Profile 11 min read

Lessons from Noam Brown

Noam Brown built AI systems that defeated players in No-Limit Texas Hold’em and Diplomacy. His work shows why giving models time to think at inference can outperform more training data, connecting applied game theory to OpenAI’s o1.
Profile 8 min read

Lessons from Riley Goodside

Riley Goodside, Scale AI’s first Staff Prompt Engineer, helped turn prompting into a structured discipline. His work on prompt injection, chain-of-thought reasoning, and model mechanics gives developers a practical baseline for eliciting stronger outputs.
Profile 8 min read

Lessons from Chip Huyen

Chip Huyen is an engineer, author, and educator who maps the realities of production machine learning. Treating AI as rigorous software engineering, she clarifies the MLOps, system complexity, language-model challenges, and hiring choices behind dependable deployment.
Profile 7 min read

Lessons from Suhail Doshi

Suhail Doshi, co-founder of Mixpanel and founder of Playground AI, applies first-principles rigor to product growth. His five-lever formula frames questions about early retention, fundraising realities, and the transition from analytics to generative AI.
Profile 7 min read

Lessons from Logan Kilpatrick

Logan Kilpatrick built OpenAI’s developer relations program and leads product for Google AI Studio and Gemini API. His idea of vibe coding captures a shift toward directing AI in natural language while preserving human agency in software creation.
Profile 7 min read

Lessons from Matt Shumer

Matt Shumer, CEO of OthersideAI and creator of HyperWrite, argues AI is becoming an autonomous worker, not merely a software tool. He explores modular agent systems, cognitive-labor displacement, and the shift from executing tasks to managing agents.
Profile 8 min read

Lessons from Shawn Wang

Shawn “swyx” Wang moved from finance into software engineering, popularized Learning in Public, and coined “AI Engineer.” His work examines how careers, developer tools, and applied product development can adapt through major shifts in the technology industry.
Profile 7 min read

Lessons from Jim Fan

Jim Fan leads NVIDIA’s work on embodied agents across games, simulations, robots, dexterous hands, and world models. His systems view treats robotics as a data problem: collect behavior broadly, bootstrap skills, then transfer them into the physical world.

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