Guides
Open vs Closed Source AI: When Each Wins, with Examples
A clean decision framework for when open weights beat closed APIs, when they don't, and the convergence trend reshaping the choice for builders.
3️⃣ Frameworks
Guides
The Data Flywheel Decoded: Real Compounding vs. Pitch Theatre
Every AI deck mentions a data flywheel. Most don't have one. How to tell when the loop is real — and the single test that settles it.
Guides
What's Actually a Moat in AI — and What Just Looks Like a Moat
Most "AI moats" are marketing. A short field guide for builders, investors, and readers who want to tell defensible from disposable in 2026.
Guides
Build vs Buy: A Practical Decision Tree for AI Products in 2026
A defensible framework for the build-versus-buy call every product lead is making right now. Four layers, one decisive question, zero hype.
Guides
How to Read an LLM Benchmark — and Why Most of Them Are Gamed
A practical guide to MMLU, GPQA, SWE-Bench and the rest — what they actually measure, how labs cook the numbers, and what to trust instead.