A pattern I keep seeing in AI consulting:
The proof of concept becomes a miniature production system before anyone agrees what it has to prove.
The team starts with architecture:
Which model?
Which integrations?
Which dashboard?
Which permissions?
Which agent behavior?
Each decision sounds reasonable. Together, they create a much bigger project.
The question that should come first is simpler:
What uncertainty is this POC supposed to remove?
Before building, write down:
- who will use it
- which workflow it changes
- what happens today
- what decision the result should support
- what would count as failure
If the POC needs a full dashboard, permissions layer, and production workflow, ask whether it is still an experiment—or whether you are quietly building the product.
A POC should buy clarity, not hide the lack of it.l
@luvchanan We’ve observed that AI founders often focus on refining their work over UI changes, spending weeks or months on it. Our client’s dashboard was approved by GTM in April, but they’re still working on making it pixel-perfect.
Let user demand dictate what’s needed and launch.
@luvchanan We see this from the development side too. A good engineering team can always find another legitimate thing worth fixing. Part of the job is knowing when to tell a founder, “Yes, you’ll need that eventually. No, you don’t need it before your first customers.”
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@jappleby Well it’s a perfect ad- hidden message is there, and good enough to create a buzz on social platforms.
Ad doesn’t have to be actual product demo, capability showcase.
This ad is fun and it helps present brand to new audiences and create buzz
@sama You might want to check response quality on the model specifically where long context is there- hallucinations are there mid response that leads to more messages and higher cost
@elonmusk@CommunityNotes Numbers as it is look really horrible for some countries - having said that these countries are the ones with most restrictions and when people are moving out they are engaging in violent crimes.