Creativity is a luxury. It demands time, energy and space: things that feel scarce when rent, groceries, and the next shift loom larger than any poem or prototype. Most of us are caught in a slow-spinning loop of laundry, commutes, and alarms that reset before the dream has even ended. It is also a luxury that needs literal room: a quiet corner, a desk that isn’t the dinner table, a door that closes....
OpenAI GPT-5 Router is like Apple removing headphone jack. It sucks but everyone will follow it. — immortal (@immortal_0698) August 14, 2025 What is GPT-5 Router The GPT-5 router picks the right model for each request in real time. In plain English: easy stuff goes to the small model; complex stuff goes to the big brain. The goal is simple, better answers per dollar and millisecond by mixing models instead of forcing a single static choice....
Why Your Retriever is Failing and How Context Can Save It Imagine asking “I want to buy apple” – do you mean Apple Inc. stock, the latest iPhone, or simply fruit? Without context, your retriever may serve you the wrong results. 1. What Is the Problem in Your Retriever & Embedding? Modern retrievers map queries and documents into high-dimensional vectors (embeddings) and rank by cosine similarity. But when a query is ambiguous, plain embeddings struggle:...
Improving Retrieval in RAG (via Recall, Precision, and NDCG) Introduction Retrieval-Augmented Generation (RAG) is the superhero sidekick that grounds your Large Language Model (LLM) in cold, hard facts. But here’s the dirty secret: if your retrieval sucks, your RAG system is just a fancy chatbot with a broken brain. Weak retrieval = missed documents, irrelevant results, and rankings that make no sense. This guide cuts through the noise. You’ll learn how to turbocharge your RAG retrieval with a no-fluff, step-by-step approach to maximize recall, sharpen precision, and nail NDCG....
Amazon Bedrock is a fully managed service that makes high-performing foundation models (FMs) from leading AI startups and Amazon available for your use through a unified API. You can choose from a wide range of foundation models to find the model that is best suited for your use case. Amazon Bedrock also offers a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With Amazon Bedrock, you can easily experiment with and evaluate top foundation models for your use cases, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources....