AI & LLMs

How large language models, embeddings, RAG and modern AI systems actually work — and how to ship them in production.

AI & LLMs · AI Agents & Automation · Software Development · Data Engineering · Data Science & ML · Web Scraping · System Design · Scalability & Performance · Product Management · Cloud & DevOps

• What Is Computer Vision? Tasks, Models and Uses

  What computer vision is and how it works: image classification, detection and segmentation, CNNs and vision transformers, real uses, limits and how to start.

• LLM Tokens Explained: Cost, Thinking, Caching, Batch

  LLM tokens explained: input vs output pricing, hidden thinking tokens, why models have no memory, prompt caching, batch discounts and how to pick the right model.

• Jev vs LLM: What TypeSafe's System One Model Changes

  Jev vs LLM compared: TypeSafe's decision-only System One model against GPT-style LLMs on speed, cost, accuracy and use cases, with a clear rule for choosing.

• Programmers Are 75% 'Exposed' to AI. What That Means

  Anthropic's labor study says programmers are 75% AI-exposed, yet unemployment hasn't risen. What the number measures, the one warning sign, and what it can't prove.

• Claude Code Skills Setup for Vibe Coding (Zero to Hero)

  Add Ponytail, Superpowers, OmniRoute and your own Agent Skills to Claude Code. Verified install commands, a full workflow, risks and a starter stack.

• How Large Language Models Actually Work: Tokens, Transformers and Attention Explained

  A clear, engineer-friendly explanation of how LLMs work — tokenization, embeddings, transformer attention, training, inference and why they hallucinate.

• RAG Explained: How Retrieval-Augmented Generation Works (and How to Build It Right)

  Retrieval-augmented generation grounds LLM answers in your own data. Learn how RAG works end to end, the architecture, chunking, retrieval, reranking and common failure modes.

• Vector Databases Explained: Embeddings, ANN Indexes and When You Actually Need One

  What a vector database is, how approximate nearest neighbor indexes like HNSW and IVF work, and how to choose between pgvector, dedicated vector DBs and search engines.

• Prompt Engineering for Developers: Patterns That Hold Up in Production

  A practical prompt engineering guide for software engineers — structure, few-shot examples, structured outputs, chain-of-thought, versioning and testing prompts like code.

• Fine-Tuning vs RAG vs Prompt Engineering: How to Choose the Right Approach

  A decision guide for customizing LLMs. When to use prompt engineering, retrieval-augmented generation or fine-tuning — with costs, trade-offs and real-world examples.

• How to Evaluate LLM Applications: Evals, Metrics and Test Sets That Catch Regressions

  Learn how to build evals for LLM apps — golden datasets, code-based checks, LLM-as-a-judge, RAG metrics, online monitoring and running evals in CI.

• How to Reduce LLM Hallucinations in Production Systems

  Why LLMs hallucinate and 12 proven techniques to reduce it in production — grounding with RAG, abstention, structured outputs, tool use, verification and evals.

• LLM Cost Optimization: Cutting Token Spend Without Hurting Quality

  Practical strategies to reduce LLM API costs — model routing, prompt caching, batching, shorter prompts, output limits, semantic caching and measuring cost per task.

• Text Embeddings Explained: The Intuition and Math Behind Semantic Search

  What text embeddings are, how they capture meaning, how similarity is measured, and how to use embeddings for semantic search, clustering, recommendations and RAG.

• Building Safe AI Features: Guardrails, Prompt Injection and Data Privacy

  How to ship LLM features safely. Covers prompt injection, data leakage, output filtering, least-privilege tools, PII handling, audit logs and a practical security checklist.