Find your next rabbit hole.

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.

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.

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.