Find your next rabbit hole.

Part 2 of 4 follows Cursor’s careful hiring, Perplexity’s move into model serving, Metaphor’s search architecture, and Answer.ai’s plans to make AI useful and discover overlooked talent.

Part 3 moves from conference conversations to Cerebras’ sparse training research, FiftyOne’s dataset debugger, and Atlas’s maps of unstructured data—ending with the question of who can afford to run models.

Word2Vec’s asynchronous updates, metric-induced emergence, DPO’s algebra, repeated-data scaling, and QLoRA’s memory savings—with their mechanisms and limits.

Nomic on sustaining open source, Lightning on reducing training and infrastructure mistakes, and Jay Alammar on generation, model knowledge, and Transformer ideas worth teaching.

Open models, local inference, LoRA, and Mojo reshape the question of where lasting engineering advantages come from.

From human tumor datasets to spatial transformers: how Noetik aims to turn patient biology into better-defined clinical trials.

Sourcegraph combines search, code graphs and language models to supply useful context—and treats reliable individual changes as the foundation for greater automation.

How Hex Magic embeds AI in analytical work: small, reviewable tasks, warehouse-aware retrieval, and evaluations that turn recurring failures into engineering improvements.

Background work requires understandable permissions, dependable tools, useful evaluations, and context that extends beyond a chat.

MCP’s first year exposed the hard parts of shared agent infrastructure: enterprise identity, bidirectional transport, trusted discovery, long-running work, and neutral governance.

From PyTorch’s hardware tradeoffs to shared feedback and home robots: a reading edition of the March 2024 conversation with Soumith Chintala.

Open models gave builders more control. Shared training pipelines let researchers reuse one another’s work—but compute, data access and commercial incentives still limited participation.