Agents, IDEs, and the Blast Radius: Practical AI for Software Engineers
AI Engineering Podcast
00:00
AI in Code Review and the Problem of Continuity
Tobias observes AI reviewing AI and context loss between tools; Will discusses context windows and the need to supply deployment and architecture constraints.
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Summary
In this episode of the AI Engineering Podcast Will Vincent, Python developer advocate at JetBrains (PyCharm), talks about how AI utilities are revolutionizing software engineering beyond basic code completion. He discusses the shift from "vibe coding" to "vibe engineering," where engineers collaborate with AI agents through clear guidelines, iterative specs, and tight guardrails. Will shares practical techniques for getting real value from these tools, including loading the whole codebase for context, creating agent specifications, constraining blast radius, and favoring step-by-step plans over one-shot generations. The conversation covers code review gaps, deployment context, and why continuity across tools matters, as well as JetBrains' evolving approach to integrated AI, including support for external and local models. Will emphasizes the importance of human oversight, particularly for architectural choices and production changes, and encourages experimentation and playfulness while acknowledging the ethics, security, and reliability tradeoffs that come with modern LLMs.
Announcements
Parting Question
In this episode of the AI Engineering Podcast Will Vincent, Python developer advocate at JetBrains (PyCharm), talks about how AI utilities are revolutionizing software engineering beyond basic code completion. He discusses the shift from "vibe coding" to "vibe engineering," where engineers collaborate with AI agents through clear guidelines, iterative specs, and tight guardrails. Will shares practical techniques for getting real value from these tools, including loading the whole codebase for context, creating agent specifications, constraining blast radius, and favoring step-by-step plans over one-shot generations. The conversation covers code review gaps, deployment context, and why continuity across tools matters, as well as JetBrains' evolving approach to integrated AI, including support for external and local models. Will emphasizes the importance of human oversight, particularly for architectural choices and production changes, and encourages experimentation and playfulness while acknowledging the ethics, security, and reliability tradeoffs that come with modern LLMs.
Announcements
- Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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- Your host is Tobias Macey and today I'm interviewing Will Vincent about selecting and using AI software engineering utilities and making them work for your team
- Introduction
- How did you get involved in machine learning?
- Software engineering is a discipline that is relatively young in relative terms, but does have several decades of history. As someone working for a developer tools company, what is your broad opinion on the impact of AI on software engineering as an occupation?
- There are many permutations of AI development tools. What are the broad categories that you see?
- What are the major areas of overlap?
- What are the styles of coding agents that you are seeing the broadest adoption for?
- What are your thoughts on the role of editors/IDEs in an AI-driven development workflow?
- Many of the code generation utilities are executed on a developer's computer in a single-player mode. What are some strategies that you have seen or experimented with to extract and share techniques/best practices/prompt templates at the team level?
- While there are many AI-powered services that hook into various stages of the software development and delivery lifecycle, what are the areas where you are seeing gaps in the user experience?
- What are the most interesting, innovative, or unexpected ways that you have seen AI used in the context of software engineering workflows?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on developer tooling in the age of AI?
- When is AI-powered the wrong choice?
- What do you have planned for the future of AI in the context of Jetbrains?
- What are your predictions/hopes for the future of AI for software engineering?
Parting Question
- From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?
- Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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- JetBrains
- Simon Willison
- Vibe Engineering Post
- GitHub Copilot
- AGENTS.md
- Kiro IDE
- Claude Code
- Jetbrains QuickEdit
- Claude Agent in JetBrains IDEs
- Ruff linter
- uv package manager
- ty type checker
- pyrefly
- IDE == Integrated Development Environment
- Ollama
- LM Studio
- Google Gemma
- Deepseek
- gpt-oss
- Ollama Cloud
- Gemini Diffusion
- Django Annual Survey
- Co-Intelligence by Ethan Mollick (affiliate link)
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