Lessons from shipping AI products. Practical, opinionated, and based on real projects.
Most AI startup failures aren't about bad ideas or hallucinating models. They're boring execution patterns that repeat. Here are the 6 most common ones.
Fixed price vs hourly MVP: why fixed-price contracts protect founders, align incentives, and ship faster than open-ended hourly billing.
A practical week-by-week roadmap for building your startup's security program from scratch. Quick wins, SOC 2 foundations, and the minimum you need to close enterprise deals.
A practical guide to technical due diligence for AI startups: what investors and acquirers check, what red flags to fix, and how to prepare your codebase.
Claws AI agents: Karpathy's term for orchestrated AI systems on personal hardware. The LLM-to-Agent-to-Claws arc and why builders should care.
The definitive AI tech stack for startups in 2026: the right LLM, vector DB, orchestration, infra, and agent tools to ship fast and scale without regret.
Multi-agent orchestration: what breaks when you run 11 simultaneous AI agents, and practical patterns for keeping coordination from becoming chaos.
Moving from AI MVP to production scale requires re-architecting for reliability, cost, and observability. Here's the exact playbook we use with clients.
Hiring AI developers in 2026 requires evaluating LLM expertise, agent architecture skills, and production experience — not just ML theory. Here's the playbook.
Poorly scoped AI projects burn runway fast. This founder's guide walks you through defining scope, setting success metrics, and shipping your first AI feature.