--- title: "Linear walkthroughs" section: "raw" type: "source" created: "2026-08-22" updated: "2026-08-22" canonical: "https://pyweb.dev/wiki/raw/articles/simon-willison-linear-walkthroughs-2026" --- # Linear walkthroughs Simon Willison’s Weblog Subscribe Sponsored by: Teleport — AI agents don’t sleep and will try anything to achieve their goal. Teleport explains how to deploy AI safely, starting with an isolated ephemeral trusted runtime. Guides > Agentic Engineering Patterns Linear walkthroughs Sometimes it's useful to have a coding agent give you a structured walkthrough of a codebase. Maybe it's existing code you need to get up to speed on, maybe it's your own code that you've forgotten the details of, or maybe you vibe coded the whole thing and need to understand how it actually works. Frontier models with the right agent harness can construct a detailed walkthrough to help you understand how code works. An example using Showboat and Present # I recently vibe coded a SwiftUI slide presentation app on my Mac using Claude Code and Opus 4.6. I was speaking about the advances in frontier models between November 2025 and February 2026, and I like to include at least one gimmick in my talks (a STAR moment - Something They'll Always Remember). In this case I decided the gimmick would be revealing at the end of the presentation that the slide mechanism itself was an example of what vibe coding could do. I released the code to GitHub and then realized I didn't know anything about how it actually worked - I had prompted the whole thing into existence (partial transcript here) without paying any attention to the code it was writing. So I fired up a new instance of Claude Code for web, pointed it at my repo and prompted: Showboat is a tool I built to help coding agents write documents that demonstrate their work. You can see the showboat --help output here, which is designed to give the model everything it needs to know in order to use the tool. The showboat note command adds Markdown to the document. The showboat exec command accepts a shell command, executes it and then adds both the command and its output to the document. By telling it to use "sed or grep or cat or whatever you need to include snippets of code you are talking about" I ensured that Claude Code would not manually copy snippets of code into the document, since that could introduce a risk of hallucinations or mistakes. This worked extremely well. Here's the document Claude Code created with Showboat, which talks through all six .swift files in detail and provides a clear and actionable explanation about how the code works. I learned a great deal about how SwiftUI apps are structured and absorbed some solid details about the Swift language itself just from reading this document. If you are concerned that LLMs might reduce the speed at which you learn new skills I strongly recommend adopting patterns like this one. Even a ~40 minute vibe coded toy project can become an opportunity to explore new ecosystems and pick up some interesting new tricks. ← Agentic manual testing Interactive explanations → This is a chapter from the guide Agentic Engineering Patterns. Chapters in this guide Principles What is agentic engineering? Writing code is cheap now Hoard things you know how to do AI should help us produce better code Anti-patterns: things to avoid Working with coding agents How coding agents work Using Git with coding agents Subagents Testing and QA Red/green TDD First run the tests Agentic manual testing Understanding code Linear walkthroughs Interactive explanations Annotated prompts GIF optimization tool using WebAssembly and Gifsicle Adding a new content type to my blog-to-newsletter tool Appendix Prompts I use agentic-engineering 59 ai 2,199 llms 1,915 vibe-coding 94 coding-agents 240 ai-assisted-programming 403 swift 11 generative-ai 1,948 showboat 14 Created: 25th February 2026 Last modified: 4th March 2026 7 changes Previous: Agentic manual testing Next: Interactive explanations Disclosures Colophon © 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 --- ## Agent Navigation ### Machine endpoints - Knowledge graph: https://pyweb.dev/api/graph.json - Graph analysis: https://pyweb.dev/api/graph-analysis.json - Context index: https://pyweb.dev/llms.txt