You know the email. "Hi team, just circling back on this again as I haven't heard anything. Thanks for your attention to this matter." Reads like a sweet grandma wrote it.
A human reads that and thinks "oh no, they are about to burn the building down." AI reads it and thinks "great sentiment, very positive, 98% satisfaction score."
In the summer, one founder of a VC-backed startup approached me to manage his LinkedIn profile, through which he acquires clients (personal brand building).
It was a classic job interview, where the assumption is to create a conversion (you are active on someone's account, building their personal brand, as the account grows, people are noticing you, write to you, you arrange a call, and maybe close a sale)
I asked if there was a possibility of getting equity in this position, because the other positions they had advertised (whether tech, GTM, sales, some small percentage of equity) did offer even a small %...
The answer was "No, this position does not include equity."
For over a week, the wider Product Hunt community has been chiming in with their two cents in the discussion about where to draw the line between which product features should be free and which should require payment.
Just yesterday on X, a post started trending about a tool with 35,000+ users, but only just over 1,300 paying customers. The founder was asking the community for advice on how to increase conversions.
I've been thinking a lot about what separates AI products that people actually stick with from those they try once and forget. The pattern I keep noticing is that the ones that win aren't necessarily the most powerful they're the ones that feel like they understand your context.
Think about it: most AI tools today are essentially fancy command lines. You give them an instruction, they spit out a result. But the products gaining real traction are the ones that remember what you care about, adapt to how you work, and meet you where you are emotionally not just functionally.
In a discussion forum with @monatruong_murror , we talked about how AI can help us learn things that aren t naturally familiar to us, like programming.
The biggest challenge was/is: Getting AI to guide you toward a solution, instead of just giving you the answer.
We've spent the last few months building Genie, an AI analyst inside Databox. Tomorrow it goes live on Product Hunt.
The short version: you ask a question about your data in plain language, Genie finds the right metrics, runs the analysis, and returns an answer with a chart in seconds. No SQL, no waiting on someone else.
If you've been following along in this forum, thank you the conversations here genuinely shaped how we think about the product.
We go live at midnight PT. If you want to support the launch, the one thing that matters most: make sure you have a Product Hunt account before midnight. Votes from accounts created on launch day carry much less weight in the algorithm.
The builder internet has one dominant religion: ship fast, learn fast, iterate. And honestly? It's mostly right. I'm not here to argue against iteration.
But I've been noticing a pattern in products that actually lasted and it's uncomfortable: A lot of them were embarrassingly slow at the start. Not because the founders were lazy but because they were obsessive about the wrong thing to ship first.
Figma spent years just making the multiplayer cursor work flawlessly before talking about anything else. Notion had a tiny, nearly unusable v1 that they kept showing the same 500 people. Linear said no to mobile for two years while everyone said they were crazy.
AI agents are increasingly making real decisions in businesses. They qualify leads, respond to customers, analyze data, and sometimes trigger actions that affect revenue or customer experience. As these systems move from suggesting to actually deciding, mistakes become inevitable.
When that happens, responsibility becomes unclear. The user configured the system, the company built the product, and the underlying models often come from another provider. If an AI agent makes the wrong call and it impacts a customer or revenue, where should accountability actually sit?
Curious how others are thinking about this. Who should be responsible in such cases, and are there any legal guidelines or draft regulations emerging around this?
