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The document outlines the progression of taxis through time from 1769 to present day. It notes the years that taxis first appeared powered by steam from 1769 to 1801. It then lists decades from 1840 to 1910 where the taxi industry continued to evolve. Finally it mentions key years from 1920 to 2000 where the taxi industry modernized and became established, and concludes with taxis in 2009. The document uses dates to chronologically chart the development of taxis over approximately 240 years.

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- Aston Martin was founded in 1915 by Robert Bamford and Lionel Martin and was originally known for modifying Singer vehicles. The company name comes from its success at the Aston Clinton hill climb event. - In 1948, David Brown, a tractor and gear manufacturer, bought Aston Martin. His first model was the Two Liter Sports in 1948. - Notable subsequent models included the DB2 in 1950, DB4 from 1958-1963, and DB5 which gained fame from being James Bond's car in Goldfinger in 1963. - Ford purchased Aston Martin in 1987 and invested in the new DB7 model in 1994, helping the company's success. The DB7 Vantage was introduced in 1999

Monitoring and Managing Anomaly Detection on OpenShift Overview Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices. Key Topics Covered 1. Introduction to Anomaly Detection - Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems. 2. Understanding Edge (IoT) - Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source. 3. What is ArgoCD? - Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices. 4. Deployment Using ArgoCD for Edge Devices - Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD. 5. Introduction to Apache Kafka and S3 - Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions. 6. Viewing Kafka Messages in the Data Lake - Learn how to view and analyze Kafka messages stored in a data lake for better insights. 7. What is Prometheus? - Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices. 8. Monitoring Application Metrics with Prometheus - Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system. 9. What is Camel K? - Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes. 10. Configuring Camel K Integrations for Data Pipelines - Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow. 11. What is a Jupyter Notebook? - Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text. 12. Jupyter Notebooks with Code Examples - Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.

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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems. The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS. Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application. I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.

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Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers: * What is Vector Search? * Importance and benefits of vector search * Practical use cases across various industries * Step-by-step implementation guide * Live demos with code snippets * Enhancing LLM capabilities with vector search * Best practices and optimization strategies Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications. #MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology

Operating System Used by Users in day-to-day life.pptx

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2024 State of Marketing Report – by Hubspot

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https://www.hubspot.com/state-of-marketing · Scaling relationships and proving ROI · Social media is the place for search, sales, and service · Authentic influencer partnerships fuel brand growth · The strongest connections happen via call, click, chat, and camera. · Time saved with AI leads to more creative work · Seeking: A single source of truth · TLDR; Get on social, try AI, and align your systems. · More human marketing, powered by robots

Everything You Need To Know About ChatGPT

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Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits. To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups. Technology For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did. While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis. Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad. Age and Gender When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same. Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers. Race Affects Attitudes As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).

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