Superlinear

Give Coding Agents Eyes Beyond the Chat: Screen Recordings, Session History, and Devices

Thu, 03 Sep 2026 05:00:00 -0400

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 clips, and asking Gemini for a detailed play-by-play. We also extend the same idea to real phone test rigs, cameras, friction logs, and skills generated from recorded tasks.

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_fm
⁠⁠LinkedIn: ⁠⁠https://www.linkedin.com/company/superlinearfm⁠⁠
Brandon Kase: ⁠⁠https://x.com/bkase_⁠⁠
Christine Yip: ⁠⁠https://x.com/christinetyip⁠

Music licensed through Soundstripe. Code: O2UT6CCEQNH4KT0D

Make Your Coding Agents Expert Designers

Fri, 28 Aug 2026 07:00:00 -0400

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.

Context Hygiene for Coding Agents: Murder, Lobotomy, or Diary

Tue, 18 Aug 2026 10:00:00 -0400

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.

Beyond Root-Level Harness Engineering: A Different Environment for Every Task

Wed, 12 Aug 2026 10:00:00 -0400

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.

4 Emerging Practices in Agentic Engineering

Tue, 28 Jul 2026 12:00:00 -0400

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 behavior

We discuss ideas from AI Native DevCon London, and explore how better guides and sensors can improve a harness, why expert vocabulary helps you access more of an LLM’s capabilities, when code is a better interface than individual tool calls, and how working examples can communicate intent more precisely than a written specification.

The broader takeaway: building effectively with AI is not only about choosing a better model or writing a better prompt. It is also about designing a better environment for the agent to work within.

Understanding Loops and Graphs in Modern Coding Agents

Fri, 24 Jul 2026 14:00:00 -0400

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-agents, and why those graphs may be one reason your token limit disappears so quickly.

00:00 Introduction
03:34 How the Agent Loop Works
06:18 Goal Loops and Autoresearch Loops
14:57 From Loops to Graphs
17:06 Sub-Agents: Benefits, Costs, and Context
21:18 The Research Fan-Out Graph
26:27 Multi-Model Agent Graphs
28:37 Asynchronous Implementation and Review
33:40 How to Design Agent Graphs
36:07 When the Harness Builds the Graph for You: Token Spend vs. Performance
38:38 Execution Traces: A Profiler for Agents
41:46 Final Takeaway

Point, Don't Describe: A Better Way to Work With AI Agents

Fri, 24 Jul 2026 09:00:00 -0400

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 Roblox game
10:38 Why “point instead of describe” may become a core pattern in agent workflows
11:57 What's next: extending this to video, 3D models, simulations, and more