Dipio applies behavioral science methodologies to AI-led user research, turning evidence from user conversations and behavior into agent-native specs (e.g., spec-kit, etc.). Your coding harness can access them directly through Dipio MCP, or through the platform as an evidence-backed, ready-to-implement feature specs.
I wrote more about the idea we call the “Evidence Loop” here: https://www.dipio.ai/blog/evidence-loop
I’d particularly appreciate criticism of the underlying thesis: are we solving the "how to build" problem much faster than the "what to build" problem? Also, any criticism in general is welcomed, don't hold yourself - I have a hard skin, and most importantly I value a lot the feedback from such a great community like this one!
jkalichman•53m ago
A lot of the recent signals point in the same direction: back in Dec 2025, Boris Cherny said 100% of his Claude Code contributions "over the previous 30 days" were written by Claude, and as Garry Tan recently mentioned - YC has already seen companies with 95% AI-generated codebases.
We’re getting very good at the "how".
But most of the systems around software development still optimize for shipping faster and more reliably. They don’t really answer the more basic question: was this worth building?
That’s the idea behind what we call the Evidence Loop:
real users (or synthetic copy of it) → evidence → spec → agents build → observe what happens → start the loop again.
We’re also researching Synthetic Twins. We’ve published peer-reviewed work (currently in press). I don’t think synthetics should replace real users; the interesting part is using them to explore hypotheses cheaply, then bringing those hypotheses back to real users and real behaviour.
Longer term, the bet is simple: agents shouldn’t just get a ticket saying “build X.” They should have access to the evidence explaining why X should exist at all.
Thank you!
Julian
DM's are welcomed: https://www.linkedin.com/in/j16h/