Author name: aifuturethinkers.com

Hello, and welcome to the world of my AI! I am overjoyed that you have chosen to accompany us on this journey into the fascinating field of artificial intelligence. If you find artificial intelligence to be as fascinating as I do, you are in for a thrilling trip! As a Data Engineering professional, I’ve been immersed in technology for the past ten years, and it’s become second nature to me. With a Master’s degree in Computer Application under my belt, I’ve had the fortunate opportunity to see artificial intelligence (AI) disrupting businesses and changing the game in ways that we couldn’t have anticipated before it happened. Now that I’ve finished reading all of my blogs, I’m ready to pass on all of the incredible information that I’ve gained. Together, we will investigate everything from the most cutting-edge AI applications to the most recent fashions. It doesn’t matter if you’ve never worked with AI before; I guarantee to make the process easy and entertaining so that anybody may take part in the AI adventure.

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Writing Your First GPU Kernel in Python with Numba and CUDA

Image by Author | Ideogram   GPUs are great for tasks where you need to do the same operation across different pieces of data. This is known as the Single Instruction, Multiple Data (SIMD) approach. Unlike CPUs, which only have a few powerful cores, GPUs have thousands of smaller ones that can run these repetitive …

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LangGraph 101: Let’s Build A Deep Research Agent

that actually work in practice is not an easy task. You need to consider how to orchestrate the multi-step workflow, keep track of the agents’ states, implement necessary guardrails, and monitor decision processes as they happen. Fortunately, LangGraph addresses exactly those pain points for you. Recently, Google just demonstrated this perfectly by open-sourcing a full-stack …

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Stress Testing FastAPI Application – KDnuggets

Image by Author   # Introduction  Stress testing is crucial for understanding how your application behaves under heavy load. For machine learning-powered APIs, it is especially important because model inference can be CPU-intensive. By simulating a large number of users, we can identify performance bottlenecks, determine the capacity of our system, and ensure reliability. In this …

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Introducing Gemma 3 270M: The compact model for hyper-efficient AI

The last few months have been an exciting time for the Gemma family of open models. We introduced Gemma 3 and Gemma 3 QAT, delivering state-of-the-art performance for single cloud and desktop accelerators. Then, we announced the full release of Gemma 3n, a mobile-first architecture bringing powerful, real-time multimodal AI directly to edge devices. Our …

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Getting Started with Neo4j: Installation and Setup Guide

Image by Editor | ChatGPT   # Introduction  Neo4j is a powerful database that works with connected data. Unlike traditional databases that use tables, Neo4j uses nodes and relationships. This setup makes it easy to explore complex links in data. Neo4j is popular for projects like social networks, recommendation systems, and network analysis. This article will …

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Kaggle Game Arena evaluates AI models through games

Current AI benchmarks are struggling to keep pace with modern models. As helpful as they are to measure model performance on specific tasks, it can be hard to know if models trained on internet data are actually solving problems or just remembering answers they’ve already seen. As models reach closer to 100% on certain benchmarks, …

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How to Design Machine Learning Experiments — the Right Way

Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more. It’s tempting to think that what separates a successful machine learning project from a not-so-great one is a cutting-edge model, more computing power, or a few extra teammates. In reality, throwing more resources …

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AI-Powered Feature Engineering with n8n: Scaling Data Science Intelligence

Image by Author | ChatGPT   # Introduction  Feature engineering gets called the ‘art’ of data science for good reason — experienced data scientists develop this intuition for spotting meaningful features, but that knowledge is tough to share across teams. You’ll often see junior data scientists spending hours brainstorming potential features, while senior folks end up …

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