Alternative is reimagining financial services through lending

Alternative

alternative

Why should I use your product? I’m a web app developer and I love doing what I do. I code in a language that seems highly frowned upon, yet is one of the most powerful and used languages on the Internet. It is so powerful that it makes up about 80% of the backend code on the Internet. I have even made a few hundred dollars creating small apps as a SaaS (software as a service). However, when it comes to being the competition or the alternative, the giants will always win. By giants, I mean services like Google, Dropbox, Github, and even Medium.com. Nothing I have created hasn’t been done before — and maybe that is my fault, but oftentimes I find myself trying to solve my own pain points, using other applications to do it, only to find that something within those apps is missing — that or the fact that I’m too cheap and would just prefer to create an alternative version of the product so I can use it for free — and then charge others a cheaper price — becoming the alternative and the competition. Every time I release a new app, however, it often receives several different types of responses. They go something like this: Crickets Chirping If the crickets aren’t out, than I usually get a few people signing up and trying the service. I have had a few successful launches in the past, including , , and which have acquired a few individual users and some businesses. It has been great to have a few consistent signups paying me every month, but it has not been enough to quit my day job, so I have continued my projects anyway, creating new useful tools. MyPost Call Me Private Text Privately Recently, I created a WordPress Managed Hosting solution called . This is a hosting solution ahead of its time and is unique in the fact that it does what no other host does: it has a list of features which were born out of the complaints and suggestions of so many web developers who were hosting their websites on other WordPress Managed hosts. It automates the entire process of being a web developer and webmaster, meaning you can setup your clients on and never have to worry about updating their websites again, as it does it automatically. WP Bubble WP Bubble The launch wasn’t as successful as I hoped, but I managed to acquire an investor in the product, who is hosting their website with and they pay me slightly more than what I charge for the product in their own hopes to continue supporting a good product — and a few customers — two who churned within a day or two — trials of the product — and a third who left and returned a month later, and is actually using the product to create a few websites of his own. WP Bubble The most recent product I released is called , which is an image and file hosting solution with a very fast CDN. The most prominent features include an easy comment system, tracking of geometrics on clicks, and the ability to set a password before anyone can view the file. The CDN is also enhanced with Cloudflare to keep my bandwidth costs very low — security high— and the speed of the downloading of the file very fast. The launch wasn’t big at all, as I only released it in one or two areas, and the only response I received was: BinCDN What makes this product different from Google Drive or Dropbox? This question is what prompted me to write this article. I had to ponder: everything my product does, Dropbox and Google Drive already do. So what makes my products different? I could certainly come up with many different technical reasons — and Google and Dropbox will match every single one of them. What makes me different or what makes my product different? I’m not a million or billion dollar company, but I do pride myself on the privacy and security of my products. I do pride myself on the fact that I personally took the time to research a design for a product, spent hours upon hours writing out a plan for my product, and had complete personal say in how my product looks, what it does, how it functions, and the fact that it is personally supported by me. Best of all, I pride myself on the fact that I actually wrote the code for my product and don’t need to seek approval from anyone else about my product or go through a long review process, only to have my changes or ideas rejected by someone “higher up” telling me that my code isn’t good enough. Most of my products released are not free. I used to release them for free and keep them free for as long as I could support them — what would end up happening, I’d lose interest, no longer support them, and they would die out. Now that I have monetized them, they have a longer chance for survival and I care enough about them to continue supporting them when I know others will support them too. What happens when Google decides to discontinue a product that a few people find useful and are using? What if Dropbox decided one day that it was going start charging more for everyone, as Photobucket once did? What if Facebook or Twitter decided to filter and ban people for their freedom of speech, as they often do? They have every right — they are offering it for free — and you really have no say, especially when it comes to your data. What happens when you are paying for and supporting my small products? My small business? I’m more likely to continue supporting it and supporting you because I really do care about you, as an indivudal. I care about your small business. I care about your large business. I care that you are using my products. I can develop them to the standards of my users and my product is small enough that I care enough about making those changes and catering to the demands that my customers need my products to do for them. Personally and professionally, I’m not about you. The less I have — the better. Despite the fact that both Dropbox and Google give away most of their products for free for a time, they still sell you on the large plans — and then sell your data on top of that. Double whammy, don’t you think? collecting or keeping and selling any data to third-parties And no one seems to care what happens with their files or their data, but it became a fact of life — that your information — habits, lifestyle, likes, dislikes, loves, hates, friends data, family data — is literally available and being sold to the highest bidders, all for money and the advertising industry. Sure, your data is sitting in some top notch secured warehouse, but so are my projects as well, as I spin the servers up using as my primary hosting solution for all projects I start. Vultr I have had a few of my customers ask about the security and protection of their data — before I even began building products, I learned about SHA-256 and Hash encryption methods and a lot about securing data and keeping it protected and private — so that not even I could pry through it. While smaller companies might get hacked, larger companies, such as and , are under constant attack and often fail to report that they were ever hacked — losing millions of customers’ data to hackers every year. Google Facebook While they claim to care about your data — and they sure do — and even have programs that pay people to report their bugs to them — the fact is: they are too large to really care about you personally. And sure, they do care about you — as long as you don’t try to sue them because they have so many lawyers protecting them, you’ll have years of court to go through just to get anywhere. With my own business practices and the size of my company and customers: I cannot actually afford . Let me repeat that: I care enough about you to try and answer every email, every live chat that is opened, and respond to you personally to take care of your problem within a timely manner. I may not get it right every time, but I’m personally there to help. I’ve had a product not cancel a subscription and charge a user for an extra month — and I’ve happily refunded my former customer without incident, without question, and without issue. I have not hired other people because I am too small to afford anyone else, so I personally am the web developer, the app developer, the tech support, the salesperson, the marketer, and everything in the company. NOT TO CARE I CANNOT afford NOT TO CARE about you and your business. There is another thing: I’m not Walmart, I’m not Amazon — great big companies that I buy things from — and I’m not a big business. I even barter with other small businesses to use their products and allow them to use mine, all so we can save ourselves from having to pay cash or credit to each other. I’m still a small business and I support other small businesses. Tell me all about the large businesses that are supporting you — sure, you can get products for cheap and in bulk — and that is a great way to support your business, but as a small business owner, who is buying your products and how can you even compete with the likeness of Amazon or Walmart brands? — Or any of the other brands out there? Believe it or not: these are pretty much the top companies that own everything you and everyone else will ever buy — to them, you’re not really competition, and you barely exist, and they are truly after putting you out of business and wouldn’t hesitate to do so. The biggest corporations are notorious for buying smaller companies and oblierating their operations so they can remain the only ones without competition. Don’t hate them too much — it’s the way of the world, and they aren’t going anywhere. They will all be here long after our great grandchildren pass away, and this is mainly just the companies that deal with food. I don’t make enough from my products to quit my day job. It is my dream and the dream of many to no longer have to work at their day jobs, but rather focus on their passions, but it is still not — and is a long ways away from my reality. I’m a “local online business” that is trying to make a living from my products. I have yet to resort to selling my body for sex or science. I’m pretty sure science will accept me. Indie Hackers I did slave labor for a very long time in my teenage years and for almost half of my twenties, and for a short time in my thirties. In fact, I’m still doing slave labor, but it is more mental than physical nowadays. I go to work and I code for nearly 8 hours a day. I come home and despite being mentally exhausted, I work on my products, coding for at least another 4 hours. I have bills to pay, rent that is due every month, and food that my body requires. I have taxes to pay. I don’t live a luxurious lifestyle at all, though sometimes I do dream about waking up and being on a wonderful cruise. If you build it — they won’t come. If you advertise it — they might come. If you keep pushing it — they might use it. If you find it valuable — they might see the value in it. If you charge for it — they might pay for it. If you talk about it or blog about it, eventually, SEO might help you get discovered, and you might get a few interested people who will sign up, and maybe you will get lucky enough to get a few paying customers who like your products and don’t mind paying you their own hard earned money to solve their own pains and problems. Regardless of being the competition or used as an alternative, I have never given up on my talent, I don’t consider anything a failure — because I built my products for me first and foremost. I use most of my products every single day. As long as they are useful to me, how can they actually be a failure? I then monetize them for others to use. I try to spread the word with my story telling skills or the . little funding I have to try and hire others on Fiverr to help me So hopefully I’ve answered the question “ I’m just me, a human being, an individual trying to make it in this world. I enjoy programming. I enjoy creating products. I enjoy when others use my products and actually pay the little money I need to keep funding them and my future endeavors. I don’t have to answer to any stockholders or sell your data to companies to become the most profitable company in the world. What makes this different than Google Drive or Dropbox?” Yes, I would love to be the next Google, Facebook, Apple, or even Dropbox, but I already know it’s not going to happen, and I’m okay with that. I don’t have the intelligence of a hundred programmers. I have the intelligence of just one. What I do know is that I continue programming and solving my own pains and sharing my products with the world in hopes that maybe somewhere out there, one of you will have the same exact pain as me — love my product, use my product, and even pay me for my product. So all of the giants may be my competition, but my products are still an alternative. Image Sources: Google

Competing Against The Giants As An Alternative

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References CONCLUSION This study provides important insights into whether, although Google is by far the most popular search engine, the use of alternatives could benefit users. Our results show that using another or more than one search engine leads to seeing more diverse search results, allowing users to inform themselves more comprehensively. It should be noted that within each search engine's results, the concentration of sources shows that only a few top sources dominate the results, meaning whichever search engine a user chooses to use will shape what sources the information they get to see comes from. RESEARCH DATA Research data is available at: https://osf.io/nt3wv/ ACKNOWLEDGMENTS This work is funded by the German Research Foundation (DFG – Deutsche Forschungsgemeinschaft; Grant No. 460676551). REFERENCES Agrawal, R. (2016). Overlap in the Web Search Results of Google and Bing. Journal of Web Science, 2(1), 17–30. https://doi.org/10.1561/106.00000005 Bar-Ilan, J. (2005). Comparing rankings of search results on the Web. Information Processing & Management, 41(6), 1511–1519. https://doi.org/10.1016/J.IPM.2005.03.008 Bar‐Ilan, J., Levene, M., & Mat‐Hassan, M. (2006). Methods for evaluating dynamic changes in search engine rankings: a case study. Journal of Documentation, 62(6), 708–729. https://doi.org/10.1108/00220410610714930 Bharat, K. (1998). A technique for measuring the relative size and overlap of public Web search engines. Computer Networks and ISDN Systems, 30(1–7), 379–388. https://doi.org/10.1016/S0169-7552(98)00127-5 Bilal, D., & Ellis, R. (2011). Evaluating Leading Web Search Engines on Children’s Queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 6764 LNCS (Issue PART 4, pp. 549–558). https://doi.org/10.1007/978 3-642-21619- 0_67 Cardoso, B., & Magalhães, J. (2011). Google, bing and a new perspective on ranking similarity. Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM ’11, 1933–1936. https://doi.org/10.1145/2063576.2063858 Chignell, M. H., Gwizdka, J., & Bodner, R. C. (1999). Discriminating meta-search: a framework for evaluation. Information Processing & Management, 35(3), 337–362. https://doi.org/10.1016/S0306 4573(98)00065-X Ding, W., & Marchionini, G. (1996). A comparative study of web search service performance. Proceedings of the ASIS Annual Meeting, 33, 136–142. https://eric.ed.gov/?id=EJ557172 Edelman Trust Institute. (2022). Edelman Trust Barometer 2022 - Global Report. https://www.edelman.com/sites/g/files/aatuss191/files/2022-01/2022 Edelman Trust Barometer FINAL_Jan25.pdf European Commission. (2016). Special Eurobarometer 447 – Online Platforms. European Commission. https://doi.org/10.2759/937517 Gini, C. (1936). On the measure of concentration with special reference to income and statistics. Colorado College Publication, General Series, 208(1), 73–79. Goel, S., Broder, A., Gabrilovich, E., & Pang, B. (2010). Anatomy of the long tail. Proceedings of the Third ACM International Conference on Web Search and Data Mining - WSDM ’10, 201. https://doi.org/10.1145/1718487.1718513 González, C. G., Bonventi, W., & Rodrigues, A. L. V. (2008). Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 5249 LNAI (pp. 8–22). Springer Verlag. https://doi.org/10.1007/978-3-540-88190-2_7 Höchstötter, N., & Lewandowski, D. (2009). What users see – Structures in search engine results pages. Information Sciences, 179(12), 1796–1812. https://doi.org/10.1016/j.ins.2009.01.028 Introna, L. D., & Nissenbaum, H. (2000). Shaping the Web: Why the Politics of Search Engines Matters. The Information Society, 16(3), 169–185. https://doi.org/10.1080/01972240050133634 Lawrence, S., & Giles, C. L. (1999). Accessibility of information on the web. Nature, 400(6740), 107 107. https://doi.org/10.1038/21987 Lewandowski, D. (2019). The web is missing an essential part of infrastructure. Communications of the ACM, 62(4) 24–24. https://doi.org/10.1145/3312479 Lewandowski, D., & Kammerer, Y. (2021). Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research. Behaviour & Information Technology, 40(14), 1485–1515. https://doi.org/10.1080/0144929X.2020.1761450 Lewandowski, D., & Sünkler, S. (2019). What does Google recommend when you want to compare insurance offerings? Aslib Journal of Information Management, 71(3), 310–324. https://doi.org/10.1108/AJIM-07-2018- 0172 Mager, A. (2014). Is Small Really Beautiful? Big Search and Its Alternatives. In R. König & M. Rasch (Eds.), Society of the Query Reader (pp. 59–72). Institute of Network Cultures. Makhortykh, M., Urman, A., & Ulloa, R. (2020). How search engines disseminate information about COVID-19 and why they should do better. Harvard Kennedy School Misinformation Review, 1(May), 1–12. https://doi.org/10.37016/mr-2020-017 Meng, W., Yu, C., & Liu, K.-L. (2002). Building efficient and effective metasearch engines. ACM Computing Surveys, 34(1), 48–89. https://doi.org/10.1145/505282.505284 Newman, N., Fletcher, R., Schulz, A., Andı, S., Robertson, C., & Kleis Nielsen, R. (2021). The Reuters Institute Digital News Report 2021. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2021 06/Digital_News_Report_2021_FINAL.pdf Ortega, F., Gonzalez-Barahona, J. M., & Robles, G. (2008). On the Inequality of Contributions to Wikipedia. Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 304–304. https://doi.org/10.1109/HICSS.2008.333 Purcell, K., Brenner, J., & Rainie, L. (2012). Search Engine Use 2012. https://www.pewresearch.org/internet/wpcontent/uploads/sites/9/media/Files/Reports/2012/PIP_ earch_Engine_Use_2012.pdf Puschmann, C. (2019). Beyond the Bubble: Assessing the Diversity of Political Search Results. Digital Journalism, 7(6), 824–843. https://doi.org/10.1080/21670811.2018.1539626 Schultheiß, S., & Lewandowski, D. (2021). A representative online survey among German search engine users with a focus on questions regarding search engine optimization (SEO): a study within the SEO Effect project - Working Paper 2. https://osf.io/wzhxs Spink, A., Jansen, B. J., Blakely, C., & Koshman, S. (2006). A study of results overlap and uniqueness among major Web search engines. Information Processing and Management, 42(5), 1379–1391. https://doi.org/10.1016/j.ipm.2005.11.001 StatCounter. (2022). Search Engine Market Share. https://gs.statcounter.com/search-engine market-share/ Steiner, M., Magin, M., Stark, B., & Geiß, S. (2022). Seek and you shall find? A content analysis on the diversity of five search engines’ results on political queries. Information, Communication & Society, 25(2), 217–241. https://doi.org/10.1080/1369118X.2020.1776367 Sünkler, S., Lewandowski, D., Schultheiß, S., Yagci, N., Sygulla, D., & von Mach, S. (2022). Relevance Assessment Tool. osf.io/t3hg9 Thelwall, M. (2008). Quantitative comparisons of search engine results. Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. https://doi.org/10.1002/asi.20834 Unkel, J., & Haim, M. (2021). Googling Politics: Parties, Sources, and Issue Ownerships on Google in the 2017 German Federal Election Campaign. Social Science Computer Review, 39(5), 844–861. https://doi.org/10.1177/0894439319881634 This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References CONCLUSION This study provides important insights into whether, although Google is by far the most popular search engine, the use of alternatives could benefit users. Our results show that using another or more than one search engine leads to seeing more diverse search results, allowing users to inform themselves more comprehensively. It should be noted that within each search engine's results, the concentration of sources shows that only a few top sources dominate the results, meaning whichever search engine a user chooses to use will shape what sources the information they get to see comes from. RESEARCH DATA Research data is available at: https://osf.io/nt3wv/ ACKNOWLEDGMENTS This work is funded by the German Research Foundation (DFG – Deutsche Forschungsgemeinschaft; Grant No. 460676551). REFERENCES Agrawal, R. (2016). Overlap in the Web Search Results of Google and Bing. Journal of Web Science, 2(1), 17–30. https://doi.org/10.1561/106.00000005 Bar-Ilan, J. (2005). Comparing rankings of search results on the Web. Information Processing & Management, 41(6), 1511–1519. https://doi.org/10.1016/J.IPM.2005.03.008 Bar‐Ilan, J., Levene, M., & Mat‐Hassan, M. (2006). Methods for evaluating dynamic changes in search engine rankings: a case study. Journal of Documentation, 62(6), 708–729. https://doi.org/10.1108/00220410610714930 Bharat, K. (1998). A technique for measuring the relative size and overlap of public Web search engines. Computer Networks and ISDN Systems, 30(1–7), 379–388. https://doi.org/10.1016/S0169-7552(98)00127-5 Bilal, D., & Ellis, R. (2011). Evaluating Leading Web Search Engines on Children’s Queries. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 6764 LNCS (Issue PART 4, pp. 549–558). https://doi.org/10.1007/978 3-642-21619- 0_67 Cardoso, B., & Magalhães, J. (2011). Google, bing and a new perspective on ranking similarity. Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM ’11, 1933–1936. https://doi.org/10.1145/2063576.2063858 Chignell, M. H., Gwizdka, J., & Bodner, R. C. (1999). Discriminating meta-search: a framework for evaluation. Information Processing & Management, 35(3), 337–362. https://doi.org/10.1016/S0306 4573(98)00065-X Ding, W., & Marchionini, G. (1996). A comparative study of web search service performance. Proceedings of the ASIS Annual Meeting, 33, 136–142. https://eric.ed.gov/?id=EJ557172 Edelman Trust Institute. (2022). Edelman Trust Barometer 2022 - Global Report. https://www.edelman.com/sites/g/files/aatuss191/files/2022-01/2022 Edelman Trust Barometer FINAL_Jan25.pdf European Commission. (2016). Special Eurobarometer 447 – Online Platforms. European Commission. https://doi.org/10.2759/937517 Gini, C. (1936). On the measure of concentration with special reference to income and statistics. Colorado College Publication, General Series, 208(1), 73–79. Goel, S., Broder, A., Gabrilovich, E., & Pang, B. (2010). Anatomy of the long tail. Proceedings of the Third ACM International Conference on Web Search and Data Mining - WSDM ’10, 201. https://doi.org/10.1145/1718487.1718513 González, C. G., Bonventi, W., & Rodrigues, A. L. V. (2008). Density of Closed Balls in Real-Valued and Autometrized Boolean Spaces for Clustering Applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 5249 LNAI (pp. 8–22). Springer Verlag. https://doi.org/10.1007/978-3-540-88190-2_7 Höchstötter, N., & Lewandowski, D. (2009). What users see – Structures in search engine results pages. Information Sciences, 179(12), 1796–1812. https://doi.org/10.1016/j.ins.2009.01.028 Introna, L. D., & Nissenbaum, H. (2000). Shaping the Web: Why the Politics of Search Engines Matters. The Information Society, 16(3), 169–185. https://doi.org/10.1080/01972240050133634 Lawrence, S., & Giles, C. L. (1999). Accessibility of information on the web. Nature, 400(6740), 107 107. https://doi.org/10.1038/21987 Lewandowski, D. (2019). The web is missing an essential part of infrastructure. Communications of the ACM, 62(4) 24–24. https://doi.org/10.1145/3312479 Lewandowski, D., & Kammerer, Y. (2021). Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research. Behaviour & Information Technology, 40(14), 1485–1515. https://doi.org/10.1080/0144929X.2020.1761450 Lewandowski, D., & Sünkler, S. (2019). What does Google recommend when you want to compare insurance offerings? Aslib Journal of Information Management, 71(3), 310–324. https://doi.org/10.1108/AJIM-07-2018- 0172 Mager, A. (2014). Is Small Really Beautiful? Big Search and Its Alternatives. In R. König & M. Rasch (Eds.), Society of the Query Reader (pp. 59–72). Institute of Network Cultures. Makhortykh, M., Urman, A., & Ulloa, R. (2020). How search engines disseminate information about COVID-19 and why they should do better. Harvard Kennedy School Misinformation Review, 1(May), 1–12. https://doi.org/10.37016/mr-2020-017 Meng, W., Yu, C., & Liu, K.-L. (2002). Building efficient and effective metasearch engines. ACM Computing Surveys, 34(1), 48–89. https://doi.org/10.1145/505282.505284 Newman, N., Fletcher, R., Schulz, A., Andı, S., Robertson, C., & Kleis Nielsen, R. (2021). The Reuters Institute Digital News Report 2021. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2021 06/Digital_News_Report_2021_FINAL.pdf Ortega, F., Gonzalez-Barahona, J. M., & Robles, G. (2008). On the Inequality of Contributions to Wikipedia. Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), 304–304. https://doi.org/10.1109/HICSS.2008.333 Purcell, K., Brenner, J., & Rainie, L. (2012). Search Engine Use 2012. https://www.pewresearch.org/internet/wpcontent/uploads/sites/9/media/Files/Reports/2012/PIP_ earch_Engine_Use_2012.pdf Puschmann, C. (2019). Beyond the Bubble: Assessing the Diversity of Political Search Results. Digital Journalism, 7(6), 824–843. https://doi.org/10.1080/21670811.2018.1539626 Schultheiß, S., & Lewandowski, D. (2021). A representative online survey among German search engine users with a focus on questions regarding search engine optimization (SEO): a study within the SEO Effect project - Working Paper 2. https://osf.io/wzhxs Spink, A., Jansen, B. J., Blakely, C., & Koshman, S. (2006). A study of results overlap and uniqueness among major Web search engines. Information Processing and Management, 42(5), 1379–1391. https://doi.org/10.1016/j.ipm.2005.11.001 StatCounter. (2022). Search Engine Market Share. https://gs.statcounter.com/search-engine market-share/ Steiner, M., Magin, M., Stark, B., & Geiß, S. (2022). Seek and you shall find? A content analysis on the diversity of five search engines’ results on political queries. Information, Communication & Society, 25(2), 217–241. https://doi.org/10.1080/1369118X.2020.1776367 Sünkler, S., Lewandowski, D., Schultheiß, S., Yagci, N., Sygulla, D., & von Mach, S. (2022). Relevance Assessment Tool. osf.io/t3hg9 Thelwall, M. (2008). Quantitative comparisons of search engine results. Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. https://doi.org/10.1002/asi.20834 Unkel, J., & Haim, M. (2021). Googling Politics: Parties, Sources, and Issue Ownerships on Google in the 2017 German Federal Election Campaign. Social Science Computer Review, 39(5), 844–861. https://doi.org/10.1177/0894439319881634 This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Exploring Benefits of Using Alternative Search Engines

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References DISCUSSION Our study examined whether there are differences in the sources of the top search results between Google and alternative search engines based on popular search queries. An evaluation of root domains of the most popular sources shows only a small overlap between Google and the alternative search engines (RQ1). Overall, we found an overlap of 27% to 28% between Google and the alternatives in German results, and in the US, the overlap ranges from 24% to 25%. There is a significantly higher overlap between the alternative search engines of about 63% to 70% in German results and 62% to 65% in US results. This may be explained by all three alternative search engines using Bing's index at least in part. However, our findings show that Metager consistently has the highest overlap with Bing, going up to 78% and only overlapping as much as 64% with DuckDuckGo. The lower overlap of Google with the alternative search engines had already been shown in the studies of Agrawal et al. (2016) and Makhortykh et al. (2020). Our study provides further evidence for this. When looking at the uniqueness of sources across all German queries, we found that the search engines returned a similar total number of sources, ranging from 2,693 to 2,841. However, for the US queries, the variety in Google was noticeably higher, with over 4,000 total sources compared to around 3,500 of the alternatives. The most popular domain was Wikipedia, followed by sources we classified as News services (RQ2). This is likely exacerbated by selecting Google Trends as the source of our search queries, which usually includes queries related to popular news stories, sports, and celebrities. Still, it is consistent with previous studies' findings (Steiner et al., 2022) which already showed that Wikipedia and news made up the majority of search results. Furthermore, in our research, Wikipedia and news sources were the top domains across all results and the most frequent sources for each search engine individually. When comparing the result sets of the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US, it is interesting to see that news sources are far more prevalent in German results, with social media being very infrequent. On the other hand, the US results had more social media websites. Interestingly, in the US results, YouTube was in the top 10 most popular domains in all search engines but Google. The same was the case for the top 50 domains in German results. This is unexpected because YouTube is a subsidiary of Google. However, this finding may be explained by the fact that we only collected organic search results, and Google might be using universal search results to display YouTube results. Another interesting difference is the greater preference of Wikipedia in Google in US results (1,892, 10,2% of all domains) compared to German results (658, 4% of all domains). Furthermore, the second and third most popular sources on Google are Instagram and Facebook for the US results, while they are not even in the top 10 of German Google sources. Finally, in terms of what is missing from Google in the US results, it is notable that Fox News is not found in the top 50 sources, while it is present in all of the alternatives. The concentration of sources and source diversity showed a tendency for only a few root domains to make up a large share of search results. The Gini Index values of 0.73 and 0.79 in Germany and the United States, respectively, are a clear indicator (RQ3). This is consistent with findings from previous studies (Höchstötter & Lewandowski, 2009) that showed that only a few top sources dominate the search results in search engines. Of course, our study is not without limitations. First, the selection of search queries is the most significant factor in compiling the data that was evaluated. Even though the number of queries is high and there is some diversity in the queries, the topics are almost always focused on news, celebrities, and sports, which inevitably leads to many news sources in the search results. A refined approach to selecting search queries would be appropriate for a more accurate evaluation of source diversity. For example, this could be achieved by focusing on socially controversial topics and choosing the queries accordingly. Further limitations arise in the evaluation of source types. The classification we used in this study is very broad. A more precise classification would help make statements about the actual kinds of sources and thus to determine even more precisely which preferences and biases exist in different search engines. For example, grouping sources according to seriousness and reliability could serve as an explanation for the selection of sources to be displayed in search results. Regarding the search results collected in this study, another limitation is that only organic search results were considered. We did not consider advertisements or universal search results, although these have a strong influence on what users see on the SERP. The relevance of the search results was also not considered. This paper is available on arxiv under CC 4.0 license.
Lead image by Edho Pratama on Unsplash Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References DISCUSSION Our study examined whether there are differences in the sources of the top search results between Google and alternative search engines based on popular search queries. An evaluation of root domains of the most popular sources shows only a small overlap between Google and the alternative search engines (RQ1). Overall, we found an overlap of 27% to 28% between Google and the alternatives in German results, and in the US, the overlap ranges from 24% to 25%. There is a significantly higher overlap between the alternative search engines of about 63% to 70% in German results and 62% to 65% in US results. This may be explained by all three alternative search engines using Bing's index at least in part. However, our findings show that Metager consistently has the highest overlap with Bing, going up to 78% and only overlapping as much as 64% with DuckDuckGo. The lower overlap of Google with the alternative search engines had already been shown in the studies of Agrawal et al. (2016) and Makhortykh et al. (2020). Our study provides further evidence for this. When looking at the uniqueness of sources across all German queries, we found that the search engines returned a similar total number of sources, ranging from 2,693 to 2,841. However, for the US queries, the variety in Google was noticeably higher, with over 4,000 total sources compared to around 3,500 of the alternatives. The most popular domain was Wikipedia, followed by sources we classified as News services (RQ2). This is likely exacerbated by selecting Google Trends as the source of our search queries, which usually includes queries related to popular news stories, sports, and celebrities. Still, it is consistent with previous studies' findings (Steiner et al., 2022) which already showed that Wikipedia and news made up the majority of search results. Furthermore, in our research, Wikipedia and news sources were the top domains across all results and the most frequent sources for each search engine individually. When comparing the result sets of the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US, it is interesting to see that news sources are far more prevalent in German results, with social media being very infrequent. On the other hand, the US results had more social media websites. Interestingly, in the US results, YouTube was in the top 10 most popular domains in all search engines but Google. The same was the case for the top 50 domains in German results. This is unexpected because YouTube is a subsidiary of Google. However, this finding may be explained by the fact that we only collected organic search results, and Google might be using universal search results to display YouTube results. Another interesting difference is the greater preference of Wikipedia in Google in US results (1,892, 10,2% of all domains) compared to German results (658, 4% of all domains). Furthermore, the second and third most popular sources on Google are Instagram and Facebook for the US results, while they are not even in the top 10 of German Google sources. Finally, in terms of what is missing from Google in the US results, it is notable that Fox News is not found in the top 50 sources, while it is present in all of the alternatives. The concentration of sources and source diversity showed a tendency for only a few root domains to make up a large share of search results. The Gini Index values of 0.73 and 0.79 in Germany and the United States, respectively, are a clear indicator (RQ3). This is consistent with findings from previous studies (Höchstötter & Lewandowski, 2009) that showed that only a few top sources dominate the search results in search engines. Of course, our study is not without limitations. First, the selection of search queries is the most significant factor in compiling the data that was evaluated. Even though the number of queries is high and there is some diversity in the queries, the topics are almost always focused on news, celebrities, and sports, which inevitably leads to many news sources in the search results. A refined approach to selecting search queries would be appropriate for a more accurate evaluation of source diversity. For example, this could be achieved by focusing on socially controversial topics and choosing the queries accordingly. Further limitations arise in the evaluation of source types. The classification we used in this study is very broad. A more precise classification would help make statements about the actual kinds of sources and thus to determine even more precisely which preferences and biases exist in different search engines. For example, grouping sources according to seriousness and reliability could serve as an explanation for the selection of sources to be displayed in search results. Regarding the search results collected in this study, another limitation is that only organic search results were considered. We did not consider advertisements or universal search results, although these have a strong influence on what users see on the SERP. The relevance of the search results was also not considered. This paper is available on arxiv under CC 4.0 license. Lead image by Edho Pratama on Unsplash This paper is available on arxiv under CC 4.0 license. available on arxiv Lead image by Edho Pratama on Unsplash Edho Pratama on Unsplash

Differences in Top Search Results Between Google and Alternative Search Engines

Tiktok Inc., and ByteDance LTD., v. Merrick B. Garland Update Court Filing, retrieved on May 7, 2024,  is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 6 of 11. E. Congress Disregarded Alternatives to Banning TikTok, Such as the National Security Measures Petitioners Negotiated with the Executive Branch. 41. Petitioners have demonstrated a commitment to addressing both of those concerns without the need to resort to the drastic, unconstitutional step of shuttering one of the most widely used forums for speech in the United States. The 90-page draft National Security Agreement that Petitioners developed with CFIUS would, if implemented, provide U.S. TikTok users with protections more robust than those employed by any other widely used online platform in the industry. 42. The draft National Security Agreement contains several means of ensuring data security without banning TikTok. All protected U.S. user data (as defined in the National Security Agreement) would be safeguarded in the United States under a special corporate structure: TikTok U.S. Data Security (a new subsidiary of TikTok Inc.). A special board, with Security Directors whose appointment would be subject to the U.S. government’s approval, would oversee TikTok U.S. Data Security, and in turn exclude ByteDance and all of its other subsidiaries and affiliates from such responsibilities. Further separation between the U.S. TikTok business and ByteDance subsidiaries and affiliates, including TikTok in the rest of the world, would be achieved by appointing a U.S.-government-approved Security Director to the board of TikTok Inc. Protected U.S. user data would be stored in the cloud environment of a U.S.-government-approved partner, Oracle Corporation, with access to such data managed by TikTok U.S. Data Security. 43. The draft Agreement would also protect against the concern about content manipulation and propaganda. Multiple layers of protection address concerns related to content available on the TikTok platform, including ensuring that all content moderation — both human and algorithmic — would be subject to third-party verification and monitoring. The concern about content manipulation would also be addressed by securing all software code through Oracle Corporation, a U.S. trusted technology provider. The TikTok U.S. platform and application would be deployed through the Oracle cloud infrastructure and subject to source code review and vetting by Oracle with another U.S.-government-approved third party responsible for conducting security inspections. As part of this process, Oracle and third parties approved by CFIUS would conduct independent inspections of the TikTok recommendation engine. 44. The draft Agreement also includes strict penalties for noncompliance, including a “shut-down option,” giving the government the authority to suspend TikTok in the United States in response to specified acts of noncompliance. The Agreement also provides significant monetary penalties and other remedies for noncompliance. 45. Although the government has apparently abandoned the draft National Security Agreement, Petitioners have not. TikTok Inc. has begun the process of voluntarily implementing the National Security Agreement’s provisions to the extent it can do so without the U.S. government’s cooperation, including by incorporating and staffing the TikTok U.S. Data Security entity, and by partnering with Oracle Corporation on the migration of the U.S. platform and protected U.S. user data to Oracle’s cloud environment. 46. To date, Petitioners have spent more than $2 billion to implement these measures and resolve the very concerns publicly expressed by congressional supporters of the Act — all without the overbroad and unconstitutional method of an outright ban. 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 retrieved on May 7, 2024, from sf16-va.tiktokcdn.com 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. Lead image by Solen Feyissa on Unsplash Tiktok Inc., and ByteDance LTD., v. Merrick B. Garland Update Court Filing, retrieved on May 7, 2024,  is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 6 of 11. Tiktok Inc., and ByteDance LTD., v. Merrick B. Garland Update Court Filing, retrieved on May 7, 2024,  is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 6 of 11. HackerNoon’s Legal PDF Series here E. Congress Disregarded Alternatives to Banning TikTok, Such as the National Security Measures Petitioners Negotiated with the Executive Branch. 41. Petitioners have demonstrated a commitment to addressing both of those concerns without the need to resort to the drastic, unconstitutional step of shuttering one of the most widely used forums for speech in the United States. The 90-page draft National Security Agreement that Petitioners developed with CFIUS would, if implemented, provide U.S. TikTok users with protections more robust than those employed by any other widely used online platform in the industry. 42. The draft National Security Agreement contains several means of ensuring data security without banning TikTok. All protected U.S. user data (as defined in the National Security Agreement) would be safeguarded in the United States under a special corporate structure: TikTok U.S. Data Security (a new subsidiary of TikTok Inc.). A special board, with Security Directors whose appointment would be subject to the U.S. government’s approval, would oversee TikTok U.S. Data Security, and in turn exclude ByteDance and all of its other subsidiaries and affiliates from such responsibilities. Further separation between the U.S. TikTok business and ByteDance subsidiaries and affiliates, including TikTok in the rest of the world, would be achieved by appointing a U.S.-government-approved Security Director to the board of TikTok Inc. Protected U.S. user data would be stored in the cloud environment of a U.S.-government-approved partner, Oracle Corporation, with access to such data managed by TikTok U.S. Data Security. 43. The draft Agreement would also protect against the concern about content manipulation and propaganda. Multiple layers of protection address concerns related to content available on the TikTok platform, including ensuring that all content moderation — both human and algorithmic — would be subject to third-party verification and monitoring. The concern about content manipulation would also be addressed by securing all software code through Oracle Corporation, a U.S. trusted technology provider. The TikTok U.S. platform and application would be deployed through the Oracle cloud infrastructure and subject to source code review and vetting by Oracle with another U.S.-government-approved third party responsible for conducting security inspections. As part of this process, Oracle and third parties approved by CFIUS would conduct independent inspections of the TikTok recommendation engine. 44. The draft Agreement also includes strict penalties for noncompliance, including a “shut-down option,” giving the government the authority to suspend TikTok in the United States in response to specified acts of noncompliance. The Agreement also provides significant monetary penalties and other remedies for noncompliance. 45. Although the government has apparently abandoned the draft National Security Agreement, Petitioners have not. TikTok Inc. has begun the process of voluntarily implementing the National Security Agreement’s provisions to the extent it can do so without the U.S. government’s cooperation, including by incorporating and staffing the TikTok U.S. Data Security entity, and by partnering with Oracle Corporation on the migration of the U.S. platform and protected U.S. user data to Oracle’s cloud environment. 46. To date, Petitioners have spent more than $2 billion to implement these measures and resolve the very concerns publicly expressed by congressional supporters of the Act — all without the overbroad and unconstitutional method of an outright ban. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on May 7, 2024, from sf16-va.tiktokcdn.com 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. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on May 7, 2024, from sf16-va.tiktokcdn.com 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. sf16-va.tiktokcdn.com Lead image by Solen Feyissa on Unsplash Lead image by Solen Feyissa on Unsplash Solen Feyissa on Unsplash

TikTok Proposes Robust National Security Measures as Viable Alternative to Banning

is a popular technology for efficient and scalable data storage and retrieval. However, maintaining its performance and data integrity requires a crucial practice called reindexing. Indexing is the initial process of adding data to Elasticsearch, while reindexing is essential for maintaining data accuracy and optimizing search performance. Elasticsearch Whether you're a seasoned Elasticsearch user or just beginning your journey, understanding reindexing is important for maintaining an efficient Elasticsearch cluster. In this article, we'll delve into the essentials of Elasticsearch reindexing, answering when it's necessary, how to trigger it, and the best practices to get the most out of your Elasticsearch cluster. Understanding Elasticsearch Reindexing In Elasticsearch, reindexing helps maintain data integrity and increase performance. Put simply, it's the process of copying data from one index to another. While this might sound straightforward, if not done correctly it can cause issues such as slow data retrieval or even incorrect results Imagine your Elasticsearch indices as well-organized libraries. Over time, books might need to be updated, rearranged, or even replaced. Reindexing is akin to rearranging the library shelves or updating the books to keep everything in order. Without it, your library can become disorganized, leading to slower searches and potential inaccuracies in your data. This analogy underscores the importance of understanding reindexing in Elasticsearch. It's not just about copying data; it's about maintaining the integrity of your "library" for efficient searching and retrieval. Let’s take a look at and how to keep on top of it. when reindexing is required When is Reindexing Necessary? Reindexing becomes essential when changes occur in your Elasticsearch data models or mappings, or when you're seeking performance enhancements. In this section, we’ll look into these scenarios in more detail to understand the nuances around why reindexing is required. Structural Changes in Data Models Structural changes in data models refer to modifications in how data is structured within Elasticsearch. These changes can include things like adding or removing new fields or altering data types of existing fields. Introducing new fields often requires a reindex to ensure Elasticsearch knows how to efficiently search for data stored in that field. Modifying data types requires a new index altogether as you cannot change data types in place. Once the new mapping has been created for the modified data type then the data needs reindexing. These structural changes require reindexing due to Elasticsearch's schema-on-write approach. Elasticsearch indexes data as it is ingested, and any changes to the data structure can lead to inconsistencies between existing data and data written with the new schema. As a result, without reindexing, search queries may yield unexpected or inaccurate results due to the schema mismatch of data items. This can have an impact on both data accuracy and search performance. Mapping Updates or Changes Mappings serve as the blueprint for how data is indexed and queried in Elasticsearch. When these mappings are modified then reindexing is usually required. Mappings define the data types and properties of fields within Elasticsearch. Any change to these mappings affects how data is indexed, stored, and retrieved. For instance, altering a text field to a date field fundamentally changes how data is processed and queried. Elasticsearch enforces data consistency based on mapping definitions. Changes to mappings can lead to inconsistencies between existing data and the updated schema if the data is not reindexed. When mappings are modified, particularly if it involves changing data types or field properties, backfilling also becomes important. Backfilling is the process of retroactively populating or updating existing data to align it with a new schema or data structure. This means that the existing data can still be queried efficiently and accurately after the mapping change. Performance Enhancements and Index Optimizations Reindexing isn't just a routine maintenance task, it's a powerful tool for optimizing search performance within Elasticsearch. For example, reindexing allows you to modify the number of shards in an index. Adjusting the shard count, or resharding, can distribute data more evenly, preventing uneven workloads on specific nodes to improve search performance. Reindexing can also be used to consolidate indices together. Let's say you have multiple small indices that share the same data structure and are frequently queried together. Reindexing can consolidate them into a single, larger index. This reduces the overhead of managing numerous small indices which can in turn enhance search speed. Finally, reindexing can be used to improve routing. By reindexing and applying routing strategies effectively, you can route queries to specific shards, minimizing the number of shards that need to be searched. This targeted approach can significantly speed up search queries if your data is frequently searched by specific keys such as a user ID. Upgrading Your Cluster When upgrading from Elasticsearch version 6.X to 8.0 (current major version) and beyond, you may need to reindex any indices that were created in version 6. Elasticsearch's data structures and underlying mechanisms changed significantly between these versions requiring reindexing for compatibility and optimal performance. The reindexing process ensures that data aligns with the updated structure and new functionality to ensure you can migrate seamlessly from old to new. Elasticsearch recommends using their upgrade assistant to help with this process. How to Trigger a Reindexing Operation Reindexing in Elasticsearch is made possible through the Elasticsearch Reindex API. The Reindex API serves as the bridge between your existing index and the new index you want to create or modify. Its primary purpose is to enable the efficient transfer of data from one index to another, on top of this, you can also: Selectively copy documents from the source index to the target index. Apply complex data transformations, such as field renaming or type conversions. Filter data based on specific criteria. Control the indexing process with options like throttling and refresh intervals. Before using the Reindex API, ensure that the target index, where you want to move or transform your data, is created and properly configured. To trigger reindexing, you then need to formulate a POST request to the endpoint, specifying the source and target indices, as well as any desired transformations or filters. An example reindex POST request could look as follows. _reindex POST /_reindex
{
  "source": {
    "index": "source_index"
  },
  "dest": {
    "index": "target_index"
  },
  "script": {
    "source": "ctx._source.new_field = 'transformed value'"
  },
  "query": {
    "term": {
      "category.keyword": "example"
    }
  }
} Once your request is built you can send the request to Elasticsearch, initiating the reindexing process. Elasticsearch will start copying data from the source index to the target index, following your defined instructions. Once the reindexing is complete, thoroughly test the data in the target index to ensure it aligns with your expectations. For example, you can compare the field mappings between the source and target indices to confirm that fields were mapped correctly during reindexing. You could also retrieve a sample of documents from both the source and target indices and compare them to verify the data was reindexed accurately. Best Practices for Reindexing When reindexing within Elasticsearch, you should look to follow these best practices to ensure the reindexing procedure is smooth with no data loss and little impact on existing cluster operations. Prioritize Data Backup Before initiating any reindexing activity, it is important to back up your cluster. This precautionary step acts as a safety net, offering a way to revert to the original state should any unexpected issues arise during the reindexing process. The source index should still exist after reindexing, however, it's a fundamental principle to always have a reliable copy of your data before making significant changes. Conduct Reindexing in a Controlled Environment First To mitigate potential risks and challenges during reindexing, it is advisable to perform the operation in a pre-production environment first. By doing so, you can identify and address any unforeseen issues without affecting the production system. Once the procedure has been completed and verified in the pre-production environment, it can then safely be run in production. Monitor Resource Usage It is important to monitor system resources during reindexing to prevent strain on your infrastructure. Reindexing can be resource-intensive, especially for larger datasets. Keeping a close eye on CPU, memory, disk usage, and network activity can help optimize resource allocation, ensuring the process runs efficiently without causing performance bottlenecks. To check resource usage you can use the node stats API. GET /_nodes/stats This will return a response that looks as follows. {
  "_nodes": {
    "total": 2,
    "successful": 2,
    "failed": 0
  },
  "cluster_name": "my_cluster",
  "nodes": {
    "node_id1": {
      "name": "node_name1",
      "process": {
        "cpu": {
          "percent": 30,
        }
      },
      "jvm": {
        "mem": {
          "heap_used_percent": 40.3,
          "heap_used_in_bytes": 123456789,
          "heap_max_in_bytes": 256000000
        }
      }
    },
    "node_id2": {
      "name": "node_name2",
      "process": {
        "cpu": {
          "percent": 50,
        }
      },
      "jvm": {
        "mem": {
          "heap_used_percent": 60.8,
          "heap_used_in_bytes": 210987654,
          "heap_max_in_bytes": 256000000
        }
      }
    }
  }
} If you find reindexing is too intensive, you can throttle the process by setting the parameter when submitting the reindex request. This will add a sleep between batches for the number of seconds set by the parameter, to provide a cooldown period between batches. requests_per_second Verify and Validate Results Once the reindexing is complete you should verify the data in the target index to ensure it looks as expected. This validation process should encompass a variety of tests including document counts, field mappings, and search queries. Alternative Solutions Elasticsearch has undoubtedly established itself as a prominent solution in the NoSQL search and analytics space. However, it's worth exploring alternative solutions that offer unique approaches to data indexing and querying, particularly one like Rockset. Rockset is a to Elasticsearch and offers a different perspective on indexing and querying data. Unlike Elasticsearch's schema-on-write approach, Rockset allows schemaless ingestion. Data can be ingested and queried without the need for upfront schema definition, offering more flexibility in handling ever-evolving datasets without the need for reindexing. cloud-native alternative In the area of index management, Rockset benefits from its model where a row index, a column index, and a search index are all created automatically for the data as it is ingested. This contrasts with Elasticsearch, where indexes are created by users and structural changes often necessitate time-consuming reindexing procedures. converged indexing While Elasticsearch remains a robust solution for various use cases, exploring alternatives like Rockset may be useful, especially if you find reindexing in Elasticsearch becoming a frequent activity. Conclusion Reindexing is a fundamental process in Elasticsearch and is important for maintaining the efficiency and accuracy of search results as data structures evolve. If you find that reindexing is becoming a constant time burden for your team it might be worth exploring alternative solutions like Rockset. Rockset offers a more streamlined index management process that enables developers to concentrate on more value-add activities.

Understanding Elasticsearch Reindexing: When to Reindex, Best Practices and Alternatives

ইলাস্টিকসার্চ রিইন্ডেক্সিং বোঝা: কখন পুনঃসূচীকরণ করতে হবে, সর্বোত্তম অনুশীলন এবং বিকল্প

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Comprendre la réindexation Elasticsearch : quand réindexer, bonnes pratiques et alternatives

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Elasticsearch の再インデックスを理解する: 再インデックスを行うタイミング、ベストプラクティス、代替手段

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Понимание переиндексации Elasticsearch: когда проводить переиндексацию, лучшие практики и альтернативы

Elasticsearch Yeniden İndekslemeyi Anlamak: Ne Zaman Yeniden İndekslenmeli, En İyi Uygulamalar ve Alternatifler

Tìm hiểu về lập chỉ mục lại Elaticsearch: Khi nào nên lập chỉ mục lại, các phương pháp hay nhất và giải pháp thay thế

了解 Elasticsearch 重新索引：何时重新索引、最佳实践和替代方案

Hey friends 👋 Auth0 and its parent company, Okta, are what developers think of when managing and authorization. Both do, , a great job at implementing secure access for our favorite apps! user authentication in general They are also the tools to help us work with the temperamental 0Auth2. 😅 Though this option deserves to be considered, many decent alternatives are on the market. In the open-source world, we are particularly excited about the below 5 alternatives! Let's dive straight into it 👇 Ory Ory is a big name in the industry. It maintains advanced open-source security software solving authentication, authorization, access control, application network security, and delegation. Ory Kratos is an open-source repository that is Auth0-like. It focuses on the identity, user management, and authentication system for the Cloud. The main features you can expect are: Registration, login, and account management flows for passkeys, biometric authentication, social sign-in, SSO, and multi-factor authentication Pre-built login, registration, and account management pages and components OAuth2 and OpenID Connect provider for single sign-on, API access, and machine-to-machine authorization Low-latency permission checks based on Google's Zanzibar model and with built-in support for the Ory Permission Language Hanko Hanko is an elegant project focused on an open-source authentication and user management solution. Their approach focuses on moving the login beyond passwords. Passkeys are the future, and this was well articulated in the blog post , an article written by Google. The Beginning of the End of the Password Since passkeys were announced only very recently, the ecosystem of devices, browsers, and operating systems is getting ready to move beyond passwords. Hanko has been preparing for this shift, and as of present, it provides: Fast integration with Hanko Elements web components (login box and user profile) API-first, small footprint, cloud-native Availability for self-hosting and on Hanko Cloud. The easiest way to get started with hank is with docker-compose: 1️⃣ Clone this repository: git clone https://github.com/teamhanko/hanko.git 2️⃣ In the newly created hanko folder, run: docker compose -f deploy/docker-compose/quickstart.yaml -p "hanko-quickstart" up --build 3️⃣ After the services are running, the login page can be viewed at . To receive emails without your own SMTP server, Hanko added mailslurper which will be available at . localhost:8888 localhost:8080 Cerbos Cerbos is an authorization layer. It enables you to define context-aware access control rules for your application resources in YAML policies, which are managed and deployed via your Git-ops infrastructure. You can set up a self-hosted Cerbos Policy Decision Point. With Cerbos you can: Define authorization logic in a collaborative IDE and testing environment Collaborate with colleagues to author and share policies in private playgrounds Deploy with a fully hosted CI/CD pipeline Build special policy bundles for client-side or in-browser authorization Here is how Cerbos works with your application (a more advanced explanation can also be found ): 👇 here To try out Cerbos, you can get started with their very fun . 😉 Cerbforce tutorial Zitadel Zitadel is an open-source user management tool that is quickly set up like Auth0. Zitadel is built with a complex multi-tenancy architecture in mind, and it provides solutions to handle B2B customers and partners. Zitadel is built with the following structure in mind: API-first approach Multi-tenancy authentication and access management Strong audit trail due to event sourcing as a storage pattern Actions to react to events with custom code Self-service for end-users, business customers, and administrators CockroachDB or a Postgres database as a storage option You should consider Zitadel if you are interested in leveraging the below features: Multi-tenancy with team management Secure Login Self-service OpenID Connect OAuth2.x SAML2 LDAP Passkeys / FIDO2 OTP If the information above makes you think Zitadel is a good fit for you, you can get started by checking out their guide . here SuperTokens SuperTokens is another open-source authentication solution. The main features you can find on the platform are the following: Various login types: Email/password, Passwordless (OTP or Magic link-based), Social / OAuth 2.0 Access control Session management User management Self-hosted/managed cloud Their architecture is unique because your backend API layer would sit in the middle of your front end and SuperTokens'. This enables easy customizations to the auth logic and allows for a secure session solution. To start with SuperTokens, you can use their practical guide to pick the login type you want and get started . here That's it for this one. ☝️ As you have learned in this article, there are many exciting alternatives to Auth0. You should look into each alternative and determine what service best suits your current or future needs. In the meantime, I invite you to consider supporting these projects by starring them. ⭐️ (We are not affiliated with them. We just think that great projects deserve great recognition.) See you next week, Your Hackernoon buddy 💚 Bap Also published . here

5 Open Source Alternatives to Auth0 to be Excited About in 2024

GRAND CAYMAN, Cayman Islands, February 7th, 2024/Chainwire/--Ondo’s Sui upcoming integration will bring native access to new tokenized assets such as treasuries, securities, and stablecoins on-chain. , the Layer 1 blockchain which has experienced explosive growth since its inception eight months ago, today announced that Ondo Finance is expanding into the Sui ecosystem. Sui The expansion will bring Sui Network’s first native dollar-denominated token (including stablecoins and interest-bearing stablecoin substitutes) in the form of Ondo USD Yield or “USDY” — a US treasury-backed and interest-bearing token issued by Ondo. Ondo’s expansion to Sui adds to Sui’s blistering DeFi momentum, demonstrating the growth and demand for financial applications and native functionality on chain. Sui’s DeFi volume is up more than 1200% since October and Sui recently broke into the top 10 DeFi ecosystems as measured by TVL. Ondo Finance is the third-largest platform bringing tokenized derivatives of real-world assets onto public blockchains with $185M in TVL and over $1B worth of its newly-launched governance token trading in its first week in late January. In addition to stablecoins, Ondo’s flagship Treasury-backed tokens, tokenized securities, and real-world assets will create countless new opportunities for teams building on Sui. Ondo’s expansion into the Sui ecosystem also continues a trend of top projects affirmatively choosing to integrate into Sui. For example, in December 2023, leading Solana lending protocol Solend announced it would launch a lending protocol native to Sui and decentralized derivatives exchange Bluefin likewise shuttered its V1 Arbitrum implementation to focus entirely on Sui. What The People Spearheading This Had to Say “The people who interact with our platform want fast and efficient transactions, which should be essential for any blockchain project,” said Ondo’s founder and chief executive officer, Nathan Allman. “Sui’s growth and network performance offer clear confirmation that its network is the perfect fit for Ondo’s ecosystem.” Tokenized treasury offerings represent tradable tokens backed by real-world assets, and their presence on Sui is a significant step toward growing DeFi in the ecosystem and across the industry. “Ondo is an amazing addition to the Sui ecosystem, providing a native yield-bearing stablecoin-like asset that will unlock new opportunities for Sui’s builders and developers and essential new functionality for the users of their applications,” said Greg Siourounis, Managing Director of the Sui Foundation. “Sui’s DeFi volume is already growing at a remarkable rate and Ondo’s participation will make that trajectory even stronger. I am excited to see how Sui’s community leverages the real-world assets and innovative financial products Ondo offers.” Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Real-World Assets And Yield-Bearing Stablecoin-Alternative Coming to Sui: Ondo Finance's Expansion

Welcome to a two-part exploration of Causal Impact analysis. In this concise overview, we aim to equip you with theoretical foundations and practical insights. of the current article provides a quick dive into the theoretical aspects of Causal Impact analysis. Discover how this method operates, its suitability, and essential limitations. The first part delves into the practical application of Causal Impact analysis. We guide you through a specific dataset, demonstrating how to implement the library and interpret results. This hands-on approach empowers you to not only use the Causal Impact library effectively but also draw meaningful conclusions from your analyses. The second part Introduction In the current work of data analysts, evaluating the effectiveness after the introduction of a particular feature is one of the most important tasks. While randomized experiments like AB tests are the traditional gold standard for effect estimation, real-world challenges sometimes make their implementation impractical. The following common scenarios illustrate these challenges. Companies may lack the inherent infrastructure or suitability for experiments The inability to experiment due to geographical limitations. For example, you have launched a broad brand marketing company Practical constraints such as cost, resource limitations, or the need to analyze past actions In these situations, need to find alternative methods to estimate the feature’s effects and understand the impact of changes except for AB tests. This article explores the CausalImpact library, an open-source tool developed by Google. What is CausalImpact? At its heart, causal impact is about figuring out the impact of an action by guessing how a certain measure would have changed if that action (like testing a feature) hadn't happened. We predict this hypothetical change, compare it to what actually occurred, and the gap between them tells us how much the action influenced the outcome. Imagine launching a wide advertising campaign in the UK to promote a new app feature. The goal is to increase installations by reaching a larger audience through bloggers. However, placing part of this audience in a control group, where they don't see the new feature, might create a negative impression. To address this, a decision is made to roll out the feature for the entire Region B, while Region A serves as the control group without the brand campaign. Here's how CausalImpact comes into play: Control and Test Groups: Region A (Control Group): No brand campaign. Region B (Test Group): Brand campaign introduced to the audience. Targeted Metric: The metric of interest is the number of installations. Date of Intervention: The date when the campaign is rolled out to all users in Region B. With CausalImpact, a model is built based on installs from Region A. This model then predicts the expected values for the same time period in Region B, assuming no brand campaign occurred. These predicted values serve as a baseline, representing what could be expected in Region B without the advertising campaign. The important step involves comparing the actual results in Region B against these expected results. This comparison reveals the impact of the advertising campaign on installations. CausalImpact essentially allows us to quantify and understand how much the intervention influenced the outcome, providing valuable insights for decision-makers and data scientists. Limitations of Using Causal Impact Analysis Causal Impact analysis has its limitations that practitioners need to be mindful of: Data Quality Matters: Causal Impact's effectiveness relies on the quality of the data it receives. Inaccurate or incomplete data can compromise the reliability of the analysis. Correlation in Control Group: Control group must exhibit reasonable correlation with test groups. It's crucial to ensure that the chosen control group is comparable to the test group to enhance the accuracy of predictions. Macro Event Impacts: Both test and control groups should be affected by macro events in a similar manner. Any significant disparities in external factors can introduce bias into the analysis, affecting the accuracy of causal effect estimation. Post-Analysis Considerations: What happens in the control groups during the post-analysis period is vital. Interrupting regular marketing activities or making changes in the control groups during this period can lead to misleading results. It's essential to maintain consistency in control group conditions. External Influences: External factors beyond the scope of the analysis can also impact the results. Understanding and accounting for these external influences is necessary for a more accurate interpretation of causal effects. Being aware of these limitations and carefully addressing them in the analysis process is crucial to obtaining meaningful insights and making informed decisions based on Causal Impact analysis. In the , I will give a practical example of using the library and tell you how to interpret the results next article

Causal Impact Analysis as an Alternative to A/B Testing

Stripe™️ is great. Everybody loves Stripe™️. It is powerful, flexible, reliable, and easy to start. It integrates with everything, even with my smart toaster. They have a fancy website and a handsome founder—pure joy. But there is a cohort of people who can't enjoy it. I'm talking about people from these countries: 🇦🇷 Argentina, 🇨🇱 Chile, 🇨🇳 China, 🇨🇴 Colombia, 🇪🇬 Egypt, 🇮🇱 Israel, 🇰🇿 Kazakhstan, 🇰🇼 Kuwait, 🇵🇪 Peru, 🇵🇭 Philippines, 🇶🇦 Qatar, 🇷🇺 Russia, 🇸🇦 Saudi Arabia, 🇰🇷 South Korea, 🇹🇼 Taiwan, 🇹🇷 Turkey, 🇺🇦 Ukraine, 🇺🇾 Uruguay, 🇻🇪 Venezuela, 🇻🇳 Vietnam. That is approximately 2.56 billion people (thx ChatGPT)! So what So I made a website that aggregates all the payment service providers other than Stripe™️ in one place: It is intended for small indie makers like myself. https://stripealternatives.com/ Why The main goal is to show other options for people from these countries. I do not them to be sad they can't use the most popular payment service. It is not a problem. There are dozens of competitors to choose from! I also want to promote the Stripe™️ competitors because it has too huge % of the market. And the monopoly can make things bad. The data I listed the payment services I used in my SaaS building experience. But I worked with only 6 of them. So I used the power of GPT4 and Reddit to add more items. I hope that you will also help me to organize the list. There could be mistakes and incorrect data. Please feel free to commit changes in the comments here. I will apply them all. Disclaimer: The project " " is not affiliated with Stripe™️®©. STRIPE is a trademark of STRIPE, INC. Please do not sue me. stripealternatives.com Also appears . here

I Created a List of All Stripe Alternatives

আমি সমস্ত স্ট্রাইপ বিকল্পের একটি তালিকা তৈরি করেছি

Ich habe eine Liste aller Stripe-Alternativen erstellt

Creé una lista de todas las alternativas de Stripe

J'ai créé une liste de toutes les alternatives Stripe

मैंने सभी स्ट्राइप विकल्पों की एक सूची बनाई

すべての Stripe 代替案のリストを作成しました

나는 모든 스트라이프 대안 목록을 만들었습니다.

Criei uma lista de todas as alternativas de Stripe

Я создал список всех альтернатив Stripe

Tüm Stripe Alternatiflerinin Listesini Oluşturdum

Tôi đã tạo một danh sách tất cả các lựa chọn thay thế sọc

我创建了所有条纹替代品的列表

I recently completed the from Sektor7 Institute. Here is my honest opinion about this course, and I will try to provide resources that are at least equal in quality to the mentioned course. RED TEAM Operator: Malware Development Essentials Course I have a problem with the course format, the scripts, and the price. I hope this review will prompt a change in the authors' approach. I don't have a problem with the overall content of the material provided. Positives Don't get me wrong, the course will teach you what they say it will teach you, and it's really good for beginners. The course is a great intro to the Windows API. Even if you never want to create , the material is mostly very high quality, and  I only criticize minor parts. If your employer offers you this course, definitely take it. malware Criticism first, the course contains 4 hours of material in videos. Some parts of these videos only repeat previously mentioned things and do not contain any informational value. For example, the narrator shows the disassembled code several times and spends much time looking for different parts in the assembler. This reflects an actual debugging process, but it is unsuitable for video. If text and screenshots were provided, the material would be much shorter and equally educational. Many things are duplicated, and I don't think the viewer must see run 15 times throughout the course. x64dbg compile.bat The course would benefit if it were in text form or supplemented by short videos explaining the complex parts. Secondly, the code is OK, but the compilation scripts are not broken down to the necessary details and work more automagically. You won't know what they do if you want to do something more custom. On the other hand, the code is broken down to the smallest detail, sometimes to the detriment. I know it is an essential course, but there is no need to explain the elementary operations . The viewer should be able to program; things like functions do not need to be explained. C++ main getchar() I would emphasize learning to search for information in the Windows API documentation and explaining what rights a given user must have to run them. I know this is a personal preference, but why are demos shown inside I prefer to show you how to set the maldev coding environment on Windows properly. Resources on how to get all needed components like compiler, make, required libraries, and how to generate shellcode. note++ And lastly, the price. This is a criticism not only of these materials but also of the certification business in general. The course costs $199, an industry-wide reasonable price for the material. I can see the value if you don't want to put effort into finding your materials and want to get them on a platter, but then I would debate whether you are even capable of learning how to create malware with that mindset. If you are a third-world country citizen, student, or someone who tries to learn it just because of passion for a subject, I don’t think this is a good investment. Providing an alternative To support my arguments, I put together several sources based on the course's table of contents, trying to show that finding the same quality sources is not difficult and all the information contained in the course is freely available. TOC of the Maldev course Portable Executable Droppers Obfuscation and Hiding Backdoors and Trojans Code Injection My alternative resources: - Although this page is designed for learning how to develop cheats for games, it does a great job of explaining how code injection, DLL injection, code caves, and other concepts work. https://gamehacking.academy - This Repo provides simple shellcode dropper exercises and also examples, and it is not limited to `C++` only, You can choose Go, Rust or Csharp. EDR and AV evasion exercises are also provided. https://github.com/chvancooten/maldev-for-dummies/tree/main/Exercises/Exercise%201%20-%20Basic%20Shellcode%20Loader and - This brilliant YouTuber covers a lot of MalDev subjects, and his repo has the following topics covered:   Shellcode Injection, NTDLL, Indirect Syscalls, Direct Syscalls and DLL Injection. https://github.com/cr-0w/maldev https://www.youtube.com/@crr0ww - Another great resource on Trojan malware development, aka embedding a shellcode inside the legitimate binary. https://www.ired.team/offensive-security/code-injection-process-injection/backdooring-portable-executables-pe-with-shellcode These resources are free and cover all sections of the course mentioned above. I hope I have provided only constructive criticism, and nobody will get offended, thank you for reading, and feel free to leave feedback.

Sektor7 - Malware Development Essentials Course Criticism: Proving an Alternative.

Email communication is a critical aspect of any business operation, and while convenience and affordability are essential, the choice of an email service provider should be driven by more than just convenience. Bare Metal Email, through extensive research on email operations, highlights the significant impact of email choices on businesses. As a potential , Bare Metal Email claims to have developed premium security and addresses deliverability issues within organizations. Let’s dive deeper to find out more. Gmail alternative Over the past decade, Bare Metal Email has concentrated on developing a comprehensive system to resolve deliverability challenges and enhance business workflows. Unlike competitors such as Gmail, their six-layer system focuses on multiple aspects of email functionality. The first layer involves scrutinizing the network for blocks and spammers that could affect reputation. The IP layer ensures the cleanliness and absence of blocks in the IPs. Moving to the domain layer, Bare Metal Email scans the customer's network for potential blocks. The ESP layer, the recipient of the email, is checked for general blocks and specifically for blocks related to the sender's domain. This thorough approach ensures that emails sent by businesses are not flagged as spam or blocked by recipients. The final two layers of the Bare Metal Email system analyze the structure and text of emails to ensure correct delivery. The Heuristic layer employs AI and algorithms to detect keywords that might trigger blocking by recipients. The layer examines the email’s code for potential issues like broken hyperlinks that could lead to spam filtering on the recipient’s end. This meticulous scanning process reflects Bare Metal Email's commitment to addressing issues at every level of email composition. HTML We’ve all questioned whether Google, and Gmail by extension, is listening to us. Those suspiciously placed ads for a product you had spoken to a friend about not 10 minutes previously. Bare Metal Email claims to distinguish itself by offering a security system that is both robust and noninvasive. The company incorporates strong security measures at each layer, eliminating the need for two-factor identification. This approach contrasts with the common advice for businesses to use complex passwords, backups, and multiple authentication methods, often resulting in account lockouts and wasted time. Bare Metal Email aims to provide a secure and accessible email experience without the restrictions and complexities associated with multiple Gmail accounts. Good email providers recognize the evolving landscape of business communication and the importance of adaptability. With a commitment to staying ahead of emerging threats and technological advancements, Bare Metal Email claims to regularly update its system to address new challenges. This approach ensures its clients benefit from ongoing improvements and the latest security measures. In a rapidly changing digital environment, having an email provider that prioritizes both current needs and future developments is a valuable asset for businesses seeking a reliable and sustainable communication solution. In a world where communication is paramount, especially through email, we can vouch that Bare Metal Email has positioned itself as a . The company assures users that its system sources and solves deliverability issues, such as blacklisting and spam filters, offering businesses a solution to ensure their emails reach the intended recipients without complications, and where complications exist - solutions. reliable alternative to Gmail - By: Tom White

Exploring Bare Metal Email as a Gmail Alternative for Business

ব্যবসার জন্য একটি Gmail বিকল্প হিসাবে বেয়ার মেটাল ইমেল অন্বেষণ

Entdecken Sie Bare-Metal-E-Mail als Gmail-Alternative für Unternehmen

Explorando el correo electrónico básico como alternativa a Gmail para empresas

Explorer la messagerie Bare Metal comme alternative à Gmail pour les entreprises

व्यवसाय के लिए जीमेल विकल्प के रूप में बेअर मेटल ईमेल की खोज

ビジネス向けの Gmail 代替としてのベア メタル メールの検討

비즈니스용 Gmail 대안으로 베어메탈 이메일 탐색

Explorando o e-mail Bare Metal como uma alternativa ao Gmail para empresas

Изучение электронной почты в качестве альтернативы Gmail для бизнеса

İşletmeler için Gmail Alternatifi Olarak Bare Metal E-postayı Keşfetmek

Khám phá email kim loại trần như một giải pháp thay thế Gmail cho doanh nghiệp

探索裸机电子邮件作为 Gmail 的企业替代品

, the leading financial media company for next-generation investors, is excited to announce a robust lineup of speakers that includes the world’s top investors, founders and entrepreneurs for its annual Nov. 14 in New York City. Benzinga Future of Digital Assets Conference This must-attend event promises to be the largest gathering of digital asset companies and institutional investors, offering a year's worth of networking opportunities in one day. Forge partnerships and build winning investment strategies. This event is where global power players, Wall Street money managers and blockchain visionaries meet and manifest a dynamic, future-forward Web3 marketplace. The Future Of Digital Assets conference will be preceded by the Nov. 13. Both events will be held at Convene at 225 Liberty St. in Manhattan. Benzinga Fintech Deal Day & Awards “We're gearing up for a monumental event this November. The lineup is unmatched, and we will truly be charting the course for how Wall Street and digital assets work with and against each other,” says Luke Jacobi, president of Benzinga. Benzinga’s impressive roster of speakers continues to grow daily. Some highlights include: Brett Harrison, Founder, CEO, Architect Richard Widmann, Head of Strategy Web3, Google Cloud Luca Netz, CEO, Pudgy Penguins Caitlin Long, Founder, CEO, Custodia Bank Mark Yusko, CEO, Chief Investment Officer, Morgan Creek Capital Management Keith Grossman, President of Enterprise, MoonPay Gracy Chen, Managing Director, Bitget David Palmer, Chief Product Officer, Vodafone Brooke Stoddard, VP, Client Team, Grayscale Investments Alex Salnikov, Co-Founder, Chief Strategy Officer, Rarible Abhishek Saxena, Investment Principal, Polygon Ventures Bobby Zagotta, CEO, Bitstamp USA, Global Commercial Officer, Bitstamp USA To see the full agenda with speakers, go here. Organizations sending speakers to this year's events include BlackRock, BNY Mellon, DTCC, OCC, State Street, Société Générale, Hedera, Citi, BMO, Dow Jones, Northern Trust, Franklin Templeton, Amazon, S&P Global, VanEck, Google, Fireblocks, Invesco, Moody’s, and EDX Markets, to name a few. The countdown to the Future of Digital Assets event has begun! With limited spaces remaining, now is the time to secure your spot and be part of a groundbreaking experience that promises networking, learning, and insights into the evolving world of digital assets. Register here. For more information, visit the . Future of Digital Assets event page Benzinga’s Fintech Deal Day & Awards | November 13, 2023 | Convene in NYC Use code HACKERNOON20 for 20% off tickets. . Get tickets here About Benzinga Benzinga is a leading content provider to global brokerages and media outlets, including Yahoo, MarketWatch, and Insider. Benzinga delivers high-quality, low-cost, and timely content directly to users through newswires, analytics software, and other data services. In addition to its core product portfolio, Benzinga hosts thought leadership, networking, and educational events to assist investors, founders, and operators in the next stage of their growth.

Build Wealth In Alternative Investments at Benzinga’s Future of Digital Assets Conference

The Noonification: Tired of ChatGPT? Try These 6 Alternatives Instead (11/10/2023)

Looking for the best ChatGPT alternatives? More and more people are turning to AI chatbots like ChatGPT to help with all sorts of tasks. But what if ChatGPT isn’t the perfect fit for you? We don’t like ChatGPT that much. Sometimes, the information is old, it can give biased answers, too, and it has many filters. But don’t worry! There are lots of other great options out there. To make your life easier, we’ve gathered a list of the best alternatives to ChatGPT. These chatbots are smart, helpful, and ready to assist you in getting your work done faster and better. So, keep reading our unbiased and unsponsored take to find your next favorite AI chatbot other than ChatGPT! The information presented in this article is based solely on my (Mukund Kapoor) independent analysis and research. Any links included within this content are not affiliate links and are provided for reader convenience only. I have not been sponsored or influenced by any companies mentioned to promote or rank their products. Best ChatGPT Alternatives If you’re looking for general ChatGPT alternatives because you don’t want to spend $20 every month or you’re not able to buy ChatGPT Plus, then this list is for you. Here are some of the best ChatGPT alternatives that you can try if ChatGPT is not what you like: 1. Chatsonic by Writesonic Chatsonic, powered by GPT-4, is a chatbot that aims to overcome the limitations of ChatGPT. It offers real-time data, image and voice searches, and a wide range of content creation abilities. Chatsonic is an AI writing tool that offers an unlimited plan for just $16 per month, making it the most affordable option compared to ChatGPT. Why Try Chatsonic as an Alternative to ChatGPT You can use Chatsonic to find accurate information online and avoid making mistakes. AI chatbots remember conversations and use them to keep talking to you. In our opinion, you should try using ChatSonic instead of ChatGPT. It is a great alternative that you might find more valuable as we have tried it among all the other tools in this list and found that Chatsonic performs far better than any other AI chatbot. The best part is it costs $4 less than ChatGPT, making this a worthy ChatGPT alternative. Though you may find many free options, our advice would be to go with Chatsonic and try this ChatGPT alternative. Key Features: At just $16 per month, it’s cheaper than ChatGPT but offers as much or even more. Low Cost: Chatsonic uses Google’s Knowledge Graph. That means you get up-to-date and correct info anytime. Google Know-How: You can talk to it like it’s a coach, a poet, or even a stand-up comedian! Makes chatting more fun. Fun Personas: Unlike ChatGPT, Chatsonic can make art from text. Just type something and see it turn into a cool image. AI Art: It remembers your past chats, which is super useful for keeping the conversation flowing. Smart Memory: You can also use voice commands, a step from just typing. More than Text: Got a phone? You can use Chatsonic. There’s an Android app that makes it a breeze to use on the go. Easy to Use Anywhere: If you have a business, you can make your own chatbot with Botsonic. No need for coding! DIY Chatbots: Pricing: ChatSonic offers a tiered pricing model to cater to different needs, which is fairly competitive in the market. When you sign up for a trial, you can access 10,000 premium words at no cost. If you already have a word balance on Chatsonic and would like to generate more, you can easily do so. In addition, a subscription option for $16/month provides access to an infinite supply of premium words. 2. Microsoft Bing When you think about AI-driven chat tools, the focus usually shifts to Google and OpenAI. But Microsoft Bing has shown it's not one to be overlooked. Offering an AI model that rivals ChatGPT, Microsoft has released an upgraded version of Bing that features their AI chat engine, codenamed "Sydney." Originally announced as the "Prometheus model," it was later confirmed that the tech behind it is none other than GPT-4. Why Try Microsoft Bing as an Alternative to ChatGPT For those who seek a search engine and a conversational AI bot in one, Bing provides this with finesse. The new Bing also features a Chat mode, which allows users to engage in contextual conversations based on web queries. The service also offers multimodal capabilities, visual answers, and enhanced accuracy. What makes it even more comparable to ChatGPT is its feature set, which includes abilities to plan trips, find recipes, and offer advice. Previously available only via a waitlist, Bing’s chat engine is now public. Key Features: This ensures the chat experience is as fluid and conversational as ChatGPT. Powered by GPT-4: Unlike some other chatbots, Bing also supports visual queries and can provide visual answers. Visual Input and Output: Bing AI is hooked to the web, delivering the latest information on any subject matter. Internet Connectivity: Bing keeps a history of your chats so that you can return to them later. Chat History: Bing offers different conversational styles, making it versatile for various users. Different Chat Modes: One standout feature is that Bing credits the sources from where it pulls information, providing an extra layer of trust. Cites Sources: : Pricing One of the best parts? Bing is free to use. So, if you’re looking for a no-cost alternative to ChatGPT, Bing offers a compelling case. 3. Google Bard There are now a lot of new AI conversational apps, but Google Bard an excellent alternative to ChatGPT after Microsoft's Bing Chat. It uses LAMDA (Language Model for Dialogue Applications) to have deep and long talks with users. Why Try Google Bard as an Alternative to ChatGPT Google Bard presents itself as an intriguing alternative to ChatGPT, particularly for those who are already invested in Google's ecosystem of products and services. Google Bard is also meant to be very good at understanding context and producing text that makes sense and is relevant to the context. Key Features: : Unlike other alternatives, Bard accepts both text and image inputs, making it a multi-modal chatbot. This opens doors to diverse use cases, from visual searches to image-based Q&As. Text and Image Uploads : Google's extensive database is a treasure trove of information. Bard taps into this to deliver answers that are accurate and incredibly exhaustive. Real-time Research : Bard relies on Google's next-gen PaLM 2 language and conversational model, which offers a more refined conversational experience compared to its predecessors. PaLM 2-Powered : Whether you're a coder or a content creator, Bard's export options are a boon. You can export your interactions to Google Docs or Colab, enhancing workflow efficiency. Export Capabilities : Bard's language capabilities are not confined to English. It supports multiple languages, making it an ideal tool for global users. Multilingual Support Pricing: As of now, Google Bard is free of cost since it is an experimental project. Google has not disclosed any future pricing plans, making it an attractive option for budget-conscious users. 4. Claude AI The fourth alternative to ChatGPT that has gained a lot of users in the past month is Claude AI, developed by Anthropic. With enhanced features and capabilities, Claude 2 takes the user experience to a new level. The company, which has received support from Google, positions itself as a direct competitor to OpenAI. Claude does not traditionally use the LLM model. The product utilizes unique AI techniques, referred to as "proprietary," which encompass a neural network, training data, and other undisclosed components. Why Try Claude AI as an Alternative to ChatGPT ClaudeAI is cool and does some things better than ChatGPT. First, you can add files like Word or PDFs. Claude reads them and then talks to you about them. This is something ChatGPT can't do. You can ask Claude questions about the file you uploaded, and it understands what you're asking. Another cool thing is Claude can handle a lot of words. You can chat about many things at once, and it will not forget what it said earlier. It's like talking to a friend who listens. That's because Claude uses 100K tokens, way more than ChatGPT's 8K. Claude knows things that happened up to early 2023, so it has fresher info than ChatGPT. If you want to know about new phones or events, Claude is better. But be careful with links. Claude tries to tell you what a website says, but sometimes it gets wrong. So don't trust it too much for this. Still, Claude has a lot going for it, and it is a good option if you need to upload files or want more recent information. Key Features: : Claude's got a 100k context window. What does that mean? It means it remembers a lot of what you said, so you don't have to keep repeating yourself. Big Chat Window : You can load a whole library of books into Claude. So, if you're a book lover, you and Claude will get along just fine. Book Smart : You can even send PDF files to Claude. I'll read them and chat about it with you. File Uploads : Claude is designed to be super safe to use. That's a big deal if you're worried about that kind of thing. Safe and Sound Pricing: Guess what? Claude is free right now if you're in certain countries. But they also have some plans where you pay based on how much you chat. It's cheaper than GPT-4, so that's something to consider if you're watching your wallet. 5. Poe AI Poe is a popular chatbot website that uses artificial intelligence to answer questions as quickly as possible. This unique Quora product is built on OpenAI GPT and Claude-version 1.2. Why Try Poe AI as an Alternative to ChatGPT Poe AI is the brainchild of Quora's CEO, Adam D'Angelo. His aim? To make bots easy for all to use. Poe AI offers something different in a busy field of bots like Google Bard and Bing AI. However, it's not free right now as you can only type 2 to 3 messages per day, and it costs $19/month, but it has some good reviews. The reason why it ranks so low on our list is that it lacks a lot of features that Chatsonic or Microsoft Bing AI Chat provides. Key Features: : Poe offers access to various bots like ChatGPT, GPT-4, Claude Plus, and Claude Instant, all in one interface. Multiple Bots in One App : Poe's mobile app makes it easy to chat on the go, a feature that many users find convenient compared to web-only interfaces. Mobile-Friendly : Poe provides a Pro service at $19.99 a month, which removes daily message limits and offers premium features. Subscription Plans : Poe aims to guide users in selecting the most appropriate bot for specific tasks, like writing or programming. Bot Recommendations : The app syncs all your chat data across different devices, so you can pick up right where you left off. Inter-Device Sync : Poe allows you to create and customize your own bots for unique use cases. User-Created Bots Pricing: Poe's Pro service is priced at . This gives you more access to bots. It's a decent deal if you plan to use bots a lot. $19.99 a month 6. Jasper Chat Jasper is a powerful tool that makes different kinds of content using AI. Recently, they made Jasper Chat, which is like ChatGPT but mainly for businesses like marketing and ads. It can create blog posts, suggest titles, make emails sound nice, and do other writing jobs. It can even make pictures for your stuff. Jasper Chat is built to help with writing, marketing, and sales. It uses GPT-3.5, like ChatGPT, and some other AI tech. It's great for customer service, sales, and marketing tasks. Why Try Jasper as an Alternative to ChatGPT Jasper Chat, ranked 6th among ChatGPT alternatives, is a helpful tool for content creators. It's part of the Jasper suite of AI tools and can do many things. It can write blog articles, suggest catchy titles, and even create AI art. One cool thing about Jasper Chat is that it works in 29 languages, so it's great for people worldwide. It also remembers past conversations, so it feels like a real chat. You can even teach it to sound like your brand. However, it's not perfect. Sometimes, it makes mistakes, so you might need to check its work. Also, it's not free; you must pay at least $59 monthly. But if you're serious about making content, it could be worth it. In short, Jasper Chat is a useful tool for content creators. It might not be the best in every way, but it's an excellent all-around choice. Key Features: : Jasper Chat can generate content in 29 different languages. Multilingual Support : The chatbot remembers previous interactions for more coherent and context-aware conversations. Context Memory : Users can train Jasper to align with their specific brand voice. Brand Training : Jasper can produce a wide range of content, from blog articles to AI-generated art. Content Variety Pricing: Jasper Chat is available to paid subscribers through Jasper's Business and Boss plans. The Boss plan starts at , offering a range of premium features, including the chatbot. $59 per month Although there is no free tier specifically for Jasper Chat, the company does offer a 5-day trial to let users explore its capabilities before committing to a subscription. ChatGPT Alternatives Comparison Table Features / Chatbots Chatsonic Microsoft Bing Google Bard Claude AI Poe AI Jasper Chat Cost ✅ $16/month ✅ Free ✅ Free ✅ Free (Limited) ❌ $19.99/month ❌ $59/month Real-Time Data ✅ ❌ ❌ ❌ ❌ ❌ Image Search ✅ ✅ Visual Input ✅ ❌ ❌ ❌ Voice Search ✅ ❌ ❌ ❌ ❌ ❌ Content Creation ✅ ❌ ❌ ❌ ❌ ✅ Memory ✅ ✅ Chat History ❌ ✅ 100K Tokens ❌ ✅ Up-to-Date Info ✅ ✅ ✅ ✅ ❌ ❌ Fun Personas ✅ ❌ ❌ ❌ ❌ ❌ Art Creation ✅ ❌ ❌ ❌ ❌ ✅ Multilingual ❌ ❌ ✅ ❌ ❌ ✅ DIY Chatbots ✅ ❌ ❌ ❌ ✅ ❌ File Uploads ❌ ❌ ✅ ✅ ❌ ❌ Mobile App ✅ ❌ ❌ ❌ ✅ ❌ Which is The Best ChatGPT Alternative? There are several worthy ChatGPT alternatives in the market. But, based on functionality, ease of use, and value for money, Chatsonic by Writesonic and Jasper stand out as top contenders. Everything I Liked : Writesonic doesn’t just limit itself to chat functionalities; it also offers a robust set of tools for content creation, from blog posts and articles to social media updates. Rich Feature Set : Writesonic’s underlying language model has been trained extensively, ensuring that the text generated is coherent, relevant, and, most importantly, human-like. High-Quality Text Generation : With a clean, intuitive UI, Writesonic makes it easy, even for those who are not tech savvy, to navigate through its numerous features. User Experience : Writesonic allows you to tweak the output according to your needs. You can customize tone, style, and word usage to align with your brand’s voice. Customization : Although you mentioned that cost isn’t a primary concern, it’s worth noting that Writesonic offers various plans for both individual freelancers and large enterprises. Pricing Flexibility Honorable Mention: Jasper If cost were not a factor, Jasper would be a strong contender for the top spot. Its capabilities are quite similar to those of Writesonic, including excellent text generation and customization features. However, the pricing model makes it a less accessible option for some users, especially those on a budget. And that's the end of this article. We hope this post helps you choose the best alternative similar to ChatGPT. If you have any questions or are stuck at any point making the right decision, please comment below. GreatAIPrompts community is always here for your help. This was also published on . The inclusion of any links or lists in this article is not promotional, affiliate, or for my personal benefit in any way. It was created by Mukund Kapoor without using any AI writing tool. Oh, and don’t forget to check out my recent post on . Disclaimer: content greataiprompts.com Best AI Image Generators

Tired of ChatGPT? Try These 6 Alternatives Instead

ChatGPT ক্লান্ত? পরিবর্তে এই 6 বিকল্প চেষ্টা করুন

Keine Lust mehr auf ChatGPT? Probieren Sie stattdessen diese 6 Alternativen aus

¿Estás cansado de ChatGPT? Pruebe estas 6 alternativas en su lugar

Fatigué de ChatGPT ? Essayez plutôt ces 6 alternatives

क्या आप चैटजीपीटी से थक गए हैं? इसके बजाय इन 6 विकल्पों को आज़माएँ

ChatGPT に飽きましたか?代わりに次の 6 つの代替案を試してください

ChatGPT에 지치셨나요? 대신 이 6가지 대안을 시도해 보세요

Cansado do ChatGPT? Experimente estas 6 alternativas

Устали от ChatGPT? Вместо этого попробуйте эти 6 альтернатив

ChatGPT'den bıktınız mı? Bunun yerine Bu 6 Alternatifi Deneyin

Bạn mệt mỏi với ChatGPT? Thay vào đó hãy thử 6 lựa chọn thay thế này

厌倦了 ChatGPT？尝试这 6 种替代方案

SEC v. Ripple Court Filing, retrieved on July 13, 2023 is part of . You can jump to any part in this filing . This part is 2 of 18. HackerNoon’s Legal PDF Series here BACKGROUND[2] I. Factual Background A. Development of the XRP Ledger and the Founding of Ripple In 2011 and early 2012, Arthur Britto, Jed McCaleb, and David Schwartz developed the source code for a cryptographically secured ledger, or a “blockchain,”[3] which is now known as the XRP Ledger. SEC 56.1 Resp. ¶ 11, ECF No. 842; see also ECF No. 668. They aimed to create a faster, cheaper, and more energy-efficient alternative to the bitcoin blockchain, the first blockchain ledger which was introduced in 2009. Id. ¶¶ 4, 12. When the XRP Ledger launched in 2012, its source code generated a fixed supply of 100 billion XRP. Id. ¶¶ 17–18. XRP is the native digital token of the XRP Ledger, and the XRP Ledger requires XRP to operate. Id. ¶¶ 13–14. Each unit of XRP is divisible into one million “drops,” and each unit or drop of XRP is fungible with any other unit or drop. Defs. 56.1 Resp. ¶¶ 17–18, ECF No. 835; see also ECF No. 663. In 2012, Britto, Defendant Larsen, and McCaleb founded Ripple.[4] Id. ¶ 41; SEC 56.1 Resp. ¶ 32. Larsen became Ripple’s CEO, a position he held until December 2016. Defs. 56.1 Resp. ¶ 41. Of the 100 billion XRP generated by the XRP Ledger’s code, the three founders retained 20 billion for themselves (including 9 billion for Larsen) and provided 80 billion XRP to Ripple. Id. ¶ 15; SEC 56.1 Resp. ¶ 21. The founders did not sell any XRP before the launch of the XRP Ledger, and Ripple never owned the 20 billion XRP retained by the three founders. SEC 56.1 Resp. ¶¶ 20, 22. Since its founding, Ripple’s mission has been to realize an “Internet of Value” by using technology to facilitate the transfer of value across the internet. Id. ¶ 35. Specifically, Ripple “seeks to modernize international payments by developing a global payments network for international currency transfers.” Id. For instance, Ripple developed a software product called RippleNet, which allows customers to clear and settle cross-border financial transactions on mutually agreed upon terms. Id. ¶ 41. One feature of RippleNet is known as “on demand liquidity” (“ODL”). Id. ¶ 45. ODL facilitates cross-border transactions by allowing customers to exchange fiat currency (for example, U.S. dollars) for XRP and then the XRP for another fiat currency (for example, Mexican pesos). Id. ¶ 46; Defs. 56.1 Resp. ¶ 740. Like ODL, some, but not all, of Ripple’s products and services rely on the XRP Ledger and XRP. SEC 56.1 Resp. ¶ 44. The XRP Ledger is based on open-source software; anyone can use the ledger, submit transactions, host a node to contribute to the validation of transactions, propose changes to the source code, or develop applications that run on the ledger. Id. ¶¶ 52, 54. Other developers have built software products that use the XRP Ledger, such as payment-processing applications. Id. ¶ 59. Ripple has also funded companies as part of its “Xpring” initiative to incentivize the development of other use “cases” on the XRP Ledger. Id. ¶¶ 58–59. [2] The facts in this section are taken from the parties’ Rule 56.1 statements, counterstatements, and responses, unless otherwise noted. Disputed facts are so noted. Citations to a paragraph in a Rule 56.1 statement also include the opposing party’s response. “[W]here there are no citations[,] or where the cited materials do not support the factual assertions in the [s]tatements, the Court is free to disregard the assertion.” Holtz v. Rockefeller & Co., 258 F.3d 62, 73 (2d Cir. 2001) (alteration omitted). [3] A blockchain is an electronically distributed database or ledger “shared among a computer network’s nodes.” See Adam Hayes, Blockchain Facts: What Is It, How It Works, and How It Can Be Used, Investopedia (updated Apr. 23, 2023), https://www.investopedia.com/terms/b/blockchain.asp/. A blockchain is a system for recording information. Each transaction is recorded as a “block” of data on the digital ledger, which is connected to the blocks before and after it. SEC 56.1 Resp. ¶ 1, ECF No. 842. Blockchains are typically recorded across a distributed network of computers. Id. ¶ 2. [4] Ripple was originally named NewCoin, Inc. and incorporated under California law. SEC 56.1 Resp. ¶ 33. It was then renamed OpenCoin, Inc. in October 2012. Id. In 2013, the company was renamed Ripple Labs, Inc., and in 2014, it was incorporated under Delaware law. Id. In this order, the Court shall refer to the company as Ripple, even when referring to its forerunners, NewCoin and OpenCoin. 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 1:20-cv-10832-AT-SN retrieved on September 7, 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. dropbox

SEC v. Ripple: XRP's Founders Wanted to Create a Faster, Cheaper Alternative to Bitcoin Blockchain

The following is an abstract of some thoughts I had from the RWKV podcast found here: https://www.latent.space/p/rwkv#details Why do Alternatives Matter? Transformer architectures are currently at their peak with the AI revolution of 2023. However, in the rush to adopt them due to their recent success, it's easy to ignore alternatives that we can learn from. As engineers, we should not take a one-size-fits-all approach, using the same hammer for every nail. We should find trade-offs in every solution. Failure to do so would trap us within the limitations of one particular platform while remaining blissfully unaware of all other alternatives. This could potentially hold back development for years. This problem is not unique to . It's a historical pattern that has repeated itself since ancient times up to the modern day. AI A page from the SQL war history... A notable recent example in software development was the trend towards NoSQL when SQL servers started hitting physical limitations. Startups everywhere made for "scale" reasons despite being nowhere near those scales. the move to NoSQL However, over time, as eventual consistency and NoSQL management overhead emerged, and hardware capabilities made giant leaps in SSD speed and capacity, we've seen a recent trend back to SQL servers for their simplicity and now sufficient scalability for 90%+ of startups. Does this mean SQL is better than NoSQL or vice versa? No, it simply means each technology has preferred use cases with pros/cons and learning points that can cross-pollinate among similar technologies. I hope you view this article as not an attack on transformers, but a healthy appreciation on alternatives. With that, lets get on to the main topic What is the biggest pain point for existing transformer architecture? Generally, this covers compute, context length, datasets, and alignment. For this discussion, we'll focus on compute and context length: Quadratic computational costs due to O(N^2) increase per token used/generated. This makes context sizes >100k incredibly expensive, which impacts inference and training. The current exacerbates this issue. GPU shortage Context size limits attention, heavily restricting "smart agent" use cases (like ), and forcing workarounds. Larger contexts need fewer workarounds. smol-dev So, how do we solve this? Introducing RWKV - a linear transformer/modern large-scale RNN The following summarises the RWKV paper found: here For a deeper technical dive, see the: wiki here , along with , are among the first of a new class called RWKV Microsoft RetNet "Linear Transformers." It tackles the above three limitations directly by supporting the following: Linear computational cost regardless of context size. Lower requirements allow reasonable token/sec output on CPU (especially ARM) in RNN mode. No hard context size limit as an RNN. Any limits in docs are guidelines - you can finetune beyond them. As we continue to scale up AI models to large context sizes. From 100k and beyond. The quadratic computational costs start to increase exponentially. Linear Transformers, however, instead of abandoning the Recurrent Neural Network architecture and their bottlenecks, which forced them to be replaced by transformers. Took lessons learned from making transformers scalable, re-engineered RNNs to work similarly to transformers, and removed all those bottlenecks. Bringing them back into the race with transformers in terms of training speeds. Allowing them to both operate efficiently at O(N) cost while scaling past a billion parameters in training. All while maintaining similar performance levels. As you scale quadratically vs. linear, you will hit over 10x at a 2k token count & over 100x at a 100k token length. At 14B parameters, RWKV is the largest open-source linear transformer, on par with GPT-NeoX and others on similar datasets (e.g. The Pile). But what does this mean, in simpler terms? Pros 10x++ cheaper inference/training than transformers at large context sizes Can run slowly on very limited hardware in RNN mode Similar performance to transformers on the same datasets No technical context size limit as RNN (infinite context!) Cons Sliding window problem, with lossy memory past a certain point Not proven to scale beyond 14B parameters yet Less optimized and adopted than transformers So, while RWKV isn't yet at 60B+ parameter scales like LLaMA2, with the right support/resources, it has the potential to get there at lower cost and larger contexts, especially as expert models trend towards smaller and more efficient. Consider it if efficiency matters for your use case. But it's not the final solution - healthy alternatives are still key. Also one last thing i forget to mentioned: Linear Transformers typically do not use classic Transformer attention, but an approximate, meaning ... Attention is Not All You Need Other alternatives and their benefits we should probably learn from : Slow to train for text but extremely resilient to multi-epoch training. Figuring out why could help mitigate Diffusion Models the token crisis : techniques could be used to train desired skill sets to a specific goal without datasets. Even for text-based models Generative Adversarial Networks / Agents I am a code contributor to the RWKV project and an active member of the community. However, it does not mean I view RWKV as the final answer; even if in the future, where "Linear Transformers" beat out traditional "Transformer" networks in the future, research into alternatives is still important for healthy ecosystem development. Disclosure: I strongly believe there is no such thing as "one architecture to rule them all.” Additionally, I am aware there have since been developments like TransformerXL to more efficiently scale transformers, somewhere between quadratic cost and linear cost. However, with very limited known implementation of this method in public, it is hard for me to evaluate its pros and cons. Also published . here

Introducing RWKV: The Rise of Linear Transformers and Exploring Alternatives

Have you ever thought about how money is created? Well, typically it is made out of nothing by banks. When someone takes out a loan, the bank just adds the borrowed money to the amount already recorded in its database for the bank account. So if 100$ was already present in the database entry for the bank account and someone takes out a loan of 10000$, the bank will just rewrite the figure to 10100$. Thus, the bank loans out money it does not have. Of course, the loan must be repaid. When the loan is repaid, the money that was created by the loan disappears. So this money comes out of nothing and disappears into nothing (let's not get into the subject of interest which complicates matters a bit). This money thus only exists temporarily. There is a parable in relation to the point of money which exists only temporarily. A cowboy walks into the bar. After a few drinks, the barkeep asks him if he would lend him $10 to fix a hole in the roof. The cowboy agrees to lend the $10, with the condition that it would be paid back in a week. The barkeep calls a roofer, who fixes the roof and the barkeep pays the $10. Meanwhile, the roofer's shoe falls apart and he goes to the cobbler to buy a new one for another $10. The cobbler is delighted by his unexpected windfall and quickly has the money spent in the same bar. The $10 thus returns to the barkeep. When a week passes, the cowboy returns and the barkeep returns the $10. The cowboy thanks him and then pulls out a match and burns the money. The barkeep, eyes wide, asks 'Why did you do that?', to which the cowboy replies, 'It's fake money, it's worth nothing.' In the parable, the cowboy is like a bank. He lends money he doesn't have. He creates this money out of thin air, and when it is burned, it disappears like bank loans. Nevertheless, from this temporary money, the bar got a new roof, the roofer received a new pair of shoes, and the cobbler had enough money to down a few drinks. These are tangible results of money that exists only temporarily. So what we use as money is nothing more than somebody's debt. Money is debt! The value of money is ultimately based on a form of trust.  We trust that others value it as well, so when we do something for someone in exchange, we accept money, because we trust that it will be accepted when we require something in return. This trust makes money a universal medium of exchange. A good example of this is Bitcoin. Our Bitcoin balance is nothing more than an entry in a distributed ledger. There is nothing to back it up, yet people around the world are willing to pay real money for Bitcoin. The value of Bitcoin is thus purely a matter of trust. Money is trust! These were the basic ideas that inspired the concept of karma money. What if everyone operated like a bank? Instead of taking money on credit, they would create it themselves. Of course, such a system would only work if there was a limit to the amount of money that could be created. So everyone's debt would be public. If I had to choose who to sell my products or services to, I would give it to the one with the least debt, as this gives the best chance of it being paid back quickly. Therefore, everyone is interested in keeping their debt low by repaying it. Let's take a look at how a cowboy parable would look in this system. There is no need for a cowboy (bank) as the barkeeper can create the $10 for the roofer himself. This creates a $10 debt for the barkeeper visible to everyone but in return, his roof is fixed. The roofer passes this $10 on to the cobbler and when the cobbler passes it back to the barkeeper, the $10 is destroyed along with the barkeeper's debt. Thus the barkeeper's debt is again zero. It is thus evident that the process of creating transient money works well even without banks. All that is needed is for the roofer to trust the money created by the barkeeper and to believe that it is equally good money as the $10 created by the bank. I have realized this karma money on the Ethereum blockchain with a simple Solidity smart contract. My karma implementation is an ERC20 contract, which is great because it can easily be used with any Ethereum wallet. The peculiarity of karma is that, while in the case of other currencies, we spend from our balance, here our balance starts from 0 and increases with every spending (money creation). The greater our balance, the more indebted we become, so here the goal is to reduce our balance. Let's look at a brief example. Alice wants to buy apples from John, so she pays John 10 karma dollars for the apples. Alice's balance thus increases to 10$. Later, John buys oranges from Alice, so when he pays 10$ to Alice, the debt is settled and Alice's balance is reduced back to 0$. After the theory, let us take a look at the code ( ): available here on GitHub // SPDX-License-Identifier: MIT
pragma solidity ^0.8.19;

import "@openzeppelin/contracts/token/ERC20/ERC20.sol";
import "@openzeppelin/contracts/token/ERC20/extensions/IERC20Metadata.sol";

contract Karma is IERC20, IERC20Metadata {
    mapping(address => uint256) private _balances;
    mapping(address => mapping(address => uint256)) private _debts;
    mapping(address => mapping(address => uint256)) private _allowances;

    string private _name;
    string private _symbol;
    uint private _cycleReward;

    constructor(string memory name_, string memory symbol_, uint cycleReward) {
        _name = name_;
        _symbol = symbol_;
        _cycleReward = cycleReward;
    }

    function name() public view virtual override returns (string memory) {
        return _name;
    }

    function symbol() public view virtual override returns (string memory) {
        return _symbol;
    }

    function decimals() public view virtual override returns (uint8) {
        return 18;
    }

    // totalSupply is meaningless
    function totalSupply() public view virtual override returns (uint256) {
        return 0;
    }

    function balanceOf(
        address account
    ) public view virtual override returns (uint256) {
        return _balances[account];
    }

    function debtOf(
        address debtor,
        address creditor
    ) public view virtual returns (uint256) {
        return _debts[debtor][creditor];
    }

    function transfer(
        address to,
        uint256 amount
    ) public virtual override returns (bool) {
        _transfer(msg.sender, to, amount);
        return true;
    }

    function allowance(
        address owner,
        address spender
    ) public view virtual override returns (uint256) {
        return _allowances[owner][spender];
    }

    function approve(
        address spender,
        uint256 amount
    ) public virtual override returns (bool) {
        require(spender != address(0), "ERC20: approve to the zero address");

        address owner = msg.sender;
        _allowances[owner][spender] = amount;
        emit Approval(owner, spender, amount);
        return true;
    }

    function transferFrom(
        address from,
        address to,
        uint256 amount
    ) public virtual override returns (bool) {
        _spendAllowance(from, msg.sender, amount);
        _transfer(from, to, amount);
        return true;
    }

    function mineCycle(
        address[] memory nodes,
        uint256 amount
    ) public virtual returns (bool) {
        // checking debts in cycle from 0..n
        for (uint i = 0; i < nodes.length - 1; i++) {
            require(
                _debts[nodes[i]][nodes[i + 1]] >= amount,
                "Karma: Not enough debt for the cycle"
            );
        }

        // checking the last debt (end of cycle)
        require(
            _debts[nodes[nodes.length - 1]][nodes[0]] >= amount,
            "Karma: Not enough debt for the cycle"
        );

        // decreasing the debts and balances and pay cyleReward
        for (uint i = 0; i < nodes.length - 1; i++) {
            _debts[nodes[i]][nodes[i + 1]] -= amount;
            _balances[nodes[i]] -= amount;
            _transfer(nodes[i], msg.sender, _cycleReward);
        }

        _debts[nodes[nodes.length - 1]][nodes[0]] -= amount;
        _balances[nodes[nodes.length - 1]] -= amount;
        _transfer(nodes[nodes.length - 1], msg.sender, _cycleReward);

        return true;
    }

    function _transfer(
        address from,
        address to,
        uint256 amount
    ) internal virtual {
        require(to != address(0), "ERC20: transfer to the zero address");

        _balances[from] += amount;
        _debts[from][to] += amount;

        emit Transfer(from, to, amount);
    }

    function _spendAllowance(
        address owner,
        address spender,
        uint256 amount
    ) internal virtual {
        uint256 currentAllowance = allowance(owner, spender);
        if (currentAllowance != type(uint256).max) {
            require(
                currentAllowance >= amount,
                "ERC20: insufficient allowance"
            );

            uint256 newAmount = currentAllowance - amount;
            _allowances[owner][spender] = newAmount;
            emit Approval(owner, spender, newAmount);
        }
    }
} As can be seen, this is a completely standard ERC20 token. In the constructor, we can specify the token name and symbol and also a parameter called cycleReward which we will discuss later. Karma can represent dollars, euros, any money, but also time (e.g. I will work for you for 1 hour in exchange for 1 hour of work) or anything else. So karma is a general lending system. The ERC20 standard's method is for querying any individual's balance. It is very important that each person is only linked to one Ethereum address, if that were not the case, then if someone got very indebted, they would simply open a new account, thus the whole system would be pointless. Fortunately, there are solutions such as or , which are able to ensure that each individual has a unique address. balanceOf Proof of Humanity WorldCoin I also introduced a function, which can precisely query who owes how many points to whom. debtOf The standard transfer function is used for moving the balance. On the one hand, this increases the user's balance and, on the other, it records the debt to the receiving party. Therefore, if Alice pays John 10$, Alice's balance will be increased by 10$ and it will be noted that Alice owes John 10$. It may appear that the code isn't exactly doing what I described earlier. For instance, if John now pays Alice 10$, instead of both Alice and John having a balance of 0$, Alice's balance will stay at 10$ and John's balance will also become 10$. In addition, in the debt matrix, Alice will now owe John 10$, and John will owe Alice 10$. Why doesn't the smart contract resolve these debts? Alice and John's case is very straightforward, but typically much more complicated debt chains form. In the cowboy example, for example, there were 3 people long the chain, however, it is conceivable for a chain to contain 10 or more people. Finding such chains is by no means trivial. This is why karma miners are needed. The karma miners constantly watch the debt graph, and if they discover a cycle, they can submit it to the smart contract. This is what the mineCycle method is for. The method checks the submitted chain, and if it is valid, it will execute the modifications to balances. For this, the miner will receive a small karma from each member of the chain, for reducing their balances. Thus, karma mining is almost exactly the same as mining Bitcoin. The member runs a program on their machine, consuming electricity and computation power (finding cycles in a graph), which in return produces karma. There are still a few ERC20 standard-related methods, but these are not of interest from a system standpoint. Karma can be an ideal solution for communities seeking an alternative to the traditional money system. But members of such a system can also be companies or projects. For example, an open source project can accept donations in the form of karma and similarly pay developers in karma, who can then exchange this for, say, locally grown vegetables and fruits in a community using karma. Of course, there is no legal framework backing up karma like there is for actual money, but Bitcoin has shown that a currency can work purely on trust, without any backing. Compared to Bitcoin, karma does not require huge computing power, nor does it have a country's energy consumption. The system relies on trust, and the money is backed by our very own identity.

Karma: an ERC20-compatible Alternative Money on the Ethereum Blockchain

United States Of America. v. Microsoft Corporation Court Filing by Thomas Penfield Jackson, November 5, 1999, is part of . You can jump to any part in this filing . This is part 17 of 58. HackerNoon’s Legal PDF Series here C. Viable Alternatives to Windows That Microsoft’s market share and the applications barrier to entry together endow the company with monopoly power in the market for Intel-compatible PC operating systems is directly evidenced by the sustained absence of realistic commercial alternatives to Microsoft’s PC operating-system products. OEMs are the most important direct customers for operating systems for Intelcompatible PCs. Because competition among OEMs is intense, they pay particularly close attention to consumer demand. OEMs are thus not only important customers in their own right, they are also surrogates for consumers in identifying reasonably-available commercial alternatives to Windows. Without significant exception, all OEMs pre-install Windows on the vast majority of PCs that they sell, and they uniformly are of a mind that there exists no commercially viable alternative to which they could switch in response to a substantial and sustained price increase or its equivalent by Microsoft. For example, in 1995, at a time when IBM still placed hope in OS/2's ability to rival Windows, the firm nevertheless calculated that its PC company would lose between seventy and ninety percent of its sales volume if failed to load Windows 95 on its PCs. Although a few OEMs have announced their intention to pre-install Linux on some of the computers they ship, none of them plan to install Linux in lieu of Windows on any appreciable number of PC (as opposed to server) systems. For its part, Be is not even attempting to persuade OEMs to install the BeOS on PCs to the exclusion of Windows. OEMs believe that the likelihood of a viable alternative to Windows emerging any time in the next few years is too low to constrain Microsoft from raising prices or imposing other burdens on customers and users. The accuracy of this belief is highlighted by the fact that the other vendors of Intel-compatible PC operating systems do not view their own offerings as viable alternatives to Windows. Microsoft knows that OEMs have no choice but to load Windows, both because it has a good understanding of the market in which it operates and because OEMs have told Microsoft as much. Indicative of Microsoft’s assessment of the situation is the fact that, in a 1996 presentation to the firm’s executive committee, the Microsoft executive in charge of OEM licensing reported that piracy continued to be the main competition to the company’s operating system products. Secure in this knowledge, Microsoft did not consider the prices of other Intel-compatible PC operating systems when it set the price of Windows 98. As the Court found above, the growth of server- and middleware-based applications development might eventually weaken the applications barrier to entry. This would not only make it easier for outside firms to enter the market, it could also make it easier for non-Microsoft firms already in the market to present a viable alternative to Windows. But as the Court also found above, it is not clear whether ISVs will ever develop a large, diverse body of full-featured applications that rely solely on APIs exposed by servers and middleware. Furthermore, even assuming that such a movement has already begun in earnest, it will take several years for the applications barrier to erode enough to enable a non-Microsoft, Intelcompatible PC operating system to develop into a viable alternative to Windows. Continue Reading Here About We bring you the most important technical and insightful public domain court case filings. HackerNoon Legal PDF Series: This court case Civil Action No. 98-1232 (TPJ) retrieved on 2-6-2023, 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. justice.gov

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