Artificial intelligence (AI) is the buzzword today with AI-based applications demonstrating great performance, speed, and accuracy. Their deployment is widespread in domains like healthcare, finance, retail, automotive, manufacturing, logistics, education, agriculture, telecom, travel, insurance, etc (the list includes almost every major field of work).

AI-based models are being trained with datasets and used for predictive analytics, data engineering and many high-performance applications. These include health diagnostics, finance fraud detection, recommendation systems, autonomous cars, smart grids, route optimisation, crop monitoring, network optimisation, customer segmentation, price prediction, threat detection, chatbots, risk assessment, gaming, data analysis, and many other real world applications.

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Ollama is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more. Quickstart Get up and running with your first model Download Ollama Download Ollama on macOS, Windows or Linux Cloud Ollama’s cloud models offer larger models with better performance. API reference View Ollama’s API reference

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Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It is built around universal standards, supporting Ollama and OpenAI-compatible Protocols (specifically Chat Completions). This protocol-first approach makes it a powerful, provider-agnostic AI deployment solution for both local and cloud-based models.

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Google's aggressive artificial intelligence (AI) push has not slowed down in 2026. The company has already announced a partnership with Apple, released new shopping tools and a protocol, introduced Personal Intelligence in Gemini and added the chatbot to its Trends website. Now, the company has shifted its focus towards the open community with the release of TranslateGemma models. These multilingual AI models are designed to support translation between a large number of languages across text and image (input only) modalities.

TranslateGemma Models Released In a blog post, the Mountain View-based tech giant released three different variants of the TranslateGemma AI models. These models are available to download on Google's Hugging Face listing and Kaggle's website. Additionally, developers and enterprises can also access them via Vertex AI, the company's cloud-based AI hub. These models are available with a permissive licence allowing both academic and commercial use cases.

TranslateGemma is available in 4B, 12B, and 27B sizes (where 4B refers to four billion parameters). The smallest model is said to be optimised for mobile and edge deployment, and the 12B variant is designed for consumer laptops. The largest 27B model offers maximum fidelity and can be run locally on a single Nvidia H100 GPU or TPU.

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Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

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Moves that are in the public domain - including: It's a Wonderful Life, Metropolis, All Quiet not he Western Front, The Gold Rush, A Streetcar Named Desire,

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A large language model’s parameters are often said to be the dials and levers that control how it behaves. Think of a planet-size pinball machine that sends its balls pinging from one end to the other via billions of paddles and bumpers set just so. Tweak those settings and the balls will behave in a different way.  

OpenAI’s GPT-3, released in 2020, had 175 billion parameters. Google DeepMind’s latest LLM, Gemini 3, may have at least a trillion—some think it’s probably more like 7 trillion—but the company isn’t saying. (With competition now fierce, AI firms no longer share information about how their models are built.)

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What is Ollama? Ollama is an open-source platform for running and managing large-language-model (LLM) packages entirely on your local machine. It bundles model weights, configuration, and data into a single Modelfile package. Ollama offers a command-line interface (CLI), a REST API, and a Python/JavaScript SDK, allowing users to download models, run them offline, and even call user-defined functions. Running models locally gives users privacy, removes network latency, and keeps data on the user’s device. Install Ollama Visit the official website to download Ollama https://ollama.com/. It’s available for Mac, Windows, and Linux.

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NeatoCal is a tiny JavaScript app that outputs a printable calendar with a full year on a single page. I love the view where all the weekends line up.

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We aren’t just using these AI tools as assistant anymore; they’re fixing code bugs on their own, making full movies from a sentence, and staying focused for days without forgetting the plan. We went from having helpful assistants to creating actual digital coworkers in less than a year. 

The biggest thing that happened in 2025? Specialisation. The big tech companies finally stopped pretending one “super brain” could do everything perfectly and started building specialists instead. It’s way better this way because now picking a model is just like hiring a pro: you don’t hire a plumber to do your taxes.  

Whether you need a poet, a mathematician, or a filmmaker, the question isn’t “which AI is smartest” anymore—it’s just about picking the right tool for the specific mess you’re trying to clean up. 

Here are the best AI models of 2025 categorised based on what they do: 

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3,000,000+ Systems Tested and 5,700 + CPU Models PassMark Software has delved into the millions of benchmark results that PerformanceTest users have posted to its web site and produced a comprehensive range of CPU charts to help compare the relative speeds of different processors from Intel, AMD, Apple, Qualcomm and others.

Included in these lists are CPUs designed for servers and workstations (such as Intel Xeon and AMD EPYC processors), desktop CPUs (Intel Core Series and AMD Ryzen), in addition to ARM processors (Apple M1 and Qualcomm Snapdragon) and mobile CPUs.

Read the full article at www.cpubenchmark.net

This chart made up of millions of PerformanceTest benchmark results and is updated daily with new graphics card benchmarks. This high end chart contains high performance video cards typically found in premium gaming PCs. Recently introduced AMD video cards and nVidia graphics cards using the PCI-Express (or PCI-E) standard are common in our high end video card charts.

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NVIDIA today announced the NVIDIA Nemotron™ 3 family of open models, data and libraries designed to power transparent, efficient and specialized agentic AI development across industries.

The Nemotron 3 models — with Nano, Super and Ultra sizes — introduce a breakthrough hybrid latent mixture-of-experts (MoE) architecture that helps developers build and deploy reliable multi-agent systems at scale.

As organizations shift from single-model chatbots to collaborative multi-agent AI systems, developers face mounting challenges, including communication overhead, context drift and high inference costs. In addition, developers require transparency to trust the models that will automate their complex workflows. Nemotron 3 directly addresses these challenges, delivering the performance and openness customers need to build specialized, agentic AI.

“Open innovation is the foundation of AI progress,” said Jensen Huang, founder and CEO of NVIDIA. “With Nemotron, we’re transforming advanced AI into an open platform that gives developers the transparency and efficiency they need to build agentic systems at scale.”

NVIDIA Nemotron supports NVIDIA’s broader sovereign AI efforts, with organizations from Europe to South Korea adopting open, transparent and efficient models that allow them to build AI systems aligned to their own data, regulations and values.

Early adopters, including Accenture, Cadence, CrowdStrike, Cursor, Deloitte, EY, Oracle Cloud Infrastructure, Palantir, Perplexity, ServiceNow, Siemens, Synopsys and Zoom, are integrating models from the Nemotron family to power AI workflows across manufacturing, cybersecurity, software development, media, communications and other industries.

“NVIDIA and ServiceNow have been shaping the future of AI for years, and the best is yet to come,” Bill McDermott, chairman and CEO of ServiceNow. “Today, we’re taking a major step forward in empowering leaders across all industries to fast-track their agentic AI strategy. ServiceNow’s intelligent workflow automation combined with NVIDIA Nemotron 3 will continue to define the standard with unmatched efficiency, speed and accuracy.”

As multi-agent AI systems expand, developers are increasingly relying on proprietary models for state-of-the-art reasoning while using more efficient and customizable open models to drive down costs. Routing tasks between frontier-level models and Nemotron in a single workflow gives agents the most intelligence while optimizing tokenomics.

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Here you will quickly learn all about local LLM hardware, software & models to try out first. There are many reasons why one might try to get into local large language models. One is wanting to own a local and fully private, personal AI assistant. Another is a need for a capable roleplay companion or story writing helper. Whatever your goal is, this guide will walk you through the basics of local LLMs including hardware requirements, inference software options, and lightweight models to start with. Enjoy!

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The real problem of humanity is the following: we have paleolithic emotions; medieval institutions; and god-like technology.

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Almost every technological innovation of the past several years has been laser-focused on one thing: generative AI. Many of these supposedly revolutionary systems run on big, expensive servers in a data center somewhere, but at the same time, chipmakers are crowing about the power of the neural processing units (NPU) they have brought to consumer devices. Every few months, it’s the same thing: This new NPU is 30 or 40 percent faster than the last one. That’s supposed to let you do something important, but no one really gets around to explaining what that is.

Experts envision a future of secure, personal AI tools with on-device intelligence, but does that match the reality of the AI boom? AI on the “edge” sounds great, but almost every AI tool of consequence is running in the cloud. So what’s that chip in your phone even doing?

What is an NPU?

Companies launching a new product often get bogged down in superlatives and vague marketing speak, so they do a poor job of explaining technical details. It’s not clear to most people buying a phone why they need the hardware to run AI workloads, and the supposed benefits are largely theoretical.

Many of today’s flagship consumer processors are systems-on-a-chip (SoC) because they incorporate multiple computing elements—like CPU cores, GPUs, and imaging controllers—on a single piece of silicon. This is true of mobile parts like Qualcomm’s Snapdragon or Google’s Tensor, as well as PC components like the Intel Core Ultra.

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SAN FRANCISCO (AP) — OpenAI CEO Sam Altman has set off a “code red” alert to employees to improve its flagship product, ChatGPT, and delay other product developments, according to The Wall Street Journal.

The newspaper reported that Altman sent an internal memo to staff Monday saying more work was needed to enhance the artificial intelligence chatbot’s speed, reliability and personalization features.

This week marks three years since OpenAI first released ChatGPT, sparking global fascination and a commercial boom in generative AI technology and giving the San Francisco-based startup an early lead. But the company faces increased competition with rivals, including Google, which last month unleashed Gemini 3, the latest version of its own AI assistant.

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Micron is retiring the Crucial brand, marking the end of its line of budget-friendly solid-state drives (SSDs) and RAM kits, as reported earlier by VideoCardz. In an announcement on Wednesday, Micron says winding down its consumer-focused business will “improve supply and support for our larger, strategic customers in faster-growing segments” — a.k.a. AI companies.

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Nike’s new “neuroscience-based footwear” is designed to activate an athlete’s brain before and after a big game. The two shoes, a mule (the $95 Mind 001) and a lace-up sneaker (the $145 Mind 002), feature a distinctive array of 22 orange foam nodes embedded in each sole. Nike says the nodes each move up and down independently, like “pistons and gimbals,” as the athlete walks, mimicking the feeling of walking on the ground in a way that is “scientifically shown” to stimulate the foot and thus activate the brain’s sensory areas.

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Kiwix is an offline reader for online content like Wikipedia, Project Gutenberg, or TED Talks. It makes knowledge available to people with no or limited internet access. The software as well as the content is free to use for anyone.

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TRANSFER YOUR PLAYLISTS AND FAVORITES The most reliable and fast solution to recreate your music collection across music services.

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This is cool: Internet-in-a-Box. “Up to 32 users who are within about 100m of the hotspot can connect to the device and access or download the content that exists on the device: Wikipedia slices, medical knowledge, videos, and books.”

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Fun & simple little browser game: Dodge This. “Move to dodge the bullets. How long can you survive?”

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