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By Multiplayer
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.
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By Multiplayer
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.
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By Multiplayer
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.
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By Multiplayer
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.
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By Multiplayer
The Multiplayer debugging agent is purpose-built for developers working with coding agents. It captures all the data observability tools miss and manages the whole process from bug identified to bug fixed. AI coding assistants are great at writing code. They are not great at fixing bugs in production and the reason is simple: they don’t have runtime visibility.
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By Multiplayer
Custom logging can technically capture everything, but in practice, it rarely does. Coverage degrades over time, external APIs get forgotten, and during incidents, you're left asking "did anyone log this?" instead of debugging. Automatic capture solves this. If you're a technical leader, there's a good chance your team is spending significant time on custom logging… and you might not even realize how much it's costing you in productivity and incomplete debugging data.
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By Multiplayer
There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue. The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data. There's a common belief in the observability space: if you just collect more data, you'll have what you need to debug any issue. The reality is more frustrating: even with 100% unsampled observability, you're still missing critical debugging data.
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By Multiplayer
Better specs and clearer task decomposition are a significant step forward. But specs and plans describe intentions. What AI agents also need is visibility into what systems actually do at runtime.
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By Multiplayer
The next generation of debugging doesn’t depend exclusively on the quality of AI models, but it’s heavily dependent on feeding AI tools the context they need to be useful. AI coding assistants have transformed how we write code. For example, GitHub Copilot, Cursor, and ChatGPT can generate Stripe integration boilerplate in seconds. They'll scaffold your payment flow, suggest error handling patterns, and even write unit tests.
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By Multiplayer
In 2025 we focused on a simple but ambitious goal: making debugging faster, less fragmented and less manual. Check out all our releases to make that possible. 2025 was a defining year for Multiplayer. We focused on a simple but ambitious goal: making debugging faster, less fragmented and less manual. That meant meeting developers where they were already working and capturing the right context at the right time.
- June 2026 (4)
- May 2026 (1)
- February 2026 (3)
- January 2026 (1)
- December 2025 (1)
- November 2025 (3)
- September 2025 (4)
- August 2025 (1)
- July 2025 (1)
- June 2025 (1)
- May 2025 (1)
- April 2025 (1)
- March 2025 (4)
- December 2024 (1)
- November 2024 (2)
- October 2024 (1)
- August 2024 (2)
- June 2024 (3)
- May 2024 (2)
- April 2024 (1)
- February 2024 (2)
Multiplayer is a collaborative platform that makes it easier for teams to work on distributed software.
By connecting to your services and your code, it brings together disconnected SaaS services and shows dependencies so users can make changes to backend software without breaking things. It also offers platform version control, visualization, and deep integrations with third party providers, so teams have an always up-to-date picture of their SaaS sprawl and a better way to work on distributed systems.
A collaborative tool for teams that work on system design and architectural documentation:
- Effortless Architecture Visualizations: Leverage the built-in auto-layout feature to keep your platform diagram tidy and accessible.
- Collaborate on System Design: Plan, discuss and review ideas together with everyone sharing the same view.
- Single Source of Truth for your System Information: A central knowledge base for everything in your backend software platform
Design, develop and manage distributed software better.