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-loopI’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!