products in 12 months with more features and better quality? What's the community's opinion?