Show HN: An "Evidence Loop" for steering the agents what to build

  • Posted 1 hour ago by jkalichman
  • 1 points
https://www.dipio.ai
Fellow builders, my name is Julian - I'm a PhD candidate in Behavioural Science at LSE, where I focus on Human-AI Alignment (specifically the field of Machine Psychology), and we built Dipio AI around a problem I think is becoming more important as coding agents get better: implementation is getting dramatically cheaper, but knowing what is worth building isn't.

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!

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