How the SIGPLAN Budget Works
SIGPLAN runs on a simple arithmetic: around $2M USD a year in conference registration income from POPL, PLDI, ICFP, and SPLASH/OOPSLA must cover the conferences themselves, some $300K in annual community support (PLMW, PAC travel funding, summer schools, and awards), and new student presenter travel grants — all while keeping the reserve ACM requires. In this post, the SIGPLAN Treasurer explains where the money comes from, why conferences ran at a loss for five years after COVID, how the new $300-per-day registration target was set, and what it all means for the PL community.
The Transformation of Documents: Repositories Are the New Unit of Knowledge Work
How will documents evolve when AI agents become ubiquitous? In a world of AI agents, does the repository become the source of truth—where humans declare intent, agents turn it into executable artifacts, and documents represent a targeted view of something more powerful?
Repositories Are Human/Agent Knowledge Factories
Repositories aren’t where code lives anymore—they’re where **knowledge is manufactured**.
And increasingly, it’s not humans doing the work—it’s agents, with humans supervising the factory.
Think Globally, Discuss PL Locally
The Reserve Reviewer Policy
The Reserve Reviewer Policy is an experimental policy to help conferences scale to increased workloads. We outline its design and first implementation.
AI Models Need a Virtual Machine
Neural networks are more useful when placed in a suitable, specialized environment.
The Academic Pipeline Stall: Why Industry Must Stand for Academia
This post was cross-published from the SIGARCH blog. The Research Pipeline is Stalling The U.S. National Science Foundation (NSF) froze all outgoing funding, including new awards and scheduled payments on active grants. Over 1,000 NSF research...
How to Give a Good Talk
Testing AI Software Isn’t Like Testing Plain Old Software
AI software demands new approaches to testing that go far beyond existing software testing methodologies. And with the rapid evolution of AI model capabilities, the need for the software engineering community to innovate new approaches to AI software testing is urgent.
