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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 The 7 Most Useful Jupyter Notebook Extensions for Data Scientists In this article, we will explore seven different Jupyter Notebook extensions that will improve your work.
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SAN MATEO, CA – June 18, 2025 — Analytics automation company Savant Labs today launched its Summer 2025 Release, including their Agentic Analytics Suite and Intelligence Graph, one-click integration with Anthropic Claude, and migration tools to help enterprises modernize from legacy self-service analytics platforms.
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.
AI bots are quietly overwhelming the digital infrastructure behind our cultural memory. In early 2025, libraries, museums, and archives around the world began reporting mysterious traffic surges on their websites. The culprit? Automated bots scraping entire online collections to fuel training datasets for large AI models. What started as a few isolated incidents is now becoming a global pattern.
Skip to main content Skip to secondary menu Skip to primary sidebar Skip to footer Geeky Gadgets The Latest Technology News Home Top News AI Apple Android Technology Guides Gadgets Hardware Gaming Autos Deals About Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything 1:13 pm June 18, 2025 By Julian Horsey What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time?
home I counted all of the yurts in Mongolia using machine learning Jun 17, 2025 Table of Contents Counting all the yurts in Mongolia Training a model to identify yurts Refining the search area Building a model backend for labeling Monitoring accuracy of each model Scaling training of models Deploying models and searching Mongolia The resulting count The people of the yurts Further questions The Fall of Civilizations podcast put out a 6¾-hour episode on the history of the Mongol Empire, which I e
Apache Cassandra is a distributed NoSQL database for managing massive data with high availability. This guide covers its installation on Linux, Windows, and macOS.
Large Language Models (LLMs) have demonstrated remarkable performance in many applications, including challenging reasoning problems via chain-of-thoughts (CoTs) techniques that generate ``thinking tokens'' before answering the questions. While existing theoretical works demonstrate that CoTs with discrete tokens boost the capability of LLMs, recent work on continuous CoTs lacks a theoretical understanding of why it outperforms discrete counterparts in various reasoning tasks such as dir
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.
Imagine looking for a flight on a travel website and waiting for 10 seconds as the results load up. Feels like an eternity, right? Modern travel search platforms must return results almost instantly, even under heavy load. Yet, not long ago, our travel search engine’s API had a p95 latency hovering around 10 seconds. This […] The post From 10s to 2s: Complete p95 Latency Reduction Roadmap Using Cloud Run and Redis appeared first on Analytics Vidhya.
When it comes to artificial intelligence, more intensive computing uses more energy, producing more greenhouse gases. From uninvited results at the top of your search engine queries to offering to write your emails and helping students do homework, generative A.I.
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Sitemap Open in app Sign up Sign in Medium Logo Write Sign up Sign in Member-only story Meta Offering $100 Million Salaries to Top OpenAI Researchers Is Peak Silicon Valley Obscenity This is not exaggeration Alberto Romero Follow 5 min read · Just now -- Share Everyone knows the AI industry moves money. Few know how much. Let’s start with the facts: Meta is losing.
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
Homomorphic encryption allows a computer to run programs on encrypted data. Learn how homomorphic encryption works through interactive examples, build a homomorphically encrypted CRDT and see whether it has promise for local-first software.
Home | Projects | Publications | Blog The Unreasonable Effectiveness of Fuzzing for Porting Programs A simple strategy of having LLMs write fuzz tests and build up a port in topological order seems effective at automating porting from C to Rust. Agents are starting to produce more and more code A week or 2 back, I was reflecting on some code Claude had generated for me and I had a sort of moment of clarity.
The Chinese AI company, MiniMaxAI, has just launched a large-scale open-source reasoning model, named MiniMax-M1. The model, released on Day 1 of the 5-day MiniMaxWeek event, seems to give a good competition to OpenAI o3, Claude 4, DeepSeke-R1, and other contemporaries. Along with the chatbot, MiniMax has also released an agent in beta version, capable […] The post MiniMax-M1 and MiniMax Agent: China’s Biggest Open-source Reasoning Model and Agent appeared first on Analytics Vidhya.
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.
There’s a lot of talk in the startup world about how AI makes individuals so productive that it could give rise to a generation of “solo unicorns” — one-person companies worth over $1 billion.
There’s a lot of talk in the startup world about how AI makes individuals so productive that it could give rise to a generation of “solo unicorns” — one-person companies worth over $1 billion.
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
Data Lakehouse has emerged as a significant innovation in data management architecture, bridging the advantages of both data lakes and data warehouses. By enabling organizations to efficiently store various data types and perform analytics, it addresses many challenges faced in traditional data ecosystems. This powerful model combines accessibility with advanced analytics capabilities, making it a game-changer for businesses seeking to leverage their data.
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Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
Data integration is an essential aspect of modern businesses, enabling organizations to harness diverse information sources to drive insights and decision-making. In today’s data-driven world, the ability to combine data from various systems and formats into a unified view is paramount. This ensures that all stakeholders have access to accurate and timely data, fostering collaboration and efficiency across departments.
It feels like every other AI announcement lately mentions “agents.” And already, the AI community has 2025 pegged as “the year of AI agents,” sometimes without much more detail than “They’ll be amazing!” Often forgotten in this hype are the fundamentals. Everybody is dreaming of armies of agents, booking hotels and flights, researching complex topics, and writing PhD theses for us.
Dimension tables play a critical role in data warehousing, serving as the backbone for organizing and interpreting vast amounts of business data. These structured tables enable data analysts to derive meaningful insights from information stored in fact tables. Essentially, dimension tables enhance the understanding of data by providing descriptive context to numerical measurements, making them indispensable for effective business intelligence.
With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
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