It doesn't even have to be a big dramatic failures, more the small moments where something clicked and you went from trusting the output by default to double checking everything. For me it was watching an agent confidently rename a function across twelve files, then leave the original function untouched in a thirteenth file it apparently didn't search, with zero indication anything had been missed. It wasn't even a hard case, the file just wasn't in the directory it happened to grep first.
What was your moment? And did it actually change your workflow afterward, or did the trust creep back in after a week like it always seems to for me?

On Product Hunt, I can see many people launching their products using "vibe-coding tools" like @Lovable , @bolt.new , or@Replit

I reckon many people who created something with them are usually developers who didn't have enough time for building a side idea before, but with AI, they could make it happen.

Daily reality: Claude Code in one pane, Codex in another, Cursor in a third, plus a few shells. An agent stops and waits for "yes". By the time I find the right pane, 20 min gone.

Just me? What's your setup notifications, layout tricks, or you've accepted the tax?

Last summer, the idea for my SaaS, Xolora, started to take shape. Around the same time, the concept of vibe coding was blowing up. As a non-technical founder, it sounded like a dream come true. No coding experience? No problem, just let AI handle it.

The beginning was incredibly promising. Using Emergent made me feel unstoppable. I was seeing my idea come to life.

Had Claude Code refactor our checkout flow to add an annual billing option alongside monthly. It worked in every test I ran, annual customers got charged correctly, monthly customers got charged correctly, everything looked clean.

What I didn't catch was a specific combination, someone applying a discount code and then switching plans before completing checkout, that caused the final price to recalculate against the wrong base amount. Instead of erroring out or defaulting to full price, it just quietly landed on zero.
Nobody typed in a hack, nobody exploited anything on purpose, the flow itself just handed out free subscriptions to whoever happened to click things in that particular order.

Took six days to notice, and only because I was manually checking Stripe for an unrelated reason and saw a cluster of $0.00 charges I couldn't explain. Roughly 40 people had gotten a free year of the product by the time I found it.
Fixing the bug was fast. Deciding what to do about the 40 people who were already in, upgrade them to properly paying, quietly let them keep it, reach out and explain, was the actually hard part.

Ended up honoring it for existing users and just fixing it going forward, felt like the least damaging option even though it cost real money.

Hey everyone,

I've built my product around traditional SaaS pricing (monthly tiers), but I m starting to wonder if that model is getting outdated, especially with more AI-powered and compute-heavy tools entering the market.
That shift requires real architectural changes, instrumentation, metering, billing logic, and UI changes, not just pricing tweaks. It s something I m starting to seriously think about for my own product.

In particular, AI usage has real COGs (every prompt costs money), and I m seeing more platforms experimenting with usage-based models, or hybrids like SaaS base + usage + overage.

For those of you building AI or compute-intensive tools:

Honest question for this forum.

I save AI tool threads constantly. New model drops, someone's slick Cursor workflow, "10 tools that'll change everything." I open maybe one in twenty. And I still feel behind every single week.

Lately I think the problem isn't information, there's an ocean of it. The problem is that almost all of it tells you about AI instead of showing you. A thread describes a workflow. A launch video is scripted so everything works on the first try. None of it shows the real thing: the prompt someone throws away, the tool they reach for and why, the moment it breaks and how they dig out.

And that's the part that actually teaches you. Nobody got good at cooking from recipes or at chess from the rulebook. You get good watching people better than you do the real work.

If the agent writes the code who s actually testing it?

Do you still go through edge cases and user scenarios manually, or are you also delegating please break my app to another agent now?

AI dev tools are evolving crazy fast , every few weeks there s a new must-try for vibe coders.

Some people are building full products with @ChatGPT by OpenAI and @Replit , others swear by @Cursor and @Claude by Anthropic , and a few are mixing @Lovable + @v0 by Vercel + @bolt.new to ship apps in record time.

I ve been refining my own vibe stack lately, trying to find that sweet spot between speed, control, and creativity.
It made me wonder ,what does your setup look like right now?

We run an automated pipeline that drafts articles for our product. During our first test attempts we let the model do almost everything, including the mechanical bits: fixing dashes and quotes, tracking which stage each article was in, creating SEO json, naming image files etc. It worked, but the quality was uneven, sometimes the model would miss some things, and, of course, it was rather expensive in terms of tokens.
Then we changed our approach and started treating AI like a creative employee and providing it with "software" (python scripts) to help with any mechanical tasks that could be automated by code. The model only does the part that actually needs intelligence and creativity now.
And honestly, the result surprised me - quality went up, because we now let it focus on what it's actually good at. As a pleasant bonus - the costs of running the pipeline dropped too. One of the first things we scripted was stripping the em-dashes the model loves to add but my co-founder has an allergy for

How do you improve the quality of your AI results, especially automated pipelines?