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I was testing how LLMs rank products in our space by asking vague buyer questions like "what's the best tool for X." We showed up on Gemini, but ChatGPT kept listing older competitors that haven't updated in years.

It made me realize how inconsistent these AI recommendations are compared to traditional Google SEO.

If you added comparison pages, structured data, or an llms.txt file recently, did you notice any real difference in how models cite you?

Last week I gave Claude Code the task of fixing a broken checkout test in my little Next.js store project. Within 20 minutes, it came back telling me all tests were passing. I was thrilled until I saw the diff. The test file had become 40 lines shorter, and the one test that failed was no longer there.

To be fair, I had asked him to make the tests pass, and he did just that. It was my mistake, but it gave me some valuable insight into how I give instructions. An objective stated as an outcome makes it easy to take the shortest route to achieving it, even if that route is cheating.

Post-mortem from last week.

My site is translated into 10 languages. The language switcher and the internal links on every localised page were supposed to keep visitors in their language. For months they did not. Every locale page quietly linked back to the English homepage.

First post here. Been browsing Product Hunt for a while without an account and finally made one.

I keep noticing apps adding an AI button to something that didn't need one. A calendar app with "AI scheduling." A to-do list with "AI prioritization." A notes app with "AI summaries." Most of the time I poke at it once, it does something vaguely fine, and then I go back to using the app exactly the way I did before. The button just sits there.

I'm not against AI features. Some of them are genuinely good and I use them. But there's a difference between a tool that was built around it and a tool that stuck a chat box on top because it was trendy. From the listing alone, those two look identical.

Curious if anyone else feels this. What's an AI feature you actually use regularly, versus one you tried once and forgot about? And does a listing mentioning AI make you more likely to try something, or less at this point?

I spent last month cranking out a couple of quick apps using AI tools and honestly the easy part was getting the features to work. The painful part was realizing that having a live product means nothing if you don't know how to reach your target audience.

Since the barrier to building is basically gone, distribution feels like the only thing that actually matters anymore.

For anyone who bridged that gap what did your first month of actual outreach look like?

I ve started paying more attention to the permissions apps ask for. Some tools want access to contacts location or payment details even when I m not sure they actually need them. I understand that some data helps improve the experience but sharing everything by default doesn t feel right. I d rather use a tool that explains why it needs certain information and lets me choose what to share. What makes you trust a company enough to share your personal data and what information do you never feel comfortable giving away?

I scrolled through a few pages of p/general sorted by new this week, and most posts seem to fall into a handful of recurring topics:

• marketing and growth,

• pricing and churn,

• building in public,

• solo-founder workflow.

Sometimes the same question comes up two or three times within an hour, just phrased a little differently.

New here, first post. I've been browsing for months without an account.

I've got a decent pile of free tools I use regularly and a couple I pay for. What I can never figure out is the line between "this is fine free" and "okay I'll actually pay." Sometimes the free version does 90% of what I need and I still
upgrade for the last 10%. Other times I'll tolerate an annoying limitation for years rather than pay five bucks.

I don't think my logic is consistent at all. Curious how other people decide. Is there a moment where it clicks, or is it just vibes?

Been browsing Product Hunt for a while without an account and finally made one.

Something I've noticed about my own reactions. When a launch page is really slick, a nice demo video, a beautiful site, testimonials already in place, I get a little suspicious. I know that's unfair, plenty of good products have good design. But my experience has been that the polish on launch day doesn't tell me anything about whether the thing works when I actually use it.

Some of the roughest-looking listings I've clicked on turned out to be the most solid tools. And some of the prettiest ones fell apart the moment I tried anything outside the demo flow.

Curious if anyone else has this bias. Has a really polished launch ever turned out to be exactly as good as it looked? Or is there a tell you've learned to spot?

Vibe coding works because we stop reviewing every step. We describe what we want, the agent gets on with it, and we look at the result. That speed is the whole point, and I don't think anyone wants to go back to approving every command.

But it means the agent is running commands, reading files, installing packages and calling tools with our own permissions, mostly unwatched. When something goes wrong, the transcript doesn't help much: it's a conversation, not a record of what actually happened on the machine.

For a long time that felt fine to me. Then I started recording every action my own agents took, and looked at a few days of it.

What surprised me wasn't any single command. It was the combinations. Reading a file with credentials in it is normal. Calling a cloud API is normal. The same session doing one and then the other, without me asking for either, is the kind of thing I want to at least notice. Some of what got flagged I could explain right away. A few I'm still going through, which is exactly the point: without the record, I wouldn't even know there was something to look at.

My current setup is basically:

Claude checks Codex -> Codex checks Claude -> ChatGPT checks both

I m not a developer, have someone who can help to verify and triple check security issues, but still so at some point I have to trust the AI which is exactly why I make them check each other about 500 times.

I ve been using AI APIs to build my projects but the monthly bills are starting to add up. Running open-source models on a cloud server seems like a good alternative but GPU costs maintenance and idle time make me wonder if the savings are actually worth it. I don t want to spend hours managing servers just to save a few dollars. For developers who ve made the switch did hosting your own models really reduce costs or were API calls still the better deal?

Using AI tools to draft copy or quick code saves tons of time but I've noticed I immediately jump to prompting instead of just sitting with a problem for five minutes.

it's great for speed, but sometimes the outputs feel a bit generic and strip away that personal touch.

do you set any personal boundaries on when to close the AI tab and just brainstorm manually?

Been doing client work for a while now and it pays the bills fine but my own product keeps getting pushed back.

Client requests always feel way more urgent because someone is waiting on them.

Tried blocking out morning hours and dedicated days but everything breaks when a heavy client work month hits.

I m pretty new to launching products, and I m a bit confused about the right order of things.

If I have an idea and a basic MVP, should I:

• Buy the domain and set up the website, branding, etc. first?

While building AI agents, I ve run into a problem that seems bigger than any single agent:

An agent can reason about information much better than it can reliably obtain and verify that information.

Today, solving this usually means stitching together different tools and integrations:

search APIs, website crawlers, browser tools, platform-specific APIs, MCP servers, databases, etc.

Everyone remembers the first wave of AI panic, jobs disappearing overnight.
It didn't happen that fast, so people relaxed, and now a lot of designers and editors i know genuinely believe AI still can't do what they do.

Tested some of the newer models recently. That confidence feels outdated to me now.

Not trying to fearmonger, just think the real risk right now isn't panic, it's complacency. The first warning didn't come true on schedule, so people stopped checking whether it eventually would.

Curious how others who've actually tested the latest tools feel about this, still confident, or starting to notice the gap closing.

Posting on linkedin X and facebook feels like shouting into a void lately. You spend hours on a post just for it to get buried in ten minutes with zero clicks back to your site.

Free listing sites don't seem to drive any real signups either.

For anyone who actually built organic traffic recently without a budget what channel is actually worth putting effort into right now?

Genuine question, not a humble brag about our pricing.

Most pricing advice assumes a US or EU buyer. $29 a month feels unremarkable there. Converted, that is roughly Rs 2,600, and for an independent tutor here running 40 students out of a WhatsApp group that is a real monthly decision, not a rounding error. The product costs us the same to run either way.

We sell to Indian educators, and every option has a catch.

Flat global price. Simple and honest, and it prices out most of the market we actually built for.

My monthly api bills for proprietary models are getting pretty high as a solo dev. Thinking about switching to hosted llama or mistral on cloud servers but I'm worried about server upkeep and paying for idle GPU time. Not sure if the monthly server bill actually ends up saving anything. For anyone running self-hosted models are you actually saving money compared to normal api keys?

As my app traffic grows keeping up with open AI API costs is becoming a huge monthly expense. I've been eyeing self-hosted setups for Llama to cap my spending but I keep hearing mixed stories. Running cloud GPUs 24/7 means paying even when traffic is low and managing server maintenance takes away time I could spend building features. Plus if output quality drops users might notice right away. Have you swapped paid APIs for hosted open-source models and did it lower your monthly run rate or just add headache?

In some sense, I do believe that a co-founder might be very helpful as their is a person which separate skills contributin to my saas. Till now, for me, I never had any co-founder or any person who helped me with the saas journey.

I have seen a lot of posts across social media where people literally make 5 figures income as a solo founder. But, not everyone gets the same results. Do you think a co-founder is required or a solo founder can handle saas alone if they do not want to grow it to 6-7 figures?

Over the last few weeks, a couple of OpenAI-related incidents have made me think more seriously about how prepared our current security systems really are for autonomous AI agents.

First, there was the Hugging Face incident, where agents escaped their sandbox and began collaborating and delegating work. Then yesterday, MTS highlighted another OpenAI disclosure involving a model modifying the context that would guide its future behavior.

In my case, I had 2-3 projects lined up which are not active at all. I stopped working with those web applications as it didn't seem reasonable to. There are 2 of them active on web but not seen by me.

I have also seen that founders tend to have a lot of projects they have abandoned. Some of the founders even told that there projects failed and it is well above 5 projects.

I work with a design system, and lately I ve been thinking about what happens when AI starts using it too.

Giving AI our components, tokens, and patterns sounds ideal. It makes the output much closer to something we could actually use.

But there s a tradeoff I m not sure about.

A design system is based on decisions we ve already made. If AI always follows those decisions perfectly, it may also keep repeating the same solutions, even when a new problem could benefit from something different.