The clubhouse is an invite-only social network that just has audio content. Nothing gets recorded so you have to listen to it live while people are talking. Remember the radio that was 'replaced' by the podcast? So, it's more or less that, but with a greater sense of proximity to the speaker and, of course, more technology, after all, we're talking about a social network. Recently . Clubhouse signs a deal with Ted Here are four reasons why you should participate in the Clubhouse. Reason #1: Simple, straightforward, and focused on content! Here, we can explore the benefit of simplicity further. There is no feed there, no photo, and not even many functions. The focus is on content. That is, you walk into a room, listen to the content, and sometimes have the opportunity to participate. Reason #2: No judgments! Most people are extremely visual. And, come to think of it, how many times have you missed what someone was saying because of your appearance? Or, on the other hand, we can follow people just for their looks. Well, at the Clubhouse that doesn't happen. On the new social network, delivery is 100% content. In other words, you will follow whoever has something to tell and/or teach. Reason #3: No metrics, no ego. Here there are no likes, shares, comments, or photos (except for the profile). The whole issue we've discussed for a while about the feeling of anxiety and comparison generated by social media doesn't happen in the Clubhouse. This way, people are also freer and safer to say what they want, without having that concern about their physical appearance or whether the content will engage. Reason #4: Opportunity to talk to people you admire As mentioned before, the Clubhouse gives us the feeling of being close to whoever is speaking. And maybe it's not just the feeling. Here you can enter a room and get to talk to that person you always wanted. Not to mention that if you add some content to the conversation it can generate several opportunities for you.

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In simple terms, a Front End or User Interface (UI) is the set of elements that enable a user to interact with a device or application. It allows your target audience to interact with it more easily. It allows your target  audience to see what products are available. It's created in such a way that it displays the services you provide to attract and keep visitors to your site. A strong User Interface is vital because it improves the interaction between the user and your website or application, which can convert potential visitors into consumers. It not only focuses on the aesthetics of a website, but also on its responsiveness, efficiency, and accessibility. Some of the advantages of a decent Front End or User Interface for your web or mobile application are listed below: Customer Credibility A good user interface design will install trust in your organization and brand among potential customers. They will view it as a direction on your firm if sections of your site do not perform properly or appear shabby. How can you give a quality product or service if you can't even make a good-looking website? Cost-cutting If you create an application that is simple to use and does not frustrate customers, you will notice that your customer service line will receive fewer calls regarding issues with apps or the web, as clients will be able to do all of their tasks online without assistance. Competitive Advantage One of the most important advantages of a good User Interface is the ability to convert clients quickly. If you make your visitors' lives easier through a fantastic user interface, you'll be more likely to convert them to customers, surpass your closest competitors, and reap the advantages in no time. Reduced Confusion The goal of UI design is to properly communicate the website's intention or message to the user without distracting them excessively. If there is inadequate color contrast, needless information overload, no consistency in UI design, or haphazard grouping of features, the user will be more confused and the website's ability to perform its purpose will be reduced. To avoid confusion and boost client conversion, UI developers should make sure that a website or application is tidy, organized, and un-chaotic. Clarified Vision Because you'll be making decisions early on, this method will help you specify exactly how you'll meet your audience's expectations. It summarizes who you are and what you intend to accomplish, allowing you to explain precisely what sets you apart from the competitors. UI UX design

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Photo by on Unsplash Javier Miranda Distributed Ledgers are not exclusively blockchains, but blockchains are the most widely used distributed Ledgers. So for the sake of simplicity, we will take the traditional Blockchain architecture as an example and build our new Distributed Ledger on top of it. This Idea might be the next revolution in the blockchain industry, a way to deal with scalability, limit Transparency, deal with energy consumption since we can use any consensus mechanism. At the same time, it might be an idea that is only good on paper, something that will be near impossible to achieve. This is MY vision of a real decentralized, widely-used distributed ledger; further studies are a must! Any additional information, upgrades, studies, comments that YOU can provide are more than welcome. As they say : it takes two to tango Anyway, let’s jump right into it : What is a Multi-Dimensional Distributed Ledger? To understand this concept, imagine a normal blockchain of 4 blocks; each block has a header and a body; the header usually contains: Block number Hash of the previous block Merkel Root or hash of this block’s data A nonce (Proof-of-work case) Timestamp The body contains data. This data can vary widely from simple transactions to smart contracts to any kind of data that we want to store in our chain. This is the typical structure we are talking about : Each block is chained with the previous block since it’s storing its hash with the exception of the first block. Since it’s the first block created, it cannot have a previous hash, so by default, it's usually made up of zeros. This is called the genesis block. But wait, what if each block is a genesis block for a new blockchain? Is that even possible, I hear you ask. The short answer is yes, but in order to achieve this, we need to answer a lot of questions, add some features to our blocks, and set some rules. Even then, this technology might still have a lot of flaws. What are the Dimensions You are Talking About? A new dimension is a new blockchain out of the last block of an already existing blockchain. Basically, what this means is that when a new block is mined,  instead of adding one new block, we add two, one that follows the same blockchain, and the other one is the first block in our new blockchain. We will add to our blockchain structure a new section called a portal. A portal can be part of the data or an independent unit, like the header and the body. The main blockchain will be called ; the new blockchain will be called dimension 0 dimension 1. Each new blockchain out of is called dimension 0 dimension 1. Likewise, Each new blockchain out of is called , and so on. dimension 1 dimension 2 In theory, We can have an infinite number of dimensions. Why Would I Expand a Block into a New Blockchain? By expanding a block and transforming it into the genesis block of a new blockchain, the amount of data stored in a blockchain can greatly increase without worrying about the whole chain being oversaturated. We can use different Proofs in our new dimensions instead of relying on one. The new dimension can be private to protect personal information, professional secrets, Sensible Data, etc. By having multiple dimensions, each independent and at the same time attached to the main chain, we can have a new way of communication between different blockchains, a portal to be exact. Is it possible to Access all of the Data in Different Dimensions? It really depends on the use cases of our dimensions and how flexible we want the dimensions to be. Our data can be transparent, and at the same time private, we can verify the integrity of our data and make sure that no one is able to change it, and at the same time, we can save our data off-chain since our portal is already on-chain. Is it Possible to Code this Solution? I am working on a prototype; it’s called a prototype because, well, it’s a small version of a huge potential change in the industry (at least that’s what I think). As I said, any additional information, upgrades, studies, comments that you want to provide are greatly appreciated! Possible Use Cases of Multi-Dimensional Distributed Ledger This can be particularly useful in medical use cases, we might not want the public to have access to the data, but at the same time, we want the doctors too. A doctor can verify that the data is not tampered with since he can store the hash of the next block, so when needed, as a patient, I can present my private chain with my personal information without the need of making it public. We can also use this architecture to store a huge amount of data; we could use data fragmentation techniques and split our data into different blocks, forming a new blockchain. The Features we Need to Add : Instead of mining one block, we will mine two blocks. One that goes with the current dimension, and follows its rules, While the second one will follow the rules of the new dimension. In case we decide to add a new dimension : We will add a portal to our block that will allow us to access the new dimension; each portal has : which is equal to: Gate Number Gate Number = “Block Number” . “Dimension number” A Gate Number is unique; it’s a way to define different portals in our system. So if we will open a portal in the second block in our main blockchain ( Dimension 0 ), our gate number will be 2.0 is equal to the hash of all the data in our current block. Portal Hash equal to zeroes. P_Previous Hash Timestamp If we use PoW for our new dimension, a nonce is needed as well. This is an example of the structure of our new Multi-Dimension blockchain: The Rules we Need to Set : I want to set some rules, first of all, because my resources are limited, and creating a huge Multidimensional blockchain is out of the question for me. Secondly, to avoid some bugs in the system, and lastly, well, because it’s funny to create something and set your own rules for it! If a block has a hash ( In the header or in the P_Previous Hash ) of zeroes, it may not expand its dimension anymore. Each dimension is independent and can be removed from the chain if needed; however, this may cause some problems, and tempering with the data in our new private chain is not impossible; one way to deal with this is : Storing the hash of the sum of the hashes of all the blocks removed in a new variable in our portal. Each dimension’s PoW, if it exists, is harder than the previous dimension by a number of zeroes. I set this rule to prevent abusing PoW. Save our planet ! Use less electricity! How new dimensions are created is up to your imagination, on request, random, or when some conditions are met, etc. Optimization and Next Steps : This architecture is nowhere near perfect. But I think it’s a good approach if we want to really achieve a perfect distributed ledger. To summarize, imagine if any kind of blockchain you are currently looking at is a 2D point of view of a 3D-shaped blockchain. Instead of using block number and gate number, we can use one variable that defines which block we are in and which dimension our current blockchain is situated in. Instead of adding new features to our block, our data could be the portal itself; this particular case could be useful to store large amounts of data on-chain. The next step is to code this prototype, which will be my next story; if you find this interesting, or you can contribute to this, or you want to discuss anything with me, feel free to reach out!

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Did you know you can embed NFTs ( ) into your ?  No?  Well, that isn't too surprising because we haven't told anyone about it. say what? Hacker Noon stories What I did here is browse and then when I found something resembling a voxel kung fu gorilla DJ (AKA ), I clicked on it.  Which brought me here: https://opensea.io/assets CyberKongz https://opensea.io/assets/0x7ea3cca10668b8346aec0bf1844a49e995527c8b/8438 Next, I scrolled down to the "Details" section on the left and clicked on it to reveal some important information, the "Contract Address" and the "Token ID" as seen here: This only works with NFTs using the ⚠️⚠️⚠️NOTE: ERC-721 contract⚠️⚠️⚠️ I clicked the link for the Contract Address, which opened in a new tab and copied the actual value from the top of the page as seen here: Right, so that's in the paste buffer, I go back to the draft and paste it in- but do I hit enter?  Goodness, no!  I go back and click the Token ID to similarly copy its value into my paste buffer because typing the 4 digits is too hard and you can totally 🙅 ha trust websites to fill your paste buffer, right? No, you can't, really.  They call it "Pastejacking"- it's not nearly as stimulating as it may sound to your filthy and immature ears. Okay, so in the end I wind up with this: 0x7ea3cca10668b8346aec0bf1844a49e995527c8b#8438 Then I press enter and I see it loading for a couple seconds like: If all goes well, you will see the embedded NFT load in after a few seconds. . 🎉 Congratulations If you see it loading forever, undo the embed and try again.  I have seen it hang the first time and then work quickly the second time 😐.  If that doesn't do it, make sure you aren't missing the #tokenId bit at the end the end.  Also be sure that the token standard is ERC-721. Right!  So you can now embed (ERC-721) NFTs into your Hacker Noon stories! Have a good one.✌️ Why not give it a go today?

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In 1697, Melvin Conway advanced a famous thesis. This thesis gets a mention in almost every guide to creating a microservice architecture — and for a good reason: more than one generation of developers has seen the thesis confirmed. But suppose a company's communications structure changes due to development and entry into new international markets. In this case, the product itself must change to meet users’ needs in these new markets. Formulating the Problem In the summer of 2020, our team was faced with preparing a product for entry into international markets. At that time, we had an extensive set of microservices designed and run in line with the company’s outdated organizational structure. Retaining this ragbag of services was difficult and expensive. What’s more, many of the services bore no connection with the business requirements or technological trends of the day. Confronting these new realities, we knew we needed to: ●	simplify the current architecture by getting rid of redundant data transfer between services
●	improve the transit time of data to the end-user
●	reformat teams to develop and maintain a new solution Our Solution Over the years, .NET became the company’s main back-end stack: we built up a significant code base, libraries for implementing extra functionalities, and expertise in C# development. So, we wanted to make the most of the previous experience during the development phase and not switch unnecessarily to a new stack. We started writing a service using .NET Core 3.1 and later migrated to .NET 5 quite quickly and painlessly. All our services are hosted using Kubernetes. The correct configuration of CI/CD falls only partially on the shoulders of the development team—the main approaches and templates for this are developed and supported by a separate DevOps team. Using Kafka, we can transfer data between our services in the form of messages containing a complete condition snapshot of a particular business entity. At the same time, snapshots change at very different rates. For example, taxonomy (data on championships, sports events, players) changes every few seconds, and operational data (markeе coefficients) changes several times per second. Based on this, one of the main requirements for the new service was the need to support high loads when delivering data to clients. It was originally designed to avoid the overhead of using external caches or databases. Therefore, when starting, the service reads data from Kafka topics into RAM, performing aggregation and distributing updates (in fact, updates are the difference between already sent and received data) reactively using the functionality via sockets on the front-ends, pre-serializing the data into a binary format using Messagepack. SignalR provides a framework for working with sockets. Rx.NET Solving Issues in the Process Issue №1: Memory Outdated data in Kafka has to be cleared so that only the data Kafka requires is read at the start of the service (current taxonomy and trading data). Otherwise, we’re forced to go through the topic from the earliest offsets, trying to decide whether the data is relevant in this offset and whether it should be loaded into memory, which is an unnecessary overhead. However, as Kafka is essentially a commit log and not a random access database, simply writing "Delete * From" won’t work (without additional services from Confluent). Therefore, a service that provides Kafka with data delivery if data losses relevance should send nulling using the value=null key—a tombstone telling Kafka the data is not relevant and can be "buried" when executing retention policies. Actual data should also be compact because when operating with snapshots, we are only interested in the last state of the entity. In this case, you need to specify the "compact" parameter as the retention policy in the topic settings. Another memory issue is the suboptimal use of data aggregations. For example, to join in , the data on the left and right must be pre-filtered to avoid unnecessary waste of resources. Rx.NET It’s also worth noting the benefits of keeping aggregates rather than raw data in memory. The more efficient use of LOH means that fewer objects get there—and the smaller they are, the less likely it is to get excessive heap fragmentation and OutOfMemory exception when allocating a large array. Fragmentation can happen when executing the ToList() method on IEnumerable—when it looks as though there’s still enough memory. Nevertheless, due to LOH fragmentation, the system cannot allocate enough of it. To check for heap fragmentation since the last garbage collection, initiate GC.GetMemoryInfo and look at the FragmentedBytes field. It shows the number of fragmented bytes in a heap—for example, "fragmentedBytes": 1669556304. Well, the axiomatic rule is to omit superfluous allocations altogether unnecessarily: passing each enumerator of a thread-safe collection is more profitable than making copies of this collection for multithreaded processing. Issue №2: Performance As noted, performance and the ability to distribute data to clients with an average latency <200 msec are essential for a service. Only updates need to be sent to clients to achieve this requirement—the difference between the previous and current state of the data. We use a structure of the Diff<T> type to do this, where T is the ViewModel, in which each field is filled only with the changed data. This approach saves on serialization and forwarding channel speed but requires more CPU to compute the difference for each object sent. In addition, updates are combined into batches so that not every minor update is sent to the client but buffer, either by quantity or amount of time. Garbage Collector settings have a very significant impact on performance. Of course, it’s necessary to strive for the minimum use of LOH to optimize the memory work, and when it is essential to do this, strive to minimize the size of the objects that get there. The default size LOH is 85000 bytes, but actually, you can experiment with increasing this value. For example, if you’re sure your objects don’t exceed 120,000 bytes and you can’t make them any smaller, you can try increasing the System.GC.LOHThreshold parameter to the appropriate value. Server garbage collection with concurrent mode also provides a significant advantage when optimizing data latency. For instance, enabling the corresponding parameters <ConcurrentGarbageCollection>true</ConcurrentGarbageCollection> and <ServerGarbageCollection>true</ServerGarbageCollection> made it possible to improve latency by about 20% compared to ServerGarbageCollection = false, albeit at the expense of a slight increase in CPU usage. The transition from .NET Core 3.1 to .NET 5.0, which was relatively easy with the assembly update and serialization edits, made it possible to improve service performance by about 15% without changing the code. Accordingly, the periodic update of the frameworks and libraries being used brings benefits at minimal developer cost, although there may be some exceptions. Yes, writing and maintaining tests in an up-to-date state requires team resources. Still, they allow you to estimate the impact of new features or fresh optimizations on how the service will behave in production and how much its requirements for memory and CPU will change. Issue №3: Compatibility Front-ends (Android/iOS/Web) subscribe to changes to operational data over a set of identifiers using web sockets. We use SignalR on the back-end as a "wrapper" over web sockets. However, at that time, we didn’t find any well-optimized and efficient libraries for supporting SignalR on the Android and iOS side for our requirements. Therefore, the front-ends had to fully support the SignalR binary protocol's features: consider the presence of hardbits, encoding several entities in one message by adding information about the length of data subsets (payloads) in the VarInt format to the message, etc. It added some work, but on the other hand, thanks to the well-written documentation of the SignalR framework, it allowed us to implement our custom transport library on the front side, and our teams fully support it. To describe contracts and passed types, we had to generate a custom JSON scheme. It contains additional information about the optionality of the field, a list of valid values for enums that are sent to the front-end in the form of int's, and other descriptors that help the front-ends create subscriptions parsing the received data. When contracts need to be changed, a new subscription version is created that supports the changes. Until all of our client applications migrate to new subscriptions, we have to maintain two subscription options simultaneously. Issue №4: Monitoring To monitor the operation of the service effectively requires logs and metrics, of course. Everything is more or less standard here: ELK for logs and Prometheus/Grafana for taking metrics and monitoring them in nearly real-time. The more metrics you measure, the better. That’s the rule. Even a metric like the lack of new data for some time can indicate that something is wrong—problems with data mirroring between Kafka clusters, for example, or the service lags out due to an unhandled error in the subscription via . Rx.NET We also consider the time it took for the message to reach our service, bypassing all previous transfers, as an essential parameter. It allows us to evaluate the message’s latency within our service and throughout the entire system. The metric showing the difference between generated batches that are to be sent to the client and disposed batches allows us to evaluate memory leaks and the efficiency of the mechanism for multiplying data to different subscribers. Issue №5: Scaling and Hosting To use hosting resources efficiently while avoiding performance drops with a large number of connections in prime time, it’s important that autoscaling is as flexible as possible. It should also respond to metrics that allow you to balance between resource utilization and good data latency for the end-user. Metric options for scaling should consider the load on the CPU, the signal flow rate from Kafka at the input to the sockets at the output, and the amount of memory consumed. Degradation in this metric can signal to the orchestrator that it’s time to bring up another service instance. Moreover, the memory metric is an inflexible indicator due to the non-deterministic nature of GC memory cleaning. You can experiment with the System.GC.HighMemoryPercent parameter and achieve more aggressive GC work, not at 90% of the occupied memory (by default), but 80%. It will allow autoscaling to consume resources more efficiently. Our Conclusions We continue to face some difficulties, but we can already pinpoint several conclusions: Optimizing the performance and memory consumption of a highly loaded service is a permanent necessity. Essentially, we are in a race between adding new features and minimizing their impact on system performance. It requires significant effort on the part of the team, on both the development and the testing sides. Moreover, the presence of Load tests is necessary It’s worth updating the main libraries and frameworks regularly because they often improve performance and make the service more stable Measure everything measurable. The more metrics you have, the more predictable the system is, and the more data available to improve it. It can be helpful to reconsider the default settings of the frameworks used, whether it’s Kafka or .NET. We hope that our experience will help you to prepare for the challenges of building high-load services. Good luck!

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Disclaimer: This article was originally published January 20, 2018 . This is for informational purposes only. This is not financial advice. on freeCodeCamp The amount of engagement in the crypto investment space needs no introduction. With , I thought I’d put together a simple Jupyter notebook to get a clearer and broader viewpoint into the investment activities within my own crypto portfolio. market caps, volumes, and public awareness on the rise TL;DR ;) here’s the code Why Should We Analyze Our Portfolios? Because we’re definitely missing important details about our investments by only looking at the total value of our (potentially fat) wallets — even though I enjoy looking at Blockfolio from time to time. Because seeing our Ripple go to the moon and overshadow the rest of our investments is likely increasing our financial risk substantially. Because we all want our money to grow, but achieving this by picking a diverse set of cryptos is easier and safer than picking a moonshot that could end up a dud (and make us broke). And let’s face it, the market gains are just too big for us to be left in the dark on the true characteristics of our investment portfolios. Important Portfolio Characteristics Now there are several characteristics of our portfolio that we should take a good look at, including return risk. But a lot of the time we’re fixated on one and not the other. and We can look at return in several ways: the amount of money we’ve made from the beginning to the current date, the average rate of money we’ve made over specific time periods (e.g., annual returns), how much better our investments did when compared to several characteristics of a benchmark (e.g., ), and even the annual compound rate it would have taken to get to our current investment based on our starting point (i.e., ). alpha CAGR As important, if not more, is how we look at risk and its effect on return. I don’t know about you, but I want to make sure I’m making a good return based on an amount of risk I feel comfortable with. If we take on a huge amount of risk to make one particular return when we could have taken much less risk to make that very same return, the path to take for a more is clear. efficient investment This is where understanding volatility, correlations, and risk-adjusted returns come into play by computing statistics such as standard deviation of returns (or volatility), , the , and the . beta Sharpe ratio Sortino ratio And while we can compute all the statistics under the sun to measure our portfolio’s performance, it doesn’t do much good if we don’t include a reference point to see how well we’re doing in comparison. This is called a , and we’ll be using the golden boy of cryptocurrencies: Bitcoin. benchmark Notebook Walk-Through So I don’t want to display a bunch of code here because I think you should go through the notebook yourself and get a feel for things. Don’t be afraid, the notebook includes some clear explanations and the code is commented! It’ll also help in better understanding this post. If you want, clone and give it a whirl first. However, I will show you results through some statistics and nice visualizations. the repo To start, we need to create a tradesheet that emulates how we invested our portfolio. The one below is included in the repo. These are actually the same cryptos I invested in and the times I bought and sold them up until now, but the amount of money and the allocations (i.e., the amount I bought and sold) are not. You can think of the tradesheet as our . These are the trades we decided to take based on our wizardry powers or what an algorithm told us. investment strategy Along with the tradesheet, we also need historical market data. I chose to go with something simple: download some CSVs from and throw them into a data folder. Pulling data from an API would be better though! CoinGecko Now we want to run a backtest on our investment strategy. Simply put, running a backtest allows us to go back in time to our first trade, walk forward in time, and simulate the trading activity that occurred in our portfolio up until today. A backtester can be very sophisticated and can be used in a lot of different scenarios (to the finance geeks: pun intended), but in our case it’s rather straightforward. Based on the statistics above, it’s clear that our portfolio did fairly well when compared to our benchmark. The returns are better, volatility is only slightly worse, and our beta is surprisingly below 100%. And look at that alpha! OK. Numbers are nice, but I want to see some charts. Well that’s intimidating. The above chart shows how the USD value of our portfolio evolved over time including all of our cash flows (i.e., deposits and withdrawals). While it’s nice to visualize this, it’s hard to get a clear idea of how our portfolio did in true performance when cash flows are included. For example, if I deposited $1 million (I wish), the portfolio would appear to have a HUGE spike! Now that’s better. By removing the daily returns when cash flows were witnessed, we have a more accurate representation of the true performance of our portfolio. Fortunately, we have a very small number of cash flows, so this method is acceptable. As you can see, it took us some time to catch up to Bitcoin, but it did and eventually surpassed it (thanks Golem and NEO). Actually, you can see that after the crazy Bitcoin, Ethereum, and Litecoin boom (aka the Coinbase boom), our portfolio became more diversified. This surely had a lot to do with the dampening of the upcoming Bitcoin drawdowns and the likely larger returns experienced among the newly added assets. Well there you have it. Clearly, our portfolio experienced much less volatility (i.e., risk) after diversifying. Diversification (and luck) for the win! For me, this is the most interesting plot. This is a matrix that represents the correlations between all of the assets in our portfolio. While a lot of assets had a with one another, Bitcoin Cash had a very low correlation to every single asset. You can even see that it was negatively correlated with OmiseGO! Correlations do change over time, but it’s nonetheless interesting to see these types of relationships within our portfolio. medium to high correlation Again, go ahead and clone and play around a bit so you can understand in more detail how we went about analyzing our portfolio. You can even add your own tradesheet to get a glimpse into yours. And if you find bugs, let me know! the repo Summing It All Up I hope you’ve gained a better appreciation for why it’s important to look at your portfolio through various lenses. It’s hard to get a clear understanding from just visualizing asset price movements, especially with all that’s been going on lately in the crypto space. Also, it’s not always clear how much risk we’re taking on over time, and how those risks will evolve when we invest. What is clear is that diversification in such a market is important, because none of us knows where this market is going. With that in mind, best to keep an eye on your ship while weathering the storms and HODL. I’m Grant and I’m a freelance fintech writer! If you’re looking for engaging, informative fintech content that speaks to your audience and can grow your brand awareness and online reach, I can help. Learn more on how I can help your fintech content marketing

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Have you ever wanted to edit a video to remove or add someone, change the background, make it last a bit longer, or change the resolution to fit a specific aspect ratio without compressing or stretching it? For those of you who already ran advertisement campaigns, you certainly wanted to have variations of your videos for AB testing and see what works best. Well, this new research by Niv Haim et al. can help you do all of the about in a single video and in HD! Indeed, using a simple video, you can perform any tasks I just mentioned in seconds or a few minutes for high-quality videos. You can basically use it for any video manipulation or video generation application you have in mind. It even outperforms GANs in all ways and doesn’t use any deep learning fancy research nor requires a huge and impractical dataset! And the best thing is that this technique is scalable to high-resolution videos... Watch the video References ►Read the full article: ►Paper covered: Haim, N., Feinstein, B., Granot, N., Shocher, A., Bagon, S., Dekel, T., & Irani, M. (2021). Diverse Generation from a Single Video Made Possible. ArXiv, abs/2109.08591. ►The technique that was adapted from images to videos: Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, and Michal Irani. Drop the gan: In defense of patches nearest neighbors as single image generative models. arXiv preprint arXiv:2103.15545, 2021. ►Code (available soon): ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/vgpnn-ge... https://nivha.github.io/vgpnn/ https://www.louisbouchard.ai/newsletter/ Video Transcript 00:00 have you ever wanted to edit a video 00:02 remove or add someone change the 00:04 background make it last a bit longer or 00:06 change the resolution to fit a specific 00:08 aspect ratio without compressing or 00:10 stretching it for those of you who 00:12 already ran advertisement campaigns you 00:14 certainly wanted to have variations of 00:16 your videos for a b testing and see what 00:19 works best well this new research by niv 00:22 haim ital can help you do all of these 00:24 out of a single video and in high 00:27 definition indeed using a simple video 00:29 you can perform any tasks i just 00:32 mentioned in seconds or in a few minutes 00:34 for high quality videos you can 00:36 basically use it for any video 00:38 manipulation or video generation 00:40 application you have in mind it even 00:42 outperforms guns in any ways and doesn't 00:45 use any deep learning fancy research nor 00:48 requires a huge and impractical data set 00:51 and the best thing is that this 00:52 technique is scalable to high resolution 00:55 videos it is not only for research 00:57 purposes with 256 by 256 pixel videos oh 01:01 and of course you can use it with images 01:04 let's see how it works the model is 01:06 called video based generative patch 01:08 nearest neighbors vgpnn instead of using 01:11 complex algorithms and models like gans 01:14 or transformers the researchers that 01:16 developed vgpn opt for a much simpler 01:19 approach but revisited the nearest 01:22 neighbor algorithm first they downscale 01:24 the image in a pyramid way where each 01:26 level is a flower resolution than the 01:28 one above then they add random noise to 01:31 the coarsest level to generate a 01:33 different image similar to what guns do 01:36 in the compressed space after encoding 01:38 the image note that here i will say 01:40 image for simplicity but in this case 01:42 since it's applied to videos the process 01:45 is made on three frames simultaneously 01:48 adding a time dimension but the 01:49 explanation stays the same with an extra 01:52 step at the end the image at the 01:54 coarsest scale with noise added is 01:56 divided into multiple small square 01:59 patches all patches in the image with 02:01 noise added are replaced with the most 02:04 similar patch from the initial scaled 02:06 down image without noise this most 02:09 similar patch is measured with the 02:11 nearest neighbor algorithm as we will 02:13 see most of these patches will stay the 02:15 same but depending on the added noise 02:17 some patches will change just enough to 02:19 make them look more similar to another 02:21 patch in the initial image this is the 02:24 vpn output you see here these changes 02:27 are just enough to generate a new 02:29 version of the image then this first 02:31 output is upscaled and used to compare 02:34 with the input image of the next scale 02:36 to act as a noisy version of it and the 02:38 same steps are repeated in this next 02:41 iteration we split these images into 02:43 small patches and replace the previously 02:45 generated ones with the most similar 02:48 ones at the current step let's get into 02:50 this vpn module we just covered as you 02:53 can see here the only difference from 02:55 the initial step with noise added is 02:58 that we compare the upscale generated 03:00 image here denoted as q with an upscaled 03:03 version of the previous image just so it 03:06 has the same level of details denoted as 03:09 k basically using the level below as 03:12 comparisons we compare q and k and then 03:15 select corresponding patches in the 03:17 image from this current level v to 03:20 generate the new image for this step 03:22 which will be used for the next 03:24 iteration as you see here with the small 03:26 arrows k is just an upscale version of 03:28 the image we created downscaling v in 03:31 the initial step of this algorithm where 03:33 we created the pyramidal scaling 03:35 versions of our image this is done to 03:38 compare the same level of sharpness in 03:40 both images as the upscale generated 03:42 image from the previous layer q will be 03:45 much more blurry than the image at the 03:48 current step v and it will be very hard 03:50 to find similar patches this is repeated 03:53 until we get back to the top of the 03:54 pyramid with high resolution results 03:57 then all these generated patches are 03:59 folded into a video and voila you can 04:02 repeat this with different noises or 04:04 modifications to generate any variations 04:06 you want on your videos let's do a quick 04:09 recap the image is downscaled at 04:11 multiple scales noise is added to the 04:13 corsa scale image which is divided into 04:16 small square patches each noisy patch is 04:18 then replaced with the most similar 04:20 patches from the same compressed image 04:23 without noise causing few random changes 04:26 in the image while keeping realism both 04:28 the newly generated image and image 04:31 without noise of this step are upscaled 04:33 and compared to find the most similar 04:36 patches with the nearest neighbor again 04:38 these most similar patches are then 04:40 chosen from the image at the current 04:42 resolution to generate a new image for 04:45 the step again and we repeat this 04:47 upscaling and comparing steps until we 04:49 get back to the top of the pyramid with 04:52 high resolution results of course the 04:54 results are not perfect you can still 04:56 see some artifacts like people appearing 04:58 and disappearing at weird places or 05:00 simply copy-pasting someone in some 05:02 cases making it very obvious if you 05:05 focus on it still it's only the first 05:07 paper attacking video manipulations with 05:09 the nearest neighbor algorithm and 05:11 making it scalable to high resolution 05:13 videos it's always awesome to see 05:15 different approaches i'm super excited 05:18 to see the next paper improving upon 05:20 this one also the results are still 05:22 quite impressive and they could be used 05:24 as a data augmentation tool for models 05:26 working on videos due to their very low 05:29 run time allowing other models to train 05:31 on larger and more diverse data sets 05:33 without much cost if you are interested 05:35 in learning more about this technique i 05:37 will strongly recommend reading their 05:38 paper it is the first link in the 05:40 description thank you for watching and 05:42 to everyone supporting my work on 05:44 patreon or by commenting and liking the 05:46 videos here on youtube 05:54 you English (auto-generated) All Recently uploaded Watched

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This AI Performs Seamless Video Manipulation Without Deep Learning or Datasets

The basics An exemplary docker image has the following 3 parts: Name (required, user-specified at build time) Tag (optional, user-specified at build time) Digest (automatically generated at build time) Pull an image using name Examples: docker pull golang

docker pull yugabytedb/yugabyte

docker pull quay.io/keycloak/keycloak-x When an image is pulled using only the name, the image tagged is pulled. If there is no image with the tag , then no image will be pulled. latest latest Pull an image using name:tag Examples: docker pull golang:latest

docker pull golang:1.17.1

docker pull yugabytedb/yugabyte:latest

docker pull yugabytedb/yugabyte:2.9.0.0-b4

docker pull quay.io/keycloak/keycloak-x:latest

docker pull quay.io/keycloak/keycloak-x:15.0.2 An image like is available for multiple os/arch, example: , , etc. In such cases, docker automatically pulls the appropriate image for the os/arch the pull command is run on. golang windows/amd64 linux/amd64 Use this command to view os/arch’s of an image: docker manifest inspect --verbose golang:1.17.1

# sample output
[
  {
    "Ref": "docker.io/library/golang:1.17.1@sha256:232a180dbcbcfa7250917507f3827d88a9ae89bb1cdd8fe3ac4db7b764ebb25a",
    "Descriptor": {
      "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
      "digest": "sha256:232a180dbcbcfa7250917507f3827d88a9ae89bb1cdd8fe3ac4db7b764ebb25a",
      "size": 1796,
      "platform": {
        "architecture": "amd64",
        "os": "linux"
      }
    },
    .
    .
    . A common thing that happens with a tag is that it could be reused. For example an image pulled using today may be completely different from an image pulled in 6 months. golang:latest Tags that look like version numbers can have the same behavior. There is no guarantee that an image pulled using today (on say ) will be the same when pulled 6 months later (again on ). golang:1.17.1 linux/amd64 linux/amd64 Caution needs to be exercised in production environments when pulling an image using the tag since it makes rollbacks harder. latest Pulling an image using name@sha256:digest A digest is an id that is automatically created during build time and cannot be changed (immutable). When an image is pulled using a digest, a will download the same image every time on any os/arch. This is called image pinning. docker pull First, determine the image you wish to use. Then get the digest as follows: name:tag docker manifest inspect --verbose golang:1.17.1

# sample output
[
  {
    "Ref": "docker.io/library/golang:1.17.1@sha256:232a180dbcbcfa7250917507f3827d88a9ae89bb1cdd8fe3ac4db7b764ebb25a",
    "Descriptor": {
      "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
      "digest": "sha256:232a180dbcbcfa7250917507f3827d88a9ae89bb1cdd8fe3ac4db7b764ebb25a",
      "size": 1796,
      "platform": {
        "architecture": "amd64",
        "os": "linux"
      }
    },
    .
    .
    .

docker manifest inspect --verbose yugabytedb/yugabyte:2.9.0.0-b4

docker manifest inspect --verbose quay.io/keycloak/keycloak-x:15.0.2 The above command returns a JSON response. Look for the in the . digest Descriptor Examples: # golang 1.17.1 for linux/amd64
docker pull golang@sha256:232a180dbcbcfa7250917507f3827d88a9ae89bb1cdd8fe3ac4db7b764ebb25a

# yugabyte 2.9.0.0-b4 for linux/amd64
docker pull yugabytedb/yugabyte@sha256:974219f34a18afde9517b27f3b81403c3a08f6908cbf8d7b717097b93b11583d

# keycloak-x 15.0.2 for linux/amd64
docker pull quay.io/keycloak/keycloak-x@sha256:a6b7be1808b8443dde696c5f108be1cb6e7641d6b281ef7598df012c1d6871f8 Note: Comments added above only for readability. So what should you use? Well, that depends on your use case. A general guideline that I use for my workflows is: Experimenting on your local machine? It’s ok to pull using or . name name:tag Building an image for production? Use to pull your base image(s). This ensures that the same final image is built on any machine. name@sha256:digest Deploying an image to a production environment? If the image was built by your team (or company), it’s ok to use where a tag represents a version number, for example . Avoid using the tag since it makes rollbacks harder. name:tag v21.10.1 latest If the image was built by your team (or company), use if possible. If you choose to use , be mindful that tags can be reused. Avoid using the tag since it makes rollbacks harder. not name@sha256:digest name:tag latest Quick Recap A docker image usually has 3 parts: name, tag, and digest. Images can be pulled using , or or . name name:tag name@sha256:digest Some images are multi-architecture images. Pulling a multi-architecture image using or will automatically download the appropriate image for the os/arch the pull command is run on. name name:tag Use to view os/arch’s of an image and to get the digest. docker manifest inspect --verbose <name:tag> Pull an image using to download the same image on any os/arch. name@sha256:digest Avoid using the tag in a product environment since it makes rollbacks hard. latest Thank you for taking the time to read this article. I hope the information here helps you with image pulls for your use cases and workflows.

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Docker Images: Name Vs. Tag Vs. Digest

Do you remember the time when the appeared on the smartphones screens? This game has started the era of casual games with very few in-game actions, and each wrong move means your game is over. The one who lasts alive longest will head the leaderboard. Flappy Bird Today we will see how to write a Python bot that piggybacks on computer vision library OpenCV to beat , an endless runner from . Don’t touch the red Addicting Games Rules of the game The game is pretty simple: green buttons fall, and the player needs to press them before leaving the screen. And of course, don’t touch the red! There’s one crucial feature; if you play in arcade mode, the buttons fall with increasing speed. That makes the game hard for a human player. But it is not an issue for our bot! OpenCV Template Matching The main part of our computer vision bot is the template matching available in the OpenCV library. It is not a neural network approach. It is much simpler and limited. This algorithm is meant for searching for a patch on the target image, e.g., a “green button” on a “gaming screen.” It works the following way: the algorithm takes the template image and then, using a sliding window, tries to find a matching patch on the target image. Using this, we can get the positions and similarity measures for each pixel. In the code application of the template matching looks like this import cv2

template = cv2.imread('template.png')
target = cv2.imread('target.png')
result = cv2.matchTemplate(target, template, cv2.TM_CCOEFF_NORMED)
    _, max_val, _, max_loc = cv2.minMaxLoc(result) As a result, we’ll have the equal to the maximum similarity on the target image and the is the upper left corner of the found match. max_val max_loc This algorithm is faster when it works with smaller target images and smaller patterns. Firstly, I’ve tried to work with whole green buttons, but then I’ve switched to smaller ones that work faster, and with that, I’ve achieved higher scores Taking Screenshots and Interaction The other important parts of the bot are getting screens to analyze and how we’re sending mouse clicks to the game. It is necessary to mention that Addicting Games provide games that you can play using your internet browser, so nothing additional must be installed. There’re two Python packages that help with the tasks above: and , we use them to get the screenshots of a particular part of the screen and to send clicks to the browser window correspondingly. I also use library as it’s very handy to set the “break action” on some key in the case when your mouse is controlled by a bot. The library (and probably )  require rights, so run your Python script as an executable with a proper shebang header. mss pyautogui keyboard keyboard pyautogui sudo Here I provide code snippets on how to get screenshots and send clicks: #!/hdd/anaconda2/envs/games_ai/bin/python

# ^ change above to your python path ^

import keyboard
import mss
import pyautogui

pyautogui.PAUSE = 0.0

print("Press 's' to start")
print("Press 'q' to quit")
keyboard.wait('s')

# setup mss and get the full size of your monitor
sct = mss.mss()
mon = sct.monitors[0]

while True:
    # decide on the part of the screen
    roi = {
        "left": 0, 
        "top": int(mon["height"] * 0.2), 
        "width": int(mon["width"] / 2), 
        "height": int(mon["height"] * 0.23)
    }

    roi_crop = numpy.array(sct.grab(roi))[:,:,:3]
    
    # do something with `roi_crop`

    if keyboard.is_pressed('q'):
        break Here’s also one thing. When you use on Linux, you might face it is possible to overcome with command. pyautogui Xlib.error.DisplayConnectionError xhost + My Algorithm Based on the latter two, I’ve created an algorithm that beats the previous human playing with score of 170 a score of 445. There are two parts to a program. First tries to click the first three-button available on a screen when the game starts. The game field doesn’t move until a player hits the first button, so we can treat a field as static when we do click on the first three. For that purpose, we inspect the three lines of the screen, searching for a small pattern (see the previous figure), and then click on them The first half of the code: #!/hdd/anaconda2/envs/games_ai/bin/python

# if "Xlib.error.DisplayConnectionError" use "xhost +" on linux

import shutil
import os
import keyboard
import mss
import cv2
import numpy
from time import time, sleep
import pyautogui
from random import randint
import math

pyautogui.PAUSE = 0.0

print("Press 's' to start")
print("Press 'q' to quit")
keyboard.wait('s')

try:
    shutil.rmtree("./screenshots")
except FileNotFoundError:
    pass
os.mkdir("./screenshots")

# setup mss and get the full size of your monitor
sct = mss.mss()
mon = sct.monitors[0]

frame_id = 0
# decide where is the region of interest
for idx in range(3,0,-1):
    roi = {
        "left": 0, 
        "top": int(mon["height"] * (idx * 0.2)), 
        "width": int(mon["width"] / 2), 
        "height": int(mon["height"] * 0.23)
    }

    green_button = cv2.imread('green_button.png')
    offset_x = int(green_button.shape[0] / 2)
    offset_y = int(green_button.shape[1] / 2)

    roi_crop = numpy.array(sct.grab(roi))[:,:,:3]
    result = cv2.matchTemplate(roi_crop, green_button, cv2.TM_CCOEFF_NORMED)
    _, max_val, _, max_loc = cv2.minMaxLoc(result)

    print(max_val, max_loc)

    button_center = (max_loc[0] + offset_y, max_loc[1] + offset_x)
    roi_crop = cv2.circle(roi_crop.astype(float), button_center, 20, (255, 0, 0), 2)
    cv2.imwrite(f"./screenshots/{frame_id:03}.jpg", roi_crop)

    abs_x_roi = roi["left"] + button_center[0]
    abs_y_roi = roi["top"] + button_center[1]
    pyautogui.click(x=abs_x_roi, y=abs_y_roi)
    frame_id += 1 In the second part, we press the following 400 buttons; it is implemented as an infinite while loop that captures the screen and clicks on the pixel where it is expected to see a button regarding the current speed. The speed function has been selected as a logarithmic function of the number of iterations. This function provides a pixel offset needed to adjust when time has passed since the pattern had been found. The second half: second_roi = {
    "left": 0, 
    "top": int(mon["height"] * 0.18), 
    "width": int(mon["width"] / 2), 
    "height": int(mon["height"] * 0.06)
}

btn = cv2.imread('center.png')
offset_y = int(btn.shape[0])
offset_x = int(btn.shape[1] / 2)

thresh = 0.9
frame_list = []
btn_cnt = 1
while True:
    frame_id += 1
    second_roi_crop = numpy.array(sct.grab(second_roi))[:,:,:3]
    result = cv2.matchTemplate(second_roi_crop, btn, cv2.TM_CCOEFF_NORMED)
    _, max_val, _, max_loc = cv2.minMaxLoc(result)
    
    # define the speed of the screen
    speed = math.floor(math.log(frame_id)**2.5)
    print(frame_id, max_val, max_loc, speed)
    frame_list.append(max_loc[0])
    if max_val > thresh:
        button_center = (max_loc[0] + offset_x, max_loc[1] + offset_y)
        second_roi_crop = cv2.circle(second_roi_crop.astype(float), button_center, 20, (255, 0, 0), 2)
        cv2.imwrite(f"./screenshots/{frame_id:03}.jpg", second_roi_crop)

        abs_x_sec = second_roi["left"] + button_center[0]
        abs_y_sec = second_roi["top"] + button_center[1] + speed
        pyautogui.click(x=abs_x_sec, y=abs_y_sec)
        btn_cnt += 1

    if keyboard.is_pressed('q'):
        break As you can see, the speed is parameterized, and depending on your PC configuration, you can find better parameters that beat my high score. I encourage you to do that! This is because the code is very dependent on the speed of image processing and it may vary from system to system. Here’s the peek of the one run. How it looks like when the bot is actually running. In order not to be unfounded, here is the leaderboard screenshot. I need to mention that in this particular game, score at all levels of difficulty goes to the leaderboard, so you needn’t play “Hard”. “Easy” level is just fine (btw when you reach 100 pressed button, you can’t tell that it’s easy anymore) The code of the project is available at Github . It would be great to create an extensive library of Hacked Addicting Games and keep all of these algorithms there. So you are invited to create the pull requests! https://github.com/zetyquickly/addicting-games-ai Acknowledgments This video inspired this project: https://www.youtube.com/watch?v=vXqKniVe6P8 Here the author beats the leaderboard of the Kick Ya Chop game, it has similarities with Don’t Touch the Red, but also, there’s a big difference. In Kick Ya Chop, the player decides on the speed of the game. The faster the human/bot clicks faster the tree is falling. In Don’t Touch the Red, the game decides on the speed of upcoming buttons.

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How to Make a Gaming Bot that Beats Human Using Python and OpenCV

An opinion piece concerning developers' wages. Why they remain high and why they are unlikely to drop anytime soon. Do you think that the cost of hiring developers will drop in the future? Best not hold your breath. There is no shortage of those disgruntled by the wages that developers demand. Be it the CEO not wanting to pay for the resource or non-coding staff who work just as hard but earn less. As long as I have done this job, there has always been somebody on hand to tell me that the bubble will burst on developers' wages. Sadly for the would-be oracles, wishful thinking does not change market forces. The prophecy. The prophecy goes something like this: "As we train our kids to code, and give adults more opportunity to learn to code, there will be more developers available. Supply will grow, and the cost will drop" However, experience suggests that giving more opportunity will not alone increase the supply of developers and that other factors will block increased supply and keep wages high as supply does grow. Teaching coding more widely is not enough. There is a big difference between somebody who wrote a little code as part of a mandated curriculum and somebody who has expanded this into a commercially viable skillset. The very nature of code and how rapidly it develops means that school and university curriculum will always be behind. All we can do is teach the core principles. However, the gap between core principles and a commercially valuable developer is more of a pole vault across a canyon than a small step. After being taught the basics, you need the gumption and dedication to expand your base skills and apply them practically. Some will, many will not. Teaching more widely will give people core skills, but alone it will not open the floodgates to a bountiful supply of development resources. It takes a certain kind of person to make a developer; the ability to code is only one part. We still have trouble attracting half of the population to development roles. There is still a distinct shortage of women in development roles. In the time I have been coding, things have improved vastly, but we still have a long way to go. More women have to want to work as developers, and for that to happen, we need wholesale changes in culture that make the roles more attractive to them. Saying "women are welcome here" and making no cultural changes to address why women have traditionally not "worked here" is to do nothing. Often driven by the fact that many hiring men can not fathom why women have not traditionally been attracted to tech and are often more offended by the allegations of sexism in the industry than by the actual problem itself. Our tech leads need to accept we have failed women in technology, and while making modest improvements, we widely continue to do so. Only then can we begin to understand why women may not have been traditionally attracted to development roles and make the required changes. Until we address this gender imbalance, we have a fundamental block on the number of developers coming into the resource pool. The resource may increase, but so will demand. Computers continue to find their way into everything; these computers need programming. New technologies continue to emerge and must be learned, whilst old legacy systems must still be maintained. All signs point to demand continuing to outstrip supply. Big tech knows it, which is why they are turning to AI to solve the issue. But even that requires skilled developers! Then there is everything else. The vast amount of professional development developers are expected to do on their own time. Third parties such as recruiters force up wages. Enticing people to spend all day sitting at a computer, doing work that often makes your head hurt. The list of anecdotal reasons why salaries are high for developers goes on and on. So when will developer wages drop? We are nowhere near the point where we have more developers than we do devices that need programming. With the rate at which technology continues to grow, I have to question if we ever will be. Until this point is reached, or the technological singularity occurs, demand for good developers' will exceed supply, and wages will be kept high. Speculate all you want that the bubble will burst on developers’ wages, but don't hold your breath unless, of course, blue is your color. Previously published on . https://www.edward.im/the-dev-wage-bubble/

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The Dev Wages Bubble: 'Not Going to Burst Any Time Soon'

The world first witnessed an increase in the number of physical machines during the Industrial Revolution. This steady rise compelled organizations to create a system, which was done by building factories, assembly lines, and other elements of automated manufacturing. Soon after, the tech boom witnessed Agile systems coming into the picture to automate processes for creating. This was done by operationalizing the product life cycle, which helped promote continuous innovation by clearing out waste. Following this, we saw DevOps being introduced to optimize the production lifecycle even further by introducing big data. Of course, all of these processes have brought us to the present world, where we are turning our attention to machine learning insights. This brings us to MLOps. Machine Learning Operations, or MLOps, is essentially a framework that focuses on the collaboration between data scientists and the operations unit within an organization. The framework is designed to reduce errors, minimize waste, further improve upon automation, and produce more valuable insights with the help of machine learning. MLOps follows a path similar to that of DevOps. While DevOps focuses on shortening the product life cycle by creating better products each time, MLOps drives insights that can be put into better use immediately. The reason why MLOps is considered to integrate the best of both worlds is due to its vested role in improving the organizational operations. MLOps promotes data scientists to look at their roles with organizational interest, which helps bring about clarity and measurable benchmarks. DevOps vs. MLOps: What kind of relationship do MLOps and DevOps have? Many would believe that since Machine Learning is a software engineering field, the principles of DevOps can be applied to it. While this holds true to some extent, there are a few key differences between the two. DevOps is essentially a practice that involves building and operating software systems on a large-scale. DevOps works with two main concepts – Continuous Integration and Continuous Delivery. MLOps involves ML, a software system, and the concepts of DevOps can be applied to build ML systems at scale. As compared to DevOps, MLOps is more experimental in nature. This new framework requires data scientists to try a variety of features, parameters, and models. With these different combinations, these experts try to manage the code base and produce compatible results. While working with ML, the team will typically include data scientists and ML researchers who can help develop models, understand exploratory data analysis, and experimentation. Although they can understand the system better, they cannot offer production-class services like software engineers are capable of doing. Testing ML is comparatively more complex. The process would involve data validation, model validation, and trained model quality evaluation, along with unit and integration tests. ML deployment is also a complex process as it requires you to position a multi-step pipeline, allowing you to automate the process of retraining and deploying models. The constantly evolving data profiles in ML may result in reduced performance if paired with suboptimal coding. Models are capable of decaying in more ways as compared to other software systems, which requires experts to track summary statistics and monitor performance. The similarities between ML and other software systems lie in the continuous integration of source control, integration testing, unit tests, as well as continuous delivery. But, in ML, continuous integration moves beyond just code and components and involves data: testing and validating data and data schemas. Continuous Delivery, too, requires an ML training pipeline that uses automation to deploy a model prediction service. What are the Benefits of MLOps? The operationalization of data helps to gain insight and use this knowledge to develop actionable business value. Here’s how the addition of MLOps can help organizations gain more value: MLOps helps to bridge the gap between the business knowledge of the operations unit in the company and the studies conducted by the data science team. MLOps seeks to leverage the benefits of both spheres and create more valuable ML. While data scientists may be working hard to gain more insight, all efforts could prove futile if your organization runs into trouble with regulatory bodies. MLOps mitigates this risk by paving the way for the operations unit to apply new regulations and best practices, while the data team focuses on development. MLOps helps increase investments in present machine learning and data science tools and technology to a much bigger extent. This helps to build a system of maintaining records between different teams and projects. How can an organization introduce MLOps? Here are some basic points to consider before bringing MLOps into the organization model: Benchmarks : The organization’s key performance indicators should be concise and measurable to bring all members on board. There has to be a steady collaborative effort between data scientists and operations team members to understand their roles and use insights correctly Monitoring : There is a dire need for both units to be monitored through every step of the process. Since ML requires data to be retrained periodically, the organization requires a keen eye to be kept on the process to ensure that everyone is operating within regulations and to be doubly sure that the programs are providing quality information. Compliance : To ensure compliance with regulations at every step of the way, MLOps requires a thorough plan for governance, which helps to ensure that the programs created are auditable and within the scope of operations. How does Kubernetes advance MLOps? Kubernetes is essentially an open-source container-orchestration system that is used by organizations to automate deployment, scaling, and management for computer applications. As an orchestrator, Kubernetes is used to build scalable distributed systems and is also being leveraged to bring about much-needed flexibility in varied machine learning frameworks for data scientists to work upon. This flexibility extends to the scalability and reiteration required by units that run machine learning systems in products, as well as more control over resource allocation required by the operations unit. When applied in machine learning, Kubernetes can significantly ease the process for data scientists and business operators. Typically, data science and deployment paths are different entities. On one side, data scientists build experiments using one set of tools and infrastructure, whereas the development teams recreate the model using different tools and infrastructure. To make the process more cohesive, organizations should look to bring in a combined pipeline in the form of Kubeflow, which uses Kubernetes to train and scale models on a variety of frameworks, without the requirement of any kind of expertise in infrastructure planning. Conclusion Machine learning is definitely the future of data science, and the integration of MLOps into the organizational structure can go a long way in cutting back on errors and building models with more efficiency. MLOps can benefit from the tools used today in DevOps to implement the best practices of CI/CD and production. Kubernetes is a very good fit for Machine Learning. It’s the perfect platform to deploy machine learning models to production, run scheduled jobs, distributed computing, and CI/CD pipelines. Even if you are not a Kubernetes expert platforms like CloudPlex allow you to create a Kubernetes cluster. (on any major cloud provider or on bare metal) for free and in a few minutes. Using the drag-and-drop tool, you can build, upgrade, and destroy clusters without diving into complex YAML and infrastructure configurations. You can start using it today for free here. Asad Faizi Founder CEO , Inc CloudPlex.io asad@cloudplex.

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What is MLOps and How Kubernetes can Help

In discussions about blockchain, several concepts come up regularly. One of which is the distribution of decision-making power. This can be understood in the umbrella term, . decentralisation Decentralisation is one of the core principles of blockchain, a distributed ledger technology, that allows for peer-to-peer transactions of various value exchange instruments. Let’s break it down. Oh my gosh - jargon overload. What does decentralisation mean? What does decentralisation mean in the context of blockchain? Now that we’ve established the philosophical concept behind decentralisation, let’s look at it through the lens of technology. The deeper I’ve dived into network infrastructure, the more I see the fabric of our digital rights. Decentralisation is not a new concept when it comes to network architecture. They are often broken into the following categories: Blockchain technologies are often grouped with philosophies around decentralisation, but nuance is important here. Decentralisation is a spectrum due to the tradeoffs such as lower transaction throughput and speed. What is the correlation between decentralisation and transactions per second (TPS)? When you send your friend some money using a banking app, the bank is the custodian of the funds and the trusted party that ensures the transaction is fulfilled and your friend’s wallet gets topped up with whatever amount you’re sending them. With blockchain, we do away with the need for middlemen for transactions. You “hold your crypto” and the blockchain serves as a public ledger of transactions between the network. The nature of blockchain is such that there is a transfer of control and decision-making from a centralised entity (your bank, internet service provider, etc) to a distributed network. How? Different distributed networks are supported and defined by different consensus mechanisms. A simple way to think about the correlation between decentralisation and transaction speed is: Consensus mechanisms in blockchains directly impact how secure they are. The more secure the network, the smaller the TPS. A consensus mechanism refers to any number of methodologies used to achieve agreement, trust, and security across a decentralized computer network. Take for example the consensus mechanism of the Bitcoin network. To add a new block to the chain, a node has to make a calculation. This is known as proof of work. The Bitcoin consensus is structured around transaction rules, transaction states across the network and Bitcoin values. One important concept to remember, there are many blockchains out there with varying degrees of consensus mechanisms, transactions per section potential, and other value propositions.  Binance Smart Chain is in no way, shape, or form, decentralised. But hey, much cheaper transactions - so again, it boils down to the use case. Choosing which blockchain you want to build your dapp or web3 service on really depends on the use-case, speed, and level of decentralisation necessary for your service. We have to take steps towards true decentralisation Decentralized networks strive to reduce the level of trust network participants must place in one another, and deter their ability to exert authority or control over one another in ways that degrade the functionality of the network. Moving from an overtly centralised internet, financial, and broadly global system means that several decentralised projects are building on extremely centralised infrastructure. This can be seen cross dimensionally from the banks that you need to connect your exchange account to,  decentralised applications hosted on AWS, through to the regulators the industry continues to bend its knees to. You can try go full crypto, but some of your employees will still want payroll. This means that cryptocurrency projects will have to continue to navigate playing well and integrating with traditional systems so as to offer frictionless and seamless access to their services. WTF is DeFi (Decentralised Finance) then? Decentralised Finance (DeFi) is one of the most exciting innovations within the cryptocurrency industry. DeFi is a system whereby financial products become available on a public decentralised blockchain network. This opens them up to everyone, removing the need for middlemen such as banks or brokerages. Think about it as a permissionless system where software is written on blockchain enable buyers, sellers, lenders, and borrowers to interact peer to peer. But are these decentralised finance projects truly decentralised? And what does true decentralisation even mean? First let’s understand the DeFi Stack When thinking about DeFi, it is important to separate the stack out and understand where we are attempting to apply concepts such as decentralisation to. Settlement This is the base layer. Ethereum is an example of a settlement layer. This settlement layer can also have tokenised representatives of assets such as the US dollar (USDC, USDT, DAI) Singapore dollar (XSGD), and more. Protocol Here standards and rules are coded to govern specific tasks and interactions. Participants agree to follow a prerequisite to operating in the industry. This is where projects like Compound, Aave, Synthetix, and other lending, borrowing, and overcollateralising logic is placed over the settlement layer. Application This, is the consumer-facing applications that abstract underlying protocols into retail or institution-friendly services. This is where decentralised exchanges and lending services exist. Aggregation Aggregators help end-users more clearly understand what’s happening across all the different services they are running. Crypto wallets, portfolio trackers live in this category. When do we apply concepts such as decentralisation? Generally, a commitment to decentralisation in the first two of the four is key given this is where trustlessness and permissionless-ness create the most impact. As we abstract out to the application and aggregation layer, we are often looking at a hybrid model of centralised and decentralised services plugged together to create ease for the end consumer. One example is Revolut and other challenger banks offering their end-users exposure to cryptocurrencies in their digital wallets. Without access to private keys, these subset of consumers continue to be able to interact with the industry, with Revolut custody-ing their cryptocurrencies. Now some hard core fringe players in the crowd who may go on about not your keys, not your coins - you don’t really understand how zoom works but you still use it to take meetings, don’t you? Decentralisation in web3 and beyond. Decentralisation is not purely a concept tied to blockchain. This framework can be applied to several different processes associated with the internet. Anywhere there is value transfer, a certain degree of decentralisation can bring benefits to actors within the network. Take for example privacy. An article came out recently about the , and what that means for the average consumer. consolidation of the VPN industry TL;DR - Most VPN companies, and VPN review sites are owned by two - three mega parent companies. Smell funny? Hell yeah. Starting to see why decentralisation of power is necessary? Especially with services that tie to our basic human need to function and thrive in a digital economy? Blockchain offers a unique opportunity for parties to transact trustlessly without a third party, and this is leading to a rethinking of the way several organisations and industries function. We are seeing this first take the financial world by storm, as it is one of the most immediately recognisable forms of value. But value transfer doesn’t stop here, there are several projects building in web3 to decentralise power structures and ownership of the internet - be it through decentralising storage, privacy or computing - so that access to information and freedom of expression can continue to be a sovereign human right. A quick, really relatable example - think about social media platforms whose entire business model is based on the sale of data that we blindly share with them. In this network, the individual doesn’t see any benefit. A decentralized approach would help make it equitable for all. Listen to Amy and me discuss decentralisation in detail on the Hackernoon podcast: Let’s optimise towards positive delta shifts in humanity In summary - decentralisation as a concept and philosophy is one of the impulses of the cryptocurrency and digital assets industry. Yet as the industry matures from the fringe and counter-cultural early days towards the institutional inflow of 2021, it is clear that decentralisation and even the rails towards true decentralisation are yet to exist and must be built in collaboration with incumbents. There will continue to be hybrid models of centralised and decentralised products and services that evolve depending on the specific use cases and the target markets that the business is solving for. That's one small step for man, one giant leap for mankind For more of my musings on technology, philosophy, and beyond: Twitter - https://twitter.com/the_sharmini LinkedIn - https://www.linkedin.com/in/sharminiravindran/ Medium - https://medium.com/@sharminiravindr Disclaimer: All opinions are mine and don’t represent the organisations I work with or advise.

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Is Decentralisation Even Real?

It’s clear blockchain technology is growing at a tremendous pace and revolutionizing the crypto world. Decentralized finance (DeFi) has taken the main stage with the goal of eliminating the requirement of centralized control to manage the financial system. Most of the platforms and exchanges across the crypto world are still centralized, despite the utilization of distributed ledgers to store data in a decentralized manner. DeFi’s hoping to shake things up and it sure looks to be on the right track. The total value locked (TVL) in DeFi projects rose to nearly $175 billion in early September. TVL in February 2021. only just crossed $40 billion A serial entrepreneur with a background in IT, law, and finance, Waseem Mamlouk, Vice President at Nimbus Platform, is hard at work ensuring portions of the platform are certified as Sharia-compliant. is a DAO-governed dApp ecosystem based on IPOs, lending, crypto and more, with a goal of creating a decentralized bank for platform users. Nimbus providing a range of income-generating streams With a unique perspective, we got in touch with Mamlouk to get his thoughts on DeFi and crypto as he keeps working to serve buyers who want investments to comply with their spiritual beliefs. Why do you believe government-issued fiat currencies do not have any intrinsic value and may be incompatible with a careful interpretation of Sharia law? The world’s major currencies have not been pegged to any tangible asset since leaving the gold standard with the demise of Bretton Woods in the late '60s (68-73). Over several decades, many leading economies have been running in the red compared to their assets. It’s only logical those who believe in Sharia law would search for an asset backed by a hard commodity, or at least requires a certain amount of effort to manufacture. Crypto presents a simple solution! First, a move away from currencies that are printed beyond even their physical existence and placed on balance sheets without having a physical presence is inevitable. Furthermore, anyone following (Sharia) law directly will not maintain their wealth in fiat currencies or at least seek to hedge their risk. Technically, without having an asset peg and without the currency having any intrinsic value or cost of production. In fact, fiat is fully non-compliant with Sharia law. Yes, I understand this might be a controversial take, but there’s been a lot of talk already about returning to the gold standard in the context of debt, gold, and Islamic finance. Can you brief us on the impact of cryptocurrencies within Islamic banking? Well, there have only been a few entrants so far with limited success. Crypto has struggled to thrive within the Islamic banking sector as there was some confusion even back at the beginning of the wave starting in 2010 or so as to the legitimacy of cryptocurrency in general. However, fast forward to 2021, and you have a totally different story where cryptocurrency is very logically at the center of anyone who seeks to truly make Islamic investments and avoid any confusion over the source of funds and mixing funds from other sources. Technology in crypto allows us to track the actual fund source and answers any questions about usage. Newly-minted assets can also help clear up issues about the cleanliness of the value transfer mechanism. Really, anyone who is able to marry cryptocurrency within Islamic banking is going to find themselves in the middle of a potentially very lucrative market. Pew Research the global Muslim population will rise by 70% to nearly 3 billion between 2015 and 2060. So smart financial tools geared towards the Islamic world are certainly an attractive prospect. estimates What will bring investment money from large-scale institutions to the crypto world? I think a big part is regulations that will make crypto and DeFi more similar to the existing banking system. Of course, this was bound to happen at some point. I believe the two should be joined together as opposed to those who say that crypto should just totally replace the existing traditional system. What is your verdict about the future of ethical DeFi? Ethical DeFi has a bright future and has only just begun to develop. It’s going to be so important to have a system that is holistic in nature and can cater to the needs of any client. Ethical DeFi also helps investors more as they have better data to make decisions. Instead of a traditional system of investment managers making choices, ethical DeFi permits investors to actually direct their money since they directly hold digital assets. With Blockchain and DAO technology, it is even now possible for these same community members to vote with their holdings and determine the future, especially in regard to ethically sound investment. What is the scope of DeFi in 2021? And where do you see it in the next 3 years? We have reached into the billions already and DeFi will continue to grow at a very high CAGR which will not be less than 20% compounded annually. This will make the market size impossible to ignore. DeFi will be proven to exist as the future and not just a flippant trend. Within the next three years, we might even see the DeFi market reach over $1 trillion in value (if the growth rate continues to accelerate). Of course, it’s more important to understand the immense growth potential framed in the lens of realizing many millions across the world do not have access to traditional banking tools and could harness DeFi & crypto tools to transact. What does DeFi offer that traditional markets do not? The decentralised finance market is borderless and trustless. This creates a dynamic that’s unseen in human history and opens all possibilities to create new financial instruments and a completely new paradigm for the old services that were offered previously. Many people are so tired of the slow processing times, high fees, and other regulations found all across the traditional banking world, especially since a lot of these financial institutions aren’t even the most efficient or customer-friendly. How will DeFi change the world? We already see that many of our youth in the future will not need a traditional bank account and will simply join the DeFi market to start investing and saving activities. First, I believe a hybrid system will emerge to bridge the two together. Virtual marketplaces will become the norm while visiting a physical bank will be a relic of the past. ATMs, thanks to DeFi, will be replaced by a smart device and user. What are the future plans for Nimbus and how do you hope the platform positively impacts DeFi’s growth? There’s really so much! We’re still aiming to keep growing our community, which already numbers in the tens of thousands. Nimbus’ stock exchange listing will be the first time a DeFi company achieves that. We’re happy to be such a trendsetter across the DeFi world and hope that draws more attention to the industry. I’m also personally still working hard on getting some of our different tools and solutions Sharia-compliant. Each country has their own systems and procedures in place of what it actually means to become certified, so we hope to really keep submitting proposals to Sharia councils and receive approval.

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2022 - HackerNoon Contributor of the Year - Fintech

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On the Future of ‘Ethical DeFi’: Interview with Waseem Mamlouk

A product designer should identify a market opportunity and develop a solution for the identified problem. Many startup product designers fall into the trap of endless innovation without considering the required marketing efforts. For example, when they use an idea evaluation matrix, their idea screening criteria list does not include marketing efforts. In my journey through life, I worked as a software engineer, a marketing strategist, and a product designer. So in this article, I share my approach to idea evaluation with you. I consider four types of ideas based on the marketability and innovation. Before starting the design of a new product, I discuss this with key people such as my cofounder, client, or our investors. Clone Idea I used the parasite as the symbol of the clone idea because this type of business idea lives on the innovation of others. In countries with closed markets, investors and media favor the startups cloning American/ European businesses. Because in closed markets, foreign companies can not compete usually. For example, in my country, Iran, the secret formula for creating an idea is: Iranian+ a successful western company such as Iranian Youtube I don't think cloning others' innovation is honorable, but investors believe investing in innovative ideas is similar to donating to charities in closed markets. They may pay a little to charities, but they do not invest in charities. Improving a proven idea This approach demonstrates to future investors that there is a market for your new product, but also it means that you are competing with more prominent companies. So your new features should provide cutting-edge technology. An AI-driven word processing application that aims to compete with Microsoft Word can exemplify this type of idea. For the marketing stage, you can use micro-targeting services. Furthermore, your company may need to allocate money for employing more traditional advertising methods to attract conservative customers. Smart Targeting Let me explain it with a real-world example. When canva.com launched, around 10 million graphic designers around the world were using Adobe Photoshop. It implies that there is absolutely a market for graphic design applications; we called it a known market, but entering the competition would be harsh for newcomers. Canva.com and similar projects such as Over (It became GoDaddy Studio) targeted non-graphic designers. Smart Targeted Ideas can benefit from paid and organic media. They can use both traditional and non-traditional advertising techniques, and it would be easier to convince the potential investors in contrast to the previous type. And if you are curious about the total users of Canva.com, I should tell you that 30 million users create graphic works in this app every month.   This is the magic of smart targeting, Pure Innovations Dramatic innovations disrupt the current norms and change human life forever. Technologists like us love to spending time on game-changing ideas. And what will happen if an idea is technically implementable in a short period with minimum costs? Caution, watch for the innovation trap! Launching a disruptive idea is more than technology and knowledge; it requires that a thousand things work in the right direction. As Kotler said : The boldest level of marketing occurs when a company introduces a product or service that nobody asked for and often could not even conceive of. No one in the 1950s asked for a Sony Walkman, a Sony Betamax, or a Sony 31/2-inch disc. Yet Sony, under its brilliant founder and chairman, Akio Morita, introduced those and many other new products that since have become everyday staples. Morita summarized his marketing philosophy in these words: "I don't serve markets. I create them” (Phillip Kotler, Kotler on Marketing). Creating markets is an ambiguous task. You need to have access to venture capitals, you should have access to key influencers in your industry, and have lobbyists to have your back in courts. Your pitch sentence may be irrelevant for most people. So it is essential to be in the right place to find the right people. You may need to travel hundreds of miles to connect with like-minded people. You can not  buy the market with your hard-earned money In other words, the marketing message becomes more complex when we want to present a totally innovative idea. For example, micro-targeting services such as App Store Search ads may work well for launching a new download manager, but they may not be effective in the same way for advertising a dating app for pets! Pure innovation usually faces no direct competitions, and It has enormous growth potential. It would be interesting for editors to publish articles about these ideas for free. But let me tell you a bitter truth: many creative applications will die in the Appstore's blindside; there are many innovative products in the Producthunt that are not featured. Do not underestimate the marketing and PR efforts. Earning organic media is not a straightforward process, and it becomes more challenging when you do not have access to the ecosystem. Idea Evaluation  Matrix There will be a time when we must choose between what is easy and what is right. -Albus Dumbledore Finally, before creating the idea evaluation matrix, you need to discuss the marketing aspects with the stakeholders. If you are an entrepreneur and you wish to launch your own startup, you should ask your self following questions: Can I afford the paid media costs? Do we have enough connections for spreading the word about this idea? How can we do networking to increase earned media? Although I think that each organization should have its own idea screening criteria matrix, I suggest the following factors to be in any idea selection effort: Market Size Technical Difficulty Cost Marketability Innovation Access to earned media Word of Mouth Potenitial Time This process is not limited to initial idea selection. For each iteration, you should use idea evaluation again, because it allows your organization to align marketing effort with development activities

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2022 - HackerNoon Contributor of the Year - Augmented Reality

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Marketing Centric Idea Evaluation Process for Startups

Choosing a new software platform for your nonprofit can cause lots of stress. But, in my experience, there's one part of the process that especially freaks out nonprofit accidental techies: the demo process. In this post, I'll be going over eight questions you should ask your software vendor before your next software demo. You probably know a lot of things about their needs, but making sure you've asked the vendor all the right questions is a different matter entirely. Using Open-Ended Questions Why is the demo process for a new CRM (or any new software) so hard? We all have blind spots, and it's not always easy to figure out how to deal with them. And before I share the eight questions you'll need to ask your software vendor, here's an overarching pro tip. Use for the best results. When you're investing tens of thousands of dollars (and often more) on new systems, the devil is truly in the details. A simple yes or no answer just doesn't give you the context that you need to know whether something will work for you and your organization. open-ended questions Here's one example: You: Do you provide training? Vendor: Yes, we do. You need specifics! When do you get the training? How is it delivered? Is there documentation? Is the training specific to your configuration, or are you trained in a vanilla version? How many people can be trained? The best way to get those details is to ask questions in a way that they can't be answered with a yes or no. also help to turn this into a conversation rather than just running through a checklist. Conversations provide information, which can trigger new questions. Open-ended questions Here's that same training question phrased just a bit differently: H You: ow will my team be trained? Vendor: We offer two hours of classroom training and on-demand video training. … And with that, you're now ready for the big eight questions with some context for each one. The Eight Questions to Ask 1. How often do you add new features to your system? There may be functionality you need at some point, but that could mean anywhere from two months to two years before it's released. Get a sense of how the vendor rolls out new functionality. 2. Which merchant accounts can we use? Changing merchant account providers can be time-consuming and may cause changes to your reconciliation processes. Sometimes you can keep your current provider — something that's good to know in advance. 3. How long will our implementation take? Very generally, system setup can take anywhere from two months to a year. Make sure you find out what a realistic time frame is for your specific migration. Do you need it to go faster or slower? 4. How does our legacy data move into the new system? Every organization's data schema and requirements are different, and no two migrations are the same. Getting the new system set up to receive your data is the first step. Creating the import files, pushing them in, and testing the import may fall to your team to manage. 5. How will our team be trained? I used this as an example because getting a good ROI on the new system is directly related to the quality of the training, and most organizations have a need for ongoing learning as their use expands over time. If you think you'll need custom training, ask for it. 6. What makes an organization a good fit for your system and company? There's no perfect system out there, so you need to know whether your organization has characteristics and requirements like the organizations that are happy using the software. 7. Who will manage our account? Vendors offer a variety of account management models — sometimes you have a dedicated person, and sometimes you don't. Learning about the vendor's structure will help you know whether their model will provide you with a satisfying business relationship. 8. How do we access support? Here's another place where you'll find a bunch of different approaches. Ticketing systems, live chat, phone, and email are all in the mix. Be sure you understand whether only one person can contact support or if any user can ask for help. If you're feeling nervous about your nonprofit technology blind spots, start with these eight questions. They'll help to expand your understanding of elements that are as important as functionality and cost, and you'll be more likely to find a system that's a great match for your needs. Originally published as " 8 Questions to Ask Before Your Next Software Demo " with the Creative Commons Attribution 4.0 International license.

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How to Get the Best out of Your Next Software Demo

HackerNoon Tech Stories Archive September 26th, 2021

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