The Streisand Effect and Nick Fuentes
submitted by /u/Knorssman [link] [comments]
Like
0
Liked
Liked
The Best Web Scraping APIs for AI Models in 2026
For powering next-generation AI models in 2026, Bright Data’s Web Scraper API delivers on all fronts: dynamic site support, anti-bot automation, structured output, and global reach.
Like
0
Liked
Liked
10 GitHub Repositories to Master Machine Learning Deployment
Master the essential skill of deploying machine learning models with courses, projects, examples, resources, and interview questions.
Like
0
Liked
Liked
Innovation to Impact: How NVIDIA Research Fuels Transformative Work in AI, Graphics and Beyond
The roots of many of NVIDIA’s landmark innovations — the foundational technology that powers AI, accelerated computing, real-time ray tracing and seamlessly connected data centers — can be found in the company’s research organization, a global team of around 400 experts in fields including computer architecture, generative AI, graphics and robotics. Established in 2006 and led since 2009 by Bill Dally, former chair of Stanford University’s computer science department, NVIDIA Research is unique among corporate research organizations — […]
Like
0
Liked
Liked
Empromptu raises $2M pre-seed to help enterprises build AI apps
Empromptu claims all a user has to do is tell the platform’s AI chatbot what they want — like a new HTML or JavaScript app — and the AI will go ahead and build it.
Like
0
Liked
Liked
As AI Grows More Complex, Model Builders Rely on NVIDIA
Unveiling what it describes as the most capable model series yet for professional knowledge work, OpenAI launched GPT-5.2 today. The model was trained and deployed on NVIDIA infrastructure, including NVIDIA Hopper and GB200 NVL72 systems. It’s the latest example of how leading AI builders train and deploy at scale on NVIDIA’s full-stack AI infrastructure. Pretraining: The Bedrock of Intelligence AI models are getting more capable thanks to three scaling laws: pretraining, post-training and test-time scaling. Reasoning models, which […]
Like
0
Liked
Liked
Stop building RAG pipelines like it’s 2023
Author(s): AI Rabbit Originally published on Towards AI. Agentic Era If your architecture still looks like “User Query Vector DB LLM,” you aren’t building an AI application; you’re building a hallucination engine. The “naive” RAG era where we just dumped PDFs into Pinecone and prayed for the best is officially over. Here is the technical reality of RAG in 2025.The article discusses the evolution of Retrieval-Augmented Generation (RAG) pipelines, emphasizing the transition from outdated linear architectures to more […]
Like
0
Liked
Liked
AI and machine learning for engineering design
Artificial intelligence optimization offers a host of benefits for mechanical engineers, including faster and more accurate designs and simulations, improved efficiency, reduced development costs through process automation, and enhanced predictive maintenance and quality control. “When people think about mechanical engineering, they’re thinking about basic mechanical tools like hammers and … hardware like cars, robots, cranes, but mechanical engineering is very broad,” says Faez Ahmed, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT. “Within mechanical […]
Like
0
Liked
Liked
The Rise of Specialized LLMs for Enterprise
In this article, I discuss the main problems of standard LLMs (OpenAI and the likes), and how the new generation of LLMs addresses these issues. The focus is on Enterprise LLMs. LLMs with Billions of Parameters Most of the LLMs still fall in that category. The first ones (ChatGPT) appeared around 2022, though Bert is an early precursor. Most recent books discussing LLMs still define them as transformer architecture with deep neural networks (DNNs), costly training, and reliance […]
Like
0
Liked
Liked