AI

In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three agents, Q-Learning with epsilon-greedy exploration, Upper Confidence Bound (UCB), and Monte Carlo Tree Search (MCTS), to navigate a grid world and reach a goal efficiently while avoiding obstacles. Also, we experiment with different ways of…

Can an open source MoE truly power agentic coding workflows at a fraction of flagship model costs while sustaining long-horizon tool use across MCP, shell, browser, retrieval, and code? MiniMax team has just released MiniMax-M2, a mixture of experts MoE model optimized for coding and agent workflows. The weights are published on Hugging Face under…

Can we render long texts as images and use a VLM to achieve 3–4× token compression, preserving accuracy while scaling a 128K context toward 1M-token workloads? A team of researchers from Zhipu AI release Glyph, an AI framework for scaling the context length through visual-text compression. It renders long textual sequences into images and processes…

Pyversity is a fast, lightweight Python library designed to improve the diversity of results from retrieval systems. Retrieval often returns items that are very similar, leading to redundancy. Pyversity efficiently re-ranks these results to surface relevant but less redundant items. It offers a clear, unified API for several popular diversification strategies, including Maximal Marginal Relevance…

In this tutorial, we create a fully interactive, visually compelling data visualization dashboard using Bokeh. We start by turning raw data into insightful plots, then enhance them with features such as linked brushing, color gradients, and real-time filters powered by dropdowns and sliders. As we progress, we bring our dashboard to life with Custom JavaScript…

In this tutorial, we build an advanced Agentic Retrieval-Augmented Generation (RAG) system that goes beyond simple question answering. We design it to intelligently route queries to the right knowledge sources, perform self-checks to assess answer quality, and iteratively refine responses for improved accuracy. We implement the entire system using open-source tools like FAISS, SentenceTransformers, and…

Large language model serving often wastes GPU memory because engines pre-reserve large static KV cache regions per model, even when requests are bursty or idle. Meet ‘kvcached‘, a library to enable virtualized, elastic KV cache for LLM serving on shared GPUs. kvcached has been developed by a research from Berkeley’s Sky Computing Lab (University of…

Large language models (LLMs) offer several parameters that let you fine-tune their behavior and control how they generate responses. If a model isn’t producing the desired output, the issue often lies in how these parameters are configured. In this tutorial, we’ll explore some of the most commonly used ones — max_completion_tokens, temperature, top_p, presence_penalty, and…

In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO and A2C, and develop our own training callbacks for performance tracking. As we progress, we train, evaluate, and visualize agent performance to compare algorithmic efficiency, learning curves, and decision…

AI companies use model specifications to define target behaviors during training and evaluation. Do current specs state the intended behaviors with enough precision, and do frontier models exhibit distinct behavioral profiles under the same spec? A team of researchers from Anthropic, Thinking Machines Lab and Constellation present a systematic method that stress tests model specs…