AI coding assistants, developer platforms, MLOps, APIs, and infrastructure for building with AI.
RAG is the technique that lets a language model answer using your own documents instead of only what it memorized in training. Here is how it works and why almost every serious AI app uses it.
GitHub Copilot brought AI code completion to millions of developers inside their editors. Here is what it does well, where it needs supervision, and how it fits a modern workflow.
Fine-tuning takes a general-purpose AI model and specializes it for your task, tone, or format by training it further on your examples. Here is when it helps — and when prompting or RAG is the better tool.
Cursor rethinks the code editor around AI rather than bolting AI onto one. With codebase-aware chat and multi-file edits, it pushed the 'AI-native IDE' idea into the mainstream.
Embeddings are how AI turns words into numbers that capture meaning — the quiet engine behind semantic search, recommendations, and RAG. Here is the concept without the math.
Ollama turned 'running an LLM locally' from a weekend project into a single command. Here is what it does well, where it hits limits, and when local models beat cloud APIs.
Fine-tuning a giant model used to require giant resources. LoRA showed you can adapt one by training a tiny number of extra parameters — and it became the default method for customizing open models.
AI coding assistants are the most widely adopted enterprise AI use case so far. Here is where the gains are real, where teams get burned, and what a sensible rollout looks like.