The wiki

DatologyAI builds automated training-data curation infrastructure, with research spanning synthetic pretraining data, multilingual datasets, fast embeddings, vision-language models and autonomous data research.

Creator of Claude Plays Pokémon, with a background in machine learning infrastructure and public views on prompting, fine-tuning, and agent reliability.

Retool’s founder and CEO on AI-enabled internal software, developer control, and governance beneath generated code.

AI research and engineering leader who co-founded Adept, advocated reliable computer-use agents, and led Amazon’s AGI SF Lab at its December 2024 launch.

Dreamer cofounder and former Stripe CTO whose work spans consumer AI agents, developer infrastructure and Android Wear.

Co-creator and lead maintainer of the Model Context Protocol, David Soria Parra builds shared infrastructure for connecting AI applications to tools and data.

Creator of OpenCode and Zen, focused on terminal-based AI coding, model choice, and the quality of inference deployments.

Daytona shifted from development environments for human engineers to stateful computers for AI agents, pairing fast sandbox startup with persistent state and infrastructure for bursty research workloads.

Generating tests is one step toward a larger goal: keeping specifications, tests, and code aligned, with developers deciding what the software should do.

Search engineer and AI investor, from Glean’s founding team and Glean Assistant to early-stage investing at Menlo Ventures.

The AI research organization behind AlphaGo and AlphaFold, combined with Google Brain in 2023 to form Google DeepMind and develop Gemini.

A Hangzhou AI company whose model generations connect sparse computation, compressed attention, reinforcement learning and GPU communication with changing products and economics.

DeepMind founder and AI researcher recognized for AlphaFold, with a career spanning game development, cognitive neuroscience and AI for scientific discovery.

Part 2 of the NVIDIA Brev and Dynamo conversation: adapting inference to changing workloads, giving agents access to compute, and distinguishing longer runs from useful autonomous work.

A San Diego edition of the developer writing retreat series, combining structured instruction, focused writing time and sponsor-supported fellowships.

Apps SDK gives developers an interface inside ChatGPT. AgentKit brings workflow design, evaluation, and deployment together—with consequential limits at launch.

Part 1 of 4 follows developers’ first reactions to OpenAI’s announcements: less conversation plumbing, more tools, and practical questions about billing, context, control and reliable action.

Part 3 follows expense assistants, browser agents, code search, and open models through the practical questions behind DevDay: usable context, reliable outputs, and responsibility for automated actions.

Part 4 of 4 moves from the Keep AI Open party to Julius AI’s code-execution workflows: why latency matters, how parallel tools could shorten repairs, and where startups can build alongside a platform.

Part 2 follows the move from conversational configuration to connected workflows—and the consequences for retrieval control, hosting costs, and developers building on OpenAI.

HumanLayer founder and author of 12-Factor Agents, focused on context management, human oversight, and reliable AI-assisted software development.

HubSpot’s co-founder and CTO, creator of Agent.ai, and an advocate for practical, composable AI workflows.

Founder and CEO of Supermemory, working on persistent AI memory and the choices that determine what agents remember, retrieve and forget.

Anthropic’s first technical product manager, whose work spans Claude model releases and the incubation of developer tools and agent capabilities.

How Genesis combines physics-guided co-folding, demanding evaluations, and laboratory feedback—and why useful drug-discovery agents depend on all three.

A voice, video and text platform whose developer ecosystem hosted large AI communities and experiments, alongside an evolving focus on gaming.

Why API traffic cannot easily reveal training intent, why the strongest model may be a poor teacher, and how coding benchmarks can reward knowledge their tasks never supplied.

AI researcher and entrepreneur building agents that act across screens, with a focus on reliability, human approval, and consumer access.

AI application development, auditable agent architectures, and selective use of autonomous coding tools.

An April 2023 conversation with Mike Conover about shaping model behavior, building open datasets, and evaluating what applications actually need.