Insights
Technical strategy and hard-won lessons on building AI-native products.
010 · Founder Perspective
The constraint isn't headcount anymore
AI has genuinely changed what one person can build. A solo founder today can ship what used to take a team of eight. The part nobody talks about: that just moved the bottleneck. It didn't remove it.
Read →009 · Founder Perspective
When the model stops being the moat
The capability gap between top AI models has compressed to the point where your users can't tell the difference. So if the model isn't the moat, what is?
Read →008 · Technical Strategy
Your prompts aren't in version control. They should be.
You wouldn't ship application code with no git history and no rollback. Most AI products do exactly that with their prompts — the part that changes most often and breaks things most quietly.
Read →007 · Technical Strategy
You're probably using the wrong model. Here's how to choose.
Defaulting to the most capable model feels safe. It isn't. Model selection is one of the biggest cost and latency levers in your AI product — and most founders never touch it.
Read →006 · Technical Strategy
Do you actually need agents?
Everyone is building agents right now. Most of them don't need agents. Here's how to tell which camp you're in — before you spend two months on infrastructure that's solving the wrong problem.
Read →005 · Technical Strategy
Your prompts aren't the problem. Your context is.
Founders spend hours tweaking prompt wording when the real issue is what surrounds the prompt. Context engineering is the discipline most AI products are missing — and it's not complicated once you see it.
Read →004 · Technical Strategy
RAG vs. fine-tuning: how to choose the right tool for your AI product
At some point your AI product will need to know things the base model doesn't. You have two main paths. Most founders pick the wrong one — not because the answer is complicated, but because nobody explained the actual tradeoffs.
Read →003 · Technical Strategy
Evals: how to know if your AI product is actually working
Most founders ship an AI feature, watch it work in a demo, and call it done. Then users find the edge cases. Here's how to build a feedback loop that catches problems before your users do.
Read →002 · Engineering
Vibe coding is real. But it still needs a pilot.
AI coding tools can now build in weeks what used to take months. The catch? Speed without judgment is just a faster way to build the wrong thing.
Read →001 · Technical Strategy
What "AI-native" actually means — and why most founders get it wrong
Everyone says they're building AI-native. Most of them aren't. Here's how to tell the difference, and why it matters more than you think in the first few weeks of your build.
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