Build
PewDiePie goes all-in on self-hosting AI using modded GPUs, with plans to build his own model soon — YouTuber pits multiple chatbots against each other to find the best answers
[ad_1] PewDiePie has built a custom web UI for self-hosting AI models called “ChatOS” that runs on his custom PC with 2x RTX 4000 Ada cards, along with 8x modded RTX 4090s with 48 GB of VRAM. Running open-source models from Baidu and OpenAI, PewDiePie made a “council” of bots that voted on the best…
Blackwell GPU’s exclusion from high-level trade talks highlights deepening AI ecosystem rift between nations — China aims to build sovereign hardware and software systems without Nvidia
[ad_1] The newly announced China-U.S. trade ceasefire appears, at first glance, to be a long-overdue cooling-off period. For now, China will delay its sweeping new export controls on rare earth elements, while the U.S. holds off on tariff increases, which will remain at 10%. This will come as a much-welcome reprieve for hardware manufacturers, who…
OpenAI’s Microsoft contract negotiation is a necessary step toward a future IPO — Altman’s goal is to build 30 gigawatts of compute infrastructure, valued at $1.4 trillion
[ad_1] After months of negotiations and with a looming end-of-year deadline, Microsoft and OpenAI have finally agreed on the wording for their working relationship going forward, clarifying several salient points that had proven difficult for both companies to agree upon until now. The key points include the fact that Microsoft now owns a 27% stake…
How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3
[ad_1] 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…
An Implementation to Build Dynamic AI Systems with the Model Context Protocol (MCP) for Real-Time Resource and Tool Integration
[ad_1] In this tutorial, we explore the Advanced Model Context Protocol (MCP) and demonstrate how to use it to address one of the most unique challenges in modern AI systems: enabling real-time interaction between AI models and external data or tools. Traditional models operate in isolation, limited to their training data, but through MCP, we…
Ivy Framework Agnostic Machine Learning Build, Transpile, and Benchmark Across All Major Backends
[ad_1] In this tutorial, we explore Ivy’s remarkable ability to unify machine learning development across frameworks. We begin by writing a fully framework-agnostic neural network that runs seamlessly on NumPy, PyTorch, TensorFlow, and JAX. We then dive into code transpilation, unified APIs, and advanced features like Ivy Containers and graph tracing, all designed to make…
How to Build an End-to-End Data Science Workflow with Machine Learning, Interpretability, and Gemini AI Assistance?
[ad_1] In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We begin by preparing and modeling the diabetes dataset, then we dive into evaluation, feature importance, and partial dependence. Along the way, we bring in Gemini as our AI data scientist…
How to build AI scaling laws for efficient LLM training and budget maximization
[ad_1] When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To…
How to Build a Multilingual OCR AI Agent in Python with EasyOCR and OpenCV
[ad_1] class AdvancedOCRAgent: “”” Advanced OCR AI Agent with preprocessing, multi-language support, and intelligent text extraction capabilities. “”” def __init__(self, languages: List[str] = [‘en’], gpu: bool = True): “””Initialize OCR agent with specified languages.””” print(“🤖 Initializing Advanced OCR Agent…”) self.languages = languages self.reader = easyocr.Reader(languages, gpu=gpu) self.confidence_threshold = 0.5 print(f”✅ OCR Agent ready! Languages: {languages}”)…
How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints
[ad_1] In this tutorial, we aim to understand how LangGraph enables us to manage conversation flows in a structured manner, while also providing the power to “time travel” through checkpoints. By building a chatbot that integrates a free Gemini model and a Wikipedia tool, we can add multiple steps to a dialogue, record each checkpoint,…