The wiki

Founder of Hercules, an AI app builder for business, and former founder and CEO of Fig, acquired by AWS in 2023.

AI researcher working on autonomous research agents, specialized language models, and learning environments, with earlier contributions to retrieval and image-classifier training.

Sierra co-founder and chair of OpenAI’s Foundation and Group PBC boards, with earlier work spanning Google Maps, FriendFeed, Facebook, Quip and Salesforce.

Limitless co-founder focused on user research, data science, and the role of human decisions in AI-assisted work.

A developer infrastructure company that evolved from shareable development environments into GPU access and one-click AI software deployment, acquired by NVIDIA in 2024.

A corporate-card and finance platform that developed AI agents for employee expenses, finance teams and internal operations, and was acquired by Capital One in April 2026.

OpenAI Applied Evals contributor and entrepreneur whose public work includes a proposal for multi-turn agent evaluation.

Technology podcaster, internet historian and investor whose AI work centers on product differentiation, usable interfaces and practical tools for creators.

An AI research company whose financial-analysis assistant evolved into autonomous research agents with dedicated computing environments, source-linked deliverables and infrastructure controls.

Selecting evidence, preserving its meaning, and turning it into investment questions—with expert judgment guiding the system.

CRV general partner investing in AI infrastructure and writing about agent experience, software distribution, and expertise embedded in products.

AI leader, recommendation-systems author and teacher whose work connects model capabilities with evaluation, editable workflows and software lifecycle management.

Claude’s SWE-bench reference agent shows how much depends on tool design. Computer use and robotics extend the lesson to integration cost, reliability, and trust.

Part 2 of the OpenAI DevDay 2024 conversation moves from voice interfaces and application-specific evaluation to Cosine’s software-engineering models, then to planning products around unpredictable research advances.

Part 3 of Latent Space’s OpenAI DevDay 2024 coverage explores deployment safety, agent reliability, product discovery, internal workflows, and the gap between generated code and useful software.

Part 1 follows realtime voice from database lookups to a 400-strawberry order, then explores evaluation-driven model improvement and o1-assisted application development.

Modular connects asynchronous execution, compiler fusion and Mojo’s Python-oriented language design to a common goal: making new computations easier to express without giving up hardware performance.

Singapore’s AI strategy connects workplace adoption, practical skills and compute access—with distinct roles for government, employers and engineers.

Superhuman connects deep search, focused tools and demanding evaluations with a harder product question: how much work can an assistant remove without creating more review?

A reading edition of the Microsoft Build 2026 conversation on company-specific learning, reusable software assets, and AI whose value appears in completed work.

Google Labs’ coding agent puts independent execution, simpler orchestration, and persistent context in service of a harder problem: specifying and verifying useful software.

From capability discovery and conversational interfaces to product differentiation, model evaluation, and personalized AI.

Saving an insight takes a triple-tap. Making it searchable and useful later takes audio alignment, careful model choices and an interface built around listening habits. This conversation was published [March 14, 2025](https://www.latent.space/p/snipd).

From calendar classifiers to company-wide search: useful AI, the infrastructure around it, and the judgment needed to rethink work.

Axiom founder and CEO whose technical vision connects mathematical discovery, informal intuition and machine-verifiable proofs.

Adobe scientist exploring intent-driven websites and AI-assisted deployment recovery, with a background in open-source software automation.

Claude Code product leader whose work centers on rapid experimentation, minimal scaffolding, and developer control as model capabilities improve.

Together AI co-founder and machine learning systems researcher whose work spans data quality, distributed infrastructure and efficient model architectures.

Cerebras builds wafer-scale AI processors and complete computers around them, supplying training systems and low-latency inference through on-premises hardware and cloud services.

A consumer platform for AI characters and stories, with model development driven by live user feedback, conversation-level blending and reward models.