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Ravasi said that Francis’s physical presence on Sunday was “not only a gesture of institutional respect, but also respect for a man who always had within himself a moral dimension and an ...

Pope makes history to pay respects to former Italian president

Timothy Ravasi, study coauthor and marine scientist at OIST, says that faster growth in a warming world could become a problem for wild clownfish. If climate change causes clownfish larvae to develop too quickly, they might arrive on an anemone when there ...

In Hot Water, Clownfish Grow Up Quick

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Debugging effectively requires a nuanced approach, similar to using tongs that tightly grip the problem from both sides. While low-level tools have their place in system-level service debugging, today's focus shifts towards a more sophisticated segment of the development stack: advanced management tools. Understanding these tools is crucial for developers, as it bridges the gap between code creation and operational deployment, enhancing both efficiency and effectiveness in managing applications across extensive infrastructures. https://www.youtube.com/watch?v=rQjHAMM3XfY&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers his subject. If you have friends who are learning to code, I'd appreciate a reference to my If you want to get back to Java after a while, check out my Debugging book t Java Basics book. Java 8 to 21 book . The Need for Advanced Management Tools in Development Development and DevOps teams utilize an array of tools, often perceived as complex or alien by developers. These tools, designed for scalability, enable the management of thousands of servers simultaneously. Such capabilities, although not always necessary for smaller scales, offer significant advantages in application management. Advanced management tools facilitate the navigation and control over multiple machines, making them indispensable for developers seeking to optimize application performance and reliability. Introduction to JMX (Java Management Extensions) One of the pivotal standards in application management is Java Management Extensions (JMX), which Java introduced to simplify the interaction with and management of applications. JMX allows both applications and the Java Development Kit (JDK) itself to expose critical information and functionalities, enabling external tools to manipulate these elements dynamically. Although activating JMX falls outside this discussion, its significance cannot be overstated, with ample resources available for those interested in its implementation. Setting up JMX JMX isn't enabled by default; to enable it, we need the following steps: : To enable JMX on a Java application, you must adjust the Java Virtual Machine (JVM) startup parameters. This involves adding specific flags to your application's startup command. The essential flags for enabling JMX are: Modify the JVM Startup Parameters : This flag activates the JMX remote management and monitoring. -Dcom.sun.management.jmxremote : Replace with a specific port number where the JMX remote connection will listen. -Dcom.sun.management.jmxremote.port=<PORT> <PORT> : This flag disables SSL for JMX connections. For development environments, SSL might be disabled for simplicity, but for production environments, consider enabling SSL for security. -Dcom.sun.management.jmxremote.ssl=false : This flag disables authentication. Similar to SSL, authentication may be disabled in development but should be enabled in production to ensure secure access. -Dcom.sun.management.jmxremote.authenticate=false : With the JVM parameters set, restart your application. This will apply the new startup parameters, activating JMX. Restart Your Application : After restarting your application, you can verify that JMX is enabled by connecting to it using a JMX client such as JConsole, VisualVM, or a custom management application. Use the port number specified in the startup parameters to establish the connection. Verify JMX Connectivity JMX Security Considerations While enabling JMX provides powerful management capabilities, it's crucial to consider security implications, especially when JMX is exposed over a network. When deploying applications in production, always enable SSL and authentication to protect against unauthorized access. Additionally, consider firewall rules and network policies to restrict JMX access to trusted clients. Understanding MBeans Central to JMX are Management Beans (MBeans), which serve as the control points within an application. These beans enable developers to publish specific functionalities for runtime monitoring and configuration. The ability to export application metrics to dashboards through MBeans is particularly valuable, facilitating real-time decision-making based on accurate, up-to-date information. Furthermore, operations such as user management can be exposed through MBeans, enhancing administrative capabilities. Spring and Management Beans Spring Framework's Actuator module exemplifies the integration of management capabilities within development, offering extensive metrics and operational details. This integration propels applications to "production-ready" status, allowing developers to monitor and manage applications with unprecedented depth and efficiency. Tooling for JMX Management While JMX can be accessed through various web interfaces and administrative tools, command-line tooling offers a direct, efficient method for interacting with JMX-enabled applications on production servers. Tools like JMXTerm complement visual tools by providing a streamlined interface for rapid insights, especially in environments unfamiliar to the developer. Getting Started with JMXTerm JMXTerm is a powerful utility for managing JMX without the need for graphical visualization, ideal for quick diagnostics or high-level server insights. After enabling JMX on the JVM and setting up the necessary configurations, developers can connect to servers, explore different JMX domains, and manipulate MBeans directly from the command line. We can accomplish all of the following via visual tools and sometimes using a web interface. Normally, that's the approach I use. However, as a learning tool I think JMXTerm is fantastic since it exposes things in a way that's consistent and verbose. If we can understand JMXTerm, the GUI version would be a walk in the park... We can launch JMXTerm using the command line. In my case, I used the following command: java -jar ~/Downloads/jmxterm-1.0.2-uber.jar --url localhost:30002 Once the connection is made, we can issue commands to JMX and retrieve information about the JVM or the application e.g., I can list the domains that you can think of as similar to "packages" or "modules," a way to organize the various beans: $>domains
#following domains are available
JMImplementation
com.sun.management
java.lang
java.nio
java.util.logging
javax.cache
jdk.management.jfr I can select a specific domain and thus perform future operations within said domain: $>domain java.util.logging
#domain is set to java.util.logging Once inside the domain, I can select a specific bean and perform operations on it. For this I need to first list the beans in the domain, in this case there's only the logging bean. I can then select that bean using the command: bean $>beans
#domain = java.util.logging:
java.util.logging:type=Logging
$>bean java.util.logging:type=Logging
#bean is set to java.util.logging:type=Logging I can perform many operations on beans. Perhaps the most useful is the command, which lets me query a bean. Notice that a bean can have attributes, think of them like object fields and operations which you can think of as methods. There are also notifications which you can think of as events: info $>info
#mbean = java.util.logging:type=Logging
#class name = sun.management.ManagementFactoryHelper$PlatformLoggingImpl
# attributes
  %0   - LoggerNames ([Ljava.lang.String;, r)
  %1   - ObjectName (javax.management.ObjectName, r)
# operations
  %0   - java.lang.String getLoggerLevel(java.lang.String p0)
  %1   - java.lang.String getParentLoggerName(java.lang.String p0)
  %2   - void setLoggerLevel(java.lang.String p0,java.lang.String p1)
#there's no notifications I can run operations and pass various commands e.g., I can get the logger level, set it, and then check that the logger level was indeed updated: $>run getLoggerLevel "org.apache.tomcat.websocket.WsWebSocketContainer"
#calling operation getLoggerLevel of mbean java.util.logging:type=Logging with params [org.apache.tomcat.websocket.WsWebSocketContainer]
#operation returns:
$>run setLoggerLevel org.apache.tomcat.websocket.WsWebSocketContainer INFO
#calling operation setLoggerLevel of mbean java.util.logging:type=Logging with params [org.apache.tomcat.websocket.WsWebSocketContainer, INFO]
#operation returns: 
null
$>run getLoggerLevel "org.apache.tomcat.websocket.WsWebSocketContainer"
#calling operation getLoggerLevel of mbean java.util.logging:type=Logging with params [org.apache.tomcat.websocket.WsWebSocketContainer]
#operation returns: 
INFO This is just the tip of the iceberg. We can get many things, such as spring settings, internal VM information, etc. In this example, I can query VM information directly from the console: $>domain com.sun.management
#domain is set to com.sun.management
$>beans
#domain = com.sun.management:
com.sun.management:type=DiagnosticCommand
com.sun.management:type=HotSpotDiagnostic
$>bean com.sun.management:type=HotSpotDiagnostic
#bean is set to com.sun.management:type=HotSpotDiagnostic
$>info
#mbean = com.sun.management:type=HotSpotDiagnostic
#class name = com.sun.management.internal.HotSpotDiagnostic
# attributes
  %0   - DiagnosticOptions ([Ljavax.management.openmbean.CompositeData;, r)
  %1   - ObjectName (javax.management.ObjectName, r)
# operations
  %0   - void dumpHeap(java.lang.String p0,boolean p1)
  %1   - javax.management.openmbean.CompositeData getVMOption(java.lang.String p0)
  %2   - void setVMOption(java.lang.String p0,java.lang.String p1)
#there's no notifications Leveraging JMX in Debugging and Management JMX stands out as a robust tool for wiring management consoles, allowing developers to expose critical settings and metrics for their projects. Beyond its conventional use, JMX can be leveraged as part of the debugging process, serving as a pseudo-interface for triggering debugging scenarios or observing debugging sessions within the management UI. This approach not only simplifies the management of server applications but also enhances the developer's ability to diagnose and resolve issues efficiently. Exposing MBeans in Spring Boot Up until now, we discussed the process of working with beans that are a part of the JVM or Spring. But what about our own application logic? We can expose our own application’s internal state so we (and our SREs) can review these in production and staging. Instead of building a custom control panel or logging , we can just expose the data. If a flag is problematic we can change it in production, if you want to query a specific state it too can be exposed. everything Spring Boot simplifies the management and monitoring of applications through its comprehensive support for JMX. By leveraging Spring's infrastructure, we can easily expose their application's beans as JMX Managed Beans (MBeans), making them accessible for monitoring and management via JMX clients. Understanding Spring Boot JMX Support Spring Boot automatically configures JMX for you and exposes any beans annotated with as JMX MBeans. This feature, combined with Spring Boot’s Actuator, provides a rich set of management endpoints, covering various aspects of the application, from metrics to thread dumps. @ManagedResource Expose an MBean in Spring Boot To expose a bean, we need to take the following steps: : Create an interface that defines the operations and attributes you wish to expose via JMX. This interface should be annotated with JMX annotations such as for methods and for fields or getter/setter methods. Define a Management Interface @ManagedOperation @ManagedAttribute : Implement the interface in a class that performs the actual logic for the operations and attributes defined. This class represents your MBean and can be a regular Spring-managed bean. Implement the MBean : Annotate your MBean implementation class with to indicate that it should be exposed as an MBean. You can specify the object name for the MBean in this annotation, which is how it will be identified in JMX clients. Annotate the Bean with @ManagedResource @ManagedResource : Ensure that JMX is enabled in your Spring Boot application. This is usually the default behavior, but you can explicitly enable it by setting in your or file. Enable JMX in Spring Boot spring.jmx.enabled=true application.properties application.yml : Once your application is running, you can access the exposed MBean through any standard JMX client, such as JConsole, VisualVM, or a custom client. Connect to the Spring Boot application's JMX domain, and you'll find the MBean you exposed, ready for interaction. Access the MBean via a JMX Client Example: Exposing a Simple Configuration MBean // Define a management interface
public interface ConfigurationMBean {
    @ManagedAttribute
    String getApplicationName();

    @ManagedOperation
    void updateApplicationName(String name);
}

// Implement the MBean
@Component
@ManagedResource(objectName = "com.example:type=Configuration")
public class Configuration implements ConfigurationMBean {
    private String applicationName = "MyApp";

    @Override
    public String getApplicationName() {
        return applicationName;
    }

    @Override
    public void updateApplicationName(String name) {
        this.applicationName = name;
    }
} In this example, the class is annotated with , making it available as an MBean with operations and attributes accessible via JMX clients. Configuration @ManagedResource Exposing MBeans in Spring Boot is a powerful feature that enhances the management and monitoring capabilities of applications. By following the steps outlined above, developers can provide external tools with dynamic access to application internals, offering a window into the runtime behavior and allowing for adjustments on the fly. This not only aids in debugging and performance tuning but also aligns with best practices for building manageable, robust applications. Final Word Advanced management tools, particularly JMX and its integration with frameworks like Spring, offer developers powerful capabilities for application monitoring, configuration, and debugging. By understanding and utilizing these tools, developers can achieve a deeper level of control over their applications, enhancing both performance and reliability. Whether through graphical interfaces or command-line utilities like JMXTerm, the dynamic manipulation and monitoring of applications in runtime environments open new avenues for effective software development and management. As the bridge between development and operations continues to narrow, mastering these advanced tools becomes essential for any developer looking to excel in today's fast-paced technological landscape. Also published . here

Leveraging JMX for Efficient Debugging, Revisited

In the realm of system debugging, particularly on Linux platforms, strace stands out as a powerful and indispensable tool. Its simplicity and efficacy make it the go-to solution for diagnosing and understanding system-level operations, especially when working with servers and containers. In this blog post, we'll delve into the nuances of strace, from its history and technical functioning to practical applications and advanced features. Whether you're a seasoned developer or just starting out, this exploration will enhance your diagnostic toolkit and provide deeper insights into the workings of Linux systems. https://www.youtube.com/watch?v=bgi7PJXtEzc&embedable=true As a side note, if you like the content of this and the other posts in this series check out my his subject. If you have friends that are learning to code I'd appreciate a reference to my get back to Java after a while check out my Debugging book that covers t Java Basics book. If you want to . Java 8 to 21 book Understanding strace and its Origins A Look Back: strace and dtrace The journey of strace begins with its predecessor, dtrace, . However, dtrace's availability is limited, particularly on Linux systems where most server and container debugging takes place. This is where strace comes into the picture, offering a simpler yet effective alternative. which we covered last time Originating from Sun Microsystems strace, like dtrace, traces its roots back to Sun Microsystems, emerging in the 90s (a decade before dtrace). This isn't surprising given the impressive array of technologies that originated from Sun. However, strace differentiates itself by its straightforwardness in both usage and capabilities. Unlike DTrace, which demands deep operating system support and thus remained absent as an official feature in common Linux distributions, strace thrives in the Linux environment. Its simplicity and ease of implementation make it a popular choice for Linux users, offering a distinct approach to system diagnostics. Technical Functioning of strace The Role of ptrace in strace The cornerstone of strace's functionality is the ptrace kernel feature. ptrace, pre-existing in Linux, spares users from the need to add additional kernel code or modules, a requirement often associated with DTrace. This fundamental difference not only simplifies the use of strace but also broadens its accessibility. Comparing with DTrace While DTrace offers a more in-depth analysis through deeper kernel support, strace operates on a more surface level. This simplicity, however, does not undermine its effectiveness. strace works essentially by logging every kernel call made by a process, providing verbose but incredibly detailed insights into the system's operation. This method allows users to trace the inner workings of a process, understanding each interaction with the kernel. Practical Usage and Advantages Ease of Use and Accessibility One of the most appealing aspects of strace is its user-friendly nature. It doesn't require special privileges or complex setup procedures. This ease of use is particularly beneficial for developers and system administrators who need to quickly diagnose and address issues in a Linux environment. Unlike DTrace, strace is readily available and doesn’t demand advanced configurations or permissions. Favored in Linux Environments strace's popularity in Linux circles is not only due to its accessibility but also its practicality. Being able to run without special privileges makes it a go-to tool for diagnosing various system-related issues. However, it's important to note that strace should be used cautiously in production environments. Its extensive logging can create a significant performance overhead, potentially impacting the efficiency of a live system. This is why strace is generally recommended for use in development or isolated testing environments rather than in production. strace in Action: A Closer Look at System Calls Basic Usage and Output Analysis Using strace is straightforward: you simply pass the command line to it. strace java -classpath . PrimeMain This simplicity belies its power, as the output offers a wealth of information. Each line in the strace output corresponds to a system call made by the process as you can see below: execve("/home/ec2-user/jdk1.8.0_45/bin/java", ["java", "-classpath.", "PrimeMain"], 0x7fffd689ec20 /* 23 vars */) = 0
brk(NULL)                               = 0xb85000
mmap(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x7f0294272000
readlink("/proc/self/exe", "/home/ec2-user/jdk1.8.0_45/bin/j"..., 4096) = 35
access("/etc/ld.so.preload", R_OK)      = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
stat("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64", 0x7fff37af09a0) = -1 ENOENT (No such file or directory)
open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory)
stat("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls", 0x7fff37af09a0) = -1 ENOENT (No such file or directory) By analyzing these calls, users can gain insights into the intricate operations of their applications. For instance, if a Java process attempts to load a library and fails, strace can reveal the underlying system call and its exit code, providing clues about potential issues like missing files or directories. E.g. in this line: COPY COPY open("/home/ec2-user/jdk1.8.0_45/bin/../lib/amd64/jli/tls/x86_64/libpthread.so.0", O_RDONLY|O_CLOEXEC) = -1 ENOENT (No such file or directory) Java tries to load the library from the directory using a system call open to load the file. The exit code of the system call is which means that the file isn't there. Under normal circumstances, we should get back a file descriptor value from this API (positive non-zero integer). Looking in the directory, it seems the directory is missing. I'm guessing that this is because of a missing (Java Cryptography Extensions) installation. This is probably OK but might have been interesting in some cases. pthread tls -1, tls JCE Interpreting System Calls for Debugging The output of strace, while verbose, is a goldmine for troubleshooting. For example, a negative exit code in a system call indicates an error, such as a missing file, which could be crucial for diagnosing issues in an application. This level of detail, although overwhelming at times, is invaluable for understanding the interactions between your application and the Linux system. Advanced Features and Tips Filtering System Calls for Efficiency A common challenge with strace is managing its voluminous output. Fortunately, strace offers options to filter system calls, significantly enhancing its usability. By using the argument, you can instruct strace to log only specific types of system calls, such as or e.g.: -e open connect strace -e open java -classpath . PrimeMain This selective logging not only makes the output more manageable but also allows for focused troubleshooting, speeding up the debugging process. Exploring a Variety of System Calls strace's utility extends beyond just tracking file access or network interactions. It can be used to monitor a range of system calls, offering insights into various aspects of application behavior. By understanding and utilizing different system calls, users can gain a comprehensive view of their application's interaction with the operating system, leading to more effective debugging and optimization. strace and Java: A Special Case strace with the JVM While strace predates Java and operates at a low level with no specific awareness of the Java Virtual Machine (JVM), it remains highly effective for debugging Java applications. The JVM, like most platforms, relies on system calls for its operations, which strace can monitor and report. However, certain aspects of the JVM's behavior may be less visible to strace due to its unique approach to problem-solving. Allocations and Threading in Java For instance, Java's memory management differs significantly from standard system tools. While typical applications use malloc, which directly maps to kernel allocation logic, Java manages its own memory. This approach, aimed at efficiency and streamlined garbage collection, means that some memory allocation activities are obscured from strace's view. Similarly, Java threading is currently well-represented in strace output, but this is changing with Java 21 and Project Loom. Java 21 added support for Virtual Threads which are only partially visible to the operating system hence 1,000 threads can seem like 16 threads. These changes could affect the clarity of strace outputs in complex, heavily threaded Java applications. Final Word strace stands out as an exceptionally versatile and powerful tool in the Linux debugging arsenal. Its ability to provide detailed insights into system calls makes it invaluable for diagnosing and understanding the inner workings of applications. Despite its simplicity, strace is capable of handling complex debugging scenarios, especially when used with its advanced filtering options. For developers and system administrators working in Linux environments, strace is more than just a diagnostic tool; it's a lens through which the intricate interactions between applications and the operating system can be viewed and understood. As technologies evolve, tools like strace adapt, continuing to offer relevant and critical insights into system behaviors. Whether you are troubleshooting a stubborn issue or simply curious about how your applications interact with the Linux kernel, strace is a tool that you will likely find yourself returning to time and again.

Revisiting Strace: Simple is Beautiful

When we think of debugging, we think of breakpoints in IDEs, stepping over, inspecting variables, etc. However, there are instances where stepping outside the conventional confines of an IDE becomes essential to track and resolve complex issues. This is where tools like DTrace come into play, offering a more nuanced and powerful approach to debugging than traditional methods. This blog post delves into the intricacies of DTrace, an innovative tool that has reshaped the landscape of and system analysis. debugging https://youtu.be/3M0AhZnVoUk?embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my . If you want to get back to Java after a while, check out my Debugging book Java Basics book Java 8 to 21 book . DTrace Overview DTrace was first introduced by Sun Microsystems in 2004. DTrace quickly garnered attention for its groundbreaking approach to dynamic system tracing. Originally developed for Solaris, it has since been ported to various platforms, including MacOS, Windows, and Linux. DTrace stands out as a dynamic tracing framework that enables deep inspection of live systems – from operating systems to running applications. Its capacity to provide real-time insights into system and application behavior without significant performance degradation marks it as a revolutionary tool in the domain of system diagnostics and debugging. Understanding DTrace’s Capabilities DTrace, short for Dynamic Tracing, is a comprehensive toolkit for real-time system monitoring and debugging, offering an array of capabilities that span across different levels of system operation. Its versatility lies in its ability to provide insights into both high-level system performance and detailed process-level activities. System Monitoring and Analysis At its core, DTrace excels in monitoring various system-level operations. It can trace system calls, file system activities, and network operations. This enables developers and system administrators to observe the interactions between the operating system and the applications running on it. For instance, DTrace can identify which files a process accesses, monitor network requests, and even trace system calls to provide a detailed view of what's happening within the system. Process and Performance Analysis Beyond system-level monitoring, DTrace is particularly adept at dissecting individual processes. It can provide detailed information about process execution, including CPU and memory usage, helping to pinpoint performance bottlenecks or memory leaks. This granular level of detail is invaluable for performance tuning and debugging complex software issues. Customizability and Flexibility One of the most powerful aspects of DTrace is its customizability. With a scripting language based on C syntax, DTrace allows the creation of customized scripts to probe specific aspects of system behavior. This flexibility means that it can be adapted to a wide range of debugging scenarios, making it a versatile tool in a developer’s arsenal. Real-World Applications In practical terms, DTrace can be used to diagnose elusive performance issues, track down resource leaks, or understand complex interactions between different system components. For example, it can be used to determine the cause of a slow file operation, analyze the reasons behind a process crash, or understand the system impact of a new software deployment. Performance and Compatibility of DTrace A standout feature of DTrace is its ability to operate with remarkable efficiency. Despite its deep system integration, DTrace is designed to have minimal impact on overall system performance. This efficiency makes it a feasible tool for use in live production environments, where maintaining system stability and performance is crucial. Its non-intrusive nature allows developers and system administrators to conduct thorough debugging and performance analysis without the worry of significantly slowing down or disrupting the normal operation of the system. Cross-Platform Compatibility Originally developed for Solaris, DTrace has evolved into a cross-platform tool, with adaptations available for MacOS, Windows, and various . Each platform presents its own set of features and limitations. For instance, while DTrace is a native component in Solaris and MacOS, its implementation in Linux often requires a specialized build due to kernel support and licensing considerations. Linux distributions Compatibility Challenges on MacOS On MacOS, DTrace's functionality intersects with System Integrity Protection (SIP), a security feature designed to prevent potentially harmful actions. To utilize DTrace effectively, users may need to disable SIP, which should be done with caution. This process involves booting into recovery mode and executing specific commands, a step that highlights the need for a careful approach when working with such powerful system-level tools. We can disable SIP using the command: csrutil disable We can optionally use a more refined approach of enabling SIP without dtrace using the following command: csrutil enable --without dtrace Be extra careful when issuing these commands and when working on machines where dtrace is enabled. Back up your data properly! Customizability and Flexibility of DTrace A key feature that sets DTrace apart in the realm of system monitoring tools is its highly customizable nature. DTrace employs a scripting language that bears similarity to C syntax, offering users the ability to craft detailed and specific diagnostic scripts. This scripting capability allows for the creation of custom probes that can be fine-tuned to target particular aspects of system behavior, providing precise and relevant data. Adaptability to Various Scenarios The flexibility of DTrace's scripting language means it can adapt to a multitude of debugging scenarios. Whether it's tracking down memory leaks, analyzing CPU usage, or monitoring I/O operations, DTrace can be configured to provide insights tailored to the specific needs of the task. This adaptability makes it an invaluable tool for both developers and system administrators who require a dynamic approach to problem-solving. Examples of Customizable Probes Users can define probes to monitor specific system events, track the behavior of certain processes, or gather data on system resource usage. This level of customization ensures that DTrace can be an effective tool in a variety of contexts, from routine maintenance to complex troubleshooting tasks. Following in a simple hello world dtrace probe: sudo dtrace -qn 'syscall::write:entry, syscall::sendto:entry /pid == $target/ { printf("(%d) %s %s", pid, probefunc, copyinstr(arg1)); }' -p 9999 The kernel is instrumented with hooks that match various callbacks. dtrace connects to these hooks and can perform interesting tasks when these hooks are triggered. They have a naming convention, specially: . In this case the provider is a system call in both cases. We have no module, so we can leave that part blank between the colon ( ) symbols. We grab a write operation and entries. When an application writes or tries to send a packet, the following code event will trigger. provider:module:function:name : sendto These things happen frequently, which is why we restrict the process ID to the specific target with . This means the code will only trigger for the PID passed to us in the command line. The rest of the code should be simple for anyone with basic C experience; it's a printf that would list the processes and the data passed. pid == $target Real-World Applications of DTrace DTrace's diverse capabilities extend far beyond theoretical use, playing a pivotal role in resolving real-world system complexities. Its ability to provide deep insights into system operations makes it an indispensable tool in a variety of practical applications. To get a sense of how dtrace can be used, we can use the command whose output on my Mac is below: man -k dtrace bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace
Tcl_CommandTraceInfo(3tcl), Tcl_TraceCommand(3tcl), Tcl_UntraceCommand(3tcl) - monitor renames and deletes of a command
bitesize.d(1m)           - analyse disk I/O size by process. Uses DTrace
cpuwalk.d(1m)            - Measure which CPUs a process runs on. Uses DTrace
creatbyproc.d(1m)        - snoop creat()s by process name. Uses DTrace
dappprof(1m)             - profile user and lib function usage. Uses DTrace
dapptrace(1m)            - trace user and library function usage. Uses DTrace
dispqlen.d(1m)           - dispatcher queue length by CPU. Uses DTrace
dtrace(1)                - dynamic tracing compiler and tracing utility
dtruss(1m)               - process syscall details. Uses DTrace
errinfo(1m)              - print errno for syscall fails. Uses DTrace
execsnoop(1m)            - snoop new process execution. Uses DTrace
fddist(1m)               - file descriptor usage distributions. Uses DTrace
filebyproc.d(1m)         - snoop opens by process name. Uses DTrace
hotspot.d(1m)            - print disk event by location. Uses DTrace
iofile.d(1m)             - I/O wait time by file and process. Uses DTrace
iofileb.d(1m)            - I/O bytes by file and process. Uses DTrace
iopattern(1m)            - print disk I/O pattern. Uses DTrace
iopending(1m)            - plot number of pending disk events. Uses DTrace
iosnoop(1m)              - snoop I/O events as they occur. Uses DTrace
iotop(1m)                - display top disk I/O events by process. Uses DTrace
kill.d(1m)               - snoop process signals as they occur. Uses DTrace
lastwords(1m)            - print syscalls before exit. Uses DTrace
loads.d(1m)              - print load averages. Uses DTrace
newproc.d(1m)            - snoop new processes. Uses DTrace
opensnoop(1m)            - snoop file opens as they occur. Uses DTrace
pathopens.d(1m)          - full pathnames opened ok count. Uses DTrace
perldtrace(1)            - Perl's support for DTrace
pidpersec.d(1m)          - print new PIDs per sec. Uses DTrace
plockstat(1)             - front-end to DTrace to print statistics about POSIX mutexes and read/write locks
priclass.d(1m)           - priority distribution by scheduling class. Uses DTrace
pridist.d(1m)            - process priority distribution. Uses DTrace
procsystime(1m)          - analyse system call times. Uses DTrace
rwbypid.d(1m)            - read/write calls by PID. Uses DTrace
rwbytype.d(1m)           - read/write bytes by vnode type. Uses DTrace
rwsnoop(1m)              - snoop read/write events. Uses DTrace
sampleproc(1m)           - sample processes on the CPUs. Uses DTrace
seeksize.d(1m)           - print disk event seek report. Uses DTrace
setuids.d(1m)            - snoop setuid calls as they occur. Uses DTrace
sigdist.d(1m)            - signal distribution by process. Uses DTrace
syscallbypid.d(1m)       - syscalls by process ID. Uses DTrace
syscallbyproc.d(1m)      - syscalls by process name. Uses DTrace
syscallbysysc.d(1m)      - syscalls by syscall. Uses DTrace
topsyscall(1m)           - top syscalls by syscall name. Uses DTrace
topsysproc(1m)           - top syscalls by process name. Uses DTrace There's a lot here; we don't need to read everything. The point is that when you run into a problem, you can just search through this list and find a tool dedicated to debugging that problem. Let’s say you're facing elevated disk write issues that are causing the performance of your application to degrade... But is it your app at fault or some other app? rwbypid.d can help you with that. It can generate a list of processes and the number of calls they have for read/write based on the process ID, as seen in the following screenshot: We can use this information to better understand IO issues in our code or even in 3rd party applications/libraries. is another tool that helps us track IO operations but with more details: iosnoop In diagnosing elusive system issues, DTrace shines by enabling detailed observation of system calls, file operations, and network activities. For instance, it can be used to uncover the root cause of unexpected system behaviors or to trace the origin of security breaches, offering a level of detail that is often unattainable with other debugging tools. Performance optimization is the main area where DTrace demonstrates its strengths. It allows administrators and developers to pinpoint performance bottlenecks, whether they lie in application code, system calls, or hardware interactions. By providing real-time data on resource usage, DTrace helps in fine-tuning systems for optimal performance. Final Words In conclusion, DTrace stands as a powerful and versatile tool in the realm of system monitoring and debugging. We've explored its broad capabilities, from in-depth system analysis to individual process tracing and its remarkable performance efficiency that allows for its use in live environments. Its cross-platform compatibility, coupled with the challenges and solutions specific to MacOS, highlights its widespread applicability. The customizability through scripting provides unmatched flexibility, adapting to a myriad of diagnostic needs. Real-world applications of DTrace in diagnosing system issues and optimizing performance underscore its practical value. DTrace's comprehensive toolkit offers an unparalleled window into the inner workings of systems, making it an invaluable asset for system administrators and developers alike. Whether it's for routine troubleshooting or complex performance tuning, DTrace provides insights and solutions that are essential in the modern computing landscape. Also published . here

Advanced Debugging Techniques: Revisiting DTrace for Troubleshooting

The differences between blockchain networks Blockchain networks have three main distinctive features: Architecture Ecosystem Consensus Architecture is a code, and all the rules of its maintenance and interaction with the program are written in the code. Blockchain Code describes: how the chain remains cohesive. (Hashing is the most widespread but not the only option) how many transactions can be stored within one block and what additional data fields it has. whether transactions are only coin transfers or can store additional data, for example, text. The structure of a chain: The block size: Transaction types: The rules for regulating various parameters are also described at the code level. For example, information on how often you can add blocks to the chain. In some , this parameter is adjusted so that, on average, a certain amount of time must pass between adding blocks into the chain. blockchain networks All these attributes may differ depending on each blockchain network. Different blockchains use different architectures. Ecosystem At the code level, the blockchain core and tools for expanding its capabilities are defined. The ability to create additional blockchains based on it, implement programs working inside the blockchain, receive data from outside the blockchain, create various applications based on it, interact with other blockchains, and much more depends on the architecture. All applications and services are built on the same blockchain and constitute its ecosystem. It is the architecture of the blockchain network that determines the possibility of developing an ecosystem based on it. And then, developers and users create their applications based on the blockchain and expand the ecosystem. A separate ecosystem with its products and applications is being created around each blockchain network. In many ways, it is the presence and development of an ecosystem based on a blockchain network that determines its benefit and value, among others. Consensus mechanism We already said that new entries are entered into the blockchain with the community’s consent. Blockchain users determine, according to their rules, what will be entered into the blockchain and what will not. The mechanism by which users reach an agreement is called consensus. If consensus is not reached, adding new blocks may stop on the network. There are many different mechanisms of agreement between participants, but at the moment, the two most popular are the Proof-of-Work and Proof-of-Stake. The general principle of these algorithms is as follows. Whoever adds a new block to the blockchain receives a reward from the network. Therefore, participants compete for the opportunity to add a new block. The difference between Proof-of-Work and Proof-of-Stake is how the user who adds a block and receives a reward is chosen. Proof-of-Work (PoW) In such a consensus algorithm, the competition between the participants is arranged as follows. To add a new block, you need to find a specific hash, which, for example, starts with several zeros. That is, it is necessary to iterate over a unique number added to the block and hash the entire block with different variants of this number. After each change of this number, the block’s hash will also change. The number that produced the required hash will be included in the block. The miner who finds this number will include a new block in the chain and receive a reward. This process of hash selection is called mining. Let's analyze this example to understand how mining protects against malicious intent. Let’s say John-the-scammer wants to single-handedly choose which transactions to include in the new block and which not to include or change their order. That is, to manage the blockchain centrally. To do this, John-the-scammer must be the first to pick up the hash of all the other transactions included in the block. Since John-the-scammer will compete with all the other miners on the network, they will be unable to pick up the hash faster than they do. The only way for John the Scammer to carry out his malicious actions is to build a mining farm more powerful than all the other miners combined. The cost of such a farm will be higher than it will be possible to earn on the . In addition, when new miners are included in the network, John-the-scammer will also have to increase his capacity, but by an even larger amount. centralization of the blockchain Thus, the security of the blockchain with the PoW consensus mechanism is achieved by spending a huge amount of electricity. To avoid these costs, we have developed another algorithm — PoS. Proof-of-Stake (PoS) With such a consensus algorithm, participants also compete with each other for a reward. But the winner is determined differently than in PoW. Instead of selecting the data to get the desired hash, the participants freeze the coins in their accounts. And then, from all the participants who have frozen funds in this way, the winner is randomly selected. To understand how the PoS algorithm protects against malicious actions, let’s go back to the example with John. Let’s assume that John-the-scammer wants to manage the network again centrally, but now in a different blockchain. To do this, John will have to redeem most of the coins. Here, several problems appear on his way. Firstly, very often, most of the coins are already blocked. That is, even if desired, John-the-scammer will not be able to buy most of the coins since they are not on the market. Secondly, even if the coins are on the market, John the scammer will have to spend a lot of money to buy them. His purchases will entail an increase in the price of the coin. Thirdly, even with half of all coins, they will not be able to manage the blockchain alone since the choice is still conditionally random. Therefore, the cost of buying coins will exceed the possible benefits obtained from the centralization of the blockchain. The security of the blockchain with the PoS consensus mechanism is achieved with blocked coins. These differences are not the main criteria for choosing a network. There are higher—level characteristics for this, and architecture, ecosystem, and consensuses are part of them. Criteria for choosing a blockchain network The blockchain network is selected by the following criteria: Security User Activity Technical capacity First of all, our main priority is safety because we are talking about money exchange. The level of blockchain safety can be evaluated by the age of the chain, coin capitalization, and after studying network security protocol. The longer the blockchain has been functioning, and the more developers work with it, the higher the level of trust in the blockchain. Especially when large companies use the blockchain for their products. It’s important to choose a network with a developed ecosystem. The more developers and users, the more reliable the project is considered within the ecosystem and the easier to enter the market. Now you know how blockchain networks differ from each other and by which criteria they are selected. Study the report describing the networks and choose which one is more suitable for building an exchange. Ethereum The network was launched in 2015 and functioned on a Proof-of-Work consensus algorithm. Recently, however, the network transitioned to Proof-of-Stake. Age. Ethereum has almost the largest capitalization and huge popularity. The network also has a wide ecosystem that includes various decentralized applications. For example, exchanges, wallets, bridges for interaction with other blockchain networks, and many others. User activity. Many developers are working on them, thanks to which the number of users is high, and trading volumes occupy 3rd place. We can say that the network is a decentralized operating system. With the Ethereum Virtual Machine, you can write various programs running inside the blockchain. Ethereum has an extensive development database, which includes various development environments, documentation, and additional materials. Technical capabilities. Ethereum blockchain Binance Smart Chain (BSC) The network, launched in 2020, runs on a modified Proof-of-Stake consensus and implements EVM support. It means that this network supports all the tools implemented by EVM. Age. BSC gained popularity among users for its network capacity, which is significantly higher than that of Ethereum. That's why the blockchain coin is among the top ten in the rankings of crypto asset capitalization and trading volumes. User activity. Since the development rules on this network are the same as on Ethereum, it's easy to enter into programming here. That is why many developers turned to this network. Technical capabilities. Any Solidity developer can start writing programs in this or EVM-supporting networks. Solana The Solana network was launched in 2020 and is based on the Proof-of-Stake consensus. Age. Solana is still among the top ten in coin capitalization and trading volume rankings. The network has gained widespread popularity, although opinions on the network differ. Solana has a large ecosystem that includes a variety of decentralized applications. User activity. Solana implements Rust programming and has a small documentation and development manuals base. Technical capabilities. Cosmos Blockchain was launched in 2019 and is based on a Proof-of-Stake consensus mechanism. Age. Cosmos has implemented a modular system that supports any design programming language. It's called TendermintCore. As a result, Cosmos has created a huge ecosystem of blockchains and applications based on them. However, now, the capitalization and trading volumes are only included in the top 30 and 60. User activity. The implementation of TendermintCore makes Cosmos very popular among developers. To launch your blockchain, you may use Cosmos as the basis. Then, you may create a parallel blockchain that will use all of its advantages. Technical capabilities. Avalanche The network was launched in 2020 but has already gained widespread popularity among users. Age. Avalanche implements connected networks and supports programming within the blockchain. This has created a broad ecosystem that has brought in many developers. Now, Avalanche is among the top twenty in capitalization ranking and the top thirty in trading volumes ranking. User activity. The network is based on three compatible blockchains. It also allows you to connect to other networks and AVM (Avalanche Virtual Machine). Technical capabilities. compatible with EVM Blockchain networks comparison Blockchain network Age, years User and developers’ activity Technological capability Ethereum 8+ +++ +++ Binance Smart Chain 3+ ++ +++ Solana 3+ +++ ++ Cosmos 4+ ++ ++ Avalanche 3+ ++ ++ Now you get what a blockchain is, how it differs from a centralized network, and know its main pros. And we need to talk about payments with cryptocurrency. We will use the most popular EVM-compliant blockchain wallet, Metamask. I'll tell you all you have to know about it — from creating a wallet to getting information about transactions and addresses. Stay tuned!

Revisiting Blockchain Basics Part 2: Differences in Blockchains

is a very popular concept you can hear about from many different sources. It seems to be a completely new technology, but it can already be applied in many areas of life. Now we will learn what blockchain is and how it differs from traditional networks and databases. Blockchain Typically, individuals rely on traditional banks or payment systems for currency exchange. These are centralized systems where information is stored and processed on dedicated servers. However, the use of centralized networks, which are managed and maintained by a single entity, limits control over one's funds and data. These systems often require identity verification and do not offer transparent, publicly accessible transactions, making them incompatible with your style of exchange. The blockchain network allows you to manage your funds all by yourself and make fast and cheap transfers from anywhere in the world where there is an Internet connection. Blockchain is decentralized since it is not under the control of a small group of people. Also, unlike traditional centralized services and payment systems, the blockchain network has no restrictions on the user’s location, age, or nationality. The data is processed with the collaboration of thousands of computers worldwide. Blockchain as a data structure In a traditional , information is stored in an independent order. It can be freely edited and deleted without damaging the integrity of other data. In the blockchain, the data is in blocks. The blocks are securely connected in a strict chain. The chain can be represented as a book, where new pages, in our case, blocks, are constantly added. All pages are numbered, bound together, and go in strict order, one after the other. database The data chain has two properties that ensure its reliability: It is impossible to take an already included block and rewrite it. Entries can only be made to a new block. Other community members check new entries. Therefore, scammers cannot write what they want into the book. Next, let’s look at how these features are implemented inside the blockchain. Blockchain Properties Property #1. What is recorded in the blockchain cannot be overwritten For the chain to be complete, each subsequent block must be connected to the previous one. This connection between blocks is similar to the connection between commits in GitHub. To understand this comparison, let’s look at how commits work. When we commit, GitHub gathers data about all the file states in the repository. From this data, it calculates the unique identifier of the commit. In this case, the commit ID is obtained not from the state of all files but from the changes that occurred from the previous commit. Thus, the hash is obtained from the following data: links to the previous commit. a list of changes that have taken place Thanks to this, the commits are tightly connected: the identifier of the new commit is obtained from the data containing the identifier of the previous one. This way, we can easily check the integrity of the data. To do this, you need to get a new identifier for the current commit and compare it with the original one. If the data has not changed, we will always get the same result. Hash: a link to the block The data in the blocks are linked by their hash identifier. A special data modification algorithm, the hashing function, is responsible for its creation. Hashing is a function that converts a string of data of any length into a string of data of a certain length. That is, any input data is changed according to a certain algorithm. As a result, we get a unique fixed-length data string. For example, each commit has a different number of modified lines of code, but the hash length of each commit is the same. If we hash one string and get a hash, then change just any one character in the original string and hash it again, we will get a completely different hash. This is called the “avalanche effect” of the hash. In this case, the same string will always give the same result. For example, we applied a hash function to the string “example” and got a hash. If we apply the function to the string “eXample”, we will get a completely different hash, but of the same length. hashing There is no algorithm that can perform the reverse hashing operation. Therefore, the hashing function is a one-way function. But you can find the source data from the hash. To do this, hash all possible variants of the source data until the result is the right hash. However, doing this requires a huge amount of computing power and time and is considered unfeasible. It is generally assumed that the hash is unique for almost every data set. In reality, several different datasets can have the same hash. But this is unlikely due to the huge number of hash options. If we change one block in the blockchain, we will immediately change all the blocks that follow the changed one. That is, we will get a new chain, which will be discarded by community members. This mechanism allows you to quickly and unambiguously verify the integrity and reliability of any link in the chain or even the entire chain. To do this, calculate the hashes of all blocks in the plot and compare the results with the hashes in the corresponding blocks. A chain of blocks that are strictly interconnected. Each block stores a certain number of records and the result of hashing the entire previous block. Each record that updates data in the blockchain is called a transaction. If you replace at least one symbol in a block, the hash of this block and all subsequent blocks will change. This guarantees the integrity of the chain. Here’s an example. Let’s take a few blocks already included in the chain and try to add a transaction to the block that was previously not in it. Since the block body has changed, therefore its hash has also changed. As a result, all hashes of subsequent blocks will change. Thanks to hashing, each transaction and each block are closely related to each other. If you try to change any data, all the hashes of subsequent blocks will completely change. The blockchain is decentralized, and you must provide your new changeable block to all community members. Each member will independently check your block and might disagree with the changes you have included. But we’ll talk about how it works further. Property #2. It is impossible to record false data False data cannot be written to the blockchain because new blocks and data are checked for authenticity. Let’s analyze how it works. Each participant has a copy of the chain and tools for verifying the validity, such as asymmetric and proof of work. Later we will look at what it is and how it works. When one of the participants wants to add a new block, the other participants check that the data of this block does not contradict the data from their copy and that all transfers from addresses were initialized by their owners. They make sure that the data in this new block is not falsified and can be added to the blockchain. At the same time, participants do not trust one data source separately. For a block to get into the blockchain, its authenticity must be confirmed by most participants. It is important to remember that only those blocks verified for authenticity and approved by most network participants get into the blockchain. cryptography Native currency and community members Like any database or information system, blockchain must ensure viability and security. Someone has to keep the entire history of transfers, check transactions, and add new blocks. You need a computer or server with special software to perform these tasks. It exchanges information with many of the same devices and forms a network. For example, a computer can become a network node that communicates with all other devices on the network. It can synchronize with the entire network and store the full history of the blockchain, starting from the first block. Such a computer is called a full node. Full — because it stores the entire history of transactions and blocks. There are different nodes with different functions. For example, storing the entire blockchain, adding new blocks, and verifying transactions. But to use the blockchain, installing a node at home is not necessary. For this purpose, a user node that performs minimal load is suitable. It is enough to work with personal transactions and addresses. It is called the light client and is usually presented as a wallet of digital assets. It is not entirely clear what the motivation of users is to install special software and spend resources and time to run a node. Besides enthusiasm… You can only have a light client, which does not require anything. But if you are an enthusiast, then… The blockchain has a mechanism for encouraging community members who ensure the operability and security of the blockchain network. For the blockchain to be decentralized, anyone has the opportunity to install a node and become a participant in the life of the network. To implement this, the source code contains a special mechanism for rewarding those who support the efficiency and security of the blockchain. Blockchains and their general characteristics There are a lot of blockchain networks. Among the most popular are Bitcoin, Ethereum, Binance Smart Chain, Solana, Cosmos, Polkadot, and Avalanche. They all differ in many ways but also have some common properties. First, let’s look at what all blockchains have in common: — if you have made a transfer or other action in the blockchain, it is impossible to simply cancel it. Irreversible transactions — users can view any block or transaction initialized in the public blockchain. Transparency — blockchain is a structure in which changes occur only with the consent of the majority of community members. No one can make decisions about anything alone. Decentralization — there are no conditions to become a community member or developer. Anyone can access the technology. Open access — each participant has a copy of the blockchain. They can exchange new data with other users, and only after verification by the majority will they be valid. Distribution — any data stored in the blockchain can be verified for authenticity. That is, you can easily find out if something has been changed. Verifiability These are the main blockchain properties, but not the only ones. There are many use cases for blockchain technology, and it is essential to look at the differences between networks in detail. In the next article, I will go into more detail about the differences between the different blockchains

Revisiting Blockchain Basics: Part 1—What is a Blockchain? 

First published in 1991, by , outlines the harrowing journey of taking a technology product from early adoption to mainstream success. Crossing the Chasm Marketing and Selling High-Tech Products to Mainstream Customers Dr Geoffrey A. Moore It remains exceedingly relevant for B2B startups, even in our current hyper-accelerated technological landscape. Below are five lessons from this iconic treatise that I believe every B2B startup should scrutinize anew. The Chasm is Not a Metaphor; It’s a Graveyard If you think the chasm is an overblown metaphor, you’re deluding yourself. This is where startups go to die, and it’s astonishing how many promising B2B startups fall into oblivion here. The chasm occurs post-innovators and early adopters, right before the early majority stage in the technology adoption lifecycle. It’s a trap because product/market fit among early adopters is not automatically replicable for the broader market. Early adopters are forgiving. They can see the future and are willing to deal with incomplete or buggy products because they see the potential. The early majority does not share this magnanimity. They want solutions, not potential. They require robustness, full-feature sets, and most importantly, minimal adoption risk. This is why you should never mistake initial success with early adopters as a sign that you’ve made it. You need a separate, dedicated strategy to cross the chasm, focusing on a singular compelling reason for mainstream customers to adopt your product. Establishing this will give you the focal point around which you can evolve and refine your offering, thus enabling you to satisfy the stringent demands of the early majority. Whole Product Concept: No Product is an Island Every B2B entrepreneur needs to understand that no product exists in isolation. Early adopters might be willing to piece together the ‘whole product’ by integrating your solution with other services, but the mainstream market won’t. They want a complete, end-to-end solution that solves their problem out-of-the-box. The Whole Product Concept is not just the generic product you've made; it’s the complete package required to achieve the desired result. It includes customer support, complementary products, system integration, and even the know-how to use it effectively. By fulfilling the whole product, you don't just sell a product; you sell a promise of a solved problem. The best way to tackle this is through strategic partnerships, API integrations, and robust customer support systems, thereby creating an ecosystem around your product. You have to make the customer feel that everything has been thought of, leaving no rough edges or unanswered questions. Niche Marketing is Not a Luxury; It’s a Necessity The chasm-crossing strategy that most resonates with B2B startups is the "bowling pin" strategy. The principle here is to first dominate a niche market and then use that as a springboard to enter adjacent markets. This is antithetical to the 'big bang' approach that tries to capture as much market share as possible, as quickly as possible. Instead, focus on a narrow target where you can become the indisputable leader. This is crucial for two reasons: First, becoming a leader in a niche market allows you to make mistakes and learn without fatal consequences. Second, it creates referenceable clients that serve as social proof when you enter adjacent markets. Your reputation precedes you, and the next market is more likely to trust you because you've already proven yourself elsewhere. The Sales Process: High Touch Over High Tech Many B2B startups invest in highly scalable, low-touch sales processes. While this works for early adopters, crossing the chasm demands a high-touch approach. The mainstream market doesn’t want to self-serve; they want to be guided. Here, a charismatic sales team that can hand-hold potential clients becomes invaluable. These are not just salespeople; they are consultants who understand the client’s domain, needs, and apprehensions. The consultative sales approach works because you're no longer selling a product; you're selling a vision of how their business will look after implementing your solution. You must train your sales team to articulate this vision convincingly, providing prospects with a tangible roadmap of how you'll solve their issues and bring value. Crossing the Chasm is an Ongoing Process Crossing the chasm is not a one-time event; it’s a constant process. The moment you think you’ve crossed it, there's another one staring you in the face as market dynamics change, competitors emerge, or technologies evolve. The chasm keeps reappearing, and your strategies must evolve with it. The success factors that helped you cross the first chasm may not work for the second or third. Thus, you need to maintain a culture of constant learning and adaptation. Conclusion This book represents an adaptation of the innovation adoption model known as the " " theory, originally formulated by the eminent scholar, . Geoffrey Moore’s Crossing the Chasm should not be a book that you read once and put back on the shelf. It should be a manual that you consult regularly, making necessary adjustments to your strategy as you move forward. Diffusion of Innovations Everett Rogers While the tech landscape has evolved dramatically since the book was first published, the core principles remain as valid as ever for B2B startups looking to make a lasting impact.

5 Lessons From Crossing The Chasm That Every B2B Startup Needs to Revisit

প্রতিটি B2B স্টার্টআপকে পুনরায় দেখার প্রয়োজন যে খাদ অতিক্রম করা থেকে 5টি পাঠ

5 Lehren aus der Überwindung der Kluft, die jedes B2B-Startup noch einmal überdenken muss

5 lecciones aprendidas de cruzar el abismo que toda startup B2B debe revisar

5 leçons tirées du franchissement du gouffre que chaque startup B2B doit revisiter

खाई को पार करने के 5 सबक जिन पर हर बी2बी स्टार्टअप को दोबारा विचार करने की जरूरत है

すべての B2B スタートアップが再検討する必要があるキャズムを越えることから得た 5 つの教訓

모든 B2B 스타트업이 재검토해야 하는 캐즘 건너기에서 얻을 수 있는 5가지 교훈

5 lições de como cruzar o abismo que toda startup B2B precisa revisitar

5 уроков преодоления пропасти, к которым должен вернуться каждый B2B-стартап

Her B2B Startup'ın Yeniden Ziyaret Etmesi Gereken Uçurumu Aşmaya İlişkin 5 Ders

5 bài học từ việc vượt qua vực thẳm mà mọi công ty khởi nghiệp B2B cần xem lại

每个 B2B 初创公司都需要重新审视跨越鸿沟的 5 个教训

As a content creator, I’ve always been fascinated with Ads. And growing up in India during the 90s has given me a treasure trove of these to cherish. I can spend hours looping the same minute-long video and learn something new every single time. These Ads were brilliantly crafted and, dare I say, are the perfect examples of how short-form content should be written and executed. So, if you’re a writer who hit the wall and is looking for some inspiration, maybe it’s time to revisit some classics and reignite the flame. Here are my top five Indian ads that every writer must know. So let’s dive straight in! #1 Melody https://www.youtube.com/watch?v=EdJSuddDfl4&t=4s&embedable=true “Melody itni chocolatee kyu hain?” “Melody khao, khud jaan jao.” If you grew up in India during the 90s, you’ve probably seen the immensely catchy Melody Ad. It’s been decades since the Ad first launched, yet these two simple lines still stick with us. And the reason? It’s quirky humor. Through Melody’s ads, this question is posed at the most surprising of times — during a PT drill, in a school classroom, or on the red carpet! And it is the sheer absurdity of the timing that sticks with the audience while also communicating the product’s essence. This brilliantly tempts the chocolate and encourages viewers to try the toffee themselves. Lesson for you The rule of 3s! This is a classic trick used in the Ad, where the writer has used three different scenarios to make a couple of dialogues stick. This is also what Apple does. copywriting “iPhone 14 Plus has 21% more screen, making it supersized but surprisingly lightweight.” They use three phrases to describe each feature, so there’s a sense of rhythm to the copy. If you want to persuade your audience, try the rule of three next time. #2 Amul https://www.youtube.com/watch?v=zIQTwukIDyg&embedable=true This homegrown brand’s jingle is one of those tunes that just doesn’t leave your head even after a week! You could be working at your desk or preparing dinner, and you would catch yourself humming it. And this is what makes the jingle such an essential reason for this ad’s success. It solidifies the brand’s image with the audience and makes it a familiar name. Lesson for you Through the ad, the writer has incorporated almost every viewer the brand targets under the single banner of “India”, which makes it relatable to the masses. By invoking a sense of belonging, the writer appealed to the emotion of the viewers and hence, made the Ad memorable. #3 Coca Cola https://www.youtube.com/watch?v=inF-9sFATE8&embedable=true When you read the newspaper or scroll through your feed, there are umpteen reminders of what’s going wrong with the world. Amid this, launched a heart-warming ad spreading positivity and hope. Coca-Cola During the ad, Coca-Cola shares a positive practice for every bad news. And by doing this, it asks the viewers to concentrate on the good and live a better life. Just like Amul’s ad, the ad’s song also became an anthem with the viewers. ‘umeedo waali dhoop’ Lesson for you As a writer, consider working backwards. Where most of the time, we’ll work from the main objective (product launch, brand awareness etc.). Maybe keeping it aside for a moment could help you brainstorm. Take a look at the bigger picture and knit it back to your objective. You could find some interesting results, such as this timeless Coca-Cola ad. #4 Dove https://www.youtube.com/watch?v=E3kWzRB6Yy8&t=3s&embedable=true Where most personal care brands concentrate on how you’re lacking, this company took a different approach. Dove’s #StopTheBeautyTest Ad questioned Indian society’s gender inequality and what it means to be a young woman in India. Not only that, but it also worked with real women instead of paid actors to further the impact of its ad campaign. Through this ad, Dove implores society to look beyond size, color, and physical looks to appreciate their unique personalities and beings. Growing up in India, we all know this is very much the ground reality, but by fashioning an ad around this problem, Dove calls out people to be better and do better. Lesson for you Whatever content you’re working on, make sure to find a common pain point you can use in your brand storytelling. This will help you better iterate your key messaging. #5 WhatsApp https://www.youtube.com/watch?v=YMeDtdWdGpU&t=1s&embedable=true launched its #ItsBetweenYou — ad in 2020 to clarify its privacy policy. And to make this point, the ad shows us a relationship between two sisters. Both live different lives but remain connected through WhatsApp. WhatsApp Apna Sa Lagta Hain Apno Ke Bich The ad covers a video call where one sister virtually holds the other one’s hand through a haircut. The writers took a simple, mundane idea and effectively communicated WhatsApp’s respect for privacy and struck a chord with the general public. Lesson for you Ads like these are great examples of how writers can take moments from their personal lives and enhance them into something bigger. Ordinary everyday activities can sometimes be a great source of inspiration, especially when trying to connect with the audience. Wrapping Up From tickling our funny bones with some wacky humor to showing the mirror to society, Indian ads have played an important role in all our lives. And the credit for this impact goes to the writers behind them. Coming up with clever, interesting, and engaging ad plots is a task, no doubt. But I hope revisiting these iconic ads helps you escape the frustrating writer’s block. So, put pen to paper and begin again! Connect with me on . LinkedIn Read the latest from my team on content writing, editing, and marketing on our blog . here

Revisiting Iconic Indian TV Ads: Lessons in Brand Storytelling

As I write this, my interview on so if you subscribe to my blog or you might be able to catch it live. If not, the recording should appear right here: DevCentral hasn't started yet follow me on socials https://www.youtube.com/watch?v=CFL--dAX3FQ&embedable=true Either way, this isn't the first time I wrote about or talked about logging and the common pitfalls we see when logging in production or debugging. I covered this extensively in . I also covering these ideas. But my ideas somewhat evolved around some of the concepts I discussed. the old blog did a video In my original post, I was a bit harsh on AOP logging. My . I think the main problem with AOP logging is that it is often used as a sledgehammer when debugging. Another problem is leaving it on in production. But when it is used surgically it can uncover problems that would be much harder to uncover in any other way. opinion on this has evolved The main message of the original post is still the most important part: we need company-wide standardization of logging. Without that our code review process is useless. Logging is Precognitive Debugging In my , I spent quite a bit of time talking about logging. First, it's important to understand that logging is very different from print debugging. When you use print statements for debugging they are ephemeral, in a bad way. You should . But more importantly, logging is about the bug that hasn't happened yet. Print debugging is about the bug that is already there. debugging book use tracepoints They are nothing alike. A log should describe our system. When we read a log we can often see the code quality without inspecting a single line of source. Uniformity, consistency, conciseness, order and value. These are all properties of a good log which is the output of a well-oiled machine. Logging is a user interface designed for your field-work engineering team. If it is written badly, they won't be as effective when carrying out their jobs. Your product will suffer. Logging will pay back dividends with early detection of problems and simpler debugging of tests. But to do that we need to give a lot of thought to the core process. Which variables should we log? Where should we place the log? How many logs should we have per block of code? These are all questions that we can answer for the general case. I answered them all in the original blog. But that isn't an authoritative answer, it's an opinion. We need to enforce standards around this. Let me qualify that last statement. We need standards. I love that we can measure coverage and then standardize the amount of test coverage. I think that can be very helpful. However, standardizing a fixed number like test coverage without flexibility leads to terrible code that's only designed to reach the unattainable metric. We need flexibility, and a baseline to align against. Not rigid rules. Costs, Energy and Performance While managers might look at the financial bottom line for overlogging. To me, the more significant aspect is the environmental impact. This has a cascading effect throughout our industry. More logging and ingestion require more computing services. If big companies take up more computing resources it drives up pricing for all of us due to scarcity. We can do our part for the environment, the company's bottom line and our industry. There are many strategies we can take to reduce logging to a reasonable minimum. Setting the log levels intelligently and consistently. Monitoring our logs regularly, etc. It is often that a request that would have been served only by the cache is forced to perform an IO operation to satisfy logging. The impact on overall system performance can be tremendous yet hard to notice. If our production and dev environments differ these subtle differences can further mask such inconsistencies. Join Us I hope to see your questions in the live stream or here before/after the fact. When I gave my logging talk before, I got amazing and highly engaging feedback from the audience. Either way, the recording should be there after the fact so check it out. Logging got a lot of attention as a pillar of observability. However, the developer perspective of logging seems to have fallen to the wayside and doesn't enjoy the same level of attention. Let's change that. Also published . here

Revisiting Logging Best Practices

Satoshi Nakamoto, the pseudonymous creator of the world's first decentralized cryptocurrency, , had a vision that went far beyond just creating a new digital currency. His vision was to create a financial system that was truly decentralized, secure, and independent of any central authority. Bitcoin Bitcoin was created as a response to the , which exposed the flaws of the traditional banking system. Satoshi saw that the centralized nature of the banking system made it vulnerable to manipulation, corruption, and systemic risk. He realized that the traditional financial system was in need of a radical change, and that's when he came up with the idea of creating a decentralized digital currency. 2008 financial crisis Satoshi's vision for Bitcoin was to create a financial system that was completely transparent, secure, and tamper-proof. He envisioned a world where transactions would be conducted directly between two parties, without the need for intermediaries like banks or payment processors. He wanted to create a currency that was truly independent, one that couldn't be manipulated by any government, central authority, or corporation. Satoshi also wanted to create a more level playing field for everyone. In the traditional financial system, access to financial services and products is often limited to those with a high net worth or good credit. This seemed unfair to Satoshi, and he wanted to create a financial system that was accessible to everyone, regardless of their wealth or credit history. In addition to creating a decentralized financial system, Satoshi also had a vision for the technology behind Bitcoin. He realized that the blockchain /DLT (distributed ledger technology) that powers Bitcoin could be used for much more than just creating a new currency. Satoshi's decentralized ethos has inspired countless people to get involved not only in the Bitcoin project but the space in its entirety. Today, the Bitcoin network has grown to become one of the largest and most secure networks in the world, with millions of users and billions of dollars in value. On average, Bitcoin settles 600k transactions per day. Bitcoin's all-time high for daily settlement was . $11.6 Billion dollars Satoshi Nakamoto has been AWOL since 2010 but his vision continues to be realized and built upon daily. The first few "shadowy super coders" indeed created a financial system that was truly decentralized, secure, and accessible to everyone. His vision has inspired the growth of a whole new industry and has given rise to a whole new way of thinking about money and finance. His vision for Bitcoin continues to be a source of inspiration for those who believe in the potential of decentralized technology to create a better world. New applications are being built as I type this, which will make onboarding to decentralized applications much simpler.

Revisiting Satoshi's Vision

Miriam Revisited

If you are looking for a magic wand to make crypto money from thin air, then this post is not for you. I am not gonna share any moon math or crypto moonshot projects. But instead I am going to share few concepts from a notable personal finance book called “The Richest Man in Bablyon” so you can apply its learning in your pursuit of generating wealth through crypto. The book lists for generating wealth: 7 principles Start thy purse to fattening: save money. Control thy expenditures: don't spend more than you need. Make thy gold multiply: invest wisely. Guard thy treasures from loss: avoid investments that sound too good to be true. Make of thy dwelling a profitable investment: own your home. Ensure a future income: protect yourself with life insurance. Improve thy ability to earn: strive to become wiser and more knowledgeable. Subscribe here for all the updates like this! Here’s how you should apply those principles when it comes to crypto Make thy gold multiply: invest wisely. Invest only in projects that makes sense to and not only to your YouTube influencers. You cannot be making money in crypto if you are not left with money to buy the dips, DCA or invest otherwise. YOU Guard thy treasures from loss: avoid investments that sound too good to be true. Don’t chase higher APY with money you can’t afford to lose. If you want to chase higher APY, do so with only 1% or 0.5% of your total portfolio. Ensure a future income: protect yourself with life insurance. Similar to the previous point, don’t invest your entire life savings on crypto to earn a quick buck, particularly when it comes to new and non-established tokens. Only allocate those funds to crypto that you’re comfortable living without. Improve thy ability to earn: strive to become wiser and more knowledgeable. Invest in yourself: the better your skillset, the better the opportunities — be it crypto or otherwise. Improve your knowledge and craft, and increase your ability to earn more. Start thy purse to fattening: save money. Always keep some money in reserves; you will get plenty of opportunities to DCA your way into a profitable crypto portfolio or to buy low and sell high. Over the period of time, your money will accrue faster than you can imagine. Other general tips to safe guard your crypto portfolio Diversify your portfolio, including stable coins. Self custody your crypto and use hardware wallets. Use two factor authentication wherever possible (avoid SMS authentication and use authenticator if possible). Don’t use public WIFI to check your wallets or log in to exchanges. Educate yourself about projects you’re investing in; crypto markets are dynamic so stay informed on day to day events. I generally follow and have their installed, they provide good and timely updates from variety of news channels. Thinkmoon extension Validate your influencers and tweets, do your own research about the projects you are investing in, its your money and not theirs Compounding interest is your best friend and cash flow is the king Some food for thoughts from the book- Gold slips away from the person who invests gold into purposes through which they are not familiar. Gold flees the person who tries to force it into impossible earnings. Stick with the plan. Money accrues surprisingly quickly and debts are gone fast with discipline and consistency. Thats all, Savvy? for all the cool zk-tech and web3 stuff! Subscribe here References: https://hackernoon.com/4-must-have-crypto-browser-extensions https://positiveaffirmations.one/richest-man-in-babylon-summary/ https://chrome.google.com/webstore/detail/think-moon/baaaoogjikmghkiefbpggookljakdfpb

Revisiting Making Money in Crypto

Revisando Ganar Dinero en Crypto

Product-Market Fit Tips for Startups: Insights From 20 Experts

Pull Request (PR) stacking is another name for stacked diffs, a concept that has existed for several years. We’ll discuss what it is, the tools we can use, and where PR stacking may benefit us. What Is PR Stacking? In traditional Pull Requests (PRs), the developer makes all the changes in one or more commits and then opens the PR to merge the change into the main branch. The approach in PR stacking instead is to separate the functionality to be delivered into pieces. Let’s say we’re working on a new POST request API. 1️⃣ The first PR could be the happy path where what we submit works okay, and that would be opened against the main branch. 2️⃣ The next piece of work could be the error scenario when the payload is too large, and that PR would be created to merge into the branch from the first PR. 3️⃣ Another PR could be added for the scenario when the data being sent is not in a format the API can understand and would be stacked on top of the second PR. A PR stack moves us away from having to deliver our significant change in one go with a long review. Instead, we break it down into pieces so they can be reviewed quickly. What Are the Benefits of PR Stacking? easier to review: the smaller changes make it easier for the reviewer to understand what is happening. They get to know the code better.
catch problems: with smaller PRs, it is easier to see when a bug is introduced both for the author and reviewer.
less waiting: the reviews are quicker since they are simple, and developers can continue developing their changes while waiting for reviews.
improved communication: using stacked PRs means there is a lot more communication about the code. This is very useful when multiple developers are working together on one feature.
clearer code changes: since the PRs are isolated to a single change, it’s clearer what each change does, and if coupled with good PR descriptions, it can educate the reviewers. What Tool Should I Use for PR Stacking? We could perform PR stacking with the git command, but it doesn’t offer any of the features of a dedicated tool, such as: applying the changes from the current PR to all of the PRs that are stacked on it by doing an automatic rebase


annotating our PR with the details of the entire PR stack


rebasing and restacking PRs when we merge or close a downstream PR Using PR Stacking in a Community Project A challenge in community (open source) projects is that the dependent libraries can have new security vulnerabilities, or coding practices that change a lot. Robert C Martin has a quote: “Always leave the code you’re editing a little better than you found it”. When we want to update a feature in the project, we want to leave it better than when we came to it. We could perform refactoring in the first PR to clarify the code rather than confusing the reviewer with what is added and refactored. In the next PR, we could update the tests and functionality. Another PR could be stacked upon that to fix up library dependencies that are out of date. The benefit to the reviewers is that they can see the changes and why with each PR, an essential aspect in community projects that helps with communication. This leads to faster reviews and the opportunity to continue building the functionality while feedback is gathered. What Are the Next Steps? Now that we’ve learned about PR stacking, experiment with the different PR stacking tools. Look for opportunities to use the tools in the next feature or open-source submission. As with any experiment, please note the impact that PR stacking and the tool have on our work. The lead image was generated using Microsoft Designer. Pull Request (PR) stacking is another name for stacked diffs, a concept that has existed for several years. We’ll discuss what it is, the tools we can use, and where PR stacking may benefit us. What Is PR Stacking? In traditional Pull Requests (PRs), the developer makes all the changes in one or more commits and then opens the PR to merge the change into the main branch. The approach in PR stacking instead is to separate the functionality to be delivered into pieces. Let’s say we’re working on a new POST request API. 1️⃣ The first PR could be the happy path where what we submit works okay, and that would be opened against the main branch. 2️⃣ The next piece of work could be the error scenario when the payload is too large, and that PR would be created to merge into the branch from the first PR. 3️⃣ Another PR could be added for the scenario when the data being sent is not in a format the API can understand and would be stacked on top of the second PR. A PR stack moves us away from having to deliver our significant change in one go with a long review. Instead, we break it down into pieces so they can be reviewed quickly. What Are the Benefits of PR Stacking? easier to review: the smaller changes make it easier for the reviewer to understand what is happening. They get to know the code better. catch problems: with smaller PRs, it is easier to see when a bug is introduced both for the author and reviewer. less waiting: the reviews are quicker since they are simple, and developers can continue developing their changes while waiting for reviews. improved communication: using stacked PRs means there is a lot more communication about the code. This is very useful when multiple developers are working together on one feature. clearer code changes: since the PRs are isolated to a single change, it’s clearer what each change does, and if coupled with good PR descriptions, it can educate the reviewers. easier to review : the smaller changes make it easier for the reviewer to understand what is happening. They get to know the code better. easier to review catch problems : with smaller PRs, it is easier to see when a bug is introduced both for the author and reviewer. catch problems less waiting : the reviews are quicker since they are simple, and developers can continue developing their changes while waiting for reviews. less waiting improved communication : using stacked PRs means there is a lot more communication about the code. This is very useful when multiple developers are working together on one feature. improved communication clearer code changes : since the PRs are isolated to a single change, it’s clearer what each change does, and if coupled with good PR descriptions, it can educate the reviewers. clearer code changes What Tool Should I Use for PR Stacking? We could perform PR stacking with the git command, but it doesn’t offer any of the features of a dedicated tool, such as: applying the changes from the current PR to all of the PRs that are stacked on it by doing an automatic rebase annotating our PR with the details of the entire PR stack rebasing and restacking PRs when we merge or close a downstream PR applying the changes from the current PR to all of the PRs that are stacked on it by doing an automatic rebase applying the changes from the current PR to all of the PRs that are stacked on it by doing an automatic rebase annotating our PR with the details of the entire PR stack annotating our PR with the details of the entire PR stack rebasing and restacking PRs when we merge or close a downstream PR rebasing and restacking PRs when we merge or close a downstream PR Using PR Stacking in a Community Project A challenge in community (open source) projects is that the dependent libraries can have new security vulnerabilities, or coding practices that change a lot. Robert C Martin has a quote: “Always leave the code you’re editing a little better than you found it”. When we want to update a feature in the project, we want to leave it better than when we came to it. We could perform refactoring in the first PR to clarify the code rather than confusing the reviewer with what is added and refactored. In the next PR, we could update the tests and functionality. Another PR could be stacked upon that to fix up library dependencies that are out of date. Robert C Martin Robert C Martin The benefit to the reviewers is that they can see the changes and why with each PR, an essential aspect in community projects that helps with communication. This leads to faster reviews and the opportunity to continue building the functionality while feedback is gathered. What Are the Next Steps? Now that we’ve learned about PR stacking, experiment with the different PR stacking tools. Look for opportunities to use the tools in the next feature or open-source submission. As with any experiment, please note the impact that PR stacking and the tool have on our work. The lead image was generated using Microsoft Designer. The lead image was generated using Microsoft Designer . Microsoft Designer Microsoft Designer

A Beginner's Guide to Pull Request Stacking

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 6 Conclusion Understanding how recommender systems shape our behaviour is essential to avoid manipulation. In this paper, we investigated the Amazon recommender system concerning whether it has made it harder to change preferences. By analysing the Barrier-to-Exit (Rakova & Chowdhury, 2019) of more than 50,000 users, we found a highly significant growth in Barrier-to-Exit over time, which indicates that it has indeed become harder to change preferences for the analysed users. However, sampling bias induced by the calculation of Barrier-to-Exit makes it difficult to draw conclusions about the general population of Amazon customers. This highlights the dilemma of portability in measuring socio-technical systems (Selbst et al., 2019): accurately evaluating a concept like ”changing preferences” requires adapting to the context of the system, which makes it more difficult to generalise (and compare) to other systems. Comparing recommender systems is necessary for ensuring that these respect human autonomy (Varshney, 2020) and live up to new regulations such as the EU AI Act (Kop, 2021). Further work, should aim to create auditing procedures and metrics that allow third parties to measure potential preference manipulation in a way that fits within the context of the industry and allows for comparisons between different systems. This will help assess the pressures of Surveillance Capitalism (Zuboff, 2019) on human autonomy. References Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59 (4), 390–412. 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Internet: A change agent an overview of internet penetration & growth across the world. International Journal of Information Dissemination and Technology, 7 (2), 83–91. Retrieved 2023-01-11, from https://www.proquest.com/docview/1920419032/abstract/14B253C0DD6A4277PQ/1 (Num Pages: 83-91 Place: Ambala, India Publisher: Maharishi Markandehwar University) Papakyriakopoulos, O., Serrano, J. C. M., & Hegelich, S. (2020). Political communication on social media: A tale of hyperactive users and bias in recommender systems. Online Social Networks and Media, 15 , 100058. (Publisher: Elsevier) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12 , 2825–2830. (Publisher: JMLR. org) Poole, M. A., & O’Farrell, P. N. (1971). The assumptions of the linear regression model. Transactions of the Institute of British Geographers, 145–158. (Publisher: JSTOR) Popper, K. R. (1970). Normal science and its dangers. Cambridge University Press Cambridge. Rakova, B., & Chowdhury, R. (2019, September). Human self-determination within algorithmic sociotechnical systems. arXiv. Retrieved 2022-09-07, from http://arxiv.org/abs/1909.06713 (arXiv:1909.06713 [cs]) Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11 (4), 193. (Publisher: Multidisciplinary Digital Publishing Institute) Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021). Enhancing trust in AI through industry self-governance. Journal of the American Medical Informatics Association, 28 (7), 1582–1590. (Publisher: Oxford University Press) Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry, 22 , 4349–4357. Satterthwaite, F. E. (1946, December). An Approximate Distribution of Estimates of Variance Components. Biometrics Bulletin, 2 (6), 110. Retrieved 2023-01-11, from https://www.jstor.org/stable/10.2307/3002019 ?origin=crossref doi: 10.2307/3002019 Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., . . . Araya-Ajoy, Y. G. (2020, September). Robustness of linear mixed-effects models to violations of distributional assumptions. Methods in Ecology and Evolution, 11 (9), 1141–1152. Retrieved 2022-12-27, from https://onlinelibrary.wiley.com/ doi/10.1111/2041-210X.13434 doi: 10.1111/2041-210X.13434 Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). Atlanta GA USA: ACM. Retrieved 2022-09-07, from https://dl.acm.org/doi/10.1145/3287560.3287598 doi: 10.1145/3287560.3287598 Smith, B., & Linden, G. (2017, May). Two Decades of Recommender Systems at Amazon.com. IEEE Internet Computing, 21 (3), 12–18. Retrieved 2022-12-20, from http://ieeexplore.ieee.org/document/7927889/ doi: 10.1109/MIC.2017.72 Timmers, H., & Wagenaar, W. A. (1977, November). Inverse statistics and misperception of exponential growth. Perception & Psychophysics, 21 (6), 558–562. Retrieved 2023-01-10, from https://doi.org/10.3758/BF03198737 doi: 10.3758/BF03198737 Van Rossum, G. (2007). Python Programming language. In USENIX annual technical conference (Vol. 41, pp. 1–36). (Issue: 1) Villadsen, A. R., & Wulff, J. N. (2021, July). Statistical Myths About Log-Transformed Dependent Variables and How to Better Estimate Exponential Models. British Journal of Management, 32 (3), 779–796. Retrieved 2023-01-12, from https://onlinelibrary.wiley.com/doi/10.1111/1467-8551.12431 doi: 10.1111/1467-8551.12431 Wells, J. R., Danskin, G., & Ellsworth, G. (2018). Amazon. com, 2018. Harvard Business School Case Study(716-402). Wood, S. N., Pya, N., & S¨afken, B. (2016, October). Smoothing Parameter and Model Selection for General Smooth Models. Journal of the American Statistical Association, 111 (516), 1548–1563. Retrieved 2023- 01-12, from https://doi.org/10.1080/01621459.2016.1180986 (Publisher: Taylor & Francis eprint: https://doi.org/10.1080/01621459.2016.1180986) doi: 10.1080/01621459.2016.1180986 Zhu, F., & Liu, Q. (2018). Competing with complementors: An empirical look at Amazon.com. Strategic Management Journal, 39 (10), 2618–2642. Retrieved 2023-01-11, from https://onlinelibrary.wiley.com/doi/ abs/10.1002/smj.2932 ( eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.2932) doi: 10.1002/ smj.2932 Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power: Barack Obama’s Books of 2019. Profile Books. This paper is under CC 4.0 license. available on arxiv

Ensuring Ethical AI: Lessons from Amazon's Barrier-to-Exit Analysis

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References V. EXPERIMENTAL RESULTS A. Comparison of CUP against Baselines • LLM-Rec also performs poorly. Solely relying on the LLM’s latent knowledge about books is not sufficient when coping with long-tail items that appear sparsely in the LLM’s training data. Popularity and position bias [31] further aggravate this adverse effect. Thus, to address RQ1 (see Section I-B), merely and fully resorting to LLMs alone is not a viable solution. We also ran extended CUP configurations where precomputed CF vectors were incorporated (see Section III-B). These achieved only very minor gains in some of the settings, and there was even a detrimental effect on some cases. We omit these hybrid methods here. We will revisit them in Subsection V-C for sensitivity studies on data density. Another observation is that the weighted training almost always improves performance. Therefore, the following subsections focus on results with weighted training samples. B. Comparison of CUP Configurations • On the GR-1K-rich data, the overall performance is higher than for AM across all configurations. This can be attributed to the fact that GR has longer reviews, and these texts tend to be more informative than the ones in the AM data (which sometimes refer to packaging, shipment and other non-content aspects). C. Influence of Interaction Density The slices AM-1K-rich and GR-1K-rich are constructed with a text-centric stress-test in mind. Both are extremely sparse in terms of user-item interactions. To study the influence of sparseness in isolation, we created two larger samples of both datasets, one still sparse by design and the other denser in terms of users sharing items. We refer to these as AM-10Ksparse and AM-10K-dense, and analogously for GR. All these samples cover 10K users: 10x more users than our previous text-rich slices, to allow more potential for learning from interactions. Table I in Section IV-B shows statistics. The datasets are constructed as follows. To ensure connectivity in the interaction graphs, we first select 500 users and sample 2000 books connected to these users, both uniformly at random. This is the seed for constructing two variants of data, based on the cumulative item degrees of users, that is, the sum of the #users per book for all books that the user has in her collection: • 10K-dense: We randomly select 10K (minus the initial 500) users from all users in proportion to the users’ cumulative item degrees. This favors users with many or popular books. • 10K-sparse: We select users inversely proportional to their cumulative item degrees, thus favoring sparse users. In this sensitivity study, we focus on comparing CF (based on matrix factorization) against our default CUPtext configuration (with idf-selected sentences) and the hybrid combination CUPtext +CF. Tables V and VI show the results for the AM10K data and GR-10K data, comparing the sparse vs. the dense variants with search-based evaluation (corresponding results with standard evaluation are on the project web page). Key insights are: • As the 10K-sparse data is already much denser than the stress-test 1K-rich slices, CF can achieve decent results on the sparse data. Especially in search-based mode, CF is almost on par with CUP. Note that these gains come from the seen items alone, as CF is bound to fail on unseen items. • CUPtext is still the clear winner on the sparse variant. The hybrid configuration, with text and CF vectors combined, is inferior to learning from text alone. • For the 10K-dense data, CF alone performs well, as this is the traditional regime for which CF has been invented. An interesting point arises for the search-based mode and with dense data. Here, the negative test points are closer to the positive sample (after the BM25 search), and pose higher difficulty for text cues alone to discriminate them. Thus, CF becomes more competitive. On GR, hybrid CUP+CF performs best. D. Efficiency of CUP Two architectural choices make CUP efficient: • input length restricted to 128 tokens, and • fine-tuning only the last layer of BERT and the FFN layers. For more analysis, we measured the training time and resulting NDCG on the GR-1K-rich data, comparing different input size budgets and choices of which parameters are tuned and which ones are kept frozen. Figure 2 shows the results. We observe that the 128-token configuration has the lowest training cost: significantly less time per epoch than the other variants and fast convergence (reaching its best NDCG already after 15 epochs in ca. 3000 seconds). The 256- and 512-token models eventually reach higher NDCG, but only by a small margin and after much longer training time. This confirms that concise user profiles, with a small token budget, are the best approach in terms of benefit/cost ratio. As for configurations with more or less tunable parameters, we observe that the variant with frozen BERT takes much longer to converge and is inferior to the preferred CUP method even after more than 50 epochs. The other extreme, allowing all of BERT parameters to be altered together with the FFN parameters, performs best after enough training epochs, better than the CUP configuration that tunes only the last BERT layer. However, it takes almost twice as much time per epoch. So again, from the benefit/cost perspective, our design choice hits a sweet spot in the spectrum of model configurations. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References V. EXPERIMENTAL RESULTS A. Comparison of CUP against Baselines A. Comparison of CUP against Baselines • LLM-Rec also performs poorly. Solely relying on the LLM’s latent knowledge about books is not sufficient when coping with long-tail items that appear sparsely in the LLM’s training data. Popularity and position bias [31] further aggravate this adverse effect. Thus, to address RQ1 (see Section I-B), merely and fully resorting to LLMs alone is not a viable solution. We also ran extended CUP configurations where precomputed CF vectors were incorporated (see Section III-B). These achieved only very minor gains in some of the settings, and there was even a detrimental effect on some cases. We omit these hybrid methods here. We will revisit them in Subsection V-C for sensitivity studies on data density. Another observation is that the weighted training almost always improves performance. Therefore, the following subsections focus on results with weighted training samples. B. Comparison of CUP Configurations • On the GR-1K-rich data, the overall performance is higher than for AM across all configurations. This can be attributed to the fact that GR has longer reviews, and these texts tend to be more informative than the ones in the AM data (which sometimes refer to packaging, shipment and other non-content aspects). C. Influence of Interaction Density C. Influence of Interaction Density The slices AM-1K-rich and GR-1K-rich are constructed with a text-centric stress-test in mind. Both are extremely sparse in terms of user-item interactions. To study the influence of sparseness in isolation, we created two larger samples of both datasets, one still sparse by design and the other denser in terms of users sharing items. We refer to these as AM-10Ksparse and AM-10K-dense, and analogously for GR. All these samples cover 10K users: 10x more users than our previous text-rich slices, to allow more potential for learning from interactions. Table I in Section IV-B shows statistics. The datasets are constructed as follows. To ensure connectivity in the interaction graphs, we first select 500 users and sample 2000 books connected to these users, both uniformly at random. This is the seed for constructing two variants of data, based on the cumulative item degrees of users, that is, the sum of the #users per book for all books that the user has in her collection: • 10K-dense: We randomly select 10K (minus the initial 500) users from all users in proportion to the users’ cumulative item degrees. This favors users with many or popular books. • 10K-sparse: We select users inversely proportional to their cumulative item degrees, thus favoring sparse users. In this sensitivity study, we focus on comparing CF (based on matrix factorization) against our default CUPtext configuration (with idf-selected sentences) and the hybrid combination CUPtext +CF. Tables V and VI show the results for the AM10K data and GR-10K data, comparing the sparse vs. the dense variants with search-based evaluation (corresponding results with standard evaluation are on the project web page). Key insights are: • As the 10K-sparse data is already much denser than the stress-test 1K-rich slices, CF can achieve decent results on the sparse data. Especially in search-based mode, CF is almost on par with CUP. Note that these gains come from the seen items alone, as CF is bound to fail on unseen items. • CUPtext is still the clear winner on the sparse variant. The hybrid configuration, with text and CF vectors combined, is inferior to learning from text alone. • For the 10K-dense data, CF alone performs well, as this is the traditional regime for which CF has been invented. An interesting point arises for the search-based mode and with dense data. Here, the negative test points are closer to the positive sample (after the BM25 search), and pose higher difficulty for text cues alone to discriminate them. Thus, CF becomes more competitive. On GR, hybrid CUP+CF performs best. D. Efficiency of CUP D. Efficiency of CUP Two architectural choices make CUP efficient: • input length restricted to 128 tokens, and • fine-tuning only the last layer of BERT and the FFN layers. For more analysis, we measured the training time and resulting NDCG on the GR-1K-rich data, comparing different input size budgets and choices of which parameters are tuned and which ones are kept frozen. Figure 2 shows the results. We observe that the 128-token configuration has the lowest training cost: significantly less time per epoch than the other variants and fast convergence (reaching its best NDCG already after 15 epochs in ca. 3000 seconds). The 256- and 512-token models eventually reach higher NDCG, but only by a small margin and after much longer training time. This confirms that concise user profiles, with a small token budget, are the best approach in terms of benefit/cost ratio. As for configurations with more or less tunable parameters, we observe that the variant with frozen BERT takes much longer to converge and is inferior to the preferred CUP method even after more than 50 epochs. The other extreme, allowing all of BERT parameters to be altered together with the FFN parameters, performs best after enough training epochs, better than the CUP configuration that tunes only the last BERT layer. However, it takes almost twice as much time per epoch. So again, from the benefit/cost perspective, our design choice hits a sweet spot in the spectrum of model configurations.

Recommendations by Concise User Profiles from Review Text: Experimental Results

The evolution of the digital landscape is picking up again. This time, we’re heading into Web3 - a stage in the history of the internet where customers are able to achieve more control of their data and what is done with it. Web3 represents a departure from the centralized architectures of Web2, offering a vision of a more open, inclusive, and democratized digital ecosystem. At the heart of this transition lies the need for speed, as users demand seamless, real-time interactions and transactions across decentralized networks. Whether it's executing smart contracts, minting NFTs, or facilitating microtransactions, the ability of blockchains to deliver swift and scalable solutions is paramount to unlocking the full potential of Web3. Web3 is expected to be ruled by several nascent technologies,- including blockchain. In fact, blockchain is expected to be the foundational layer of Web3, powering other technologies and delivering us into a new age of the internet. But, for this to happen, it is important for blockchains to be better - and on that front, speed is one area where a lot of progress needs to be made. Speed plays a pivotal role in enhancing user experience and driving widespread adoption of blockchain-based applications. In an era where convenience and efficiency reign supreme, sluggish transaction speeds and high latency can deter users and stifle growth. By investing in speed, blockchains can lower barriers to entry, attract a broader user base, and foster a vibrant ecosystem of dApps, NFT marketplaces, and DeFi protocols. To lend more insights into this, we spoke with the founders of Spedy - a new blockchain that touts itself as the fastest on the internet at the moment - to understand their view of the market: 1. Does the focus on speed mean that it is the most important part of the blockchain trilemma? Solving the scalability problem is indeed one of the most important parts of the trilemma, and with the Sonic update, Fantom has become one of the fastest L1 chains with 2,000 TPS and sub-second finality. Sonic is the new best-in-class shared sequencer for L1 and L2 chains, capable of processing over 180 million daily transactions with real, sub-second confirmation times. 2. What are the states of security and decentralization on Speedy? Just as the Sonic update is set to solve the scalability problem, Speedy is meant to be the cultural catalyst for Fantom. As a project, attracting new users on-chain who can experiment and witness not only the best-in-class speed but also the entire ecosystem that has been built to be secure and decentralized from the very beginning. Particularly for Speedy, decentralization stands in the hands of the community, with 90% of the funds going into a locked LP; 5% were airdropped to sGOAT holders—the first meme foundation on Fantom; and 5% used for CEX listings and outreach. 3. In the context of value and money transfers, how does speed play a role in optimizing blockchain operations? We recently reached a new milestone with 1 billion smart contracts deployed in the crypto space, with Fantom accounting for 16% with over 161,600,000 contractd. So, your question couldn't come at a better time. Fantom has always been known for low transaction fees, but with recent upgrades to the network and revisions to Fantom’s fee structure, it has become cheaper than ever to engage on the network. For most transactions, fees are less than $0.01, even during high-traffic periods. Regarding speed again, Rabby wallet tested how fast Sonic is exactly, and here is the conclusion: 4. What are the primary use cases for Speedy, and what should end users expect? Our primary use case as a project is to become the dominant meme project on Fantom, but generally, there is a catch. Speedy is also a catalyst for other creators to join and use the ecosystem we’ve built also on the cultural side. We already have teams developing in the SocialFi area, and new iGaming platforms will soon be launched. Having a growing community is, in my opinion, one of the most attractive metrics, as it means people have formed a strong culture and are eager to see the ecosystem get bigger to benefit everyone. 5. Do you believe that developers will continue to focus on speed optimization going forward? I’ve recently read Andre Cronje’s article called 'Frustrations of a Dapp Developer', and once you understand the challenges you are bound to overcome, the dedication one needs, the complexity, and the error margin, you can only conclude that the devs who succeeded in making Sonic best-in-class will never stop. It’s in the code." Conclusion: The internet has reached a point where pace is everything. And for blockchains, being able to process transactions faster isn’t optional - it’s a must. For blockchain developers, choosing how to optimize for speed will be critical if they’re to achieve mass adoption. That said, the blockchain trilemma is one that continues to bother many developers, and it’s also important not to forget the place of security and decentralization. The evolution of the digital landscape is picking up again. This time, we’re heading into Web3 - a stage in the history of the internet where customers are able to achieve more control of their data and what is done with it. Web3 represents a departure from the centralized architectures of Web2, offering a vision of a more open, inclusive, and democratized digital ecosystem. At the heart of this transition lies the need for speed, as users demand seamless, real-time interactions and transactions across decentralized networks. Whether it's executing smart contracts, minting NFTs, or facilitating microtransactions, the ability of blockchains to deliver swift and scalable solutions is paramount to unlocking the full potential of Web3. Web3 is expected to be ruled by several nascent technologies,- including blockchain. In fact, blockchain is expected to be the foundational layer of Web3, powering other technologies and delivering us into a new age of the internet. But, for this to happen, it is important for blockchains to be better - and on that front, speed is one area where a lot of progress needs to be made. Speed plays a pivotal role in enhancing user experience and driving widespread adoption of blockchain-based applications. In an era where convenience and efficiency reign supreme, sluggish transaction speeds and high latency can deter users and stifle growth. By investing in speed, blockchains can lower barriers to entry, attract a broader user base, and foster a vibrant ecosystem of dApps, NFT marketplaces, and DeFi protocols. To lend more insights into this, we spoke with the founders of Spedy - a new blockchain that touts itself as the fastest on the internet at the moment - to understand their view of the market: 1. Does the focus on speed mean that it is the most important part of the blockchain trilemma? Solving the scalability problem is indeed one of the most important parts of the trilemma, and with the Sonic update, Fantom has become one of the fastest L1 chains with 2,000 TPS and sub-second finality. Sonic is the new best-in-class shared sequencer for L1 and L2 chains, capable of processing over 180 million daily transactions with real, sub-second confirmation times. 2. What are the states of security and decentralization on Speedy? Just as the Sonic update is set to solve the scalability problem, Speedy is meant to be the cultural catalyst for Fantom. As a project, attracting new users on-chain who can experiment and witness not only the best-in-class speed but also the entire ecosystem that has been built to be secure and decentralized from the very beginning. Particularly for Speedy, decentralization stands in the hands of the community, with 90% of the funds going into a locked LP; 5% were airdropped to sGOAT holders—the first meme foundation on Fantom; and 5% used for CEX listings and outreach. 3. In the context of value and money transfers, how does speed play a role in optimizing blockchain operations? We recently reached a new milestone with 1 billion smart contracts deployed in the crypto space, with Fantom accounting for 16% with over 161,600,000 contractd. So, your question couldn't come at a better time. Fantom has always been known for low transaction fees, but with recent upgrades to the network and revisions to Fantom’s fee structure, it has become cheaper than ever to engage on the network. For most transactions, fees are less than $0.01, even during high-traffic periods. Regarding speed again, Rabby wallet tested how fast Sonic is exactly, and here is the conclusion: 4. What are the primary use cases for Speedy, and what should end users expect? Our primary use case as a project is to become the dominant meme project on Fantom, but generally, there is a catch. Speedy is also a catalyst for other creators to join and use the ecosystem we’ve built also on the cultural side. We already have teams developing in the SocialFi area, and new iGaming platforms will soon be launched. Having a growing community is, in my opinion, one of the most attractive metrics, as it means people have formed a strong culture and are eager to see the ecosystem get bigger to benefit everyone. 5. Do you believe that developers will continue to focus on speed optimization going forward? I’ve recently read Andre Cronje’s article called 'Frustrations of a Dapp Developer', and once you understand the challenges you are bound to overcome, the dedication one needs, the complexity, and the error margin, you can only conclude that the devs who succeeded in making Sonic best-in-class will never stop. It’s in the code." Conclusion: Conclusion: The internet has reached a point where pace is everything. And for blockchains, being able to process transactions faster isn’t optional - it’s a must. For blockchain developers, choosing how to optimize for speed will be critical if they’re to achieve mass adoption. That said, the blockchain trilemma is one that continues to bother many developers, and it’s also important not to forget the place of security and decentralization.

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