关于这个播客 🔗
Superlinear is a podcast about emerging practices for building with AI and coding agents.
Hosted by Brandon Kase and Christine Yip.
Homepage: https://superlinear.fm
Twitter: https://x.com/superlinear_fm
LinkedIn: https://www.linkedin.com/company/superline...
Brandon Kase: https://x.com/bkase_
Christine Yip: https://x.com/christinetyip
Hosted by Brandon Kase and Christine Yip.
Homepage: https://superlinear.fm
Twitter: https://x.com/superlinear_fm
LinkedIn: https://www.linkedin.com/company/superline...
Brandon Kase: https://x.com/bkase_
Christine Yip: https://x.com/christinetyip
最新一集 🔗
How do you build an agent like Dots, Muse, Instinct, or Grok Bot? Start high up the stack, and move down only when you need more control.YC F26-startup Agent 37 founder, Vishnu Krishnaprasad, explains when to use Hermes, OpenClaw, or Pi, why each user needs a separate sandbox, and how to turn a p...
以前的节目 🔗
Bend2 puts code and proofs in the same language, so coding agents can write both. Tests check examples. A checked proof can establish that a specific rule holds for every allowed input.This is formal verification, an additional verification layer alongside types and tests. You still need to revie...
Your coding agent does not need a frontier LLM call for every judgment. Deciding which tests to run, which skill to load, or whether a prompt needs more context can be a bounded question.Jev and other decision models return typed choices, scores, or yes/no probabilities quickly enough to sit insi...
GPT-6 Astra and Fable 5.1 are changing how we start new software projects:Less harness engineering, less process, less prescribing. More deliberate steering on the few decisions that compound through the whole project.We walk through the Codex conversation of an actual project that we built: wher...
Your project still carries decisions made before the latest model arrived: tests for removed features, slow checks, outdated code, and instructions that keep accumulating.A new model is a useful prompt to revisit those decisions. Give your agent a concrete problem to investigate, then review what...
Your coding agent can inspect its own chat, but it cannot see you switching between browser research, documents, local files, and devices.Screen recordings can turn that invisible work into model context effectively with video native models like Gemini.In this episode, we show how Screenpipe expo...
AI can generate a website in seconds. So why does so much AI-generated design still look generic?The problem is not the model. It’s the workflow.In this episode, we break down three real things we built: the Superlinear website, a custom minimap for our podcast editor, and a 3D game character. We...
A million-token context window doesn’t mean you should fill it.Every piece of information you keep in a coding agent’s active context has a cost. Not just in tokens and dollars, but in the model’s attention.In this episode, we explore context hygiene for coding agents: how to decide what deserves...
Harness engineering usually happens at the root level of a project.However, it should go much deeper.Frontend work, debugging, performance optimization, reviews, and other hard tasks can each have their own purpose-built environment, and even sub-agents inside those tasks can have different harne...
Coding agents are becoming more capable, but getting better results increasingly depends on the systems, interfaces, and feedback loops we build around them.In this episode of the Superlinear podcast, we unpack four emerging practices in agentic engineering:→ Shifting left in your agent harness→ ...
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