io_uring (previously known as aioring) is a Linux kernel system call interface for storage device asynchronous I/O operations addressing performance issues with similar interfaces provided by functions like read()/write() or aio_read()/aio_write() etc. for operations on data accessed by file ...

io_uring - Wikipedia

Discover io_uring, an async I/O API for Linux that can potentially benefit networking, and learn how to integrate it into your applications and I/O libraries.

Why you should use io_uring for network I/O | Red Hat Developer

Delightful io_uring packages and resources. Contribute to espoal/awesome-iouring development by creating an account on GitHub.

GitHub - espoal/awesome-iouring: Delightful io_uring packages and ...

Check Iouring Private Limited registration details online. Get financial highlights, company network &amp; industry details. Sign Up now for more reports &amp; updates

IOURING PRIVATE LIMITED - Company Profile, Directors, Revenue & ...

Provides easy-to-use async IO interface with io_uring - GitHub - Iceber/iouring-go: Provides easy-to-use async IO interface with io_uring

GitHub - Iceber/iouring-go: Provides easy-to-use async IO interface ...

IOURING PRIVATE LIMITED - company information, business information and directors/partners details

IOURING PRIVATE LIMITED - Company, directors and contact details ...

IOURING PRIVATE LIMITED incorporated on 03-Sep-2021 Whose CIN is U72900TN2021PTC145930 and registered in Tamil Nadu State.

IOURING PRIVATE LIMITED Company Information | Insider Biz

generated by cgit v1.2.3-59-gdc87 (git 2.39.0) at 2024-02-17 10:02:22 +0000

Efficient IO with io_uring

Iouring Private Limited, established in 2021, is engaged in the Information Technology sector in Triplicane, Chennai. Explore their Corporate Structure, Financials, and More.

Iouring Private Limited - Company Details | The Company Check

One year ago, the io_uring subsystem did not exist in the mainline kernel; it showed up in the 5.1 release in May 2019. At its core, io_uring is a mechanism for performing asynchronous I/O, but it has been steadily growing beyond that use case and adding new capabilities.

The rapid growth of io_uring [LWN.net]

Iouring

iouring

JavaZone Turned Out to be an Amazing Conference This Year

ON THE MANAGEMENT OF YOUNG CHILDREN.

Frankenstein or, The Modern Prometheus by Mary Wollstonecraft (Godwin) Shelley is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Letter I: To Mrs. Saville, England. LETTER I. To Mrs. Saville, England St. Petersburgh, Dec. 11th, 17—. You will rejoice to hear that no disaster has accompanied the commencement of an enterprise which you have regarded with such evil forebodings. I arrived here yesterday, and my first task is to assure my dear sister of my welfare and increasing confidence in the success of my undertaking. I am already far north of London, and as I walk in the streets of Petersburgh, I feel a cold northern breeze play upon my cheeks, which braces my nerves and fills me with delight. Do you understand this feeling? This breeze, which has travelled from the regions towards which I am advancing, gives me a foretaste of those icy climes. Inspirited by this wind of promise, my daydreams become more fervent and vivid. I try in vain to be persuaded that the pole is the seat of frost and desolation; it ever presents itself to my imagination as the region of beauty and delight. There, Margaret, the sun is for ever visible, its broad disk just skirting the horizon and diffusing a perpetual splendour. There—for with your leave, my sister, I will put some trust in preceding navigators—there snow and frost are banished; and, sailing over a calm sea, we may be wafted to a land surpassing in wonders and in beauty every region hitherto discovered on the habitable globe. Its productions and features may be without example, as the phenomena of the heavenly bodies undoubtedly are in those undiscovered solitudes. What may not be expected in a country of eternal light? I may there discover the wondrous power which attracts the needle and may regulate a thousand celestial observations that require only this voyage to render their seeming eccentricities consistent for ever. I shall satiate my ardent curiosity with the sight of a part of the world never before visited, and may tread a land never before imprinted by the foot of man. These are my enticements, and they are sufficient to conquer all fear of danger or death and to induce me to commence this laborious voyage with the joy a child feels when he embarks in a little boat, with his holiday mates, on an expedition of discovery up his native river. But supposing all these conjectures to be false, you cannot contest the inestimable benefit which I shall confer on all mankind, to the last generation, by discovering a passage near the pole to those countries, to reach which at present so many months are requisite; or by ascertaining the secret of the magnet, which, if at all possible, can only be effected by an undertaking such as mine. These reflections have dispelled the agitation with which I began my letter, and I feel my heart glow with an enthusiasm which elevates me to heaven, for nothing contributes so much to tranquillise the mind as a steady purpose—a point on which the soul may fix its intellectual eye. This expedition has been the favourite dream of my early years. I have read with ardour the accounts of the various voyages which have been made in the prospect of arriving at the North Pacific Ocean through the seas which surround the pole. You may remember that a history of all the voyages made for purposes of discovery composed the whole of our good Uncle Thomas’ library. My education was neglected, yet I was passionately fond of reading. These volumes were my study day and night, and my familiarity with them increased that regret which I had felt, as a child, on learning that my father’s dying injunction had forbidden my uncle to allow me to embark in a seafaring life. These visions faded when I perused, for the first time, those poets whose effusions entranced my soul and lifted it to heaven. I also became a poet and for one year lived in a paradise of my own creation; I imagined that I also might obtain a niche in the temple where the names of Homer and Shakespeare are consecrated. You are well acquainted with my failure and how heavily I bore the disappointment. But just at that time I inherited the fortune of my cousin, and my thoughts were turned into the channel of their earlier bent. Six years have passed since I resolved on my present undertaking. I can, even now, remember the hour from which I dedicated myself to this great enterprise. I commenced by inuring my body to hardship. I accompanied the whale-fishers on several expeditions to the North Sea; I voluntarily endured cold, famine, thirst, and want of sleep; I often worked harder than the common sailors during the day and devoted my nights to the study of mathematics, the theory of medicine, and those branches of physical science from which a naval adventurer might derive the greatest practical advantage. Twice I actually hired myself as an under-mate in a Greenland whaler, and acquitted myself to admiration. I must own I felt a little proud when my captain offered me the second dignity in the vessel and entreated me to remain with the greatest earnestness, so valuable did he consider my services. And now, dear Margaret, do I not deserve to accomplish some great purpose? My life might have been passed in ease and luxury, but I preferred glory to every enticement that wealth placed in my path. Oh, that some encouraging voice would answer in the affirmative! My courage and my resolution is firm; but my hopes fluctuate, and my spirits are often depressed. I am about to proceed on a long and difficult voyage, the emergencies of which will demand all my fortitude: I am required not only to raise the spirits of others, but sometimes to sustain my own, when theirs are failing. This is the most favourable period for travelling in Russia. They fly quickly over the snow in their sledges; the motion is pleasant, and, in my opinion, far more agreeable than that of an English stagecoach. The cold is not excessive, if you are wrapped in furs—a dress which I have already adopted, for there is a great difference between walking the deck and remaining seated motionless for hours, when no exercise prevents the blood from actually freezing in your veins. I have no ambition to lose my life on the post-road between St. Petersburgh and Archangel. I shall depart for the latter town in a fortnight or three weeks; and my intention is to hire a ship there, which can easily be done by paying the insurance for the owner, and to engage as many sailors as I think necessary among those who are accustomed to the whale-fishing. I do not intend to sail until the month of June; and when shall I return? Ah, dear sister, how can I answer this question? If I succeed, many, many months, perhaps years, will pass before you and I may meet. If I fail, you will see me again soon, or never. Farewell, my dear, excellent Margaret. Heaven shower down blessings on you, and save me, that I may again and again testify my gratitude for all your love and kindness. Your affectionate brother, R. Walton About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Shelley, Mary Wollstonecraft (Godwin), 1993. Relativity:  Frankenstein or, The Modern Prometheus. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/84/84-h/84-h.htm#letter1 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.

Frankenstein or, The Modern Prometheus: Letter I - To Mrs. Saville, England

A Hiring Platform’s Nine-Part Guide for Programmers BY AMMON BARTRAM ON MAR 8, 2016. This post started as the preparation material we send to our candidates, but we decided to post it publicly. Being a good programmer has a surprisingly small role in passing programming interviews. To be a productive programmer, you need to be able to solve large, sprawling problems over weeks and months. Each question in an interview, in contrast, lasts less than one hour. To do well in an interview, then, you need to be able to solve small problems quickly, under duress, while explaining your thoughts clearly. This is a different skill . On top of this, interviewers are often poorly trained and inattentive (they would rather be programming), and ask questions far removed from actual work. They bring bias, pattern matching, and a lack of standardization. [1] Running Triplebyte, I see this clearly. We interview engineers without looking at resumes, and fast-track them to on-sites at top tech companies. We’ve interviewed over 1000 programmers in the last nine months. We focus heavily on practical programming, and let candidates pick one of several ways to be evaluated. This means we work with many (very talented) programmers without formal CS training. Many of these people do poorly on interviews. They eat large sprawling problems for breakfast, but they balk at 45-min algorithm challenges. The good news is that interviewing is a skill that can be learned. We’ve had success teaching candidates to do better on interviews. Indeed, the quality that most correlates with a Triplebyte candidate passing interviews at companies is not raw talent, but rather diligence. But the status quo is what it is. We’re working at Triplebyte to change this. If you’re interested in what we’re doing, we’d love you to . In the meantime, if you do want to get better at interviewing, this blog post describes how we think you can most effectively do so. I fundamentally do not believe that good programmers should have to learn special interviewing skills to do well on interviews. check out our process 1. Be enthusiastic Enthusiasm has a huge impact on interview results. About 50% of the Triplebyte candidates who fail interviews at companies fail for non-technical reasons. This is usually described by the company as a “poor culture fit”. Nine times out of ten, however, culture fit just means enthusiasm for what a company does. Companies want candidates who are excited about their mission. This carries as much weight at many companies as technical skill. This makes sense. Excited employees will be happier and work harder. The problem is that this can be faked. Some candidates manage to convince every company they talk to that it’s their dream job, while others (who are genuinely excited) fail to convince anyone. We’ve seen this again and again. The solution is for everyone to get better at showing their enthusiasm. This is not permission to lie. But interviewing is like dating. No one wants to be told on a first date that they are one option among many, even though this is usually the case. Similarly, most programmers just want a good job with a good paycheck. But stating this in an interview is a mistake. The best approach is to prepare notes before an interview about what you find exciting about the company, and bring this up with each interviewer when they ask if you have any questions. A good source of ideas is to read the company’s recent blog posts and press releases and note the ones you find exciting. This idea seems facile. I imagine you are nodding along as you read this. But (as anyone who has ever interviewed can tell you) a surprisingly small percentage of applicants do this. Carefully preparing notes on why you find a company exciting really will increase your pass rate. You can even reference the notes during the interview. Bringing prepared notes shows preparation. 2. Study common interview concepts A large percentage of interview questions feature data structures and algorithms. For better or worse, this is the truth. We gather question details from our candidates who interview at companies (we’ll be doing a in-depth analysis of this data in a future article), and algorithm questions make up over 70% of the questions that are asked. You do not need to be an expert, but knowing the following list of algorithms and data structures will help at most companies. Hash tables Linked lists Breadth-first search, depth-first search Quicksort, merge sort Binary search 2D arrays Dynamic arrays Binary search trees Dynamic programming Big-O analysis Depending on your background, this list may look trivial, or may look totally intimidating. That’s exactly the point. These are concepts that are far more common in interviews than they are in production web programming. If you’re self-taught or years out of school and these concepts are not familiar to you, you will do better in interviews if you study them. Even if you do know these things, refreshing your knowledge will help. You need to be able to write a BFS cold, and you need to understand how a hash table is implemented. A startlingly high percentage of interview questions reduce to breadth-first search or the use of a hash table to count uniques. Learning these things is not as hard as many of the people we talk to fear. Algorithms are usually described in academic language, and this can be off-putting. But at its core, nothing on this list is more complicated than the architecture of a modern web app. If you can build a web app (well), you can learn these things. The resource that I recommend is the book by Steven Skiena. Chapters 3 through 5 do a great job of going over this material, in a straightforward way. It does use C and some math syntax, but it explains the material well. Coursera also has several good algorithms courses. , in particular, focuses on the concepts that are important in interviews. The Algorithm Design Manual This one Studying algorithms and data structures helps not only because the material comes up in interviews, but also because the approach to problems taken in an algorithm course is the same approach that works best in interviews. Studying algorithms will get you in an interview mindset. 3. Get help from your interviewer Interviewers help candidates. They give hints, they respond to ideas, and they generally guide the process. But they don’t help all candidates equally. Others are judged harshly for any hints they are given. You want to be helped. Some programmers are able to extract significant help, without the interviewer holding it against them. This comes down to process and communication. If the interviewer likes your process and you communicate well with them, they will not mind helping. You can make this more likely by following a careful process. The steps I recommend are: Ask questions Talk through a brute-force solution Talk through an optimized solution Write code After you are asked an interview question, start by clarifying what was asked. This is the time to be pedantic. Clarify every ambiguity you can think of. Ask about edge cases. Bring up specific examples of input, and make sure you are correct about the expected output. Ask questions even if you’re almost sure you know the answers. This is useful because it gives you a chance to come up with edge cases and fully spec the problem (seeing how you handle edge-cases is one of the main things that interviewers look for when evaluating an interview), and also because it gives you a minute to collect your thoughts before you need to start solving the problem. Next, you should talk through the simplest brute-force solution to the problem that you can think of. You should talk, rather than jump right into coding, because you can move faster when talking, and it’s more engaging for the interviewer. If you retreat into writing code, however, you’ll miss this opportunity. If the interviewer is engaged, they will step in and offer pointers. Candidates often skip the brute-force step, assuming that the brute-force solution to the problem is too obvious, or wrong. This is a mistake. Make sure that you always give a solution to the problem you’ve been asked (even if it takes exponential time, or an NSA super computer). When you’ve described a brute-force solution, ask the interviewer if they would like you to implement it, or come up with more efficient solution. Normally they will tell you to come up with a more efficient solution. The process for the more efficient solution is the same as for the brute force. Again talk, don’t write code, and bounce ideas off of the interviewer. Hopefully, the question will be similar to something you’ve seen, and you’ll know the answer. If that is not the case, it’s useful to think of what problems you’ve seen that are most similar, and bring these up with the interviewer. Most interview questions are slightly-obscured applications of classic CS algorithms. The interviewer will often guide you to this algorithm, but only if you begin the process. Finally, after both you and your interviewer agree that you have a good solution, you should write your code. Depending on the company, this may be on a computer or a whiteboard. But because you’ve already come up with the solution, this should be fairly straightforward. For extra points, ask your interviewer if they would like you to write tests. 4. Talk about trade-offs Programming interviews are primarily made up of programming questions, and that is what I have talked about so far. However, you may also encounter system design questions. Companies seem to like these especially for more experienced candidates. In a system design question, the candidate is asked how he or she would design a complex real-world system. Examples include designing Google Maps, designing a social network, or designing an API for a bank. The first observation is that answering system design questions requires some specific knowledge. Obviously no one actually expects you to design Google Maps (that took a lot of people a long time). But they do expect you to have some insight into aspects of such a design. The good news is that these questions usually focus on web backends, so you can make a lot of progress by reading about this area. An incomplete list of things to understand is: HTTP (at the protocol level) Databases (indexes, query planning) CDNs Caching (LRU cache, memcached, redis) Load balancers Distributed worker systems You need to understand these concepts. But more importantly, you need to understand how they fit together to form real systems. The best way to learn this is to read about how other engineers have used the concepts. The blog is a great resource for this. It publishes detailed write-ups of the back-end architecture at real companies. You can read about how every concept on the list above is used in real systems. High Scalability Once you’ve done this reading, answering system design questions is a matter of process. Start at the highest level, and move downward. At each level, ask your interviewer for specifications (should you suggest a simple starting point, or talk about what a mature system might look like?) and talk about several options (applying the ideas from your reading). Discussing tradeoffs in your design is key. Your interviewer cares less about whether your design is good in itself, and more about whether you are able to talk about the trade-offs (positives and negatives) of your decisions. Practice this. 5. Highlight results The third type of question you may encounter is the experience question. This is where the interviewer asks you to talk about a programming project that you completed in the past. The mistake that many engineers make on this question is to talk about a technically interesting side-project. Many programmers choose to talk about implementing a neural network classifier, or writing a Twitter grammar bot. These are bad choices because it’s very hard for the interviewer to judge their scope. Many candidates exaggerate simple side projects (sometimes that never actually worked), and the interviewer has no way to tell if you are doing this. The solution is to choose a project that produced results, and highlight the results. This often involves picking a less technically interesting project, but it’s worth it. Think (ahead of time) of the programming you’ve done that had the largest real-world impact. If you’ve written a iOS game, and 50k people have downloaded it, the download number makes it a good option. If you’ve written an admin interface during an internship that was deployed to the entire admin staff, the deployment makes it a good thing to talk about. Selecting a practical project will also communicate to the company that you focus on actual work. A programmer who is too focused on interesting tech is an anti-pattern that companies screen against (these programmers are sometimes not productive). 6. Use a dynamic language, but mention C I recommend that you use a dynamic language like Python, Ruby or JavaScript during interviews. Of course, you should use whatever language you know best. But we find that many people try interviewing in C , C++ or Java, under the impression these are the “real’ programming languages. Several classic books on interviewing recommend that programmers choose Java or C++. At startups at least, we’ve found that this is bad advice. Candidates do better when using dynamic languages. This is true, I think, because of dynamic languages’ compact syntax, flexible typing, and list and hash literals. They are permissive languages. This can be a liability when writing complex systems (a highly debatable point), but it’s great when trying to cram binary search onto a whiteboard. No matter what language you use, it’s helpful to mention work in other languages. An anti-pattern that companies screen against is people who only know one language. If you do only know one language, you have to rely on your strength in that language. But if you’ve done work or side-projects in multiple languages, be sure to bring this up when talking to your interviewers. If you have worked in lower-level languages like C, C++, Go, or Rust, talking about this will particularly help. Java, C# and PHP are a problematic case. As we described in our , we’ve uncovered bias against these languages in startups. We have data showing that programmers using these languages in the interview pass at a lower rate. This is not fair, but it is the truth. If you have other options, I recommend against using these languages in interviews with startups. last blog post 7. Practice, practice, practice You can get much better at interviewing by practicing answering questions. This is true because interviews are stressful, but stress harms performance. The solution is practice. Interviewing becomes less stressful with exposure. This happens naturally with experience. Even within a single job search, we find that candidates often fail their initial interviews, and then pass more as their confidence builds. If stress is something you struggle with, I recommend that you jumpstart this process by Get a list of interview questions (the book is one good source) and solve them. Set a 20-minute timer on each question, and race to answer. Practice writing the answers on a whiteboard (not all companies require this, but it’s the worst case, so you should practice it). A pen on paper is a pretty good simulation of a whiteboard. If you have friends who can help you prepare, taking turns interviewing each other is great. Reading a lot of interview questions has the added benefit of providing you ideas to use when in actual interviews. A surprising number of questions are re-used (in full or in part). practicing interview stress. Cracking the Coding Interview Even experienced (and stress-free) candidates will benefit from this. Interviewing is a fundamentally different skill from working as a programmer, and it can atrophy. But experienced programmers often (reasonably) feel that they should not have to prepare for interviews. They study less. This is why junior candidates often actually do better on interview questions than experienced candidates. Companies know this, and, paradoxically, some tell us they set lower bars on the programming questions for experienced candidates. 8. Mention credentials Credentials bias interviewers. Triplebyte candidates who have worked at a top company or studied at a top school go on to pass interviews at a 30% higher rate than programmers who don’t have these credentials (for a given level of performance on our credential-blind screen). I don’t like this. It’s not meritocratic and it sucks, but if you have these credentials, it’s in your interest to make sure that your interviewers know this. You can’t trust that they’ll read your resume. 9. Line up offers If you’ve ever read fund-raising advice for founders, you’ll know that getting the 1st VC to make an investment offer is the hardest part. Once you have one offer, more come pouring in. The same is true of job offers. If you already have an offer, be sure to mention this in interviews. Mentioning other offers in an interview heavily biases the interviewer in your favor. This brings up the strategy of making a list of the companies you’re interested in, and setting up interviews in Doing well earlier in the process will increase your probability of getting an offer from you number one choice. You should do this. reverse order of interest. Conclusion Passing interviews is a skill. Being a great programmer helps, but it’s only part of the picture. Everyone fails some of their interviews, and preparing properly can help everyone pass more. Enthusiasm is paramount, and research helps with this. As many programmers fail for lacking enthusiasm as fail for technical reasons. Interviewers help candidates during interviews, and if you follow a good process and communicate clearly, they will help you. Practice always helps. Reading lots of interview questions and inuring yourself to interview stress will lead to more offers. This situation is not ideal. Preparing for interviews is work, and forcing programmers to learn skills other than building great software wastes everyone’s time. Companies should improve their interview processes to be less biased by academic CS, memorized facts, and rehearsed interview processes. This is what we’re doing at Triplebyte. We help programmers get jobs without looking at resumes. We let programmers pick one of several areas in which to be evaluated, and we study and improve our process over time. We’d love to help you get a job at a startup, without jumping through these hoops. You can get started . But the status quo is what it is. Until this changes, programmers should know how to prepare. here Thanks to Jared Friedman, Emmett Shear, Garry Tan, Alexis Ohanian and Daniel Gackle for reading drafts of this. [1] This is not to say that interview performance does not correlate with programing skill. It does. But the correlation is far weaker than most companies assume, and factors other than programing skill explain a large part of interview variance. ↩ Originally Published On Triplebyte’s Blog: https://triplebyte.com/blog/how-to-pass-a-programming-interview

How to Pass a Programming Interview

Epicenter is a decentralized economic ecosystem of financial products, tools, and services put together. Consisting of three components: a store of value (Epic Cash), an ecosystem of utility governance (ECR token), and a medium of exchange (EUSD token), it serves as a potent alternative to underserved financial systems. Epicenter provides a secure means of exchange, with wide distribution. It comprises: Epic Cash which is based on Bitcoin with a 21M hard cap supply but utilizes cutting edge Mimblewimble PoW technology for superior efficiency; ECR which is the governance token and EUSD which is the soft algo dollar price peg. The core of Epicenter Epicenter is an open-source economic system that was devised to bridge the gap between the fiat world and the crypto world. It provides an integrated and connected DeFi ecosystem that includes lending, staking, swapping payments, and fiat on/off rails. Ultimately, it will allow for two-way fiat-to-crypto conversions without compromising payments with a credit card and gold. Also rewarding Epic Cash token holders with new dApp distributions as projects launch in the Epicenter ecosystem is a functional strong point. Designing a currency that effectively combines the privacy-protecting MimbleWimble protocol with sound economics and fair distributionhas been a major problem area targeted to be solved by Epic. Epic cash was created to remediate this dearth. Epicash provides a 100% proof-of-work mined currency with no special nodes Epicenter mission statement: Make crypto usable in the real world through a store of value, medium of exchange and unit of account. Strong points of Epicenter • The Epicenter ecosystem is designed to connect to other blockchains with cross-chain threading and atomic swaps. • This connectivity with other blockchains creates a broad audience for users to create value within the Epicenter ecosystem. • Epicenter provides a broad ecosystem of composable components with multi-chain connectivity. • Its unique connectivity feature provides both end-users and developers flexible options for defi including lending, staking, swapping, payments, future features, ALL with crypto to fiat convertibility. • The diversity of the ecosystem reduces risks by offering a variety of monetization options for developers that cater to a broad user base. More about Epic cash It is no news that 1.7 billion adults have no access to the global financial system, while another 1.3 billion are underserved. The presence of these challenges has shown signs of possible strains on human capital. In a bid to provide secure, scalable, and private means of exchange, designed for wide distribution, Epic cash was curated. Epic Cash is the original vision of Bitcoin. It is designed with the capability of being a medium of exchange as well as a store of value. It is Nakamoto consensus-compliant and with a fixed supply of 21 million. It can also be described as a novel implementation of the original Bitcoin Standard of deflationary digital scarcity, updated for fungibility, scalability, censorship resistance, centralization resistance, regulatory compatibility, and accessibility What Epic cash does? • Epic cash strictly believes that privacy is a fundamental right hence, it is a currency for everyone and combines privacy-protecting efficiently. • In combination with the best of public and private blockchains, it ensures adequate protection of user privacy at the protocol level while being completely fungible with each coin being identical to every other coin. • Epic operates on light and efficient blockchain, reducing blockchain size by nearly 90% through an automated process known as Cut-Through, allowing for more scalability and improved anonymity. • The security of ASICs with the accessibility of CPU and GPU mining using tiered consensus and emission is perfectly combined by Epic. Hence, it can be multi-functional, acting as CPU, GPU, and ASIC-mined. As a result of this, block rewards are distributed according to a schedule that balances wide distribution with long-term security. • Rust-based Mimblewimble blockchain is used to ensure that Epic Cash is productive in unlocking human capital through connecting individuals to the global market. • Through the use of Epic Cash, improvements in all of the attributes that will be necessary to foster mass adoption, such as fungibility, scalability, censorship resistance, centralization resistance, regulatory compatibility, and accessibility, will be recorded. • It is fast, virtually free to use, and open to all. Why you should opt-in The Epicenter project is community-driven, from the ground up. It has successfully spread its tentacles to accommodate over 80 volunteer contributors and is open to more. Although some token has to be paid, the development fee goes only for software, with nothing allocated for promotion or marketing activities. Fungibility is assured by the problems of transaction censorship and miner extractable value that are currently bedevilling BTC and ETH are completely taken care of through the incorporation of Mimblewimble protocol, which assures fungibility of Epic cash. The blockchain is extremely compact (currently 1.2 Gb), which means that a full node can be hosted on a low-end smartphone. Although a variety of different proof-of-work algorithms are employed to allow mining to be done on home computers and even mobile devices, it still ensures a noncustodial vision of individual financial sovereignty.  The company's structure is explicitly designed for regulatory compliance, particularly about the Howey Test for securities, so there is no controlling authority.

Institutional Investment Keep Pouring Into Bitcoin

We’re starting a new column with this article — . The author of the column is , our investment expert and co-founder of . It’s here where we’ll scan the market for the newest and most remarkable upcoming ICOs and analyze them in-depth, and our focus will be on the viability of the business concepts behind these projects. This won’t concern pre-ICO/ICO price gaps, no gasps and groans about the team, and no code checking. Here, we will try to break everything down to see if there are any market prospects for the product if, and only if, that product can be delivered. #icobusted Alexander Savinkin Howtotoken We would appreciate any feedback you may have about our new format, so please feel free to express your opinions in the comments section — which parts were you the most interested in and how we could improve the topics to make them more interesting to you. To stay up to date about what we’re doing, follow us on: Twitter Facebook Telegram Or just subscribe to our email newsletters (to receive updates and exclusive materials that are only distributed through email): One of the most interesting upcoming ICOs that is about to pop up is called . In a few words, Akropolis aims to be the first pension fund marketplace. It is one of the few projects that is going to leverage the blockchain within the traditional sectors of economy. And that particular sector happens to be pretty darn big. Akropolis Product The core idea is to build a platform that unites separate pension funds into one marketplace where individuals can compare and choose the most appropriate pension fund and plan (from the wide variety that’s out there), and fund managers will be able to use the platform for customer acquisition. Data trustworthiness and data ownership will be supported via smart contracts. I won’t say that it’s impossible to make such a service without the blockchain; but the blockchain can provide the advantages of truthfulness as a public control mechanism, mostly because of the distributed ledger for the history of interactions and a fund’s performance/reputation. Also, can be utilized to reduce fractions between pension funds and fund managers, for example. It’s also a great way to experiment and see how the things work out in practiсe while having a centralized backup. DAO Akropolis has decided to begin with a semi-decentralized solution, because the blockchain is not a necessary element in this case, especially in the beginning. But as we have all seen, the retirement fund market is very unstructured and difficult to properly analyze. In this way, the blockchain architecture of data storage might be one of the ways of bringing order into this chaotic market. Also, this might bolster smart-contract usage on the neutral territory of Akropolis’ platform, which by itself might turn this into a more customer-oriented market. And sure, blockchain implementation into Akropolis’ platform is also a good reason to have an ICO. These days, the typical way of choosing a pension fund is to consult with your bank manager, insurance agent, or financial consultant. And most of these intermediaries will just send you off to their partners. Meaning your choices will be severely limited and biased, since these “partners” usually pay out a finders fee. Yes, there are a lot of on how to manage your pension savings. There are a range of out there that try to help you make a choice. There are also some fund-rating sites and . Still, with the lack of uniformity that this market is facing, customers come along with a lack of data and extended expenses. surveys and calculators projects in the UK the US But there is no such marketplace yet where you can compare all the choices by different characteristics, buy them directly on the site, or even make your own personal order for a pension program and set an auction for the funds you’re interested in. So if anyone can provide an unbiased and easy-to-understand tool to select the most suitable and reliable pension fund, then it’s a big opportunity to hit the jackpot. Market Akropolis’ potential market is really, really big. In 2017, US pension assets reached , and it is about half of the total global volume of pension assets. $25,400 billion with a 12.7% annual growth rate By the way, all that happened at a time when the average level of unfunded government pension liabilities analyzed in 20 OECD countries was ~190% of the GDP, dwarfing the reported amount of all government debt, which totals only at 109% of the GDP (due to CityGroup report cited in Akropolis’ white paper)! The central idea for the market is this: the ageing population in developed countries is a steady process that coincides with worsening state finances. Everyone has to earn his or her pension mostly for themselves. Akropolis can pretend to fund marketing budgets for customer acquisition. To assess the size of potential stake let’s take mutual funds for instance, which are the main client hunters in the market. have managed a total of $11.6 trillion in 2016. This industry’s revenue is in the order of $100 billion, with over one third of this amount representing expenditures in marketing, which largely consists of sales loads and broker commissions (known as 12b-1 fees). Active mutual funds in the U.S. So if Akropolis becomes a customer’s point of entry to pension funds, then it could have a potential market in the volume of at least $30 billion per year. Analogues As a matter of fact, there is not much innovation in the traditional pension fund industry. And any project promising a real step forward for both the customers and service providers can expect a good market response. We can take the as an example, which provides an investment advisory application designed for the management of employees’ 401k accounts (for $10 per month with an estimated average of $5,000 in annual savings). Blooom collects basic user data like name, email, age, and timeframe to retirement and provides free allocation and fee analysis. For a monthly fee, clients can sign up to have their accounts managed by Blooom. Blooom project In less than three years, Blooom had reached $2 billion in assets under management by January 2018. Round A (October 2015) was at $4 million and then Blooom landed $9 million for Round B (January 2017). Blooom revenue is not available, but by multiplying the 16,000 clients declared on the site by the $120 annual fee we get a figure that it’s about $2 million. Assuming that 90 million Americans, on their own, are bungling their most valuable retirement asset — the 401K — only 1% of the market is needed to boost Blooom revenue beyond $100 million. It looks like quite a unicorn, guys! Summary Akropolis doesn’t seem to have a high risk of development failure, and they could very well deliver on all of their promises. The $25 million hardcap also looks reasonable for such an endeavour. But the problem here is that, according to Akropolis’ draft of materials, $12.5 million in technology and talent expenses seems like a lot, and the $2.5 million for marketing also raises questions. Is it really enough money for a nationwide B2C (business-to-customer) marketing campaign, let alone for global marketing? It may come to pass that extra funding will be required before the project starts to earn enough by itself. And it is my own personal belief that having a security token is a must. Whatever is embedded in a utility token, nothing else will secure investor`s future profits with 100% guarantee. This is not a cryptocurrency to cash in on during a stock-price uprise. And final : pro et contra (+) Huge and rising national market, and global as well (+) Lack of direct and strong competitors in the marketplace (+) Product is not so hard to deliver (-) Unthought-out budget (at least in the draft version) (-) Token is not a security one, although dividend payouts suggest otherwise All in all, Akropolis deserves a more detailed consideration from investors. You can read now only a draft of the WP. And folks, please, make your WP beta-version more human-readable! ___________________________________________________________________ Useful materials Why people are abandoning their 401(k)s by Marketplace Weekend Staff Global pension assets study 2018 by Thinking Ahead Institute Pension Research Reports by Society of Actuaries (SOA) 2018 Corporate Pension Funding Study by Milliman Building a better retirement income solution by Ernst & Young (EY) New Jersey issues RFI for investment options for private-sector retirement marketplace by Robert Steyer New Jersey Small Business Retirement Marketplace by State of New Jersey New Retirement Plan Marketplace Makes It Easier for Americans to Save by John Mangan Research & commentary by Vanguard Best Retirement Plans: Choose the Right Plan for You by Dayana Yochim Marketing Mutual Funds by Nikolai Roussanov, Hongxun Ruan, and Yanhao Wei Washington State Launches Online Retirement Marketplace by Melanie Waddell A New Frontier for Saving for Retirement? The Creation of State Retirement Savings Marketplaces by Rockefeller Institute of Government All materials are for informational purposes only. None of the material should be interpreted as investment advice. Originally published at howtotoken.com on August 28, 2018.

The $21 Trillion Pension Fund Market is Pouring Money into the Blockchain — #icobusted analysis

Digital Marketing Retainers Have Dissolved Into Wallet Joke For Agencies

Financial institutions are investing heavily in AI, with North American banks leading the way in adopting gen AI for enhanced customer service and operational efficiency. The financial and banking services sector has entered a new phase in its digital transformation journey: the Artificial Intelligence (AI) era. While the use of AI in financial services is not new, and AI-powered applications such as trading algorithms and autonomous rule-based credit decision systems have been around for a while— the recent influx of capital from financial institutions to build future-proof AI infrastructure emphasizes the technology’s growing significance.According to a recentIDC report, the financial sector is projected to be the largest spender on AI solutions between 2024 and 2028, with banking at the forefront. The financial services industry is anticipated to account for over 20% of all AI expenditures, representing the highest growth rate among major industries. Today, AI's impact is evident across an array of financial applications, from automated knowledge management to personalized banking services. Specifically, North American banks are heavily investing in generative AI (genAI) to drive innovation and improve operational transparency. GenAI is helping enhance banking and chatbot capabilities, offering experiences that are distinct from traditional automated services. GenAI agents are now available 24/7 in multiple languages, to assist customers with banking queries and account management. To support these efforts, they are acquiring crucial hardware like AI chips and data processing infrastructure, while simultaneously channeling resources to hire human talent. Finance and AI: A Synergy in the Making Financial institutions are among the most data-rich organizations, handling billions of customer transactions and interactions across both digital and physical platforms. This wealth of data is essential to train A.I. systems, which is why banks have been pouring significant resources into maintaining the technology. The goal is to refine existing customer processes and explore high-impact use cases for inculcating A.I., scaling innovative prototypes into robust solutions. For instance, Paris-based Amundi, managing €2 trillion ($2.1 trillion) in assets, utilizes genAI tools to customize investment portfolios for its more than 100 million clients. By engaging clients in discussions about their risk preferences through its chatbot, the asset management firm crafts portfolios that meet individual needs while also gaining real-time insights into client sentiment. “Younger demographics, who will make up an increasingly large percentage of overall target customers for the finance industry - are very comfortable interacting with chatbots and often prefer this option over traditional options like call centers,” Ranjit Tinaikar, CEO of Ness Digital Engineering, told me. “AI can drastically improve the risk insights in pricing consumer loans. The banking industry is tapping into these trends to future-proof the way they deliver customer service and optimize experiences” Renowned North American investment bank and wealth management firm Morgan Stanley, recently introduced its new internal genAI model designed for its financial advisors and support staff. Powered by OpenAI’s GPT-4, the ‘AI @ Morgan Stanley Assistant’ helps thousands of wealth managers to swiftly retrieve information from an extensive internal knowledge base. It can also summarize client meeting notes and automatically draft follow-up emails. Despite its promise, the use of AI for handling sensitive financial decisions does have its drawbacks. AI models’ decision-making abilities are often hindered by unreliable data or unexpected infrastructural errors, which in turn impacts their effectiveness. Challenges and Limitations of AI in Financial Services A.I.'s ability to disrupt industry dynamics appears closely tied to the nature of customer data and the challenges it addresses. In banking service areas like wealth management or lending, where data is less abundant and unstable, A.I.'s impact has been more incremental than transformational. The finance industry’s experience with AI also raises questions about whether AI will democratize or consolidate power within the sector. “Most banking corporations don’t have the requisite organizational expertise and governance to deploy A.I. solutions. GenA.I. is only as smart as the data used to train the large language models (LLMs),” Tinaikar added. “Access to this data, which is often fragmented across internal and external systems, is a major challenge to create reliable financial A.I. models.” It is crucial to ensure that A.I. operates within a secure, ethical, and compliant framework to prevent potentially sensitive data misuse. In this context, larger companies with vast resources for technology and data investments hold a notable edge over their smaller rivals. For small and medium-sized businesses (SMBs), the real challenge to creating smarter and reliable A.I. systems is their inability to afford A.I. experts and invest in security management architectures. “You do need a “human-in-loop” for sensitive decisions around A.I.-generated pricing and risk recommendations,” said Tinaikar. “In the interim, SMB corporates have to develop their self-governance to protect their consumers, investors, and other key stakeholders.” Why Human Judgment Remains Crucial in AI-Enhanced Finance GenA.I. represents a transformative shift, enabling banks to fully harness the power of their customer data. By analyzing structured and unstructured data from diverse sources and understanding the relationships between them, it offers enhanced visibility and insights into both risks and opportunities within and beyond the bank. But, as AI makes data analysis more efficient and cost-effective, the role of human judgment becomes even more crucial. AI systems currently lack the nuance and creativity of human insight, making them unreliable for sensitive use cases. Although AI is an incredibly powerful tool, financial managers and institutions stand to gain the most from combining AI's power with unique human abilities. “The difference lies, of course, in the type of emotional judgment applied by human experts,” explained Tinaikar. “As AI introduces the potential for a disruptive shift where machines could replace human judgment, we need to develop regulatory and technological solutions to ensure that humans remain involved in mission-critical tasks where machine-simulated judgment may not be reliable.” Financial institutions are investing heavily in AI, with North American banks leading the way in adopting gen AI for enhanced customer service and operational efficiency. Financial institutions are investing heavily in AI, with North American banks leading the way in adopting gen AI for enhanced customer service and operational efficiency. The financial and banking services sector has entered a new phase in its digital transformation journey: the Artificial Intelligence (AI) era. While the use of AI in financial services is not new, and AI-powered applications such as trading algorithms and autonomous rule-based credit decision systems have been around for a while— the recent influx of capital from financial institutions to build future-proof AI infrastructure emphasizes the technology’s growing significance. According to a recent IDC report , the financial sector is projected to be the largest spender on AI solutions between 2024 and 2028, with banking at the forefront. The financial services industry is anticipated to account for over 20% of all AI expenditures, representing the highest growth rate among major industries. IDC report IDC report Today, AI's impact is evident across an array of financial applications, from automated knowledge management to personalized banking services. Specifically, North American banks are heavily investing in generative AI ( genAI ) to drive innovation and improve operational transparency. GenAI is helping enhance banking and chatbot capabilities, offering experiences that are distinct from traditional automated services. GenAI agents are now available 24/7 in multiple languages, to assist customers with banking queries and account management. genAI To support these efforts, they are acquiring crucial hardware like AI chips and data processing infrastructure, while simultaneously channeling resources to hire human talent. Finance and AI: A Synergy in the Making Finance and AI: A Synergy in the Making Financial institutions are among the most data-rich organizations, handling billions of customer transactions and interactions across both digital and physical platforms. This wealth of data is essential to train A.I. systems, which is why banks have been pouring significant resources into maintaining the technology. The goal is to refine existing customer processes and explore high-impact use cases for inculcating A.I., scaling innovative prototypes into robust solutions. For instance, Paris-based Amundi , managing €2 trillion ($2.1 trillion) in assets, utilizes genAI tools to customize investment portfolios for its more than 100 million clients. By engaging clients in discussions about their risk preferences through its chatbot, the asset management firm crafts portfolios that meet individual needs while also gaining real-time insights into client sentiment. Amundi Amundi “Younger demographics, who will make up an increasingly large percentage of overall target customers for the finance industry - are very comfortable interacting with chatbots and often prefer this option over traditional options like call centers,” Ranjit Tinaikar, CEO of Ness Digital Engineering, told me. “AI can drastically improve the risk insights in pricing consumer loans. The banking industry is tapping into these trends to future-proof the way they deliver customer service and optimize experiences” Renowned North American investment bank and wealth management firm Morgan Stanley, recently introduced its new internal genAI model designed for its financial advisors and support staff. Powered by OpenAI’s GPT-4, the ‘ AI @ Morgan Stanley Assistant ’ helps thousands of wealth managers to swiftly retrieve information from an extensive internal knowledge base. It can also summarize client meeting notes and automatically draft follow-up emails. AI @ Morgan Stanley Assistant AI @ Morgan Stanley Assistant Despite its promise, the use of AI for handling sensitive financial decisions does have its drawbacks. AI models’ decision-making abilities are often hindered by unreliable data or unexpected infrastructural errors, which in turn impacts their effectiveness. Challenges and Limitations of AI in Financial Services Challenges and Limitations of AI in Financial Services A.I.'s ability to disrupt industry dynamics appears closely tied to the nature of customer data and the challenges it addresses. In banking service areas like wealth management or lending, where data is less abundant and unstable, A.I.'s impact has been more incremental than transformational. The finance industry’s experience with AI also raises questions about whether AI will democratize or consolidate power within the sector. “Most banking corporations don’t have the requisite organizational expertise and governance to deploy A.I. solutions. GenA.I. is only as smart as the data used to train the large language models (LLMs),” Tinaikar added. “Access to this data, which is often fragmented across internal and external systems, is a major challenge to create reliable financial A.I. models.” It is crucial to ensure that A.I. operates within a secure, ethical, and compliant framework to prevent potentially sensitive data misuse. In this context, larger companies with vast resources for technology and data investments hold a notable edge over their smaller rivals. For small and medium-sized businesses (SMBs), the real challenge to creating smarter and reliable A.I. systems is their inability to afford A.I. experts and invest in security management architectures. “You do need a “human-in-loop” for sensitive decisions around A.I.-generated pricing and risk recommendations,” said Tinaikar. “In the interim, SMB corporates have to develop their self-governance to protect their consumers, investors, and other key stakeholders.” Why Human Judgment Remains Crucial in AI-Enhanced Finance GenA.I. represents a transformative shift, enabling banks to fully harness the power of their customer data. By analyzing structured and unstructured data from diverse sources and understanding the relationships between them, it offers enhanced visibility and insights into both risks and opportunities within and beyond the bank. But, as AI makes data analysis more efficient and cost-effective, the role of human judgment becomes even more crucial. AI systems currently lack the nuance and creativity of human insight, making them unreliable for sensitive use cases. Although AI is an incredibly powerful tool, financial managers and institutions stand to gain the most from combining AI's power with unique human abilities. “The difference lies, of course, in the type of emotional judgment applied by human experts,” explained Tinaikar. “As AI introduces the potential for a disruptive shift where machines could replace human judgment, we need to develop regulatory and technological solutions to ensure that humans remain involved in mission-critical tasks where machine-simulated judgment may not be reliable.”

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