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    #production

    7 articles tagged production.

    AI Engineering
    Sep 9, 2026

    Designing the AI Request Pipeline: 8 Layers Between User Input and Your LLM

    A production architecture blueprint for every layer between a user's message and a safe, reliable LLM response — with Spring Boot code you can actually use.

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    AI Engineering
    Sep 8, 2026

    We Let Claude Code Refactor Our 200K-Line Java Monolith. Here's the Honest Result.

    A real post-mortem on 6 weeks of AI-assisted refactoring — what it got right, what it silently broke, what we'd do differently, and the hybrid model we actually shipped.

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    AI
    Sep 1, 2026

    Feature Flags for AI: The Deployment Pattern That Saved Us From 3 Production Disasters

    Why LLM features need flags more than anything else you've ever shipped — and the four flag patterns that let us swap models, A/B test prompts, and kill broken AI features in under 60 seconds.

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    Java
    Aug 31, 2026

    10 Spring Boot Mistakes That Silently Kill Production Apps

    A practical breakdown of the most common Spring Boot anti-patterns that cause degraded performance, memory leaks, and outages — with before/after code, real query plans, and how to catch them before they reach production.

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    AI
    Aug 31, 2026

    Event-Driven AI: How Kafka Streams Changed the Way We Run LLMs in Production

    From blocking HTTP calls to async event streams — the architecture shift that halved user-visible latency, eliminated 3AM pages, and lets us process 2M AI requests a day without hitting rate limit walls.

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    AI
    Aug 18, 2026

    Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists

    Your LLM forgets everything the moment the request ends. Here's how production systems build short-term, long-term, and semantic memory — with Spring AI, Redis, and pgvector examples.

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    AI
    Aug 13, 2026

    Why Most Enterprise AI Projects Die in Production (And the 5 Patterns That Actually Survive)

    I've deployed AI systems to 10,000+ enterprise users across healthcare, logistics, and financial services. The projects that failed didn't fail because of the model. They failed because of decisions made before the first line of model code was written.

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