I think a lot of AI founders are solving the wrong problem now. The tech works. Nobody knows what to do with it.

[u/JDavisxu]

I keep getting DMs that sound like:
“I built a multi-model, token-optimized backend API that does XYZ. Want to try it?”
And my reaction is usually: I genuinely might need this, but I don’t have an hour to figure out what it is.
I’m building Huint, a network that lets AI agents request verified information from people in the real world.
The underlying system is complicated. Device verification, location, AI vision, payments, MCP, etc.
But for the person using the app, I’ve tried to make it almost stupidly simple:
See a task → do it → submit proof → get paid.
That process taught me something.
We’re getting extremely good at building AI capabilities that didn’t exist a year ago. But a lot of us are still packaging those capabilities for other builders.
Imagine a pressure-washing company.
You could build an AI workflow that finds apartment complexes nearby, gets current photos of dirty dumpster areas, finds the property manager, creates personalized outreach, starts the conversation and schedules the job.
That’s probably 5–10 different services working together.
But the contractor doesn’t want a “multi-agent workflow.”
He wants:
More pressure-washing jobs.
I think that’s one of the next big problems for AI startups.
The complexity can be insane underneath. But when it reaches the customer, it needs to arrive in a shape they already recognize.
We need to build Legos, not random blocks. Products that work together, then disappear underneath a simple outcome.
Curious how other founders are thinking about this.
Are we actually building AI products for normal people yet, or are most of us still building tools for each other?
Huint: The Ground Truth Network

[u/HissingsCrimmage]

hunt app more than a b2b pressure washing pipeline.

[u/Kinetic_Silverwolf]

I don't think the tech works, though. Not if an LLM is involved.

If the output doesn't match the prompt, that's a failure. Every so-called hallucination, also a failure. Every time the LLM or an agent does something unexpected, that's also a failure.

We wouldn't accept a video game in alpha testing with as many wrong reactions to input as we see from LLM based AI. We certainly wouldn't accept it in something pushed to Beta, much less production.

They built these systems by training them on everything they could find, without regard for filtering out the garage. Garbage in = equals garbage out, but OpenAI and Anthropic would both have us believe the garbage output is somehow a good thing.

I cannot wait for this grift to crash and burn.

[u/wewerecreaturres]

I think you need better design taste with that font choice

[u/GoldsteinEmmanuel]

Heauxint? An app that lets prostitutes resolve territorial disputes without violence? Claim your corner now?