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We have been building since late 2024. In that time we were a gen AI agency, then a voice AI company, then a procurement tool, before landing on the thing that actually worked. Every one of those pivots was decided by the same wrong diagnosis. Something was not selling, we concluded the product was not good enough yet, and we went and built more. It is a very comfortable conclusion because building is the part you control and the part you enjoy. It also has a satisfying story attached: not there yet, nearly there, one more release. What was actually happening both times was that nobody knew we existed, and the people who did know had no reason to try it this week rather than some other week. That is not a capability problem and no amount of shipping fixes it. The way we eventually found the real thing was not analysis. We ran the catalogue on ourselves, and the one item we kept reaching for was the cheapest thing on the list, the one we had been treating as a feature to sell the expensive stuff around. Two things I would tell myself at the start. First, if a customer signs and then does not use it, that is not a customer, that is a very expensive compliment, and it should be counted as churn on the day it happens rather than revenue. Second, when something is not selling, force yourself to write down the distribution version of the explanation before you are allowed to write down the product version. The product version will still be there afterwards. It just should not get to go first. Curious how many people here have made the same call. When something stalled, did you build or did you go and find out why nobody came back?

Hi everyone,

Thank you to everyone who tried Flyout, left a comment, or sent feedback since we launched here on July 14. Today's release is a direct result of that: 1.0.12 ships the tagging system several of you asked about in the comments, plus two other things people kept running into.

I ve been thinking about this while working on a B2B website.
A lot of early-stage SaaS sites seem to have the same gap:
1. Book a demo feels too heavy for casual visitors
2. Contact forms feel like sending a message into a void
3. Full live chat tools create the pressure to be online all day
4. Email links are simple, but easy to ignore
So I m curious: if you re a solo founder or small team, how do you turn a silent website visitor into a real conversation?
Do you usually use:
- a contact form
- live chat
- Calendly / demo booking
- plain email
- Telegram / WhatsApp / Discord
- something else?
Mostly trying to understand how founders actually handle this before we ship our first version.

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.

Wow, still a bit surreal to see it on the Today tab.

Quick recap for anyone who missed the launch (it hit #7 here that day): murmur is a flight simulator for phone calls I built solo: you rehearse the calls you keep putting off - a job interview, calling the doctor, telling your mom you're changing the major - with an AI that talks back and pushes you a bit, and then provides feedback.
There is a lot of stuff going on behind the curtains to make it as smooth as it is: hundreds of AI evals and simulated conversations, end-to-end testing, that kind of stuff.

One lesson that changed my perspective was realizing that building more features doesn't automatically create a better product.

Early on, I believed that if we kept adding functionality, users would naturally see more value. We spent a lot of time improving the product, but adoption barely changed. The problem wasn't the number of features it was that we hadn't spent enough time understanding what users actually needed.

I can't believe that this is in real time, I just can't believe it!
NeuralAgent is using interfaces AT LIGHTNING SPEED.
If you ever used Claude Cowork computer use or other computer use agents, you'd know how powerful this is!
The video is in real time, it's at playback 1.0, no speeding at all, all computer use demos you were seeing were at least 8x speeded (including our own old demos)!
And the task is:
"Send a Whatsapp desktop message to Khalid Shadid about the future of AI, attach an image in the message from my downloads, it's a chatgpt generated image. GO!"
Our fast model is now performing at its best ever in terms of speed and accuracy! It now checks its work while maintaining high speed!
You must try it now, download NeuralAgent here:
https://getneuralagent.com/downl...

A lot of the feedback we got on the Ciaro Pro launch was around character consistency and scene consistency.

That makes sense. It is probably the biggest frustration in AI filmmaking right now.

A lot of tools can generate impressive individual clips. But once you try to make something longer where characters need to stay recognizable, scenes need to feel connected, and the visual logic has to hold together across a sequence things start to break down.

Here's a pattern I can't unsee:

AI threatens my profession "This is unethical."

AI helps my profession "This is innovation."

Six years ago I got into Y Combinator and built the first version of @Basedash: AI data analyst. It was a simple tool that let you edit data in your database.
Now, six years later, we have a whole new product, and again we're shipping... database editing. But this time it's a look into the future of how companies are going to be run.
At the surface, this feature is kinda neat but nothing new. You can ask the Basedash AI to edit things in your database or call MCP servers to take action in other tools.
But, being a BI product, Basedash has a super deep understanding of your business. Probably better than you do yourself.
Soon, Basedash will proactively start suggesting ways to make your business better.
"If you extend this user's trial, they'll have a 50% higher chance of converting"
"If you tweak your ad sequence with this copy, it'll increase conversion rate by 20%"
One click to approve and run the experiment.
This is how we transition to AI-run companies.

Please check out our PH launch for actions and give us any support or feedback: https://www.producthunt.com/prod...

Open LinkedIn for five minutes as a developer.

You will see a founder crying about failure in an airport lounge. A recruiter posting "Excited to share that I'm hiring!" for the fourteenth time this week. A motivational quote about hustle printed over a sunset. Three humblebrags disguised as life lessons. One "I'm grateful and blessed" announcement with 4,000 likes.

And somewhere, buried under all of it, maybe one actually useful post.

Six months ago HirePilot had zero organic presence. One person handling all of marketing. No budget for writers, no appetite for agency overhead.

The answer we landed on: build a system instead of hiring.

Here is what we run today. Airtable is the content database and the trigger layer. Every piece of content lives as a row in one table. The status field drives everything. Make is the automation engine. Claude API handles generation.

The biggest change is the integration of a music player.

I will continue to update and improve boobar every 1-2 weeks. I would be very grateful if you would like to support my work.

Something I keep thinking about while building LineageLens Trellis: LLMs hallucinate package names *consistently*. The same fake names recur across models and prompts. Attackers have started harvesting those names and registering them on npm/PyPI with malicious install hooks so when the next agent hallucinates `fastapi-utils-extra`, the install *succeeds*.
The part that unsettles me is that it beats code review completely. The import looks normal, the lockfile resolves, nothing in the diff is wrong. The malicious code isn't in the diff it's on the registry, behind a name a model invented.
We ended up treating dependency introduction as a separate trust event from code edits: registry checks (package age, release count) plus an offline edit-distance pass for typosquats like reqeusts`, tied back to *which agent session* introduced the name.
But I'm genuinely curious what other makers do here. If you're using Copilot/Cursor/Claude Code daily does anything stand between the agent and `npm install` in your setup? Or is the agent fully trusted the moment it suggests a dependency?

Follow our substack:https://lineagelens.substack.com...
Github Repo:https://github.com/karnati-prave...

Lately I've noticed most discussion around AI coding tools focuses on which assistant or terminal agent people use, but not much on the actual architecture decisions behind that, things like framework, hosting, database, and auth choices that affect cost and security down the line.

For context, my current default stack is Next.js on the frontend, Supabase for database and auth, Vercel for hosting, and Stripe for payments. I picked it mainly for speed of shipping and because it keeps infra costs predictable early on, but I'm not sure it's the best call for security or scaling past MVP stage.

A few things I'm curious about:

• Do you settle on a default stack before prompting your AI agent, or does the agent end up influencing your stack choice?

• What's been your biggest regret, cost, security, or scaling wise, with a stack you picked early on?

• Any stack you'd actively avoid for a new SaaS in 2026?

The Product Hunt playbook has changed. Here is what the data says actually works.

The Old Playbook (2025 and earlier)

You built a "Notify me" page. You set up an email sequence. You posted on launch day. You hoped.

That still works, but it is no longer enough. Product Hunt is no longer a transactional "launch and vanish" platform. It has become a community, and the real conversations now start in forum threads .

I wanted to thank everyone who took the time to check out RegionMirror and share feedback on launch day I really appreciate it. I ll be working on several improvements based on that feedback, with support for multiple saved screen-region presets at the top of the list.

I also wanted to clarify what happened with the 7-day trial. If you visited RegionMirror in the Mac App Store during the launch, you may have seen a $19.99 purchase required before downloading the app. That was a pricing configuration mistake on my part.

Ten replies in on X, still at 10 followers, so I stopped guessing and pulled the actual numbers.

The spread on real replies I posted: one got 2 views under a 1,100 view post. Another got 2 views under a 5,842 view post. One got 1,204 views, replying under a post at 1.9 million views.

Every one of them landed at roughly 0.03% to 0.17% of the parent post's reach, no matter what I wrote or when I posted it. My own follower count never showed up as a variable at all.

The harder part: even the 1,204 view reply got zero likes and zero replies back. So reach was never the actual bottleneck, whatever I said in the reply was.

This morning, OpenAI released their new GPT-5.6 models. We sprinted to add support in Toyo, and it's now live!

Mine is from a sales call last year. Someone told me they'd "figure out budget later" and I just moved on. Should have asked who actually signs off. Found out six weeks later it was never going to happen.

Curious what everyone else's is. We're using these to work out what Ruby should be catching.

We're in the middle of setting up an affiliate program for AI Context Flow and I wanted to get some feedback from the community here:
- what makes you want to promote a product? (commissions, category or being a power user yourself?)
- what do you expect from the product team to provide you?
- is it something that would interest you if you aren't a professional content creator already?
Curious to hear the thoughts from other founders and people in the community

Quick confession I opened my browser today and counted
14 tabs. 9 of them were "utility" sites:
- 2 different JSON formatters (one for big files, one prettier)
- A JWT decoder
- A base64 encoder
- A regex tester
- A UUID generator
- A cron expression parser
- Two SQL formatters (because one doesn't do BigQuery)
Every single one has ads. Half of them upload my data to
"the cloud" for a task that's literally 3 lines of JavaScript.
Here's what's wild I've been doing this for 8+ years.
Every dev I know does this. We just accept it.
So I wanted to ask the PH community:
1. What's the ONE utility tool tab you can never close?
2. Have you ever pasted something sensitive (JWT, API
response, DB dump) into a random online tool and later
thought "wait... where did that data just go?"
3. If you could merge 5 of these tools into one keyboard
shortcut ( K style), which 5 would they be?
Genuinely curious what your daily "utility stack" looks
like I've been building something in this space and
the patterns I'm seeing are wild.
(No link drop this is a real question. Happy to share
what I'm working on in the comments if anyone's curious.)

Quick follow-up from the Osaurus team.

On Tuesday, PrismML released Bonsai 27B, a big open source AI model compressed to a fraction of its normal size. A model like this usually needs 54 GB of memory, which means expensive cloud servers or a maxed-out workstation. This one fits in under 8 GB. That's small enough for a regular MacBook.

As you may know, we just launched Dirac yesterday!

This is one small step towards building a better email for all of us who are stuck being our own email assistants.

The launch was incredibly fun for me as the Maker of Dirac. Many of you guys signed up for Dirac and our 100 founder members are starting to fill up spots.

We didn't expect this. We came in hoping to make it to the top 5 and woke up at #1 Product of the Day!