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Burn less, ship more: the case for token optimization
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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Discover how AI can supercharge debugging, testing, and feature development with full-stack context. Insights on AI-assisted coding, MCP servers, and developer productivity.
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How to curate observability data for AI agents
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.
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Multiplayer is now open source
The Multiplayer debugging agent is open source under MIT. Here's why, and what it means for how you use it.
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The death of the dashboard: why agentic AI is choking on legacy observability tools
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.
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The debugging agent for developers: runs locally and eliminates PR slop
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.
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Why observability tools are missing critical debugging data (no matter how you sample)
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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With Spec-Driven Development we've come full circle to Big Design Up Front
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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Why AI can't debug your API integrations (yet)
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.
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Multiplayer MCP server: brings full stack session recordings into your IDE of choice
Multiplayer MCP Server streams full stack session data into your IDE. Give AI tools complete context for accurate fixes: frontend, backend, annotation.
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Can AI solve two of the biggest problems in distributed systems: documentation and debugging?
Documenting and debugging distributed systems is still one of the most painful reality of engineering teams. Can AI tools help with that and what are their limits?
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AI will transform how we debug distributed systems
Even the most talented and experienced developers come across several challenges when debugging a large distributed system. AI can help with that.
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How to win the AI dev-tools arms race
AI alone can't disrupt a business, the companies that survive and thrive are the ones that are using the technology to solve real problems.