SOLID Principles, Repository Pattern, and Dependency Injection Explained with One Example

·1 views

A technical tutorial published on DEV Community breaks down two commonly conflated software design concepts: the Repository Pattern and the distinction between Dependency Inversion Principle (DIP) and Dependency Injection (DI). The Repository Pattern is described as a dedicated class that handles all data-source communication, shielding business logic from direct database or API access. DIP is explained as a design rule requiring high-level code to depend on abstractions rather than concrete implementations, while DI is the practical mechanism that enforces this by supplying dependencies externally, typically via constructors. To ground these concepts, the article builds a small library book reservation system in C#, featuring a Book entity, a repository interface and its implementation, a notification component, and a service layer. Each of the five SOLID principles is then mapped to a specific part of this single codebase, with analogies and concrete implementation notes provided for each.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Flutter Animations: How to Build Smooth, Production-Grade Motion from Scratch

A Flutter developer guide outlines the full journey from basic animations to production-ready implementations that maintain 60 fps on low-end devices. The article distinguishes between implicit animations, which handle simple state-driven transitions automatically, and explicit animations, which give developers full control over timing, sequencing, and physics. A key insight shared is that frame drops in production often stem not from animation duration but from what is being animated and how the widget tree rebuilds. The guide introduces the mental model of 'controller drives value, value drives paint' as the foundation for understanding all Flutter animation patterns. Practical code examples are provided at each stage, covering AnimationController setup, Tween mapping, and the use of AnimatedBuilder for efficient rebuilds.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

MonkeyCode Offers 10M Free AI Tokens and a Sandbox Server — But Read the Fine Print

MonkeyCode, an open-source project, advertises a 10M token grant paired with a free server sandbox, but the actual utility depends on real-world usage patterns and throttling behavior. At an average of 500–2,000 tokens per chat request, the 10M token budget translates to roughly 5,000–20,000 usable requests — far less than the headline figure implies. The platform's free tier can be stress-tested in minutes using a simple shell script that measures HTTP response codes and latency, helping developers assess whether throttling makes it impractical. Because MonkeyCode is open source, users can audit the codebase directly for telemetry or data-collection calls before trusting it with sensitive prompts. This article was produced as part of MonkeyCode's product outreach, and developers are advised to verify all claims independently before committing to the stack.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Port Harcourt Developer Builds Offline AI Tool on Budget Windows Laptop Without a GPU

A software developer based in Port Harcourt, Nigeria, built StacksNG — an offline AI coding assistant tailored to African payment APIs — using only a Dell laptop with an Intel i5, 16GB RAM, and integrated graphics, running Ollama and a RAG pipeline. His 2020 Intel MacBook Air with 8GB RAM proved too limited for local AI inference due to throttling, lack of Apple Silicon, and insufficient memory. The experience challenges the popular argument that cloud subscriptions always beat local AI setups, since existing hardware can make local inference effectively free. Before upgrading to an Apple Silicon machine, he plans to rent GPU time on platforms like RunPod at under $0.55 per hour to determine his actual compute needs. He also notes that unreliable power infrastructure and variable API latency over submarine cables make a hybrid local-cloud strategy more practical for developers in his region.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Practical Project Roadmap to Learn Generative AI and LLM Engineering

A structured learning roadmap for Generative AI and LLM engineering recommends building hands-on projects rather than relying solely on tutorials. The four-stage path progresses from core LLM usage to Retrieval-Augmented Generation (RAG), fine-tuning, and model evaluation. Beginners are advised to start by building a lead-qualification system that uses prompt engineering and structured outputs to assess sales leads. The next stage introduces RAG, where users connect an LLM to a custom knowledge base of company documents to answer grounded questions. Advanced stages cover fine-tuning open-source models using techniques like LoRA and QLoRA, as well as evaluating model performance against a baseline.

0 comments Read more at DEV Community