Superlinear

Episodi

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  How to Make Coding Agents Faster: Crabbox + 3 Fixes 15.10.2026 33min

  Your coding agent can spend more time waiting for tests and builds than writing code. As inference gets faster, tool calls can become a larger share of the wait. Speeding up that feedback loop means examining which checks run, how they run, and where they run. Crabbox, from Peter Steinberger and the OpenClaw team, keeps agents local while expensive work runs on other machines. In this episode, we share our experience offloading Electron tests with Crabbox, discuss the tradeoffs of selective...
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  How to Build AI Agents Like Dots, Instinct, Muse & Grok Bot 07.10.2026 53min

  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 proven manual workflow into skills. He also demos a Dots-like agent built on Hermes. Superlinear is a podcast about emerging practices for building with AI. Hosted by Brandon Kase and Christine Yip....
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  How to Build and Verify Agent-Written Code with Bend 2 02.10.2026 52min

  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 review whether the rule captures the behavior you actually want. In this episode, we show how to choose rules worth proving, get the agent to finish the proofs, and decide where the extra effort pays of...
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  Jev and Decision Models: A New Layer for AI Agents 24.09.2026 1h 5min

  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 inside repeated workflows. A valid answer can still be wrong, so placement and fallback rules matter. We show a live prompt checker and a small, six-commit test-selection experiment, then explore agent...
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  The Fall 2026 Workflow for Starting Projects with Coding Agents 18.09.2026 1h 17min

  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: where we applied engineering judgment, what we left to the model, and how we planned verification so the agent could detect mistakes and keep building autonomously. From the first ramble to the...
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  New Model Dropped? 4 Ways to De-slop Your Project 11.09.2026 46min

  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 its proposed changes would improve or put at risk. In this episode, we cover four maintenance tasks through our own projects: pruning low-value code and tests, turning recurring mistakes into lint...
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  Give Coding Agents Eyes Beyond the Chat: Screen Recordings, Session History, and Devices 03.09.2026 47min

  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 exposed Christine acting as the context bridge around Codex, and Brandon's workflow for recording focused work sessions, compressing them to 720p at one frame per second, splitting them into 15-minute...
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  Make Your Coding Agents Expert Designers 28.08.2026 50min

  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 show how breaking design into intermediate steps can give you better results and more places to steer. Superlinear is a podcast about emerging practices for building with AI. Hosted by Brandon K...
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  Context Hygiene for Coding Agents: Murder, Lobotomy, or Diary 18.08.2026 43min

  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 to stay in active context, what to move elsewhere, and when to throw context away entirely. Superlinear is a podcast about emerging practices for building with AI. Hosted by Brandon Kase and Chr...
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  Beyond Root-Level Harness Engineering: A Different Environment for Every Task 12.08.2026 58min

  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 harnesses. Superlinear is a podcast about emerging practices for building with AI. Hosted by Brandon Kase and Christine Yip. Homepage: ⁠⁠https://superlinear.fm ⁠⁠Twitter: ⁠⁠https://x.com/superlinear_f...
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  4 Emerging Practices in Agentic Engineering 28.07.2026 47min

  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 → Using precise expert language to communicate intent → Choosing deliberately between MCP, CLI tools, Bash, and Code Mode → Using test oracles to help agents explore, verify, and reproduce complex ...
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  Understanding Loops and Graphs in Modern Coding Agents 24.07.2026 39min

  When you ask Fable or GPT-5.6 Sol to complete a task, it feels like you’re talking to one agent. But behind that single conversation, the harness may be spawning multiple sub-agents, giving each one a fresh context window, running them in parallel, and passing summaries between them. You gave one prompt. It built a graph. In this episode of Superlinear, Christine Yip and Brandon Kase unpack how modern agent harnesses grow from simple agent loops into hidden graphs of sub-...
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  Point, Don't Describe: A Better Way to Work With AI Agents 24.07.2026 13min

  Most AI workflows still happen in one-dimensional chat. But the best tools are moving toward higher-dimensional interactions: visual, spatial feedback loops where humans can point instead of describe. 00:00 Intro 00:35 Why text is “one-dimensional” and visual interfaces feel higher bandwidth 04:06 The render → annotate → translate loop 05:27 Planotator for giving feedback on plans and specs 07:00 Agentation, Claude, and Codex for frontend feedback 08:47 Building spatial feedback mode into a ...