Building a RAG Pipeline with llama.cpp in Python
Using llama.
FastVLM: Efficient Vision encoding for Vision Language Models
Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency. At different operational resolutions, the vision encoder of a VLM …
Read more “FastVLM: Efficient Vision encoding for Vision Language Models”
Build a FinOps agent using Amazon Bedrock with multi-agent capability and Amazon Nova as the foundation model
AI agents are revolutionizing how businesses enhance their operational capabilities and enterprise applications. By enabling natural language interactions, these agents provide customers with a streamlined, personalized experience. Amazon Bedrock Agents uses the capabilities of foundation models (FMs), combining them with APIs and data to process user requests, gather information, and execute specific tasks effectively. The …
Identity as the new perimeter: National Oilwell Varco’s approach to stopping the 79% of attacks that are malware-free
NOV’s CIO led a cyber strategy fusing Zero Trust, AI, and airtight identity controls to cut threats by 35x and eliminating reimaging.Read More
Best Sports Bras for Women, Tested and Reviewed (2025)
Our top picks keep everything in place, even if your workout is just a walk to the fridge.
Novel technique overcomes spurious correlations problem in AI
AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.
Detecting & Handling Data Drift in Production
Machine learning models are trained on historical data and deployed in real-world environments.
Quantization in Machine Learning: 5 Reasons Why It Matters More Than You Think
Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.
Introducing Gemini 2.5 Flash
Gemini 2.5 Flash is our first fully hybrid reasoning model, giving developers the ability to turn thinking on or off.
Disentangled Representational Learning with the Gromov-Monge Gap
Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretability, or fairness. Although remarkably challenging to solve in theory, disentanglement is often achieved in practice through prior matching. Furthermore, recent works have shown that prior matching approaches can be enhanced by leveraging …
Read more “Disentangled Representational Learning with the Gromov-Monge Gap”