Announcing Our Next Open-Source Initiative: Building AI Applications with the Claude API Using Ruby & Ruby on Rails
Introducing: Building AI Applications with the Claude API Using Ruby & Ruby on Rails
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AI & Automation
AI Harness vs Frontier Models: Why Enterprises Need More Than Just Powerful AI
Artificial Intelligence is evolving at an incredible pace. Every few months, a new "most powerful" AI model is announced, capable of writing code, generating content, solving complex problems, or even acting as an autonomous agent. While these frontier models receive most of the attention, successful AI implementations inside businesses rely on something equally important—an AI Harness. Understanding the distinction can help organizations avoid expensive AI mistakes and build scalable, secure, and future-ready AI solutions.
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Choosing a Vector Database for AI Search
pgvector, Pinecone, Qdrant, Weaviate — how to pick a vector store based on scale, filtering needs, and operational appetite.
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Building Reliable AI Workflows with LangChain
LangChain helps compose LLM steps, tools, and memory. Where it helps, where it gets in the way, and how to keep chains debuggable.
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AI Coding Assistants: Getting Real Value from Pair Programming with LLMs
AI assistants speed up the right tasks and slow down the wrong ones. How senior engineers actually use them well.
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MCP Explained: The Model Context Protocol for Tool-Using AI
MCP standardises how AI apps connect to tools and data sources. Why a common protocol matters and how servers and clients fit together.
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RAG Done Right: Retrieval-Augmented Generation in Production
Naive RAG is easy; good RAG is mostly retrieval engineering. Chunking, hybrid search, reranking, and evaluation that actually moves quality.
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Agentic AI: From Chatbots to Autonomous Workflows
Agents turn a language model into a system that plans, calls tools, and acts. What actually makes an agent reliable in production.
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