In today's world, getting tech talent and bringing them on board is one big challenge for companies

BarRaiser

barraiser

In 2017, my partner and I launched the first mobile app for children. I took the position of CEO. Because of the lack of a lot of relevant experience, I began to make mistakes almost from the very beginning. I didn’t really know how to “swim,” - yet, I suddenly found myself in the open sea. Nevertheless, even back then, I understood that it is impossible to build a business completely without mistakes. Yes, I did a lot of things, but every time it gave me experience, which I invested in the further development of the project. Let me introduce myself. My name is Mikhail Kotlov. I am a co-founder of , a start-up build around educational applications for children that have been translated into more than 30 languages. Our apps are in the top 10 in the Apple App Store (Kids 5 & Under category) in 40 countries worldwide. We are mainly focused on the European and United States markets, now launching in China. Our product line includes 3 children's applications, an app for parents, a project with online lessons, and an educational animated series "Team Mendeleev". IntellectoKids Today I will share the mistakes that I made while building IntellectoKids, and most importantly, the lessons I learned in the process. Started with the "discovery of America" For many startups, the United States is the most desirable market and main target. I was no exception. Launching IntellectoKids, we thought ambitiously and decided to immediately promote in the United States. In our case, this turned out to be a mistake because advertising in America was more expensive than we expected when planning the budget. In addition, at that time, we did not have enough competencies for successful promotion. Simply put, we weren't ready - and we could do it. Therefore, we quickly changed our strategy and turned to less competitive markets. We have localized the product into Swedish, Spanish, and French markets. It worked: the company began to grow. Of course, then we returned to the USA, but now we had more experience and money, so everything turned out well. Now our applications are very popular in the States. So if you have already tried to storm the US market and failed, do not worry: look at the countries of Latin America and Europe. Perhaps, you will have a better chance there. Locked all processes on me When you only have 8-10 employees, you are thoroughly immersed in everything that happens in your startup. Almost no phone call in the team is complete without your participation. This is not uncommon, and in the beginning, this is the right strategy. However, at some point, the company was already growing in terms of metrics, the team was getting larger, the processes were growing, but the approach stayed the same. This is what was happening at IntellectoKids. I was aware of the smallest details: any problem, any solution passed through me. As a result, I had practically no time left for strategic planning. And the CEO's task is to constantly check the course, anticipating everything several steps ahead. It's good that we have two founders at IntellectoKids, and my partner partially covered this problem. Immersion in routine processes interferes with researching trends and communicating with people from the field. However, this is necessary to keep the pulse on market trends and news. Eventually, new ideas come less often because you are too busy with operations and troubleshooting every small issue. Unintentionally, my behavior looked like distrust for my team. It partly prevented the company's team leads from developing professionally. Until I dared to trust my team, they entirely relied on my opinion and could not become independent "players". As a result: It started hurting business goals; It made the team bored and burned them out Fortunately, it didn’t come to a critical point, and I got it together. We now call the team leaders once a week to discuss global product and marketing issues and adjust the action plan if required. Plus, I do regular one-on-one calls to make sure things are going as planned. This tactic saves resources and avoids distraction through trivial issues. As it turns out, my team is capable of brainstorming ad creatives or controlling the release of a new version of the application without me. Today, the creative team and the development team are now working independently. I would like the same thing to happen over time with all teams, but it takes time to cultivate talent and find candidates with leadership skills in the job market. For example, currently, we are now looking for a competent CMO. As of right now, I take care of the CMO duties as well. Worked with digital agencies There was a time when I thought it was easier to outsource marketing and creativity. I was wrong. Although for many companies the agency is a great option, it turned out that in my case, it was not suitable. The reason is the particular aspects of our niche market. It is difficult to advertise an app for preschoolers. First, you have two target audiences: children and parents. The child needs an exciting product, and the parent needs a useful one. Therefore, it is important to show the educational value of the app, not just the demo of the game. The second difficulty is that parents have a very wide range of interests. When we advertise a children's app, we compete with all products and services of interest to women between the ages of 25 to 55 - this can be anything from match 3 games to beauty salons. We must catch moms' attention in a limited period of time while they are on Facebook. The field of children's applications is quite young. Most agencies do not have a sufficient understanding of how to work in this field. It is very difficult to gain experience in conducting such advertising campaigns if you work in an agency and are not thoroughly immersed in the project, like the guys at IntellectoKids. Therefore, at some point, I had to abandon the idea of outsourcing. I've built an in-house team of creators and marketers who do a better job than agencies. Didn't share ideas "Sitting inside a box" was a big mistake on my part. I did not talk about projects and plans. I was a little afraid that my ideas would be stolen and was superstitious: I did not want to announce any new features until they became a reality. In fact, you are unlikely to come up with something completely new. In other words, there is nothing new under the Sun. So you shouldn't be afraid of collaborations or discussions. By staying silent about your ideas, you rob yourself of the opportunity to get useful feedback. I think I missed a lot of useful comments from smart and experienced people who, at that time, understood business processes better than I did. Moreover, if you don’t share ideas and plans, you will never learn how to make a great product or promote it. In the long run, exchanging ideas and feedback is a lot more valuable than staying silent. Finally, there was a need to raise capital, and I realized that it is wrong to stay silent. One of the founders' responsibilities is to get the attention of potential investors. It means constantly telling people about the company. You never know which contact will invest or direct you to the right investor, so you need to keep your business network informed and excited about your company. When asked for advice, I always say: "Be generous." Share your ideas and news. The established connections will definitely come in handy one day. Didn't develop a recruiting team For three years, I had just one recruiter, and we built up the marketing, development, and product teams together. At the time, I didn’t think about the need to build a recruiting team to optimize the hiring process. The Russian market is not the target market for IntellectoKids; we work little with this category of users. People are not familiar with our product, and practically, not familiar with the company. The fact that we are not heard of significantly narrows our opportunities for recruiting specialists and reduces the number of applications for vacancies. To increase the company's recognition in the Russian job market, we began to engage in PR. Currently, we are working to create our HR brand. But it should not stop there. It's really great in our company: we create high-quality content, work with modern development technologies, offer various pleasant bonuses - from voluntary medical insurance to pay for specialized training. But someone has to “sell” all this to candidates, hunt people from well-known companies, and use non-standard search methods. In this case, you cannot do without a strong recruitment team, so I recently allocated resources to create a recruiting department. Interviewed people incorrectly I didn't always know how to conduct interviews. I thought I was asking the right questions. But in fact, I missed the important details, which subsequently influenced the person's work and his relationships with other team members. These details were mostly values ​​- and if a candidate’s value matched the company values. It is much easier to assess professional skills in a regular interview than soft skills. And it can be difficult to understand whether a newcomer can organically integrate into the team. The simplest example: once, I hired a girl who, as it turned out, was used to swearing at her colleagues. This is not accepted in the culture of IntellectoKids: it’s crazy for us to even raise our voices. Unfortunately, the conversations did not affect, and we had to part ways with that employee. And I had to continue to search for a suitable person for her role. Now I stick to this rule: during interviews, I pay attention to soft skills. If a candidate doesn’t check all the boxes on soft skills, I won't hire this candidate, even if he/she is a genius. After a couple of unfortunate incidents, I decided to dive deeper into the issue and look for a system that would help me hire competent people. One of my acquaintances recommended “The Who method”, as described in the book by Jeff Smart and Randy Street. (See, not being silent and talking to people helps!) The selection process is quite long. On average, it takes two to three weeks from the first interview to an offer. But the method works: during the time that we have been using it, there have been almost no cases in IntellectoKids that a person did not pass the probationary period. So, the system is useful for everyone involved. How does it work? First, the interview is conducted by the recruiter to check whether the person is generally suitable. For example, we check if the candidate’s goals align with the company's goals. Then, we give a test task. With the test task, we obviously check the hard skills. However, we can see how this candidate thinks and how he/she can immerse themselves in a topic. The next step: the team-lead interviews the candidate to see if the candidate has professional skills. The next step is an interview with me. If the candidate is not on my team, I act as a barraiser. Barraiser is an expert invited for an interview who is not directly interested in the candidate. The interview is all about identifying the strengths and weaknesses of soft skills. This is done, among other things, so that both parties understand how comfortable it would be to work together. If the conversation went well, we would collect references from past jobs. Candidates are often intimidated by this point. However, there is no reason to be scared here. We might not extend an offer because of negative recommendations, only when something truly terrible that this candidate has done in their previous job. Finally, after this stage, we extend an offer. It turns out that getting to us is quite difficult. But we have a low turnover rate. Usually, each hire is a win-win match. The main thing is not to be lazy and not to skip any of the selection steps. There were a few times when we didn’t follow each step in our hiring method. As a result, the newcomer either left or got fired. Not enough investment in server development Until recently, our server development was based on part-time employees. We did not have a full-fledged team in this direction. Meanwhile, server-side development is very important for monetization, analytics, and marketing, because all analytical and marketing tools require server-side solutions. Otherwise, it is impossible to draw the right conclusions, analyze the data and understand what is more profitable for you to sell and how to promote the product. The lack of a competent full-time server development team began to hinder the development of our marketing and analytics. And in the end, when it was necessary to grow, we were engaged in the development of server architecture. Actually, we are still fixing this mistake. Stopped running CustDev I started my business researching parenting pain. But at a certain stage of company growth, I stopped further researching on the subject. It was definitely my fault. CustDev must be persistent. First, the needs of your target audience are always changing, and you need to improve your content in accordance with those new needs. Secondly, your product changes, and you need to understand if you are on the right track. We recently started to focus on CustDev again, but due to lack of time, we are not as active as we would like to be. Got stuck on Facebook We researched different promotion channels; our main focus was Facebook. Our ads worked so well; we were tempted to spend our entire marketing budget on Facebook. But through practice, we learned that it wasn’t the best approach. Even when you find the most profitable channel for your product, it is important to use other channels as well. Currently, we are looking for new team members who will be engaged in promotion in Google Ads, influence marketing, CPA networks, and different partnerships. I wanted to get results without investing in the team When we launched online lessons, we needed to look for teachers. At first, I took care of it myself. I looked for candidates myself and attracted the head of the online lessons department. However, real results started to happen when we hired a dedicated recruiter with the appropriate expertise. There are many similar examples in my practice. As a result, I strongly believe in hiring the right people for the task. Instead of dividing new responsibilities between existing employees, who may or may not have enough competencies to succeed with this particular task. If you are trying to reduce financial risks without investing in people, you are still taking risks, the risk of undesirable outcomes. When you get these outcomes, you will think that the reason is in the product itself and in the lack of demand. The chances are that your company simply does not have the expertise that would allow the product to grow. Or maybe your current team doesn't have enough time to devote to the project. In both cases, the solution is to hire people who will only focus on the new task. Staked on game content We develop educational games for children. Previously, the process of creating content was structured as follows: first, the game designer came up with a scenario for the game, and then we "built" learning mechanics into it. During the pandemic, the demand for systematic education of children through applications has grown. The parents wanted the child to not just play and, perhaps, learn a new skill as a bonus. Our target audience needed something more: a product that carries an understandable educational value, and only then, just as a bonus, entertains. Therefore, our apps now help children prepare for school through gamification. First, the teaching methodologist writes an educational program, and only after that, the game designer packs it up and makes an exciting game. This point can be attributed to errors only conditionally since we rebuilt the processes in time. Still, we could have done this earlier; it would only help us gain more users. How I learned and still learning from my mistakes To be wrong in the “right” way, I carefully analyze past experiences and understand: what exactly went wrong; what risks in the future does a particular mistake carry; what opportunities it opens up for me; what can I fix - and how; how can I minimize losses at the same time; what needs to be done so that the mistake does not repeat itself. Also, I advise everyone to read the book by Carol Dweck, “Flexible Mind.” The author of the book, a professor at Stanford University, explores the nature of the fear of mistakes and gives advice on how not to be afraid of new experiences. The book also talks about how we can only learn through our mistakes.

Mistakes I Made While Building an EdTech Startup With 400K Active Users

Much talk has been thrown around regarding the importance of diversity; however, as Dave McClure of 500 Startups noted, In general, that means statistically more “white male nerds” get hired and receive funding Dave said, “There’s a soft bias towards doing things that are familiar.” “ — I think we are making progress,” “Now it’s brown and yellow nerds that get funding.” As a small , your first hire can make or break your team, and as teams grow, there is a tendency to bring on people who remind you of yourself. What are actual ways to make sure you are hiring a diverse team in backgrounds as well as skillsets? startup natural I recently attended the x Women in Tech event with Alexandra Wilkis Wilson (Co-Founder of Glamsquad, previously Co-Founder of Gilt), Sutian Dong (Partner at Female Founders Fund), Kate Shillo (Director at Galvanize Ventures), and Ana Mahoney (Uber General Manager). Here are my actionable takeaways from the event: Uber Vettery Your core attracts your applicants. Take a hard look at your core team The core of your team will inevitably attract similar applicants. People are drawn to people who remind them of themselves. Sutian retells the story of two male co-founders, sons of immigrants who were adamant about ensuring diversity in hiring. Only when they took a company picture at the end of the year, did they realize there were no women in the picture — they had resort to pulling random women to stand in the picture. The lesson? When people look for jobs and think about fit, they’re going to look at the current makeup of the team, and it’s easier to ensure there is diversity early on when there are five people rather than when there are 50. It is difficult to “hire for diversity” when your core itself is not diverse. Adding in a simple sentence at the end of job descriptions that underscores your commitment to diversity, e.g. “we encourage people of all backgrounds, and all experiences to apply,” will change the makeup of applicants. Actionable tip: Consider bringing on a “bar-raiser” for your hiring process The bar raiser is an objective party that does not work for the company and is tasked to ask the questions that the hiring team doesn’t think to ask in order to prevent assumptions or biases that otherwise might be overlooked. The bar raiser should effectively moderate interview debriefs, making sure all concerns are aired and At Uber, Anna stresses, “We hire for teams, and when you have a diverse group of people as part of that process, then you naturally attract diverse applicants, and that mindset is reflected in those we hire.” be able to candidly say, “Here are the criteria we have for hiring, here is what we have, and here is what we’re missing.” Use an assessment test like Myers Briggs to measure diversity of strengths Early on at Gilt, Alexandra brought on an executive coach to build out a highly functional team. At the time a team of seven, they used the Myers Briggs test to assess their profiles. The result? When all team members were mapped out on 16 quadrants, they discovered they were all very similar in how they thought and how they processed information. It’s human tendency to hire people that remind you of yourself, and “It’s important to think about diversity not only in how people look, but also in how they think.” entrepreneurs tend to be “idea people, glass half-full optimists. Make sure to balance this out with others who are are analytical, realistic, and metrics-driven. Test for skills rather than for previous experiences first There’s a natural inclination to ask about someone’s past work experiences. However, at Uber, they test for skills first by administering real life case studies that reflect what you would actually be working on in the role. If you’ve enjoyed doing those assignments — whether that’s structuring a marketing campaign, writing a blog post, organizing operational flow — then both sides actually get a sense of if you would thrive in that role. If you’re on the hiring side, ask these questions: Ask questions about a puzzle you are trying to figure out, and see how they analyze the pieces. “Are they structured in their thinking? Are they articulate? Are they creative? Can they pick up on a hint and build upon it?” Finally, conduct a thorough background check Think you’ve found your dream candidate? Make sure you don’t overlook reference checks. To make sure you are unbiased in hiring, Sutian recommends having your company’s board members meet any executive team hires. Reference check who they’ve worked with before, even check with people on LinkedIn that you are mutually connected with. One challenging question that Alexandra always asks references is References tend to err on reviewing candidates positively, but this question can shed light on potential red flags such as, “x tends to be impatient and had some difficulty working in our small team.” Kate encourages people to “check [the candidate’s] credit, especially if they’re managing your money.” “What’s the worst thing you can say about this person?” At SheWorx , we believe you need to be authentic about your reasoning behind why you believe diversity is important to you. Make sure that if it is a priority, you are clear about that during your interactions with the candidate. If you fundamentally don’t know what your reasoning is behind supporting diversity, then take the time learn the facts, read the stories, and analyze different viewpoints, so you can formulate an opinion that aligns with your values and beliefs. The worst thing you can do is to say you are committed to diversity and not be able to articulate why it’s important to you. When you share your reasoning, cite a story that makes you believe that improving diversity is important. People can smell inauthenticity and that’s the last thing you want to project to a potential hire. SheWorx is a collective of ambitious female entrepreneurs and changemakers redefining the next wave of leadership. RSVP for our next SheWorx Breakfast Wednesday 2/3 with Owen Davis , Founder of Overlap, NYC Seed, and Alpha Ventures. He’ll be providing actionable strategies for how to raise capital efficiently.

How to Build a [Strong and Diverse] Winning Team

Breaking Down Organizational Barriers to Accelerate Software Development

সফ্টওয়্যার বিকাশকে ত্বরান্বিত করতে সাংগঠনিক বাধাগুলি ভেঙে ফেলা

Abbau organisatorischer Barrieren zur Beschleunigung der Softwareentwicklung

Derribar barreras organizacionales para acelerar el desarrollo de software

Supprimer les barrières organisationnelles pour accélérer le développement de logiciels

सॉफ्टवेयर विकास में तेजी लाने के लिए संगठनात्मक बाधाओं को तोड़ना

組織的障壁を打破しソフトウェア開発を加速する

조직의 장벽을 허물어 소프트웨어 개발 가속화

Quebrando barreiras organizacionais para acelerar o desenvolvimento de software

Разрушение организационных барьеров для ускорения разработки программного обеспечения

Yazılım Geliştirmeyi Hızlandırmak için Organizasyonel Engelleri Yıkmak

Phá vỡ các rào cản tổ chức để tăng tốc phát triển phần mềm

打破组织障碍，加速软件开发

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps B Other Models B.1 Model without activity-level However, when we ran the model (also using lmerTest (Kuznetsova et al., 2017)) and plotted the residuals, we got the following: Just from a brief visual inspection, it is clear to see that the residuals are not randomly distributed: There are two distinct ”bands” that both seem to trend upward. This breaks the assumption that the residuals are randomly distributed (Poole & O’Farrell, 1971). While many assumption violations are reduced with enough data (Baayen et al., 2008; Schielzeth et al., 2020), non-linearity is not one of them (Poole & O’Farrell, 1971). Fortunately, the non-random residuals were (partially) fixed by introducing activity-level for the reasons described in section 3.3. B.2 Problematic Categories Removed Here we fit the main model (eq. 5, with problematic categories removed. We define a problematic category as a category with a fitted random effect of less than -0.5. We obtain this threshold by visually inspecting Fig. 7. The results of this fit can be seen below in 2: B.3 Gamma mixed-effects model In the following, we refit the main model (eq. 5) using a Gamma regression. This is the most widely recommended solution in the literature on fitting right-tailed, heteroscedastic outcomes (Feng et al., 2014; Villadsen & Wulff, 2021). However, since we discovered this after running our initial models, we could only justify doing this as a post-hoc test. I use the lme4-package to fit the model (Bates et al., 2014). To avoid convergence errors and adapt the model to the formula, we make the following alterations: Add a gamma log-link function (Fox, 2015) change the year (β1) estimate to decades. This has the effect of rescaling the effect size. We still log-transform the activity-level to rescale it. As this is not part of the hypothesis, this does not affect our interpretation. This leads us to the interpretation. The effect size per decade is 0.31, which is highly significant (SE=0.006, T=24, p ≪ 0.001). This translates into 34% increase per decade or 3% increase per year. This is the same direction as our transformed linear model, with somewhat larger results. However, as these come from an almost unidentifiable fit with extremely non-random residuals, no inferences can be drawn from this. Some of this indicates that the conditional distribution of Barrier-to-Exit is not a Gamma distribution. Pursuing the GLMM path would require further assessments of the best-fitting distribution. This could be by e.g. applying the Box-Cox method as described by Villadsen and Wulff (2021). This paper is under CC 4.0 license. available on arxiv

Assessing Non-linearities and Distribution Assumptions in Barrier-to-Exit Analysis

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps A Validation of Assumptions There are several assumptions to validate for crossed-linear mixed effects models (Baayen et al., 2008). Specifically, we need to assess: • : Is the phenomenon the model captures actually linear? This can be tested by looking for trends in the residuals. If no trends are found, linearity holds (Poole & O’Farrell, 1971). Linearity Is the variance across residuals equal? This can be tested by plotting residuals against response and looking for cone-like shapes (Fox, 2015). • Homogeneity of Variance: Are the residuals normally distributed? We can assess this by a QQ-plot over the residuals. • Normality of residuals: Per the definition of crossed-effects mixed effects models (Baayen et al., 2008), we expect the residuals to be normal. This can be assessed similarly to the random effects. • Normality of random effects: Note that testing the assumptions relies on visual tests rather than statistical tests. This is not for a lack of statistical tests (see e.g. Fox, 2015). Rather it is because statistical tests tend to become oversensitive for large datasets (Ghasemi & Zahediasl, 2012). Given our dataset has more than 50,000 observations, it is safer to use visual tests. First, let us test for linearity and heteroscedasticity. The residual plot can be seen in fig. 5: At first glance, there seems to be no strong heteroscedasticity or apparent linearity. However, at a closer look, there are some weird outliers in the bottom left corner. Furthermore, there is a weird kind of straight line going from the top left corner and down towards the middle. This indicates that there may be something wrong with the model. Let us now assess the normality of the residuals: We notice that the residuals are generally lower than the normality line. This indicates a left skew in the residuals, i.e. there are smaller outliers. While the fixed effects are robust against this type of skew, the estimates for group-level variances are more affected (Schielzeth et al., 2020). Finally, let us consider the normality of the random effects. A qq-plot of the random effects for the two levels can be seen below: Here we see a dramatic skew toward the lower level of the fitted residuals. This implies that there are weird dynamics in the lower values of Barrier-to-Exit. Part of this may be because of the relatively few observations per user (see fig 8). This highlights another issue with measuring Barrier-to-Exit across time: because it requires many observations over a long period of time to calculate sufficient precision of revealed preferences, it’s hard to measure changes on the individual level. While the effects of non-normality on the fixed effects should be minor (Schielzeth et al., 2020), we nevertheless conduct a robustness check. In the robustness check, we remove the problematic categories and refit the main model (eq. 5). The results of this can be seen in Appendix B.2 This paper is under CC 4.0 license. available on arxiv

Ensuring Model Validity: A Closer Look at Assumptions in Amazon's Barrier-to-Exit Analysis

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 6 Conclusion Understanding how recommender systems shape our behaviour is essential to avoid manipulation. In this paper, we investigated the Amazon recommender system concerning whether it has made it harder to change preferences. By analysing the Barrier-to-Exit (Rakova & Chowdhury, 2019) of more than 50,000 users, we found a highly significant growth in Barrier-to-Exit over time, which indicates that it has indeed become harder to change preferences for the analysed users. However, sampling bias induced by the calculation of Barrier-to-Exit makes it difficult to draw conclusions about the general population of Amazon customers. This highlights the dilemma of portability in measuring socio-technical systems (Selbst et al., 2019): accurately evaluating a concept like ”changing preferences” requires adapting to the context of the system, which makes it more difficult to generalise (and compare) to other systems. Comparing recommender systems is necessary for ensuring that these respect human autonomy (Varshney, 2020) and live up to new regulations such as the EU AI Act (Kop, 2021). Further work, should aim to create auditing procedures and metrics that allow third parties to measure potential preference manipulation in a way that fits within the context of the industry and allows for comparisons between different systems. This will help assess the pressures of Surveillance Capitalism (Zuboff, 2019) on human autonomy. References Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59 (4), 390–412. (Publisher: Elsevier) Barto´n, K. (2022, September). MuMIn: Multi-Model Inference. Retrieved 2023-01-08, from https://CRAN.R-project .org/package=MuMIn Bates, D., M¨achler, M., Bolker, B., & Walker, S. (2014, June). Fitting Linear Mixed-Effects Models using lme4. arXiv. Retrieved 2023-01-12, from http://arxiv.org/abs/1406.5823 (arXiv:1406.5823 [stat]) Bennett, J., & Lanning, S. (2007). The netflix prize. In Proceedings of KDD cup and workshop (Vol. 2007, p. 35). New York. urrell, J. (2016, June). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3 (1), 2053951715622512. Retrieved 2022-10-11, from https://ezproxy-prd.bodleian.ox.ac.uk:2246/doi/full/10.1177/2053951715622512 (Publisher: SAGE Publications Ltd) doi: 10.1177/ 2053951715622512 Calvo, R. A., Peters, D., Vold, K., & Ryan, R. M. (2020). Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry. In C. Burr & L. Floridi (Eds.), Ethics of Digital Well-Being: A Multidisciplinary Approach (pp. 31–54). Cham: Springer International Publishing. Retrieved 2022-08-16, from https://doi.org/10.1007/ 978-3-030-50585-1 2 doi: 10.1007/978-3-030-50585-1 2 Carroll, M. D., Dragan, A., Russell, S., & Hadfield-Menell, D. (2022). Estimating and penalizing induced preference shifts in recommender systems. In International Conference on Machine Learning (pp. 2686–2708). PMLR. Feng, C., Wang, H., Lu, N., Chen, T., He, H., Lu, Y., & Tu, X. M. (2014, April). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 26 (2), 105–109. Retrieved 2023-01-10, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120293/ doi: 10.3969/j.issn.1002-0829.2014.02.009 Floridi, L., Holweg, M., Taddeo, M., Amaya Silva, J., M¨okander, J., & Wen, Y. (2022, March). capAI - A Procedure for Conducting Conformity Assessment of AI Systems in Line with the EU Artificial Intelligence Act (SSRN Scholarly Paper No. ID 4064091). Rochester, NY: Social Science Research Network. Retrieved 2022-04-01, from https://papers.ssrn.com/abstract=4064091 doi: 10.2139/ssrn.4064091 Fox, J. (2003). Effect Displays in R for Generalised Linear Models. Journal of Statistical Software, 8 (15). Retrieved 2023-01-10, from http://www.jstatsoft.org/v08/i15/ doi: 10.18637/jss.v008.i15 Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications. Franklin, M., Ashton, H., Gorman, R., & Armstrong, S. (2022, May). Missing Mechanisms of Manipulation in the EU AI Act. The International FLAIRS Conference Proceedings, 35 . Retrieved 2022-12-31, from https:// journals.flvc.org/FLAIRS/article/view/130723 doi: 10.32473/flairs.v35i.130723 Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daum´e III, H., & Crawford, K. (2021, December). Datasheets for Datasets. arXiv. Retrieved 2022-10-20, from http://arxiv.org/abs/1803.09010 (arXiv:1803.09010 [cs]) Ghasemi, A., & Zahediasl, S. (2012, December). Normality Tests for Statistical Analysis: A Guide for NonStatisticians. International Journal of Endocrinology and Metabolism, 10 (2), 486–489. Retrieved 2023-01-11, from https://brief.land/ijem/articles/71904.html doi: 10.5812/ijem.3505 Gorelick, M., & Ozsvald, I. (2020). High performance Python: practical performant programming for humans (Second edition ed.). Beijing [China] ; Boston [MA]: O’Reilly. Harper, F. M., & Konstan, J. A. (2016, January). The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 5 (4), 1–19. Retrieved 2022-12-29, from https://dl.acm.org/doi/10.1145/ 2827872 doi: 10.1145/2827872 Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., . . . Oliphant, T. E. (2020, September). Array programming with NumPy. Nature, 585 (7825), 357–362. Retrieved 2021-12-27, from https://www.nature.com/articles/s41586-020-2649-2 doi: 10.1038/s41586-020-2649-2 Jannach, D., & Adomavicius, G. (2016, September). Recommendations with a Purpose. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 7–10). Boston Massachusetts USA: ACM. Retrieved 2022-09-01, from https://dl.acm.org/doi/10.1145/2959100.2959186 doi: 10.1145/2959100.2959186 Jiang, R., Chiappa, S., Lattimore, T., Gy¨orgy, A., & Kohli, P. (2019, January). Degenerate Feedback Loops in Recommender Systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 383–390). Honolulu HI USA: ACM. Retrieved 2022-09-07, from https://dl.acm.org/doi/10.1145/3306618 .3314288 doi: 10.1145/3306618.3314288 Kipf, T. N., & Welling, M. (2017, February). Semi-Supervised Classification with Graph Convolutional Networks. arXiv. Retrieved 2022-12-14, from http://arxiv.org/abs/1609.02907 (arXiv:1609.02907 [cs, stat]) Knijnenburg, B. 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The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power: Barack Obama’s Books of 2019. Profile Books. This paper is under CC 4.0 license. available on arxiv

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

Authors:
(1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 4 Results The results from the model can be seen in table 1. The coefficient for time is 0.018 (SE=0.001). This implies growth in Barrier-to-Exit of 1.8% per year. This is highly significant (T=29.95, p ≪ 0.0001). The coefficient for activity level is 0.614 (SE=0.001), which is also highly significant (T=450.11, p ≪ 0.0001). A visual representation of these models can be seen in figure 4. The partial effects plot (Fox, 2003) in fig 4a shows an increase in Barrier-to-Exit from approximately 1.15 to 1.5. This translates into a growth of approximately 43% over the duration of the study. Fig. 4b shows the effect plot with residuals. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jonathan H. Rystrøm. Authors: Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Abstract and Introduction Previous Literature Previous Literature Methods and Data Methods and Data Results Results Discussions Discussions Conclusions and References Conclusions and References A. Validation of Assumptions A. Validation of Assumptions B. Other Models B. Other Models C. Pre-processing steps C. Pre-processing steps 4 Results The results from the model can be seen in table 1. The coefficient for time is 0.018 (SE=0.001). This implies growth in Barrier-to-Exit of 1.8% per year. This is highly significant (T=29.95, p ≪ 0.0001). The coefficient for activity level is 0.614 (SE=0.001), which is also highly significant (T=450.11, p ≪ 0.0001). A visual representation of these models can be seen in figure 4. The partial effects plot (Fox, 2003) in fig 4a shows an increase in Barrier-to-Exit from approximately 1.15 to 1.5. This translates into a growth of approximately 43% over the duration of the study. Fig. 4b shows the effect plot with residuals. 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

Quantifying Preference Manipulation: Growth Trends in Amazon's Barrier-to-Exit Over Time

Understanding Barrier-to-Exit in Amazon's Recommender System

অ্যামাজনের সুপারিশকারী সিস্টেমে বাধা-থেকে-প্রস্থান বোঝা

Verständnis der Ausstiegsbarrieren im Empfehlungssystem von Amazon

Comprender la barrera de salida en el sistema de recomendación de Amazon

Comprendre les barrières à la sortie dans le système de recommandation d'Amazon

अमेज़न की अनुशंसा प्रणाली में बैरियर-टू-एग्जिट को समझना

Amazon のレコメンデーション システムにおける退出障壁を理解する

Amazon 추천 시스템의 종료 장벽 이해

Compreendendo a barreira de saída no sistema de recomendação da Amazon

Понимание барьера выхода в рекомендательной системе Amazon

Amazon'un Tavsiye Sisteminde Çıkış Engelini Anlamak

Tìm hiểu về Rào cản thoát trong Hệ thống đề xuất của Amazon

了解亚马逊推荐系统的退出壁垒

WOMBO, the makers of viral AI avatar apps, and io.net, a pioneer in decentralized GPU compute networks, have just struck a partnership that represents a major step towards expanding the possibilities of AI computing. This collaboration comes at a time when artificial intelligence (AI) is pushing the boundaries of technology. This agreement marks a significant step forward in the development of artificial intelligence's computing capabilities. This cooperation not only represents a milestone in the development of artificial intelligence-powered applications, but it also addresses the issue of restricted processing capacity that many AI startups are now confronting. It is anticipated that over 200 million copies of WOMBO's applications have been downloaded from the Google App Store and for iOS. The firm's most popular and successful apps are WOMBO, Dream, and WOMBO Me. Because they need a significant quantity of processing power, they have found themselves drawn to io.net's cutting-edge decentralized GPU computing network. Io.net emerges as a pioneering force, offering decentralized, geo-distributed computing networks with the potential to transform the artificial intelligence infrastructure. Io.net is a pioneering organization. The ability of WOMBO to employ Apple silicon chip clusters as fuel for its machine learning (ML) models is critical to the success of this collaboration. This would help artificial intelligence enterprises overcome the challenges of cloud computing, such as high cost and limited accessibility. Partnership Goals and Technology The partnership between WOMBO and io.net comes against the background of the AI industry's battle with high cloud computing operational costs and a restricted supply of hardware. The novel technology developed by Io.net aggregates decentralized and geographically dispersed GPUs, enabling clusters to be deployed on demand for a fraction of the typical cost. This strategy has opened up new avenues of opportunity for WOMBO, allowing it to grow its AI applications more effectively and efficiently. WOMBO CEO Ben-Zion Benkhin expressed his enthusiasm for the partnership, stating, “We are excited about partnering with io.net to help bring unused computing power and put it to use in groundbreaking AI applications - together, our teams have the potential to put a serious dent in the GPU supply shortage.” This sentiment captures the collaborative spirit and the ambitious vision shared by both companies to overcome the challenges of GPU supply constraints. Tapping into Apple’s Silicon Chips Central to this strategic alliance is an innovative endeavor that utilizes the extraordinary capabilities of Apple silicon chip clusters. This initiative is poised to leverage the Neural Engine capabilities on Apple’s chips along with io.net’s mega-clustering capabilities, tapping into hundreds of millions of consumer devices for AI workloads. This approach not only showcases the technical synergy between WOMBO and io.net but also highlights the potential for scalable AI application development on a global scale. Final Thoughts Io.net is a decentralized physical infrastructure network (DePIN) that develops and administers decentralized GPU clusters produced on demand from sources distributed throughout the globe. Through its infrastructure of over 520 thousand graphics processing units (GPUs) and central processing units (CPUs), io.net democratizes access to GPU computing power, lowering prices and extending opportunities for both engineers and enterprises. This network has been optimized for use cases that need high processing demand and low latency, such as artificial intelligence and machine learning activities, as well as cloud gaming, by inventing a new compute network standard. In terms of artificial intelligence, the partnership between WOMBO and io.net represents a change towards computer solutions that are more environmentally friendly, accessible, and efficient. This cooperation, which employs distributed computing and the power of Apple silicon processors, not only prepares the path for WOMBO's future growth but also sets a pattern for how artificial intelligence firms might overcome limitations in computing capacity and cost. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. WOMBO, the makers of viral AI avatar apps, and io.net, a pioneer in decentralized GPU compute networks, have just struck a partnership that represents a major step towards expanding the possibilities of AI computing. This collaboration comes at a time when artificial intelligence (AI) is pushing the boundaries of technology. This agreement marks a significant step forward in the development of artificial intelligence's computing capabilities. This cooperation not only represents a milestone in the development of artificial intelligence-powered applications, but it also addresses the issue of restricted processing capacity that many AI startups are now confronting. It is anticipated that over 200 million copies of WOMBO's applications have been downloaded from the Google App Store and for iOS. The firm's most popular and successful apps are WOMBO, Dream, and WOMBO Me. Because they need a significant quantity of processing power, they have found themselves drawn to io.net's cutting-edge decentralized GPU computing network. Io.net emerges as a pioneering force, offering decentralized, geo-distributed computing networks with the potential to transform the artificial intelligence infrastructure. Io.net is a pioneering organization. The ability of WOMBO to employ Apple silicon chip clusters as fuel for its machine learning (ML) models is critical to the success of this collaboration. This would help artificial intelligence enterprises overcome the challenges of cloud computing, such as high cost and limited accessibility. Partnership Goals and Technology The partnership between WOMBO and io.net comes against the background of the AI industry's battle with high cloud computing operational costs and a restricted supply of hardware. The novel technology developed by Io.net aggregates decentralized and geographically dispersed GPUs, enabling clusters to be deployed on demand for a fraction of the typical cost. This strategy has opened up new avenues of opportunity for WOMBO, allowing it to grow its AI applications more effectively and efficiently. WOMBO CEO Ben-Zion Benkhin expressed his enthusiasm for the partnership, stating, “We are excited about partnering with io.net to help bring unused computing power and put it to use in groundbreaking AI applications - together, our teams have the potential to put a serious dent in the GPU supply shortage.” This sentiment captures the collaborative spirit and the ambitious vision shared by both companies to overcome the challenges of GPU supply constraints. Tapping into Apple’s Silicon Chips Central to this strategic alliance is an innovative endeavor that utilizes the extraordinary capabilities of Apple silicon chip clusters. This initiative is poised to leverage the Neural Engine capabilities on Apple’s chips along with io.net’s mega-clustering capabilities, tapping into hundreds of millions of consumer devices for AI workloads. This approach not only showcases the technical synergy between WOMBO and io.net but also highlights the potential for scalable AI application development on a global scale. Final Thoughts Io.net is a decentralized physical infrastructure network (DePIN) that develops and administers decentralized GPU clusters produced on demand from sources distributed throughout the globe. Through its infrastructure of over 520 thousand graphics processing units (GPUs) and central processing units (CPUs), io.net democratizes access to GPU computing power, lowering prices and extending opportunities for both engineers and enterprises. This network has been optimized for use cases that need high processing demand and low latency, such as artificial intelligence and machine learning activities, as well as cloud gaming, by inventing a new compute network standard. In terms of artificial intelligence, the partnership between WOMBO and io.net represents a change towards computer solutions that are more environmentally friendly, accessible, and efficient. This cooperation, which employs distributed computing and the power of Apple silicon processors, not only prepares the path for WOMBO's future growth but also sets a pattern for how artificial intelligence firms might overcome limitations in computing capacity and cost. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Breaking Barriers in AI Development: WOMBO and io.net Unite to Tackle GPU Shortage

Although there are plenty of initiatives encouraging young women into a career in tech, the reality is that men still outnumber women by a . ratio of 3:1 Additionally, by the time students choose their majors in college, only , and just 19% of computer science majors are women. 21% of engineering majors But one woman who has lived this experience, while also managing to carve out a successful career in tech despite the systemic obstacles standing in her way, is Lori Laub. She is the CEO and founder of TruCentive, an incentives delivery platform that allows businesses to send gifts, rewards, and incentives to employees. Driven by “pure passion” to pursue a tech career, Laub credits her parents with instilling the self-belief that she could achieve anything she set her mind to. “They presented reality without sugar-coating it,” she explains. “They informed me that some people might have advantages, such as better education, stronger connections, or greater resources, which could hinder or facilitate my journey. Nonetheless, they impressed upon me that, ultimately, the outcome would hinge on how I utilized the opportunities presented to me. Ambition counts “Although they didn't phrase it in these exact terms, I've come to realize they were imparting a crucial lesson: my gender, my background, or any other aspect of my identity were not the defining factors; rather, what counted was my ambition, my commitment to my goals, and the consistency of my efforts every single day. That doesn’t mean Laub’s career path has been smooth sailing. In fact, she admits that she’s had to overcome gender stereotypes throughout her career, especially in her twenties, when clients struggled to believe she was “in charge”, and also internally when a male colleague consistently referred to her as “little lady”. “His seniority in both position and age within the company was daunting. However, I reached a point where I felt compelled to take action,” she reveals. “Initially, I politely requested that he refrain from calling me that. Still, when the behavior continued, I decided to address him as 'little man' respectfully. This prompted a dialogue between us, after which he started to use my actual name. While he occasionally reverted to the old moniker, gently correcting him became easier over time, and he eventually ceased using it altogether.” While this approach might not work for everyone, Laub advocates that all workers, regardless of rank or gender, could benefit from some self-awareness. “Ultimately, the key is consistently presenting your best version of yourself while being truthful about your strengths and improvement areas… Perfection is unrealistic; it's essential for each of us to dedicate ourselves to improving our contributions to the organization and the way we deliver them.” Tech’s doors are opening Her advice to women hoping to break into tech is similar in sentiment: “The doors to the tech industry are increasingly open to women, and the opportunities continue to expand. The question now centers on how women leverage these opportunities to enhance the workplace for the generations of women who will follow.” Ready to accelerate your career path with a challenging new role? Focus your search on the which features thousands of roles in companies actively hiring, such as the three below. Hackernoon Job Board For those looking for a way to carve out a career in cybersecurity, Infoblox is hiring a . Reporting to the senior director of software engineering, you will design leading-edge, market-disrupting cybersecurity solutions and work alongside a team of people who share a passion for building the next generation of networking technologies. Interested? . Staff Software Engineer Apply here If aeronautics is more your thing, ENSCO, Inc. is hiring an to join its U.S. Space Force, Space Systems Command’s MILSATCOM Program. In this role you will support a diverse set of strategic and tactical systems and infrastructure in a multidisciplinary and collaborative environment and apply model-based system engineering to support the development of technical baselines. . Enterprise Requirements & Integration Engineer Find out more here Alternatively, if you want to delve into the world of AI, DataAnnotation is seeking a to train its AI chatbots to code. This flexible, part-time or full-time role which allows you to choose the projects you want to work on, involves coming up with diverse problems and solutions for a coding chatbot, writing high-quality answers and code snippets and evaluating the code quality produced by AI models for accuracy and performance. Want to know more? . Software Developer See the full job description here Find your next opportunity today via the HackerNoon Job Board By Aoibhinn Mc Bride

This Is How You Can Break Barriers as a Young Woman in Tech

এইভাবে আপনি প্রযুক্তিতে একজন তরুণী হিসাবে বাধাগুলি ভাঙতে পারেন

So können Sie als junge Frau in der Tech-Branche Barrieren überwinden

Así es como puedes romper barreras como mujer joven en tecnología

Voici comment vous pouvez briser les barrières en tant que jeune femme dans le domaine de la technologie

इस तरह आप तकनीक के क्षेत्र में एक युवा महिला के रूप में बाधाओं को तोड़ सकती हैं

これがテクノロジー業界の若い女性として障壁を打ち破る方法です

기술 분야의 젊은 여성으로서 장벽을 허물 수 있는 방법

É assim que você pode quebrar barreiras quando jovem na área de tecnologia

Вот как вы можете преодолеть барьеры, будучи молодой женщиной в сфере технологий

Teknoloji Alanında Genç Bir Kadın Olarak Engelleri Bu Şekilde Kırabilirsiniz

Đây là cách bạn có thể phá vỡ rào cản khi còn là một phụ nữ trẻ trong lĩnh vực công nghệ

作为一名科技界的年轻女性，你可以这样打破障碍

The Great Barrier Reef in Australia is stunningly beautiful and the largest coral reef in the world, home to an incredible diversity of marine life. Unfortunately, the reef faces threats from crown-of-thorns starfish (COTS) that eat coral. To control COTS outbreaks, reef managers use a method called Manta Tow surveys where divers are towed behind a boat, visually assessing sections of the reef. However, this method has limitations in efficiency, data quality, and scalability. To improve COTS surveillance and control, the Great Barrier Reef Foundation started an innovation program. A key part is using underwater cameras to collect reef images and applying AI to detect COTS automatically. To develop the machine learning technology for this video-based surveying at scale, Australia's national science agency . Their goal is to create systems that can accurately and efficiently analyze large volumes of imagery to pinpoint COTS outbreaks across the vast Great Barrier Reef in near real-time. CSIRO has partnered with Google This would greatly assist conservation efforts and help secure the long-term protection of the reef ecosystem. In summary, AI-powered image analysis is being applied to automate detecting crown-of-thorns starfish from footage of the enormous Great Barrier Reef, enabling better monitoring and control at massively increased efficiency and scale to safeguard the reef. The is to accurately identify starfish in real-time by building an object detection model trained on underwater videos of coral reefs. In this blog post, we will build an ML pipeline to analyze the reef’s images and predict the presence and position of crown-of-thorns starfish. goal of this competition We will be using - an Opensource package that uses data-centric AI to clean up the issues in the image data and , , and - used for computer vision tasks like objection detection and image classification. Cleanvision Tensorflow KerasCV YOLOv8 !pip install -q cleanvision keras-cv kaggle As the data size is huge, we are directly using Kaggle to download and run the tutorial. For that, we need to configure your Kaggle creds and give you the appropriate permissions; ! read more here ! cp kaggle.json ~/.kaggle/
! chmod 600 ~/.kaggle/kaggle.json Downloading and Extracting the Kaggle Dataset !kaggle competitions download -c tensorflow-great-barrier-reef !unzip /content/tensorflow-great-barrier-reef.zip Using Cleanvision to Clean Up the Data CleanVision is an open-source data-centric AI package that automatically identifies problematic images that could negatively impact computer vision systems. It scans image datasets and detects common data issues like blur, inadequate lighting, duplicate images, etc. Fixing these problems in the training data is an essential first step before developing machine learning models to ensure their reliability. CleanVision provides a simple unified interface - just run the same few lines of Python code to audit any image collection, regardless of size or origin. This makes it extremely easy to clean dirty image data. By proactively flagging low-quality, duplicates, and other undesirable images upfront, CleanVision helps save significant time and improves outcomes when building computer vision applications. It's a fast way to boost data hygiene as a prerequisite before applying machine learning to image-based tasks. from cleanvision import Imagelab

# Specify path to folder containing the image files in your dataset
imagelab = Imagelab(data_path="train_images/")

# Automatically check for a predefined list of issues within your dataset
imagelab.find_issues()

# Produce a neat report of the issues found in your dataset
imagelab.report() import os
for st in imagelab.info['exact_duplicates']['sets']:
  os.remove(st[0]) Importing Relevant Libraries import pandas as pd
# show images inline
%matplotlib inline

import keras
import tensorflow

# import miscellaneous modules
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
import time

import tensorflow as tf Preparing Dataset for Preprocessing We will read the dataset and then preprocess it to be able to use it for model training. We begin by loading the train CSV file and converting the annotations to integer format from the original JSON format. df_train = pd.read_csv("train.csv")

df_train=df_train.loc[df_train["annotations"].astype(str) != "[]"]
df_train['annotations'] = df_train['annotations'].apply(eval)

df_train['image_path'] = "train_images/video_" + df_train['video_id'].astype(str) + "/" + df_train['video_frame'].astype(str) + ".jpg"
df_extrain=df_train.explode('annotations') # Single annotation per row
df_extrain.reset_index(inplace=True)
df_extrain.head() df_extrain_main=pd.DataFrame(pd.json_normalize(df_extrain['annotations']), columns=['x', 'y', 'width', 'height']).join(df_extrain)
df_extrain_main['class']=0
df_extrain_main=df_extrain_main[['image_path','x','y','width','height','class','video_id','video_frame']]
df_extrain_main.head(10) After the CSV processing, we try to split the CSV into a dataset and use the appropriate bounding box format, classes, and image paths to create the preliminary dataset ready to be processed further. def create_tf_example(rowss,data_df):


    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []

    # Convert ---> [xmin,ymin,width,height] to [xmins,xmaxs,ymins,ymaxs]
    xmin = rowss['x']
    xmax = rowss['x']+rowss['width']
    ymin = rowss['y']
    ymax = rowss['y']+rowss['height']


    #main_data.append((rowss['image_path'],xmins,xmaxs,ymins,ymaxs))
    return rowss['image_path'],xmin,ymin,xmax,ymax from PIL import Image, ImageDraw
paths = []
bboxes = []
classes = []
for index, row in df_extrain_main.iterrows():
            if index % 1000 == 0:
                print('Processed {0} images.'.format(index))
            image_path,xmins,ymins,xmaxs,ymaxs=create_tf_example(row,df_extrain_main)
            paths.append(image_path)
            bboxes.append([[float(xmins),float(ymins),float(xmaxs),float(ymaxs)]])
            classes.append([0]) Here, we are using to create ragged tensors from the and classes lists. A ragged tensor is a type of tensor that can handle varying lengths of data along one or more dimensions. tf.ragged.constant bbox This is useful when dealing with data that has variable-length sequences, such as text or time series data. In this case, the and classes lists have different lengths for each image, depending on the number of objects in the image and the corresponding bounding boxes and classes. To handle this variability, ragged tensors are used instead of regular tensors. bbox Later, these ragged tensors are used to create a using the method. This method creates a dataset from the input tensors by slicing them along the first dimension. tf.data.Dataset from_tensor_slices By using ragged tensors, the dataset can handle varying lengths of data for each image and provide a flexible input pipeline for further processing. bbox = tf.ragged.constant(bboxes)
classes = tf.ragged.constant(classes)
image_paths = tf.ragged.constant(paths)

data = tf.data.Dataset.from_tensor_slices((image_paths, classes, bbox)) num_val = int(bbox.shape[0] * 0.2)

# Split the dataset into train and validation sets
val_data = data.take(num_val)
train_data = data.skip(num_val) includes pre-trained models for popular computer vision datasets, such as , which can be used for transfer learning. KerasCV also provides a range of visualization tools for inspecting the intermediate representations learned by the model and for visualizing the results of object detection and segmentation tasks. KerasCV ImageNet, COCO, and Pascal VOC In this particular notebook, we are using YOLOV8, so we have to format the dataset which is in a compatible format according to the YOLOV8 model. The bounding box input format should be as follows. bounding_boxes = {
    # num_boxes may be a Ragged dimension
    'boxes': Tensor(shape=[batch, num_boxes, 4]),
    'classes': Tensor(shape=[batch, num_boxes])
} The dictionary has two keys, ' ' and ' ', each of which maps to a TensorFlow or Tensor object. The ' ' Tensor has a shape of , where batch is the number of images in the batch and is the maximum number of bounding boxes in any image. boxes classes RaggedTensor boxes [batch, num_boxes, 4] num_boxes The 4 represents the four values needed to define a bounding box: xmin, ymin, xmax, ymax. The 'classes' Tensor has a shape of , where each element represents the class label for the corresponding bounding box in the ' ' Tensor. The num_boxes dimension may be ragged, which means that the number of boxes may vary across images in the batch. [batch, num_boxes] boxes The final model input looks something like this: {"images": images, "bounding_boxes": bounding_boxes} def load_image(image_path):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(image, channels=3)
    return image


def load_dataset(image_path, classes, bbox):
    # Read Image
    image = load_image(image_path)
    bounding_boxes = {
        "classes": tf.cast(classes, dtype=tf.float32),
        "boxes": tf.cast(bbox, dtype=tf.float32),
    }
    return {"images": tf.cast(image, tf.float32), "bounding_boxes": bounding_boxes} Data Augmentation Applying effective data augmentation is critical yet challenging when building object detection models. Transformations like crops, flips, etc. must update the bounding box coordinates correctly. Doing this manually is complex and error-prone. KerasCV provides specialized layers to handle augmentation aware of bounding boxes. It offers a wide range of transformations that automatically adjust bboxes to match the augmented images. This integrated bounding box handling enables easily incorporating powerful augmentation into object detection pipelines with KerasCV. By leveraging tf.data pipelines, the augmentation can be done on-the-fly during training. With KerasCV's robust bbox-aware augmentation capabilities, developers can achieve more diverse and useful training data and improved generalization in object detection models, while avoiding cumbersome manual handling of coordinates. The layers handle that complexity behind the scenes. augmenter = keras.Sequential(
    layers=[
        keras_cv.layers.RandomFlip(mode="horizontal", bounding_box_format="xyxy"),
        keras_cv.layers.RandomShear(
            x_factor=0.2, y_factor=0.2, bounding_box_format="xyxy"
        ),
        keras_cv.layers.JitteredResize(
            target_size=(640, 640), scale_factor=(0.75, 1.3), bounding_box_format="xyxy"
        ),
    ]
)
resizing = keras_cv.layers.JitteredResize(
    target_size=(640, 640),
    scale_factor=(0.75, 1.3),
    bounding_box_format="xyxy",
) Training Dataset With Augmentation We are preparing the training dataset with augmentation for our object detection model. We start by setting the to 4, and then use the function to load the data. batch_size load_dataset Next, we shuffle the data to ensure that our model doesn't overfit on a particular kind of data. We then use the function to batch the data with a fixed size of 4 and drop any remaining data. ragged_batch Finally, we apply the function to the dataset to perform data augmentation, which helps to increase the diversity of the dataset and improve the model's accuracy. augmenter The parameter is set to to allow TensorFlow to dynamically tune the number of parallel calls to improve performance. num_parallel_calls tf.data.AUTOTUNE BATCH_SIZE =4
train_ds = train_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.shuffle(BATCH_SIZE * 4)
train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)
train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE) Validation Dataset In this code block, we are creating a validation dataset by mapping the 'load_dataset' function to the 'val_data' set. This function helps in loading the data from the dataset and preprocessing it for the model. We are also setting 'num_parallel_calls' to 'tf.data.AUTOTUNE' to enable parallelism for faster processing. Next, we are shuffling the validation dataset by a factor of 'BATCH_SIZE * 4' to increase randomness and prevent overfitting. Finally, we are creating a 'ragged_batch' of 'BATCH_SIZE' with 'drop_remainder=True' to ensure that all batches have the same size. This helps in making the model more efficient and consistent during training. val_ds = val_data.map(load_dataset, num_parallel_calls=tf.data.AUTOTUNE)
val_ds = val_ds.shuffle(BATCH_SIZE * 4)
val_ds = val_ds.ragged_batch(BATCH_SIZE, drop_remainder=True) Bounding Box Visualisation We use the library for bounding box visualization. It imports the necessary packages and defines a class mapping for the dataset. The function takes in the dataset inputs and uses the function to display the images with bounding boxes overlaid. You can see that via and datasets. keras_cv visualize_dataset() plot_bounding_box_gallery() train_ds val_ds import keras_cv
from keras_cv import bounding_box
from keras_cv import visualization
class_mapping = {0:'fish'}

def visualize_dataset(inputs, value_range, rows, cols, bounding_box_format):
    inputs = next(iter(inputs.take(1)))
    images, bounding_boxes = inputs["images"], inputs["bounding_boxes"]
    visualization.plot_bounding_box_gallery(
        images,
        value_range=value_range,
        rows=rows,
        cols=cols,
        y_true=bounding_boxes,
        scale=5,
        font_scale=0.7,
        bounding_box_format=bounding_box_format,
        class_mapping=class_mapping,
    )


visualize_dataset(
    train_ds, bounding_box_format="xyxy", value_range=(0, 255), rows=2, cols=2
)

visualize_dataset(
    val_ds, bounding_box_format="xyxy", value_range=(0, 255), rows=2, cols=2
) Next, we unpack the dataset to be able to feed it into the model training function of the Keras API. def dict_to_tuple(inputs):
    return inputs["images"], inputs["bounding_boxes"]


train_ds = train_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)

val_ds = val_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)
val_ds = val_ds.prefetch(tf.data.AUTOTUNE) Model Building is the newest iteration of the popular YOLO (You Only Look Once) family of models used for computer vision tasks like object detection and image classification. It was created by Ultralytics, building on their previous YOLOv5 model. YOLOv8 Compared to prior versions, YOLOv8 integrates numerous architectural upgrades for improved accuracy and performance. The creators also aimed to enhance the overall developer experience. As the latest release in the industry-leading YOLO lineup, YOLOv8 represents the current state-of-the-art in domains like object detection. It incorporates both algorithmic advances to push the boundaries of CV capability and usability improvements for practitioners. Keras-CV offers multiple models- , , , , and . For this example, we choose the backbone and load it with the pre-trained weights. YOLOV8 YOLOv8n YOLOv8s YOLOv8m YOLOv8l YOLOv8x YOLOV8s coco Next, we build a model using the which accepts a feature extractor as the backbone argument, a num_classes argument that specifies the number of object classes to detect based on the size of the class_mapping list, a bounding_box_format argument that informs the model of the format of the bbox in the dataset, and finally, the feature pyramid network (FPN) depth is specified by the argument. YOLOV8 YOLOV8Detector fpn_depth backbone = keras_cv.models.YOLOV8Backbone.from_preset(
    "yolo_v8_s_backbone_coco"  # We will use yolov8 small backbone with coco weights
)
yolo = keras_cv.models.YOLOV8Detector(
    num_classes=1,
    bounding_box_format="xyxy",
    backbone=backbone,
    fpn_depth=1,
) Compiling the Model The training process of a YOLOv8 model involves the use of two types of losses - classification loss and box loss. The classification loss measures the difference between the predicted class probabilities and the true class probabilities for each detected object. Binary cross entropy loss is utilized in this case, as each detected item either belongs to a particular class or does not. This loss function helps optimize the model for accurate object classification. On the other hand, the box loss calculates the dissimilarity between the predicted bounding boxes and the ground truth boxes. YOLOv8 uses Complete IoU (CIoU) instead of basic IoU for box loss calculation. CIoU considers additional factors like the boxes' aspect ratio, center distance, and scale. This helps better represent box similarity by looking at more box attributes beyond just overlap. By jointly optimizing for object classification and precise localization during training, the classification and box losses help YOLOv8 generate highly accurate object detection by minimizing discrepancies between outputs and label data across classes and bounding box coordinates. optimizer = tf.keras.optimizers.Adam(
    learning_rate=10e-3,
)

yolo.compile(
    optimizer=optimizer, classification_loss="binary_crossentropy", box_loss="ciou"
) Training the Model During each epoch, the model iteratively adjusts its parameters to minimize the difference between its predicted output and the actual output. This process helps the model learn how to accurately detect crown-of-thorns starfish in the underwater videos of coral reefs. The number of epochs can be adjusted based on the training dataset size, complexity of the model, and desired accuracy. After training, the model can be used to predict the presence and position of starfish in real-time, helping researchers and conservationists monitor and control COTS outbreaks more efficiently and effectively. yolo.fit(
    train_ds,
    epochs=3,
) Conclusion The integration of , , , and in this project exemplifies the power and versatility of AI in environmental conservation, particularly for the Great Barrier Reef. Cleanvision TensorFlow KerasCV YOLOv8 By leveraging the strengths of these advanced tools, we've developed a robust model capable of accurately identifying crown-of-thorns starfish in complex underwater environments. This not only marks a significant step forward in protecting one of the world's natural wonders but also demonstrates the immense potential of AI in addressing ecological challenges.

How to Use TensorFlow and Cleanvision to Detect Starfish Threats in the Great Barrier Reef

The driver education sector is evolving rapidly with the advent of digital learning. Andrii Sheverdin, a pioneer in this realm, transforms traditional driver training with his innovative app, “Driving School in the Phone.” This platform disrupts the status quo, making quality driver education universally accessible, particularly for teenagers. In a revealing interview, Sheverdin shares insights into his groundbreaking approach and dedication to making driver training inclusive, driven by his philosophy: "Empowering every teenager to learn without barriers." Since 2021, the driving school industry has experienced significant changes. A major development is the adoption of online education. From the start of 2023, driver's license applicants in the USA must complete a five-hour online course for a "Pre-Licensing Course Certificate," an hour longer than in previous years. This change in teaching methods has sparked intense discussions. Many drivers worry that reducing in-person lessons with instructors might increase road accidents. , a driving school founder and winner of the International IT Project Competition Bridge in the EdTech Visionary category and the Skolkovo award, launched the innovative online platform "Driving School in the Phone." This platform played a crucial role in assisting countless educational institutions in maintaining their operations remotely amid the pandemic. It demonstrated that educational quality can be sustained even when shifting to digital platforms. In November 2023, Sheverdin expanded his operations by establishing CoreWaY LLC in New York, marking his entry into the American market. Our interview explores the future of driver training and the role of technology in this evolution. Andrii Sheverdin Andrii, before the launch of your driving school application, the online education market in this area was somewhat underdeveloped. Having been involved in driving instruction since the mid-2000s, could you share some insights into the challenges faced by students and schools before your innovative solution came into play? The vast majority of driving schools functioned solely in a traditional, offline manner. Theoretical training was conducted exclusively through in-person sessions, adhering to a fixed schedule. This necessitated students to accommodate, often involving significant travel time. Managing payments and handling administrative tasks previously required in-person attendance. This method restricted the frequency of interaction between the students and staff at the driving schools. Operations within these schools were predominantly paper-based, with little to no digital integration. Before our intervention, no complete strategies were in place for driving schools to an online educational model. Your product was a trailblazer in the field, combining mobility and modern technology, a feat acknowledged by the Russian National Business Awards. But what sparked the concept for the highly successful "Driving School in the Phone" application? The idea to create a mobile application for driving schools came after many years of working in this field and a deep understanding of its problems and needs. From my own experience, I encountered that the learning process in driving schools was often inconvenient for students precisely because of the need to adjust to the schedule and spend time on the road - about which I already mentioned - there were other inconveniences. As the head of a driving school, I saw difficulties in organizing effective interaction between employees and students within the driving school. In communicating with young students, I understood that they needed a more modern, mobile learning format. Eventually, I realized it was necessary to create an application that would allow for online learning, remote handling of organizational issues, and make getting a driver's license more comfortable for students. These ideas formed the basis of "Driving School in the Phone." Frequently, there's skepticism about online educational platforms, particularly for something as critical as learning to drive. Yet, the training provided through your application matches the effectiveness of conventional, in-person learning methods. How were you able to accomplish this? Yes, one of the critical tasks we set for ourselves when creating the "Driving School in the Phone" application was to ensure the same high quality of online learning as in the traditional offline format. And we put a lot of effort into this. First, our educational platform includes modern interactive tools: tests, video lessons, and 3D animations. This allows the student to immerse deeply in the material and gain the necessary knowledge. Moreover, we implemented functions for remote interaction of students with teachers through chats and video conferencing. Any question can be asked online and promptly receive qualified consultation. Also, in the application, there is an option to take theory exams online to prepare for the traffic police exam fully. Numerous driving schools have adopted your product and have reached thousands of driving students in your home country. Would this level of uptake be a success? Success is a relative concept. There is always something to strive for; however, we achieved impressive growth and business scaling thanks to the introduction of our application in driving schools. Our clients increased students' graduation several times with the same number of employees. This was made possible by optimizing and automating business processes using our platform. But the most important thing is that we are fundamentally changing the approach to training in driving schools, making it genuinely modern and convenient for students thanks to mobile technologies. My team and I take great pride in seeing how our product enhances the quality of educational services in driver training. And this is the best reward for us as a company-developer. With a substantial client base utilizing your platform, how does your company's organizational structure facilitate effective interaction with them? Can you describe how you established this structure? At CoreWaY LLC, we have formed a unique and multifunctional team that works in different corners of the world. This affords us a distinctive advantage: we can identify and utilize top talents, irrespective of their geographic location. This is incredibly crucial in our line of work. Managing such a distributed team requires flexibility and an innovative approach. We strive to maintain constant communication and ensure that all our employees, regardless of location, feel involved in the company's overall process and course. We use various technological tools and platforms to stay on the same wavelength, even in different time zones. It also helps us be closer to our clients worldwide and better understand their needs. Our company generally puts flexibility, open communication, and teamwork at the forefront, allowing us to effectively respond to rapidly changing market demands and stay at the forefront of innovation in driver training. Andrii, could you share some details about the new company you've established in the USA? What are your strategies for entering the American market, and how do you intend to tailor your approach to accommodate the unique aspects of driver training in the United States? Our new company, CoreWaY LLC, which I registered in November of this year in New York, represents an important step in our endeavor to bring innovation to driver training in the USA. We plan to offer a fresh look at the educational process, combining proven methodologies with modern technologies. The main goal of our company in the USA is to provide affordable, convenient, and practical driver training. We realize that educational requirements and preferences in the USA differ from those in Russia. Therefore, we will adapt our approach to each state's local features and legislative requirements to make our training as relevant and effective as possible. In the American market, the main focus will be on creating a comprehensive educational program for drivers of all categories: from passenger cars (category D, in Russia - category B) to motorcycles and trucks (CDL - Commercial Driver's License). Considering the popularity and demand for category CDL in the USA, we will pay special attention to the training of truck drivers. We also plan to cooperate closely with local driving schools and educational institutions to make our programs and methods entirely relevant. Our goal is to create a holistic educational ecosystem that will meet the needs of modern American drivers and contribute to road safety. With your driver training application gearing up for its imminent IOS release in the USA, could you highlight this app's distinctive features and functions? How does it stand out from other existing solutions in the market? Yes, we eagerly await the upcoming release of our new driver training application on mobile platforms in the USA. This application is developed with our commitment to innovation and providing high-quality training. In addition to adaptive learning and artificial intelligence support, we have introduced some unique features that make our application more inclusive and accessible. Firstly, we thought about schoolchildren. You know, teenagers learn differently; they need a different approach. Therefore, we introduced a special mode for grades 9-12 students. In it, everything is more straightforward and more enjoyable: simplified цtests, interactive guides, and all that. And most importantly - for school children, it is entirely free. We want every teenager to have the opportunity to learn to drive without any barriers. Another thing we cared about was support for immigrants. Our application is now available in eight languages. This means that no matter who you are and where you come from, you can easily switch to your native language in the application. Our application serves more than just the purpose of learning how to drive. It signifies the progression toward establishing a more inclusive, accessible educational environment. We want everyone, regardless of age or background, to have the opportunity to learn and develop. What is your planned course of action or strategy to implement? We are setting our sights high with plans to enhance our application's features and break into new markets. An essential advancement we've made is incorporating artificial intelligence to personalize learning experiences and support students from various countries. This involves real-time translation of messages, enabling seamless communication between clients and driving school staff in their preferred languages. Additionally, our goal is to collaborate with driving schools in every state across the U.S. This will allow us to provide a more complete range of services to those learning to drive.

Andrii Sheverdin: 'Every Teenager Should Have the Opportunity to Learn Without Barriers'

আন্দ্রে শেভারডিন: 'প্রতিটি কিশোরের বাধা ছাড়াই শেখার সুযোগ থাকা উচিত'

Andrey Sheverdin: „Jeder Teenager sollte die Möglichkeit haben, ohne Barrieren zu lernen“

Andrey Sheverdin: "Todo adolescente debería tener la oportunidad de aprender sin barreras"

Andrey Sheverdin : « Chaque adolescent devrait avoir la possibilité d'apprendre sans barrières »

एंड्री शेवरडिन: 'प्रत्येक किशोर को बाधाओं के बिना सीखने का अवसर मिलना चाहिए'

アンドレイ・シェバーディン：「すべてのティーンエイジャーは障壁なく学ぶ機会を持つべきだ」

Andrey Sheverdin: '모든 십대는 장벽 없이 학습할 수 있는 기회를 가져야 합니다'

Andrey Sheverdin: 'Todo adolescente deveria ter a oportunidade de aprender sem barreiras'

Андрей Шевердин: «Каждый подросток должен иметь возможность учиться без барьеров»

Andrey Sheverdin: 'Her Genç Engelsiz Öğrenme Fırsatına Sahip Olmalı'

Andrey Sheverdin: 'Mọi thanh thiếu niên nên có cơ hội học tập không rào cản'

安德烈·谢维丁：“每个青少年都应该有无障碍学习的机会”

In the dynamic world of startups, where innovation races against time, operational efficiency isn't just a goal – it's a lifeline. Picture this: visionary founders and passionate teams, brimming with groundbreaking ideas yet tethered down by the weight of day-to-day operations. This is a common narrative in the ecosystem, where the brilliance of innovation often risks being dimmed by the complexities of administrative tasks. But what if there was a way to keep the focus firmly on growth and creativity while seamlessly managing those essential yet time-consuming operations? startup Enter the realm of strategic outsourcing – a game-changing approach reshaping the startup landscape. This isn't about offloading tasks to the lowest bidder; it's about empowering businesses with the freedom to innovate, expand, and excel. It's here that companies like Levy emerge as service providers and pivotal partners in the startup journey. Read on as we delve into the real-life stories of five startups. Each narrative unfolds how strategic outsourcing, particularly with Levy, transformed their operational challenges into stories of triumph. From reclaiming valuable hours to streamlining complex processes, these testimonials are not just success stories – they are a testament to the power of smart collaboration in the startup world. As we navigate these transformative journeys, remember that every startup has a unique path, but the challenges are often universal. The insights shared here are more than individual success stories; they are blueprints for efficiency, growth, and sustained innovation. So, whether you're a budding entrepreneur or a seasoned founder, join us in uncovering how strategic outsourcing can be the key to unlocking your startup's full potential. The Operational Odyssey of Parallel Bio Embarking on a Biotech Venture Under CEO Robert DiFazio's leadership, Parallel Bio embarked on an ambitious journey in the biotech industry. Their mission was to blend the complexities of organoids with artificial intelligence, aiming to pioneer groundbreaking immunotherapies. While their scientific vision was clear, the operational realities of running a startup soon cast a shadow over their aspirations. The transition from focusing on scientific research to grappling with the multifaceted demands of business operations is a common challenge for many startups. For a scientist like Robert, delving into the world of HR intricacies, legal compliance, and multi-state registrations was akin to navigating uncharted territory. These operational tasks, crucial for the smooth running of any business, began to consume an inordinate amount of time and resources, detracting from the core focus of scientific innovation. The Wisdom of Outsourcing Back-Office Operations Robert recognized the need to streamline these essential yet burdensome tasks, and Parallel Bio turned to the strategic solution of outsourcing their back-office operations. This decision was pivotal! Outsourcing allowed them to hand over complex, time-consuming tasks to seasoned experts. It was not just about finding a service provider but about forming a partnership that understood and aligned with their mission and values. In this context, Levy's role became instrumental. They offered more than just operational support; they provided a network of expertise that ensured no aspect of Parallel Bio's operations was overlooked. This meant that tasks from HR management to tax filings were handled with precision, allowing the Parallel Bio team to redirect their energies back to what they did best: finding the fastest way to produce better drugs that work for more people. Reclaiming Time: A Startup's Most Valuable Asset The most significant benefit of this was the extra time and energy that Parallel Bio was able to reclaim and redirect into their cor emission. For Robert and his team, this meant saving around 10 to 15 hours a week, a substantial amount that could now be invested in research, development, and strategic growth. This efficiency gain was about more than just offloading tasks; it was about streamlining operations to enhance overall productivity and focus. Parallel Bio's experience sheds light on a broader truth in the startup ecosystem: . By entrusting specialized tasks to expert hands, startups can alleviate the burden of back-office complexities and concentrate on their core objectives. operational efficiency is key to unlocking a startup's full potential Reflecting on the Strategic Choice of Outsourcing In retrospect, the decision to outsource back-office operations was not just a tactical move for Parallel Bio; it was a strategic choice that significantly impacted its growth trajectory. It allowed them to maintain their agility and innovation focus, which is crucial in the fast-paced, cut-throat biotech sector. Learning: A Lesson in Strategic Focus Parallel Bio's journey is a powerful lesson for startups: operational challenges, if not managed wisely, can impede growth. Outsourcing back-office tasks is a strategic decision that can free up valuable resources, allowing startups to remain focused on their primary goals and drive forward their mission with renewed vigor. ViralMoment's Transformation with Strategic Outsourcing What does ViralMoment do? ViralMoment, under the dynamic leadership of CEO Chelsea Hall, embarked on an ambitious journey in the digital realm. Their mission is to revolutionize how brands engage with audiences through social video, aiming to become a powerhouse in the entertainment industry. However, as ViralMoment began to gain traction and grow their company, operational challenges started to emerge, threatening to derail their creative momentum. The Operational Bottleneck Chelsea's passion was evident. She was deeply invested in nurturing her product and user base. Yet, as ViralMoment grew, the backend operations, crucial for any thriving business, became increasingly cumbersome. Tasks such as accounting and insurance, essential for the smooth functioning of the company, consumed an excessive amount of Chelsea's time and focus. This operational overload was not just a hurdle; it was a barrier to innovation and growth. As Chelsie recognized the need to streamline these operational complexities, ViralMoment turned to outsourcing as a strategic solution. This move was about more than just delegating tasks; it was about realigning the company's focus to its core strengths – creative content and market expansion. Outsourcing backend operations meant that these essential yet non-core activities could be managed by experts, allowing Chelsie and her team to concentrate on providing the best insights for social video. In this strategic shift, Levy played a crucial role. With their expertise in handling operational intricacies, Levy provided ViralMoment with the support it needed to overcome its backend challenges. Levy helped ViralMoment negotiate and purchase the correct business insurance and handled the onboarding of all of ViralMoment’s contractors, allowing them to grow at a faster pace without eating up any of the team’s precious time. This partnership resulted in significant time savings for Chelsie – approximately 8-10 hours a week. More importantly, it brought about a transformation in how ViralMoment operated, turning previous inefficiencies into streamlined processes. Beyond Time Savings: Gaining Operational Confidence The partnership with Levy did more than just free up time; it instilled a newfound sense of confidence within ViralMoment; they no longer had to waste time doing things they were not good at. As the company expanded, building a remote team and catering to a global clientele, the complexities of managing operations grew faster than imaginable. Levy's support in these areas meant Chelsie could face these challenges with assurance, knowing that the operational side of the business was in good hands, capable of scaling with them, and giving her peace of mind that everything was taken care of in the correct way. Chelsie's experience with Levy highlights a common trend among startups – the struggle with backend operations. Her advice to fellow founders is insightful: f This approach allows founders to channel their energies into growth and innovation while experts handle the complex and intricate backend machinery. ocus on your vision and delegate the operational tasks. Learnings: ViralMoment's Leap Forward For ViralMoment, partnering with Levy was more than an operational decision; it was a strategic move that catalyzed their growth. Chelsie's story is a testament to the transformative power of outsourcing in the startup world. It underscores the importance of focusing on core competencies and leveraging expert support for operational tasks, a strategy that can lead startups to new heights of success. Lava.xyz's Leap Forward with Levy What does Lava.xyz do? In the rapidly evolving cryptocurrency landscape, Lava.xyz emerged as a front-runner to simplify the self-custody of digital assets. At the helm was a CEO deeply immersed in the world of Bitcoin and stablecoins, committed to making Lava a pivotal player in the crypto space. However, the journey of innovation is seldom without its hurdles, and for Lava, these came in the form of operational challenges. As Lava.xyz grew, the CEO found himself increasingly entangled in the web of back-office operations. Managing these tasks single-handedly, from HR duties to IT management, began to take a toll, consuming valuable time that could otherwise be spent on product development and strategic planning. This operational overload was not just a drain on time; it was a diversion from Lava's core mission. Recognizing the need to streamline operations in order to focus on their primary objectives, Lava.xyz turned to outsourcing as a strategic solution. This decision was pivotal in reshaping the company's operational framework. Outsourcing allowed Lava to hand over the repetitive yet essential tasks to a team of specialists, freeing up the CEO and his team to concentrate on the heart of their business – cryptocurrency innovation that would change the world! Levy's Impact on Lava.xyz Levy's integration into Lava's operations marked a significant turning point. With Levy's expertise, the once cumbersome process of managing back-office tasks became streamlined and efficient. The CEO's weekly schedule, previously packed with administrative duties, was now significantly lighter, with just a short weekly call to synchronize tasks with Levy's team. This shift resulted in a drastic reduction in administrative duties, allowing the CEO to dedicate more time to product-related tasks and propel Lava's business forward. The CEO of Lava.xyz believed that the primary focus of any leader, especially in the tech industry, should be on product development. Levy's specialized knowledge in operational domains meant that Lava could excel in its area of expertise without being bogged down by the repetitive nature of back-office tasks. The Early-Stage Startup Dilemma In the early stages of a company, CEOs often find themselves juggling numerous responsibilities. For Lava.xyz, tasks like accounting and onboarding could consume up to 20-25 hours a week. Levy's intervention in handling these issues proved to be a game-changer, offering a solution that was more efficient than hiring in-house or part-time employees. For Lava.xyz, the thought of operating without Levy's services was daunting. The company had come to rely heavily on Levy for managing operational tasks, allowing the CEO and his team to focus on their core competencies. Levy's role was critical in ensuring that Lava.xyz could continue its growth trajectory without being hindered by operational complexities. Learnings: Lava.xyz's Operational Transformation Lava.xyz's partnership with Levy stands as a powerful example of how strategic outsourcing can revolutionize a startup's operations. By entrusting back-office tasks to Levy, Lava was able to reclaim time and focus, essential for navigating the competitive and fast-paced world of cryptocurrency. This story highlights the importance of recognizing when to delegate and the transformative impact it can have on a startup's journey. Conclusion The journeys of Parallel Bio, ViralMoment, and Lava.xyz collectively underscore a pivotal lesson for the startup ecosystem: the strategic importance of outsourcing back-office operations. Each of these startups, driven by innovative missions, encountered operational challenges that threatened to impede their growth and distract from their core objectives. However, the decision to partner with Levy marked a turning point, transforming these challenges into opportunities for accelerated growth, enhanced focus, and peace of mind that can only be achieved with the trust that everything is going to be done properly and on time, every time. The stories of these diverse startups reveal a common thread – the transformative power of strategic outsourcing. By entrusting operational tasks to Levy, these companies were able to reclaim valuable time, streamline processes, and refocus on their primary missions. The benefits extended beyond mere time savings; they encompassed enhanced operational efficiency, access to specialized expertise, and the ability to scale operations seamlessly. Levy's role in these transformations was not just as a service provider but as a strategic partner. The comprehensive approach to managing back-office tasks, coupled with their understanding of each startup's unique needs and goals, gained through years of experience, made them an indispensable asset. The partnership with Levy allowed these startups to navigate the complexities of growth without being bogged down by the intricacies of operational management. In short, the experiences of Parallel Bio, ViralMoment, and Lava.xyz highlight a crucial strategy for startups aiming to thrive in today's competitive landscape: the strategic delegation of back-office operations. This approach enables startups to maintain their agility, innovate continuously, and stay focused on their growth trajectories. These success stories demonstrate that smart outsourcing is not just about operational efficiency; it's about empowering startups to unlock their full potential and chart a course for sustained success.

Breaking Barriers: How 3 Startups Used Outsourcing to Fuel Their Growth Stories (With Numbers!)

ব্রেকিং ব্যারিয়ারস: কিভাবে 3টি স্টার্টআপ তাদের বৃদ্ধির গল্পে জ্বালানি দেওয়ার জন্য আউটসোর্সিং ব্যবহার করেছে (সংখ্যা সহ!)

Barrieren überwinden: Wie 3 Startups Outsourcing nutzten, um ihre Wachstumsgeschichten voranzutreiben (mit Zahlen!)

Rompiendo barreras: cómo tres empresas emergentes utilizaron la subcontratación para impulsar sus historias de crecimiento (¡con números!)

Briser les barrières : comment 3 startups ont utilisé l'externalisation pour alimenter leur croissance (avec des chiffres !)

बाधाओं को तोड़ना: कैसे 3 स्टार्टअप्स ने अपनी विकास गाथाओं को बढ़ावा देने के लिए आउटसोर्सिंग का उपयोग किया (संख्याओं के साथ!)

障壁の突破: 3 つのスタートアップがアウトソーシングを利用して成長ストーリーを加速させた方法 (数字とともに!)

장벽 깨기: 3개 스타트업이 아웃소싱을 통해 성장 스토리를 촉진한 방법(숫자 포함!)

Quebrando barreiras: como três startups usaram a terceirização para impulsionar suas histórias de crescimento (com números!)

Ломая барьеры: как три стартапа использовали аутсорсинг для стимулирования своего роста (с цифрами!)

Engelleri Aşmak: 3 Startup, Büyüme Hikayelerini Desteklemek İçin Dış Kaynak Kullanımından Nasıl Yararlandı (Rakamlarla!)

Phá vỡ các rào cản: Cách 3 công ty khởi nghiệp sử dụng dịch vụ thuê ngoài để thúc đẩy câu chuyện tăng trưởng của họ (Bằng những con số!)

打破障碍：3 家初创公司如何利用外包来推动其成长故事（用数字！）

When you go camping, plan a move, or try to make something more complicated than a cup of coffee, you need a checklist. When you try to convince the whole planet that they need to use strange tokens in strange wallets, you definitely need one. So today I want to talk about global adoption and the barriers we need to overcome going forward. crypto According to the , roughly 69% of adults (3.8 billion) had access to banking services in the covered year. Measuring the number of people who use crypto is a bit harder since one person can have thousands of wallets. However, even if we use the , that’s still only 320 million people—a long road ahead. World Bank’s Annual Report 2018 most optimistic data Infrastructure: The Foundation for Success To achieve global adoption, cryptocurrencies must address critical infrastructure concerns. These include decentralization, transaction processing speed (TPS), and network stability. A. Decentralization: Decentralization is a fundamental principle of cryptocurrencies. It eliminates the need for intermediaries and central authorities, making transactions transparent and trustless. Achieving higher levels of decentralization remains a goal for many cryptocurrencies. This would enhance security and trust in the system, as it would become less susceptible to control or manipulation by any single entity. Usually, the level of decentralization is measured by the Nakamoto Coefficient. Basically, it’s the lowest number of validators that would need to collude in order to censor the network. Currently, the most decentralized blockchain is Solana, with a Nakamoto Coefficient of 31. Avalanche and Ethereum have 27 and 25, respectively. B. TPS and Stability: Transaction processing speed and network stability are essential for cryptocurrencies to be used in everyday transactions. Bitcoin, while decentralized, has faced scalability challenges, which have resulted in high transaction fees and slower processing times during peak usage. To address this issue, cryptocurrencies must explore solutions like Layer 2 scaling solutions (e.g., the Lightning Network for Bitcoin), sharding (as proposed for Ethereum), or usage of POS networks, which allow for the processing of thousands of transactions per second, with fees of less than a cent. C. Examples of Blockchain Failures: Learning from past blockchain failures is critical. Instances like the shutdown of the BitConnect lending platform or the DAO hack on the Ethereum blockchain highlight the need for better security and risk management practices. Solana blockchain was down ten times since its inception. While it’s normal at the early stages, blockchains won’t be able to afford even 1 second of downtime when getting closer to global adoption. Imagine if everyone suddenly couldn’t transfer money. Millions won’t get that morning coffee, a cab to work, or an important business transfer. Compliance: Bridging the Regulatory Gap Crypto markets need robust regulatory frameworks to attract institutional and individual investors alike. A. Legislation Improvement: The lack of clear and consistent regulations has been a major barrier to global adoption. Governments and regulatory bodies should work collaboratively to craft comprehensive legislation that promotes innovation while safeguarding consumers. Regulatory clarity can help remove uncertainty and attract more institutional investments. Countries like Switzerland, South Korea, and Hong Kong, which have implemented crypto-friendly regulations, have witnessed significant growth in crypto-related businesses. B. Compliance Tools: The development of compliance tools is vital for both B2C and B2B transactions. KYC (Know Your Customer) and AML (Anti-Money Laundering) solutions should be user-friendly and readily accessible for crypto businesses and users. Additionally, smart contract auditing services play a crucial role in ensuring that crypto transactions are legally compliant and secure. We are sure that with the growing popularity of crypto, more players will enter the market of crypto compliance services. C. Crypto Taxes: Taxation in the crypto space remains a complex issue, often leading to confusion for users. Governments should provide clear and comprehensive guidance on how cryptocurrencies are taxed. Businesses should also create tools that make it easy for users to calculate and report their crypto gains and losses for tax purposes. Paying your fiat taxes is already hard as it is; we don’t need to make it harder. Ease of Use: Simplifying the Crypto Experience To promote global adoption, cryptocurrencies must become more accessible and user-friendly. A. User Interface (UI): The user interface of crypto platforms and wallets should be designed with user-friendliness in mind. The complexities of blockchain technology should be hidden behind intuitive and aesthetically pleasing interfaces. Projects like MetaMask and Coinbase have made significant strides in creating user-friendly interfaces that guide users through the process of buying, storing, and using cryptocurrencies. The goal is to make crypto transactions as straightforward as traditional online banking. B. Wallet Names Instead of Addresses: Simplifying wallet addresses with human-readable names can reduce the risk of errors in transactions. Ethereum's Ethereum Name Service (ENS) and similar initiatives have shown that this can significantly improve user experience. For example, instead of sending funds to a long and complex alphanumeric address, users can send funds to a simple, memorable name like "JohnsCryptoWallet.eth." C. Crypto ATMs: The deployment of crypto ATMs around the world makes it easier for people to buy and sell cryptocurrencies in a physical location. These ATMs provide a bridge between the digital and physical worlds, fostering familiarity and trust in digital assets. Users can deposit cash and receive cryptocurrency directly into their wallets, making the process more accessible to those who may not be tech-savvy or comfortable with online exchanges. Conclusion The potential for cryptocurrency adoption on a global scale is enormous, but several challenges must be addressed. Improving infrastructure, enhancing compliance, and simplifying the user experience are vital steps to reach this goal. With continued innovation, regulatory support, and an unwavering focus on user-friendliness, cryptocurrencies can become a crucial part of our financial ecosystem, changing the way we think about and use money. Global adoption is on the horizon, and the crypto market is steadily working towards it. As these challenges are met and surpassed, we can expect cryptocurrencies to continue their journey toward global adoption.

Breaking Barriers: The Global Crypto Adoption Checklist

Embarking on a revolutionary journey through the fast-evolving landscape of distributed ledger technology, we unveil a transformative perspective on scalability and interoperability. In this unique exploration, we introduce an innovative formal model poised to unravel the complexities of achieving atomic composability in Multi-Rollup Environments on , setting the stage for a new era of decentralized systems. Ethereum 🚀 Navigating the Blockchain Horizon In the dynamic realm of distributed ledger technology, Ethereum has been a trailblazer, addressing the formidable blockchain trilemma of scalability, security, and decentralization. Rollups emerge as key players, offering a solution to this trilemma. However, the promise of scalability through rollups raises concerns about fragmented composability. To bridge this gap, we present a one-of-a-kind formal model designed to ensure the seamless integration of decentralized applications (dApps) across multiple rollups while upholding atomic composability—a crucial factor for the future of decentralized systems. 🌐 Understanding Rollups and Composability: Crafting a Blockchain Symphony Rollups, the unsung heroes of Ethereum scaling, operate off-chain to boost network throughput without overwhelming the main Ethereum chain. Composability, the art of harmonious interaction between dApps and smart contracts, faces challenges as multiple rollups proliferate. Our formal model emerges as the solution, promising to unite these elements and guarantee atomic transactions across rollups. 🧩 The Formal Model Unveiled: A Symphony of Systems Our formal model is not just a fusion of established distributed system theories and cutting-edge cryptographic practices; it's a unique orchestration tailored for the nuances of decentralized ledgers within Ethereum's ecosystem. Dive into the intricacies of buffering, dependency management, concurrency control, and groundbreaking . This model addresses the multifaceted nature of blockchain ecosystems, ensuring transactional integrity and reliability in an unprecedented manner. zero-knowledge proofs 🛡️ Rollup Punitive Measures: Fortifying the Fabric of Atomic Composability Strengthening the robustness of our model, we introduce punitive measures, including a staking mechanism, monitoring and reporting, and a decentralized common pool (DCP). By staking tokens, rollups commit collateral monitored by third-party nodes. Misbehavior proofs and a decentralized common pool add layers of security, fostering trust and resilience in the multi-rollup environment. 🔄 Decentralized Common Pool (DCP): A Symphony of Security and Redundancy Continues Step into the realm of the Decentralized Common Pool (DCP), a decentralized masterpiece designed for security and redundancy. With a consensus mechanism, data redundancy, and cryptographic verification, the DCP ensures the integrity and authenticity of the transaction pool. However, the introduction of such measures comes with trade-offs, addressing challenges while fortifying the atomic composability model. 🔒 Atomic Composability & ZK-Proofs: An Unprecedented Cryptographic Ballet Enter the world of Zero-Knowledge Proofs (zk-proofs), where and zk-STARKs take center stage. Discover how these cryptographic gems validate transactions, verify dependencies, manage concurrency, and maintain efficiency with their compactness. The integration of zk-proofs into the model introduces cryptographic complexity but promises swift validation, reduced buffering, and enhanced privacy. zk-SNARKs 🔍 Incorporating ZK-Proofs: Navigating the Cryptographic Maze To seamlessly integrate zk-proofs into the model, rollups must be zk-proof compatible and capable of efficient proof generation and verification. Standardizing proofs related to atomic composability ensures smooth inter-rollup operations. From verification functions to privacy maintenance, explore the manifold applications of zk-proofs in fortifying the atomic composability model. 🚀 Application of the Formal Model: A Universal Symphony for Ethereum's Evolution As the Ethereum ecosystem evolves, the formal model transcends its initial purpose, extending its applicability to shared sequencers and other scaling solutions. Whether dealing with the centralized nature of sequencers or diverse architectures of rollups, the model consistently upholds the principles of atomic composability, fostering a cohesive, interoperable, and scalable Ethereum ecosystem. 🌐 Shared Sequencers: A Harmonious Blend of Centralization and Efficiency Shared sequencers, a rising star in the scaling solutions constellation, bring centralization to the transaction ordering mechanism without compromising main chain security. Explore the implications of our formal model on shared sequencers, where buffering, concurrency control, and zk-proof validation emerge as key players in ensuring atomic composability. 🔍 Alternative Solutions: A Comparative Odyssey Embark on a comparative analysis as we juxtapose shared sequencers with alternative scaling solutions like zk-rollups, optimistic rollups, and sidechains. Each solution, with its unique architecture, finds resonance within the comprehensive framework of our formal model. Uncover the strengths, potential drawbacks, and the universal applicability of atomic composability across diverse scaling landscapes. 🔒 Conclusion: Charting a Unique Path to a Universal Future In conclusion, our formal model stands as a unique beacon, offering a comprehensive guide to achieving atomic composability in the intricate web of Multi-Rollup Environments on Ethereum. From exhaustive definitions to detailed operations, robust dependency handling, flexibility, scalability, and security through verification, the model charts a unique roadmap for a universal future. As blockchain technology evolves, this model serves as a flexible blueprint, ready to adapt to new challenges and complexities, ensuring the seamless integration of decentralized applications and services into the ever-expanding Ethereum ecosystem. Welcome to the future, where atomic composability reigns supreme! 🚀🌐🔒 References E. Ben-Sasson, A. Chiesa, D. Genkin, E. Tromer, and M. Virza. Snarks for c: Verifying program executions succinctly and in zero knowledge. In Proceedings of the 33rd Annual Cryptology Conference, pages 90–108. Springer, 2013. V. Buterin. A next-generation smart contract and decentralized application platform. White paper, 2014. Available at . https://ethereum.org/en/whitepaper/ V. Buterin. Ethereum’s scalability and decentralization challenge, 2020. S. Micali. Atomic transactions in blockchain, 2016. Solidity Academy, Unlocking the Future of Ethereum with Zero-Knowledge Rollups (ZK-Rollups) <https://link.medium.com/AjD0OcrfZEb > F. Schär. Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 2020. H. Williamson. Optimistic rollups: Layer 2 scaling. Blog post, 2020. Available at . https://optimism.io/

Breaking Barriers: A Pioneering Approach to Seamless Multi-Rollup Environments on Ethereum

Major game publishers like , , and have entered the blockchain gaming space, signaling real utility for the tech industry in this brutal consumer market. But why haven’t we seen developers take the plunge? Why, despite validation from triple-A firms, does onchain gaming remain a niche? Square Enix Nexon Ubisoft The root of the issue: consensus is and – two deal-breaking factors for consumer tech adoption. Realistically, leveraging blockchain for registering lightning-fast player reactions remains impractical, especially for real-time genres like FPS. Most blockchain games eventually pivot to utilize the technology selectively in the stack, primarily for managing the distribution and ownership of in-game items, skins, and currency. slow expensive In this article, we look at the limits faced by pure on-chain games and how offers a solution. We also share an example implementation powered by MEM and the NEAR blockchain. MEM Distinguishing between on-chain and blockchain games Onchain gaming operates exclusively on the blockchain, excluding the game's frontend. Here, the game logic is embedded within smart contracts, and both the game's state and data storage resides on the blockchain (onchain). Conversely, blockchain games are essentially web2 games (centralized server-hosted games) that integrate specific blockchain technology, often for trading cards or tokenizing game assets as NFTs. In a blockchain game, the logic, data storage, and state are housed off-chain on centralized servers. The advantages of on-chain gaming While developing fully on-chain games poses several challenges, the on-chain gaming ecosystem presents numerous advantages that closely resonate with the benefits of blockchains and smart contracts. : Actions and transactions recorded on the blockchain are transparent and immutable, building trust and preventing fraud. Transparent & immutable actions Players truly own in-game assets as non-fungible tokens (NFTs) on the blockchain, allowing secure transfers and verifying their scarcity. Ownership of digital assets: Reduces reliance on centralized servers, distributing control among players and fostering a more decentralized environment. Decentralization: Players can earn tokens within the game and sell them elsewhere, opening avenues for earning through gameplay, trading, and content creation. Monetization possibilities: The transparency of blockchains makes cheating more difficult, as actions are publicly recorded and immutable. Anti-cheating measures: In the on-chain gaming realm, game logic can be reused to birth new game varieties, craft diverse visual interfaces, and develop applications for smoother, more accessible, and entertaining gameplay experiences. Composability: on-chain games typically require code-based interactions, so tech-savvy players and developers can link game logic represented in smart contracts to various frontends, enabling diverse aesthetic representations. This allows for multiple user interfaces within a single on-chain game, where two players could experience the same game differently (e.g., one in a medieval world and the other in a space-themed setting). Support for multiple clients: Navigating the challenges of EVM based on-chain gaming Onchain games face strict technical limitations and burdensome user experiences, which serve as primary deterrents for both developers and players. Here are some challenges: : Blockchain networks often struggle with scalability, causing issues with transaction speed and volume, which can hinder the smooth operation of complex games and in return result in poor UX. Scalability The technical nature of interacting with on-chain games via wallets or code commands, or requiring the players to have a wide understanding of web3 can be off-putting for less tech-savvy players, impacting user adoption and retention. User experience: Transaction fees and confirmation times on certain blockchains can be costly and relatively slow, affecting the affordability and real-time nature of gameplay. Cost and speed: Storing large amounts of game-related data on the blockchain can be expensive and impractical due to limitations in storage capacity and costs. And because of that, the game’s developers opt for cheaper data storage solutions that are off chain and centralized. Data storage: Solidity, the primary language for EVM smart contracts, requires gaming developers to learn a new skill set. Its unique syntax and structure can be challenging, especially for traditional game developers accustomed to different programming languages. Specialized programming languages: Leveraging MEM for on-chain gaming MEM is a decentralized serverless functions network based on the Verifiable Atomic Computing Paradigm – chain-agnostic, highly scalable, and with near-instant finality. It executes functions written in various programming languages within a sandboxed environment. web3 Revisiting the challenges faced in on-chain gaming, MEM emerges as a possible solution to lift the UX and computational limits of the EVM while maintaining transparency and interoperability. MEM facilitates the development of gasless, chain-agnostic, highly scalable games with millisecond latency. Instead of embedding their game's core logic in an EVM smart contract and relying on EVM L1/L2 for data storage and storing the game state, game developers can opt for MEM. Using their preferred programming language (such as or TypeScript), they can construct the same game, leveraging MEM's integration with the Arweave network to maintain a permanent state. This integration, facilitated by MEM, not only ensures enhanced scalability but also delivers a smoother Developer Experience (DX) and User Experience (UX) within the gaming landscape. JavaScript At the time of writing, MEM handles the expenses tied to storing serverless function states' data on Arweave. Consequently, within its ecosystem, MEM provides free permanent data storage for these functions. Example: YoctoManji YoctoManji is a fusion of classic board games like Monopoly with the spirit of Jumanji and the aesthetics of Minecraft. The twist: landing on special plots marked by black gems triggers unique rules the user must take. Whether it involves paying fees to the plot owner or engaging in transactions on decentralized applications, each gem-bound rule triggers new challenges to complete. The game's contract orchestrates player movements, and turns, and verifies on-chain plot rules. Failure to comply with a plot's rule incurs a cryptocurrency penalty for the player. This gaming prototype showcases 's role in managing the core game logic, verifying plot actions, and handling data and state storage. Additionally, it leverages for financial features, including fee payments, entry fees, and executing plot-related rules on the NEAR blockchain. MEM NEAR While this MVP presently relies on NEAR for its financial operations, the deterministic feature offered by MEM introduces versatility. This feature allows for the utilization of any network, be it Ethereum, , Arweave, or others, for financial processing. Optimism The game’s serverless function backend can be found here: https://github.com/decentldotland/yman/tree/main/contract And the game’s frontend here: https://github.com/decentldotland/yman/tree/main/src/pages This board game operates in a completely serverless, on-chain, and gasless environment, ensuring near-instant finality while delivering a user experience reminiscent of Web2 applications. In conclusion, on-chain and blockchain gaming represent an emerging sector with vast untapped potential and a significant market share. While blockchain technology offers unique benefits such as censorship-resistant finance, complete asset ownership, and inherent tamper-proof characteristics, these advantages come with trade-offs. Technical scalability limitations, technological constraints, suboptimal user experiences, and higher operational costs often deter users and developers from further investment in this domain. The Molecular Execution Machine (MEM) presents a range of features poised to empower the infrastructure of Web3 gaming, whether on-chain or blockchain-based. MEM's serverless functions offer a means to reduce operational costs, enhance game scalability, and address several technical limitations prevalent in the gaming sector. EVM Are you constantly hitting bottlenecks in your game development?? Join the community and let us show you how MEM can save you development time and infrastructure costs! discord.gg/decentland decent.land

How MEM Lifts the Adoption Barrier for On-Chain Gaming

মেম কীভাবে অনচেইন গেমিংয়ের জন্য দত্তক নেওয়ার বাধা তুলে নেয়

Wie Mem die Akzeptanzbarriere für On-Chain-Gaming überwindet

Cómo Mem elimina la barrera de adopción de los juegos en cadena

Comment Mem lève la barrière d'adoption pour les jeux en chaîne

मेम ऑनचेन गेमिंग के लिए एडॉप्शन बैरियर को कैसे हटाती है

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What's the most vital part of web3? It's not 3D or Virtual Reality, or Augmented Reality. All these things are already in web2. is about the next step of democracy on the internet. Web3 In order to step up democracy, web3 should address being accessible for everyone in ways that will foster diversity, equity, and inclusion, such that everyone's voice can be heard. Not everyone can use a VR. Some people are blind, some people can get motion sickness, some people have low vision, color blindness, etc. Many people can't afford it. The core of the inclusivity issues is accessibility. Once accessibility is gotten right, it gets easier to build a future where barriers dissolve, diversity flourishes, and everyone–regardless of ability or background, finds a meaningful place in the digital narrative. Here are some things we need to fix to be more accessible to people: Access to Technology: According to the , people still pay more than 30% of their monthly income to purchase the cheapest smartphone available in their markets and a magnitude of people can't even afford it at all (they're hopeless). Alliance for Affordable Internet 2.5 billion You can't compare the price of the cheapest smartphones to VR headsets. Web3 should be accessible on different lower-end platforms for people who can't afford headsets till they get cheaper. Web3 builders should build solutions that keep compatibility and optimization in mind for lower-end smartphones and require less processing capabilities and less bandwidth. For me, I feel like rich people keep getting better technology and leaving the poor behind. It's a pressing concern in the digital age. As technology surges forward, there's a risk of widening the gap between the privileged and the underserved. The swift pace of technological progress can unintentionally widen this divide. This goes beyond personal convenience; it affects education, employment, and even civic engagement. There's a critical need for efforts to ensure that serves as a force for inclusivity in order to bridge the gap. web3 technology Only through collective action can we hope to build a world where technology uplifts all, regardless of economic status. Please help share this article when you're done reading--to improve visibility and foster collective action. Age (Ex. KYC4KIDS): Yes, age is often taken for granted. Just like other differences/disabilities, you may not notice if you don't come across people in the category. For example, many Web3 projects require users to upload a picture of a government-issued ID for identity verification purposes and a photo of the user themselves. It is referred to as KYC (Know Your Customer); this process is meant to stop fraud and is based on . banking laws established in 2001 As a minor myself, I can't really participate in many web3 projects because of age restrictions in the KYC (Know Your Customer) process. I was telling my friend about this and how I couldn't take part in the action, and he told me to wait till I was 18. I told him to go away. I did some thinking and realized that it was possible to conduct KYC for minors, and I asked, “Why don't these projects support it?” I built a basic demo as Proof of Concept to conduct parents/guardians supervised KYC for minors, with seamless, secure, and awesome verification powered by metamaps. I call it KYC4KIDS. Now, I'm no legal expert, but I'm hoping web3 projects everywhere could offer inclusivity for kids like me!
I'm hoping these projects can include children in their roadmaps/whitepapers and make us part of the plan.
I hope they can support minors in their in-house KYC, and I hope more KYC services will jump on this bandwagon. Let's make it happen; help get and relay the message out there so we can have #KYC4Kids. Children need to be included in web3. They say children are the future, and web3 is the future, but why are children less inclusive in web3? For all it's worth, web3 should be all about children. Fun fact: people talk about their various use cases and argue who started web3 and for what, but the truth is that Web3 was started by children: children and older gamers were the ones who opened this up with gaming, film, and animations. So don't exclude kids when you're thinking, talking, or building web3. KYC (Know Your Customer): Still on the KYC issue, while the process involved in KYC is still tedious for those with an ID, more than 3 million people in the US alone lack a government-issued ID ( ). Talk less of the rest of the world, and these KYC systems don't even support all countries. NPR The most common form of government ID is a driver's license. How many people drive today? Old people, the poor, some in big cities, and even people in rural areas don't drive. Many old people around the world don't have birth certificates, and some of their names were incorrectly typed on the documents. Which if there's a discrepancy between your birth certificate and other forms of ID, you won't be able to get KYCed or some other IDs. While there are certainly ways around these obstacles, each problem that arises will dissuade more people from accessing and taking advantage of Web3. Onboarding Process: Just think about it: you want to use a web3 site, you're asked to connect your wallet, you try to figure out what it means, and if you're really smart, you understand it is similar to "Sign up with Google.” Okay, you find a wallet app, you download it, and install it. Now you have to sign up for the wallet; you go through the email verification, phone number verification, and set a strong password and everything. Then they give you a security phrase of 12 random words and tell you it's very important for your wallet security, but you can't put it online so that it won't be hacked; writing on paper is a great choice, but you have to make sure you don't lose the paper. You do that, and some of the projects will say that you need to have some balance in the wallet to proceed. You go through all the stress of figuring out how to top up your unreadable wallet address that you can't get wrong and must be sure that it's the right token/coin on the right blockchain. Then they tell you to do KYC, and you go through the difficulties I mentioned about KYC. I don't know about you, but I can barely type this short summary of the process. Imagine doing all these for  something you can easily do elsewhere or for an app you'll probably use only once There are impressive solutions addressing the UX issues, like now you can use simpler names like vitalik.eth to make crypto transactions, and there are features to keep your 12 random words on the cloud and similar things. However, the UX issues are still massive and need more action. Thankfully, incredible solutions are being built, like , for example. Sendtokens Enter Sendtokens, a non-custodial decentralized wallet designed with inclusivity at its core. The platform reimagines how we interact with cryptocurrencies, removing the hurdles of intricate alphanumeric keys. Instead, users can seamlessly send and receive digital assets using familiar emails and phone numbers. With features like Cryptopins and a commitment to scalability, Sendtokens sets a new standard for user-friendly transactions. Its compatibility with a wide range of tokens further cements its position as a trailblazer in the Web3 landscape, offering a bridge to a more accessible and inclusive digital future. Check out Sendtokens Cognitive Overload: You need a high level of expertise to feel comfortable in Web3. The learning curve is very steep, the technology is cumbersome, things are always changing, and mistakes can be very expensive. Many of the terms used are unnecessarily confusing or abstract when they don’t need to be, and using them can stop people from trying Web3. Using terms like "HODL" and "BUIDL" has become a distinctive part of the cryptocurrency culture, reflecting a sense of community and shared enthusiasm. However, it's crucial to consider the audience and context. While these terms may resonate within the crypto community, they can be confusing or alienating to those unfamiliar with them. Striking a balance between industry-specific jargon and accessible language is key. When addressing a broader audience, opting for standard spelling and providing explanations can enhance clarity. There are more reasonable terms that are still confusing, like smart contracts, gas fees, etc. People won't understand what they mean easily unless they understand the core of how smart contracts work or how gas fees work. This is unnecessary because millions of people use web2 every day without knowing the meaning of HTTPS. But if you referred to gas fees as transaction fees instead, it's easier to understand that way. I found the following table in about how we do a bad job of this article communicating web3 It's sad, but it's true. I agree with the author when he said: "Simpler, more inviting language is an urgent first step, but it’s not enough on its own. To be impactful, the words of Web3 must be genuine, too. Less about our awesome tech and more about the user. Let’s show people we really care about solving their problems. Capturing curiosity is the key to welcoming the mainstream. Disabilities: According to the , over 1 billion people in the world have some sort of disability. So, the importance of inclusivity for these people cannot be overemphasized. World Health Organization As a web3 company, you should be conscious of being accessible to diverse individuals and kinds of people. The truth about your target audience and market segments is they're not as similar as you think. I read on Forbes that companies that are more inclusive have more ROI. In just the United States alone, 1 in 4 Americans have a disability and totally spend over $490 billion annually. The disability community encompasses a $1.2 trillion market. Businesses need to be informed that investing in diversity, inclusivity, and equity is not only about compliance but a smart business opportunity and the right thing to do. Authenticity: One very prominent issue is that people aren't what they seem like online in Web2; they only present the most ideal versions of themselves. (formerly Facebook) outline that Instagram is toxic to teenage girls. Even internal documents in Meta I salute projects that do an awesome job helping people to get authentic avatars in the metaverse, authentic NFTs (like trumiinfts), and the like. I asked chatGPT about how to promote Authenticity, and it mentioned things like raising awareness, setting examples, etc. I strongly agree that these are vital steps to driving a more authentic environment to foster inclusivity, but the thing about promoting authenticity is; the people have to want to be authentic. Now, it's difficult trying to convince people to be authentic, given what social media looks like now and statistics on mental health. If we're doing anything at all to convince them, I don't think it's working. On the other hand, in web3–would you want to be inauthentic when you're anonymous? The evolution of technology, driven by decentralization and blockchain, is revolutionizing the way we interact with the digital realm. From static profile pictures to three-dimensional avatars, we've witnessed a remarkable progression in how we present ourselves online. Web3, as the third generation of the internet, marks a monumental shift. It liberates us from the constraints of flat images and gaming environments, allowing us to fully embody three-dimensional avatars in a virtual world. This agency promises an unprecedented level of self-expression and immersion. Through avatars, we have the canvas to represent ourselves as anything we like. Authenticity takes center stage, prompting us to explore facets of our identity that may have remained hidden otherwise. Digital cosplay, for instance, offers a genuine glimpse into our inner selves. Avatars cease to be mere representations; they become windows into our passions, beliefs, and affiliations. Whether it's enthusiasts adopting on-brand avatars or individuals proudly displaying their interests on social media, avatars serve as a powerful tool for personal expression. We just need to keep directing it to encourage authenticity. Data Ownership & Ads Yet, this newfound freedom comes with a critical consideration: data privacy. to reclaim ownership of their personal data, challenging the traditional model where corporations harvest data for advertising. Web3 empowers individuals The emergence of "data unions" ensures individuals retain control, paving the way for a future where fair compensation for data becomes the norm. For brands, Web3 presents a unique opportunity for unfiltered customer insight. Platforms like Discord and Reddit, where users engage with anonymity, offer a window into authentic perspectives. This opens doors for more genuine customer engagement, allowing brands to better understand their audience. In essence, Web3 has the potential to revolutionize how brands connect with their audience, fostering a more authentic and mutually beneficial relationship. This is what's being built at Slakenet, plus the platform rewards people with internet access to make it possible to fund your internet by using it. Marginalized Groups: According to a report by in December 2021, prices for digital avatars from CryptoPunks NFTs fluctuate based on race, gender, and skin color. Mid- and dark-skinned avatars were priced lower on average than lighter-skinned NFTs. Bloomberg Bloomberg also cited data from DeGenData (a CryptoPunks sales tracker), which showed lower average prices for female Punks compared to male punks. Even on the developer side of things, A of 82,286 global software developers revealed that 91.7% of all respondents were men. 2021 Statista survey There are lots of projects out there solving these challenges, and I'm glad about the progress so far. It's also important to keep raising awareness and advocating for inclusion and equity for marginalized groups, including (but not limited to) women, racial and ethnic minorities, LGBTQIA community members, and those from economically disadvantaged backgrounds. Conclusion: In the horizon of Web3, inclusivity is not just a principle; it's a powerful force driving digital empowerment. Inclusivity is not a feature but a fundamental right. Through Web3, we have the opportunity to dismantle barriers, amplify voices, and champion a future where everyone is seen, heard, and valued. Together, let us ensure that the promise of Web3 is not just realized but celebrated as a testament to the boundless potential of human connection in the digital age. You can share some other inclusivity issues and raise awareness. I would love to hear your thoughts on the matter

Web3 Inclusivity: Replacing Digital Barriers With Bridges

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

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

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