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How do AI agents get long-term memory or access to your documents? A vector database. The short version: you convert your data into embeddings, which are numerical representations of meaning, store them, then give the agent a search tool so it pulls only the relevant information for whatever you're asking. This pattern is called RAG, retrieval augmented generation, and it's how agents answer questions about documents and remember long-term facts even when the context window is full of the curren
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SQL vs. NoSQL Databases The choice between SQL and NoSQL impacts how your application stores, scales, and manages data. SQL (Relational Databases) SQL databases use a structured, table-based format with rows and columns. They rely on a predefined schema, meaning you must define the data structure before adding any information. Strengths: Excellent for complex queries, multi-row transactions, and maintaining strict data integrity (ACID compliance). Best Use: Financial systems, accounting software
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Follow us @oddsontechnology for more tech archives and tech videos. Even the creator of C doesn’t know all of C . When developers asked Bjarne Stroustrup how they could learn every part of C , his answer was surprisingly simple: don’t. After more than four decades of evolution, the language has grown so large that even its own creator regularly looks things up. His advice wasn’t to memorize every function, STL container, library, or template—it was to understand the fundamentals and know where t
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Your reads and your writes want completely opposite things. CQRS stops them from fighting. 👇 Most apps use one model for everything. But writes and reads pull in different directions. Writes want a clean, normalized schema, split across many tables so nothing is duplicated and updates stay consistent. Reads want the opposite: everything pre-joined and denormalized so a query is a single fast lookup. Force them to share one model on one database and they contend for the same rows and locks, and
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