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Perspective on AI product strategy
Long-form pieces on the decisions teams face when moving AI from prototype to production, and on the organizational shifts that make AI adoption actually compound.
New here? Start with Is AI Context Actually Your New IP? . It frames the larger thesis the rest of the writing builds on.
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Roughly monthly. Long-form perspective on AI product strategy, the prototype-to-production gap, and what's actually working in the field.
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Your scan is actually two scans
Reality capture splits cleanly into render work and model work. They use different tools, target different audiences, tolerate different amounts of AI inference, and answer to different parts of the org. Conflating them is the most expensive mistake in the field today.
ReadWe ran Mask3D on a terrestrial scan it was never trained on
An honest field report on running an aerial-trained 3D instance segmentation model against a ground-based LiDAR capture of a small civic building. What worked, what broke, what it means for clients evaluating off-the-shelf 3D AI.
ReadFrom Personal AI to Organizational AI: A Pragmatic Migration Path
Karpathy showed us the architecture for personal LLM knowledge bases. The harder problem is organizational AI adoption. A pragmatic framework for moving from individual experimentation to compounding organizational capability.
ReadIs AI Context Actually Your New IP?
The teams getting real value from AI aren't the ones with the best models. They're the ones building structured, shareable context that encodes how their organization actually works.
ReadWhen Your AI Initiative Needs an Outside Perspective
Most teams don't need outside help at the start. But there's a point where more internal discussion stops producing clarity. Here are eight signals that your team has hit that point.
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