Procys

procys

HackerNoon is back with Startups of The Week! Each week, we pull attention to featured, prominent startups from our exclusive Startups of The Year database. All these startups have been nominated as one of the best in their respective category or region. This week, we spotlight Procys, Moxie AI, and Coffey & Consult. Want to be nominated for HackerNoon’s Startups of The Year? Learn how here. Meet The Startups of The Week Procys Procys transforms how businesses handle routine tasks by providing automation solutions to streamline businesses’ operations. With ensured efficiency, accuracy, and compliance, Procys is committed to automating 50 billion hours of repetitive work, freeing resources for strategic priorities. Their services include data extraction, invoice processing, and customized automation solutions tailored to specific needs. Procys has been nominated as one of the best IT Services, SaaS, and Web Development startups. Support Procys - Vote here! Moxie AI Founded from the passion for good storytelling and striking visuals, Moxie AI is dedicated to moving small business brands away from safe, conventional strategies by urging clients to embrace bold and out-of-the-box content that truly stands out. Based in Denver, CO, Moxie AI has been nominated as one of the best startups in Marketing, Creative Agency, and Social Media. Support Moxie AI - Vote here! Coffey & Consult Understanding how technology is a powerful tool for business growth in the digital age, Coffey & Consult sets out to spread their knowledge in digital branding, creating “aha” moments, and guiding clients from initial confusion to harnessing the power of innovations. Coffey & Consult has been nominated as one of the best startups in Norman, OK; with additional nominations in Consulting, Business Intelligence & Training and Consulting categories. Support Coffey & Consult - Vote here! Featured Interview of The Week Did you know? Being nominated for HackerNoon’s Startups of The Year wins you a free interview where you can share about your startup and its journey, the services it provides and call for community voting! Create your own business page on HackerNoon, and use one of the following interview templates here. This week, we are looking into the São Paulo Startup Interview. Showcase your startup’s vision, achievements, and development strategies in São Paulo. Provide valuable insights into the São Paulo startup scene, trends, and forecast. https://hackernoon.com/meet-procys-startups-of-the-year-2024-nominee?embedable=true Here’s a quote from Procys in their published interview, telling us what Startups of The Year means to them: Being part of HackerNoon’s Startups of the Year means recognition for the hard work and innovation that has gone into building Procys. It also gives us a platform to connect with a wider audience, attract potential partners, and share our vision of simplifying and automating document management for businesses worldwide.
We hope this initiative will help us gain visibility and introduce Procys to businesses that are still relying on inefficient, manual processes and could benefit from automation. Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template.
Here’s how the 2023 nominees did it: Meetanshi, Sniper.xyz, Wallet Factory, Grownu Workforce Management System, Motif, Lightrun Featured Startups Specials Package Make the most out of Startups of The Year! Create branding buzz and generate leads with HackerNoon’s specifically designed packages. Today we’re introducing the Content Marketing Package. Content Marketing: 10x Distribution! With this package, you’ll get: Your business page with your logo, introduction, call to action, and socials on HackerNoon


3 stories published on HackerNoon, along with editorial support to enhance their impact.

Your articles are converted into audio stories and distributed via audio RSS feeds
Each story is translated into 12 different languages
Social media promotions of your stories



Multiple permanent placements on HackerNoon


Your Evergreen Tech Company News Page Learn more about this package here. That will be all for today! Congrats to all the nominated startups, and best of luck with your Startups of The Year 2024 campaign! Catch y’all later hackers! The HackerNoon Team About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many storieshave been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of the Year Merch Shophere. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packagesto solve your marketing challenges. Meet our sponsors: Wellfound: At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams, and customers in one easy-to-use scalable platform that grows with your business. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only combining powerful keyword and natural language processing in a single API. HackerNoon is back with Startups of The Week! Each week, we pull attention to featured, prominent startups from our exclusive Startups of The Year database . All these startups have been nominated as one of the best in their respective category or region. Startups of The Year database Startups of The Year database This week, we spotlight Procys , Moxie AI , and Coffey & Consult . Procys Moxie AI Coffey & Consult Want to be nominated for HackerNoon’s Startups of The Year? Learn how here. Want to be nominated for HackerNoon’s Startups of The Year? Learn how here . here here Meet The Startups of The Week Procys Procys Procys transforms how businesses handle routine tasks by providing automation solutions to streamline businesses’ operations. With ensured efficiency, accuracy, and compliance, Procys is committed to automating 50 billion hours of repetitive work, freeing resources for strategic priorities. Their services include data extraction, invoice processing, and customized automation solutions tailored to specific needs. Procys has been nominated as one of the best IT Services , SaaS , and Web Development startups. IT Services SaaS Web Development Support Procys - Vote here! Support Procys - Vote here! Support Procys - Vote here ! here here Moxie AI Moxie AI Founded from the passion for good storytelling and striking visuals, Moxie AI is dedicated to moving small business brands away from safe, conventional strategies by urging clients to embrace bold and out-of-the-box content that truly stands out. Based in Denver, CO , Moxie AI has been nominated as one of the best startups in Marketing , Creative Agency , and Social Media . Denver, CO Marketing Creative Agency Social Media Support Moxie AI - Vote here! Support Moxie AI - Vote here! Support Moxie AI - Vote here ! here Coffey & Consult Coffey & Consult Understanding how technology is a powerful tool for business growth in the digital age, Coffey & Consult sets out to spread their knowledge in digital branding, creating “aha” moments, and guiding clients from initial confusion to harnessing the power of innovations. Coffey & Consult has been nominated as one of the best startups in Norman, OK ; with additional nominations in Consulting , Business Intelligence & Training and Consulting categories. Norman, OK Consulting Business Intelligence Training and Consulting Support Coffey & Consult - Vote here! Support Coffey & Consult - Vote here! Support Coffey & Consult - Vote here ! here here Featured Interview of The Week Did you know? Being nominated for HackerNoon’s Startups of The Year wins you a free interview where you can share about your startup and its journey, the services it provides and call for community voting! Create your own business page on HackerNoon , and use one of the following interview templates here . Create your own business page on HackerNoon here here This week, we are looking into the São Paulo Startup Interview . Showcase your startup’s vision, achievements, and development strategies in São Paulo. Provide valuable insights into the São Paulo startup scene, trends, and forecast. São Paulo Startup Interview https://hackernoon.com/meet-procys-startups-of-the-year-2024-nominee?embedable=true https://hackernoon.com/meet-procys-startups-of-the-year-2024-nominee?embedable=true Here’s a quote from Procys in their published interview, telling us what Startups of The Year means to them: Here’s a quote from Procys in their published interview , telling us what Startups of The Year means to them: Procys published interview Being part of HackerNoon’s Startups of the Year means recognition for the hard work and innovation that has gone into building Procys. It also gives us a platform to connect with a wider audience, attract potential partners, and share our vision of simplifying and automating document management for businesses worldwide. We hope this initiative will help us gain visibility and introduce Procys to businesses that are still relying on inefficient, manual processes and could benefit from automation. Being part of HackerNoon’s Startups of the Year means recognition for the hard work and innovation that has gone into building Procys . It also gives us a platform to connect with a wider audience, attract potential partners, and share our vision of simplifying and automating document management for businesses worldwide. Procys We hope this initiative will help us gain visibility and introduce Procys to businesses that are still relying on inefficient, manual processes and could benefit from automation. Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template. Here’s how the 2023 nominees did it: Meetanshi, Sniper.xyz, Wallet Factory, Grownu Workforce Management System, Motif, Lightrun Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template. Want to be featured on HackerNoon’s Startups of the Week? Share your startup's story - use this interview template . use this interview template use this interview template Here’s how the 2023 nominees did it: Meetanshi , Sniper.xyz , Wallet Factory , Grownu Workforce Management System , Motif , Lightrun Meetanshi Meetanshi Sniper.xyz Sniper.xyz Wallet Factory Wallet Factory Grownu Workforce Management System Grownu Workforce Management System Motif Motif Lightrun Lightrun Featured Startups Specials Package Make the most out of Startups of The Year! Create branding buzz and generate leads with HackerNoon’s specifically designed packages . Today we’re introducing the Content Marketing Package . HackerNoon’s specifically designed packages HackerNoon’s specifically designed packages Content Marketing Package Content Marketing Package Content Marketing: 10x Distribution! With this package, you’ll get: With this package, you’ll get: Your business page with your logo, introduction, call to action, and socials on HackerNoon 3 stories published on HackerNoon, along with editorial support to enhance their impact.

Your articles are converted into audio stories and distributed via audio RSS feeds
Each story is translated into 12 different languages
Social media promotions of your stories Multiple permanent placements on HackerNoon Your Evergreen Tech Company News Page Your business page with your logo, introduction, call to action, and socials on HackerNoon Your business page with your logo, introduction, call to action, and socials on HackerNoon 3 stories published on HackerNoon, along with editorial support to enhance their impact. Your articles are converted into audio stories and distributed via audio RSS feeds
Each story is translated into 12 different languages
Social media promotions of your stories 3 stories published on HackerNoon, along with editorial support to enhance their impact. Your articles are converted into audio stories and distributed via audio RSS feeds Each story is translated into 12 different languages Social media promotions of your stories Your articles are converted into audio stories and distributed via audio RSS feeds Each story is translated into 12 different languages Social media promotions of your stories Multiple permanent placements on HackerNoon Multiple permanent placements on HackerNoon Your Evergreen Tech Company News Page Your Evergreen Tech Company News Page Evergreen Tech Company News Page Evergreen Tech Company News Page Learn more about this package here. Learn more about this package here . here That will be all for today! Congrats to all the nominated startups, and best of luck with your Startups of The Year 2024 campaign! Catch y’all later hackers! The HackerNoon Team The HackerNoon Team About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview Evergreen Tech Company News Visit our FAQ page to learn more. FAQ Download our design assets here . here Check out the Startups of the Year Merch Shop here . here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages Meet our sponsors: Meet our sponsors: Wellfound: At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot : If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams, and customers in one easy-to-use scalable platform that grows with your business. Hubspot Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Algolia: Algolia NeuralSearch is the world's only combining powerful keyword and natural language processing in a single API. Algolia:

Meet Procys, Moxie AI, and Coffey & Consult: HackerNoon Startups of The Week

Procys, Moxie AI, ukat Coffey ukanakamp uñt’asipxam & Consult: HackerNoon Startups of The Week

Пазнаёмцеся з Procys, Moxie AI і Coffey & Consult: стартапы тыдня HackerNoon

Запознайте се с Procys, Moxie AI и Coffey & Consult: HackerNoon стартиращи фирми на седмицата

Conozca a Procys, Moxie AI y Coffey & Consult: las startups de la semana de HackerNoon

Découvrez Procys, Moxie AI et Coffey & Consult : les startups HackerNoon de la semaine

Procys、Moxie AI、Coffey & Consult のご紹介: HackerNoon の今週のスタートアップ

Procys, Moxie AI және Coffey & Consultпен танысыңыз: HackerNoon аптаның стартаптары

Procys, Moxie AI, Coffey & Consult нартай уулзаарай: HackerNoon долоо хоногийн гарааны бизнес

Temui Procys, Moxie AI, dan Coffey & Consult: HackerNoon Startups of The Week

प्रोसिस, मोक्सी एआई, र कफी एण्ड कन्सल्टलाई भेट्नुहोस्: ह्याकरनून स्टार्टअप्स अफ द वीक

Kopana le Procys, Moxie AI, le Coffey & Rerišana: Dithomelo tša HackerNoon tša Beke

Faceți cunoștință cu Procys, Moxie AI și Coffey & Consult: Startup-urile săptămânii HackerNoon

Hey Hackers, Procys has been nominated in HackerNoon’s annual Startups of The Year awards in Rotterdam, Netherlands. Please vote for us here. Read more about us below to understand why we deserve your vote. Meet Procys Procys is revolutionizing document management for businesses with its AI-powered platform that automates document processing, data extraction, and certification. We help companies save time, reduce manual errors, and ensure compliance with industry regulations, like the AEAT (Tax agency) in Spain, ISO Certification, as you can see, we prioritize safeguarding your data, ensuring a secure environment for all your information needs. How Procys achieved product-market fit Procys achieved product-market fit by addressing a clear need: businesses struggling with manual, error-prone document workflows. From finance teams processing hundreds of invoices to legal departments managing contracts, our customers wanted a solution that could save time and increase accuracy. The market responded overwhelmingly to our ability to automate document processing and meet compliance standards like AEAT certification. Indicators of product-market fit include: High adoption rates among mid-sized companies in sectors like finance, legal, and healthcare.
Positive feedback from customers who report significant time savings and error reduction.
Repeat customers and a growing user base from key industries that rely on document automation. The evolution of Procys Like most startups, Procys has gone through its own evolution. Initially, we focused solely on automating data extraction for financial documents. However, through user feedback and industry demands, we expanded our platform to cover full workflow automation and compliance features, like AEAT (Spanish Tax Certification/Homologation), ISO 27001:2013, 2022 and we adhere to the the highest standards. A pivotal moment came when we realized that mid-sized businesses in regulated industries were looking for more than just data extraction—they needed secure, legally compliant digital solutions. This insight guided our pivot toward creating an end-to-end document automation platform that integrates with existing systems and ensures compliance across industries. Milestones and achievements Some of the key milestones for Procys include: Official AEAT certification, allowing us to offer legally compliant digitalization for businesses in Spain.
Successful integrations with industry-leading platforms like Sage and A3ERP to streamline document workflows for our users.
Processing millions of documents across sectors like finance, legal, and healthcare.
Growing our customer base across Spain, Europe, and beyond.
Reaching over 500% growth in customer adoption since our launch. Lessons learned Building Procys has not been without challenges. One of the biggest hurdles was balancing customer feedback with our vision. Early on, we realized that businesses needed a more customizable solution, which led us to redesign parts of our platform to allow greater flexibility. Another challenge was navigating the complex regulatory landscape, particularly around document certification. This led us to invest heavily in ensuring compliance, resulting in our official AEAT certification, which has become a critical selling point for us. Through these experiences, we learned that listening to our customers and being flexible with our roadmap was key to building a product that solves real-world problems. What Startups of The Year means to us Being part of HackerNoon’s Startups of the Year means recognition for the hard work and innovation that has gone into building Procys. It also gives us a platform to connect with a wider audience, attract potential partners, and share our vision of simplifying and automating document management for businesses worldwide. We hope this initiative will help us gain visibility and introduce Procys to businesses that are still relying on inefficient, manual processes and could benefit from automation. Conclusion Procysis changing the way businesses handle documents by delivering fast, accurate, and compliant document processing solutions. We’re proud to be nominated for HackerNoon’s Startups of the Year, and we believe our journey is just beginning. With your support, we can continue to help businesses automate their workflows and focus on what truly matters—growing their operations. Vote for us today! Welcome to HackerNoon’s Startups of The Year 2024 interview series, spotlighting startups from around the world that are changing the way we live, one solution at a time. If you’ve been nominated, create a brand profile and answer these questions here.
Want to nominate a startup? Follow the steps here.Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here, here, and here. Hey Hackers, Procys has been nominated in HackerNoon’s annual Startups of The Year awards in Rotterdam, Netherlands . Procys Rotterdam, Netherlands Please vote for us here. Please vote for us here . here Read more about us below to understand why we deserve your vote. Meet Procys Procys is revolutionizing document management for businesses with its AI-powered platform that automates document processing, data extraction, and certification. We help companies save time, reduce manual errors, and ensure compliance with industry regulations, like the AEAT (Tax agency) in Spain, ISO Certification, as you can see, we prioritize safeguarding your data, ensuring a secure environment for all your information needs. Procys How Procys achieved product-market fit How Procys achieved product-market fit Procys achieved product-market fit by addressing a clear need: businesses struggling with manual, error-prone document workflows. From finance teams processing hundreds of invoices to legal departments managing contracts, our customers wanted a solution that could save time and increase accuracy. The market responded overwhelmingly to our ability to automate document processing and meet compliance standards like AEAT certification. Procys Indicators of product-market fit include: Indicators High adoption rates among mid-sized companies in sectors like finance, legal, and healthcare. Positive feedback from customers who report significant time savings and error reduction. Repeat customers and a growing user base from key industries that rely on document automation. High adoption rates among mid-sized companies in sectors like finance, legal, and healthcare. Positive feedback from customers who report significant time savings and error reduction. Repeat customers and a growing user base from key industries that rely on document automation. The evolution of Procys Like most startups, Procys has gone through its own evolution. Initially, we focused solely on automating data extraction for financial documents. However, through user feedback and industry demands, we expanded our platform to cover full workflow automation and compliance features, like AEAT (Spanish Tax Certification/Homologation), ISO 27001:2013, 2022 and we adhere to the the highest standards. Procys A pivotal moment came when we realized that mid-sized businesses in regulated industries were looking for more than just data extraction—they needed secure, legally compliant digital solutions. This insight guided our pivot toward creating an end-to-end document automation platform that integrates with existing systems and ensures compliance across industries. Milestones and achievements Some of the key milestones for Procys include: Procys Official AEAT certification, allowing us to offer legally compliant digitalization for businesses in Spain. Successful integrations with industry-leading platforms like Sage and A3ERP to streamline document workflows for our users. Processing millions of documents across sectors like finance, legal, and healthcare. Growing our customer base across Spain, Europe, and beyond. Reaching over 500% growth in customer adoption since our launch. Official AEAT certification , allowing us to offer legally compliant digitalization for businesses in Spain. Official AEAT certification Successful integrations with industry-leading platforms like Sage and A3ERP to streamline document workflows for our users. Sage A3ERP Processing millions of documents across sectors like finance, legal, and healthcare. millions of documents Growing our customer base across Spain, Europe, and beyond . Spain, Europe, and beyond Reaching over 500% growth in customer adoption since our launch. 500% growth in customer adoption Lessons learned Building Procys has not been without challenges. One of the biggest hurdles was balancing customer feedback with our vision. Early on, we realized that businesses needed a more customizable solution, which led us to redesign parts of our platform to allow greater flexibility. Procys Another challenge was navigating the complex regulatory landscape, particularly around document certification. This led us to invest heavily in ensuring compliance, resulting in our official AEAT certification, which has become a critical selling point for us. Through these experiences, we learned that listening to our customers and being flexible with our roadmap was key to building a product that solves real-world problems. What Startups of The Year means to us Being part of HackerNoon’s Startups of the Year means recognition for the hard work and innovation that has gone into building Procys . It also gives us a platform to connect with a wider audience, attract potential partners, and share our vision of simplifying and automating document management for businesses worldwide. Procys We hope this initiative will help us gain visibility and introduce Procys to businesses that are still relying on inefficient, manual processes and could benefit from automation. Conclusion Procys is changing the way businesses handle documents by delivering fast, accurate, and compliant document processing solutions. We’re proud to be nominated for HackerNoon’s Startups of the Year, and we believe our journey is just beginning. With your support, we can continue to help businesses automate their workflows and focus on what truly matters—growing their operations. Procys Vote for us today ! Vote for us today Welcome to HackerNoon’s Startups of The Year 2024 interview series, spotlighting startups from around the world that are changing the way we live, one solution at a time. If you’ve been nominated, create a brand profile and answer these questions here. Want to nominate a startup? Follow the steps here. Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here, here, and here. Welcome to HackerNoon’s Startups of The Year 2024 interview series, spotlighting startups from around the world that are changing the way we live, one solution at a time. If you’ve been nominated, create a brand profile and answer these questions here . Startups of The Year 2024 create a brand profile create a brand profile create a brand profile here here Want to nominate a startup? Follow the steps here . here here Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here, here, and here. Looking for inspiration? Check out how other startups responded to last year’s Startups of The Year interview questions and examples of company responses here , here , and here . last year’s Startups of The Year interview questions last year’s Startups of The Year interview questions here here here here here here

Meet Procys, Startups of the Year 2024 Nominee

Securing an official job offer is nothing short of exciting. The hard work has paid off and you’re ready to dive right in. Securing an official job offer is nothing short of exciting. The hard work has paid off and you’re ready to dive right in. With a heavy focus on supporting the digital nomad community, the onboarding process at Social Discovery Group may look a little different than that of other companies. Social Discovery Group Social Discovery Group One of our newest hires for a managerial position, a Delivery Manager, recently wrote about their onboarding experience at SDG and stated, “For most of my career, I have worked in companies ranging from 15 to 350 employees. Naturally, every new job comes with an onboarding process, and I have had different experiences with how this process is structured in various organizations.” He shared key observations with us about the onboarding process and how it differed from other experiences. Step One: Onboarding with the System Administrator The onboarding process had three stages, first, a meeting with the system administrator. 👩‍💻 The admin meeting is usually no more than an hour long and incredibly useful. The system administrator walked the newcomer through all the software used at SDG. While tools like Slack, Jira, and Confluence may be familiar, setting up the computer and understanding the company’s analytical tools required additional explanation, as these aspects vary across organizations. SDG SDG This session also served as a cultural onboarding experience. The admin explained how meetings are typically scheduled in Google Calendar within the company, which Slack channels to use for different inquiries, and most importantly, how the company’s Confluence wiki is structured.🔍 The system administrator is a go-to person for technical questions, so new employees never feel lost. *¨Onboarding is a crucial part of integrating into the job and the team. It significantly impacts how quickly a newcomer adapts to the workflow, gets to know the team, and understands their tasks.*¨ — Eugene Bandarenka, Delivery Manager *¨Onboarding is a crucial part of integrating into the job and the team. It significantly impacts how quickly a newcomer adapts to the workflow, gets to know the team, and understands their tasks.*¨ — Eugene Bandarenka, Delivery Manager Eugene Bandarenka, Delivery Manager Step Two: Onboarding with your Mentor The Social 3.0 team is continuously expanding and SDG has many experienced managers that take on the role of mentor to newcomers. 🌱 During the first meeting, expect mentors to cover the following topics: A detailed overview of the team, its structure, members’ strengths and weaknesses, their motivations, and career expectations.


Established processes within both the team and the company, and understanding the different workflows.


Key documentation about the team and products, as well as a separate list of needed access permissions.


A template for message requests, the relevant contacts for approvals, and the purpose of each access level.


Most importantly, the mentor will go through key responsibilities during the probation period, including (for managerial positions) delivery metrics, establishing strong connections with the team, and defining objectives. Key responsibilities will differ depending on the position. A detailed overview of the team, its structure, members’ strengths and weaknesses, their motivations, and career expectations. A detailed overview of the team, its structure, members’ strengths and weaknesses, their motivations, and career expectations. Established processes within both the team and the company, and understanding the different workflows. Established processes within both the team and the company, and understanding the different workflows. Key documentation about the team and products, as well as a separate list of needed access permissions. Key documentation about the team and products, as well as a separate list of needed access permissions. A template for message requests, the relevant contacts for approvals, and the purpose of each access level. A template for message requests, the relevant contacts for approvals, and the purpose of each access level. Most importantly, the mentor will go through key responsibilities during the probation period, including (for managerial positions) delivery metrics, establishing strong connections with the team, and defining objectives. Key responsibilities will differ depending on the position. Most importantly, the mentor will go through key responsibilities during the probation period, including (for managerial positions) delivery metrics, establishing strong connections with the team, and defining objectives. Key responsibilities will differ depending on the position. 🔑 During the 3-month probation period, newcomers will have weekly check-ins with their mentor to discuss questions and track progress. Step Three: Onboarding with HR 🎯 The next stage of onboarding is meeting with a team HR representative. Some key topics that are covered: Team insights from an HR perspective, included details on salary review timelines, employee motivations, and any personal circumstances to be aware of


HR processes, such as salary adjustments, professional development requests, and the team’s feedback system


Probation period evaluation


Gamification and internal rewards Team insights from an HR perspective, included details on salary review timelines, employee motivations, and any personal circumstances to be aware of Team insights from an HR perspective, included details on salary review timelines, employee motivations, and any personal circumstances to be aware of HR processes, such as salary adjustments, professional development requests, and the team’s feedback system HR processes, such as salary adjustments, professional development requests, and the team’s feedback system Probation period evaluation Probation period evaluation Gamification and internal rewards Gamification and internal rewards 👨‍🏫 Additionally, the HR representative provides a structured checklist to guide adaptation, divided by priorities for the first few days, the first week, and the following months. Newcomers are highly encouraged to get involved to learn through hands-on experience and participate in collaborative decision-making. SDG utilizes a structured and data-driven approach to enhance employee satisfaction. The feedback process consists of surveys tied to measurable indicators, and HR and management implement targeted initiatives to assess the impact of their actions. Final Thoughts on the Onboarding Process Joining a technical team can come with a steep learning curve, but the onboarding process at SDG is designed to make this transition as smooth as possible. The structured documentation, clear processes, and support from multiple people ensure that new employees not only understand key information but also apply it instantly. 📅 Written by Eugene Bandarenka, Delivery Manager at Social Discovery Group Written by Eugene Bandarenka, Delivery Manager at Social Discovery Group Written by Eugene Bandarenka, Delivery Manager at Social Discovery Group Social Discovery Group Social Discovery Group

What the Onboarding Process Looks Like at Social Discovery Group 

Com és el procés d'incorporació a Social Discovery Group 

Πώς φαίνεται η διαδικασία ενσωμάτωσης στο Social Discovery Group 

Cómo es el proceso de Onboarding en el Grupo de Descubrimiento Social

איך התהליך של Onboarding נראה בקבוצת גילוי חברתי

Bagaimana proses onboarding terlihat di kelompok penemuan sosial

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Social Discovery Group のオンボーディング プロセス

How the Onboarding Process Looks Like at Social Discovery Group- ს

អ្វី​ដែល​ដំណើរ​ការ​ចូល​ដំណើរ​ការ​មើល​ទៅ​ដូច​ជា​នៅ Social Discovery Group 

Si duket procesi i hyrjes në Social Discovery Group 

Hur Onboarding-processen ser ut på Social Discovery Group

Social Discovery Group'ta Yerleştirme Süreci Nasıl Görünüyor?

Singapore, March 21, 2025 — Mira, the pioneer in decentralized AI verification technology, announces unprecedented growth with 2.5 million users and two billion tokens processed daily across its ecosystem applications. The milestone demonstrates growing market demand for AI that can operate autonomously without human oversight. Processing two billion tokens daily is equivalent to approximately half of Wikipedia's content, generating 7.9 million images, or processing over 2,100 hours of video content per day. "This growth confirms we're addressing the critical barrier to AI's transformative potential," said Karan Sirdesai, Co-founder and CEO of Mira. "Today's AI remains constrained by the need for human verification—we're removing that bottleneck to enable truly autonomous intelligence capable of operating independently in high-stakes scenarios." "This growth confirms we're addressing the critical barrier to AI's transformative potential," said Karan Sirdesai, Co-founder and CEO of Mira. "Today's AI remains constrained by the need for human verification—we're removing that bottleneck to enable truly autonomous intelligence capable of operating independently in high-stakes scenarios." Enabling the Shift from 'Baby AI' to Autonomous Intelligence Current AI systems excel at generating content and solving problems, but their probabilistic nature makes them fundamentally unreliable. This creates a paradox: the more powerful AI becomes, the more human hours are required to verify its output, severely limiting AI's potential. Mira is addressing this challenge through decentralized verification that enables AI systems to operate autonomously in consequential domains. By transforming the verification process from a human bottleneck to a scalable, trustless system, Mira is paving the way for AI's next evolutionary leap. Applications Demonstrating the Vision This vision is coming to life through several applications built on Mira's verification infrastructure: Klok — A multi-LLM chat application providing access to leading AI models including DeepSeek-R1, GPT-4o mini, and Llama 3.3 70B Instruct within a unified interface. Klok treats each AI model as a trustless independent node, with verification features set to roll out in coming weeks.


WikiSentry — An AI agent that autonomously fact-checks Wikipedia articles against verified sources, identifying hallucinations, biases, and misinformation—a task previously requiring extensive human oversight.


Astro — A guidance platform helping users navigate life's important decisions through personalized, AI-powered insights that leverage verified information for reliable advice.


Amor — A supportive AI companion offering conversation and emotional connection for those seeking meaningful interaction, with verification ensuring consistent and trustworthy responses.
Public Testnet Enables Developer Innovation Klok — A multi-LLM chat application providing access to leading AI models including DeepSeek-R1, GPT-4o mini, and Llama 3.3 70B Instruct within a unified interface. Klok treats each AI model as a trustless independent node, with verification features set to roll out in coming weeks. Klok — A multi-LLM chat application providing access to leading AI models including DeepSeek-R1, GPT-4o mini, and Llama 3.3 70B Instruct within a unified interface. Klok treats each AI model as a trustless independent node, with verification features set to roll out in coming weeks. WikiSentry — An AI agent that autonomously fact-checks Wikipedia articles against verified sources, identifying hallucinations, biases, and misinformation—a task previously requiring extensive human oversight. WikiSentry — An AI agent that autonomously fact-checks Wikipedia articles against verified sources, identifying hallucinations, biases, and misinformation—a task previously requiring extensive human oversight. Astro — A guidance platform helping users navigate life's important decisions through personalized, AI-powered insights that leverage verified information for reliable advice. Astro — A guidance platform helping users navigate life's important decisions through personalized, AI-powered insights that leverage verified information for reliable advice. Amor — A supportive AI companion offering conversation and emotional connection for those seeking meaningful interaction, with verification ensuring consistent and trustworthy responses.
Public Testnet Enables Developer Innovation Amor — A supportive AI companion offering conversation and emotional connection for those seeking meaningful interaction, with verification ensuring consistent and trustworthy responses. Public Testnet Enables Developer Innovation Mira has today launched its Public Testnet, enabling developers, enterprises, and users to explore its blockchain-based verification network at explorer.mira.network, where all AI inferences are recorded as transparent, verifiable events. Read more here. explorer.mira.network explorer.mira.network here here Strategic Partnerships Expanding the Ecosystem Mira has integrated with leading agent frameworks including Eliza, SendAI, Arc, ZerePy, and FereAI, while establishing partnerships with blockchain networks like Monad and Plume Network. Infrastructure collaborations with io.net, Aethir, Hyperbolic, Exabits, and Spheron, along with industry partnerships with KernelDAO and Delphi Digital, further strengthen Mira's ecosystem. Monad Monad Plume Network Plume Network Backed by $9 million in funding from Framework, Accel, Mechanism, and Bitkraft, with angel investors including Balaji Srinivasan and Sandeep Nailwal, Mira is positioned to transform how AI systems operate at scale. About Mira Mira is building the foundational verification layer that enables trustless AI systems through advanced consensus mechanisms. The network combines sophisticated claim transformation and distributed verification protocols to achieve reliable AI execution at scale. By solving the fundamental challenge of error rates in AI, Mira is establishing new standards for reliability—paving the way for autonomous intelligence that can operate without human oversight, unlocking AI's transformative potential across society. Follow Mira on X and join their Discord for real-time updates. X X Discord Discord For more information, visit mira.network. mira.network mira.network This press release is brought to you by EAK Wire, the leading Web3 PR Newswire via HackerNoon Business Blogging Program. Web3 PR Newswire Web3 PR Newswire

Mira Reaches Major Milestone: 2.5M Users and 2B Daily Tokens Processed

Mira дасягнула важнай вехі: 2,5 мільёна карыстальнікаў і 2 мільярды штодзённых апрацаваных токенаў

Mira alcanza un hito importante: 2,5 millones de usuarios y 2 mil millones de tokens procesados diariamente

Mira dosegla veliku prekretnicu: 2,5 milijuna korisnika i 2 milijarde obrađenih dnevnih tokena

Mira が大きなマイルストーンを達成: ユーザー数 250 万人、1 日あたり 20 億トークンの処理

Мира негизги этапка жетти: 2,5 миллион колдонуучу жана 2B күнүмдүк Токендер иштетилди

Mira Akomi na Milestone ya munene: 2.5M ya ba usagers na 2B ya ba jetons ya mokolo na mokolo oyo esalemi

Mira sasniedz galveno pavērsienu: 2,5 miljoni lietotāju un 2 miljoni dienā apstrādāti marķieri

Mira atinge marco importante: 2,5 milhões de usuários e 2 bilhões de tokens diários processados

Mira Yafikia Hatua Kubwa: Watumiaji 2.5M na Tokeni 2B za Kila Siku Zimechakatwa

ሚራ ኣብ ዓቢ ምዕራፍ በጺሓ: 2.5M ተጠቀምቲን 2B መዓልታዊ ቶከናት ተሰሪሖም

Mira досягла важливого рубежу: 2,5 млн користувачів і 2 млрд щоденних токенів оброблено

UMira Ufinyelela Ingqophamlando Eyinhloko: Abasebenzisi abangu-2.5M kanye namathokheni ansuku zonke angu-2B Acutshunguliwe

As someone who's worked in the trenches of financial markets, I've seen firsthand the importance of real-time data processing. During my time at Two Sigma and Bloomberg, I witnessed how even minor delays can have significant consequences. In this article, I'll share my insights on the challenges of real-time data processing in distributed systems, using examples from the financial industry. Data Consistency: The Achilles' Heel of Distributed Systems Imagine you're a trader, relying on real-time market data to make split-second decisions. But what if the data you're receiving is inconsistent? Perhaps one server thinks the price of Apple is $240, while another sees it at $241. This discrepancy might seem minor, but in the world of high-frequency trading, it can be catastrophic. To ensure data consistency, financial institutions employ various techniques, such as: Event sourcing: By storing all state changes as a sequence of events, systems can reconstruct the latest state, even in the event of a failure.
Distributed consensus algorithms: Protocols like Paxos or Raft enable nodes to agree on a single source of truth, even in cases of network partitioning. Event sourcing: By storing all state changes as a sequence of events, systems can reconstruct the latest state, even in the event of a failure. Event sourcing Distributed consensus algorithms: Protocols like Paxos or Raft enable nodes to agree on a single source of truth, even in cases of network partitioning. Distributed consensus algorithms However, these solutions can introduce additional complexity, particularly in high-throughput environments. Latency: The Need for Speed In financial markets, latency can make or break a trade. High-frequency trading firms invest heavily in infrastructure to minimize latency, and even the smallest inefficiency can have significant consequences. Real-time market data must be processed and delivered to consumers with extremely low latency. To address latency, financial institutions employ strategies such as: Edge processing: By processing data closer to its source, systems can reduce latency and improve performance.
Hardware acceleration: Utilizing specialized hardware, such as FPGA, can significantly reduce processing times for critical tasks.
Optimized message brokers: Choosing the right message broker, such as Kafka or Pulsar, is crucial in ensuring that data is pushed to consumers as quickly as possible. Edge processing: By processing data closer to its source, systems can reduce latency and improve performance. Edge processing Hardware acceleration: Utilizing specialized hardware, such as FPGA, can significantly reduce processing times for critical tasks. Hardware acceleration Optimized message brokers: Choosing the right message broker, such as Kafka or Pulsar, is crucial in ensuring that data is pushed to consumers as quickly as possible. Optimized message brokers Fault Tolerance: What Happens When Things Go Wrong? No system is immune to failure, but in financial markets, fault tolerance is paramount. If a single node or service goes down, consumers cannot afford to lose critical market data. To ensure fault tolerance, financial institutions employ strategies such as: Replication: Market data is replicated across multiple servers or regions to ensure continuity in the event of a failure.
Automatic failover: Systems are designed to detect failure and route traffic to healthy servers without human intervention.
Distributed logging: Distributed logs, such as Kafka's log-based architecture, ensure that all actions are recorded and can be replayed in the event of a crash. Replication: Market data is replicated across multiple servers or regions to ensure continuity in the event of a failure. Replication Automatic failover: Systems are designed to detect failure and route traffic to healthy servers without human intervention. Automatic failover Distributed logging: Distributed logs, such as Kafka's log-based architecture, ensure that all actions are recorded and can be replayed in the event of a crash. Distributed logging Scalability: Handling Explosive Growth Financial markets are inherently unpredictable, and systems must be designed to handle sudden surges in traffic. Scalability is critical to ensure that systems can handle explosive growth without degrading performance. To achieve scalability, financial institutions employ strategies such as: Sharding: Market data is divided into smaller chunks and distributed across different servers to improve performance.
Load balancing: Incoming data is efficiently distributed across multiple nodes or servers to avoid bottlenecks.
Elasticity: Systems are designed to scale up or down based on demand, ensuring that resources are allocated efficiently. Sharding: Market data is divided into smaller chunks and distributed across different servers to improve performance. Sharding Load balancing: Incoming data is efficiently distributed across multiple nodes or servers to avoid bottlenecks. Load balancing Elasticity: Systems are designed to scale up or down based on demand, ensuring that resources are allocated efficiently. Elasticity Security: Protecting Critical Market Data Finally, security is paramount in financial markets. Distributed systems, by their nature, involve multiple servers, databases, and services spread across various regions, making them vulnerable to attacks. To ensure security, financial institutions employ strategies such as: Encryption: Data is encrypted both in transit and at rest to prevent eavesdropping or unauthorized access.
Authentication and authorization: Only authorized parties can access sensitive market data feeds, using techniques like OAuth and API keys.
DDoS mitigation: Systems are designed to detect and prevent Distributed Denial of Service (DDoS) attacks, ensuring availability and performance. Encryption: Data is encrypted both in transit and at rest to prevent eavesdropping or unauthorized access. Encryption Authentication and authorization: Only authorized parties can access sensitive market data feeds, using techniques like OAuth and API keys. Authentication and authorization DDoS mitigation: Systems are designed to detect and prevent Distributed Denial of Service (DDoS) attacks, ensuring availability and performance. DDoS mitigation Conclusion Real-time data processing in distributed systems is a complex challenge, particularly in high-stakes environments like financial markets. By understanding the challenges of data consistency, latency, fault tolerance, scalability, and security, financial institutions can design and implement more efficient, resilient, and scalable systems. As the financial industry continues to evolve, the quest for near-zero latency, high availability, and real-time data processing will only become more critical. By sharing my insights and experiences, I hope to contribute to the ongoing conversation about the challenges and opportunities in real-time data processing. Image Courtesy: https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges
https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/
https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges https://www.nber.org/digest/202110/race-exploit-stock-price-differences-between-exchanges https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/ https://www.linkedin.com/pulse/distributed-systems-design-pattern-lease-based-stock-shanoj-kumar-v-0bawc/ https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries https://blog.e-boks.com/understanding-data-encryption-a-comprehensive-guide-for-regulated-industries

Challenges of Real-Time Data Processing in Financial Markets

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Survey with Industry Professionals 2 Survey with Industry Professionals 3 RQ1: Real-World use cases that necessitate output constraints 3 RQ1: Real-World use cases that necessitate output constraints 4 RQ2: Benefits of Applying Constraints to LLM Outputs and 4.1 Increasing Prompt-based development Efficiency 4 RQ2: Benefits of Applying Constraints to LLM Outputs and 4.1 Increasing Prompt-based development Efficiency 4.2 Integrating with Downstream Processes and Workflows 4.2 Integrating with Downstream Processes and Workflows 4.3 Satisfying UI and Product Requirements and 4.4 Improving User Experience, Trust, and Adoption 4.3 Satisfying UI and Product Requirements and 4.4 Improving User Experience, Trust, and Adoption 5 How to Articulate output constraints to LLMS and 5.1 The case for GUI: A Quick, Reliable, and Flexible Way of Prototyping Constraints 5 How to Articulate output constraints to LLMS and 5.1 The case for GUI: A Quick, Reliable, and Flexible Way of Prototyping Constraints 5.2 The Case for NL: More Intuitive and Expressive for Complex Constraints 5.2 The Case for NL: More Intuitive and Expressive for Complex Constraints 6 The Constraint maker Tool and 6.1 Iterative Design and User Feedback 6 The Constraint maker Tool and 6.1 Iterative Design and User Feedback 7 Conclusion and References 7 Conclusion and References A. The Survey Instrument A. The Survey Instrument 4.2 Integrating with Downstream Processes and Workflows Because LLMs are often used as sub-components in larger pipelines, respondents emphasized that guaranteed constraints are critical to ensuring that the output of their work is compatible with downstream processes, such as downstream modules that expect a specific format or functional code as input. Specifically for code generation, they highlighted the necessity of constraining the output to ensure “executable” code that adheres to only “methods specified in the context” and avoids errors, such as hallucinating “unsupported operators” or “SQL ... in a different dialect.” Note that while the “function calling” features in the latest LLMs [8, 26] can “select” functions to call from a predefined list, users still have to implement these functions correctly by themselves. “executable” “methods specified in the context “unsupported operators” “SQL ... in a different dialect.” “function calling” “select” Many studies indicate that LLMs are highly effective for creating synthetic datasets for AI training [9, 15, 38], and our survey respondents postulated that being able to impose constraints on LLMs could improve the datasets’ quality and integrity. For instance, one respondent wished that model-generated movie data would “not say a movie’s name when it describes its plot,” as they were going to train using this data for a “predictive model of the movie itself.” Any breach of such constraints could render the data “unusable.” “not say a movie’s name when it describes its plot,” “predictive model of the movie itself.” “unusable.” Furthermore, given the industry trend of continuously migrating to newer, more cost-effective models, respondents highlighted the importance of “canonizing” constraints across models to avoid extra prompt-engineering after migration (e.g., “if I switch model, I get the formatting immediately”). This suggests that it could be more advantageous for models to accept output constraints independent of the prompt, which should now solely contain task instructions. “canonizing” “if I switch model, I get the formatting immediately”) This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors:
(1) Michael Xieyang Liu, Google Research, Pittsburgh, PA, USA (lxieyang@google.com);
(2) Frederick Liu, Google Research, Seattle, Washington, USA (frederickliu@google.com);
(3) Alexander J. Fiannaca, Google Research, Seattle, Washington, USA (afiannaca@google.com);
(4) Terry Koo, Google, Indiana, USA (terrykoo@google.com);
(5) Lucas Dixon, Google Research, Paris, France (ldixon@google.com);
(6) Michael Terry, Google Research, Cambridge, Massachusetts, USA (michaelterry@google.com);
(7) Carrie J. Cai, Google Research, Mountain View, California, USA (cjcai@google.com). Authors: Authors: (1) Michael Xieyang Liu, Google Research, Pittsburgh, PA, USA (lxieyang@google.com); (2) Frederick Liu, Google Research, Seattle, Washington, USA (frederickliu@google.com); (3) Alexander J. Fiannaca, Google Research, Seattle, Washington, USA (afiannaca@google.com); (4) Terry Koo, Google, Indiana, USA (terrykoo@google.com); (5) Lucas Dixon, Google Research, Paris, France (ldixon@google.com); (6) Michael Terry, Google Research, Cambridge, Massachusetts, USA (michaelterry@google.com); (7) Carrie J. Cai, Google Research, Mountain View, California, USA (cjcai@google.com).

How We Integrated Downstream Processes and Workflows

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Approach and 3.1 Differential Testing for XML Processors 3 Approach and 3.1 Differential Testing for XML Processors 3.2 XPath Expression Generation 3.2 XPath Expression Generation 3.3 XML Generation 3.3 XML Generation 4 Evaluation 4 Evaluation 4.1 Effectiveness 4.1 Effectiveness 4.2 Efficiency 4.2 Efficiency 4.3 Comparison to the State of the Art 4.3 Comparison to the State of the Art 4.4 Analysis of BaseX Historical Bug Reports 4.4 Analysis of BaseX Historical Bug Reports 5 Related Work 5 Related Work 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 5 RELATED WORK While various related approaches to our work exist, to the best of our knowledge, we propose the first general approach to testing XML processors to find logic bugs. As discussed above, the most closely related work proposed testing the index support of SQLServer in the context of XPath and XQuery [41], which, to the best of our knowledge, is the only work that has tackled the test-oracle problem for XML processors, but is limited in scope. Testing XPath functionality. Various approaches to benchmarking XPath implementations or test suites for them have been proposed, the most representative being XPathMark and the W3C qt3 test suite. XPathMark [25] is a benchmark for testing XML processors’ XPath standard 1.0 functionality, containing both correctness as well as performance tests. The W3C qt3 test suite developed by the W3C XQuery and XSLT Working Groups [19] contains around 30,000 tests for XPath and XQuery targeting XPath 3.0 and later versions, which cover a broad range of functions and expressions. Testing XPath functionality. XML-related automated synthetic data generation. Previous works have proposed approaches for automatically generating XML-related data, such as XML documents, XPath, and XQuery expressions. Aboulnaga et al. proposed an XML document generator to generate synthetic, but complex, structured XML data by introducing recursion and repetition on tag name assignment and controlling the element frequency distribution [20]. Rychnovský and Holubová proposed an approach to generate XML documents related to given XPath queries from a specific XML schema to improve query efficiency [37], which is useful for developers to create micro-benchmarks for testing performance over certain XPath expressions. XQGen [42] is a tool for generating XPath queries that conform to a given XML schema, allowing users to specify multiple parameters, such as the percentage of empty queries desired and the percentage of queries with predicates. XPath generated by XQGen includes only direct node tests without introducing complex expressions, such as axes or function transformations. Similarly, the XQuery generator designed by Todic and Uzelac [41] includes XQuery FLWOR expressions, but the logic predicate consists only of simple operations, such as value comparisons. Neither of these works tackled the test oracle problem, and, as indicated by the results in Section 4.3, given their different focus, they cannot be effectively combined with a differential testing oracle. XML-related automated synthetic data generation. Targeted test case generation. Many testing tools guide their test case generation process to improve testing efficiency, for random approaches such as random byte mutation used in fuzzing approaches generate a large proportion of invalid queries [47]. DynSQL [27] guides the fuzzing process of DBMSs towards increased code coverage and high statement validity. APOLLO [28] is a system for detecting performance regression bugs in DBMSs. It increases the probability of including components from previously encountered performance issues. Cynthia [39] was proposed to test Object Relational Mappers (ORMs) and generates targeted databases dependent on generated abstract SQL queries, which are likely to return non-empty results. Query Plan Guidance (QPG) [22] guides testing towards exploring more unique query plans. Targeted test case generation. Pivoted Query Synthesis. The targeted node in XPress was inspired by the pivot row in Pivoted Query Synthesis (PQS) [36], which was originally proposed to test relational DBMSs. PQS’ and XPress’ commonality is that they select a random element, in PQS, a row in the database, while for XPress, a node in an XML document, based on which they generate a query that is guaranteed to fetch the element. However, both the purpose and use of the targeted node and pivot row differ. In PQS, the pivot row is used both for test-case generation and to construct the test oracle, by evaluating an expression and ensuring that it evaluates to true for the pivot row so that it can be used in a query that is guaranteed to fetch the row. Doing so requires a naive reimplementation of all the DBMSs’ operators that should be tested, which incurs a high implementation effort, as highlighted in follow-up work [? ]. In XPress, the targeted node is used only for test-case generation, to improve testing efficiency and to ensure non-empty intermediate results; to this end, XPress uses the XML processor to determine the result of the expression, rather than requiring the reimplementation of operators. In addition, for predicate rectification, XPress provides operator-specific rules, rather than relying on a generic one, aiming to generate more interesting test cases. The high-level idea of a pivot element also inspired other works; for example, recent work on Android testing introduced the concept of a pivot layout [40]. Pivoted Query Synthesis. pivot layout This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com);
(2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg). Authors: Authors: (1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com); (2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg).

Finding XPath Bugs in XML Document Processors: Testing XPath Functionality and Other Related Work

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Approach and 3.1 Differential Testing for XML Processors 3 Approach and 3.1 Differential Testing for XML Processors 3.2 XPath Expression Generation 3.2 XPath Expression Generation 3.3 XML Generation 3.3 XML Generation 4 Evaluation 4 Evaluation 4.1 Effectiveness 4.1 Effectiveness 4.2 Efficiency 4.2 Efficiency 4.3 Comparison to the State of the Art 4.3 Comparison to the State of the Art 4.4 Analysis of BaseX Historical Bug Reports 4.4 Analysis of BaseX Historical Bug Reports 5 Related Work 5 Related Work 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 4.2 Efficiency Existing-generator baselines. We considered the only two—to the best of our knowledge—approaches to generate XPath expressions. Neither of them was specifically designed to be combined with a XPath test oracle. XQgen [42] generates XPath queries for micro benchmarking. Its generated predicates only check for sub-element existence. The XQuery generator designed by Todic and Uzelac [41] generates XPath queries for automatically testing index support in DBMSs. Given that indexes apply only to sargable queries (i.e., simple comparisons), the expressions it generates are simple. Both approaches generate XPath expressions based on an XML schema, while XPress generates XPath expressions based on the actual XML document. Based on this, we expect both of them to have low applicability for our differential-testing approach. Given that neither implementations are publicly available, we re-implemented them based on the description in the papers. Existing-generator baselines. Self-constructed baselines. We also constructed our own baselines to investigate the efficiency of the separate components of XPress. XPress has two main components, namely (1) the targeted predicate generation by using the targeted node to refer to existing nodes and attributes and (2) the predicate rectification to avoid empty result sets. To evaluate the effect of the components individually, we enabled them individually to test whether they improve XPress’s bug detection efficiency. Self-constructed baselines. Configurations. We considered four configurations for our selfconstructed baselines. Apart from our proposed approach introduced in Section 3.2 as (1) Targeted, we derive configuration (2) Targeted without Rectification, (3) Untargeted with Rectification, and (4) Untargeted without Rectification. In (2) Targeted without Rectification, we disable the rectification process, which would otherwise ensure targeted node selection. Since selecting a targeted node for predicate generation guidance always requires at least one node in the result set, we stop generating new sections after an empty result set is produced. In (3) Untargeted with Rectification, we generate predicates without using targeted node information to supply parameters that reference existent context and trigger corner cases for function nodes, while keeping the rectification to ensure that at least one node from the candidate set is included in the result set. In (4) Untargeted without rectification, we remove both components to generate predicates randomly, while omitting rectification. Configurations argeted, we derive configuration Targeted without Rectification Untargeted with Rectification Untargeted without Rectification Untargeted with Rectification Untargeted without rectification, Methodology. We set each baseline to run for 24 hours [30]. We repeated each experiment 10 times to account for potential performance deviations, and report the arithmetic mean for all metrics. As our testing target, we selected BaseX 10.4, which is the BaseX version that we first started testing. The reason for selecting BaseX as a representative is that we found most bugs in BaseX and all bugs were fixed, allowing us to determine the number of unique bugs we found in a testing campaign by deduplicating bug-inducing test cases automatically. Specifically, given two bug-inducing test cases, we could determine whether they trigger the same underlying bug by identifying their fix commits; only if their associated fix commit are different, do we consider the bugs unique. This is a best-effort technique, as, for example, one fix commit might address multiple bugs. We disabled the generation of the has-children functions as well as using relative XPath expressions in predicates, as they consistently lead to crashes, triggering known bugs. Methodology Results of existing generators. Neither XQGen nor the Combined XML/XQuery generator found bugs in our experiment. This is expected, as previously proposed approaches were not designed for Results of existing generators. automated testing. As mentioned above, XQGen generates predicates that only check for element existence. The XQuery generator designed by Todic and Uzelac generates simple predicates that include at most one comparison operator. Results of different configurations. As Figure 8 shows, our proposed approach, Targeted outperforms the other configurations. Within 24 hours, it found the most number of unique bugs (namely 12.5). Both configurations with targeted generation clearly outperformed the untargeted approaches, while rectification shows a similar performance in the speed of bug detection. As shown in Table 3, both targeted generation and rectification reduce the testing throughput, as they obtain intermediate results using the XML processor under test. Despite generating only 50% of the number of test cases as compared to (4) Untargeted without Rectification, (1) Targeted detected 20× more bug-inducing test cases and 2× more unique bugs. The results show that selecting a target node to guide the XPath generation process improves testing efficiency significantly. As observed above when discussing the small-scope hypothesis, most of the bugs that we found can be reproduced using a single section, explaining the limited effectiveness of rectification. However, we still believe that rectification is an important component, since without it, bugs requiring multiple sections with non-empty results could hardly be found. Results of different configurations. Code coverage. We collected code coverage for three processors’ core modules for XPress for 24 hours [30] of execution. The result is shown in Table 4. To put the numbers in relation, we collected coverage also for the projects’ test suites; Saxon has no publicly available test suites and is therefore excluded. For the three XML processors, the line coverage ranged from 15% to 20%, and the Code coverage. branch coverage ranged from 10% to 16%. The coverage percentages are low, which is expected. The main reason for low code coverage is that XML processors typically also have other components than XPath processing. Taking BaseX as an example, around 21% of uncovered code was GUI-related, 10% was due to lack of full-text functionality support, and 5% were database commands. In Saxon, as another example, XSLT modules have not been covered. A further 18% uncovered code in BaseX involved unimplemented functions; it would be straightforward to implement many additional ones, such as math functions, but the many functions available would make this a tedious task. In Section 4.4, we detail unsupported XPath features, implementing which might allow us to find more bugs. XPress’s test-case generation process primarily aims at generating semantically valid expressions, which results in low error-checking branch coverage, quantifying which is difficult, as the relevant code is spread throughout the code base. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com);
(2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg). Authors: Authors: (1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com); (2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg).

Finding XPath Bugs in XML Document Processors: Existing-Generator Baselines and More

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Approach and 3.1 Differential Testing for XML Processors 3 Approach and 3.1 Differential Testing for XML Processors 3.2 XPath Expression Generation 3.2 XPath Expression Generation 3.3 XML Generation 3.3 XML Generation 4 Evaluation 4 Evaluation 4.1 Effectiveness 4.1 Effectiveness 4.2 Efficiency 4.2 Efficiency 4.3 Comparison to the State of the Art 4.3 Comparison to the State of the Art 4.4 Analysis of BaseX Historical Bug Reports 4.4 Analysis of BaseX Historical Bug Reports 5 Related Work 5 Related Work 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 4 EVALUATION In the evaluation, we sought to investigate whether our technique is effective and efficient in finding bugs for XPath expression processors. Specifically, we were interested in the following questions: Q1. Is XPress effective in finding new XPath-related bugs in established XML processors (see Section 4.1)? Q1. Q2. Does the query generation approach described in Section 3.2 improve the bug-finding efficiency of XPress with respect to real-world baselines and a random generation approach (see Section 4.2)? Q2. Q3. How does the differential testing test oracle compare to the state-of-the-art oracle (see Section 4.3)? Q3. Q4. What kind of XPath-related bugs might be overlooked by XPress (see Section 4.4)? Q4. Tested XML Processors. We tested our method on six mature, well-known, and actively maintained XPath processors: BaseX, exist-DB, Saxon-HE, PostgreSQL, libXML2, and a commercial DBMS, whose name we have omitted due to its “DeWitt clause” [24]. We started testing on BaseX 10.4, eXist-DB 6.2.0, Saxon Home Edition 12.2, PostgreSQL version 15, and libXML2 commit version 106153. As bugs were resolved, we constantly updated to the latest available version. We selected BaseX, eXist-DB, and Saxon to be our main testing targets, because they all implement the more recent XPath 3.0 standard. BaseX ranks as the most popular Native XML DBMS on the DB-Engines Ranking [9]. eXist-DB is widely applied in data centers, systems, and platforms, as referenced on the eXist-DB reference page [11]. Saxon is an in-memory processor and therefore is not included in the DB-Engines rankings. However, the official website of Saxon [16] states: "More than 170 software vendors have built Saxon into their own applications" and "6 of the world’s top 10 software vendors are Saxonica clients", demonstrating that Saxon is a widely-used and popular XML processor. For XPath 1.0 standard implementations, we tested PostgreSQL, libXML2, and the commercial DBMS. PostgreSQL is a popular open-source DBMS, which ranks 4 on the DB-Engines ranking and has 12.8k stars on GitHub. libXML2 is a software library developed for the GNOME project. The commercial DBMS is often considered the most popular and important DBMS overall, as also reflected in various rankings. All XML processors have been actively maintained for over 15 years. Tested XML Processors. Experimental setup. We implemented the tool, XPress, in around 8,000 LOC in Java. In our experiments, we configured it to generate XML documents that contain 1 to 50 nodes. We create half as many node templates as element nodes. For each XML document, we generated 200 XPath expressions. Each XPath expression had an equal possibility to hold 1 to 7 sections. We set one predicate to hold at most 10 subjects (see Section 3.2) and the depth of the predicate tree to be at most 10. We used the default settings of each XML processor. We conducted all our experiments using a personal computer with a 64-Core AMD EPYC 7763 CPU at 2.45GHz and 512GB memory running Ubuntu 22.04. Experimental setup. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com);
(2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg). Authors: Authors: (1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com); (2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg).

Is Our Technique Effective in Finding Bugs for XPath Expression Processors? An Investigation

Table of Links Abstract and 1 Introduction 2 Background 3 Approach and 3.1 Differential Testing for XML Processors 3.2 XPath Expression Generation 3.3 XML Generation 4 Evaluation 4.1 Effectiveness 4.2 Efficiency 4.3 Comparison to the State of the Art 4.4 Analysis of BaseX Historical Bug Reports 5 Related Work 6 Conclusion, Acknowledgments, and References 3 APPROACH Figure 3 shows an overview of the approach using the same example as in Figure 1. At a high level, our approach consists of three main steps. First, we randomly generate an XML document as the context for the following queries (step 1). We then generate an XPath expression that we will subsequently validate (step 2 to step 5). Finally, we execute the XPath expression on the XML document using all engines under test and compare the resulting outputs to detect potential bugs (step 6). In the subsequent subsections, we explain these steps in reverse order to reflect their importance. We guide the XPath expression generation towards queries that reference nodes and attributes present in the XML document and result in non-empty result sets based on the intuition that they are more likely to stress the underlying logic of the tested systems. To generate XPath expressions with non-empty result sets, we construct the query section-by-section and ensure that a non-empty result set is produced before proceeding with the next section. Each section consists of a section prefix and predicates, and we first generate the prefix (step 2) and then the predicate. By restricting the section prefix, we guarantee that the result contains at least one node. From the nodes selected by the section prefix, we randomly select a node as a target (step 3). We then generate a predicate aiming to select the targeted node using a bottom-up tree construction method (step 4). We rectify the predicate to ensure that the result set contains the targeted node (step 5). We repeat this process until the XPath query reaches the desired length. 3.1 Differential Testing for XML Processors As detailed subsequently, differential testing enables us to find both logic bugs as well as internal errors when comparing the results of XML processors implementing the same XPath standard. Query execution. When passing XML documents and XPath queries to different systems, we must account for the different input interfaces. For example, DBMSs use database connection interfaces to store and query data, while Saxon can be used as a library. To abstract this, we treat every XML processor implementation as a function that returns a result set and expects two string values, namely an XML document XML and an XPath query XPATH. Listing 1 shows an implementation of this interface for Oracle Database using SQL statements. It creates a table t, inserts the XML document—the XMLType constructor is used to convert the string to an XML data type—and uses an XMLQuery function call in a SELECT statement to compute the result set. For BaseX and eXist-db, similar to the commands shown for Oracle Database, we also start with an empty database and subsequently insert an XML document. Listing 2 shows an excerpt of the Java code for Saxon. First, the call to compile converts the textual XPath query to an executable object, which is then loaded. Unlike for the DBMSs, which require inserting data into a database, for Saxon, the XML document is simply associated with the query using the setContextItem call. The evaluate call computes the result, which is returned for comparison. Bug identification. We identify both logic bugs and internal errors by comparing the returned results of different processors on the same XML document and query. We identify logic bugs when the tested systems return different node-set outputs for the same test cases. To parse and track the results easily under different output formats, we use unique node ids to identify element nodes. We detect internal errors as discrepancies with respect to errors. Rather than checking for an exact error message match, we validate whether all the systems produce an error, or all execute the XPath query successfully. If only a subset of the systems report an error for the same XPath query, we found a potential bug. Different XPath standards. Our approach and tool are applicable to both XPath 1.0 and XPath 3.0. However, due to the differences in the formats, only processors using the same standard can be tested. Functionality that is supported only in XPath 3.0, can be disabled while generating test cases for processors that implement XPath 1.0. For example, sequence functions, such as subsequence, are defined only for the XPath 3.0 standard. When generating test cases for XPath 1.0 processors, we omit to generate subsequence function nodes for predicates. We did not encounter any functions or operators that were removed in the XPath 3.0 standard, so all expressions that we generate when testing XPath 1.0 processors can be used also when testing XPath 3.0 processors. By comparing only processors with the same XPath standard against each other, the difference in the results between different XPath standards (see Section 2) has no influence on the testing process. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com);
(2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Approach and 3.1 Differential Testing for XML Processors 3 Approach and 3.1 Differential Testing for XML Processors 3.2 XPath Expression Generation 3.2 XPath Expression Generation 3.3 XML Generation 3.3 XML Generation 4 Evaluation 4 Evaluation 4.1 Effectiveness 4.1 Effectiveness 4.2 Efficiency 4.2 Efficiency 4.3 Comparison to the State of the Art 4.3 Comparison to the State of the Art 4.4 Analysis of BaseX Historical Bug Reports 4.4 Analysis of BaseX Historical Bug Reports 5 Related Work 5 Related Work 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 3 APPROACH Figure 3 shows an overview of the approach using the same example as in Figure 1. At a high level, our approach consists of three main steps. First, we randomly generate an XML document as the context for the following queries (step 1). We then generate an XPath expression that we will subsequently validate (step 2 to step 5). Finally, we execute the XPath expression on the XML document using all engines under test and compare the resulting outputs to detect potential bugs (step 6). In the subsequent subsections, we explain these steps in reverse order to reflect their importance. We guide the XPath expression generation towards queries that reference nodes and attributes present in the XML document and result in non-empty result sets based on the intuition that they are more likely to stress the underlying logic of the tested systems. To generate XPath expressions with non-empty result sets, we construct the query section-by-section and ensure that a non-empty result set is produced before proceeding with the next section. Each section consists of a section prefix and predicates, and we first generate the prefix (step 2) and then the predicate. By restricting the section prefix, we guarantee that the result contains at least one node. From the nodes selected by the section prefix, we randomly select a node as a target (step 3). We then generate a predicate aiming to select the targeted node using a bottom-up tree construction method (step 4). We rectify the predicate to ensure that the result set contains the targeted node (step 5). We repeat this process until the XPath query reaches the desired length. 3.1 Differential Testing for XML Processors As detailed subsequently, differential testing enables us to find both logic bugs as well as internal errors when comparing the results of XML processors implementing the same XPath standard. Query execution. When passing XML documents and XPath queries to different systems, we must account for the different Query execution. input interfaces. For example, DBMSs use database connection interfaces to store and query data, while Saxon can be used as a library. To abstract this, we treat every XML processor implementation as a function that returns a result set and expects two string values, namely an XML document XML and an XPath query XPATH. Listing 1 shows an implementation of this interface for Oracle Database using SQL statements. It creates a table t, inserts the XML document—the XMLType constructor is used to convert the string to an XML data type—and uses an XMLQuery function call in a SELECT statement to compute the result set. For BaseX and eXist-db, similar to the commands shown for Oracle Database, we also start with an empty database and subsequently insert an XML document. Listing 2 shows an excerpt of the Java code for Saxon. First, the call to compile converts the textual XPath query to an executable object, which is then loaded. Unlike for the DBMSs, which require inserting data into a database, for Saxon, the XML document is simply associated with the query using the setContextItem call. The evaluate call computes the result, which is returned for comparison. Bug identification. We identify both logic bugs and internal errors by comparing the returned results of different processors on the same XML document and query. We identify logic bugs when the tested systems return different node-set outputs for the same test cases. To parse and track the results easily under different output formats, we use unique node ids to identify element nodes. We detect internal errors as discrepancies with respect to errors. Rather than checking for an exact error message match, we validate whether all the systems produce an error, or all execute the XPath query successfully. If only a subset of the systems report an error for the same XPath query, we found a potential bug. Bug identification. Different XPath standards . Our approach and tool are applicable to both XPath 1.0 and XPath 3.0. However, due to the differences in the formats, only processors using the same standard can be tested. Functionality that is supported only in XPath 3.0, can be disabled while generating test cases for processors that implement XPath 1.0. For example, sequence functions, such as subsequence, are defined only for the XPath 3.0 standard. When generating test cases for XPath 1.0 processors, we omit to generate subsequence function nodes for predicates. We did not encounter any functions or operators that were removed in the XPath 3.0 standard, so all expressions that we generate when testing XPath 1.0 processors can be used also when testing XPath 3.0 processors. By comparing only processors with the same XPath standard against each other, the difference in the results between different XPath standards (see Section 2) has no influence on the testing process. Different XPath standards This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com); (2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg). Authors: Authors: (1) Shuxin Li, Southern University of Science and Technology China and Work done during an internship at the National University of Singapore (shuxin.li.lv@gmail.com); (2) Manuel Rigger, National University of Singapore Singapore (rigger@nus.edu.sg).

The Approach and Differential Testing for XML Processors

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