Project management

Aug 10, 2026

Peter Steinberger: What Happens When 4.7 Million People Let It Cook

Peter Steinberger, founder of OpenClaw, shares the candid story of building one of the world's largest open-source AI projects. Starting from a personal annoyance, OpenClaw went viral, leading to both immense success and unforeseen challenges, including burnout, security pressures, and feature creep. He offers invaluable lessons on product-market fit, managing hyper-growth in open source, the peril of dependencies, and the philosophy that "fun is velocity" in building impactful technology.

Jul 08, 2026

What do we build now? — Theo Browne, @t3dotgg

Theo Browne's keynote from AIEWF2026 urges software engineers to fundamentally change product development in response to rapidly evolving AI models (Sonnet 3.5 to Mythos). He advocates for rejecting legacy mental models and tools (skeuomorphism), embracing a new "Markdown tier" for projects, and thinking "wider" instead of just "deeper" by building extensible platforms that can challenge industry giants. The core message is to be more ambitious, as AI has drastically lowered the barrier to entry for complex, broad-reaching solutions.

Mar 27, 2026

What Tesla and SpaceX Teach Founders About Building Hardware | a16z

Ex-Tesla and SpaceX engineers Chandler Luzsicza and Turner Caldwell share the practical lessons they learned about building complex hardware and how they apply them at their own startups. They cover core principles like decision velocity, strategic vertical integration, managing the critical path without creating new bottlenecks, and using aggressive milestones as a forcing function to reveal true priorities. The discussion also delves into the value of a 'factory mindset,' the importance of a rigorous hiring process, and advice for young engineers.

Nov 24, 2025

Backlog.md: Terminal Kanban Board for Managing Tasks with AI Agents — Alex Gavrilescu, Funstage

Alex Gavrilescu introduces Backlog.md, a Git-based project management tool designed to structure AI-driven development. By breaking down features into Markdown tasks and using a multi-step review process, it helps prevent AI agents from running out of context or deviating from requirements, enabling a more predictable and efficient workflow.