Patrick surveys the rapidly evolving landscape of AI-powered coding tools, tracing the journey from basic tab-completion in GitHub Copilot to a fundamentally new way of developing software. He demonstrates how features like instant apply, multi-line edits, predictive cursor jumps, bring-your-own-model support, and reasoning-based architecture planning have transformed the IDE experience. Multi-file editing, pioneered by Cursor, marked a particularly significant leap, allowing developers to issue a single prompt and see changes propagated across an entire codebase.
A major theme of the talk is the role of context in improving AI-generated code. Patrick walks through the different ways developers can now feed richer context into their tools: referencing specific files, terminals, documentation folders, URLs, tickets, and even entire workspaces. He highlights tools that crawl documentation, convert repositories into single-file context packages, and even ingest YouTube videos or npm packages. Beyond technical context, developers can now specify coding rules, component libraries, and personal style preferences, enabling the AI to produce output that aligns with team conventions and architectural decisions.
The talk explores how the developer workflow is shifting from writing code to reviewing and managing generated code. Patrick discusses emerging features that reduce cognitive load during review: condensed diff views that explain what changed and why, step-by-step breakdowns of multi-file changes, partial acceptance of individual edits, and multiple generated variants to choose from. He also covers new modalities like voice coding, canvas-style exploration of code alongside specifications, and executable notebook environments, all pointing toward editors that adapt to the mental model of the user rather than forcing text-only interaction.
Patrick dedicates significant attention to the question of trust and autonomy in AI coding agents. He describes the feedback loop pioneered by tools like Devin, where the agent continuously iterates between the editor, browser, and terminal to self-correct. He discusses mechanisms for feeding terminal errors, browser feedback, linting results, and even production logs back into the AI loop. The talk raises provocative questions about when to let AI agents auto-commit, how to implement access control over what agents can modify, and whether end-user feedback from production should drive automated code changes. Patrick concludes that developers are entering an era where they no longer build the things directly but instead build the systems that build the things.
Watch on YouTube — available on the jedi4ever channel
This summary was generated using AI based on the auto-generated transcript.