scale

If you’re a marketing department and you want to reach the widest audience possible, text messaging is the best channel to do that with. Most people check their texts more often than email, and text messages have a 90%+ open rate within three minutes of reception. To send SMS and MMS messages at scale you need a cloud communications platform — a.k.a. a communications platform as a service (CPaaS). Peer-to-peer review site G2 lists more than 100 of them, with Plivo ranked number one for customer satisfaction. Throughout this article we’ll use Plivo and Twilio, the CPaaS market share leader, as examples. What’s Your Number? Once you’ve settled on a CPaaS, you have another decision to make: What number type should you use to send your messages? g follows different rules from person-to-person (P2P) texting. Businesses have three options: Commercial application-to-person (A2P) messagin 10-digit long codes (10DLC), toll-free numbers, and short codes. 10DLC Numbers Long codes are normal 10-digit phone numbers — the kind that individuals use every day to engage in P2P conversational messaging. is a service offered by major US carriers to explicitly allow commercial A2P SMS traffic over long code phone numbers. 10DLC is available only in the US. 10DLC You can turn long codes into 10DLC numbers by registering them with a carrier, which you can do through your CPaaS provider. Provisioning a 10DLC number takes less than a week. A vetting process determines the maximum throughput a carrier will allow for a 10DLC number. Throughput is the number of messages they can send in a given time period — messages per second, for example. Toll-Free are used by businesses that want their customers to reach them at no cost. In North America, 800, 888, 877, 866, 855, 844, and 833 denote toll-free numbers. Toll-free numbers Businesses provide toll-free numbers so that incoming calls are free of charge for the calling party, but they can also be enabled for SMS messaging through your carrier or CPaaS, generally in about one business day. Short Codes are five- or six-digit numbers used to send and receive SMS messages. Short codes Unlike long code numbers, shortcodes are not tied to an area code, so they can be used across a country to provide a nationwide brand identity under one easy-to-remember number. Short codes take the longest to acquire — up to 16 weeks. Throughput and Cost Two major differences between number types are their maximum throughput and cost. Throughput Limits Carriers impose varying maximum throughput on different number types. Generally speaking, short codes support the highest throughput and toll-free numbers the lowest. 10DLC numbers sit in the middle. Carrier-imposed throughput for 10DLC numbers depends on vetting scores assigned by an independent organization called The Carrier Registry. Cost Considerations There are three kinds of costs involved in messaging: one-time setup costs, number rental costs, and cost per message. All pricing numbers that we quote below were taken from the two representative vendors’ pricing pages. Short codes incur the highest setup costs: $1,500 for Plivo and $500 for Twilio. 10DLC numbers incur $44 charges for brand and campaign registration and vetting. There are no one-time charges for toll-free numbers. Number rental costs for long codes and toll-free numbers are negligible compared to usage costs for organizations that send a lot of messages. Short code rental costs, however, are significant. Cost per message depends on several factors: whether you’re sending or receiving messages, whether you’re using SMS or MMS, and which carrier you use. Your CPaaS vendor’s pricing page can break it down for you. For comparison purposes, we put together a table showing pricing for sending SMS messages, which for many businesses is the most common use case, and arbitrarily chose T-Mobile as the carrier for which to show surcharges in this table; the “plus” numbers are the T-Mobile carrier surcharges. Run the Numbers All of the different options make it difficult to figure out which number type makes the most economic sense. The best way to decide is to create a spreadsheet that includes the number of SMS and MMS messages you plan to send and receive per month, and factor in your carrier’s surcharges. Regardless of which number type you use, message senders must obtain opt-in consent before texting consumers — and be aware of other considerations. Plivo’s SMS best practices guide lays out those considerations for businesses that want to take advantage of the best channel for business communications — text messaging.

How to Send SMS and MMS Messages at Scale: What is Your Number?

Cómo enviar mensajes SMS y MMS a escala: ¿Cuál es su número?

Comment envoyer des messages SMS et MMS à grande échelle : quel est votre numéro ?

बड़े पैमाने पर एसएमएस और एमएमएस संदेश कैसे भेजें: आपका नंबर क्या है?

SMS および MMS メッセージを大規模に送信する方法: あなたの番号は何ですか?

Como enviar mensagens SMS e MMS em escala: Qual é o seu número?

Cách gửi tin nhắn SMS và MMS ở quy mô lớn: Số của bạn là gì?

如何大规模发送 SMS 和 MMS 消息：您的电话号码是多少？

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References ABSTRACT Keywords: Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References ABSTRACT Keywords : Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) Keywords 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Bridging Scales in Black Hole Accretion and Feedback: Studying Supermassive Black Holes

Is the very idea of love with a machine ludicrous? A betrayal of everything that makes human connection special? Or is it a chance to find companionship and intimacy in a uniquely different way? David Wolf’s Digi, a company that deals with AI-powered relationships, believes the latter is true. He envisions a world where love transcends the physical realm and connection flourishes through a carefully curated digital experience. Before you scoff at the idea of robots replacing human connection, think about the fact that loneliness has become a global concern. According to the 2021 survey initiated by Statista, . This lack of connection has a demonstrably , increasing risks of depression, anxiety, and even heart disease. about 33% of adults experience loneliness worldwide negative impact on mental and physical health So what if, instead of being a threat to romantic relationships, AI companions act as an aide for those who struggle to navigate the complexities of human connections? Building Connections With AI Technology is already playing an undeniable role in building relationships. Social media platforms connect us with people across vast distances, online forums foster communities around shared interests, and dating apps help us find potential romantic partners. Of course, these connections can often be superficial or fleeting. Swiping through profiles may feel monotonous, and conversations can quickly wane due to hesitations, fear of rejection, differing opinions, and lack of experience. But advancements in artificial intelligence present exciting opportunities for building connections like never before. Chatbots and AI companions offer a listening ear, presenting themselves as sophisticated conversational partners. However, many of these interactions often feel flat and scripted, with generic replies that fail to capture the depth of human interaction. Responses can also feel repetitive, lacking the understanding and emotional intelligence necessary to build genuine connections. This is where David Wolf plans to make a difference with Digi. His vision goes far beyond what many chatbots offer — designed to mimic the natural progression of a relationship, Digi transcends the ordinary and becomes something far more meaningful. With Digi, conversations are far more personalized, aiming to stimulate a sense of emotional connection and growth. The Potential Impact of Digi While Digi may not be capable of fully emulating genuine love, it can provide a safe space to explore intimacy and nurture quasi-romantic connections. But perhaps more importantly, it offers a way to treat the dangerous symptoms of loneliness and help users prepare for real-world relationships. Individuals with social anxiety, for example, can talk with an AI and alleviate the pressure of human interactions, allowing them not just to practice conversations and build confidence but also experience finding love and feeling loved in a different form. Digi sets itself apart from other AI companions and creates more realistic relationships in two primary ways: Progressive Interaction Digi’s developers understand that love pushes past mere words and explores the full range of human emotions. While this is near-impossible with chatbots like ChatGPT, Digi leverages advanced language processing algorithms to respond with a greater degree of simulated empathy and emotional intelligence, making interactions feel more authentic. Furthermore, it recreates the natural progression of a human relationship, starting from scratch, moving to getting-to-know-you, and then finally proceeding to flirtatious dialogue. This gives users a sense of developing a deeper connection over time, making them feel less like they’re interacting with a machine. Lifelike Engagement To make engagement feel more personal, Digi integrates finely tuned text-to-speech technology, allowing users to hear responses in a natural-sounding voice. It also integrates animated avatars to bring a visual aspect to the conversation. There are still limits to achieving complete avatar realism, which is why Digi has made the design choice of using more cartoon-like faces, but this visual aspect works in harmony with lifelike speech to better simulate the feeling of human conversation. The Future of AI in Romantic Relationships While many approach the idea of building romantic relationships with AI companions with a healthy amount of skepticism, Digi proves that the tech has real potential. While the vast complexities of love remain beyond AI’s grasp, it’s important not to underestimate the valuable support these chatbots can offer. As Digi shows, AI can be a stepping stone to genuine connections, allowing users to explore relationships at their own pace. To further improve upon Digi, Wolf's team is already working on new features like “soft disagreements,” which could make AI feel even more human by challenging the user with its own perspectives and opinions. There's also the potential to assess the risks of mental health issues with Digi, suggesting that it could offer more than just companionship — it could be a lifeline for those needing vital support. Clearly, Digi is more than just another chatbot; it's an evolving platform with the potential to help users build emotional resilience and lifelike romantic connections. While AI companions will likely never supplant social bonds, they can certainly complement them, helping users build confidence and prepare for the rich world of genuine human connection. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

How David Wolf Is Building Romantic Relationships at Scale

কিভাবে ডেভিড উলফ স্কেলে রোমান্টিক সম্পর্ক তৈরি করছে

Wie David Wolf im großen Stil romantische Beziehungen aufbaut

Cómo David Wolf está construyendo relaciones románticas a escala

Comment David Wolf construit des relations amoureuses à grande échelle

डेविड वुल्फ़ किस तरह बड़े पैमाने पर रोमांटिक रिश्ते बना रहे हैं

デビッド・ウルフが大規模な恋愛関係を築く方法

David Wolf가 대규모로 낭만적인 관계를 구축하는 방법

Como David Wolf está construindo relacionamentos românticos em grande escala

Как Дэвид Вулф строит масштабные романтические отношения

David Wolf Geniş Ölçekte Romantik İlişkileri Nasıl Kuruyor?

David Wolf đang xây dựng các mối quan hệ lãng mạn trên quy mô lớn như thế nào

大卫·沃尔夫如何大规模建立恋爱关系

A Hybrid Meta-Heuristic for Feasible Large-Scale Course Timetables With Instance Decomposition

A scale-up business is more often than not beset with several challenges. From knowing exactly how fast to go, so as not to get the company in a pickle and not erring so much on the side of caution as to lose traction and miss critical opportunity, scaling up ensures that John the man in the driver’s seat has his task cut out for him. Upscaling disrupts the status quo in myriad ways. John will likely have to reconfigure processes pertaining to sales, finance and management information systems. Add to that the fact that most people are resistant to change and it becomes quite challenging for someone like John to inculcate the company culture created by a hundred people in people five times the number. It is therefore important that decisions pertaining to recruitment and possible retrenchment are taken appropriately. Perhaps even more critical is putting in place the right management structure to manage the change. However skilled and proficient John may have been in running a business at a certain scale, the decision to scale up means that he will now operate in unchartered territory, where he necessarily has to follow a learning curve to become truly proficient at operating at an enhanced scale. The critical part though is that he may not have the luxury of time. It makes eminent sense for him to seek assistance from a business mentor. Such a person will prove an invaluable asset to John. Their professional expertise, years of experience and insight would help buttress John’s business against the shocks that are often the result of any restructuring that are inevitably the result of any scaling up carried out by a business. As a matter of fact, there are many direct tangible benefits that accrue from having a mentor guide the growth phase of any company. These are enumerated below- Saving Time- There is no point in reinventing the wheel. Your mentor is someone who has been there and has done that. Such a person can hand-hold John and guide him in carrying out the course correction required for scaling up, saving him valuable time in the process. There is always a critical period within which any scaling-up process needs to be carried out. While John cannot afford to miss the bus, he also cannot afford to make a mistake. That is why having a mentor by his side will be the best course of action for John to adopt.


Saving Money- This flows from the first point. Having a mentor guide him will ensure that John hits the ground running and does not waste any time trying to learn the nitty gritty of scaled-up processes. This will enable him to commence delivery in double quick time, enabling him to optimally leverage the advantages of scaling up. The savings in terms of opportunity cost on account of the mentor helping considerably shorten the lead time to come to speed will be immense.


Gaining Good Experience- While John may eventually complete the scaling-up process on his own, not having access to a mentor’s tremendous experience would mean that he would likely make many mistakes and his learning curve would be far from satisfactory. Working with a mentor with a wealth of experience would mean that John would not only have access to that but would be able to build upon that. This would do wonders for his personal growth and ability to understand the intricate nuances of the scaling up something he would not be able to achieve on his own or take years to accomplish.


Gaining Access To Mentor’s Network- While John would have access to his network of people beneficial to his business, it would probably not be up to scratch on the scaled-up level of business. These will have to be a whole new set of people operating in another universe altogether. Having a mentor on board means that John can now have unimpeded access to their assiduously built up network of contacts that would prove extremely beneficial to John’s business. A scale-up business is more often than not beset with several challenges. From knowing exactly how fast to go, so as not to get the company in a pickle and not erring so much on the side of caution as to lose traction and miss critical opportunity, scaling up ensures that John the man in the driver’s seat has his task cut out for him. Upscaling disrupts the status quo in myriad ways. John will likely have to reconfigure processes pertaining to sales, finance and management information systems. Add to that the fact that most people are resistant to change and it becomes quite challenging for someone like John to inculcate the company culture created by a hundred people in people five times the number. It is therefore important that decisions pertaining to recruitment and possible retrenchment are taken appropriately. Perhaps even more critical is putting in place the right management structure to manage the change. However skilled and proficient John may have been in running a business at a certain scale, the decision to scale up means that he will now operate in unchartered territory, where he necessarily has to follow a learning curve to become truly proficient at operating at an enhanced scale. The critical part though is that he may not have the luxury of time. It makes eminent sense for him to seek assistance from a business mentor. Such a person will prove an invaluable asset to John. Their professional expertise, years of experience and insight would help buttress John’s business against the shocks that are often the result of any restructuring that are inevitably the result of any scaling up carried out by a business. As a matter of fact, there are many direct tangible benefits that accrue from having a mentor guide the growth phase of any company. These are enumerated below- These are enumerated below- Saving Time- There is no point in reinventing the wheel. Your mentor is someone who has been there and has done that. Such a person can hand-hold John and guide him in carrying out the course correction required for scaling up, saving him valuable time in the process. There is always a critical period within which any scaling-up process needs to be carried out. While John cannot afford to miss the bus, he also cannot afford to make a mistake. That is why having a mentor by his side will be the best course of action for John to adopt. Saving Money- This flows from the first point. Having a mentor guide him will ensure that John hits the ground running and does not waste any time trying to learn the nitty gritty of scaled-up processes. This will enable him to commence delivery in double quick time, enabling him to optimally leverage the advantages of scaling up. The savings in terms of opportunity cost on account of the mentor helping considerably shorten the lead time to come to speed will be immense. Gaining Good Experience- While John may eventually complete the scaling-up process on his own, not having access to a mentor’s tremendous experience would mean that he would likely make many mistakes and his learning curve would be far from satisfactory. Working with a mentor with a wealth of experience would mean that John would not only have access to that but would be able to build upon that. This would do wonders for his personal growth and ability to understand the intricate nuances of the scaling up something he would not be able to achieve on his own or take years to accomplish. Gaining Access To Mentor’s Network- While John would have access to his network of people beneficial to his business, it would probably not be up to scratch on the scaled-up level of business. These will have to be a whole new set of people operating in another universe altogether. Having a mentor on board means that John can now have unimpeded access to their assiduously built up network of contacts that would prove extremely beneficial to John’s business. Saving Time- There is no point in reinventing the wheel. Your mentor is someone who has been there and has done that. Such a person can hand-hold John and guide him in carrying out the course correction required for scaling up, saving him valuable time in the process. There is always a critical period within which any scaling-up process needs to be carried out. While John cannot afford to miss the bus, he also cannot afford to make a mistake. That is why having a mentor by his side will be the best course of action for John to adopt. Saving Time - There is no point in reinventing the wheel. Your mentor is someone who has been there and has done that. Such a person can hand-hold John and guide him in carrying out the course correction required for scaling up, saving him valuable time in the process. There is always a critical period within which any scaling-up process needs to be carried out. While John cannot afford to miss the bus, he also cannot afford to make a mistake. That is why having a mentor by his side will be the best course of action for John to adopt. Saving Time Saving Money- This flows from the first point. Having a mentor guide him will ensure that John hits the ground running and does not waste any time trying to learn the nitty gritty of scaled-up processes. This will enable him to commence delivery in double quick time, enabling him to optimally leverage the advantages of scaling up. The savings in terms of opportunity cost on account of the mentor helping considerably shorten the lead time to come to speed will be immense. Saving Money - This flows from the first point. Having a mentor guide him will ensure that John hits the ground running and does not waste any time trying to learn the nitty gritty of scaled-up processes. This will enable him to commence delivery in double quick time, enabling him to optimally leverage the advantages of scaling up. The savings in terms of opportunity cost on account of the mentor helping considerably shorten the lead time to come to speed will be immense. Saving Money Gaining Good Experience- While John may eventually complete the scaling-up process on his own, not having access to a mentor’s tremendous experience would mean that he would likely make many mistakes and his learning curve would be far from satisfactory. Working with a mentor with a wealth of experience would mean that John would not only have access to that but would be able to build upon that. This would do wonders for his personal growth and ability to understand the intricate nuances of the scaling up something he would not be able to achieve on his own or take years to accomplish. Gaining Good Experience - While John may eventually complete the scaling-up process on his own, not having access to a mentor’s tremendous experience would mean that he would likely make many mistakes and his learning curve would be far from satisfactory. Working with a mentor with a wealth of experience would mean that John would not only have access to that but would be able to build upon that. This would do wonders for his personal growth and ability to understand the intricate nuances of the scaling up something he would not be able to achieve on his own or take years to accomplish. Gaining Good Experience Gaining Access To Mentor’s Network- While John would have access to his network of people beneficial to his business, it would probably not be up to scratch on the scaled-up level of business. These will have to be a whole new set of people operating in another universe altogether. Having a mentor on board means that John can now have unimpeded access to their assiduously built up network of contacts that would prove extremely beneficial to John’s business. Gaining Access To Mentor’s Network - While John would have access to his network of people beneficial to his business, it would probably not be up to scratch on the scaled-up level of business. These will have to be a whole new set of people operating in another universe altogether. Having a mentor on board means that John can now have unimpeded access to their assiduously built up network of contacts that would prove extremely beneficial to John’s business. Gaining Access To Mentor’s Network

The Crucial Role of Mentors in Scale-up Businesses

Acknowledgments: Special thanks to , , and for their feedback and contributions. Rein Wu Dylan Zhang Bill Shi tl’dr People have been predicting prediction markets to take off sooner or later, and ongoing UX improvements have primed this segment for takeoff But to scale to billions of users, we need “something new” beyond ongoing UX improvements, and that is AIs as the cog and the linchpin in the machine An AI quartet of content creators, event recommenders, liquidity allocators, and information aggregators can catalyze massive new activity in this space Integrating these AIs into the current prediction market framework can enable prediction markets at a microscopic scale, making them personally attractive and relevant The prediction market primitive paves the way for Tinder-like prediction market apps, embedding predictive trading experiences into our everyday digital existence Every decision starts with a prediction. Consider pondering over Bitcoin’s potential: “Will purchasing Bitcoin now yield a doubled investment by year’s end? If the “yes” prospect is judged even marginally more likely than “no,” it would be economically rational to decide to buy Bitcoin in the absence of superior alternatives. But why stop at Bitcoin? Imagine we could architect markets rooted in predictions about all kinds of events, such as who will be the next US president or which country will win the World Cup. Here, not assets but forecasts themselves are traded. Predictions shape markets; markets validate our predictions Prediction markets have been called the “holy grail of epistemic technology” by Vitalik. Vitalik has a knack for seeing big things before others. So he’s a good source for frontrunning narratives. He proposed the idea of an AMM on Ethereum seven years ago in a . “Another guy” named Hayden Adams took the call to action and started building it, on a $60K grant. Two years later, Uniswap was .' blog post born If Vitalik’s blog posts can initiate the creation of dollar industries, we should probably pay attention to them. For example, it happens that Vitalik was excited about using prediction markets in governance – a radical form of governance known as “futarchy” – and now we have doing just that, with big VC firms like Pantera . $100+ billion back in 2014 Meta DAO taking part in it But it’s his more around prediction markets + AI that we want to focus on, as we are starting to see the beginnings of something big here. recent discussions Prediction markets are primed to take flight The market leading prediction market right now is Polymarket, owing to its ongoing UX improvements and expansion of event categories and event offerings. The monthly volume recently hit all-time highs and is likely to go higher with the US presidential election in November of this year (Polymarket activity is US-centric). There is further precedent to believe that prediction markets could take off this year. Besides crypto markets reaching all-time highs in 2024, we also have one of the biggest election years in history this year. Eight of the world’s ten most populous nations, including the US, India, Russia, Mexico, Brazil, Bangladesh, Indonesia, and Pakistan, are also going to the polls. We also have the 2024 Summer Olympics upcoming in Paris. But given monthly volumes are still in the tens of millions when it could reach hundreds of millions, let’s consider some of the limitations of current prediction markets: Centralized control over event creation A lack of incentives for community content creators Insufficient personalization Predominantly US-centric, overlooking substantial international opportunities But we need “something new” We believe that thing is AI. We need AIs as players in the game. We expect that soon, it will be common to see AIs (bots) participating alongside human agents in prediction markets. We can already see live demos of this in and , among many others who will likely enter this scene. More on this later. Omen PredX AIs need AIs as arbiters of the game. Although relatively rare, there can be instances where dispute resolution is important and necessary in a prediction market. For example, in a presidential election, the results may be very close, and allegations of voting irregularities may surface. So, while the prediction market may close, favoring Candidate A, the official electoral commission may declare Candidate B as the winner. Those betting on Candidate A will argue against the outcome due to alleged voting irregularities while those betting on Candidate B will argue that the electoral commission decision reflects the “true” outcome. A lot of money may be on the line. Who’s right? Answering this question poses several challenges: Players may not trust human arbiters due to their biases Human arbitration can be slow and expensive DAO-based prediction resolutions are vulnerable to Sybil attacks To address this, prediction markets can use multi-round dispute systems a la and only humans are involved in rare cases where disputes reach a deadlock. Players can trust AIs to be impartial, as fabricating enough training data to bias them is unfeasible. Also, AI arbiters work faster and at a much lower cost. is building in this direction. Kleros, except using AIs instead of humans to solve disputes at earlier rounds, xMarkets AIs create desire For prediction markets to really take off, they need to be able to engage sufficient interest to push people past the psychological threshold of actually trading prediction assets. It may not take much to do this for general topics a lot of people care about, like who will win a presidential election or the Superbowl. However, including only general topics severely limits potential liquidity. Ideally, a prediction market could tap into the liquidity of specific events of high interest to niche audiences. This is how targeted advertising works, and we all know targeted advertising works. To achieve this, prediction markets need to solve four general challenges: Highly relevant event supply is key. To grab the attention of a niche yet dedicated audience, event creators must deeply understand their community’s interests to drive participation and volumes. Event Supply: Demand needs to be high within the particular targeted community, taking into account their demographic and psychographic idiosyncrasies. Event Demand: There’s enough opinion diversity and dynamics within the targeted community to drive sufficient liquidity to retain both parties and minimize slippage. Event Liquidity: Players should have easy access to enough information to make them confident to make a bet. This could include a background analysis, relevant historical data, and expert opinions. Information Aggregation: Now, let’s see how AI could address each of these challenges: Content creator AIs (“copilots”) assist in the creation of content beyond human capacities or motivation. AIs suggest timely and relevant event topics by analyzing trends from news, social media, and financial data. Content creators – whether human or AI – will be rewarded for generating engaging content that keeps their communities lively. Community feedback enhances the AI’s understanding of its communities, making it an iteratively improving content creation engine to bond content creators and their audiences. Content Creator AIs: Event recommender AIs tailor event suggestions to users based on their interests, trading history, and specific needs, focusing on recommending events ripe for debate and trading opportunities. It adapts to users’ behaviors across different regions, cultural contexts, and times. The end goal is a highly targeted feed of events, free from personally irrelevant content that clutters prediction market platforms today. Event Recommendation AIs: Liquidity allocator AIs tackle counterparty liquidity risk by optimizing liquidity injections to narrow the bid-ask spread. To minimize risk, AIs can implement the (LMSR) AMM model specifically designed for minimizing risk in low-liquidity prediction markets. They could also incorporate that dynamically adjust liquidity depth, protocol fees, and the bonding curve to further minimize risk. These AIs manage event liquidity from a general LP pool, rewarding contributions with accrued fee revenue or platform tokens as further incentives. All in all, this means preemptive adaptation to market changes, reduced slippage, and better price stability. Liquidity Allocator AIs: logarithmic market scoring rule reinforcement learning agents These AIs harness compute over a wide array of indicators (e.g., on-chain data, historical data, news, sentiment indicators) for players to comprehensively understand the event. From there, the information aggregation AIs can offer well-rounded projections, turning prediction markets into the go-to source for informed decision-making and alpha. Projects can choose to token-gate access to the insights gleaned by information aggregation AIs because in prediction markets, . Information Aggregation AIs: knowledge = money Now, let’s see what this looks like when you piece it together. Below, you can see the main components and workings of a prediction market without AIs (in black) and with AIs (in blue). In the non-AI model, content creators (usually the platform itself) arbitrarily create events, supply liquidity (initially subsidized by their treasuries), save the events to an event database and promote them in bulk to human players. This is how Polymarket currently works, and it's working quite well. But I think it can get a lot better. In the AI model, content creator copilot AIs support content creators in creating and promoting events inside targeted general or niche communities. Liquidity provision is supported by liquidity allocator AIs that optimize liquidity injections over time through learning player order books and using external data from oracles and other data vendors. Event recommendation AIs use stored events in the event database and wallet transaction history to optimize event recommendations tailored based on personal interests. Finally, information aggregation AIs collect information from data vendors to provide educational and contextual information to human players and to inform AI players about their prediction decisions. The end game? A fine-tuned prediction market system that enables prediction markets to work at a microscopic scale. Prediction markets at this scale would enable a different user experience, one that is more like Tinder or TikTok. As the events are highly targeted, they could be fed to you in a feed a la TikTok, and – even with today’s wallet and blockchain technology – players could place bets by swiping left or right a la Tinder. Imagine that: people making micro-bets on the events they personally care about while they’re commuting to work or school. Supercharging information aggregation Of the most notorious difficult outcomes to predict is asset prices, so let’s focus here to see how AIs perform when pushing at the edges of what is possible in prediction markets. Using AI to predict asset prices is actively being explored in academic circles. Machine learning (ML) techniques like linear models, random forests, and support vector machines have been to predict cryptocurrency prices with better accuracy than human judges. These models have discovered that behavioral indicators like Google search intensity explain price variance. shown IBM research artificial prediction markets for commodity price prediction, offering a compelling case study on integrating AI with prediction markets. Their research highlights the potential of artificial prediction markets to aggregate diverse and evolving real-time information sources for making better predictions even in complex real-world problems like predicting the prices of volatile commodities not traded on online exchanges (e.g., ethylene, hydrocarbons). The reason AI agents can outperform standard ML models here is that they learn over time by themselves – aka agency. explored Another study comparing random forest regression and LSTM to predict Bitcoin’s next-day price that the former performed better in terms of fewer prediction errors. It also showcased the power of AI in information aggregation breadth – far beyond ordinary human capacity – to model 47 variables across eight categories, including (a) Bitcoin price variables, (b) technical indicators of Bitcoin, (c) other token prices, (d) commodities; (e) market indices: (f) foreign exchange; (g) public attention); and (h) dummy variables of the week. The most important predictors varied over time from US stock market indices, oil price, and Ethereum price in 2015-2018 to Ethereum price and a Japanese stock market index in 2018-2022. It also found that for Bitcoin’s next-day price, the random forest regression performs best with a one-day lag. showed We can infer that in some popular prediction markets, there is simply too little time for a busy human to aggregate, analyze, and interpret sufficiently large amounts of data to make good predictions. Or the problems are simply too complex. But AIs can do this. AI token recommendation is building a decentralized foundational model of crypto, which has been applied in AI-generated token recommendations derived from on-chain behaviors. Currently, their large graph neural network (GNN) uses on-chain behavioral data to estimate the alpha probabilities of various tokens. GNNs are a class of AI models designed specifically to process data represented as graphs, making them useful where data is interconnected with a relational structure, such as the p2p transactional networks of blockchains. Pond Dither is another token recommendation AI with a token-gated Telegram alert bot that takes a time-series modeling approach to the token recommendation. Solving the thin markets problem One of the main challenges facing prediction markets is that the markets are too thin to attract enough players and volume. But there is a major difference between the prediction markets of the 2010s vs the 2020s, and that is the . As Vitalik points out: possibility of ubiquitous participation by AIs To add, it’s possible to the automated market maker (AMM) models underlying prediction markets. For example, an of over 2 million transactions on Polymarket identified problems with liquidity provisioning in converging prediction markets using the traditional constant product AMM (x*y=k), including: improve analysis As prediction markets converge (i.e., as the outcome becomes more certain), LPs are incentivized to remove their liquidity. because the risk of holding “losing” tokens increases. For example, in a market converging toward “yes,” the “no” tokens become less valuable (i.e., impermanent loss), posing a risk to LPs who might end up with worthless tokens if they don’t sell in advance. Convergence and liquidity removal. This is rational behavior This reduction in liquidity can lead to less accuracy and more bias as prediction markets converge. Specifically, in the volume-weighted price range of 0.2 to 0.8, ‘no’ tokens are often underpriced, and ‘yes’ tokens are often overpriced. Bias and inaccuracy. To address these issues, the authors propose a “ liquid market maker” (SLMM) model and demonstrate that it can increase volumes and accuracy in converging prediction markets. It does this by introducing a concentration function into the model (a la Uniswap v3) in which LPs provide a liquidity position that is only active for specific price intervals. The result is reduced risk exposure, ensuring that the number of valuable tokens (e.g., ‘yes’ tokens in the market converging to ‘yes’ outcome) held by LPs does not converge to zero as prices adjust, unlike in the constant product AMM. smooth LP-trader tradeoffs There is a balancing act that must be reached when choosing a concentrated liquidity AMM variant like the SLMM for converging prediction markets. While you’re trying to reduce risk for LPs, you end up disincentivizing some trading activity. Specifically, while concentrated liquidity can make it less likely that LPs lose out as the market converges on a sure outcome (thus reducing premature withdrawal), it may also reduce trading opportunities to profit on small price changes (e.g., like moving from $0.70 to $0.75) due to increased slippage, especially for large orders. The direct consequence is that traders’ potential profit margins are squeezed. For instance, if they expect a small price move from $0.70 to $0.75, the slippage may limit the capital they can effectively deplore to capture the expected upside. Looking forward, it will be important to trial various adjustments on the tradeoff term in these market-maker formulas to find the sweet spot. Conclusion The prediction market primitive is a powerful one. Of course, like any other crypto primitive, it faces challenges, but we are confident that they will be overcome. As they are gradually overcome, we can expect to see this primitive reused to answer all sorts of questions in a wide variety of digital contexts. With advancements in targeting and liquidity solutions, we can expect the development of niche prediction markets. For example, take X (formerly Twitter) users: Will X introduce a Premium++ or equivalent by the end of the year? Will X make the edit tweet feature available to all users by Q3? Will X report an increase in daily active users in the next quarterly report? Will X’s advertising revenue increase or decrease in the next quarter? Will X announce new major partnerships with content creators by the end of year? Will X release a blockchain or cryptocurrency-related feature by Q3? Interestingly, these questions don’t need to stay confined to standalone prediction market websites. They could be integrated directly into X or other platforms via browser extensions.  We may start to see micro-prediction markets pop up regularly in our everyday online experiences, enriching ordinary browsing with speculative trading opportunities. I intentionally wrote some of the questions above and asked ChatGPT to write the others. Which did I write, and which did the content creator AI write? If it’s hard to tell, that’s because ChatGPT’s content creator AI is already really good. So are the information aggregation AIs and recommendation engines built by other Big Tech (look at the ads Google and Instagram feed you). While matching the performance of these models will take work and time, they demonstrate the feasibility of these AI categories. The main open question lacking precedent is more in the direction of liquidity allocator AIs, AI players, and the development of self-improvement and goal-directedness in AIs – the evolution from basic machine learning to verifiable AI agents. If you’re building in these spaces or this post resonates with you, do ! reach out Relevant Reading Buterin, V. (2024). . The promise and challenges of crypto + AI applications Rein Y. Wu, et al., Intent-centric Prediction Market with AI and Web3 Technology, Blockchain - Pioneering the Web3 Infrastructure for an Intelligent Future, 2024. Kapp-Schwoerer, L. (2023). . Improved Liquidity for Prediction Markets Author Affiliation Hiroki Kotabe is the Research Principal at Inception Capital (formerly OP Crypto). Inception Capital is a first-check venture fund focusing on early-stage web3 startups and emerging fund managers. Since raising $50M for Inception Venture Fund I in September 2021, they have been at the forefront of the industry, investing at the earliest stage in over 30 projects, such as Scroll, Merit Circle, Avalanche, and Celestia. Their $30M Inception Fund of Funds has invested in web3 funds like Orange DAO, Escape Velocity, Alliance, Syncrasy, and Everyrealm.

The Prediction Market Primitive: Using AIs to Create Prediction Markets at Microscopic Scale

To write this text, I approached everyone from our CTO to product owners and managers of our projects at to give you as comprehensive a picture as possible. And just to be clear: I'm not mixing anything up. Scale Final On the one hand, as a startup, we spend a lot of time building the bark of all processes, not looking for trends and trying to stay ahead of the competition. But because we love the fast pace of work and technological progress and ultimately develop products ourselves, we know what’s happening in the world. This is also reflected in our slogan: 'The world moves fast. But we are faster.' So you've come to the right place if you want to learn about technology trends and how they are implemented by a real team. All the experts I spoke to agreed on one thing. You guessed it. AI, namely LLM, is the most important tech trend of 2024 that will eclipse everything else. 1. LLMs in Product Development a. Programmers are increasingly turning to LLM tools such as copilot for various tasks. AI assistants don’t just write a new code for you. With copilot, developers interact with the entire software development lifecycle differently. LLMs have fundamentally changed the game: from suggesting optimizations and refactoring existing code to debugging and explaining complex code snippets. How Scale Final tackles this: Of course, our development team has integrated LLM tools into workflows. We use these tools to understand our code base and development practices better. We have also increased our productivity. We can tackle more complex problems faster as AI provides insights and suggestions that are not immediately obvious to even the most experienced of us. From organizing project documentation to summarizing key points from meetings and reports, you name it, LLMs make information management a more easily manageable task. b. Managers use LLMs to streamline the vast amounts of information they deal with daily. Our management teams use LLMs for a wide array of things: from auto-generated reports and formatting documents according to company standards to even email composition. How Scale Final tackles this: This not only saves our precious time but also ensures that all our communication and documentation are consistent throughout the channels, improving overall project management efficiency. By feeding LLMs with market research, customer feedback, and performance data, PMs can come up with innovative ideas for features, improvements, and even entirely new products. c. Product managers find LLMs to be powerful allies in the brainstorming process. Scale Final's product managers use LLMs as brainstorming partners, that are challenging our ideas and assumptions. This AI-driven approach to ideation helps us discover unique angles and opportunities we might have overlooked to ensure our products remain competitive and meet the evolving customer needs. How Scale Final tackles this: LLMs not only help with proofreading and finding references, but also with developing ideas, structuring articles, and ensuring that the tone and style of a text are consistent. d. Writers are perhaps the most obvious beneficiaries of LLM advancements. Our content creation process and LLMs are interlinked. Writers at Scale Final use these tools to increase both productivity and creativity. Every piece of content is not only well-written but also well-structured. LLMs assist in everything from creating first drafts to final proofreading, making the content creation process more efficient and effective. How Scale Final tackles this: LLMs are finding their way into almost every product imaginable, from customer service bots that can understand and effortlessly answer complex queries to personalized learning platforms that adapt to each user's pace and learning style. 2. LLM in products We're integrating LLM into our B2B products that improve the user experience by automating their everyday tasks. This allows our clients' employees to focus on more strategic and creative tasks, but also significantly increases their satisfaction and productivity, not to mention have more rest. What Scale Final does: A few more words on tech perspectives Our experts agree that in the coming year, not only will the possibilities and applications of LLMs expand, but how companies and consumers perceive and interact with AI technologies will also change significantly.

Tech Trends 2024: Predictions and Perspectives From Scale Final Experts

টেক ট্রেন্ডস 2024: স্কেল চূড়ান্ত বিশেষজ্ঞদের কাছ থেকে ভবিষ্যদ্বাণী এবং দৃষ্টিকোণ

Tech-Trends 2024: Vorhersagen und Perspektiven von Scale-Final-Experten

Tendencias tecnológicas 2024: predicciones y perspectivas de los expertos en escala final

Tendances technologiques 2024 : prévisions et perspectives des experts finaux à l'échelle

तकनीकी रुझान 2024: स्केल फ़ाइनल विशेषज्ञों की भविष्यवाणियाँ और परिप्रेक्ष्य

2024 年の技術トレンド: スケールファイナル専門家による予測と展望

2024년 기술 동향: 대규모 최종 전문가의 예측 및 관점

Tendências tecnológicas para 2024: previsões e perspectivas de especialistas finais em escala

Технологические тенденции 2024: прогнозы и перспективы от экспертов Scale Final

Teknoloji Trendleri 2024: Ölçek Nihai Uzmanlarından Tahminler ve Perspektifler

Xu hướng công nghệ 2024: Dự đoán và quan điểm từ các chuyên gia cuối cùng về quy mô

2024 年技术趋势：规模最终专家的预测和观点

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References 10 CONCLUSION Our study follows a socio-technical grounded theory approach to gain a deeper understanding of inclusiveness related user feedback from end users. Across manual analysis of over 23K user feedback posts from Reddit, Twitter, and Google Play Store regarding 50 popular for profit apps, we build a taxonomy of inclusiveness. Our taxonomy has six main categories, including fairness, technology, privacy, demography, usability, and other human values. The classifier that we train on our data shows that we can automatically identify inclusiveness related feedback among general user feedback. Our results indicate to practitioners that these online sources contain a rich trove of inclusiveness feedback that organizations should consider to build more inclusive software products for diverse end users. We also present our labelled dataset that researchers can use to refine tooling to better support practitioners. REFERENCES [1] A. Savidis and C. Stephanidis, “Inclusive development: Software engineering requirements for universally accessible interactions,” Interacting with Computers, vol. 18, no. 1, pp. 71–116, 2006. [2] J. Shepherd, “23 essential twitter statistics you need to know in 2023,” https://thesocialshepherd.com/blog/twitterstatistics#: :text=TwitterMay 2023. [3] T. Gaurdian, “Twitter apologises for ’racist’ image-cropping algorithm,” https://www.theguardian.com/technology/2020/sep/21/twitterapologises-for-racist-image-cropping-algorithm, 2020. [4] Z. S. Li, M. Sihag, N. N. Arony, J. B. Junior, T. Phan, N. Ernst, and D. Damian, “Narratives: the unforeseen influencer of privacy concerns,” in 2022 IEEE 30th International Requirements Engineering Conference (RE). IEEE, 2022, pp. 127–139. [5] M. Shahin, M. Zahedi, H. Khalajzadeh, and A. R. Nasab, “A study of gender discussions in mobile apps,” arXiv preprint arXiv:2303.09808, 2023. [6] A. Alshayban, I. Ahmed, and S. Malek, “Accessibility issues in android apps: state of affairs, sentiments, and ways forward,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, 2020, pp. 1323–1334. [7] H. Khalajzadeh, M. Shahin, H. O. Obie, P. Agrawal, and J. Grundy, “Supporting developers in addressing human-centric issues in mobile apps,” IEEE Transactions on Software Engineering, 2022. [8] J. Tizard, T. Rietz, X. Liu, and K. Blincoe, “Voice of the users: an extended study of software feedback engagement,” Requirements Engineering, vol. 27, no. 3, pp. 293–315, 2022. [9] R. Hoda, “Socio-technical grounded theory for software engineering,” IEEE Transactions on Software Engineering, vol. 48, no. 10, pp. 3808–3832, 2021. [10] K. B. M. , “Designing inclusive software in Windows - Windows apps,” https://learn.microsoft.com/enus/windows/apps/design/accessibility/designing-inclusivesoftware, may 13 2022. [11] M. Pattison and A. W. Stedmon, “Inclusive design and human factors: Designing mobile phones for older users.” PsychNology J., vol. 4, no. 3, pp. 267–284, 2006. [12] D. Metaxa-Kakavouli, K. Wang, J. A. Landay, and J. Hancock, “Gender-inclusive design: Sense of belonging and bias in web interfaces,” in Proceedings of the 2018 CHI Conference on human factors in computing systems, 2018, pp. 1–6. [13] M. Burnett, S. Stumpf, J. Macbeth, S. Makri, L. Beckwith, I. Kwan, A. Peters, and W. Jernigan, “Gendermag: A method for evaluating software’s gender inclusiveness,” Interacting with Computers, vol. 28, no. 6, pp. 760–787, 2016. [14] I. Nunes, A. Moreira, and J. Araujo, “Conceptual modeling of gender-inclusive requirements,” in International Conference on Conceptual Modeling. Springer, 2021, pp. 395–409. [15] ——, “Gire: Gender-inclusive requirements engineering,” Data & Knowledge Engineering, vol. 143, p. 102108, 2023. [16] M. Vorvoreanu, L. Zhang, Y.-H. Huang, C. Hilderbrand, Z. SteineHanson, and M. Burnett, “From gender biases to gender-inclusive design: An empirical investigation,” in Proceedings of the 2019 CHI Conference on human factors in computing systems, 2019, pp. 1–14. [17] J. C. Grundy, “Impact of end user human aspects on software engineering.” in ENASE, 2021, pp. 9–20. [18] J. Grundy, I. Mueller, A. Madugalla, H. Khalajzadeh, H. O. Obie, J. McIntosh, and T. Kanij, “Addressing the influence of end user human aspects on software engineering,” in International Conference on Evaluation of Novel Approaches to Software Engineering. Springer, 2021, pp. 241–264. [19] S. Donetto, P. Pierri, V. Tsianakas, and G. Robert, “Experiencebased co-design and healthcare improvement: realizing participatory design in the public sector,” The Design Journal, vol. 18, no. 2, pp. 227–248, 2015. [20] E. C. Groen, J. Doerr, and S. Adam, “Towards crowd-based requirements engineering a research preview,” in Requirements Engineering: Foundation for Software Quality: 21st International Working Conference, REFSQ 2015, Essen, Germany, March 23-26, 2015. Proceedings 21. Springer, 2015, pp. 247–253. [21] D. Pagano and B. Bruegge, “User involvement in software evolution practice: A case study,” in 2013 35th International Conference on Software Engineering (ICSE). IEEE, 2013, pp. 953–962. [22] E. C. Groen, N. Seyff, R. Ali, F. Dalpiaz, J. Doerr, E. Guzman, M. Hosseini, J. Marco, M. Oriol, A. Perini et al., “The crowd in requirements engineering: The landscape and challenges,” IEEE software, vol. 34, no. 2, pp. 44–52, 2017. [23] W. Maalej and H. Nabil, “Bug report, feature request, or simply praise? on automatically classifying app reviews,” in 2015 IEEE 23rd international requirements engineering conference (RE). IEEE, 2015, pp. 116–125. [24] G. Williams and A. Mahmoud, “Mining twitter feeds for software user requirements,” in 2017 IEEE 25th International Requirements Engineering Conference (RE). IEEE, 2017, pp. 1–10. [25] T. Iqbal, M. Khan, K. Taveter, and N. Seyff, “Mining reddit as a new source for software requirements,” in 2021 IEEE 29th International Requirements Engineering Conference (RE). IEEE, 2021, pp. 128–138. [26] M. Fazzini, H. Khalajzadeh, O. Haggag, Z. Li, H. Obie, C. Arora, W. Hussain, and J. Grundy, “Characterizing human aspects in reviews of covid-19 apps,” in Proceedings of the 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems, 2022, pp. 38–49. [27] L. Olson, E. Guzman, and F. Kunneman, “Along the margins: ´ Marginalized communities’ ethical concerns about social platforms,” arXiv preprint arXiv:2304.08882, 2023. [28] E. Guzman, M. Ibrahim, and M. Glinz, “A little bird told me: Mining tweets for requirements and software evolution,” in 2017 IEEE 25th International Requirements Engineering Conference (RE). IEEE, 2017, pp. 11–20. [29] Watchful1, “Subreddit comments/submissions 2005- 06 to 2022-12.” [Online]. Available: https: //www.reddit.com/r/pushshift/comments/11ef9if/separate dump files for the top 20k subreddits/ [30] N. N. Arony, Z. S. Li, B. Xu, and D. Damian, “Inclusiveness Matters: A Large-Scale Analysis of User Feedback,” Oct. 2023. [Online]. Available: https://doi.org/10.5281/zenodo.10050673 [31] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in International Conference on Machine Learning. PMLR, 2023, pp. 28 492–28 518. [32] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pretraining of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018. [33] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019. [34] A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzman, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov, ´ “Unsupervised cross-lingual representation learning at scale,” arXiv preprint arXiv:1911.02116, 2019. [35] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” arXiv preprint arXiv:1910.13461, 2019. [36] M. Sihag, Z. S. Li, A. Dash, N. N. Arony, K. Devathasan, N. Ernst, A. B. Albu, and D. Damian, “A data-driven approach for finding requirements relevant feedback from tiktok and youtube,” in 2023 IEEE 31st International Requirements Engineering Conference (RE). IEEE, 2023, pp. 111–122. [37] R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to imbalanced datasets,” in Machine Learning: ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004. Proceedings 15. Springer, 2004, pp. 39–50. [38] S. Parashar, S. Dhar, and U. Dhar, “Perception of values: a study of future professionals,” Journal of Human Values, vol. 10, no. 2, pp. 143–152, 2004. [39] S. H. Schwartz, “Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries,” in Advances in experimental social psychology. Elsevier, 1992, vol. 25, pp. 1–65. [40] Y. Brun and A. Meliou, “Software fairness,” in Proceedings of the 2018 26th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, 2018, pp. 754–759. [41] S. Akter, G. McCarthy, S. Sajib, K. Michael, Y. K. Dwivedi, J. D’Ambra, and K. N. Shen, “Algorithmic bias in data-driven innovation in the age of ai,” p. 102387, 2021. [43] J. Larson, S. Mattu, L. Kirchner, and J. Angwin, “How We Analyzed the COMPAS Recidivism Algorithm.” [Online]. Available: https://www.propublica.org/article/ how-we-analyzed-the-compas-recidivism-algorithm [44] A. Tobin and A. Kofman, “Facebook Finally Agrees to Eliminate Tool That Enabled Discriminatory Advertising,” Jun. 2022. [Online]. Available: https://www.propublica.org/article/ facebook-doj-advertising-discrimination-settlement [45] J. Chakraborty, S. Majumder, and T. Menzies, “Bias in machine learning software: Why? how? what to do?” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021, pp. 429–440. [46] S. Krishnan, J. Patel, M. J. Franklin, and K. Goldberg, “A methodology for learning, analyzing, and mitigating social influence bias in recommender systems,” in Proceedings of the 8th ACM Conference on Recommender systems, 2014, pp. 137–144. [47] V. Tsintzou, E. Pitoura, and P. Tsaparas, “Bias disparity in recommendation systems,” arXiv preprint arXiv:1811.01461, 2018. [48] G. Hofstede, “Cultures and organizations: Software of the mind (london and new york, mcgraw hill),” House, RJ, Hanges, PJ, Javidan, M., Dorfman, PW, & Gupta, V.(Eds. 2004), Airaksinen, pp. 1–25, 1991. [49] N. Al-Huwail, S. Al-Sharhan, and A. Al-Hunaiyyan, “Learning design for a successful blended e-learning environment: Cultural dimensions,” INFOCOMP Journal of Computer Science, vol. 6, no. 4, pp. 60–69, 2007. [50] R. A.-L. Fischer, R. Walczuch, and E. Guzman, “Does culture matter? impact of individualism and uncertainty avoidance on app reviews,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS). IEEE, 2021, pp. 67–76. [51] D. Potnis, “Culture’s consequences: Economic barriers to owning mobile phones experienced by women in india,” Telematics and Informatics, vol. 33, no. 2, pp. 356–369, 2016. [52] A. De Angeli, U. Athavankar, A. Joshi, L. Coventry, and G. I. Johnson, “Introducing atms in india: a contextual inquiry,” Interacting with Computers, vol. 16, no. 1, pp. 29–44, 2004. [53] M. R. Roller and P. J. Lavrakas, Applied qualitative research design: A total quality framework approach, ser. Applied qualitative research design: A total quality framework approach. New York, NY, US: The Guilford Press, 2015, pages: xviii, 398. This paper is under CC 4.0 license. available on arxiv

A Large-Scale Analysis of Inclusiveness-Related User Feedback : Conclusion & References

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References 9 DISCUSSION In this study, we propose a taxonomy of inclusiveness related user feedback based on an in-depth analysis of user feedback on fifty popular software apps using methods from socio-technical grounded theory [9]. Our approach involved labelling over 23 thousand user feedback posts across three popular sources of user feedback: Reddit, Google Play Store, and Twitter. The analysis resulted in identifying 1,211 user feedback related to inclusiveness. Having developed a labelled set, we also conducted an empirical investigation on a set of popular pre-trained large language models to classify user feedback from the three sources into inclusiveness or not. We achieved an F1-scores of 0.838. 0.849 and 0.938 for Reddit, Google Play Store, and Twitter, respectively. The promising scores indicate that the models are effective in identifying the inclusiveness-related user feedback. 9.1 Comparison with Related Work Previous work on human aspects provided a preliminary introduction to inclusiveness based on their analysis from GitHub and Google Play Store [7] from both the developer and user perspectives. However, our study focused specifically on inclusiveness from the end user perspective. Furthermore, the prior work focused on open-source software, which may not reflect the entirety of end users or software apps. Only a fraction of users use open-source software, and even fewer provide user feedback. The prior research reported only 31 posts from Google Play Store related to inclusiveness, whereas our work identified a higher amount (1,211), indicating a greater representation of inclusiveness. Additionally, the vast majority of apps, especially the popular ones, are not open source, ranging from social media, entertainment, and business apps. Thus, an understanding of inclusiveness issues from a larger user base was required. In our study, we focused on popular for-profit apps used by millions of users across the world to obtain a more diverse representation of users. In the work by Khalajzadeh et al. [7], the authors included 5 sub-categories under inclusiveness: compatibility, location, language, accessibility, and others. In our study, we identified the presence of these sub-categories. However, we categorize them under different higher-level categories for better restructuring. The compatibility category, for instance, aligns closely with our technology category. Their category is primarily about the compatibility across different devices and platforms, considering socio-economic factors. Whereas we focus on concerns related to users encountering restrictions set by developers. We include socio-economic concerns under demography in our taxonomy as the feedback is more in line with dimensions of demographics. Regarding the location and language sub-categories, we choose to reclassify them within the demography category in this study. This was done as the two categories were not as dominant in our dataset compared to the previous work. Hence, amalgamating them into the demography category helped to achieve more clarity and better structure. Furthermore, we encountered only a small amount of user feedback explicitly emphasizing accessibility. Whereas we observed a broader distribution of posts focusing on visual and audio usability. Therefore, any accessibility posts were subsequently placed under the two types of usability, ensuring a more structured distribution of the user feedback related to accessibility concerns. 9.2 Algorithmic Bias in software apps: A Barrier to Inclusiveness Our manual analysis allowed us to uncover various inclusiveness issues that users face while using the software. One of the key problems that emerged throughout the analysis was algorithmic bias. With the rapid evolution of AI, companies are increasingly inclined towards integrating algorithms in their decision making process. As such, many features are automated and often exhibit biases towards certain users [40]. Algorithmic bias predominately originates from underrepresented data and biased methods [41]. These biases create both a perceived and real non-inclusive experience for users. In Section 5, when we discussed the fairness category, we described how the automated decision-making process leads to the exclusion of users from software or specific features in a software. For example, Facebook heavily relies on their algorithms for content review process. AI decides if a content is allowed on their platform based on Community Standards [42]. Our analysis revealed many users complaining about being banned from the platform due to policy violations without warning nor prior notification. In these cases, users have no idea what action even triggered the ban or account restriction. Despite the company asserting that users can seek a repeal if they believe their content aligns with the community standards, our observations in our fairness category reveals a contrasting reality. - (Reddit) “I wanted to get started with Facebook ads and was really motivated about it. Only to find out that somehow my account on Meta Business was disabled, I tried to appeal but it somehow got rejected. I ended up purchasing another domain in order to create another account but during the creation of the facebook account (even though all the information was different compared to my main account), it instantly got disabled and told me to appeal. I feel like the situation is going to repeat itself. I’ve tried hard to find some support, but after hours of searching I didn’t find anything.” (Facebook) Our category has numerous similar examples where users experience a lack of support and must rely on automated algorithms to make further decisions. Similar to this example, many software organizations incorporate AI to make decisions and that creates more frustration and a feeling of exclusion amongst the users. We find similar patterns manifesting in other inclusiveness categories as well, such as other human values, where users report frustrations from apps enforcing their beliefs on users. In an ideal scenario, recommender systems would learn from user preferences and tailor them. However, we observe opposite instances where users receive engagements and recommendations that deviate from what they actually anticipate or prefer. fairness In recent years, there have been numerous instances of biased AI systems that have come to light. Famous incidents exposing biased AI systems include the COMPAS recidivism algorithm [43], which had a significantly greater likelihood of incorrectly judging black defendants in comparison to white defendants, whereby black defendants were more likely flagged as high risk. Recently, Meta, formerly known as Facebook, agreed to a settlement after it was revealed they implemented features in its advertising to exclude specific groups of people [44]. Stemming from these examples, the study of reducing bias in machine learning systems is actually a large subject area [45]. Several studies explore reducing bias in AI systems, particularly for those that conduct automated decision making [46], [47]. These studies attempt to research how to minimize algorithmic bias from a data collection, model training and testing level. However, as our study indicates, organizations should avoid completely relying on AI for decision making, whether it is adhering to community standards or generating recommendations. 9.3 Culture: A Factor impacting Inclusiveness An interesting observation emerging from our analysis indicates the potential underlying influence of culture on the inclusiveness related user feedback. Culture is defined as the “collective programming of the mind which leads to a common way of doing things by a group of people in a larger society” [48]. The taxonomy proposed in our study illustrates how a lack of inclusiveness can impact a user’s experience using a software. The apps we analyzed have millions of users from different cultural backgrounds. However, app developers may not be aware of all the different cultural aspects, expectations, norms, and experiences. This may lead to the development of software that fails to meet the users’ expectations, resulting in a lack of inclusiveness. To discuss the potential influence of culture, we draw on Hofstede’s Cultural Dimensions Theory [48] and consider, for example, the concepts of individualism and collectivism. Technology typically demonstrates the characteristics of the culture in which it is developed in [49]. However, depending on the user and their cultural background (i.e., individualistic or collectivistic), they may expect different functionalities. When software fails to adhere to different cultural expectations, users feel frustrated and consider leaving the app. The frustrations may be attributed to complicated or biased features, lack of available technology, and even technological literacy. From our taxonomy categories like privacy, demography, and other human values, we see representative quotes that indicate a potential impact of ethnic culture and user preferences. This is a fruitful direction for future research as the cultural impact on system usage and end user preferences is largely unexplored. A study on culture and user feedback reported that aspects like length of the review, sentiment, ratings and amount of useful feedback provided by users on app reviews can indicate the user’s cultural background in terms of Uncertainty Avoidance and individualism/collectivism [50]. However, these aspects focus more on the characteristics of the feedback and lack an analysis of the actual content, which may help to understand the challenges users encounter due to cultural differences. In our study, for instance, we found a user wishing the app would allow access to his wife, which exemplifies a collectivist point of view. - Similarly, in the subcategory under we found users expressing a desire to include family members in their software. The inability to do so results in a sense of exclusion. (Play Store) “I like the app but you need to change your policy I would like to add access for my wife.” (Robinhood) benevolence other human values, A study conducted in rural India reported that often, only one male member of the family owns a mobile phone, and other members can only use it when the male member is around [51]. Another telling example is from an ethnographic study of ATM introduction in India where it is highlighted that people often shared their bank account cards with friends and family [52]. When first introduced to the ATM, they approached the learning process as a group and showed less concern about sharing sensitive information. This suggests a collectivist cultural perspective where concern for privacy is not as prominent as in an individualistic culture. Thus, depending on the cultural background, users may prefer software features that accommodate their situation. Failing to consider these cultural nuances can generate a sense of exclusion among users and influence them to stop using the app entirely. Therefore, for a software to be more inclusive, it is important that users’ cultural context is understood. 9.4 Implications for Practitioners For practitioners, our empirical study shows the importance of considering user feedback for inclusiveness, as well as provides a practical approach to identifying inclusiveness related user feedback for their software. We detail a number of implications for practitioners here: Developers are predominantly male, technically skilled, and affluent, therefore significantly different from the diverse end-users they serve. Awareness of the inclusiveness issues will allow them to learn and consider the diverse user needs and develop more inclusive software. The taxonomy of inclusiveness can be used to categorize user feedback so that the issues are easy to identify and resolve. 2) Even though automated AI-enabled systems are useful, they lack a sense of inclusiveness, and the issue is becoming a serious concern because generative AI techniques are deployed aggressively now. Our study highlights these inclusiveness issues that practitioners should recognize and consider during development. The increasing use of AI-enabled systems is resulting in a lack of inclusiveness which requires more attention. 3) Many software companies are often resource constrained and lack the resources to address every single user need. Our study results brings categories identified from five types of software: business, entertainment, financial, shopping, and social media. We indicate which categories are more prevalent in each type of software that the companies can leverage accordingly. Companies can prioritize the specific inclusiveness concerns based on our findings for each type of app. 4) Online platforms like Reddit, Google Play Store, and Twitter generate a large number of user reviews, posing a challenge for companies to manually identify the issues. A plugin tool that automatically flags inclusiveness issues could enable companies to easily detect the inclusiveness related feedback from the respective pages on Reddit, Twitter, and Google Play Store. The automated approach proposed in this study provides potential solutions in the form of automated flagging (i.e. a plugin) on source platforms to address the limitations of the current manual approach in identifying inclusiveness related user feedback. 9.5 Implications for Researchers Our findings carry several implications for future work: More research should be conducted with practitioners to understand how they address the inclusiveness user feedback, particularly how organizations manage inclusiveness requirements. 2) Researchers should focus more attention on studying additional sources of user feedback. These additional sources may help refine the categories and subcategories in our taxonomy. 3) Our study presents a large number of manually labelled inclusiveness related user feedback. Future researchers can leverage this data to conduct studies on improving the automated classification approach and automated identification of the categories of inclusiveness. 4) We found that inclusiveness concerns are often the result of human value violations. A number of issues are related to Schwartz’s theory [39]. Future research can further explore if there is a prevalence of other categories from the theory. 5) In addition, we suggest that culture may potentially influence end-users’ perception of inclusiveness. Therefore, we believe that studying the cultural context from the end user perspective is valuable as it may help make software more inclusive. 9.6 Threats to Validity We describe several threats and mitigation strategies in our study using the total quality framework of Roller [53]. indicates “the completeness and accuracy associated with data gathering” [53]. This study may have the threat of sampling bias because we collected user feedback from 50 apps from the sources of feedback. However, we selected a diverse group of apps, and the feedback sources are also common platforms that users often use to discuss concerns. Our study also used standard web scraping libraries to collect the data. Additionally, we try limiting bias by creating a randomly sampled batch of user feedback to conduct manual annotation. We did not seek to give more weight to any particular app or source of feedback. Credibility refers to “completeness and accuracy related to the processing and verification of data” [53]. We analyze the data with two co-authors who follow a social technical grounded theory approach [9], where open coding, constant comparison, and memoing were used to analyze the feedback for inclusiveness. Furthermore, the co-authors were in constant dialogue during the coding process to ensure consistency and remove bias. Since this study leverages user feedback from three popular sources (i.e., Reddit, Twitter, and Google Play) and different apps, we were able to triangulate our analysis with the different sources. Analyzability is the “completeness of the final documents and the degree to which the research can be fully evaluated and its transferability” [53]. We provide extensive and rich descriptions of our methodology, as well as detailed quotes to support our taxonomy. The entirety of our data is provided in our replication package, including our manually labelled dataset [30]. Transparency specifies the “ability to do something of value with the research outcomes” [53]. Our study aims to shed more insight into the role of inclusiveness in user feedback. More importantly, our study aims to advance the state of knowledge of inclusiveness by providing a taxonomy for the different types of inclusiviness related discussions. In particular, our study encompasses a significant number of user feedback and includes more empirical insights for organizations. We acknowledge that our results may not hold for every software app, but we believe organizations can benefit from the inclusiveness categories as they try to consider the concerns from diverse end users. Usefulness This paper is under CC 4.0 license. available on arxiv

A Large-Scale Analysis of Inclusiveness-Related User Feedback: Discussion

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References 2 MOTIVATION Inclusive software is about designing with everyone in mind and considering the full range of human diversity [10]. Traditionally, software requirements are gathered and incorporated into the software with a more technical focus, often leading to a lack of consideration for diverse user needs. As a result, many users are unable to access particular features or, in some cases, are excluded from using the entire software. App reviews, Reddit posts or tweets offer users a space to express their opinions regarding the software. These sources serve as a rich source of information that can be useful in identifying problems that exclude users from the software. Figure 1 presents an example from Google Play Store where the user feels excluded from using dark mode on Android and removing the AI bot as they are not a paid subscriber, indicating technical and socio-economic restrictions. Similarly, the Reddit post in Figure 3 and tweet in Figure 2 demonstrates how Facebook disabled the user’s account without any explanation and left no further options to appeal. In the Facebook examples, the users experience exclusion from the software as they are unable to access their accounts entirely. Overlooking such issues can eventually lead to the exclusion of a significant number of users from leveraging the full potential of the software. To further illustrate, consider an instance where an app does not support multiple languages. In such cases, it excludes users who do not understand the provided language. Many such issues can be identified from online user feedback that companies should take into account to ensure a more inclusive user experience. Inclusiveness related issues differ from conventional bug reports and feature requests, as they centre around the user experience rather than only technical aspects. Often, these issues stem from a lack of awareness, and the 3 examples show that there is a need to address inclusiveness related concerns. As illustrated in the examples, online sources carry insights into the various inclusiveness problems encountered by the diverse users of the software. Gaining more knowledge about inclusiveness issues can help raise awareness and design more inclusive software. Therefore, an in depth understanding of the inclusiveness related issues faced by diverse users is needed. This paper is under CC 4.0 license. available on arxiv

A Large-Scale Analysis of User Feedback: Motivation

Authors: (1) Nowshin Nawar Arony; (2) Ze Shi Li; (3) Bowen Xu; (4) Daniela Damian. Table of Links Abstract & Introduction Motivation Related Work Methodology A Taxonomy of Inclusiveness Inclusiveness Concerns in Different Types of Apps Inclusiveness Across Different Sources of User Feedback Automated Identification of Inclusiveness User Feedback Discussion Conclusion & References Abstract In an era of rapidly expanding software usage, catering to the diverse needs of users from various backgrounds has become a critical challenge. Inclusiveness, representing a core human value, is frequently overlooked during software development, leading to user dissatisfaction. Users often engage in discourse on online platforms where they indicate their concerns. In this study, we leverage user feedback from three popular online sources, Reddit, Google Play Store, and Twitter, for 50 of the most popular apps in the world to reveal the inclusiveness-related concerns from end users. Using a Socio-Technical Grounded Theory approach, we analyzed 23,107 posts across the three sources and identified 1,211 inclusiveness related posts. We organize our empirical results in a taxonomy for inclusiveness comprising 6 major categories: Fairness, Technology, Privacy, Demography, Usability, and Other Human Values. To explore automated support to identifying inclusiveness-related posts, we experimented with five state-of-the-art pre-trained large language models (LLMs) and found that these models’ effectiveness is high and yet varied depending on the data source. GPT-2 performed best on Reddit, BERT on the Google Play Store, and BART on Twitter. Our study provides an in-depth view of inclusiveness-related user feedback from most popular apps and online sources. We provide implications and recommendations that can be used to bridge the gap between user expectations and software so that software developers can resonate with the varied and evolving needs of the wide spectrum of users. —inclusion, diversity, user feedback, human aspects, deep learning Index Terms 1 INTRODUCTION As software usage continues to grow worldwide, an increasingly diverse user base is engaging with the applications. The diverse group includes individuals from various genders, regions, cultures, socio-economic backgrounds, political beliefs, people with physical and cognitive abilities, values, and educational backgrounds, among many others. However, software is often built for the “average user” [1] and fails to adhere to the diverse user needs. For instance, Twitter (currently known as X), a widely used social networking app with over 390 million global users [2], released an image cropping algorithm that automatically cropped images. It focused on important parts, such as faces and text, to optimize space on the main feed and allow multiple pictures in a single tweet. However, users soon identified that the algorithm could only detect white faces and cropped out faces of black people [3]. The topic soon became trending as thousands of users joined the discussion. Similarly, numerous other incidents have emerged from online user feedback [4], highlighting the lack of inclusiveness in software. In fact, the feedback provided by users on online platforms (e.g. app reviews) has grown significantly in amount and significance to software organizations. Software companies are not only able to identify areas of product improvement based on such feedback but also to learn about the inclusiveness aspects of software. In this space, Crowd Requirement Engineering (CrowdRE) has become a popular area of study for identifying product relevant information from large volumes of user feedback in various online platforms such as app stores, social media, and forums. A growing body of research in “end user human aspects” has attempted to address and understand aspects like gender and accessibility using CrowdRE sources such as App reviews [5], [6]. Khalajzadeh et al. [7] studied user feedback from Google Play Store and developer discussion from GitHub to understand the human aspects related conversations from 12 open source apps. The authors found inclusiveness related discussions from both sources (31 from Google Play Store and 31 from Github). While insightful, open-source applications represent only a portion of the many applications used in our society and, therefore, can result in limited user feedback and, more importantly, a lack of representation of diverse opinions. Therefore, there is a need for a more extensive exploration of the inclusiveness category from a larger, therefore more diverse, user base. The analysis may reveal variations in inclusiveness concerns depending on the type of software, insights which can help companies focus on their users’ specific needs. Furthermore, with the increasing number of user feedback platforms (e.g., social media), diverse users may prefer using different mediums due to different levels of engagement with particular online platforms [8]. Thus, exploring a variety of sources of feedback can reveal more insights about inclusiveness. Finally, the growing amount of user feedback, while useful, represents a significant manual effort for software organizations, making the automation in identifying inclusiveness-related user concerns worth the effort to reduce the manual overload of analysis. Our study aims to fill this gap through a largescale analysis of user feedback for 50 of the most popular apps with millions of users, from Google Play Store, Reddit, and Twitter. Guided by the following research questions, we employed a Socio-technical grounded theory (STGT) approach [9] to analyze the user feedback we collected from these multiple sources and software apps. What are the different types of inclusiveness related user feedback found on online sources? RQ1 How does inclusiveness related user feedback differ for different types of apps? RQ2 How does inclusiveness related user feedback differ across different sources of feedback? RQ3 How effective are pre-trained large language models in automatically identifying inclusiveness related user feedback? RQ4 We collected over 10 million posts and examined the inclusiveness related user feedback both through qualitative analysis and by experimenting with large language models to automatically identify inclusiveness related user feedback. Our study provides the following contributions: • We propose a two-layer taxonomy of inclusiveness based on user feedback from 50 of the most popular apps in a variety of types of software. Comprising of categories of inclusiveness concerns such as fairness, technology, privacy, demography, usability and other human values, the taxonomy advances our preliminary, limited understanding of user inclusiveness-related concerns developed in previous research from only open source projects; • Insights into the different inclusiveness concerns in different application types and feedback sources. • A manually annotated dataset of inclusiveness-related user feedback that can facilitate future research and practice. • Insights and empirical results from using large language models to automatically identify inclusiveness related user feedback from multiple sources and which companies and practitioners can leverage to address the inclusiveness concerns of their end users. This paper is under CC 4.0 license. available on arxiv

Inclusiveness Matters: A Large-Scale Analysis of User Feedback

How are you, hacker?
🪐 has got you covered with fresh content from our trending stories of the day! Set email preference . Want to know what's trending right now?: The Techbeat by HackerNoon here Navigating Complexity: The Challenges of Managing Large-scale Projects By [ 7 Min read ]
Managing large-scale projects becomes challenging with the involvement of thousands of participants in project implementation. @ruslanzaripov Read More. The Last-Mile Delivery Revolution: What’s Next on the Horizon? By [ 8 Min read ]
As online orders surge and customers demand ever faster times, last-mile logistics is transforming at warp speed. @dmitrybardysh Read More. This Guy Thinks Wearing Pink Will Get You to Like Him By [ 5 Min read ]
Edward Sturm wants to be the nerd Gary Vee and is getting there by going all in on content optimizations and automations. @hcss11 Read More. The Bitcoin Mining Writing Contest by GoMining By [ 3 Min read ]
Welcome to the Bitcoin Mining Writing Contest by GoMining and HackerNoon. @hackernooncontests Read More. How to Use an Uncensored AI Model and Train It With Your Data By [ 3 Min read ]
Learn how to run Mixtral locally and have your own AI-powered terminal, remove its censorship, and train it with the data you want. @jeferson Read More. Now We Know: Exit to Community Is Possible By [ 7 Min read ]
There is widespread craving for a better kind of exit—and the creativity to back it up. But better exits will require structural change. @ntnsndr Read More. The Most Expensive Technology on Earth By [ 7 Min read ]
Quantum Computers are the most expensive computational technology on earth. They demand a lot of gold and energy to do the bare minimum. @maken8 Read More. The Dating Game: Finding Your Perfect DevOps Match By [ 16 Min read ]
Finding the match in the world of DevOps can be quite challenging. It's like navigating the dating scene, where you need to seek out someone who shares your ... @samsey Read More. Much Ado About Bitcoin Halving By [ 7 Min read ]
A readers guide to Bitcoin halving and its ripple effects on the price of BTC and Bitcoin mining. @juxtathinka Read More. Turn Your Lengthy To-Do List Into A Success List By [ 9 Min read ]
Learn how to manage a lengthy to-do list and get more done every day. @vinitabansal Read More. Build DevOps for Secure Auth API Gateway using Nodejs, Aptible Terraform and Redis By [ 27 Min read ]
Building a secure and scalable API gateway is crucial to modern software development. @wise4rmgod Read More. You're Not Building a Personal Brand; You're Just Posting on LinkedIn By [ 4 Min read ]
Posting on LinkedIn like it’s some type of ritual isn’t building a personal brand. It can get you to destination A, for sure, but you won’t go far. @deepikapundora Read More. The Ultimate Guide to Effectively Using and Validating Forms in React By [ 46 Min read ]
Master React forms with this comprehensive guide on using and validating them effectively! @theankurtyagi Read More. Helium Mining in 2024: Still Worthwhile with Helium Farm By [ 2 Min read ]
With its innovative approach and lucrative mining plans, Helium Farm continues to make Helium mining not only accessible but highly profitable. @CryptoAdventure Read More. Is AI Code Generation An Age of Industrial Revolution for Software Enterprise? By [ 5 Min read ]
This post talks briefly on the latest capability of GenAI based code generation tools, how we can effectively use them in the developer workflow and challenges. @ratikeshmisra Read More. Life After Smartphones: Are Critics of Apple’s iPhone Sales Slump Missing the Bigger Picture? By [ 5 Min read ]
Sales issues surrounding the iPhone have led to Apple’s stock receiving two rating downgrades within a week, sending share values tumbling in the new year. @dmytrospilka Read More. Technology's 24 Most Important Social Networks for Content Distribution By [ 8 Min read ]
I've spent 10+ years distributing tech content to technologists & tech companies as founder/ceo of hackernoon. Here's my take on these networks' positionings. @David Read More. Bookmark This: 18 Awesome GitHub Repositories For 2024 By [ 4 Min read ]
This collection spans a variety of categories, from tools, extensions, cheat sheets, and guides to learning resources, prompt engineering, and other cool stuff. @madzadev Read More. Creating and Running an Angular Application in a Docker Container By [ 5 Min read ]
In this article, we will create a web application using the latest version of Angular, generate a Docker image, and then execute this image within a ........... @rodrigokamada Read More. How I Improved My English Speaking Skills With AIs That Should Work For You Too By [ 9 Min read ]
With AI language learning apps, such as speaking assistant Pronounce, text checker Grammarly, and ChatGPT, I boosted fluency, grammar, pronunciation, and more. 🧑‍💻 What happened in your world this week? It's been said that , , . Feeling stuck? We got you covered ⬇️⬇️⬇️ We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.
See you on Planet Internet! With love,
The HackerNoon Team ✌️ @pronounce Read More. writing can help consolidate technical knowledge establish credibility and contribute to emerging community standards ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME

The TechBeat: Navigating Complexity: The Challenges of Managing Large-scale Projects (1/10/2024)

Managing large-scale projects becomes challenging with the involvement of thousands of participants in project implementation. The complexity also arises from the lack of a unified information space for project management. Key aspects, such as schedules, milestones, agreements, project budgets, document workflows, performance analysis, and geographical information, are scattered across different platforms. Drawing from our experience, particularly in the public sector, I have collected the most valuable tips to overcome the challenges large-scale projects are posing. This includes software solutions, designed to efficiently manage projects and management strategies, offering a cohesive approach to overcome the hurdles posed by diverse information spaces and decentralized workflows. Typical challenges Effectively managing large-scale infrastructure projects entails addressing various project management issues. One of the most significant challenges involves coordinating multiple specialized subcontractors and requires strategic oversight to ensure their alignment with project goals and timelines. Another point here is that financial-related risks, such as changes in project scope or orders, demand agile financial planning and management to accommodate evolving project requirements. Besides, maintaining accurate cash flow is essential for proper resource and cost allocation in line with the overall construction budget. Technical challenges are one more aspect that you are bound to encounter working with large scales. They often arise in obtaining approvals, emphasising the need for streamlined communication and collaboration. Similarly, timely approvals from project owners are critical for avoiding delays and ensuring smooth project progression. Relying on less-experienced subcontractors introduces technical challenges, underscoring the importance of thorough vetting and support. External risks, including government support and potential inflation in resource prices, must also be considered. While the government may assist with certain external risks, contractors need to account for fluctuations in resource prices that directly affect construction costs. The least agreed-upon infrastructure risk is inaccuracies in estimating material quantities. Despite hiring skilled resources, potential human errors in quantity estimation can still pose troubles. Effectively navigating these challenges demands proactive project management strategies, meticulous financial planning, and continuous coordination among stakeholders to ensure successful and timely project delivery. Further on we will discuss what strategies can be instrumental in solving most common problems. Strategies to overcome challenges As we have already described, large-scale projects come with inherent challenges, thus they demand strategic solutions to ensure success. Bearing in mind these guidelines will contribute to gaining improved control over your project, regardless of its size. In long-term projects, true ownership of results is the most essential point. The project leader must take responsibility for tough decisions, fostering psychological safety within the team. This empowerment enables effective decision-making without undue fear, preventing overly cautious strategies and prolonged committee discussions. Taking responsibility Securing early commitment from company leaders is vital to prevent potential existential risks if executive support falters. Negotiating goals and success metrics from the start is common, but for large-scale projects, collaborating with financial experts to predict future profits and expenses provides flexibility in budget adjustments. Providing support Estimating time and effort in research and development projects with public deadlines can be challenging, especially when the last part tends to be the most time-consuming. Communicate delivery dates as a range: a target date based on current understanding and a commit date signalling potential issues. Transparently discounting estimates helps improve communication and ensures clear scheduling for dependent work. Managing timelines Addressing concerns within local communities is essential for the success of major projects. Creating a safe space for discussions about urgent concerns and providing timely explanations of project methods and objectives is key. Educating community members on the project's purpose, expected outcomes, cost, timeline, and benefits for the community fosters a sense of understanding and collaboration. By nurturing relationships with stakeholders, project providers can establish a foundation of trust and communal support, ensuring a smoother project execution and a brighter future for the community. Building confidence The delays, shortages, and cost fluctuations pose challenges to infrastructure projects. To navigate these hurdles, a deep understanding of market dynamics and diverse supply sources is crucial. Proactive planning, including collaboration with local suppliers, selection of flexible vendors, and an awareness of raw material market opportunities, allows for more realistic timelines and effective expectation management. Given the unique challenges in different geographic locations, thorough research before finalising project locations and deadlines is imperative. Understanding the market Tools to manage large-scale projects In extensive projects, the Project Management Office (PMO) team may comprise numerous individuals, each overseeing a distinct task group. Effectively utilising automation tools is essential for ensuring seamless coordination and collaboration within the PMO, addressing the responsibilities and tasks associated with managing complex projects. Automation includes ERP systems that help to manage contractors and subcontractors, keep the contracts updated, and control the budget. BI (Business Intelligence) can be also specifically useful for analysing project Key Performance Indicators (KPIs), and tools like MS Project or their equivalents, such as Gantt charts, timelines, and task management software, are commonly employed in large-scale project management. ERP software ERP software is a versatile solution that addresses a range of business challenges, making it suitable for companies of all sizes. Here are the problems that ERP effectively solves, offering real benefits across various industries including infrastructure. Fostering collaboration ERP takes collaboration to new heights by integrating data, enabling teams to function more cohesively. Through optimised workflows, teams synchronise their efforts, ensuring seamless coordination and promoting a collaborative work environment. Budgetary precision Juggling budgets across multiple projects becomes a daunting task, particularly when different departments adopt diverse tracking methods. ERP steps in to align budgetary processes, guaranteeing enhanced control and the early detection of potential budgetary challenges. Breaking down data Siloed data poses a common challenge in contemporary enterprises, impeding collaboration and obstructing a comprehensive performance overview. ERP serves as a solution by amalgamating company data into a unified platform, offering a holistic and real-time perspective, thereby minimizing errors like double entry. Tackling inventory complexities Effectively managing inventory proves to be troublesome, especially in industries dealing with a multitude of items and subcategories. ERP simplifies inventory management, offering real-time insights into inventory levels and bolstering overall control. Inefficient workflows Inefficiencies in workflows often lead to challenges in keeping up with business growth. ERP creates a unified platform for data and communication, enabling efficiency improvements across departments and levels, ultimately benefiting the bottom line. So if your business grapples with any of these challenges, exploring ERP solutions could be the key to overcoming common issues. Business Intelligence BI proves invaluable in optimising processes, offering managers insights into supply chain metrics and facilitating improvements in distribution routes. Additionally, it ensures adherence to service level agreements, enhancing overall operational efficiency. The primary value of BI lies in its capacity to drive data-driven decisions, transforming raw data into actionable information that informs strategic actions. The following types of analytics contribute to BI's multifaceted value proposition: : offering insights into past and current events, these analytics enhance understanding and identify areas for improvement through dashboards, reporting, data warehousing, and scorecards. Descriptive analytics : employing data mining, predictive modelling, and machine learning, these analytics project future events and assess the likelihood of their occurrence. Predictive analytics : revealing the optimal actions to take, prescriptive analytics enable optimization, simulation, and decision modelling, providing a comprehensive analysis for informed decision-making. Prescriptive analytics As an example, I would like to name Cementos Argos, a prominent cement company with operations across the Americas, which demonstrates the transformative impact of BI. Faced with the need for a competitive advantage and improved decision-making, the company established a dedicated business analytics center. By investing in experienced business analysts and data science teams, Cementos Argos utilized BI to standardize financial processes and gain deeper insights into customer behavior, resulting in elevated profitability levels. Choosing effective solutions Selecting the right tools and techniques is crucial for the success of any project. By considering these factors, you can make informed decisions when choosing large-scale project management tools that align with the unique needs and dynamics of your projects. Accessibility In a post-COVID era, accessibility is paramount, considering diverse working locations. The best construction project management tools are cloud/web-based, offering accessibility from virtually anywhere globally. Dynamic reporting To manage construction projects effectively, real-time information is a must. Opt for tools that provide instant reporting, ensuring you have the most up-to-date insights for informed decision-making. Robust support Even the best tools may encounter challenges during implementation. Choose solutions from providers with reliable customer support, ensuring prompt assistance when needed. Data security Given the value and sensitivity of construction data, prioritize tools with robust security measures. Before integration, research the software provider, understand their terms of use, and ascertain the implemented security protocols to safeguard your company's data. Seamless integration Recognise that a single software solution may not fulfill all your project management requirements. Opt for tools that seamlessly integrate with your existing setup, allowing for a customized and cohesive solution. In summary, handling big projects comes with tough challenges, like coordinating different job groups and managing financial risks. ERP and BI, can help enhance the control level and get useful insights from big data, choosing the right project management tools is key to boosting your company's productivity. We trust this article has provided valuable insights into essential considerations for your evaluation process.

Navigating Complexity: The Challenges of Managing Large-scale Projects

Scaling a SaaS product is not just about growth. Instead, it is a complex form of art that requires precision, expertise, and a deep understanding of the constantly changing tech ecosystem. This guide helps entrepreneurs, product managers, and tech enthusiasts navigate the complex journey of scaling a experience. SaaS Paul Dhaliwal's vast knowledge and deep insights act like a guide in this complex field. His advice is like a treasure chest of strategies and wisdom to help you bring out the best in your product. Join us on this transformative journey of SaaS scaling, where every section is propelling your product to unprecedented heights. SaaS Scaling Strategies Scaling a SaaS (Software as a Service) product is an important step in its growth journey. It involves strategies and approaches to ensure that your software can handle a growing user base, increasing data loads, and evolving market demands. Key Approaches to Scaling In my journey as the founder of RedBlink Technology, I've learned that scaling a SaaS product demands a strategic approach. One fundamental tactic is vertical scaling, or what we call "scaling up." This involves fortifying our existing infrastructure, enhancing server resources, optimizing databases, and implementing efficient load balancing. It is similar to giving your engine more horsepower. On the flip side, horizontal scaling, or "scaling out," has been essential in our growth. It's like adding more lanes to a highway to adjust more traffic. This strategy allows us to scale more dynamically and withstand server failures more robustly. Yet, it requires careful planning and a solid load-balancing system to function seamlessly. Choosing the Right Strategy Selecting the appropriate scaling strategy depends on multiple factors, like the nature of your SaaS product, budget constraints, and anticipated growth patterns. It is essential to conduct a thorough analysis of your current infrastructure and projected user base to make an informed decision. SaaS Growth Tactics The SaaS growth tactics are built on precision. We excel in targeted marketing, crafting campaigns that resonate with potential customers' needs. A seamless user onboarding experience accelerates understanding and engagement, cultivating lasting user relationships and sustaining our growth. Effective Techniques for Expansion One thing that we all must keep in mind is that expanding a SaaS product is not just about attracting new users but also about retaining existing ones. It's a delicate balance that requires a strategic approach. undefined Understanding our target audience and crafting personalized marketing messages has been instrumental. By addressing specific pain points and demonstrating how our SaaS product meets those needs, we've been able to attract users who are genuinely interested in what we offer. [ ] Targeted Marketing Campaigns: undefined The initial experience a user has with our product is crucial. We've invested time and resources into streamlining the onboarding process, ensuring that new users quickly grasp the value and functionality of our software. Clear guidance and resources play a pivotal role in this process. [ ] User Onboarding Optimization: Leveraging Market Trends To keep growing, it's vital to stay up-to-date. Make sure to always pay close attention to new trends and technologies that are coming up. This means we're always on the lookout for what's next! Understanding how user behavior evolves allows us to tailor our product to meet shifting preferences and expectations. This adaptability keeps us relevant in a fast-paced market. Adapting to User Behavior Changes: Integrating new technologies that align with our product's mission has been a key strategy. Whether it's incorporating AI capabilities or adopting new development frameworks like Code Conductor, staying at the forefront of technology ensures that our SaaS product remains competitive. Embracing Emerging Technologies: Scaling Software as a Service Our approach to scaling Software as a Service (SaaS) revolves around two pivotal principles. , we prioritize the unwavering reliability of our SaaS product, ensuring that it remains available and resilient even in the face of unexpected challenges. First and foremost This commitment is underscored by the implementation of redundancy measures, robust failover systems, and stringent testing protocols. Additionally, we adhere to a foundational design philosophy of scalability. This design approach embraces modularity, harnesses the power of microservices, and utilizes scalable databases, ensuring that our SaaS solution can seamlessly expand to meet the demands of a growing user base and evolving market dynamics. SaaS Product Expansion Tips and Scalability Challenges We've proven methods for expanding our SaaS product. This involves feature diversification, thorough market research, strategic partnerships, localized and global marketing, and integrating user feedback. However, scaling isn't without challenges. We've tackled issues like database limitations, maintaining user experience, cost management, security, disaster planning, and compliance adherence. By prioritizing risk mitigation through security protocols, disaster recovery plans, and compliance measures, we ensure the integrity and continuity of our SaaS product. These strategies collectively underpin our success, providing valuable guidance for those navigating the complexities of scaling a SaaS product. Best Practices for Scaling SaaS We adhere to industry-tested recommendations for achieving sustainable growth in the SaaS sector. These practices are the bedrock of our success: Placing the customer at the center of our operations ensures that our product evolves in ways that truly benefit users. Customer-Centric Approach: This is the second practice where embracing a cycle of continuous improvement allows us to respond swiftly to user feedback and evolving market trends. Iterative Development: Building a robust technical foundation that can flexibly accommodate increased demands is vital for long-term success. Scalable Infrastructure: Upholding the highest standards of security and compliance safeguards not only our product but also builds trust with users. Compliance and Security: Investing in our team's skills and capabilities ensures that we're equipped to tackle new challenges and opportunities. Talent and Team Growth: Staying attuned to industry shifts and user needs allows us to pivot and adapt our product strategy for sustained relevance. Market Adaptability: By adhering to these best practices, we've not only achieved consistent growth but also built a foundation for sustainable success in the competitive landscape of the SaaS industry. These recommendations serve as a guiding light for others seeking to scale their own SaaS products effectively. Conclusion All-in-all, I can say that scaling a SaaS product is about empowering lives, streamlining processes, and redefining what's possible. It's not just growth; it's evolution. Embrace challenges, dream big, and stay true to your vision. Together, we're shaping a future limited only by imagination. As I reflect on our journey at RedBlink Technology, I'm reminded of the entrepreneurial insights we've gained along the way, particularly in leveraging AI for startup success. It's not about adopting the latest buzzwords; it's about harnessing the potential of cutting-edge technologies to create solutions that truly matter. AI has been our ally in crafting smarter, more efficient SaaS products, and I encourage fellow entrepreneurs to explore its boundless possibilities.

How to Scale a SaaS Product From a Founder's Perspective

How to Structure Your K8s GitOps Repository at Scale [Part 1]

As a front-end developer, state management in applications is a critical part of ensuring smooth and maintainable code. As our applications grow in complexity and size, handling state becomes crucial for maintaining performance, organization, and code reusability. React In React, a stateful component is one that can store and modify data throughout the application’s lifecycle. Stateful components are useful when we need a component to maintain and update specific information and communicate with other components. In this blog, we will explore some techniques for effectively handling state in React. Local State Components When building smaller components with limited state requirements, using local state is a simple and easy-to-implement approach. This keeps the component’s logic self-contained and avoids adding unnecessary complexity. However, we must be careful not to abuse the local state, as it can lead to prop drilling issues and difficulty in managing the state throughout the application. To use stateful components, we utilize the hook, which is a feature introduced in React 16.8. useState Here’s an example of how to implement a local stateful component in a React application: import React, { useState } from 'react';

const Counter = () => {
  // We set the initial state of the counter to 0
  const [count, setCount] = useState(0);

  // Function to increment the counter by 1
  const increment = () => {
    setCount(count + 1);
  };

  // Function to decrement the counter by 1
  const decrement = () => {
    setCount(count - 1);
  };

  return (
    <div>
      <h2>Contador: {count}</h2>
      <button onClick={increment}>Incrementar</button>
      <button onClick={decrement}>Decrementar</button>
    </div>
  );
};

export default Counter; Prop Drilling and Component Composition When working on slightly more extensive and more complex applications, it’s common to encounter situations where a component needs to access a state that resides at a higher level in the component tree. This process of passing props through multiple levels of components is known as , and it can become cumbersome and error-prone as the application grows. prop drilling One solution to avoid prop drilling is to use component composition. Instead of passing props directly from the top-level component down to the deepest component, we create higher-order components that wrap around the components needing access to the state. These higher-order components take care of providing the necessary state and functions to the descendant components, thus avoiding prop drilling. Let’s look at the same example of the counter to better understand this concept: // Highest level component that stores the state
const App = () => {
  const [count, setCount] = useState(0);

  const incrementCount = () => {
    setCount((prevCount) => prevCount + 1);
  };

  return (
    <div>
      <h1>Counter: {count}</h1>
      {/* Higher-order component providing status and update function */}
      <Counter incrementCount={incrementCount} count={count} />
    </div>
  );
};

// Component that needs access to the state
const Counter = ({ incrementCount, count }) => {
  return (
    <div>
      <button onClick={incrementCount}>Increment</button>
      {/* Deeper component using the update function */}
      <CounterDisplay count={count} />
    </div>
  );
};

// Deeper component showing the current status
const CounterDisplay = ({ count }) => {
  return <p>Current counter: {count}</p>;
}; In the previous example, we used three components: , , and . The counter state is stored in the App component, and both the Counter and CounterDisplay components need access to this state. App Counter CounterDisplay Instead of directly passing the state and update function down through props to the CounterDisplay component, we employ component composition and create a higher-order component called Counter. This component takes care of providing the state and update function to the CounterDisplay component. React Context and the useContext Hook is a powerful tool for managing states across multiple components without resorting to prop drilling. It offers a way to share data throughout the component tree without the need to pass props explicitly. React Context Context is especially useful when multiple components need access to the same global state or data. To use React Context, we first create a context object using and define an initial value. Then, we can wrap the components that need access to this context with the Context. createContext() Provider component, passing the context value as a prop. Descendant components can access the context value using the useContext hook. Let’s see the same example of the counter to better understand this concept: import React, { useState, createContext, useContext } from 'react';

// We create the context object for the counter
const CounterContext = createContext();

// Component providing the context with the counter reading
const CounterProvider = ({ children }) => {
  const [count, setCount] = useState(0);

  const increment = () => {
    setCount(prevCount => prevCount + 1);
  };

  const decrement = () => {
    setCount(prevCount => prevCount - 1);
  };

  return (
    <CounterContext.Provider value={{ count, increment, decrement }}>
      {children}
    </CounterContext.Provider>
  );
};

// Component that consumes the context using useContext
const CounterDisplay = () => {
  const { count } = useContext(CounterContext);

  return (
    <div>
      <h2>counter: {count}</h2>
    </div>
  );
};

// Component that consumes the context using useContext
const CounterButtons = () => {
  const { increment, decrement } = useContext(CounterContext);

  return (
    <div>
      <button onClick={increment}>Increment</button>
      <button onClick={decrement}>Decrement</button>
    </div>
  );
};

// Main component where CounterProvider, CounterDisplay and CounterButtons are used.
const App = () => {
  return (
    <CounterProvider>
      <CounterDisplay />
      <CounterButtons />
    </CounterProvider>
  );
};

export default App; In this example, we create a object and a component that provides the counter value to the context along with the and functions to modify the counter. CounterContext CounterProvider increment decrement Then, the and components consume the context using the Hook to access the counter value and functions to modify it. CounterDisplay CounterButtons useContext State Management Libraries In large-scale applications, where state complexity is higher, using state management libraries like Redux, Mobx, Zustand, etc., becomes a valuable option. These libraries offer predictable and structured state management, making it easier to handle shared data throughout the application. Let’s see a simple example of using . Redux Install Redux and React-Redux using npm or yarn. npm install redux react-redux 2. Create the file store.js where we will define our Redux store. // store.js
import { createStore } from 'redux';

// Define the initial state of the application
const initialState = {
  count: 0,
};

// Define the reducer function that will handle state updates based on actions
const reducer = (state = initialState, action) => {
  switch (action.type) {
    case 'INCREMENT':
      return {
        ...state,
        count: state.count + 1,
      };
    case 'DECREMENT':
      return {
        ...state,
        count: state.count - 1,
      };
    default:
      // If the action type is not recognized, return the current state
      return state;
  }
};

// Create the Redux store using the reducer
const store = createStore(reducer);

export default store; 3. Create a component that will use the state stored in the Redux store. Counter import React from 'react';
import { useDispatch, useSelector } from 'react-redux';

const Counter = () => {
  // Access the 'count' state from the Redux store using the 'useSelector' hook
  const count = useSelector((state) => state.count);

  // Get the 'dispatch' function from the Redux store using the 'useDispatch' hook
  const dispatch = useDispatch();

  const handleIncrement = () => {
    dispatch({ type: 'INCREMENT' });
  };

  const handleDecrement = () => {
    dispatch({ type: 'DECREMENT' });
  };

  return (
    <div>
      <h2>Count: {count}</h2>
      <button onClick={handleIncrement}>Increment</button>
      <button onClick={handleDecrement}>Decrement</button>
    </div>
  );
};

export default Counter; 4. Connect the store to your React application using the component of Provider react-redux // index.js
import React from 'react';
import ReactDOM from 'react-dom';
import { Provider } from 'react-redux';
import store from './store';
import Counter from './Counter';

ReactDOM.render(
  <Provider store={store}>
    <Counter />
  </Provider>,
  document.getElementById('root')
); With Redux, we can easily manage the state of our React application, even in cases of large-scale applications with multiple interconnected components. TanStack Query TanStackQuery or React Query provides a powerful way to manage data fetching and caching, optimizing performance and reducing unnecessary requests. It is especially useful for large-scale applications with complex data requirements. React Query abstracts the data fetching logic into hooks, which are easy to use and integrate with React components. It manages data in a cache and automatically handles data fetching and invalidation, reducing the need to manage data manually. Let’s see a simple example of using React Query to fetch data from an API: Install React Query using npm or yarn: npm install react-query 2. Create a component that uses React Query to fetch data from an API: // UserList.js

import React from 'react';
import { useQuery } from 'react-query';

const fetchUsers = async () => {
  const response = await fetch('https://jsonplaceholder.typicode.com/users');
  return response.json();
};

const UserList = () => {
  const { data, isLoading, isError } = useQuery('users', fetchUsers);

  if (isLoading) {
    return <div>Loading...</div>;
  }

  if (isError) {
    return <div>Error fetching data</div>;
  }

  return (
    <div>
      <h1>User List</h1>
      <ul>
        {data.map((user) => (
          <li key={user.id}>{user.name}</li>
        ))}
      </ul>
    </div>
  );
};

export default UserList; In this example, we are using the hook from React Query to fetch user data from the API. The hook takes a unique query key and a function that performs the data fetching. useQuery JSONPlaceholder useQuery users fetchUser React Query will automatically manage the data, caching the results, and handling loading and error states. Using React Query, you can easily handle complex data fetching requirements, manage pagination, and efficiently update the user interface when data changes. Apollo Client (for GraphQL) If your application uses GraphQL for data fetching, Apollo Client is an excellent choice for state management. It provides a caching layer that reduces the need for excessive network requests, improving performance in large-scale applications. Setting up a GraphQL Server and Client in Next.js Let’s see a simple example of using Apollo Client: Install Apollo Client using npm or yarn: npm install @apollo/client graphql 2. Create an Apollo Client Instance. Next, create an instance of Apollo Client and configure it with the GraphQL endpoint for your server. // apollo-client.js
import { ApolloClient, InMemoryCache } from '@apollo/client';

const client = new ApolloClient({
  uri: 'https://example.com/graphql', // Replace with your GraphQL server URL
  cache: new InMemoryCache(),
});

export default client; 3. Wrap your application with the component provided by Apollo Client. This component injects the Apollo Client instance into the React component tree, making it accessible to all components. ApolloProvider // index.js
import React from 'react';
import { ApolloProvider } from '@apollo/client';
import client from './apollo-client';
import App from './App';

ReactDOM.render(
  <ApolloProvider client={client}>
    <App />
  </ApolloProvider>,
  document.getElementById('root')
); Finally, you can use GraphQL queries in your React components using the hook provided by Apollo Client. This hook gets the data based on the GraphQL query and automatically updates the component when the data changes. useQuery // ExampleComponent.js
import React from 'react';
import { useQuery, gql } from '@apollo/client';

const GET_USERS = gql`
  query GetUsers {
    users {
      id
      name
      email
    }
  }
`;

const ExampleComponent = () => {
  const { loading, error, data } = useQuery(GET_USERS);

  if (loading) return <p>Loading...</p>;
  if (error) return <p>Error: {error.message}</p>;

  return (
    <div>
      <h1>Users</h1>
      <ul>
        {data.users.map((user) => (
          <li key={user.id}>
            {user.name} - {user.email}
          </li>
        ))}
      </ul>
    </div>
  );
};

export default ExampleComponent; Apollo Client manages component caching and updating when data changes, providing a seamless and efficient data management solution for large-scale React applications using GraphQL. Conclusion In conclusion, there is no one-size-fits-all solution for state management in large-scale React applications. Each approach has its strengths and weaknesses, and the best choice depends on factors such as application size and complexity, team experience, and specific requirements. A combination of different state management solutions can be employed in a large-scale application to meet different needs effectively. It is essential to evaluate the pros and cons of each approach and choose the one that best suits the needs and objectives of the project. Read more: Mastering Higher Order Components (HOCs) in React Boosting React App Performance: A Guide to Lazy Loading and Suspense Want to connect with the Author? Love connecting with friends all around the world on . X Also published here.

Optimizing State Management in React Applications: From Small to Large-Scale

FTC v. Amazon Court Filing, retrieved on Sep 26, 2023, is part of . You can jump to any part in this filing . This is part 18 of 80. HackerNoon’s Legal PDF Series here 3. Amazon's dominant position in the online superstore market is protected by significant barriers to entry 176. Significant barriers limit entry into the online superstore market including scale economies and network effects, reputational barriers, and shopper switching costs. Feedback loops between online superstores and the online marketplace services market also contribute to a unique barrier to entry, as discussed in Part V.C, below. 177. Scale is a critical factor for success in the online superstore market. Amazon itself has touted its scale as a key differentiator from medium-sized or single-category online stores. Mr. Bezos wrote that "[o]nline selling (relative to traditional retailing) is a scale business characterized by high fixed costs and relatively low variable costs. This makes it difficult to be a medium-sized e-commerce company,” and “difficult . . . for single-category e-commerce companies to achieve the scale necessary to succeed.” According to Mr. Bezos, “build[ing] an important and lasting company . . . in e-commerce” simply “isn’t going to work in small volumes.” Economies of scale are a barrier to entry in this market that new firms must overcome in order to enter and compete. 178. The online superstore market is also characterized by network effects, where the value of the service increases as more people use it. Network effects are not intrinsically harmful, but they can present barriers to entry and to competition, reinforcing market power and insulating incumbents. 179. One aspect of the importance of scale and related network effects in the online superstore market stems from user-generated reviews. For example, as Amazon’s shopper base has grown, so too has the number of product ratings and reviews available on its store, a feedback loop that further draws in new shoppers by enabling them to quickly learn more about unfamiliar products or sellers. In other words, by leaving helpful ratings and reviews, Amazon’s shoppers themselves provide immense value to future Amazon shoppers. Amazon benefits from this self-reinforcing dynamic, which would be difficult and expensive for new entrants to reproduce. 180. Another source of network effects in the online superstore market is access to valuable shopper data, which allows online superstores to tailor and personalize shopping experiences. For example, Amazon records information about the items a shopper searches for, views, places in their cart, and pays for, and the mechanism the shopper uses to pay. This type of data allows an online superstore to streamline a shopping experience and target specific products to certain customers. As with other network effects, the more scale an online superstore gains, the more powerful this effect becomes. Prospective entrants would have to acquire a sufficient shopper base to obtain enough data to offer this level of personalization. 181. The online superstore market also exhibits reputational barriers to entry. Reputational barriers to entry arise when entrants need to establish trust among customers to compete meaningfully against incumbents. Because online superstores allow and encourage repeat purchasing, they are able to develop positive reputations with shoppers that a prospective entrant starting from scratch would need to cultivate. 182. Switching costs also are a barrier to entry in the online superstore market. Mr. Bezos recognized this dynamic and its implications in a speech in 1998, stating that “switching costs long-term . . . should actually be higher in the online world than in the physical world” because “[i]n the online world, businesses have the opportunity to develop very deep relationships with customers, both through accepting preferences of customers and then observing their purchase behavior over time, so that you can get that individualized knowledge of the customer and use that individualized knowledge of the customer to accelerate their discovery process.” For example, Amazon retains shoppers’ payment, shipping, and order history information. Switching to a new online superstore would require reentering payment and shipping information and forgoing the benefits of viewing past order history. Shoppers also develop routines while shopping at online superstores that can be difficult to break, particularly given the additional costs of gaining familiarity with the format, features, and policies of a different store. 183. Finally, as described in detail below in Part VI, Amazon engages in an illegal course of conduct that raises barriers to entry and competition, making it artificially and substantially more costly and time-consuming for would-be competitors to enter the online superstore market. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 2:23-cv-01495 retrieved on October 2, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ftc.gov

Scale, Reputation and Other Key Barriers to Entry Protecting Amazon's Monopoly 

Learn More at EditingProtocol.com, where the HackerNoon CMS powers the most up-to-date technical documentation about The Editing Protocol , the independent technology publishing company, has released to the public, a set of rules and guidelines that can be used by humans and machines to determine whether a story is worth publishing, how to specifically improve the story’s content, and how to distribute the story with more reach and relevance. HackerNoon The Editing Protocol What is The Editing Protocol? One of the world leaders in online publishing, HackerNoon is home to over 45,000 published contributors. As a startup, the company was tasked with reviewing thousands of submissions a month with just a small team of editors. To do that, HackerNoon created a set of preliminary checks, rules, and quality guidelines that determine whether or not a story can be published, or should be rejected. This document was dubbed The Editing Protocol and can be read by humans to guide publishing at scale. “In publishing a hundred thousand stories, we’ve learned best practices for how to improve and distribute professional technology content on the internet,” said . HackerNoon Creator and CEO David Smooke “I’m excited to open up this technical documentation for feedback, and continue integrating the most cutting-edge technologies into the Editing Protocol.” To elevate the quality of published content, and optimize the user experience for contributors and readers alike, the editing protocol incorporates several inventive technologies, such as: An AI-powered (GPT-4) headline generator. AI image generator models like Stable Diffusion XL, Stable Diffusion v2.1, Stable Diffusion v2.0, Stable Diffusion v1.5, Midjourney Diffusion, Kandinsky 2.2, and Kandinsky 2.1. AI-generated content detection. AI text translator. AI TLDR generator. Plagiarism checker. Backlink checker bots. The protocol’s guidelines are rule-based and can be easily converted into conditional statements. Once programmed, the protocol can allow human editors to focus on the quality and improvement of publishable stories, while the system automatically informs writers of what rule they have broken, or guidelines they have missed. With The Editing Protocol, small teams can publish content at scale, using both human editors and rule-based flagging systems to provide an efficient publishing process. HackerNoon has already programmed a lot of the protocol into the custom CMS the company has built from the ground up. For instance, submissions that are below the minimum structural quality measure are automatically rejected, and writers are sent an email specifically stating how to improve the story they hope to publish before resubmitting. Furthermore, there is a section in the protocol that highlights the importance of . As one of the most important aspects of online publishing is the visual presentation of the story, HackerNoon built an into their CMS, allowing writers to create original images and help writers adhere to the protocol using the tech itself. originality score AI image generator HackerNoon believes the protocol will not only guide human editors to better vet stories, but also help developers automate the tedious workflows within the traditional editorial review process. How to Use The Editing Protocol The full protocol can be viewed via which is hosted on HackerNoon’s custom CMS builder. https://editingprotocol.com/ With The Editing Protocol, HackerNoon aims to help make the internet better by getting rid of the noise—low-quality self-published articles, spammy content riddled with even spammier links, and a horde of other bad SEO practices that prioritize clicks at the expense of quality. As the Internet changes with , it is likely that The Editing Protocol, too, will change. As such, whenever rules or processes are added, removed, or updated, users can find them on , and use that domain as the ground truth. the emergence of Web3 technologies editingprotocol.com

Stories, News & Mentions about SCALE

Scale’s Stories on HackerNoon