Posts

Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto pattern for building knowledge‑aware large language model (LLM) applications. By coupling a generative model with a vector store that holds dense embeddings of documents, code, or product data, RAG systems can ground responses in up‑to‑date facts, reduce hallucinations, and dramatically cut inference costs. While prototypes can be built with a single‑node FAISS index or a managed SaaS offering, moving to production‑grade workloads introduces a new set of challenges: ...

Introduction In the world of algorithmic trading, market data is the lifeblood of every strategy. Traders and developers alike need instantaneous, reliable, and scalable pipelines that turn raw exchange events into actionable visualizations. Traditional polling approaches quickly become a bottleneck, especially when dealing with high‑frequency tick data or multi‑asset portfolios. Enter Supabase, the open‑source Firebase alternative that offers a Postgres‑backed backend with built‑in authentication, storage, and—most importantly for this article—webhooks. Coupled with Node.js streams, you can build a low‑latency, back‑pressure‑aware ingestion layer that pushes updates to a front‑end dashboard in real time. ...

Introduction Retrieval‑Augmented Generation (RAG) has emerged as a powerful paradigm for extending the knowledge and factual grounding of large language models (LLMs). Instead of relying solely on the parameters learned during pre‑training, a RAG system first retrieves relevant information from an external knowledge store and then generates a response conditioned on that retrieved context. The retrieval component is typically a vector database—a specialized datastore that indexes high‑dimensional embeddings and supports fast approximate nearest‑neighbor (ANN) search. ...

Introduction The convergence of three powerful trends—decentralized computation, neural network inference, and zero‑knowledge proofs (ZKPs)—is reshaping how we think about trust, privacy, and scalability on the blockchain. Imagine a network where participants can collectively train or infer on a neural model, yet no single party learns the raw data, and every computation can be cryptographically verified without revealing the underlying inputs or weights. Achieving this vision requires solving two intertwined problems: ...

Introduction In modern web services, protecting APIs from abuse, ensuring fair resource allocation, and maintaining a predictable quality‑of‑service are non‑negotiable requirements. Rate limiting—the practice of restricting how many requests a client can make in a given time window—addresses these concerns. While a simple in‑process limiter works for monolithic applications, today’s micro‑service ecosystems demand a distributed solution that works across multiple instances, data centers, and even cloud regions. This article walks you through the complete design and implementation of a distributed rate limiter built on Redis using the Token Bucket algorithm. We’ll cover the theory behind token buckets, why Redis is a natural fit, practical implementation details, edge‑case handling, scaling strategies, and real‑world patterns you can adopt immediately. ...