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Linear attention, state-space models and RWKV reduce the cost of processing sequences. Their harder challenge is preserving the information a task needs—and making that computation fast on real hardware.

From incompatible datasets to efficient training: how Axolotl turns model-building experience into reusable configuration.

Braintrust connects production logs, comparative evaluation, and interactive debugging so teams can improve AI behavior—and decide which work belongs in ordinary code.

LlamaIndex’s early evolution shows why connecting language models to data requires more than a larger context window.

Part 2 of the Railway conversation follows self-deploying workloads, durable workflows and cloneable environments to a practical question: how does faster code generation become safe production software?

Railway’s closing ambition is an instantaneous path from idea to shareable production deployment, pursued at every necessary layer of the stack.

Deploying an application is only the beginning. Part 1 follows Railway’s approach to evolving it through environment versioning, owned hardware, agent interfaces and safer production experiments.

Andon Labs tests whether agents can sustain a business, coordinate with people and act reliably in the physical world. Earnings matter—but so do the decisions that produce them.

Gray Swan connects adversarial testing, specialized guard models and system permissions to make agents safer without stripping away their usefulness.

How a tool for sharing research became a platform for running and customizing models—and why packaging, GPU economics and hacker culture all mattered.

From financial text analysis to Ghostwriter: a reading edition of the May 2023 conversation with Reza Shabani on training code models, evaluating their feel, and moving beyond autocomplete.

Faster code generation leaves verification work behind. Retool’s bet is on AI inside business processes, with developers controlling the steps and people reviewing the results.