I tried the new AI tool for Logseq, and the result seems promising! Of course, GPT-3 won't magically make you smarter, but it does save you some time and helps you write more.

We're now seeing more and more AI writing tools like LEX and Jasper, but are there going to be more AI note-taking tools too?

Mentioned Tools:
LEX: https://lex.page/
Jasper: https://www.jasper.ai/
Napkin: https://napkin.one/?via=shu
Mem: https://get.mem.ai/
Logseq: https://logseq.com/
OpenAI: https://openai.com/api/


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🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐉𝐚𝐯𝐚𝐒𝐜𝐫𝐢𝐩𝐭 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞: https://www.edureka.co/javascr....ipt-certification-tr
This Edureka video will teach you how to build a simple scorekeeper using HTML, CSS, and JavaScript. Coding along the project will help you understand the fundamentals of Document Object Model (DOM) Manipulation. Following are the topics covered in this video
1.What is HTML?
2.HTML Tree
3.What is CSS?
4.CSS selectors
5.What is JavaScript?
6.JavaScript Selector Functions
7.Scorekeeper Code Along

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🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐌𝐲𝐒𝐐𝐋 𝐃𝐁𝐀 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/mysql-dba (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka video on 'What is Normalization' will help you understand the basic concepts of Normalization in SQL and Databases and how it helps in organizing data and data redundancy in SQL with examples. Below are the topics covered in this Database Normalization tutorial
00:00:00 Introduction
00:00:44 Agenda
00:01:08 What is Normalization
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00:09:44 Boyce Codd Normal Form

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AGI is closer than you think, and it could change everything we know about the world. In this video, we will delve into the development of AGI and explore some of the challenges that still lie ahead. So, buckle up and get ready for a jaw-dropping ride into the world of AGI.

Welcome back to US Military Power Channel, today's episode we will uncover "The US Army's New Super Robo-Dog With Sniper Rifle".

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A massive thank you to Prof. Pace Nielsen for all his time and help with this video.

A big thank you to Dr. Asaf Karagila, Pascal Ochem, Prof. Tianxin Cai, and Prof. William Dunham for their expertise and help.

To try GIMPS out yourself: https://ve42.co/GIMPS

These sources were particularly helpful:
Perfect numbers via MacTutor - https://ve42.co/MTPerfect
Cai, T. (2022). Perfect numbers and fibonacci sequences. World Scientific. - https://ve42.co/Cai2022
Dunham, W. (2022). Euler: The master of us all (Vol. 22). American Mathematical Society. - https://ve42.co/Dunham2022

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References:
https://www.youtube.com/watch?v=T0xKHwQH-4I&pp=ygUPcGVyZmVjdCBudW1iZXJz
https://www.youtube.com/watch?v=q8n15q1v4Xo&pp=ygUPcGVyZmVjdCBudW1iZXJz
Dickson, L. E. (1919). History of the Theory of Numbers.. (Vol. 1). Carnegie Institution of Washington.
Knill, O. (2007). The oldest open problem in mathematics. NEU Math Circle, December2. - https://ve42.co/Knill2007
Perfect number via Wikipedia - https://ve42.co/WikiPerfect
Introduction to Arithmetic via HalthiTrust - https://ve42.co/IntroArithmetic
Nicomachus of Gerasa via MacTutor - https://ve42.co/MTNicomachus
Sonja, B. (1988). The First Perfect Numbers and Three Types of Amicable Numbers in a Manuscript on Elementary Number Theory by Ibn Fellûs. Erdem, c. IV, 11. - https://ve42.co/Sonja1988
Ibn Fallus via Wikipedia - https://ve42.co/WikiFallus
Mersenne prime via Wikipedia - https://ve42.co/WikiMP
List of Known Mersenne Prime Numbers - https://ve42.co/ListOfMP
Marin Mersenne via MacTutor - https://ve42.co/MTMersenne
Leonhard Euler via Wikipedia - https://ve42.co/WikiEuler
Frank Nelson Cole via Wikipedia - https://ve42.co/WikiFNCole
GIMPS History via Mersenne.org - https://ve42.co/GIMPSHistory
EFF Cooperative Computing Awards via EFF - https://ve42.co/EFFAwards
Jonathan Pace via Primewiki - https://ve42.co/PWikiPace
Book with just one number sells out in Japan via BastillePost - https://ve42.co/PrimeBook
Predicted distribution of Mersenne primes via John D. Cook - https://ve42.co/JDCookMP
Euler’s Odd Perfect Numbers Theorem via Cantor's Paradise - https://ve42.co/EulerOPN
A Perfect (Math) Mystery via Medium - https://ve42.co/Machado2024
Brent, R. P., Cohen, G. L., & te Riele, H. J. (1991). Improved techniques for lower bounds for odd perfect numbers. Mathematics of Computation, 57(196), 857-868. - https://ve42.co/Brent1991
Ochem, P., & Rao, M. (2012). Odd perfect numbers are greater than 10¹⁵⁰⁰. Mathematics of Computation, 81(279), 1869-1877. - https://ve42.co/Ochem2012
Mathematicians Open a New Front on an Ancient Number Problem via Quantamagazine - https://ve42.co/QuantaSpoofs
Descartes number via Wikipedia - https://ve42.co/WikiDescartesNumber
Andersen, N., Durham, S., Griffin, M. J., Hales, J., Jenkins, P., Keck, R., ... & Wu, D. (2022). Odd, spoof perfect factorizations. Journal of Number Theory, 234, 31-47. - https://ve42.co/Andersen2022
Pomerance’s Heuristic that Odd Perfect Numbers are Unlikely via OddPerfect.org - https://ve42.co/Heuristic

Images & Video:
Clip of Piergiorgio Odifreddi - https://www.youtube.com/watch?v=y0paHEA8-gA
Euclid’s Elements 1 via Claymath - https://ve42.co/CM1
Euclid’s Elements 2 via Claymath - https://ve42.co/CM2
Euclid’s Elements 3 via Claymath - https://ve42.co/CM3
Diophanti - https://ve42.co/Diophanti
Gauss book - https://ve42.co/GaussDis
Euler’s Archive 1 - https://ve42.co/Euler1
Euler’s Archive 2 - https://ve42.co/Euler2

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Anton Ragin, Balkrishna Heroor, Bertrand Serlet, Bill Linder, Blake Byers, Burt Humburg, Chris Harper, Dave Kircher, David Johnston, Diffbot, Evgeny Skvortsov, Garrett Mueller, Gnare, I.H., John H. Austin, Jr., john kiehl, Josh Hibschman, Juan Benet, KeyWestr, Lee Redden, Marinus Kuivenhoven, Matthias Wrobel, Max Paladino, Meekay, meg noah, Michael Krugman, Orlando Bassotto, Paul Peijzel, Richard Sundvall, Sam Lutfi, Stephen Wilcox, Tj Steyn, TTST, Ubiquity Ventures, wolfee

▀▀▀
Directed by Casper Mebius
Written by Casper Mebius and Derek Muller
Edited by Peter Nelson
Illustrated by Jakub Misiek
Animated by Fabio Albertelli, Ivy Tello, David Szakaly, Alondra Vitae, Alex Drakoulis, and Leigh Williamson
Filmed by Derek Muller, Raquel Nuno, and Peter Nelson
Additional research by Aaron Santos, Camilla Machado, and Gregor Čavlović
Produced by Casper Mebius, Gregor Čavlović, Han Evans, and Derek Muller

Thumbnail by Ren Hurley
Additional video/photos supplied by Getty Images and Pond5
Music from Epidemic Sound

An engineer came up with a plan to drop tungsten telephone poles from space - the idea has been seriously considered on multiple occasions, so we tested it. Head to https://brilliant.org/Veritasium to start your free trial. The first 200 will get 20% off Brilliant's annual premium subscription.

Special thanks to our Patreon supporters! Join the community to help us keep our videos free, forever:
https://ve42.co/PatreonDEB

Massive thanks to Archisand for building such a beautiful sandcastle. https://www.youtube.com/@GregLeBon

Huge thanks to John and Angie Miller for helping us with securing the shooting location and going above and beyond to make this shoot happen – http://highdesertlocations.com/

Thanks to Inland Empire Film Services and the San Bernardino County Film Office for portions of the video shot in the County of San Bernardino.

Massive thanks to Dr David Wright for the interview and providing invaluable guidance during the research for this video.

Here’s a great video about space-based missile defense – https://www.ucsusa.org/resourc....es/space-based-missi

Massive thanks to Adam Savage for being part of this video.

Additional photos from NASA and ESA.

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References:
USAF. (2003). The US Air Force transformation flight plan.

Preston, R., Johnson, D. J., Edwards, S. J., Miller, M. D., & Shipbaugh, C. (2002). Space weapons earth wars. Rand Corporation.

Wright, D., Grego, L., & Gronlund, L. (2005). The physics of space security. A Reference Manual, Cambridge.

DeBlois, B. M., Garwin, R. L., Kemp, R. S., & Marwell, J. C. (2004). Space weapons: crossing the US Rubicon. International Security, 50-84.

Baucom, D. R. (2017). The Rise and Fall of Brilliant Pebbles 1. In United States Military History 1865 to the Present Day (pp. 329-376). Routledge.

Hitchens, T., & Samson, V. (2004). Space-based interceptors: still not a good idea. Georgetown journal of international affairs, 21-29.

National Research Council. (2012). Making sense of ballistic missile defense: An assessment of concepts and systems for US boost-phase missile defense in comparison to other alternatives. National Academies Press.

Borger, J. (2005). Bush likely to back weapons in space. The Guardian, 19.

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Written by Petr Lebedev, Derek Muller, and Emily Zhang
Filmed by Trenton Oliver, Derek Muller, Petr Lebedev, Emily Zhang, Raquel Nuno, and Eddie Lopez
Animation by Mike Radjabov, Fabio Albertelli, and Jonny Hyman
Edited by Trenton Oliver
Slow Motion Camera: Shawn Sanders and Anthony Corrales
Sandcastle Timelapse by Greg LeBon and Archisand
Phantom rental from Panny Hire LA
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Production Assistants: Roman Bacvic and Eddie Lopez
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Additional video/photos supplied by Pond5 and Getty Images
Music from Epidemic Sound
Thumbnail by Ignat Berbeci
Produced by Derek Muller, Petr Lebedev, and Emily Zhang

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В предыдущем видео мы узнали, что поднять над головой гриф с тяжёлым диском на конце проще, если этот диск вращается. Но почему так? Неужели вращение как-то уменьшает массу объекта? Разбираемся вместе с Дереком с канала Veritasium.

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How it Works?
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- - - - - - - - - - - - - - - - -
About the Course

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in the python programming language to enhance your learning experience.

Gain insight into the 'Roles' played by a Machine Learning Engineer
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Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present

- - - - - - - - - - - - - - - - - - -
Why learn Machine Learning Using Python?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning

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