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Building RAG from Scratch: No Frameworks Needed Understand Retrieval-Augmented Generation by building it yourself — pure Python, zero dependencies. Introduction LangChain, LlamaIndex, Haystack — great tools, but they hide how RAG actually works.
Why I Stopped Trading and Started Tendering with AI For years, I was hooked. The flashing charts, the quick wins (and devastating losses), the constant hunt for the ‘edge’… I was a trader. Primarily focused on crypto, but dabbled in Forex and equities too.
Building an AI agent is getting easier. Running one reliably in production is still the hard part. At Interrupt 2026, LangChain introduced a major set of updates focused on production agent infrastructure.
A CrewAI crew writes our daily pharma brief for three cents One daily Pharma brief from a three-agent CrewAI crew costs $0.030 on Claude Haiku 4.5 when the tool hands the model the news API's key sentences: 14,452 prompt tokens, 3,042 completion tokens, 4 LLM calls, 8 stories with 8 real links.
If you give an LLM access to your code, you write tools. A tool is a function plus what a model needs to call it: a name, a description, and a schema for the arguments. Then you write the same tools again. The first version uses the AI SDK's tool().
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