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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · dev.to — MCP tag English(EN) ·

    From Problems to Patterns: Generative AI in .Net (C#)

    A new book titled "From Problems to Patterns: Generative AI in .Net (C#)" aims to equip .NET developers with the skills to build and deploy production-ready AI solutions. It focuses on the Microsoft AI stack, including Microsoft.Extensions.AI, Microsoft.Agents.AI, and Model Context Protocol, offering practical guidance and 37 runnable code examples. The book covers essential topics like multi-provider routing, robust RAG pipelines, maintainable autonomous agents, and secure deployment of AI tools. AI

    From Problems to Patterns: Generative AI in .Net (C#)

    IMPACT Empowers .NET developers to build and deploy production-grade AI applications, reducing reliance on Python-centric tools.

  2. TOOL · dev.to — LLM tag English(EN) ·

    We Tested 30 LLM APIs with 150 Real Calls — 42.7% Failed (And Why That's Good News)

    A recent test of 30 LLM APIs revealed a 42.7% failure rate, though most were due to model deprecations or rate limiting. When accounting for infrastructure issues like rate limits, the actual failure rate is closer to 4%, aligning with industry reports. The study highlighted significant instability with models hosted on GitHub, where several models were deprecated or frequently hit rate limits, necessitating fallback strategies for production use. NeuralBridge's SDK demonstrated a 100% self-healing rate for recoverable failures, potentially saving substantial energy and reducing carbon emissions. AI

    We Tested 30 LLM APIs with 150 Real Calls — 42.7% Failed (And Why That's Good News)

    IMPACT Highlights critical infrastructure instability in LLM APIs, impacting production deployments and suggesting a need for self-healing solutions.