Deep dives into .NET, AI tooling, algorithms and the honest mechanics of getting into this industry — written from production experience.
Ollama C# tutorial: run Llama and other open models locally and call them from .NET via OllamaSharp or the OpenAI-compatible API — private and free.
Enable nullable reference types in an existing .NET project without drowning in warnings: a file-by-file migration strategy, annotations, and pitfalls.
LLM structured output in C#: use OpenAI structured outputs and JSON schema to get typed, deserializable results — no more regex-parsing model prose.
Real C# interview questions for junior developers, from someone who runs the interviews — what each question tests and what a strong answer sounds like.
Minimal API vs controller in ASP.NET Core: performance, filters, validation, testability and team fit — a senior engineer’s practical decision guide.
Function calling with OpenAI in C#: how tool calls work under the hood, the invocation loop, and the security rules for exposing .NET methods to an LLM.
When to use a C# record vs class vs struct: value equality, immutability, allocation behavior, and a practical decision tree with real code examples.
Vector embeddings in .NET explained: what embedding vectors are, cosine similarity in C#, semantic search, and when you need a real vector database.
Career change to programming at 30, 35 or 40 — what actually matters: learning while working, age myths, financial planning, and the switcher’s edge.
LINQ performance best practices from production .NET: multiple enumeration, Count vs Any, allocation costs, and when a plain loop beats a query.