The wiki

A harness gives a model tools, context and boundaries. As models learn more of the coordination once supplied by software, harness design increasingly turns toward permissions, decisions and human attention.

An AIE Europe debrief on agent infrastructure, model specialization, and what changes when software generation outruns human review.

From queued business-software requests to specialized software teammates, Part 1 explores agent composition, knowledge graphs, engineering tradeoffs, and the discovery infrastructure a network of agents would need.

Part 2 follows the practical problems of delegating to agents: generating interfaces, testing candidates, choosing models, pricing work, and sharing context—with a closing turn to domain markets and long-term conviction.

Part 3 of 3 moves from accessible video tools and controlled image edits to reusable expertise—and the personal and community priorities that keep useful work sustainable.

Canvas and Tasks connect interface design with model training: choosing when to act, understanding what the user wants, and building trust through collaboration.

A conversation with Bret Taylor about building at technology’s limits, defining what agents should deliver, and making their decisions inspectable and correctable.

How Factory combines enterprise context, behavioral evaluations, retrieval, and human planning to turn coding tasks into delegated work.

An October 2023 conversation about applying models, delegating work to agents, and making useful AI software economically sustainable.

Jeremy Howard’s case for accessible deep learning leads to a harder question: how can a language model learn a specialization while retaining its broader abilities?

Part 1 of this 2024 Latent Space recap follows the path from AI investment to useful systems: stronger models, synthetic data, cheaper inference, local execution and multimodal applications.

Part 1 of 3 follows an early-2024 debate over compute, data, model competition and reliable agents, ending with how faster inference could change the applications worth building.

Latent Space’s December 2023 recap follows the competition over training data, inference economics, multimodal products, and application tooling—then turns to coding agents and personal-context hardware.

A Kyiv-based robotics company developing AI autonomy modules and drones for Ukraine’s defense, beginning with terminal guidance and expanding into interception and broader autonomous capabilities.

AI economics, inference bandwidth and chip design in Latent Space’s November 2023 conversation with Dylan Patel—ending with the question of whether training can escape a single data center.

Part 2 of 3 follows Lila Sciences from improving drug-development odds to connecting instruments, shortening experimental cycles, and training scientific models—then asks where simulation still falls short.

From a bot over Notion and Slack to SQL agents and application traces, LangChain addresses the work surrounding a model call.

Tulu’s practical post-training recipe leads to harder questions: how models learn to use tools, choose plans, recognize success, and remain useful as rewards become more complex.

Radical AI connects hypothesis generation, robotic experiments and scientific judgment. The next challenge is carrying that feedback loop into manufacturing.

George Hotz’s computer company develops tinygrad and sells tinybox computers for AI training and inference, pursuing a software-first route toward cheaper, more accessible compute.

How examples, reasoning, automated optimization, and adversarial testing shape the behavior of language models—and where prompting becomes systems engineering.

Circuit tracing turns a model’s next-token prediction into a testable account of intermediate computation. Part 1 follows the tools, their limits, and the case for understanding models before relying on them.

Circuit tracing connects internal features to model outputs, then tests those connections by changing them. Part 2 explores shared concepts, advance planning and misleading explanations before turning to the tools’ limits and the trade-offs of publishing them.

Part 3 of 3 examines the work behind clear interpretability papers, the infrastructure that speeds up subsequent investigations, and the questions newcomers can pursue.

Vector search remains useful. The question is whether it needs a separate database—and how retrieval should fit the application’s data, quality requirements, and workload.

Software developer and creator of the T3 Stack, building AI chat and agent-development tools and arguing that AI expands what individual developers can attempt.

Anthropic’s MCP product manager in 2025, explaining protocol trade-offs, task-oriented tools, and opportunities for server builders.

OpenAI’s Codex leader on practical coding agents, the shift toward broader computer work, and keeping users’ understanding intact.

From Cicero’s unusual moves to coding agents and multi-agent learning: what extra reasoning buys, and why objectives, experience and elapsed time still matter.

Thinking Machines Lab develops models, managed fine-tuning tools and interfaces for continuous human–AI collaboration.