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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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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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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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 streams full stack session data into your IDE. Give AI tools complete context for accurate fixes: frontend, backend, annotation.
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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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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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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.
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Depending on your specific use case, your approach to LLMs may vary. Here’s a breakdown of the options and best practices.