Hubs

AI’s power demand is reviving interest in nuclear energy — from tech companies signing deals for reactor output to a new wave of small modular reactors built to feed data centers. This hub tracks how nuclear power is being tied to AI infrastructure, the companies and deals involved, and what it could mean for energy-constrained markets like Georgia.

NVIDIA designs the graphics processors (GPUs) that train and run most of today’s AI models, making it a central player in the AI industry. This hub follows NVIDIA’s chips, supply and demand, and its role as the hardware backbone of the AI boom.

Oak is a cybersecurity startup building an AI-native identity platform that tracks and secures access for human, machine, and AI-agent accounts across the enterprise. It emerged from stealth with $60 million in seed funding to address the security risks created by a rapidly growing number of non-human identities. This hub follows Oak’s product, funding, and its role in the emerging AI-identity security market.

Ollama is a free, open-source tool for downloading and running AI models directly on your own computer, without a cloud account or internet connection. It has become a popular way for developers to experiment with open-weight models locally. This hub follows Ollama’s releases, funding, and its growing role in local AI.

On-device AI runs machine-learning models directly on a phone, laptop, or chip, without sending data to the cloud. It can improve privacy, work offline, and reduce latency, and it underpins features like Apple Intelligence and small local models. This hub gathers news and explainers on on-device AI and the compact models that make it possible.

OpenAI is the US research company behind ChatGPT and the GPT family of models, and one of the most influential organizations shaping modern AI. This hub tracks OpenAI’s model releases, products, business moves, and the debates that follow them.

Open source shapes AI in ways beyond just model weights — shared security tools, developer frameworks, and community-built infrastructure that anyone can inspect, use, and improve. This hub tracks open-source projects, coalitions, and initiatives across the AI ecosystem. For open-weight models specifically, see the Open-Source AI hub.

Open-source AI refers to models whose weights are released publicly, letting anyone run, study, or customize them without paying API fees. This hub covers open-weight model releases, the labs behind them, and the debate over openness in AI.

Open-source AI models are language and other models whose weights are released publicly for anyone to download, fine-tune, and run. This hub tracks releases, licensing, and capabilities from projects like Llama, Mistral, DeepSeek, and Qwen, and what they mean for developers and companies who want control over their own AI infrastructure.

Open-weight AI describes models whose trained parameters are released for anyone to download and run, even when the training data, code, or license don’t meet the stricter definition of open source. This hub tracks open-weight model releases, the labs behind them, and how they differ from fully open-source and closed, API-only models.

Open-weight models are AI models whose trained parameters are published for anyone to download and run, even though the training data, code, or full training recipe often stay private. The approach sits between fully closed, API-only models and fully open-source projects, and has become the norm for labs like Meta, Mistral, DeepSeek, and Qwen. This hub follows new open-weight releases, their licensing terms, and how they compare to closed alternatives.

Perplexity AI is an AI-powered answer engine that searches the live web and returns cited answers instead of a list of links. It positions itself as an alternative to traditional search for research and quick questions. This hub gathers news and explainers about Perplexity’s product, features, and how it compares with search engines and chatbots.

Physical AI is artificial intelligence built to sense, reason about, and act in the real world — powering robots, self-driving cars, and smart sensors, not just chatbots. This hub covers the models and hardware bringing AI into physical space.

Poolside is an AI startup building large-scale foundation models trained specifically for software development, aiming to automate coding tasks at scale. This hub tracks the company’s model releases, funding, and partnerships as it competes in the fast-growing space of AI coding tools.

Prior Labs is a German AI startup known for TabPFN, a foundation model built specifically for structured, tabular business data rather than text or images. SAP acquired the company to build a frontier AI lab around its technology. This hub tracks Prior Labs’ research, products, and its role inside SAP.

Prompt engineering is the practice of writing and refining the instructions given to an AI model to get more useful, accurate results. This hub covers practical techniques, common pitfalls, and how prompting is changing as models improve.

Prompt injection is a security attack that hides malicious instructions inside the text an AI model reads — a web page, an email, a document — hijacking what the model does. It is one of the most serious risks for AI agents that browse and act on external content. This hub explains prompt injection and how it differs from jailbreaking.

Public First Action is a bipartisan U.S. advocacy group focused on artificial intelligence policy, working to shape how lawmakers approach AI regulation and safety. This hub tracks its funding, positions, and the debates it takes part in, including support from AI companies such as Anthropic.

Qualcomm is a US chipmaker best known for the Snapdragon processors that power much of the smartphone industry, and increasingly a player in AI hardware. It builds on-device AI accelerators for phones and laptops and has moved into data-center inference chips. This hub follows Qualcomm’s AI chips, acquisitions, and its push to run AI across many kinds of hardware.

Quantization compresses an AI model’s numerical weights into smaller, lower-precision numbers so it can run on phones and laptops instead of large data centers. This hub explains how quantization works and the trade-offs between size, speed, and accuracy.

Quantum computing uses quantum-mechanical effects to perform certain calculations far faster than classical computers, with implications for AI training, drug discovery, and cryptography. This hub tracks hardware breakthroughs, the leading companies and research labs, and how quantum computing could reshape AI and computing more broadly.

Qwen is the family of open-weight AI models developed by Alibaba Cloud, spanning language, code, and multimodal tasks. This hub covers Qwen’s model releases, capabilities, and its role as one of the most widely used open model families.

Retrieval-Augmented Generation (RAG) connects a large language model to your own documents so it answers from verified sources instead of guessing. This hub explains how RAG works and where businesses use it.

Reasoning models are AI systems that work through a problem step by step before answering, which makes them more accurate on math, coding, and complex logic than standard models — but slower and more expensive. This hub explains how reasoning models work, when to use one, and follows the latest releases.

This hub tracks laws, frameworks, and government decisions shaping how artificial intelligence may be developed and deployed. Coverage spans the EU AI Act, national strategies, and sector-specific rules — with attention to how international frameworks interact with Georgia’s own regulatory environment.

The science behind the headlines: new models, research papers, and capabilities — summarised in plain language, linked to the original work. We cover academic labs and industry research teams advancing machine learning, language models, computer vision, and AI safety. Each piece connects technical findings to practical implications for developers, businesses, and policymakers.

RLHF (reinforcement learning from human feedback) is the training step that turns a raw language model into a helpful chatbot, using human rankings of its answers to shape its behavior. This hub explains how RLHF works, why it matters for AI safety and usefulness, and the news around it. Expect clear, jargon-free explainers.

AI is transforming robotics, enabling machines to perceive, reason, and act with unprecedented flexibility. This hub tracks AI-driven robots — from warehouse automation and humanoid machines to surgical and agricultural systems — along with the companies and research institutes pushing the field forward.

Safe Superintelligence (SSI) is an AI research lab co-founded by Ilya Sutskever, built around a single mission: developing superintelligent AI safely, without the product and revenue pressures of a typical AI company. This hub tracks SSI’s research direction, funding, and place in the broader race toward advanced AI systems.

Sam Altman is the CEO of OpenAI and one of the most prominent figures in the AI industry, shaping both its products and its public debates. This hub collects news and context about Altman’s decisions and statements.