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In this tutorial, we build an elastic vector database simulator that mirrors how modern RAG systems shard embeddings across distributed storage nodes. We implement consistent hashing with virtual nodes to ensure balanced placement and minimal reshuffling as the system scales. We visualize the hashing ring in real time and interactively add or remove nodes to […]
In the high-stakes world of AI, ‘Context Engineering’ has emerged as the latest frontier for squeezing performance out of LLMs. Industry leaders have touted AGENTS.md (and its cousins like CLAUDE.md) as the ultimate configuration point for coding agents—a repository-level ‘North Star’ injected into every conversation to guide the AI through complex codebases. But a recent
The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release of LFM2-24B-A2B, a 24-billion parameter model that redefines
While the tech folks obsesses over the latest Llama checkpoints, a much grittier battle is being fought in the basements of data centers. As AI models scale to trillions of parameters, the clusters required to train them have become some of the most complex—and fragile—machines on the planet. Meta AI Research team just released GCM
In this tutorial, we implement an end-to-end Practical Byzantine Fault Tolerance (PBFT) simulator using asyncio. We model a realistic distributed network with asynchronous message passing, configurable delays, and Byzantine nodes that intentionally deviate from the protocol. By explicitly implementing the pre-prepare, prepare, and commit phases, we explore how PBFT achieves consensus under adversarial conditions while
The development of large language models (LLMs) has been defined by the pursuit of raw scale. While increasing parameter counts into the trillions initially drove performance gains, it also introduced significant infrastructure overhead and diminishing marginal utility. The release of the Qwen 3.5 Medium Model Series signals a shift in Alibaba’s Qwen approach, prioritizing architectural
In the competitive arena of Multi-Agent Reinforcement Learning (MARL), progress has long been bottlenecked by human intuition. For years, researchers have manually refined algorithms like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO), navigating a vast combinatorial space of update rules via trial-and-error. Google DeepMind research team has now shifted this paradigm with
Large context windows have dramatically increased how much information modern language models can process in a single prompt. With models capable of handling hundreds of thousands—or even millions—of tokens, it’s easy to assume that Retrieval-Augmented Generation (RAG) is no longer necessary. If you can fit an entire codebase or documentation library into the context window,
For the past year, AI devs have relied on the ReAct (Reasoning + Acting) pattern—a simple loop where an LLM thinks, picks a tool, and executes. But as any software engineer who has tried to move these agents into production knows, simple loops are brittle. They hallucinate, they lose track of complex goals, and they
In the world of Generative AI, latency is the ultimate killer of immersion. Until recently, building a voice-enabled AI agent felt like assembling a Rube Goldberg machine: you’d pipe audio to a Speech-to-Text (STT) model, send the transcript to a Large Language Model (LLM), and finally shuttle text to a Text-to-Speech (TTS) engine. Each hop
