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Udaya here, founder of PlugThis. We're #1 right now and honestly still processing that.

But upvotes aren't the point of this product. Extensions are.

So here's the thread: if you used your free builds today, post what you made. One line on what it does. Screenshot if you have one.

Weird ideas especially welcome. The stranger the prompt, the more I want to see it.

We just wanted to take a moment to say thank you to the entire Product Hunt community.

Reaching #2 Product of the Day was an incredible milestone for us, but what we'll remember most are the conversations, thoughtful questions, and valuable feedback we received throughout the launch.

To everyone who supported IvyForms, shared their ideas, or simply took the time to check out what we're building, thank you. Your feedback is already helping shape what's coming next, and we can't wait to continue building and sharing our progress with you.

I've noticed a pattern in almost every "how I built this" thread here lately: before touching the keyboard, people are opening a chat window with an LLM and asking it to poke holes in their idea.

Makes sense. Vibe coding has made building fast. You can go from idea to working prototype in an afternoon. But building fast doesn't mean you're building the right thing faster. If anything, it raises the cost of a wrong guess, because now you can ship the wrong product in a day instead of a month.

So I've started using AI earlier in the process to argue with me. I'll describe an idea and ask it to play the most skeptical potential customer it can, or to list every reason the idea might already be a solved problem. Sometimes that surfaces a competitor I hadn't seen. Sometimes it forces me to actually define the problem instead of jumping straight to the feature list.

Here's the part that worries me though: AI models are notoriously agreeable. If I phrase my idea with enthusiasm, I sometimes get validation back that feels a little too easy like it's mirroring my confidence rather than actually stress-testing the logic. That's dangerous, because it can create false certainty right before you commit real time to building something nobody asked for.

Hey everyone, thanks for the great feedback on the valv launch!

Now that we are live we want to get all the feedback we can!

If you are interested in trying out valv further send me a message or book a slot here https://cal.com/joshua-knauber to get in touch!

If you ve been following along and want to support us, we d really appreciate a GitHub star:

https://github.com/ota-run/ota

A repo reaching a working state once is useful.

But the real value comes when that working state can be repeated by the next person, on the next machine, or in the next automation run.

A user recently shared detailed feedback about how Pocket Screen behaved with Stage Manager.

That sent me down a much deeper technical path than I expected.

The straightforward implementation did not work: once the source window moved to the Stage Manager shelf, macOS returned only the small shelf preview instead of the original full-size window.

Hi there,

While a complete rework of the "Add to Calendar Button" is in the making and only ~1 month away, I also want to share something else. A free and open-source boilerplate/starter/template for Astro.

Hi everyone, Jeremy here :)

35 new ideas + demos have just been added to The Eureka Database, and this batch came from somewhere different.

Online, we re constantly exposed to different takes and opinions, which can easily shape unrealistic or skewed expectations about what actually works in practice.

And it s not just life in general; it happens a lot in business too.

Building an AI agent is one challenge, but knowing it's ready for production is a completely different one.

Traditional software can often be verified with unit tests and integration tests, but AI agents introduce additional complexity. They rely on reasoning, external tools, changing context, and non-deterministic model outputs, which makes testing much less straightforward.

I'm curious how other teams approach this before deploying AI agents to real users.

It's crazy how easy building something from scratch has become, but one thing I ve noticed lately is that more and more products look fresh on the surface, yet feel almost identical underneath.

I see this every day scrolling the PH daily dashboard, struggling to spot the real difference between dozens of products claiming to be the next Lovable with a twist, another distraction-free productivity app, or yet another AI voice assistant.

Just like with copywriting or design, product concepts are starting to feel homogeneous.

https://partners.circle.com/part...

Hey hunters,

It can be a little nerve wrecking leading up to launch day, especially for a solo founder with no audience, following, or experience in the product hunt community.

We shipped something this week based on what you kept telling us.

Most AI tools make you jump through hoops to teach them anything. Upload a document. Format a spreadsheet. Follow the schema. By the time you're done, the flash sale is over.

We built the opposite.

Brain Dump. Open your dashboard, type whatever you need the AI to know, hit save. That's it. "Give anyone who asks a 20% discount with code FLASH20" becomes an active rule in seconds. No formatting. No uploads. Just type and go.

i build dev tools solo and use claude/cursor daily for API integration work. the pattern i keep seeing: agent finds a bug, fixes the code, declares done. what it does not do is rerun the exact failure scenario and prove the fix holds. that verification step was always on me.
we just shipped something in FetchSandbox to close that loop. after it catches a bug, it now nudges the agent to rerun the same request and surface a behavioral comparison, before vs after. same scenario, does it pass clean? that receipt is now automatic.
my rule: finding the bug is table stakes. proof that the behavior changed is the job. "i fixed the code" and "the fix works" are not the same claim.
curious how others handle this. do your agents ever rerun failure scenarios to verify, or does that step still fall back to you?

Hey all!
Thanks a lot for supporting our launch yesterday. It was a thrilling 24 hours to say the least. We were looking at a #3, and then suddenly got to #1 10 mins before the day was about to end...
We finally settled on #2! It was a nailbiting finish to say the least :)
If you haven't had a chance to check it out yet, take a look at our launch page: https://www.producthunt.com/prod...
And if you'd like some help to set PromptQL up for your teams, reach out to me or Tanmai on X / Twitter.

We just launched the ZapDigits MCP server beta.

This means you can now connect your ZapDigits marketing data to compatible AI agents and ask questions about your client accounts directly from the AI tool you use.

For example:

Build-in-public started as a genuinely good idea.

Be transparent. Share your journey. Let people watch you build something from nothing. The openness creates accountability, builds audience, attracts early users who care about the product before it launches.

For a lot of builders, it worked exactly like that.

Then it became a content strategy.

But it will raise the bar on what good decision-making even means.

Faster insights will stop being impressive - they ll become expected.
Broader analysis won t be a differentiator - it ll be table stakes.
Deeper visibility won t be optional - it ll be assumed.

And once that happens, a lot of traditional discovery work will start to feel outdated. Not because it was wrong, but because it was designed for a world where you could afford to be slow, partial, and reactive. That world is gone.

From experience, I see that product makers most often rely primarily on just 2 things:
1. Social media (and ads on them)
2. Influencers and UGC

As if nothing else existed.

Research has changed more in the last two years than it did in the previous twenty.

When we first built Collabwriting, research meant opening 27 tabs, reading everything yourself, highlighting the useful parts and sharing them with your team.

@Toyo is a platform where AI agents run your business operations, not just assist with them. Think of it like a team of new hires who work around the clock: researching, writing, building, following up. No technical skills required. You brief them like you'd brief a team member, they get to work.

They recently announced a $4.3M seed round, and are launching on @Product Hunt today.

Artifacts launched yesterday and the community landed it at #2 Product of the Day. Thank you for that.

We built Artifacts because we kept seeing the same pattern: someone gets a great answer from Genie, then spends the next hour turning that answer into something they can actually hand to a client or a boss. That gap between "I know the answer" and "I have something to share" is the whole reason this exists.

Your full Gmail history, a much faster app on large mailboxes, and one-click unsubscribe.

Your entire mailbox, not just the new stuff

Quartz now brings in your full Gmail history, not just mail that arrives after you connect. Older messages appear while sorting runs in the background, and you choose upfront how much of the backlog to sort the rest waits for Process now.

When we launched Second Brain for the first time, we somehow finished #3 Product of the Day.

The second launch finished #4.