.NET and C# courses to develop without stress - Pagina 3

Pair programming without dogma: when does it create value

Is pair programming worth it? Signals, costs and practical alternatives

Pair programming in practice: 5 signals to know if it clarifies decisions or adds friction. Examples on refactoring, onboarding and technical debt.

Testing Pair Programming Extreme Programming

Software architectural patterns and 7 hidden blockers

7 architectural patterns to eliminate technical debt

90% of projects accumulate debt by copying architectures without context. 7 criteria to choose patterns that avoid slowdowns.

Design Patterns Design patterns Architectural patterns

Core Web Vitals Optimization Without Rewriting Everything

Core Web Vitals and ASP.NET MVC: real performance, SEO that converts, and no cosmetic fixes

Practical guide to Core Web Vitals in ASP.NET MVC: architectural improvements that boost SEO and conversions.

Web Core Web Vitals LCP

Headless CMS: when it is needed and what changes

Headless CMS signals to understand if it's time to change

Headless CMS for growing projects. Discover the limitations of traditional CMS and how to avoid structural compromises.

Web Headless CMS RESTful APIs

C# vs Python: the difference when software grows

The difference between C# and Python and which one to use in your context

The difference between C# and Python is objective. The key factor is context. The choice can help you or complicate your work if the project evolves.

Semantic Kernel framework for connecting AI and data

The Semantic Kernel is the director that orchestrates patterns and information

Semantic Kernel is the Microsoft framework that allows you to orchestrate AI models, embeddings and vector databases in a logical and scalable flow.

RAG Semantic Kernel RAG

AI memory: how to give long-lasting memory to models

AI memory is the way to turn a model into a system that actually learns

A model has limited memory, find out how you can build persistent memory with RAG and make the AI more powerful.

Unit testing in .NET to write code that doesn't let you down

Use unit testing in .NET to write code that doesn't let you down

Why can you write stable and secure code with unit testing in .NET? Stop "hoping it works" and start checking your software.

Testing .NET TDD

AI in SMEs: governing software without rewriting it

By introducing AI into SMEs, time, costs, errors and manual work in company software are reduced

AI in SMEs reduces hidden risks and costs, restoring consistency in business rules without blocking operations.

LLM AI Automation

Software technical debt: risks and solutions

Technical debt in software: complete guide to management and resolution

Technical debt slows down teams and generates bugs. Discover causes, impacts and strategies to manage and resolve it without rewriting everything.

Design Patterns Technical debt Refactoring

Triggers in WPF: More interactive interfaces without code

XAML triggers transform your UI: the method every experienced developer should use

Triggers in WPF allow you to manage UI states and behaviors declaratively, reducing code, errors and development complexity.

AI hallucinations: what they are and how RAG fixes them

The problem of AI hallucinations, the invented answers and how to eliminate it

AI hallucinations generate false but plausible responses. With RAG and vector databases you can reduce them and make AI systems more reliable.

LLM AI Chatbots

.NET 10 gives you more flow, less friction and better focus

.NET 10: a smoother, lighter and more human way to build software

This version removes more weight than it adds: it frees your mind, reduces interruptions and lets you stay inside the creative flow.

C# or Java: choosing the right language for your future

C# or Java? The right choice is not technical but strategic. This is why the future speaks .NET

C# vs Java: which language offers more opportunities for modern, sustainable applications over time.

How vector indexing works and why it is essential

Vector indexing is the trick that makes research in artificial intelligence immediate

Vector indexing is the mechanism that allows Qdrant and other vector databases to respond quickly even with millions of saved data.

RAG Vector indexing Qdrant

RAG pipeline how to build an effective workflow

RAG pipeline: the path that connects documents to artificial intelligence responses

The RAG pipeline connects business documents to AI responses, ensuring a stable, reliable and truly useful flow.

Qdrant is the vector database that gives your AI memory

Qdrant vector database the heart of semantic search and RAG

Qdrant is a vector database that stores and searches embeddings in a fast and scalable way, giving real memory to artificial intelligence systems

RAG: eliminate hallucinations from AI models

RAG in software development: reliable AI answers without hallucinations

RAG connects real data and AI models to obtain reliable answers in software development, reducing errors and hallucinations

Semantic search: how it works and why it beats keywords

Semantic search: the natural way to find information beyond keywords

Semantic search uses meaning to find more accurate and natural information, overcoming the limitations of keyword-based search

RAG Semantics AI

Embedding AI: what it is and how it works

Embedding AI, the secret language that allows artificial intelligence to understand your data

AI embeddings transform texts into numbers useful for models and enable semantic and RAG search, making the software more intelligent and reliable.

RAG Embedding AI