Most founders talk about viral growth. Far fewer design for network effects, and that is where the durable value sits. Virality brings users in. A network effect is the reason they stay, because each new user makes the product more valuable for everyone already there. This guide covers the maths behind that, which types of…
Network effects are usually explained with apps: the more people join, the more useful the product becomes. The same maths runs your life. Who you know decides what you hear about, which doors you notice, what feels normal and even how long you live. This guide pulls together the research on friendship, careers and culture,…
Most AI agents fail in production for design reasons, not because the model is weak. The right design depends on the job, the risk and where the data lives. This catalogue covers the single-agent patterns you will actually use, the latest evidence on when several agents beat one, and the orchestration patterns that let agents…
AI startups create more value than any previous software generation, yet many capture only a fraction of it. Inference costs real money on every task, agents replace the seats that software used to charge for, and competitors can copy features in weeks. In that world, how you charge is part of what you build. This…
Startups rarely fail for lack of ideas. They fail because their systems pull against each other: sales promises outrun delivery, product ships features nobody pays for, or cost cuts wreck the customer experience. To scale and win market share efficiently, you need four connected systems that reinforce each other. Customer system Find, serve, keep and…
AI changed how fast a startup can build. It also changed how a startup should be built. The old org chart, with layers of managers, separate departments and slow handoffs, was designed for control. In an AI-native company the constraint is different: code and content are cheap to produce, so the scarce resources become clear…