Search: “microservices” Category: Code and Tech — 12 articles.
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12 articles in Code and Tech · matching “microservices”
Explore how the rise of AI impacts system resilience and the evolving role of chaos engineering. Discover whether AI integration strengthens or weakens complex digital infrastructures by 2026.
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Explore why Python, with frameworks like PyTorch and TensorFlow, remains dominant in AI, especially for LLMs and agents. Discover where systems-level languages like Rust and Go are gaining traction in performance-critical AI applications.
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Discover how integrating platform engineering with composable system design and FinOps principles can lead to highly scalable, cost-efficient, and future-proof software architectures.
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Explore the critical differences between data at rest, in transit, and in use to build a robust, holistic data security strategy. Learn about the unique vulnerabilities and protective measures for each state.
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As software development matures, core skills evolve. Learn why architecture judgment, efficient code review, and cultivating 'taste' are becoming indispensable for senior developers and how to master them.
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The Problem: Direct, synchronous HTTP/gRPC microservice calls create tight coupling and cascading failures when downstream services lag or fail. The Solution: Event-Driven Architecture (EDA) using NATS JetStream decouples API handlers from heavy background jobs using persistent, low-latency messaging streams. Core Setup: Streams: Persist events to disk (ORDERS.*) with defined retention policies. Producers: Publish JSON payloads synchronously to guarantee NATS receives the write. Durable Pull Consumers: Fetch batches of messages, execute processing, and explicitly send ACK upon success or NAK to request a retry. Production Best Practices: Transactional Outbox: Prevents database state and message publishing from drifting out of sync. Idempotency: Protects against duplicate event execution using unique order IDs in a cache like Redis. Dead Letter Queues (DLQ): Prevents permanently failing or malformed messages from blocking consumer workers.
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Explore the fundamental differences between Docker containerization and traditional bare metal deployments, and learn how to choose the optimal strategy based on your project's scale, team expertise, and long-term goals.
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Explore the capabilities and limitations of edge functions and databases like Cloudflare Workers and Deno Deploy, and understand when an "edge-first" architecture truly delivers on its promise.
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Explore a direct performance comparison between building REST APIs with Rust and Node.js, examining speed, developer experience, and ideal use cases for each technology.
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Explore the strengths and ideal use cases for Rust and Python in 2026. Discover which language is the best fit for your next learning endeavor based on performance, development speed, and career goals.
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### Short Summary The article explores the shift toward **"Backendless" Architecture** enabled by modern full-stack meta-frameworks like Next.js, Remix, and Nuxt. Rather than eliminating backend infrastructure entirely, this approach dissolves the traditional boundaries between frontend and backend into a single, unified codebase. By leveraging **Server Actions**, **end-to-end TypeScript safety**, and **global edge/serverless computing**, developers can write server-side database logic directly within their component architecture. This paradigm eliminates the need to manage dual repositories, write duplicate API boilerplate, or handle multi-service deployment pipelines—making it an incredibly powerful, cost-effective workflow for rapid full-stack product development.
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The article outlines a major shift in software development: moving from AI Copilots to Multi-Agent Systems (MAS). While traditional copilots simply act as smart autocomplete assistants requiring line-by-line human prompts, Multi-Agent Systems deploy networks of specialized AI agents (e.g., product, coding, testing, and security agents) that work together autonomously to solve complex tasks. Instead of replacing developers, this shift elevates them from manual code-writers to high-level system architects and AI orchestrators.
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