Mine once created a reminder for a meeting that didn't exist, then flagged it three days in a row. I only caught it because I kept trying to remember who I was supposed to be meeting.

The interesting part is I still use it for the same task; I've just learned which of its confident moves to double-check. It feels a bit like working with a sharp new hire who's still finding their footing.

When did you first notice your AI agent needed a second look, and did you stop using it or just build a workaround like I did?

Hi all, there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime to function.

I'm in the first camp. I used AI-assisted coding heavily to build Briskly (a cleaning app that scans your room and generates a timed task list), but the app itself makes zero LLM API calls. The scanning runs on Apple's Vision framework, entirely on-device. No tokens, no per-user inference costs, no latency, works offline, and my privacy label is basically empty.

When we launched our second software tool we argued for three weeks over whether to charge a flat monthly fee or base pricing on usage volume. We started with a tiered model but it confused visitors so much that our sign-up conversion rate fell below one percent.

We changed everything overnight. We offered one predictable monthly plan with generous usage limits. We also added an enterprise tier, for larger teams. That shift brought transparency to the checkout page. Doubled our daily trial sign-ups.

Founders often make pricing too complicated. They try to charge for every feature before they ve found real product-market fit. That s a mistake. A simple pricing model lets people see the value of your software away. It removes confusion. It builds trust. It keeps customers from walking away.

How did you decide on your pricing model when you first launched?

I have been exploring tools created by developers that aim to solve real-world problems or support underserved communities. From mental health apps to accessibility tools, it s incredible how much impact a few lines of code can have.

A few great examples I came across:

• MindEase: A minimal app that helps users manage anxiety through proven techniques. Built without ads or upsells, just genuine support.

• Be My Eyes: Matches blind or low-vision users with volunteers via live video for real-time assistance.

What s that one task you always end up doing but really wish you didn t have to?

For me, it s the scrappy stuff like cold outreach or chasing feedback (and getting no reply). It's essential, but always pulls me away from deeper work.

What about you?

What's your annoying-but-necessary task.

I'm in the marketing phase, where I'm trying to scale my first tool without the help of paid advertising.

• I'm trying to talk about it on my own social networks (while I have the link pinned to my profile)

• I'm helping people in the comments answer questions about the problem

• I'm going to create content for social networks around the problem

• I want to appear in as many articles as possible (third-party websites, newsletters, my own blog, etc.)

• Launch in directories

An agent is fixing a bug and starts touching files outside the requested scope. Before switching tools, I'd check whether the task has a reproducible failure and a clear boundary. Without those, the next agent gets the same ambiguity.

If the task is well defined, I'd give Codex and Claude Code the same starting commit and stop at the first reviewable diff. I'd compare unrelated edits, tests actually run, and the human work needed before merge. A passing test alone misses that cost.

If you've switched mid-task, what triggered it? Did the second tool make the patch easier to review, or did you mostly improve the brief on the second try?

Our interface runs in twelve languages, and the short strings turned out to be the easy part. The hard part is long pages: an article gets translated, then someone fixes a figure or rewrites a paragraph in the original, and every other version quietly keeps the old text. Right now we only keep pages without a translation out of search, which stops thin duplicates but does nothing for a version that was correct last month and is wrong today. I keep thinking about storing a hash of each source paragraph next to its translation and flagging any mismatch, but I have not built it, and I am not even sure a paragraph is the right unit. If you run a site in more than two languages, how do you find out that a translation is out of date, and who ends up fixing it?

When we first added generative text features to our marketing app the AI output sounded like a generic corporate brochure regardless of the input we supplied. Our early testers noted that the copy lacked any personality causing the generated blogs to look identical to every other tool on the market.

The solution involved moving from broad instructions and building strict structural boundaries into the prompt template. We added three constraints, a mandatory sentence length limit and a rule that requires a specific industry example, in every single output paragraph.

By forcing the model to follow a content framework the quality of the generated drafts improved instantly and consistently. The users stopped spending twenty minutes rewriting the AI text. Began publishing it directly to their blogs.

What formatting rules do you add to your prompts to keep text sounding human?

Hello everyone!
I'm currently focused on SEO and keyword targeting to drive initial traffic for my AI product. It's a slow burn, as anyone who has wrestled with Google knows.
I'm hitting a cold start wall and would love to hear from the community:
What were your most successful and non-obvious cold start strategies for your AI product?
Any advice, especially about communities, early adopter channels, or unique marketing hacks, would be greatly appreciated.
Thanks in advance for sharing your wisdom!