weCreate

wecreate

This is not the first time I have had this idea. But after watching a documentary on how bad the financial system currently is, I figured I have to share what has been revealed to me. When the thought first came to me, I assumed it was special. But I didn't do anything with it. Now, I think it is time to share. Why Gold Standard Won't Work Anymore Before sharing my idea, I want to explore the options I have heard thus far and why they won't work. First up is gold. I still heard recently that the USA needs to go back to the gold standard. But there is a big problem with that. How will the transition take place? The amount of money in the world already trumps the amount of gold in the world by a lot. Don't be deceived by the GDP numbers, the money circulating the globe is in multiples of that. Banks are not going to sit there while they watch governments kill their business by going back to the gold standard. The gold standard means that banks can't do fractional reserve banking and all the other things they've been doing. And recently (since 2020), they don't even need a reserve ratio anymore. They are dealing in trillions of dollars with the ability to create more at will. To attempt to take the US dollar back to gold is a suicide mission. Anyone in government who seriously embarks on that will likely be eliminated in some way. (Yes, it comes to that at times). Another option is a basket of commodities. This is another problem too. Those commodities flow through trades facilitated by big banks. So, it will just be playing into the hands of big banks again. It may take some time, but we will end up with the same problem if a basket of commodities is used. Moreover, that comes with complexity and it will be a hard sell to everyday people. Also, the big banks love complexity. That is how they keep prying eyes out of their business. The more complex it is for the average person, the better it is for big banks. Nothing will change. Another option is energy. The problem is that you already have a cartel with crude oil. Power will just change hands. The manipulators will change from bankers to oil producers. This may be slightly better because those are producing something useful to society (unlike banking which is just a paper service). To use renewable energy is dead on arrival. First, renewables cannot power a significant portion of the planet. And it is expensive. It would weaken the general public and money control would still be concentrated in the hands of the few. It can't be the Chinese yuan, Russian ruble, British pound or any other country's currency. And this is for the same reason the US dollar is not doing well as the global reserve. When one entity owns and controls the global reserve, they enrich themselves and suffocate all others. Now, you are thinking maybe it is bitcoin. And bitcoin looks like the salvation for a while. But it has an intricate problem. It requires acceptability and its value is still in reference to the US dollar. In other words, any currency that can be traded by the financial markets MUST NOT be the global currency if we want to have prosperity with fairness. Qualities of the International Currency Standard There are 7 qualities that a currency that the world can adopt as the new standard must have: It must not be tradable on the financial markets It must be verifiable (by anyone independent of the big banks, corporations, and countries) It must be directly connected with something that everybody needs and uses. A good connection will be to energy It must be unprintable. Meaning that you cannot create more of it from thin air All currencies can be measured in reference to it on a fair basis The value of a country's currency to it must reflect the well-being of that country Its system of operation must be unhackable such that independent people can validate or invalidate The New Standard This idea came to me while thinking about the meter. How did we define the meter? And how has it stayed that way and remained unchanged? Well, the idea is that you can't print meters. You can't simply create a program to create more meters. And just like that, the idea flashed into my mind… What if we had a currency that can't be spent? The big disadvantage of bitcoin was that you could spend it. And prices were still standardized in the US dollar (and in the currencies of other countries). So, you had to reference bitcoin to other currencies in the financial markets. Hence, the volatility swings. And the volatility swings make it improper to be the new global standard. So, to eliminate the volatility swings, why not have a currency that can't be spent? A currency whose unit is fixed. Just like the meter, it remains what it is. You can't buy it. You can't spend it. You can't save it. It is just a unit of money. And every day, the currencies of the world are measured to that unit. And business is conducted based on that arrangement. So, let's call this currency - Kash What Is Kash? . It is pegged to energy. It cannot be bought, sold, or shorted. It is not printable. No country or bank has a monopoly over it. Anybody can verify or validate it. Kash is a unit of money designed to be the global standard in the modern era 1 Kash is the monetary amount of energy it takes to melt 5 grams of silver under very specific conditions with a precise instrument. This means that if (in the USA) it costs $25 to melt 5 grams of silver under these precise conditions, then 25 usd = 1 Kash. The day it becomes cheaper (let's say $15) to melt 5 grams of silver under those same conditions, 15 usd = 1 Kash. The Kash never changes. The currencies fluctuate with respect to the Kash. So, every day, these experiments are carried out by private individuals and organizations across the globe for each country. And that will determine the of the country's currency to Kash. For example, in the UK, you do the same experiment and discover 1 Kash to be £13. That is what it is. If the energy situation gets bad and the cost goes up to £23. That is what 1 Kash becomes. 1 Kash will always be 1 Kash. And that is the cost of energy required to melt 5 grams of silver under very specific conditions. Now, you may be thinking how that will work for a whole country. In America for example, the value of 1 Kash will be determined in each state. Then the average becomes the value of the US dollar. As the system integrates further, each town and city will be able to measure and contribute to make the average for the state. Then, all 50 states will be calculated to make the average that will be accepted as the US dollar value for Kash. This experiment can be verified by anybody. The specific conditions for the experiment will be very simple and require tools that regular people have access to. Independent volunteer validators across the world can verify the value of their currency to Kash at least once per week. As adoption grows, people can label their goods and services in Kash. So, when people want to pay for it, they check the value of their currency in Kash and pay the equivalent value in the accepted currency. You cannot use Kash to buy or sell anything directly. I know the financial markets will create derivatives and ETFs for Kash, but only fools will buy them. They will only be investment products and the people who buy them will know that they are buying something that doesn't exist. This will be a good thing for bitcoin. This is because bitcoin has a direct relationship with energy. The value of bitcoin can be directly correlated to Kash. Hence, people can trade with bitcoin while the unit of their monetary value is measured in Kash. Kash becomes the stablecoin for bitcoin. And we render the dollar dominance useless. So, Kash, the currency that doesn't exist can save the world. Don't worry about the big banks, governments, etc. They will sort themselves out and find ways to survive. There. I just solved the biggest problem in global finance and modern economics. I hope I get a Nobel prize lol. The Specific Conditions This has to do with the process and tools used to melt this 5g of silver. And at what state of melting is deemed sufficient? This has to be fleshed out in an economic paper. Unfortunately, it is not something I can go into detail with here. I hope I can find an economics professor that we can work on this together quietly. And then launch to the world as a massive shock to the financial world. This is just a rough idea. And this is how we break away from the tyrannical rule of central banking. Many people are already hovering around this idea. So, it is something that is bound to happen one way or another. I am just pleased that I have been chosen as a vessel to communicate this. If you have the resources to make this into a proper economic paper and perhaps carry some (freedom-loving) crypto organizations along, I would be happy to stretch this out. The Aftermath Without a doubt, this will end the dollar dominance and automatically solve many economic problems. Yes, I am aware it will create new problems too. But the problems that will be created will be insignificant to the ones we have now. Just imagine BRICS adopts the Kash system. And let's assume other some countries do too. That will fix a lot in international trade. Governments will not be able to manipulate their own currency to mess with other countries. And there will be a lot of validator organizations. Since the value of Kash is determined by a simple process that anyone can check, there will be lots of organizations who can have volunteers who display the value of Kash every day. Some can track Kash to bitcoin. Some can track Kash in specific countries to their respective currencies. Some can track Kash in Asian countries. Some can track Kash in European nations. And so on like that. So, as an example, Binance can have their own system of validators that track Kash. The IMF can have theirs. A Christian organization can create a network with theirs. People, businesses, and individual sellers can then choose which validator system they trust and will be using. Validators must be volunteers. If a validator system is discovered to be fraudulent, people can see what other validators are saying and quickly switch away from the liars. And the liars will find it incredibly hard to build back up their damaged reputation. Governments can have their validation system, but of course, no one (in their right minds) would trust it. There will be an unspoken allowance percentage. For example, if 3 validator system in the USA says $25.07, $25.12, $25.16 equals 1 Kash. And then one validation system says $35.89 is Kash, you know there is a problem. That is the sign that someone is lying or manipulating something. Whoever is caught lying will have to deal with the loss of reputation that follows. No one can stop them from publishing the value of Kash, only that nobody will use their numbers anymore to calculate anything. If two parties are about to negotiate a transaction in Kash, it is important that they identify which validator system they will be using on the contract. To be safe on very large transactions, they can choose to use the average of 3 trustworthy validators. You and a group of friends can start your own validator entity for any country and publish online. At least, once a week, the reading for each city (or state) is taken. This is then computed to make the average for the country. The experiment and reading is taken from an household unit (not industrial unit). There is no specific date or time when a volunteer is to take the reading. If any validator system is discovered to be mandating their volunteers to take readings at a particular day and time, they get exposed online. The readings must be taken once a week, but day and time is left to the discretion of each volunteer. The people living in a country will know the most accurate Kash validation network. Since it works on a volunteer system, if people don't know anyone around who is a volunteer validator for a network and they try to find but can't seem to meet any one in real life like that, then you can be sure that validation network is a fraud. I recommend big organizations like OPEC, BRICS, IMF, JP Morgan, Goldman Sachs, not to have their own validation network. However, they can have 3-4 validator network that they trust. This is because if their have their own network, 95% of people won't trust them (to use them) anyway. For the first time in the history of mankind, let us have honest money manned by the people. My job is to plant the idea in as many heads as possible until this picks up real momentum. I hope to work with an economics professor to write a paper on it (and hopefully win that Nobel prize). But if it is done without me, I am happy regardless. So, the seed has been sown. Let's have a stable currency as a global standard that is not printable. Let’s change the world. Dear internet, do your thing. Also published . here

Can We Create a New International Currency to Serve as the Global Standard?

আমরা কি গ্লোবাল স্ট্যান্ডার্ড হিসাবে পরিবেশন করার জন্য একটি নতুন আন্তর্জাতিক মুদ্রা তৈরি করতে পারি?

Können wir eine neue internationale Währung schaffen, die als globaler Standard dient?

¿Podemos crear una nueva moneda internacional que sirva como estándar global?

Pouvons-nous créer une nouvelle monnaie internationale qui servirait de norme mondiale ?

क्या हम वैश्विक मानक के रूप में काम करने के लिए एक नई अंतर्राष्ट्रीय मुद्रा बना सकते हैं?

世界標準となる新たな国際通貨を創設できるか?

글로벌 표준이 될 새로운 국제 통화를 만들 수 있을까?

Podemos criar uma nova moeda internacional para servir como padrão global?

Можем ли мы создать новую международную валюту, которая станет глобальным стандартом?

Küresel Standart Olarak Hizmet Verecek Yeni Bir Uluslararası Para Birimi Yaratabilir miyiz?

Chúng ta có thể tạo ra một loại tiền tệ quốc tế mới để làm tiêu chuẩn toàn cầu không?

我们能否创造一种新的国际货币作为全球标准？

The concept of ideas being trumped by execution in the startup world is so prevalent that there is no single adage to capture it. It is such a common idea that I could have opened with quotes from any one of: , , , Goethe, (Pony), Luckey - this eclectic list could go on indefinitely. Jobs Stravinsky Neistat Ma While the sentiment of all these great thinkers is entirely correct, it also remains true that for anything to be executed on top of. ideas are a prerequisite In an age where increasingly intelligent machines are reducing the cost and pace of execution at a rate of knots, the demand for big ideas to solve both objective (i.e scientific) and subjective (i.e humanistic) functions in our world becomes ever more apparent. In 2018, USV’s Nick Grossman and Dani Grant wrote a now canonical article titled This article broke down the cycles by which new apps tend to appear en masse more or less right off the back of the development of the infrastructural foundations necessary for those apps to exist. ‘The Myth of the Infrastructure Phase’. To use their words: “First, apps inspire infrastructure. Then that infrastructure enables new apps.” We live at a critical juncture in human history where our species is fast becoming the second best of ideas. For this reason, it is worth taking a step back from Grossman and Grant’s theory of the infrastructure stack and stripping it all the way back to ‘ideafrastructure’. producers Where the infrastructure-app cycle visualises incremental improvements in technology over time, the ideafrastructure cycle appears incremental but in practice delivers complete transformations in human output and wellbeing as each new inflection point is reached. The reason that the ideafrastructure cycle can appear incremental as in the diagram above is because of the . New idea generation is by-and-large a function of the cross-pollination of two or more pre-existing ideas. composability of ideas In reality, composability =! incrementalism. The product of two cross-pollinating ideas in our case can have entirely transformative impacts on productive capacity. As our powers of execution expand, the ambitiousness and size of the ideas needed to level up collective surplus grows to fill these new capabilities and push them further. This is a weird kind of intellectual . As we improve the efficiency by which we create new things to improve our lives, our satisfaction seems to diminish. More creative and ambitious ways to increase our expected wellbeing pop up to fill the new capabilities enabled by new technology. Jevon’s Paradox Once the capacity of the latest generation of productive assets to improve wellbeing has been exhausted, a new generation of idea takes its place and inspires new productive assets altogether (or vice versa). Intentionally or not, the curves in USV’s infrastructure framework provide an interesting way of observing this. Ideas lay the groundwork for foundational changes in innovation cycles by enhancing our productive possibilities. In turn, improved production possibilities expand the ambition and scope of our ideas to drive even more advances in production and wellbeing. Now that I have had the chance to try and explain , the practical question: how do we make more? why we want/need more ideas What constitutes an Idea? Many people have tried asserting different answers to the above question. Some of them are more assertive than others (ironically, the most assertive ideas about ideas are from some of history’s most prominent idea-benders - the ). ancient philosophers For the sake of this piece, I will follow two similar definitions that I think best fit the context of ideas as they apply to entrepreneurship. Those explanations are as follows: “An idea is a or used to or something.” concept, thought representation describe explain “An idea involves the in a novel or innovative way, leading to new insights or perspectives. synthesis of existing knowledge or concepts It can be a , a , a for a product or system, a philosophical , an artistic , or a for the future.” solution to a problem hypothesis to be tested design concept expression vision The Market for Ideas Today The would-be founder in the market for ideas is fortunate and unfortunate in equal measure. Fortunate, because they have the pleasure of competing against some of the most rigid, unprogressive institutions on the face of the earth. Unfortunately, these institutions hold significant power in the public domain and an entrenched position in the marketplace of ideas. If having a marketplace for ideas dominated by a small number of institutions sounds paradoxical, that’s because it is. IBISWorld values the market for scientific research & development (both academic and institutional) at $227.5bn annually. This covers the functions of research & discovery, peer review & validation, IP transfers, contributions to policy formation and much more. Because of the foundational role that research plays in any market (being the basis for technological development and all), its disproportionately large problems have disproportionately large flow-on effects. To name but a few: Multibillion dollar institutions like Elsevier, Harvard, thrive on a price-making ability driven through manufactured scarcity. As mentioned above, ideas are composable. By close-sourcing this knowledge, the reputed gatekeepers of today’s knowledge are kneecapping the potential growth in production possibilities cited above. Inaccessibility. The incentives for academics are largely geared such that they are remembered for their discoveries. This creates a bias for publishing positive results. Often, these positive results are actually not so positive. But who has time for verifying these when you need to be publishing your own positive results? Incentives are needed to ensure that 1) research is validated and 2) academics are rewarded for producing productive research. Publication Bias and the Replication Crisis. actually To be an expert in any field requires niching down to an extreme degree. The corpus of existing research has been so large for a long enough time that in order to ‘make it’ in academia requires devoting one’s self to a field and taking the time to learn that field inside out. Siloes. First, the time is spent learning informational precedents inside out. Only after the decades that this step takes can researchers strive to create original knowledge. By the time they’re creating this original knowledge, their existing knowledge base is often so esoteric that it is hard to apply at scale to real-world problems that tend to have problem drivers from multiple disciplines. This creates a culture of academic introspection and incrementalism. More mechanisms need to be in place to break up interdisciplinary boundaries and incentivise cross-pollination of research. Anyone who has read a research paper from an unfamiliar field knows this one too well. New knowledge, almost by definition, is esoteric. This is not helped by the silo effect described above. Without the ability to sell research through stories that people can relate to and understand, it is hard to drum up the reach needed for the idea to leave a footprint on the world. Dissemination & Storytelling. If there are so many problems, why are the incumbents still winning? . The most obvious example of economies of scale in the market for new knowledge creation is the ability of major institutions to gain access to advanced technology that is difficult to produce or distribute at scale (e.g. large Hadron colliders). Beyond being symbols of progressiveness, these are key resources for driving new discoveries and ideas as outlined in the Ideafrastructure diagram above. A second, derivative version of economies of scale comes through the exclusivity effect that gatekeepers like Elsevier or Taylor & Francis possess. Economies of Scale . Research institutions, particularly universities, have accumulated immense brand value over time. Harvard is not just a research institution. Like a few other old universities, It has evolved into a universal symbol for intelligence and achievement. This provides institutions like Harvard with both incredible reach and credence - two points that will be expanded upon below. Legacy Effects Which Ideas win? Once again, a major philosophical question is posed that is very open to interpretation. For the sake of any founders reading this Substack, I will again oversimplify it. To bastardise Justin Kan’s theory of product above: The ideas that win are those that achieve the most effective distribution with regards to 1) reach, 2) credence and 3) validity. refers to the size of the audience that engages with the idea. This is best achieved through the media platforms and KOLs that promote any given idea. For example, the idea of killer AGI. The idea of red wine being an aid for {insert most human problems} appeals to the masses and achieves reach through many a weekend magazine and lifestyle article. Reach Beyond getting the research into the right hands (i.e magazines, newspapers, Twitter KOLs, non-fiction books), a key part of achieving reach through research is via storytelling. Research around red wine works because it’s easy to digest and gets hits. Quantum physics less so. is concerned with the reputation of the institutions and KOLs that originate or promote a given idea. This most obviously applies to the reputations of research institutions and professors that originate new discoveries in research papers, but also appears in less obvious contexts. To borrow the example of red wine extending longevity used above - this argument gained a lot of credence through the empirical evidence lended to it by . Credence centenarians who attribute their drinking habits to their longevity shares a lot of crossover with credence. Validity here refers to the replicability of ideas to ensure their truth in practice. This is a key area where . The incentives for scientists today revolve around producing ideas to achieve acclaim. Validity science suffers today If protocols were to come online that take their place as the pre-eminent producers of knowledge, it is my belief that a larger portion of scientific practice can be devoted to validating the ideas of others. In designing generative knowledge creators, some of the major long-term questions that need to be asked will be around how to balance and optimise this triumvirate in order to ensure that new ideas can be distributed effectively at scale whilst also remaining true. The more replicable ideas are distributed at scale, the more validity will be optimised and the more trust will be instilled in synthetic new knowledge. The Future of Ideas Different Forms of Ideas Beofre going deeper, a breakdown of the essential ingredients for idea-generation protocols. Taking the bolded wording from the definitions used above, we can divide the idea of the idea into varying levels of actionability from a product development perspective. For the sake of the theme of ‘Requests for Startups’ as a newsletter, I intend to focus on the leftmost items on the venn diagram above to help inspire would-be ideamakers. That is not to discount the forms of ideas covered elsewhere as many of them are also necessary ingredients to the next generation of idea-creation protocols. The aim of condensing the components of what makes an idea are ultimately to help define the objective functions of future products & protocols who specialise in synthesising, judging, distributing and creating competitions for ideas. What is important in each of these potential functions of new idea-centric products? Synthesis The current methods of knowledge creation practiced in academia and corporate R&D facilities follow very straightforward processes. Hypotheses are made around potential outcomes of an experiment based on previous knowledge, tested in a laboratory setting and then validated again against the pre-existing knowledge base. This is essentially how new ideas are rubber-stamped today. The dependence of this system on previous citation, often within a single field, fosters incrementalism. What if methods for new knowledge creation occurred in a manner similar to which a neural network operates? Two fields of knowledge are thrown in as inputs, with all pre-existing research and ideas comingling with one another to develop a massive array of potential hypotheses. Humans or agents can enter a marketplace to vote on the potential impacts of any given hypothesis, with those ‘winners’ being submitted for validation and testing in laboratories. The function of ideation is outsourced to the machines, whilst humans (for the time being) are still required in the loop for the physical validation of these ideas. Competition How should a marketplace be designed to evaluate the potential future impacts of new ideas - whether human or machine generated? The most common idea to date has been the idea of prediction markets, first credited to . This is how Hanson views the application of prediction markets to science: Robin Hanson Imagine , with the going odds available to the popular media, and treated socially as the current academic consensus. a betting pool or market on most disputed science questions Imagine that academics are expected to "put up or shut up" and accompany claims with at least token bets, and that statistics are collected on how well people do. Imagine that funding agencies subsidize pools on questions of interest to them, and that research labs pay for much of their research with winnings from previous pools. And imagine that anyone could play, either to take a stand on an important issue, or to insure against technological risk. This would be an market, which I offer as an alternative to existing academic social institutions. "idea futures" Somewhat like a corn futures market, where one can bet on the future price of corn, here one bets on the future settlement of a present scientific controversy. This is admittedly an unusual suggestion. But consider what might happen. Inspired by Hanson, the protocol (disclaimer: incubated by my employer) aims to create a market for knowledge whereby researchers upload papers and vote on the possible future effects of it. This creates an incentive for replication as other researchers/financial professionals can gain a financial advantage by proving or disproving the current validity of the research. Rosetta The rewards from future research are distributed 1) to those who staked a vote on the winning side of the argument, 2) to the researchers who posited the argument and 3) to the precedent researchers cited as contributors to the researcher’s idea. Storytelling Many a non-fiction writer (see: Gladwell, Harari, Taleb) has made a killing by interweaving academic knowledge with experiences in the real-world to create digestible ideas for the layman. These are prominent literary careers built off the back of arbitraging the obnoxious tendency of research papers to be virtually incomprehensible. With the expanding context windows available in today’s LLMs, the scope for summarising and reframing research in a real-world context is becoming infinitely easier. Finding broadly applicable examples and affects to apply to once esoteric research becomes infinitely easier. We can also have markets for how research stories are told in order to elicit the greatest public interest whilst not compromising the integrity of the actual ideas themselves. Twitter operates as the largest story marketplace today - what if there were new media platforms that replaced social’s currency of likes and follows with monetary incentives for understanding and communicating new knowledge. Besides the obvious educational impacts this may have, better stories also serve the function of condensing complex research into easy-to-digest logic that can be used as inspiration for product development. Commercialisation has a strong claim to being the godfather of commercialisation theory in the startup world. His claim (reflected by Marc Andreesen ) is that any startup innovation can be traced to basic (often federal) research funding from as long as 50 years prior waiting to be productised. This means that today, there is a full and growing repository of insights waiting to be converted into entrepreneurial opportunity. Bill Janeway here With the proliferation of autonomous agents that are taking on large portions of roles in (admittedly simple) startups, how far away could we be from near instant commercialisation or commercial testing of novel ideas? already Picture this. A researcher or neural network-based hypothesis creator posits a novel idea. A storytelling protocol summarises the research and turns it into a digestible story that is matched to existing customer needs - these needs can be determined by media sentiment analysis or from some other form of intuition. From here, a human (or autonomous) manager can segment the key tasks or functions to be completed in order to find the best method for this research to be productised based on the market needs identified above. This can trigger a task execution loop spanning years or even decades to ensure the consistent growth of the product/business/software/protocol. These loops can be monitored by a single human manager overseeing its operation. Rewards can be distributed to all of those who contributed research in the training data used by the agent to breakdown the product strategy at the beginning. Requests for Startups: This concerns both knowledge made ‘organically’ by today’s scientists or hypothesised ‘synthetically’ via neural networks trained on the existing corpus of research. How can we a) allow researchers to have to vote on the potential impacts of their research. Genuine, No BS Markets for new knowledge creation. skin in the game There are volumes upon volumes of research in existence that are i) hard for entrepreneurs to understand and ii) difficult to commercialise by turning into product & marketing logic. The capabilities of LLMs even today to summarise and reframe arguments means that this unnecessary complexity can be a thing of the past. Can anyone build a living protocol that scrapes & trains on popular recent research on arXiv or other open-source research platforms, combines ideas and retells them as stories that would-be founders can turn into solutions to problems? Autonomous Storytellers. Marco Mascorro has already made some inroads with the paper summariser . What does this look like if it goes a step further and finds stories that interweave between different fields and identifies the problems they may be able to solve? arXiv GPT . What if methods for new knowledge creation occurred in a manner similar to which a neural network operates? Two fields of knowledge are thrown in as inputs, with all pre-existing research and ideas comingling with one another to develop a massive array of potential hypotheses. Humans or agents can enter a marketplace to vote on the potential impacts of any given hypothesis, with those ‘winners’ being submitted for validation and testing in laboratories. Pseudo-Laboratories Also published . here

Idea Factories — How Do we Create More and Better Ideas for Humanity

AI has been with us since 70s or maybe some would say earlier than that. There were a lot of ideas on the field, but the progress was slow and the results far from what we had hoped. That changed about 10 years ago. Breakthroughs in computing and also the need to introduce “intelligent” products brought back the excitement for what AI is capable of. So we have been introduced to things like Siri or Alexa , Smart home devices, internet services and so many more that all use some kind of AI to make the experience even better. Advanced or not, AI is already in many things in our lives and it is making it better. Until recently most of the algorithms that controlled the way a “bot” should behave were hard coded in the software that empowered it. In a way the behaviour was limited by the programmer’s skills but also by the human nature itself. We tried to create an emulation of ourselves. A few years back and established as the next big step in AI evolution. At last, we are trying to free machines from the human limitations and give them the ability to learn all by themselves. We are at the start, kind of, but as the field tests have shown they can learn faster, better and most importantly never forget. Memory is maybe the greatest tool that helped humans go one step further through the years. machine learning deep learning Now we have software that can learn to drive cars, fly planes, create art, . Really big Neural networks are responsible for allowing all these machines to learn and become intelligent. Though there is a problem with that. Researchers have noticed that the deeper the network, the harder it is to understand how the machine made a particular decision. When thousands of emulated neurones are involved it is almost impossible to look into the system and understand how it works. This phenomenon is known as an AI black box and like human behaviour maybe we will never be able to fully understand it. even play video games better than us _Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The…_www.technologyreview.com There's a big problem with AI: even its creators can't explain how it works Trying to predict what a system like that would be in 10 years from now, with all the advancements in technology, i can only say one thing. It will be a lot like a human soul. Take a step back and observe the similarities here. Human behaviour or a decision we make can be explained at an extent, but the true steps that led to that conclusion are really unknown to us. In an advanced system, decisions taken can be really hard for a human to understand, accept or put a reasoning behind them . The same thing can happen between two humans that come from other cultures or are having different interests. Is this kind of frightening? Maybe it is. That’s why a lot of people in the field are starting to demand that AI should be regulated to avoid unpleasant surprises. Elon Musk recently said that AI is a bigger threat than North Korea and . The need to control how we use it is real and should be done as quickly as possible. Russia’s president Vladimir Putin said that the nation that leads in AI will be the ruler of the world So the question is, are we on the verge of playing Gods? What is a living entity? If we create a robot with composite materials instead of flesh and give it the ability to sense and make decisions, based on things it learned, is it alive? What if after a while our machine will gain context-reasoning ? What if some day it will realise that wants more than what we told it to do? What if it will say ? Do we have the right to regulate that? “I want to be free”

Playing gods. Did we create a soul through AI?

Tech professionals face heavy workloads and tight deadlines, which can take a serious toll on their well-being. Recent data from Blind, an anonymous community of tech professionals, reveals that 57% of tech employees report feeling burned out, with common stressors including long hours and constant pressure to maintain operational reliability. While top tech firms like Google and LinkedIn have implemented Site Reliability Engineering (SRE) practices to address system reliability and minimize burnout, there’s a growing consensus that more must be done to support developers on an individual level. This is where Alexandr Hacicheant, who is in charge of Reliability Engineering at Mayflower, steps in with a solution that prioritizes people first. “We often think of engineers as endlessly resilient problem-solvers, but they’re also people who need balance to thrive,” Alexandr explains. By focusing on developer well-being, Alexandr’s methods aim to build healthier, more sustainable tech teams, positioning work-life balance as a business imperative rather than an afterthought. A major part of Alexandr’s journey at Mayflower has been related to creating a workplace where developers could excel without sacrificing their well-being. “My goal wasn’t just to solve system reliability issues; it was to provide engineers with a supportive environment where they felt valued and balanced,” he shares. His approach centers on enhancing productivity through team autonomy, clear performance metrics, and streamlined workflows, helping engineers stay engaged and focused without constant firefighting. For Alexandr, preventing burnout starts with giving developers the resources and structure they need to manage their time effectively. “By reducing the amount of reactive work and empowering engineers to work on high-impact projects, we’re creating a space where they can actually enjoy the work they’re doing,” he adds. One of Alexandr’s key initiatives has been to address one of the most common sources of burnout in tech: the on-call shift. “On-call duty can be incredibly stressful for developers, especially when it disrupts their time off,” he explains. At Mayflower, his team implemented automated systems that handle routine maintenance and alert engineers only for critical issues, allowing them to have uninterrupted downtime. “Reducing unnecessary disruptions lets engineers fully recharge, which is crucial for both their health and productivity,” Alexandr notes. This approach has proven successful in decreasing stress levels among Mayflower’s engineers, enabling them to return to work with renewed focus and motivation. For Alexandr, the reduction of burnout isn’t simply a benefit to individual engineers; it’s a core business strategy. “Healthy engineers are productive engineers, and productive engineers drive a company’s success,” he emphasizes. While Alexandr’s efforts at Mayflower have yielded tangible improvements in both system reliability and employee satisfaction, he believes the industry as a whole needs to prioritize developer well-being. “If we’re going to make tech sustainable, we need to make engineers’ well-being a priority, not just system uptime,” he argues. By adopting a “People first” philosophy, Alexandr has set a standard that other leaders can look to, creating a more supportive environment for developers across the industry. Alexandr has begun sharing his insights more broadly through industry discussions, workshops, and publications. His message is simple but powerful: focusing on work-life balance isn’t just a perk; it’s an essential part of building resilient teams that can meet modern tech challenges. “When we put people first, we create a work environment that supports engineers and business success alike,” he says. Looking forward, Alexandr envisions a tech industry where developer well-being is a primary focus. “We have to shift our thinking from ‘What’s the latest tech trend?’ to ‘How do we create a sustainable workplace for our engineers?’” he reflects. His future goals include refining his “People first” approach further within Mayflower and encouraging its adoption across the industry. As Alexandr puts it, “The future of tech depends on more than innovation—it depends on creating environments where people can thrive.” By championing a work culture that respects work-life balance, he aims to inspire leaders across the industry to foster not just productive, but genuinely healthy, teams. Tech professionals face heavy workloads and tight deadlines, which can take a serious toll on their well-being. Recent data from Blind, an anonymous community of tech professionals, reveals that 57% of tech employees report feeling burned out, with common stressors including long hours and constant pressure to maintain operational reliability. While top tech firms like Google and LinkedIn have implemented Site Reliability Engineering (SRE) practices to address system reliability and minimize burnout, there’s a growing consensus that more must be done to support developers on an individual level. This is where Alexandr Hacicheant, who is in charge of Reliability Engineering at Mayflower, steps in with a solution that prioritizes people first. “We often think of engineers as endlessly resilient problem-solvers, but they’re also people who need balance to thrive,” Alexandr explains. By focusing on developer well-being, Alexandr’s methods aim to build healthier, more sustainable tech teams, positioning work-life balance as a business imperative rather than an afterthought. A major part of Alexandr’s journey at Mayflower has been related to creating a workplace where developers could excel without sacrificing their well-being. “My goal wasn’t just to solve system reliability issues; it was to provide engineers with a supportive environment where they felt valued and balanced,” he shares. His approach centers on enhancing productivity through team autonomy, clear performance metrics, and streamlined workflows, helping engineers stay engaged and focused without constant firefighting. For Alexandr, preventing burnout starts with giving developers the resources and structure they need to manage their time effectively. “By reducing the amount of reactive work and empowering engineers to work on high-impact projects, we’re creating a space where they can actually enjoy the work they’re doing,” he adds. One of Alexandr’s key initiatives has been to address one of the most common sources of burnout in tech: the on-call shift. “On-call duty can be incredibly stressful for developers, especially when it disrupts their time off,” he explains. At Mayflower, his team implemented automated systems that handle routine maintenance and alert engineers only for critical issues, allowing them to have uninterrupted downtime. “Reducing unnecessary disruptions lets engineers fully recharge, which is crucial for both their health and productivity,” Alexandr notes. This approach has proven successful in decreasing stress levels among Mayflower’s engineers, enabling them to return to work with renewed focus and motivation. For Alexandr, the reduction of burnout isn’t simply a benefit to individual engineers; it’s a core business strategy. “Healthy engineers are productive engineers, and productive engineers drive a company’s success,” he emphasizes. While Alexandr’s efforts at Mayflower have yielded tangible improvements in both system reliability and employee satisfaction, he believes the industry as a whole needs to prioritize developer well-being. “If we’re going to make tech sustainable, we need to make engineers’ well-being a priority, not just system uptime,” he argues. By adopting a “People first” philosophy, Alexandr has set a standard that other leaders can look to, creating a more supportive environment for developers across the industry. Alexandr has begun sharing his insights more broadly through industry discussions, workshops, and publications. His message is simple but powerful: focusing on work-life balance isn’t just a perk; it’s an essential part of building resilient teams that can meet modern tech challenges. “When we put people first, we create a work environment that supports engineers and business success alike,” he says. Looking forward, Alexandr envisions a tech industry where developer well-being is a primary focus. “We have to shift our thinking from ‘What’s the latest tech trend?’ to ‘How do we create a sustainable workplace for our engineers?’” he reflects. His future goals include refining his “People first” approach further within Mayflower and encouraging its adoption across the industry. As Alexandr puts it, “The future of tech depends on more than innovation—it depends on creating environments where people can thrive.” By championing a work culture that respects work-life balance, he aims to inspire leaders across the industry to foster not just productive, but genuinely healthy, teams.

How Alexandr Hacicheant Prioritizes Engineers’ Well-Being with SRE Practices

Как Alexander Hacicheant дава приоритет на благосъстоянието на инженерите с SRE практики

আলেকজান্ডার হ্যাসিচ্যান্ট কীভাবে এসআরই অনুশীলনের সাথে ইঞ্জিনিয়ারদের সুস্থতাকে অগ্রাধিকার দেয়

Cómo Alexandr Hacicheant prioriza el bienestar de los ingenieros con prácticas de SRE

Kuinka Alexandr Hacicheant asettaa etusijalle insinöörien hyvinvoinnin SRE-käytäntöjen avulla

Comment Alexandr Hacicheant donne la priorité au bien-être des ingénieurs grâce aux pratiques SRE

Alexandr Hacicheant が SRE プラクティスでエンジニアの幸福を優先する方法

Jak Alexandr Hacicheant stawia na pierwszym miejscu dobre samopoczucie inżynierów dzięki praktykom SRE

څنګه الکساندر هیچینټ د SRE تمرینونو سره د انجینرانو هوساینې ته لومړیتوب ورکوي

Imaynatachus Alexandr Hacicheant Ingenierokunap Allin Kawsayninta SRE Ruwaykunawan ñawpaqman churan

Maitiro eAlexandr Hacicheant Anotungamira Kugarika kweInjiniya neSRE Maitiro

Ndlela leyi Alexandr Hacicheant a Rhangisaka Vuhlayiseki bya Vanjhiniyara ha yona hi Maendlelo ya SRE

Alexandr Hacicheant 如何通过 SRE 实践优先考虑工程师的福祉

The journey of self-discovery often leads us to confront uncomfortable truths about human relationships and personal worth. As we navigate the complexities of connection and identity, we must learn to balance caring for others with maintaining our own sense of self. Through my experiences, I've observed a common pattern that transcends gender and culture: the tendency to lose ourselves in the pursuit of approval and acceptance. This pattern often manifests in relationships where one person gradually diminishes their own dreams, aspirations, and well-being in an attempt to prove their worth through perpetual sacrifice. However, true connection—whether in friendship, romance, or professional relationships—flourishes not through self-abandonment but through mutual growth and respect. The most enduring bonds are forged between individuals who maintain their distinct identities while supporting each other's evolution. Consider the paradox of personal value: the more we compromise our core principles and abandon our path in an attempt to please others, the more we undermine the very qualities that make us worthy partners in any relationship. Our unique perspectives, ambitions, and boundaries aren't obstacles to meaningful connections—they're essential ingredients. I've learned that self-respect isn't selfishness; it's the foundation upon which all healthy relationships are built. When we maintain our personal standards, pursue our goals, and honor our boundaries, we demonstrate not just self-love but also respect for others. We show that we value authenticity over approval, substance over superficial acceptance. The path to genuine connection requires courage—the courage to stand firmly in our truth, to maintain our course even when faced with pressure to compromise, and to recognize that our worth isn't determined by how much we're willing to sacrifice but by how authentically we live our values. Real strength lies not in proving ourselves through endless sacrifice but in maintaining our integrity while remaining open to genuine connection. It's about understanding that true partnership enhances rather than diminishes each person's individual journey. As I continue to explore these themes, I'm reminded that the most profound relationships in my life have been those where both parties remained committed to their personal growth while supporting each other's evolution. These connections weren't built on dependency or sacrifice but on mutual respect, shared values, and a commitment to individual and collective growth. Moving forward, my focus remains on fostering connections that honor both individual sovereignty and collective growth. It's about creating spaces where authenticity is celebrated, boundaries are respected, and growth is mutual. This approach requires wisdom to distinguish between genuine giving and self-diminishing sacrifice, between healthy compromise and harmful concession. The journey toward authentic connection begins with self-respect and continues through the daily practice of honoring both our own truth and the autonomy of others. It's a path that demands courage, wisdom, and an unwavering commitment to personal integrity. As we walk this path, let us remember that our greatest gift to others is not our sacrifice but our authenticity. When we stand firmly in our truth while remaining open to meaningful connection, we create the foundation for relationships that enrich rather than diminish, that elevate rather than constrain. The road continues, and with each step, we learn to balance independence with interconnection, self-respect with genuine care for others. This is the essence of true relationship wisdom—not losing ourselves in others, but finding ourselves through authentic connection while maintaining our individual paths. Still, the journey continues... The journey of self-discovery often leads us to confront uncomfortable truths about human relationships and personal worth. As we navigate the complexities of connection and identity, we must learn to balance caring for others with maintaining our own sense of self. Through my experiences, I've observed a common pattern that transcends gender and culture: the tendency to lose ourselves in the pursuit of approval and acceptance. This pattern often manifests in relationships where one person gradually diminishes their own dreams, aspirations, and well-being in an attempt to prove their worth through perpetual sacrifice. However, true connection—whether in friendship, romance, or professional relationships—flourishes not through self-abandonment but through mutual growth and respect. The most enduring bonds are forged between individuals who maintain their distinct identities while supporting each other's evolution. Consider the paradox of personal value: the more we compromise our core principles and abandon our path in an attempt to please others, the more we undermine the very qualities that make us worthy partners in any relationship. Our unique perspectives, ambitions, and boundaries aren't obstacles to meaningful connections—they're essential ingredients. I've learned that self-respect isn't selfishness; it's the foundation upon which all healthy relationships are built. When we maintain our personal standards, pursue our goals, and honor our boundaries, we demonstrate not just self-love but also respect for others. We show that we value authenticity over approval, substance over superficial acceptance. The path to genuine connection requires courage—the courage to stand firmly in our truth, to maintain our course even when faced with pressure to compromise, and to recognize that our worth isn't determined by how much we're willing to sacrifice but by how authentically we live our values. Real strength lies not in proving ourselves through endless sacrifice but in maintaining our integrity while remaining open to genuine connection. It's about understanding that true partnership enhances rather than diminishes each person's individual journey. As I continue to explore these themes, I'm reminded that the most profound relationships in my life have been those where both parties remained committed to their personal growth while supporting each other's evolution. These connections weren't built on dependency or sacrifice but on mutual respect, shared values, and a commitment to individual and collective growth. Moving forward, my focus remains on fostering connections that honor both individual sovereignty and collective growth. It's about creating spaces where authenticity is celebrated, boundaries are respected, and growth is mutual. This approach requires wisdom to distinguish between genuine giving and self-diminishing sacrifice, between healthy compromise and harmful concession. The journey toward authentic connection begins with self-respect and continues through the daily practice of honoring both our own truth and the autonomy of others. It's a path that demands courage, wisdom, and an unwavering commitment to personal integrity. As we walk this path, let us remember that our greatest gift to others is not our sacrifice but our authenticity. When we stand firmly in our truth while remaining open to meaningful connection, we create the foundation for relationships that enrich rather than diminish, that elevate rather than constrain. The road continues, and with each step, we learn to balance independence with interconnection, self-respect with genuine care for others. This is the essence of true relationship wisdom—not losing ourselves in others, but finding ourselves through authentic connection while maintaining our individual paths. Still, the journey continues...

The Long Road Home: A Story of Loss, Learning, and Renaissance - PART 3

দ্য লং রোড হোম: এ স্টোরি অফ লস, লার্নিং এবং রেনেসাঁ - পার্ট 3

El largo camino a casa: Una historia de pérdida, aprendizaje y renacimiento - PARTE 3

The Long Road Home: A Story of Loss, Learning, and Renaissance - 3. ZATIA

長い帰路: 喪失、学び、そして復興の物語 - パート 3

The Long Road Home: Uma história de perda, aprendizado e renascimento - PARTE 3

Wasi Karu Ñan: Chinkaymanta, Yachaymanta, Renacimientomanta Willakuy - 3 PARTE

Rruga e gjatë në shtëpi: Një histori e humbjes, mësimit dhe rilindjes - PJESA 3

Дуг пут кући: прича о губитку, учењу и ренесанси - 3. ДЕО

நீண்ட சாலை வீடு: இழப்பு, கற்றல் மற்றும் மறுமலர்ச்சியின் கதை - பகுதி 3

ነዊሕ መንገዲ ገዛ፡ ዛንታ ክሳራ፡ ትምህርትን ህዳሴን - 3ይ ክፋል

Uyga uzoq yo'l: Yo'qotish, o'rganish va Uyg'onish hikoyasi - 3-QISM

Ikhaya Lomgwaqo Omude: Indaba Yokulahlekelwa, Ukufunda, kanye Nokuvuselelwa Kabusha - INGXENYE 3

GRAND CAYMAN, Cayman Islands, November 13th, 2024/Chainwire/--With a 70-year legacy of offering investment solutions across diverse industries and asset classes, privately held VanEck now provides millions of customers access to SUI Investment firm VanEck has introduced a product representing SUI. VanEck, known for its financial products that provide access to Bitcoin, Ethereum, and Solana, now offers exposure to SUI, the token behind the groundbreaking Layer 1 blockchain recognized for its industry-leading performance and infinite horizontal scalability. This fully-collateralized financial instrument provides millions of investors access to SUI. With the new offering from VanEck, millions can now access SUI without needing to hold SUI in a crypto wallet. VanEck holds the referenced SUI tokens in a regulated custodian environment covered by each note, ensuring that the product's value tracks the underlying tokens' value. Like other financial products, the new VanEck offering enables users to easily invest and divest. The launch of VanEck's SUI-backed product is the latest demonstration of institutional confidence in Sui, following notable on-chain developments. These include partnerships with services like Copper, Zero Hash and Fordefi and the integration of stablecoins such as USDC, AUSD, and the stablecoin-like USDY. Over the past year, Sui has experienced remarkable growth, with total value locked (TVL) increasing by 430% and DeFi volume surging by 692%. In August, 2024, in another show of support, Grayscale, one of the world’s largest crypto asset managers, launched its Grayscale® Sui Trust. “We are excited to introduce this product as part of our growing portfolio of digital asset investment products,” said Menno Martens, Crypto Specialist and Product Manager at VanEck. “Sui’s proven track record and reputation as an industry leader, as well as its innovative and technological advancements, made this the next logical product to house in the portfolio. We believe this development will give investors a unique opportunity to access one of the most advanced blockchain networks available today.” “The creation of this SUI-based product by VanEck is a pivotal moment for the Sui ecosystem and a significant vote of confidence in Sui’s technology and long-term vision,” said Gap Kim, Head of Marketing for the Sui Foundation. “VanEck is a trusted leader in the investment industry, and the decision to offer SUI as part of their product suite underscores the growing institutional interest in Sui.” To learn more, visit https://blog.sui.io/vaneck-investor-product/ or https://www.vaneck.com/. About Sui Sui is a first-of-its-kind Layer 1 blockchain and smart contract platform designed from the ground up to make digital asset ownership fast, private, secure, and accessible to everyone. Its object-centric model, based on the Move programming language, enables parallel execution, sub-second finality, and rich on-chain assets. With horizontally scalable processing and storage, Sui supports a wide range of applications with unrivaled speed at low cost. Sui is a step-function advancement in blockchain and a platform on which creators and developers can build amazing user-friendly experiences. For more information about Sui, users can visit https://sui.io. About VanEck VanEck has a history of looking beyond the financial markets to identify trends that are likely to create impactful investment opportunities. VanEck was one of the first U.S. asset managers to offer investors access to international markets. This set the tone for the firm’s drive to identify asset classes and trends—including gold investing in 1968, emerging markets in 1993, and Exchange Traded Funds in 2006—that subsequently shaped the investment management industry. Today, VanEck offers active and passive strategies with compelling exposures supported by well-designed investment processes. The firm’s capabilities range from core investment opportunities to more specialized exposures to enhance portfolio didiversification. VanEck's actively managed strategies are fueled by in-depth, bottom-up research and security selection from portfolio managers with direct experience in the sectors and regions in which they invest. Investability, liquidity, diversity, and transparency are key to the experienced decision-making around market and index selection underlying VanEck’s passive strategies. Since VanEck's founding in 1955, putting its clients’ interests first, in all market environments, has been at the heart of the firm’s mission. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here GRAND CAYMAN, Cayman Islands, November 13th, 2024/Chainwire/--With a 70-year legacy of offering investment solutions across diverse industries and asset classes, privately held VanEck now provides millions of customers access to SUI Investment firm VanEck has introduced a product representing SUI. VanEck, known for its financial products that provide access to Bitcoin, Ethereum, and Solana, now offers exposure to SUI, the token behind the groundbreaking Layer 1 blockchain recognized for its industry-leading performance and infinite horizontal scalability. This fully-collateralized financial instrument provides millions of investors access to SUI. With the new offering from VanEck, millions can now access SUI without needing to hold SUI in a crypto wallet. VanEck holds the referenced SUI tokens in a regulated custodian environment covered by each note, ensuring that the product's value tracks the underlying tokens' value. Like other financial products, the new VanEck offering enables users to easily invest and divest. The launch of VanEck's SUI-backed product is the latest demonstration of institutional confidence in Sui, following notable on-chain developments. These include partnerships with services like Copper , Zero Hash and Fordefi and the integration of stablecoins such as USDC , AUSD , and the stablecoin-like USDY . Copper Copper Zero Hash Zero Hash Fordefi Fordefi USDC USDC AUSD AUSD USDY USDY Over the past year, Sui has experienced remarkable growth, with total value locked (TVL) increasing by 430% and DeFi volume surging by 692%. In August, 2024, in another show of support, Grayscale, one of the world’s largest crypto asset managers, launched its Grayscale® Sui Trust. “We are excited to introduce this product as part of our growing portfolio of digital asset investment products,” said Menno Martens, Crypto Specialist and Product Manager at VanEck. “We are excited to introduce this product as part of our growing portfolio of digital asset investment products,” said Menno Martens, Crypto Specialist and Product Manager at VanEck. “Sui’s proven track record and reputation as an industry leader, as well as its innovative and technological advancements, made this the next logical product to house in the portfolio. We believe this development will give investors a unique opportunity to access one of the most advanced blockchain networks available today.” “Sui’s proven track record and reputation as an industry leader, as well as its innovative and technological advancements, made this the next logical product to house in the portfolio. We believe this development will give investors a unique opportunity to access one of the most advanced blockchain networks available today.” “The creation of this SUI-based product by VanEck is a pivotal moment for the Sui ecosystem and a significant vote of confidence in Sui’s technology and long-term vision,” said Gap Kim, Head of Marketing for the Sui Foundation. “The creation of this SUI-based product by VanEck is a pivotal moment for the Sui ecosystem and a significant vote of confidence in Sui’s technology and long-term vision,” said Gap Kim, Head of Marketing for the Sui Foundation. “VanEck is a trusted leader in the investment industry, and the decision to offer SUI as part of their product suite underscores the growing institutional interest in Sui.” “VanEck is a trusted leader in the investment industry, and the decision to offer SUI as part of their product suite underscores the growing institutional interest in Sui.” To learn more, visit https://blog.sui.io/vaneck-investor-product/ or https://www.vaneck.com/ . https://blog.sui.io/vaneck-investor-product/ https://blog.sui.io/vaneck-investor-product/ https://www.vaneck.com/ https://www.vaneck.com/ About Sui Sui is a first-of-its-kind Layer 1 blockchain and smart contract platform designed from the ground up to make digital asset ownership fast, private, secure, and accessible to everyone. Its object-centric model, based on the Move programming language, enables parallel execution, sub-second finality, and rich on-chain assets. With horizontally scalable processing and storage, Sui supports a wide range of applications with unrivaled speed at low cost. Sui is a step-function advancement in blockchain and a platform on which creators and developers can build amazing user-friendly experiences. For more information about Sui, users can visit https://sui.io . https://sui.io https://sui.io About VanEck VanEck has a history of looking beyond the financial markets to identify trends that are likely to create impactful investment opportunities. VanEck was one of the first U.S. asset managers to offer investors access to international markets. This set the tone for the firm’s drive to identify asset classes and trends—including gold investing in 1968, emerging markets in 1993, and Exchange Traded Funds in 2006—that subsequently shaped the investment management industry. Today, VanEck offers active and passive strategies with compelling exposures supported by well-designed investment processes. The firm’s capabilities range from core investment opportunities to more specialized exposures to enhance portfolio didiversification. VanEck's actively managed strategies are fueled by in-depth, bottom-up research and security selection from portfolio managers with direct experience in the sectors and regions in which they invest. Investability, liquidity, diversity, and transparency are key to the experienced decision-making around market and index selection underlying VanEck’s passive strategies. Since VanEck's founding in 1955, putting its clients’ interests first, in all market environments, has been at the heart of the firm’s mission. Contact Sui Foundation media@sui.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Investment Giant VanEck Launches SUI-Based Financial Product

የኢንቨስትመንት ጃይንት ቫንኢክ በሱአይ ላይ የተመሰረተ የፋይናንሺያል ምርትን ጀመረ

Investiční gigant VanEck uvádí na trh finanční produkt založený na SUI

El gigante de las inversiones VanEck lanza un producto financiero basado en SUI

Envestisman Giant VanEck lanse pwodwi finansye ki baze sou SUI

Raksasa Investasi VanEck Luncurkan Produk Keuangan Berbasis SUI

Il gigante degli investimenti VanEck lancia un prodotto finanziario basato su SUI

投資大手ヴァンエックがSUIベースの金融商品を発売

ការវិនិយោគ Giant VanEck បើកដំណើរការផលិតផលហិរញ្ញវត្ថុផ្អែកលើ SUI

Investment Giant VanEck ले SUI-आधारित वित्तीय उत्पादन सुरु गर्‍यो

Maalgashiga Giant VanEck wuxuu bilaabay SUI-ku salaysan wax soo saarka maaliyadeed

முதலீட்டு நிறுவனமான VanEck SUI-அடிப்படையிலான நிதித் தயாரிப்பை அறிமுகப்படுத்துகிறது

Yatırım Devi VanEck, SUI Tabanlı Finansal Ürünü Piyasaya Sürüyor

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation A. Framework details Inputs. For input images, we initially apply a random horizontal flip to each image. Subsequently, the image is randomly scaled to a resolution within the range of 716 × 716 to 1075 × 1075 resolutions. Finally, a crop of 896 × 896 resolutions is extracted from the scaled image to serve as the input. Regarding the category names, we construct a sentence using a prompt and tokenise it using a lower-cased byte pair encoding (BPE). For the visual prompt, we create a uniform grid of points with dimensions h × w, which is aligned with the center of the pixels. CLIP encoder. In general, the CLIP encoder can be any architecture. Motivated by the scalability of different input resolutions, we employ a ConvNext-based CLIP model to serve as both the image and text encoders. The image encoder is configured as a ConvNext-Large model, comprising four stages. Each stage contains a different number of blocks: 3 in the first, 3 in the second, 27 in the third, and 3 in the fourth. In contrast, the text encoder is structured as a 16-layer transformer, each layer being 768 units wide and featuring 12 attention heads. We harness the power of multi-scale features extracted by the image encoder. These features are represented as feature maps of varying widths and scales: a 192-wide feature map downscaled by a factor of 4, a 384-wide map downscaled by 8, a 768-wide map downscaled by 16, and a 1536-wide map downscaled by 32. Pixel decoder. Following Mask2Former [13], we incorporate a lightweight pixel decoder based on the widely used Feature Pyramid Network (FPN) architecture [41]. We first adopt a 6-layer multi-scale deformable transformer on the multi-scale features to aggregate contextual information. Afterward, we upscale the low-resolution feature map in the decoder by a factor of 2 and then combine it with the corresponding resolution feature map from the backbone, which has been projected to match channel dimensions. This projection is achieved through a 1×1 convolution layer, followed by Group Normalization (GroupNorm). Subsequently, the merged features undergo further processing with an additional 3×3 convolution layer, complemented by GroupNorm and ReLU activation. This procedure is applied iteratively, beginning with the 32× downscaled feature map, until we attain a final feature map that is 4× downscaled. To generate pixel-wise embeddings, a single 1×1 convolution layer is applied at the end. Throughout the pixel decoder, all feature maps maintain a consistent dimension of 256 channels. Visual prompt encoder. We have two types of visual prompts, including points and bounding boxes. Each type of prompts is mapped to 256-wide embeddings as follows. A point is first converted into a small bounding box. We represent a bounding box by an embedding pair, including the sine position embedding of its top-left and bottom-right corners. Afterward, we use two extra learnable embeddings to indicate these two corners. Mask decoder. Following Mask2Former, we use a similar transformer decoder design. Each visual prompt embedding within our framework is coupled with a sine positional embedding. As depicted in Fig. 5, our approach utilizes six mask decoder layers, and we apply the same loss function after each layer. Moreover, every decoder layer includes a cross-attention layer. This layer is crucial as it ensures that the final pixel features are enriched with essential geometric information, such as point coordinates and bounding boxes. Besides, in each decoder layer, we use a cross-attention layer, which ensures that the final pixel features have access to critical geometric information (e.g., point coordinates, and boxes). Finally, visual prompt embeddings are fed to a layer normalization, followed by processing through a multiple perception layer (MLP). These processed embeddings are then dot-producted with the pixel features, culminating in the generation of mask predictions. Multi-scale feature adapter. The multi-scale feature adapter illustrated in Fig. 6 is designed to refine and enhance pixel features at various scales. This adapter is composed of multiple Low-rank Adapters (LoRA adapters) [27], each specifically tailored to update features at a particular scale. Each LoRA adapter incorporates two linear layers, denoted as A and B, with a non-linear activation function placed in between. The linear layers A and B serve to transform the input data linearly, while the activation function introduces non-linearity, allowing for the modeling of more complex relationships in the data. Each adapter is responsible for handling features at a specific scale, suggesting a hierarchical approach to feature refinement. This modular design allows for a focused and specialized treatment of features depending on their resolution and semantic complexity, which can be particularly advantageous for dense prediction tasks. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation A. Framework details Inputs . For input images, we initially apply a random horizontal flip to each image. Subsequently, the image is randomly scaled to a resolution within the range of 716 × 716 to 1075 × 1075 resolutions. Finally, a crop of 896 × 896 resolutions is extracted from the scaled image to serve as the input. Regarding the category names, we construct a sentence using a prompt and tokenise it using a lower-cased byte pair encoding (BPE). For the visual prompt, we create a uniform grid of points with dimensions h × w, which is aligned with the center of the pixels. Inputs CLIP encoder. In general, the CLIP encoder can be any architecture. Motivated by the scalability of different input resolutions, we employ a ConvNext-based CLIP model to serve as both the image and text encoders. The image encoder is configured as a ConvNext-Large model, comprising four stages. Each stage contains a different number of blocks: 3 in the first, 3 in the second, 27 in the third, and 3 in the fourth. In contrast, the text encoder is structured as a 16-layer transformer, each layer being 768 units wide and featuring 12 attention heads. We harness the power of multi-scale features extracted by the image encoder. These features are represented as feature maps of varying widths and scales: a 192-wide feature map downscaled by a factor of 4, a 384-wide map downscaled by 8, a 768-wide map downscaled by 16, and a 1536-wide map downscaled by 32. CLIP encoder. Pixel decoder . Following Mask2Former [13], we incorporate a lightweight pixel decoder based on the widely used Feature Pyramid Network (FPN) architecture [41]. We first adopt a 6-layer multi-scale deformable transformer on the multi-scale features to aggregate contextual information. Afterward, we upscale the low-resolution feature map in the decoder by a factor of 2 and then combine it with the corresponding resolution feature map from the backbone, which has been projected to match channel dimensions. This projection is achieved through a 1×1 convolution layer, followed by Group Normalization (GroupNorm). Subsequently, the merged features undergo further processing with an additional 3×3 convolution layer, complemented by GroupNorm and ReLU activation. This procedure is applied iteratively, beginning with the 32× downscaled feature map, until we attain a final feature map that is 4× downscaled. To generate pixel-wise embeddings, a single 1×1 convolution layer is applied at the end. Throughout the pixel decoder, all feature maps maintain a consistent dimension of 256 channels. Pixel decoder Visual prompt encoder. We have two types of visual prompts, including points and bounding boxes. Each type of prompts is mapped to 256-wide embeddings as follows. A point is first converted into a small bounding box. We represent a bounding box by an embedding pair, including the sine position embedding of its top-left and bottom-right corners. Afterward, we use two extra learnable embeddings to indicate these two corners. Visual prompt encoder. Mask decoder. Following Mask2Former, we use a similar transformer decoder design. Each visual prompt embedding within our framework is coupled with a sine positional embedding. As depicted in Fig. 5, our approach utilizes six mask decoder layers, and we apply the same loss function after each layer. Moreover, every decoder layer includes a cross-attention layer. This layer is crucial as it ensures that the final pixel features are enriched with essential geometric information, such as point coordinates and bounding boxes. Besides, in each decoder layer, we use a cross-attention layer, which ensures that the final pixel features have access to critical geometric information (e.g., point coordinates, and boxes). Finally, visual prompt embeddings are fed to a layer normalization, followed by processing through a multiple perception layer (MLP). These processed embeddings are then dot-producted with the pixel features, culminating in the generation of mask predictions. Mask decoder. Multi-scale feature adapter. The multi-scale feature adapter illustrated in Fig. 6 is designed to refine and enhance pixel features at various scales. This adapter is composed of multiple Low-rank Adapters (LoRA adapters) [27], each specifically tailored to update features at a particular scale. Each LoRA adapter incorporates two linear layers, denoted as A and B, with a non-linear activation function placed in between. The linear layers A and B serve to transform the input data linearly, while the activation function introduces non-linearity, allowing for the modeling of more complex relationships in the data. Each adapter is responsible for handling features at a specific scale, suggesting a hierarchical approach to feature refinement. This modular design allows for a focused and specialized treatment of features depending on their resolution and semantic complexity, which can be particularly advantageous for dense prediction tasks. Multi-scale feature adapter. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Advanced Open-Vocabulary Segmentation with Uni-OVSeg

HackerNoon: What is your company in 2–5 words? Tyler Hochman: AI Workforce Productivity Analytics Why is now the time for your company to exist? AI allows workforce algorithmic scaling at a cost that is finally competitive and reasonable for businesses. What do you love about your team, and why are you the ones to solve this problem? Our company (FORE Enterprise) blends experienced professionals with young and hungry tech talent. Innovative Stanford-educated engineers work alongside professors from Stanford, Wharton, and Kellogg to find the most effective ways to improve employee retention at companies. If you weren’t building your startup, what would you be doing? I would be building another startup. At the moment, how do you measure success? What are your metrics? We measure our success by the amount of workforce retention we create, the dollars we save businesses and the productivity we increase. Our clients’ success is our success. In a few sentences, what do you offer to whom? We offer B2B workforce analytics to operation departments and mergers and acquisitions departments. We use analytics from advanced AI models to identify key trends and issues that lead to employee turnover and then provide advice from an expert advisory council on how to intervene. We’ve developed a range of creative intervention strategies that allow our clients to take steps to combat turnover with solutions tailored to their specific companies and employees. We also factor predicted employee turnover into company valuations. What’s most exciting about your traction to date? We are excited about the amount of revenue we’ve affected for clients. We have impacted billion-dollar deals as well as saved millions for our clients in operational workforce costs. Where do you think your growth will be next year? By next year, our product will be able to seamlessly generate analytics based on data from 100,000 publicly available companies within a two-day timespan. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was a major American manufacturer, and we helped them significantly decrease the cost associated with turnover, as well as evaluate the effectiveness of the interventions for their employees. By the end of this year, we’re targeting a million and a half in revenue and by the end of next year, we hope to be at $10 to 20 million in revenue. What’s your biggest threat? Corporate ignorance. Most companies don’t understand the true cost of turnover, and even when they do, they often waste millions of dollars on ineffective interventions for employees that don’t help them retain their top talent. Many don’t know how to analyze the effectiveness of their programs and improve them to save money. We hope to show them the importance of this analysis. HackerNoon : What is your company in 2–5 words? HackerNoon Tyler Hochman : AI Workforce Productivity Analytics Tyler Hochman Tyler Hochman Why is now the time for your company to exist? AI allows workforce algorithmic scaling at a cost that is finally competitive and reasonable for businesses. What do you love about your team, and why are you the ones to solve this problem? Our company ( FORE Enterprise ) blends experienced professionals with young and hungry tech talent. Innovative Stanford-educated engineers work alongside professors from Stanford, Wharton, and Kellogg to find the most effective ways to improve employee retention at companies. FORE Enterprise If you weren’t building your startup, what would you be doing? I would be building another startup. At the moment, how do you measure success? What are your metrics? We measure our success by the amount of workforce retention we create, the dollars we save businesses and the productivity we increase. Our clients’ success is our success. In a few sentences, what do you offer to whom? We offer B2B workforce analytics to operation departments and mergers and acquisitions departments. We use analytics from advanced AI models to identify key trends and issues that lead to employee turnover and then provide advice from an expert advisory council on how to intervene. We’ve developed a range of creative intervention strategies that allow our clients to take steps to combat turnover with solutions tailored to their specific companies and employees. We also factor predicted employee turnover into company valuations. What’s most exciting about your traction to date? We are excited about the amount of revenue we’ve affected for clients. We have impacted billion-dollar deals as well as saved millions for our clients in operational workforce costs. Where do you think your growth will be next year? By next year, our product will be able to seamlessly generate analytics based on data from 100,000 publicly available companies within a two-day timespan. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was a major American manufacturer, and we helped them significantly decrease the cost associated with turnover, as well as evaluate the effectiveness of the interventions for their employees. By the end of this year, we’re targeting a million and a half in revenue and by the end of next year, we hope to be at $10 to 20 million in revenue. What’s your biggest threat? Corporate ignorance. Most companies don’t understand the true cost of turnover, and even when they do, they often waste millions of dollars on ineffective interventions for employees that don’t help them retain their top talent. Many don’t know how to analyze the effectiveness of their programs and improve them to save money. We hope to show them the importance of this analysis.

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