Systems | Development | Analytics | API | Testing

Token optimization is the new tokenmaxxing. Here's why burning fewer tokens produces better software and why the economics of AI make this shift inevitable.

Most debugging agents fail not because the model is wrong, but because the data going in is not ready for machine consumption. Here's what data curation actually looks like in practice. When we started building Multiplayer's debugging agent, we made the same mistake almost everyone makes. We gave our coding agent access to observability data and expected it to figure out what was relevant. It didn't.

The Multiplayer debugging agent is open source under MIT. Here's why, and what it means for how you use it. Today we're open sourcing the Multiplayer debugging agent: connect your favorite coding agent to prod to fix application bugs automatically. Run it locally and eliminate PR slop. The core (session-based data capture, local-first architecture, intelligent deduplication, and coding agent integration) is publicly available under MIT, free to use, and auditable by anyone.

Dashboards, sampling, and data lakes were built for human debugging. Closing the bug-to-fix loop for AI agents requires rethinking how runtime data is collected and correlated. Observability as we know it is on its way out. For over a decade, we built telemetry stacks around a single consumer: a human, staring at a dashboard, trying to make sense of a system under stress.