This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 GitHub Repositories for Mastering Agents and MCPs Learn how to build your own agentic AI application with free tutorials, guides, courses, projects, example code, research papers, and more.
Context engineering is quickly becoming the new foundation of modern AI system design, marking a shift away from the narrow focus on prompt engineering. While prompt engineering captured early attention by helping users coax better outputs from large language models (LLMs), it is no longer sufficient for building robust, scalable, and intelligent applications.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 7 DuckDB SQL Queries That Save You Hours of Pandas Work See how DuckDB outperforms Pandas in real world tasks like filtering, cohort analysis and revenue modelling all within your notebook.
In the realm of artificial intelligence, the emergence of vector databases is changing how we manage and retrieve unstructured data. These specialized systems offer a unique way to handle data through vector embeddings, transforming information into numerical arrays. By allowing for semantic similarity searches, vector databases are enhancing applications across various domains, from personalized content recommendations to advanced natural language processing.
Speaker: Jason Chester, Director, Product Management
In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.
Home Table of Contents Breaking the CNN Mold: YOLOv12 Brings Attention to Real-Time Object Detection The YOLO Evolution (Quick Recap) YOLOv8: Introducing the C2f Module and OBB Support YOLOv9: Programmable Gradient Information and GELAN YOLOv10: NMS-Free Training and Dual Assignments YOLOv11: Enhanced Speed with C3K2 Blocks and Official OBB Support Limitations of YOLOv8 to YOLOv11 Why YOLO Avoided Attention (Until Now) The Bottlenecks at a Glance YOLOv12: A New Solution What’s New in YOLOv
Data lakes have emerged as a pivotal solution for handling the vast volumes of raw data generated in today’s data-driven landscape. Unlike traditional storage solutions, data lakes offer a flexibility that allows organizations to store not just structured data, but also unstructured data that varies in type and format. This characteristic empowers businesses in various sectors to harness insights from a wide array of data sources, enabling advanced analytics and data science initiatives.
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Skip to main content Login Why Databricks Discover For Executives For Startups Lakehouse Architecture Mosaic Research Customers Customer Stories Partners Cloud Providers Databricks on AWS, Azure, GCP, and SAP Consulting & System Integrators Experts to build, deploy and migrate to Databricks Technology Partners Connect your existing tools to your Lakehouse C&SI Partner Program Build, deploy or migrate to the Lakehouse Data Partners Access the ecosystem of data consumers Partner Solutions
Semantic networks form an insightful framework in artificial intelligence (AI) by illustrating the complex web of relationships between various concepts. They not only enhance how machines comprehend and analyze data but also improve our own understanding of information through visual representation. By mapping connections, semantic networks create a structured environment where information can be retrieved and utilized more effectively.
Discover how Batch Mode in Gemini API allows developers to submit large jobs, offload processing, and get results in 24 hours, offering simplicity, convenience, and cost-savings.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
Recommendation systems are everywhere. From Netflix and Spotify to Amazon. But what if you wanted to build a visual recommendation engine? One that looks at the image, not just the title or tags? In this article, you’ll build a men’s fashion recommendation system. It will use image embeddings and the Qdrant vector database. You’ll go […] The post Build a Men’s Fashion Recommendation System Using FastEmbed and Qdrant appeared first on Analytics Vidhya.
Researchers at the Cornell Ann S. Bowers College of Computing and Information Science have developed an AI-powered process that automatically transforms a short video of a room into an interactive, 3D simulation of the space.
Introduction Artificial intelligence (AI) and machine learning (ML) systems are becoming increasingly integral to decision-making processes across various industries, including healthcare, finance, education, law enforcement, and employment. The ethical use of these technologies becomes paramount. Ethics-driven model auditing and bias mitigation are essential for ensuring the fairness of AI systems.
Software as a service (SaaS) companies managing multiple tenants face a critical challenge: efficiently extracting meaningful insights from vast document collections while controlling costs. Traditional approaches often lead to unnecessary spending on unused storage and processing resources, impacting both operational efficiency and profitability. Organizations need solutions that intelligently scale processing and storage resources based on actual tenant usage patterns while maintaining data is
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Large Language Models: A Self-Study Roadmap A complete beginner’s roadmap to understanding and building with large language models explained simply and with hands-on resources.
Fivetran’s recent acquisition of Census is a meaningful step toward streamlining how data moves through modern organizations. Rather than simply bolting on a new feature, this integration reflects a shift in thinking—from linear “pipelines” to more iterative, closed-loop systems. In this model, data doesn’t just support decisions; it cycles back into the tools where decisions are made, enabling faster, more responsive operations.
Organizations deploying video monitoring systems face a critical challenge: processing continuous video streams while maintaining accurate situational awareness. Traditional monitoring approaches that use rule-based detection or basic computer vision frequently miss important events or generate excessive false positives, leading to operational inefficiencies and alert fatigue.
Artificial Intelligence is at an inflection point where computer vision systems are breaking out of their classical limitations. While good at recognizing objects and patterns, they have traditionally been limited when it came to making considerations of context and reasoning. Introducing Retrieval Augemented Generation (RAG) to the scenario – changing the game in the way […] The post 7 RAG Applications for Computer Vision appeared first on Analytics Vidhya.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
This post is co-written with Shashank Saraogi, Nat Gale, and Durran Kelly from INRIX. The complexity of modern traffic management extends far beyond mere road monitoring, encompassing massive amounts of data collected worldwide from connected cars, mobile devices, roadway sensors, and major event monitoring systems. For transportation authorities managing urban, suburban, and rural traffic flow, the challenge lies in effectively processing and acting upon this vast network of information.
Hacker News new | past | comments | ask | show | jobs | submit login Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec 115 points by bhaktatejas922 6 hours ago | hide | past | favorite | 80 comments Hey HN, I’m Tejas at Morph. We’ve built a blazing-fast model for applying AI-generated code edits directly into your files at 4,500+ tokens/sec.
AI is growing rapidly. This list can help you stay on top of all the key terms. AI is everywhere. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, AI is completely taking over the internet.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
Today, we are excited to announce that Qwen3 , the latest generation of large language models (LLMs) in the Qwen family , is available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Qwen3 models—available in 0.6B, 4B, 8B, and 32B parameter sizes—to build, experiment, and responsibly scale your generative AI applications on AWS.
Blog Regarding Prollyferation (A Followup to "People Keep Inventing Prolly Trees") Nick Tobey July 3, 2025 9 min read Last month I published a blog post about the parallel invention of Prolly Trees , where I observed the repeated independent invention of a certain type of data structure within a relatively short period of time. In short, I described the concept of a Merkle Tree created by applying a content-defined chunker to a file, hashing the chunks, and then recursively reapplying
For July’s Book of the Month, we’re challenging our perspective on data leadership with “The Data Hero Playbook” by Malcolm Hawker. The subtitle is “Developing Your Career and Data SUPERPOWERS,” which gives a sense of the journey the reader is about to engage in. The book ultimately challenges a lot of long-held beliefs and practices in […] The post Book of the Month: “The Data Hero Playbook” appeared first on DATAVERSITY.
We present Mercury, a new generation of commercial-scale large language models (LLMs) based on diffusion. These models are parameterized via the Transformer architecture and trained to predict multiple tokens in parallel. In this report, we detail Mercury Coder, our first set of diffusion LLMs designed for coding applications. Currently, Mercury Coder comes in two sizes: Mini and Small.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Enterprises racing to adopt AI must be wary of giving into hype, downplaying ethical concerns, and focusing on use cases that won’t generate real value.
This post explores the idea that the next breakthroughs in AI may hinge more on how we collect experience through exploration, and less on how many parameters and data points we have.
Author(s): MKWriteshere Originally published on Towards AI. We trained AI to be mathematical geniuses, but inadvertently created conversational disasters. — Carnegie Mellon University (Non-Member Link) AI models are consistently outperforming math benchmarks every week. Some even beat human experts on competitions like MATH and AIME. But here’s what nobody talks about: these math geniuses often can’t handle basic conversations.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
341 
Let's personalize your content