Launch HN: Fresco (YC F24) – AI Copilot for Construction Superintendents

This is a good idea but I hope you've got some secret training data that isn't available on the open web. I've been able to stump ChatGPT with simple "gotcha" national electrical code questions that a foreman wouldn't have a problem answering(e.g sizing a breaker for a heater depending on different situations). There are far fewer subreddits and forums dedicated to trade specialists and as a community they're more hostile to DIY-ers and will tell you "get someone licensed." They're also not the types to write detailed reports and case studies on what they did.

It's not that trades are super complicated in comparison to other fields like web development, it's that there's no GitHub, no source shared among all pros like "here's what I did and how I got it to work." Without a good stack overflow how does the AI judge the quality of workmanship in photos?

You are absolutely right, btw, about google drives and one drives and hundreds of photos and all that. My experience is in dealing with general contractors on smaller jobs, not supers on mega projects, but they have similar issues. Lots of sloppy back and forths and poor tracking of change orders, etc,

What Im trying to say, since I sort of rambled there, is that while processing and sorting and making punchlists is a good idea, I have doubts about AI's current ability to accurately spot code(as in building code, which unlike JavaScript varies by zip code) issues. Does the AI know that you dont have enough clearance at X or does that have to go into the recording?

Looks neat! I don’t work in construction but I know folks in civil engineering. Are there applicabilities with Fresco you could see in that domain?

Solid idea, and best of luck.

If I could make one recommendation: hire a UX/UI designer ASAP. The less technical the audience, the more intuitive and easy to navigate the UI needs to be.

Our company focuses on home service businesses and they get roadblocked super easily. I think you'll be glad you did it earlier rather than later. Otherwise, the ux debt will pile up and it will be quite a project a year down the line.

This is super cool and there’s a ton of other industries where this is sorely needed!

FYI - this could be really useful in logistics operations and production too! (Which is my background, although I suspect the price point is unfortunately much too high for that application).

really cool application of LLMs to a big problem - nice work.

I'm in the middle of a renovation project (not as a professional developer, just a random dipshit who wanted to make a multifam building a little nicer and bit off way too much)

Anyway, I've been running around compiling and recompiling photos and punchlists, and my reaction was "Coool!"

I'm not your target audience but I have to imagine the people that are would get utility out of this.

We make an AI camera for residential home builders. I'd love to chat to see if there's any synergy here.

bedrockwireless.com

Ping me, stephen [at] bedrockwireless.com

With all due respect and while wishing you best of luck, it's always a bit worrisome when generative AI is used in the real world with real consequences...

In my experience, what LLMs, even some of the most advanced ones (o1, Gemini 1.5) are really good at is rationalization after the fact: explaining why they were right, even when presented with direct evidence to the contrary.

I just ran an experiment trying to get various models put footnote references in the OCR of a text, based on the content of the footnotes. I tested 120+ different models via OpenRouter; they all failed, but the "best" ones failed in a very bizarre and I think, dangerous way: they made up some text to better fit the footnote references! And then they lied about it, saying in a "summary" paragraph that no text had been changed, and/or that they had indeed been able to place all references.

So I guess my question is: how do you detect and flag hallucinations?