Articles in the Latest Issue:

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  YAGNI Revisited

  By: Rod Paddock

  Rod thinks that YAGNI should be treated as a flexible decision-making tool, not a rigid rule: what seems unnecessary early in a project may later become essential as requirements, data volume, and costs change. Using examples from large-scale data ingestion and optimization, he shows how reframing assumptions and revisiting old systems can improve performance and reduce expenses. He closes by reflecting on his departure from CODE Magazine and thanking the team, authors, and readers.

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  Local Vibe Coding with Gemma 4 12B

  By: Sahil Malik

  Sahil Malik suggests that local AI models like Gemma 4 12B make “vibe coding” practical by combining natural-language programming with privacy, speed, no recurring API cost, and customizable context windows. He shows how to run Gemma locally with Ollama in VS Code and Python, then build a simple developer assistant that reads a workspace, accepts a plain-English request, and generates or improves code offline.

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  Fine Tuning Large Language Models, Part 1

  By: Wei-Meng Lee

  Wei-Meng explains that “fine-tuning” covers a spectrum of adaptation methods, not one technique, and argues beginners should choose based on task, data, and hardware rather than defaulting to full training. He walks through feature extraction, partial fine-tuning, full fine-tuning, and LoRA, showing how each changes how many parameters are updated, what it costs, and when it is useful. Using Hugging Face tooling and runnable examples, Lee emphasizes that most real projects should start with prompting or feature extraction and move to LoRA before considering full fine-tuning.

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  AI-Assisted Development Without Losing Engineering Discipline

  By: Sonu Kapoor

  Sonu thinks that AI-assisted development should be treated as a productivity tool, not a replacement for engineering judgment. While AI can speed up coding, testing, and documentation, it also increases the risk of poor architecture, hidden bugs, security issues, and inconsistent code if developers skip the thinking process. He emphasizes that prompts, reviews, tests, and architectural rules must become part of the team’s discipline, and that developers remain fully responsible for AI-generated output.

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  Building Cross-Platform High-Performance Apps Using WASM and .NET

  By: Joydip Kanjilal

  Joydip describes that WebAssembly and .NET let developers build one secure, portable codebase that can run in the browser, on servers, and beyond, while still delivering high performance for compute-heavy tasks. He explains how Blazor WebAssembly, AOT compilation, WASI, interop with JavaScript, and patterns like hybrid apps, micro frontends, and PWAs fit together, then demonstrates them through a MiniCommerce e-commerce application. The central message is that WASM is ideal when you need cross-platform consistency, strong sandboxing, offline support, and efficient execution, provided you manage trade-offs like download size and interop overhead.

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  Azure Well-Architected Framework in Practice

  By: Vaibhav Gujral

  Vaibhav Gujral argues that moving to Azure only improves outcomes when teams design deliberately around the Well-Architected Framework’s five pillars: cost optimization, operational excellence, performance efficiency, reliability, and security. He emphasizes that cloud success is an ongoing discipline, not a one-time migration, and shows how practices like right-sizing, autoscaling, governance, and design patterns help organizations avoid simply relocating old inefficiencies to the cloud.

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  Building Multi-Agent Workflows in C# with Microsoft Agent Framework

  By: Mike Yeager

  Mike explains that Microsoft Agent Framework goes beyond single agents by providing orchestration patterns for building multi-agent systems. Using examples, he shows how to compose specialized agents into sequential, concurrent, handoff, and group chat workflows, stream and observe their execution, and even create custom workflows with executors, edges, and typed message contracts. His main point is that complex AI applications are best built as coordinated processes, not one all-purpose agent, and AF supplies the building blocks to model and scale those processes.

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