Speakers
Description
Overview
AI coding tools (LLMs, code assistants, AI agents) are rapidly becoming part of the developer workflow across the software industry. Open source communities are beginning to grapple with how these tools intersect with their development processes — from code generation and review assistance to documentation, debugging, and large-scale refactoring. This microconference will bring together maintainers, developers, and tooling experts to discuss the practical realities, policies, risks, and opportunities of AI-assisted development in the open source ecosystem.
The goal is not to debate whether AI tools will be used — developers are already using them — but to align on how communities should adapt their processes, what guardrails are needed, and where these tools can deliver the most value with the least risk.
Example Subtopics
- AI-assisted code generation and review — practical experiences, failure modes, and disclosure norms
- Large-scale refactoring with AI assistance, such as C-to-Rust conversions
- AI for debugging, crash analysis, and root cause identification
- Policy and process implications: attribution, copyright, licensing, and trust in AI-generated contributions
- AI-powered test case generation and fuzzing guidance
- Building project-aware AI tooling with domain-specific context and integration with existing development infrastructure
Key People Who Should Attend
- Major subsystem and project maintainers who are receiving AI-assisted contributions and need to make policy decisions
- Developers actively using AI tools in their open source workflows who can share real-world experiences
- AI tooling developers building tools targeted at open source and systems-level development
- Linux Foundation / legal experts for the policy, licensing, and attribution discussion
Previous Related Sessions
While there has not been a dedicated AI microconference at LPC before, related discussions have touched on adjacent topics in microconferences such as Kernel Testing & Dependability, Rust for Linux, and Toolchain. This would be the first session to bring together the AI-specific cross-cutting concerns that span all of these areas.
Expected Outcomes
- Community alignment on disclosure and attribution requirements for AI-assisted contributions
- Identification of high-value, low-risk use cases where AI tools should be encouraged
- Concrete next steps for building project-aware AI tooling
- Framework for evaluating AI-generated code in the review process