A Product Manager and a Software Engineer walk into a bar. They look into the menu and order their food and drinks. The Product Manager has few drinks and rants to the Engineer asking about the new fad called ‘API’. The tipsy Engineer smiles when the waiter serves the order and tells the PM that he just made an API call. The confused PM asks how? The Engineer takes a pen and a napkin, ready to answer and explain the curious PM. — Follow me as I tell you a tale of a person, an exclusive restaurant and a chef/owner to explain you what an API is and how it works! Engineer PM — Oh wow! Let’s get this party started! Authentication — Imagine you are going to this fancy restaurant which is exclusive to members alone (let’s say a Yacht Club Restaurant that allows you to dine in only if you have a Yacht Club membership). You get a membership for the club and hence can access the restaurant dine in. Engineer — So what does that mean in API sense? PM — This means that like the fancy restaurant mentioned above, APIs have some exclusivity as to who can use it. If you want to enter the restaurant, you will need to have a valid membership card along with ID proof for the Yacht Club restaurant to admit you inside. In technical terminlology, you will need to get authenticated to use the API Engineer API keys or token . Endpoint — Ok bruh! Now that you are an exclusive member of the imaginary Yacht Club, will you take me to the imaginary restaurant? Engineer — Yes of course! Mind telling me the address and directions to this imaginary restaurant? PM — Haha! This address is what they call an ‘ ’! Engineer Endpoint : This is how an endpoint looks like — Illustration http://theyachtrestaurant.com/Menu Method, Request and Response — So, now that we have landed in the imaginary Yacht Club restaurant, what do you want to do? Engineer — Isn’t it obvious? Let’s order food! PM — Perfect! So start describing your actions in the restaurant and I will justify it with API analogy. Engineer — OK cool! Going along this storylike, firstly I would like to see what all they serve! PM — Great. You have just used a ‘ ’. Engineer Get Method — Dude, can you first tell me what method is? PM — A ‘ ’ is nothing but a ‘ ’ or an ‘ ’ you ask the API to perform. The API isn’t as intelligent as you are Mr. PM, so we need to provide some instructions. We have to be very courteous and ‘ ’ the API to do something for us. For APIs to work, you will need to send some instructions in the ‘ ’ payload. Once the ‘ ’ is processed, you get a ‘ ’ payload from the API that provides you the output and outcome information. Engineer Method Verb Action Request Request Request Response — So in the example where I ask the waiter to get menu so that I can see what they serve, how would you break it into the API terminology? PM Here you go: Engineer — a. Method = Get In API analogy the GET method is used to retreive data from a server at the specified resource. In this scenario, you had asked the waiter to get you the list of items served in the restaurant. It means that you are asking for some information from a database. In this case you have requested to see the list of items served in the restaurant Request: The waiter serves you the menu. The outcome of the response is that you get a menu which shows you the list of items served in the restaurant. Response: Let me do an illustration of the API: — This is great. But why isn’t the request body there? — Rules, my lad! As a rule ‘Get’ method endpoints will have ‘ ’ which are nothing but the request body put in the URL. PM Engineer Query Parameters - Thank you comrade for the explanation. Would you like to order dinner and then go home if we are done explaining about ? PM APIs — My friend, we have barely scratched the surface. Let me dig deeper into concepts. Engineer API We have spoken a bit about ‘Get’ call. But there other methods which I would describe the scenario and API analogy for: b. = : Method Post In the imaginary Yacht Club restaurant, you did not find any food to your liking. You call the chef /owner of the restaurant and request for a customized or a new dish. Does the humble chef ask what you would like to have? You start describing your famous ‘Vegan Burger’ recipe that your mom cooked for you. The chef makes the vegan burger and serves it to you. The smell of the burger is so inviting that it makes the neighbouring tables curious! They call the chef and ask to be served the same dish. The chef decides to put this new item to the menu! In this scenario, the act of putting a new item to the menu is called ‘ ’. How to achieve it is by courteously requesting it! Post The chef will add a new item called ‘Vegan Burger’. The request should contain the item name along with the description and price to be posted to the menu. Request: The outcome would be to see the confirmation that ‘Vegan Burger’ along with ‘Description’ and ‘Price’ is posted and visible on the menu Response: — OK, what is content type and Jason Statham doing in the example? PM — Content Type is a part of the header. Before you ask me what an header is, it defines the metadata of the request and response information. In this example, the header is containing the ‘Content-Type’ and ‘Accept’ which tells us how the request and response data format should be. The Jason Statham in your language or ‘JSON’ in the technical terms is one of the type of data format which means how the content would look! Engineer c. Method = Put The ‘Vegan Burger’ is a big hit in the restaurant, but the customer’s complain about the exorbitant price. The humble chef / owner decides to slash the price by few dollars. In this scenario, the act of updating an existing item in the menu is called ‘ ’. Put The chef enters a new price for the item ‘Vegan burger’. The request payload should contain the reference of the item you are updating and the parameters that require the update along with the values. Request: The outcome would be to see the updated price in the menu for ‘Vegan Burger’ Response: d. Method = Delete With the heavy demand in ‘Vegan Burger’, the chef decides to remove an item from the menu. The act of removing an item is called ‘ ’ in API terminology Delete The chef will request to remove ‘Caviar’ from the menu. The request payload should contain the item name amongst other details to be removed from the menu. Request: The outcome would be to see the confirmation that ‘Caviar’is no longer visible on the menu. Response: API Status Codes and Error Messages — Wow! That was pretty elaborate. I like the fact that the api does all the things you want them to do. PM — Haha! In your dreams buddy! APIs are not always faithful. They are prone to error sometimes. Engineer — So how would I know when an error occurred, how it occurred and what is the indication for me to understand? PM — This brings us to our next concept called ‘ ’ . Engineer response codes means that your request was successful. 2xx : Imagine you drive up to the imaginary Yacht Club Restaurant to find a notice a signboard that says it has moved to a new address and the hotel representative telling you the direction of the new location directions. In API terms, it means that the request is redirected to another URL. 3xx : You drive up to the restaurant and order for ‘Caviar’ only to find that it is no longer served in the restaurant! This is equivalent to the Client errors (unauthorized, forbidden, page not found). 4xx: In this case you are the victim! You had ordered the vegan burger, but the queue was long, and your item took a long time to be made. You left the resto in full rage.In API terms it means Service not available or gateway timeout. 5xx: For more details on response codes check the link — https://http.cat/ Img Source : Saturday Evening Post (saturdayeveningpost.com)

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Decoding APIs for Product Managers

The second of three articles on corporate valuations and capital structures. Stock analysts say that expectations for future performance define a company’s valuation, and if the market seems disconnected from the state of the economy, it’s because investors are looking ahead. This suggests broadly shared clairvoyance among investors, something that eludes business and leaders. public policy This concept equation offers another way to understand what drives a . [Note: a valuation is the price to buy a company, based on the price for a fraction or share of it.] valuation Valuation = Analytics + Emotion + Deal Terms It states that a valuation reflects analytics, emotion, and deal terms. These variables manifest themselves differently in the public and private capital markets. Analytics Investors vary in the quantity and quality of insight that informs their perspective on a stock. There are smart and not-so-smart investors in both markets but, on balance, they tend to be smarter in the private one. A stock’s price is thought to reflect expectations for the income—dividends or capital gains— it will produce, discounted for the time value of money. Thus, the stock of a company facing difficulties might hold up if it is expected to rebound. A bad quarter will be excused if the next few should meet prior expectations. If that next quarter disappoints too, but those that follow are expected to be on track, the stock should be stable. Put another way, investors focus on what’s in the headlights, not in the rear-view mirror. Some companies will thrive in this latest incarnation of the New while others will be unaffected by the changes. For the rest, I question whether the optimism implied in the stock market makes sense, for three reasons. Economy If a company is adversely affected by the pandemic, it will take time to recover. Minimally, that means diminished projections for several quarters (i.e., lower earnings in the headlights). Business models that were already in doubt are being painted with a fresh coat of it. Expectations that were reasonable months ago are less so now. Assumptions about customer behavior and supply chains are less certain, for instance. All three reasons suggest public company valuations should be lower than they are. So perhaps another one is at play—analytic thinking that anticipates investors fueled by relief and optimism as economies are reopened. A Wall Street saying captures this approach—“Buy on the rumor, sell on the news.” [A NYT article published after this one is called " ."] Trading Sportsbooks for Brokerages, Bored Bettors Wager on Stocks Emotion Lava lamps evoke the effect of emotion on markets. The opaque fluid—the lava—represents emotion, which rises as it is heated and falls as it cools; it’s life-like energy is beguiling. Imagine two such lamps, one with more lava than the other. The one with more is the public market, and the other is the private one. That is, while emotion affects both markets, its more evident in the public one because private investors are more valuation-aware, and they get deal terms that provide “price protection.” Deal Terms Deal terms are rights granted by a company to its investors. Some address governance matters—voting rights—but the ones relevant here limit valuation risk. That is, they can protect an investor from overpaying for a position. Private investors routinely secure such deal terms. It’s hugely important for them—they would be far less successful if they had to rely on getting a valuation right when they invest. Tellingly, VCs tell entrepreneurs—“I’ll give you the valuation you want, if you give me the terms I want.” [Guess what? NO ONE can reliably value a venture-stage company.] A popular deal term is known as a price ratchet. It entitles an investor to additional shares at no cost if a subsequent investor gets the stock for a price that is insufficiently higher. The free shares lower the average price and increases the ownership of the protected investor. Another deal term is a liquidation preference. It orders how the proceeds from a liquidation event—like the company being acquired—are divvied up. Think of how animals decide who eats a kill first, second, and so on—it’s the same idea. An investor with a 1X liquidation preference receives its investment back before sharing what remains with other shareholders. One with a 5X preference receives five times it’s investment before sharing. Many deal terms are used in the private market can effectively modify a valuation. Public investors, however, don’t get price protection. It’s ironic, given that they invest in venture-stage companies at far higher valuations. That said, an ephemeral form of price protection exists for those who get shares in a hot IPO. It’s based on investment banking practices, not deal terms. Bankers decide the price and who gets the new shares. For them, a successful IPO raises the sought-after capital at a discount of 15 to 20 percent to what the shares sell at in the secondary market. That price pop attracts investors, many of whom flip their shares. It also promotes interest in other offerings the broker-dealer has. In sum, valuations work differently in the private and public markets. In the private one, they are set infrequently by valuation-savvy investors who use analytics and emotion to assess worth but rely on deal terms to protect themselves from buying in at a price that is too high. Such an investor may wind up with a larger stake, which comes out of the position of employees and investors in a weaker position. Public market valuations are set daily, and investors who are not valuation savvy help set them. That suggests weaker analytical ability and more emotion than found in the private market. Strikingly, public investors don’t get deal terms that mitigate valuation risk. Why don’t public investors get deal terms that reduce valuation risk? They don’t demand them! Also, because the capital structure used for IPOs doesn’t enable special deal terms. It’s possible with a different one, however. A capital structure defines ownership interests in a corporation. There are three types—a conventional one, a modified conventional structure, and the Fairshare Model. The first two are common. The third is an idea described in my 2019 book, The Fairshare Model: A Performance-Based Capital Structure for Venture-Stage Initial Public Offerings . A has a single class of stock. In one, all shareholders are treated alike, which means no special terms. Like in Alexander Dumas’ novel , its “All for one and one for all”—all shareholders get the same deal. This type is used by public companies and private ones with non-professional investors. It’s “conventional” with respect to a valuation for future performance is set—it happens when an equity investment is made. To illustrate, if you buy half of my new company for $1, we agree that my future performance is worth $1. Thus, the company’s pre-money valuation is $1. After you put in your money, its post-money valuation is $2. These terms are explained in this I created years before the publication of , so the information about the book is out of date. conventional capital structure The Three Musketeers when video The Fairshare Model A has multiple classes of stock. Its “conventional” because a value for future performance is set when an equity investment is made. It’s “modified” because it allows a company to treat some shareholders different from others, by providing deal terms that can reset the upfront valuation. It evokes George Orwell’s , in which “All animals are equal, but some animals are more equal than others.” Again, VC and private equity investors require a modified conventional capital structure because one is necessary to secure deal terms they demand. Public companies occasionally have multiple classes of stock for governance reasons (i.e., super-voting shares for some shareholders), but not to reduce valuation risk. modified conventional capital structure Animal Farm is unconventional. It’s also novel—no company has used it yet. The Fairshare Model uses multiple classes of stock to accomplish two goals. It limits valuation risk for IPO investors. It also balances and align the interests of investors and employees—capital and labor. There are two classes of stock—both vote but only one is tradable. IPO and pre-IPO investors get the tradable stock—employees get it too, for pre-IPO performance. It’s common stock, but for simplicity, I refer to it as “Investor Stock.” Employees get the non-tradable stock for future performance—a preferred stock I call “Performance Stock.” The Performance Stock converts to Investor Stock based on milestones described in the company’s offering document, or that both classes subsequently agree on. With this structure, public investors are more likely to profit when they invest in a company with high failure risk, because they have less valuation risk. And a well-performing team can end up with more of the wealth they create than VCs would allow. The Fairshare Model It places no value on future performance when equity capital is raised—instead it defines how to reward actual performance. To consider the significance that reduced valuation risk can have on capital markets, perform these two thought experiments. [Without deal terms to reduce valuation risk, their success would depend on having superior analytics, and not being overly swayed by emotion. I believe this would cause investing activity to dramatically shrink because private investors would be exposed to a full dose of valuation risk.] Ask yourself “How might the private capital market change if VC and private equity funds could only use a conventional capital structure?” [Public investors would be more likely to profit when they invest in a venture-stage company, and there would be better alignment between investors and employees. As a result, venture investing would grow, and the economy be more vibrant. That’s because these types of companies are the engine of economic growth and job creation, not the Fortune 500.] Now ask, “How might the public capital market change if companies used the Fairshare Model when they go public?” In closing, the Fairshare Model provides a framework to structure IPOs so that more average people benefit—public investors get price protection, and employees have the opportunity to earn more of the wealth they create. Thus, it can help address the epic economic challenge of the 21st Century—to distribute the benefits of capitalism more fairly and broadly. This is the second of three related articles. The other two in the series are: (#1) Valuations in the Pandemic (#3) Applying Complexity Theory to Valuation

Harmony

Funding

Super

The Fairshare Model reimagines capitalism

The Drivers of Company Valuation

I spent the better part of a day moving a project completely off of the ORM we were using (TypeORM). Ever since I started doing web development, I’ve been using some kind of heavy abstraction on top of my database. Whether it’s Mongoose when I used MongoDB, or TypeORM when I decided to move to TypeScript and PostgreSQL. However, it turns out that ORMs and query builders aren’t so great. I used to be that person that thought ORMs and query builders did the job for me, and I used it as an excuse to not learn SQL (which is what everyone tells you NOT to do). Given the tools that exist today, I would wholeheartedly recommend doing it the opposite of how I did if you’re starting off new to these things — write your SQL stuff by hand, or with help from the tools I mention later in the post. It’ll feel really slow at first, but you’ll thank yourself later 😄 I’m going to split this post up into three different parts: First, I’ll talk about why query builders and ORMs aren’t worth using. Second, I’ll explore specific issues I’ve had using ORMs like TypeORM — bugs that don’t actually exist when you directly interface with the DB using the popular driver library in your language or some helper functions. Third, I’ll walk through my current preferred tooling to create an ergonomic experience with the database from a Node.js project — we can replace everything the ORM used to do but better (migrations, typed database schemas and interactions, nice helper functions for simple queries, etc). Part 1: Why query builders and ORMs aren’t worth using Query builders (e.g. ) in general are an unnecessary abstraction on top of the database — just write the SQL. Instead, you’re busy learning something on top of SQL chaining a bunch of functions together and trying to figure out how a specific tool works instead of how SQL works. You’re better off learning how SQL works instead of learning a specific query builder’s toolset. That knowledge is transferrable. SQL itself is also incredibly powerful, used everywhere, and likely won’t go away anytime soon. Even if you learn new backends or frontends, you’ll still be using SQL. A lot of these benefits are lost when you only work with query builders and ORMs. Also, it can start to get frustrating sometimes when you realize that you know how to do something in SQL, that you don’t know how to do with an ORM. You’ll try to do things the “ORM” way, to use the tooling provided. Knex.js Query builders and ORMs can offer things like sanitization, some type safety, and abstracting away dealing with conflicts with your primary indexes (sometimes), but these can be better handled with the tools I mention in part 3. Also, parameterized queries in automatically sanitize your inputs — so the query builder doesn’t need to do that for you. node-postgres Edit Dec 2020: One thing ORMs/query builders are good for is being able to compose your queries — dynamic queries are easier to do since they are typically chained function calls. Part 2: Unnecessary problems and frustrations I’ve dealt with using ORMs like TypeORM I’d often have to spend time debugging issues with the ORM, that the underlying Node.js PostgreSQL driver (the wonderful , which I’ll be calling “pg” from now on) didn’t actually have any issues with. Let me walk through a non-exhaustive list of problems I’ve had working with TypeORM. I’m picking on ORMs in general here, so this isn’t a bag on TypeORM. node-postgres Trying to use it with one-off scripts, ts-node or ts-node-dev I’ve had strange issues trying to use TypeORM with these tools — it’s been somewhat unpredictable. I know that for some of the scripts, I couldn’t get the files to run with ts-node, and required compiling it to JS. As soon as I removed TypeORM and used pg, it worked as expected. Handling database connections directly with and tools I mention in part 3 make this easy. pg.Pool Working through the ORM, instead of utilizing the database directly The problem with tools like TypeORM is your entire database is managed through the ORM. It turns out that ORMs are actually not that powerful, at least not as powerful as SQL itself is. Managing your models in TypeORM leads to a lot of limitations. For example, something I often do in my database models is keep track of when a row was last updated. Doing this in TypeORM means that you do it at the JS layer, where TypeORM will get the current JS date and insert it. The problem with this is that it’s not happening at the source of truth — your database. If you try to do a query with TypeORM that doesn’t use one of its higher-level abstractions, you now have data inconsistency. You just changed some data but that column didn’t update. Instead, if you’re using Postgres, one option is to use Postgres’s triggers, so that if a row is modified at all, Postgres will do that work to figure out the rest. Another major limitation is not being able to define models outside of code written in TypeScript. We had several tables in our database that I wrote into SQL but TypeORM had no awareness of (we stopped using TypeORM for migrations first, before removing the rest of it). I could’ve made model files for all of those tables so that TypeORM would be aware of them, but it wasn’t worth it. On top of this, TypeORM allows you to make junction tables when you define a many-to-many relationship for some of your information, but these can’t be queried directly without using the query builder or dropping down to plain SQL (losing all type safety). Bulky, inefficient queries If you use TypeORM, go try something right now. Open your ormconfig.js (or .ts, .json, whichever one you use) and go add “logging”: true. Watch the queries. You might be amazed by 1. how long they are 2. how many there are 3. how inefficient and ugly they are sometimes. For example, a query as simple as (“userId”) users_followers “userId”=’blah- SELECT COUNT FROM where uuid ' is actually this in TypeORM “Users_followers_rc”.”userId” “parentId”, (“ ”.” ”) “cnt” “ ” “ ” “users_followers” “Users_followers_rc” “Users_followers_rc”.”userId” (‘blah- ’) “Users_followers_rc”.”followerId” = “ ”.” ” “Users_followers_rc”.”userId” SELECT AS COUNT users id AS FROM users users INNER JOIN ON IN uuid AND users id GROUP BY We’re often told that the ORM “takes care of the DB so you don’t have to do think about it”, but as I’ve done more development, I’ve learned that this is not the right approach. Your database is often the source of truth — and is also probably one of the slowest things in your entire stack, as it deals with stuff that’s persistent on disk, not memory like the rest of your stack probably does most of the time. Also, go check out what actually happens when you use save() in TypeORM. It makes sense when you think about it, but it’s probably more queries than you thought. ORMs also often try to support many different SQL dialects or different environments. You may find that there are specific features of a DB, say, Postgres, that you want to use. Those constructs to write better queries might not be available to you if you use an ORM. You could argue that you should aim for portability, not to be tied to a DB — but we’ll cross that bridge if it ever matters. Migrations? Meh.. Another huge problem with ORMs is that the migrations they generate aren’t the best. For example, TypeORM will generate indices of any kind with some random string of characters. I still haven’t figured out why this is necessary unless a different dialect of SQL justifies it (I only use Postgres). So you might see a query like CREATE INDEX IDX_2123asf67s6a3agab31a instead of the default convention that Postgres uses — which is easy to understand, and easy to work with. You can probably guess what it’s called without even looking at your database because the Postgres already uses is pretty straightforward. convention Sometimes the migrations you’ll get are straight-up wrong, but that’s the nature of trying to automate stuff like this. Other times, it’ll add extra indices where it doesn’t need to, or you’ll stare at your migration and realize that the default stuff it uses or even the quality of the query is not that great. Your migrations will look a lot cleaner written by hand. When you let your ORM handle migrations for you, it can’t be smart enough often to deal with things like altering columns. TypeORM will often drop your column and make a new one — that’s not a real solution in a lot of cases, unfortunately. If you write your migration by hand, these are easily solvable problems. Your migrations may also run more slowly if you need TypeScript to be compiled first. If you need to write any migrations by hand, you may notice that when you need to auto-generate migrations again, it might change or mess with the stuff you already modified (as expected, but that’s not what you would like in the ideal world). Also, does anyone actually do down migrations? More on this below in my preferred tools for doing migrations. The defaults are not that good I noticed some of the default types TypeORM recommends from the JS layer are not likely what you want in Postgres. Don’t get me wrong — this is remedied by passing in parameters in the model decorators, but if you’re relying on TypeORM to take care of it for you, these are things you won’t know about. For example, by default, TypeORM will generate a timestamp without a time zone if you use a date column. It basically makes no sense to ever do this. Use timestamptz. Also, character varying without a parameter is the same as the TEXT type in Postgres (specifically in Postgres, YMMV with other SQL dialects). Fewer characters to type for you, same underlying representation. Doesn’t feel like working with SQL (by design?) ORMs are designed to make you feel like the database is abstracted to work with your language of choice. Instead of feeling like you’re working with something where people actually understand SQL and databases and are trying to make that easier for you. It often doesn’t feel like that. SQL is such a declarative language and reads like English that it’s often nicer to use it directly. No type safety once you drop away from the high-level abstractions One general problem with ORMs is that the type safety only exists as long as you’re working through their abstractions. Once you need to drop to SQL, you lose type safety entirely. How could we make this at least somewhat better? More on this in part 3. If I had to design an ORM from scratch, it would probably have a lot of these same problems. It’s probably better to not work with this abstraction in the first place if possible with your tooling of choice. Part 3: My preferred setup, getting all the goodies without the bad stuff Now that you’ve read my mini-rant, let me share what my preferred tooling is today for completely moving off ORMs and query builders, as well as what the benefits are! Can we really have our cake and eat it too? Replacing migrations Let’s start with migrations first! This one is easy. I use (thanks for the recommendation!) https://github.com/graphile/migrate John Haley It’s super straightforward to use — you write your SQL files, and run a command. You can even run a watch command that will watch your file and execute the migrations as you work on it (I’ve never used this, personally). It’s also super fast — because it’s plain SQL. No JavaScript to deal with anywhere. The tool itself is built in TypeScript and uses pg under the hood. It’s fast. Replacing the rest of the ORM A big inspiration for me writing this piece was a I came across on GitHub a while ago. I initially didn’t want to go through it to implement it myself, but the wonderful author has made it an actual library. The package is called . With this, you can automatically generate TypeScript types from your database (originally inspired from a package called which is now unmaintained), do somewhat type-checked SQL queries, and get nifty helpers along the way, up to the level of abstraction you’d like without going overboard. Remember how I was talking about needing to keep the DB in sync with the backend? I don’t need to do that anymore. Honestly, some of the helpers here are better than what ORMs I’ve seen offer — at least for the queries that I’d use the ORM for. I would highly recommend reading the documentation (it’s straightforward to understand) and taking a look at it yourself — I won’t do it justice. project Zapatos Schemats I see the questions already… But Ani, isn’t this just like another ORM? If you use it, you’ll see that it’s not. It ultimately uses pg and has some nifty helpers to help you do better type-checking, and make certain queries easier. It does not try to write strangely complex queries, manage connection pooling, or do anything super fancy (although generating the types from your database for you is amazing and fancy in and of itself). It’ll convert whatever you pass it to pretty much the exact query you would write if you wrote it by hand (most of the time, with the exception of the highest abstractions near the bottom of the docs). It stays pretty lean in terms of what it offers and what it can do for you — most of it is ultimately on you to write a proper query. The files it generates are put directly into your project’s source code, so you can see exactly what it does. Bonus, Part 4: Abstracting the API itself Gotten this far? I thought I’d give a shoutout to some of the tools that have come in recent years to help build backends faster. Tools like generate a performant GraphQL API on top of your database. They invert the model and turn your development model into database driven design. It’ll teach you a lot about how to use your database’s powerful features instead of using it like a dummy data store you abstract away through code. PostGraphile Thanks for reading! Please let me know if you have any feedback in the comments below. I haven’t been writing that much about programming-related topics so I thought I’d kick it off with a light intro to something I did recently. Previously published at https://medium.com/swlh/how-to-move-off-query-builders-and-orms-in-javascript-typescript-4b145a865e96

BUNCH

Migrating Away From Query Builders and ORMs in JavaScript or TypeScript

In Hacker Noon’s inaugural awards last year, we wanted to recognize the brightest and best, in tech, and the award category , is for the ones who are super young, yet entrepreneurial and motivated. This is our sweet little Q&A with an awesome human named Sergio Mattei, who is the winner of the award. ‘2-or-3 under 20ish’ How did you feel when you were nominated (... and won!) the category for ‘2-or-3 under 20ish Award’? Well, it was pretty awesome! I’d been aware of the award and pushing the voting link hard within the online maker community, so I was really excited to see the final results come in with me leading the category. I’ve worked really hard building community and creating content, and seeing that get recognized was pretty fantastic. What is your full-time job? My full-time work is leading and building , a community of over 5,000 creators in tech shipping software together. It’s pretty great work: I feel like what I do is extremely impactful and has a role in impacting lives around the world. I couldn’t ask for more. Makerlog How do you keep up on your tech industry’s latest developments? I read a lot! Aggregators like Hacker News and publications like Hacker Noon are my slight addictions, haha. Every morning (and bathroom break) I hop on my phone and start getting my daily brief of satisfying tech content. Which topic do you usually read on Hackernoon.com? Naturally being a founder and in the maker community, the section is a great fit! The articles on it are pretty great morning (or, noon) reads. Startups What was the last book you read and was it any good? ‘Principles’, by Ray Dalio. It’s a pretty popular book, but I’ve just started reading it and I’m pretty hooked. What’s your biggest vice? I’ll be straight up with you: reading Hacker News. I love criticizing that place because the amount of people talking with authority is staggering (similar to Reddit). But it’s a love-hate thing -- I contribute there often and it’s my primary morning reading destination. I really love the intellectually satisfying content and in-depth discussion -- the YC folks have really inspired a great on-site culture. Would you like to share more on what you are working on right now? Absolutely! basically works by making your entire product-building process -- from idea to production -- entirely transparent and public, hence making you more productive! We’ve built an incredibly supportive community that not only helps makers stay motivated while shipping, but also loves to chip in and help -- anywhere from being initial beta testers to collaborating in products. Makerlog If you’re a maker or online creator building something, is your home! Makerlog What is one thing you’d wish to change about yourself? I’d probably improve my habit-building abilities and time awareness. It’s extremely hard for me to build habits, for some reason. I think it’s probably related to my ADD. I have attention issues, yet time awareness happens to be really difficult for me. When my mind enters a focus state, I basically completely lose track of time. Hours could go by and I wouldn’t notice. This is a serious issue when scheduling meetings and whatnot… But somehow I manage. If you could change a global issue that is one of the world’s biggest problems today, what would you change? That’s a complicated question. There’s many global issues that I would change, and I don’t want to get too political either. This will sound cliche but it’s a cause that is very dear to me -- bringing the maker movement to the places that need it most. I’ve made things since I was a kid, and for the longest time, I felt like I didn’t have a support network. That all changed after I stumbled upon the maker community - I truly began to feel that there were people that understood me, even if my economic reality distanced me from traditional startup hubs and metropolitan areas. That’s an amazing thing, and the world is set up for it. The cloud has brought high-tech to everyone at affordable costs. You can now build a startup with a cheap laptop or even from your phone with no-code. The democratization of technology is going fast and I like it. Is there anything else you wish the readers to know about you? I don’t know, I’m quite the open book. :) Follow me on Twitter for sure - I write a lot of content there and tweet often about my hustle building Makerlog. Caguas, Puerto Rico. The tropical summer heat is starting to hit but the beach makes up for it. Where are you living at right now? Pancakes, for sure. What is your favourite breakfast meal? The folks at YourStacks would be proud. Do you have a phrase or a quote you live by? “No mercy.” - Alina Sava “You didn’t wake up today to be mediocre.” - ??? Green! My eyes are green. HackerNoon would be proud. Favorite color: Favorite song at the moment: Derecho de Nacimiento by Natalia Lafourcade. Definitely Planet Money by NPR and “How I built this”. Great podcasts! Favorite podcast: Makerlog, for sure. But also Notion, TweetDeck (I am a HUGE TweetDeck fan), Spotify. Favourite tech app you use daily: I probably can’t live without Notion and tools like Roam Research. I love how these tools help me turn my thoughts into great-looking documents. A tool you can’t live without: They work like my brain does, especially Roam -- I am really effective writing in bullets. In this year’s awards, we have a category called ‘Future Heroes’, which we’ve no doubt Sergio already belongs to. Will you share your nomination for the beautiful humans shaping tomorrow’s technology in , , # , and ? web monetization tech for good BLM so much more

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mother of unicorns. not a hacker. social distancing since 2018

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Sergio Mattei: The Beautiful Humans Of Hacker Noon (Noonies Winner for Under 20ish Award, 2019)

There are and dwell time is one of those factors. Or is it not? many ranking factors Google uses It's very confusing for me until I haven't written here, but the chatters on social media platforms started prioritizing dwell time importance. In this post, I’ll be answering all of these questions like what is dwell time? How is dwell time measured? Is it REALLY a ranking factor? And if so, how can you optimize dwell time? What is Dwell Time? 3 years ago, at Bing wrote a blog post, It was in this blog post that the concept of dwell time was first introduced. Duane Forrester In simple words, Dwell time is the length of time a person spends looking at a webpage after they’ve clicked a link on a , but before clicking back to the search results. SERP page Image Source - Cognitiveseo.com Dwell time is often imprecise with session duration as well. Session duration defines the total amount of time spent on a website before the website visitor leaves the site. Dwell time tells a different tale as stated above. Also, keep in mind that Dwell time is not . Click through rate Let's Understand Dwell Time with Example For example, let’s say you search for “Pancakes” in Google. The first result looks like a good fit. So you click on a page. But when you land on the site, it looks displeasing and difficult to navigate. And the content isn’t helpful. So after 7 seconds of time on the page, you click back to the results. Your Dwell Time was 7 seconds. And that super brief visit tells Google search engine that you weren’t happy with that result. Average Dwell Time = All users total time spent before clicking Back to Result  /  Count of All Users who clicked Back to Result Can You Measure Dwell Time in Google Analytics? You will first have to measure dwell time if you are thinking of increasing it. Here is how you can do that: In Google Analytics, focus on the bounce rate & avg. time on website metrics. You will have to decrease the bounce rate and increase time on page. This indicates that your site’s visitors are spending more time on a page before bouncing-off from the site. That's how you can easily increase your dwell time. Does dwell time affect your rankings? On this question, every marketer has a different point of view, but of Google somehow considering dwell time sort of ranking signal. there are some proofs that do support the idea Steps to Increase Dwell Time Optimal use of Ads Google started to penalize mobile sites that contain intrusive pop-up and this in 2017. Let's embrace this fact that ads are sometimes annoying, which results in long dwell times on your website. If you remove ads, then certainly your dwell time will improve a lot. ads has been officially announced Right Keywords Targeting You must understand the fact that Driving relevant audiences play a vital role in increasing dwell time & this can be done only if you're targeting the right keywords. You can use tools like Keyword magic tool provided by SEMrush Image Source- SEMrush Keyword Magic Tool Page Load Time Improvement Slow websites are boring & frustrating for any of us, especially if we are browsing on mobile. I would like to recommend some tools i.e Pingdom, GTMetrix & Google Page Speed Insights, to make sure that you have a really good speed. A Case study made by shows that every single second delay impacts bounce rate, conversion rate, cart size, and page views. It seems like a one-second delay can make a huge difference in user engagement on the website. Radware Image Source: www.radware.com Better Use of Internal Linking A good Internal linking strategy will help your SEO along with that it will help in creating a better user experience. Because it creates an easy to access structure and will guide website visitors through more website pages and eventually increase your page views as well. Simultaneously, this will increase the chances to add certain links to pages that people will further relate to. This one is my favorite strategy to increase dwell time. Attractive Design of Your Website You have to make sure that your website design should be flawless, easy to navigate, and simple layout. as well as intact your users to stay more on your site, which will improve your dwell time. Good web design helps you in SEO Final Conclusion -As Google keeps on focusing more on excellent user experience & making sure that, it shows the most effective results for every search. And as Neil Patel , dwell time is less of a science and more of an art. claims Originally published on ReadWrite.

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ReadWrite

My Blog - TechBlogCorner.com

What is Dwell Time? Does Google Consider this as A Ranking Factor

A while ago, we with the AWS catalog. covered the invocation (trigger) methods supported by Lambda and the integrations available Now we’re launching a series of articles to correlate these integration possibilities with common serverless architectural patterns (covered by this ). literature review In Part I, we will cover the category. and stay tuned for the next parts of the series. Orchestration & Aggregation Subscribe to our newsletter Pattern: Aggregator Purpose A single API is used to aggregate multiple downstream resources. Solutions Entry-point Lambda as a router to other Lambdas Requests come from API Gateway, which triggers a Lambda function (L1) synchronously using the . L1 then triggers multiple other Lambda functions (L2x). The invocation from L1 to L2x could be synchronous or asynchronous, depending on the use case. proxy integration model If the client expects data that comes from L2x, the invocation trigger should be synchronous. For write-only endpoints, when the client only expects a ‘200 - OK’ response, L1 can invoke L2x asynchronously and respond to the client immediately. One disadvantage of using synchronous invocations is that the L1 function will continue to be billed for each millisecond it awaits L2x functions results. See more in this tutorial. Serverless Trilemma API Gateway as a router, client as aggregator In some cases, the client could play the role of aggregator. Consider a frontend application under your control that requires data from multiple backend sources. A single API Gateway can be deployed with several endpoints, each routing to different L2x Lambda functions (also using the ). proxy integration model The client is then responsible for parallelizing calls to all required endpoints, collecting, and aggregating results. The main benefit of this approach is removing the double-billing factor. Watch this to learn more about this. tutorial about the Serverless Trilemma Architectural concerns 1. Timeout limits API Gateway and API services’ , which can create problems if the jobs expected from the Lambda functions take longer; REST HTTP timeout limit is 29 seconds The API service , which gives more room if your jobs require 30+ seconds; Web socket supports connections for up to 2 hours Keep in mind that the Aggregator pattern (or even Lambda itself) may not be suitable for long-running processes taking several minutes, in the first place; 2. Concurrency limits Having L1 and L2x running synchronously will eat up Lambda concurrency quota faster; Consider one L1 function and four L2x functions (L2a, L2b, L2c, L2d); each invocation to the API endpoint will consume 5 concurrency credits; that means 200 requests already exhausts the entire ; default quota of 1000 concurrent executions If you are allocating concurrency 3. Potential failures L1 should have logic in place to handle failures in any of the L2x functions; Bear in mind that the AWS Lambda platform ; the problem is that this retry will not respond to the L1 function since their connection will be lost by then; will already retry a failed L2x request automatically Lambda failures can be identified using AWS CloudWatch, but monitoring retries and linking to previous executions are possible on professional platforms such as ; Dashbird If L1 is manually invoking L2x again, you’ll be faced with at least three executions: 1 failed 1 retried by AWS 1 retried by the L1 function This has the potential to triple your costs of running L2x and there isn’t much you can do about it without big changes to the architectural pattern; Pattern: Data Lake Purpose Having a central, long-term data storage that is rarely modified and supports flexible, on-demand data query and transformation according to different access pattern requirements. Solutions Push-based approach API Gateway and Lambda (using ) can serve as a passive gate to receive requests with information for the data lake. Authorized applications would send the data in JSON format through a REST endpoint. The Lambda function is responsible for packing the data and uploading it to an S3 bucket. proxy integration This bucket will serve as the data lake storage. AWS Athena is used to stored in S3 on-demand. Athena can be accessed through or drivers (opens up for the usage of GUI analytical tools), an , or even the . query the JSON data JDBC ODBC HTTP API AWS CLI In case it’s needed, a second API endpoint and Lambda function could be used to receive data requests, query Athena and send data back to the client. The benefits of this approach are: Ability to use API Gateway powerful authentication and throttling features, which is important considering that ; Athena has short limits in terms of concurrency Decoupling client requests from the data lake query service; in case it’s required to migrate to a different solution in the future, it would be much easier to do so without causing any disruption to clients depending on the data lake; Event-driven approach In case the primary data storage service supports event-driven triggers, the Lambda function can consume data for the data lake in an asynchronous way. This is the case of DynamoDB and Aurora, for example. automatically as information is entered or modified in a table. in an event-driven way. DynamoDB Streams can trigger a Lambda function Aurora (MySQL compatible only) can similarly trigger a Lambda function The asynchronously triggered Lambda would then perform the same operations to store the data in S3. Optimizing storage for fast and cheap reads JSON is a universal and easy to use structured data format, but not optimized for large scale data consumption. Athena queries will be orders of magnitude faster and cheaper with . columnar formats such as Apache Parquet An to transform JSON data into a columnar format, but AWS Kinesis would probably fit better in a serverless stack like ours. The Firehose service can supported by Athena. In this case, the data is . EMR Cluster could be used convert incoming JSON data into popular columnar formats delivered directly into S3 from Kinesis An API Gateway can also be used in front of Kinesis Firehose with the , which is beneficial for security and concurrency control purposes. AWS-type integration Architectural concerns 1. Concurrency limits There are concurrency limitations for all services listed here ( , and ), and scalability mismatches can defeat the architectural pattern; API Gateway Lambda Kinesis Firehose For example, API Gateway may ingest up to 10,000 concurrent requests, but Kinesis will start throttling way below that; It is important to model the architecture to handle peaks in demand; , for example, can increase the scalability capabilities; ; using SQS between API Gateway and Lambda or Kinesis a more scalable alternative to API Gateway is an Application Load Balancer 2. Query scalability limits Athena tables are only metadata projection of the data stored in S3, it does not store information in itself; S3 also has its limits in terms of maximum ; if Athena queries need to read data from too many objects, S3 may not be able to serve them; requests per second Kinesis Firehose is also a good option here since it’s able to concatenate multiple records in a single S3 object; 3. Data access and security Although we could have all our data bundled together and accessed by anyone, this will rarely be a good practice from an access security standpoint; A good practice is to have multiple ; Athena Tables pointing to different S3 object locations Let’s say we have human resources, logistics, and financial information within an organization and would like to keep data access restricted only to people from within each department; A prefix can be added to each S3 object to enable different access patterns within various Athena tables, such as ‘ s3://bucket/logistics/object-name’; When uploading data from Lambda to S3 it is easy to add such prefixes and ; Kinesis Firehose also support custom prefixes for S3 object names Wrapping up This was the first article in a series about Lambda triggers and architectural design patterns. We’ve covered some patterns within the category. In the coming weeks, we’ll cover more patterns in the same category, such as Fan-in/Fan-out, Queue-bases Load Leveling, Finite-state Machine. Orchestration & Aggregation Other categories of patterns will come as well, such as , , , and . Event-Management Availability Communication Authorization patterns to stay tuned for the next parts in this series! Subscribe to our newsletter In case you are looking for a solution to help you build well-architected serverless applications, cross-references your cloud stack against industry best practices to suggest performance and architectural improvements. You can today, no credit card required. Dashbird Insights try the service for free Previously published at https://dashbird.io/blog/complete-guide-lambda-triggers-design-patterns-part-1/

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Apache

2022 - HackerNoon Contributor of the Year - Aws

2022 - HackerNoon Contributor of the Year - Cloud Computing

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2022 - HackerNoon Contributor of the Year - Serverless

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Nominated for 2022 - HackerNoon Contributor of the Year - Cloud Computing

Nominated for 2022 - HackerNoon Contributor of the Year - Devops

Nominated for 2022 - HackerNoon Contributor of the Year - Serverless

Nominated for 2022 - HackerNoon Contributor of the Year - Aws

Serverless Architecture: Lambda Triggers and Design Patterns [Part 1]

The crypto market might seem incredibly boring to traders these days with Bitcoin and Ethereum behaving like stablecoins 😉. However, a handful number of crypto-assets are showing an atypical momentum non-correlated with the rest the space. Among those crypto-assets, none is capturing the imagination of crypto-speculators like ChainLink. In the last few days, ChainLink has been regularly hitting all-time highs despite challenging the lack of momentum of the top crypto-assets. The ChainLink protocol is tackling one of the hardest problems in the blockchain ecosystem by enabling the integration of off-chain data sources into smart contracts. However, the recent momentum of the LINK has little to do with the merits of the protocol and has been fueled by trading speculation. Let’s look at some revealing metrics. 1) The number of transactions over $100,000 in ChainLink has increased 15x in 3 months Large transaction used to be an exception in LINK. shows that there were only 22 LINK transactions over $100,000 in mid-April. Well, that number skyrocketed to over 300 in July 13rd. How is that for a measure of speculation. IntoTheBlock’s large transaction analysis 2) New and active addresses in ChainLink has sky rocketed The ChainLink network is growing at a healthy clip. Yes, the number of addresses is increasing but, more importantly, those addresses are active which shows a strong momentum in the network. This is shown by . IntoTheBlock’s daily active addresses indicator 3) Speculation is not coming from Asia shows that, when comes to LINK’s recent speculation, western countries are outperforming Asian nations in an interesting turn of events 😉 IntoTheBlock’s East-West analysis 4) LINK’s Turnover Ration in perpetual swaps is off the charts Almost literally 😉 Turnover ratio measure the relationship between volume and open interest in perpetual swaps. This metrics is a good indicator or speculative or hedging behavior in perpetual swaps relative to the positions in the contracts. In the case of LINK has grown to almost 14x as shown by . IntoTheBlock turnover ratio indicator 5) LINK futures show optimism The FTX LINK futures contracts expiring in September 25th has been regularly in contango which means that has been trading at a premium compared to the spot price as shown by . This is a sign of optimism and, potentially, an idea that the LINK speculation is going to stay strong for a few weeks at least. IntoTheBlock’s contango-backwardation analysis These metrics show a unique perspective about the recent momentum that ChainLink has been experiencing. With the recent lack of volatility in Bitcoin and Ethereum, ChainLink has become one of the most exciting assets in the crypto market for traders and speculators. Images Courtesy of http://app.intotheblock.com

Chainlink

Chain

A Data-Centric Perspective of ChainLink’s Madness

Written by Oleksii Tsymbal, Chief Innovation Officer at MobiDev . You’re probably familiar with the hype surrounding the business value of applied data science and machine learning. You’ve heard things like, "your data can become your competitive edge" and "your data can be turned into actionable insights." Well, that's true, but only if you're well-prepared with actual data in hand and if you learn the basics of typical project flow. How can we transform business goals into technical tasks? Why and when should we hire a machine learning expert? How do we choose the right machine learning company and go through the development process smoothly and efficiently? These are some of the questions to be answered. Machine Learning Drives Product Evolution Here is a story of two companies operating across different industries that applied machine learning (ML) algorithms to their business data and got valuable insights that resulted in new product development. A SaaS ERP solution provides for retailers in the U.S. Having been gathering warehouse and sales statistics for years, the owner and a machine learning consultant explored the data and what value it could bring. The consultant helped to set and achieve proper business goals by adding Data Science and Machine Learning features. The data was turned into a source of sales forecasting. Another case is about a U.S. insurance company needed to secure access to sensitive healthcare data with the help of custom-built software. Since high reliability was needed, the company was advised to enable using voice and facial recognition. Working with a machine learning consulting company resulted in additional security measures, such as , OCR for driver’s license recognition, and an NLP-driven question generation module. The enhancements eventually turned the product into a cutting edge enterprise verification-as-a-service (EVaaS) solution. multimodal biometric authentication deep learning-based face anti-spoofing How To Start and Deploy a Machine Learning Software Development Project Preparedness is half the battle. Here are several stages of a successful machine learning consulting and development process. Stage 1: Exploring Business Input and Aims In the SaaS retail ERP case, statistics were gathered over several years, so there was plenty of data to work with. The first step was to explore and extract the data. Then, the quality must be evaluated and the structure identified. Lastly, a consultant has to spend some time to understand what this data means from a business perspective. However, sometimes there is no existing data. In such cases, a machine learning consultant has to explore the client's aims and main use cases. Then, they must come up with ideas and technical roadmap. For example: what are the options to gather the data, and are there any existing data sets on the market? Perhaps it’s possible to use any of already pre-trained models — it would significantly reduce the amount of data, time, and effort needed. In some cases, even synthetic data could be generated. For the case of the insurance company and securing healthcare data, already pre-trained models were used for voice and facial recognition. Then, its accuracy was adjusted based on additionally gathered data. First, a machine learning consultant has to understand the business landscape and how it all works for the client and their customers. The consultant must identify the problems the client is aiming to solve and what value this serves to the client’s customers. As a result, you can expect a consultant to have a good understanding of the business and get a clear roadmap of where exactly Machine Learning could be applied and what is needed for the implementation. It’s true that in most cases at this stage there’s no way to predict what will be the end result in terms of quality and accuracy of a certain machine learning model. It can only be done with a series of experiments along the road of development. As for the timeline for exploration of business inputs and aims - it depends on the scale and complexity of each business. However, in most cases, it should not take more than a week. Clients can even expect to get this stage done free of charge as part of the pre-contract stage. Stage 2: Setting Business Goals and Tech Objectives Simple business goals such as "increase sales by 10%" must be transformed into something tangible and manageable from a technology perspective. In other words, something that could be influenced directly by technologies. However, we need to know what the metrics are to succeed. Success metrics can be a combination of your business metric (e.g. SCOR, ) and ML team’s math metric such as an absolute percentage error. FIFO, LIFO The next step is setting objectives that could be achieved with the help of technologies. For example, we might need to build a model that predicts demand for 1 week with, say, 80% accuracy, or predict how many goods of a particular type will be purchased next month based on available statistics over the past 6 months. Down the road, those metrics will be adjusted and accuracy will be improved, but it’s crucial to start with something. In the retail case, the first focus was on optimizing warehouse supply and minimizing purchases. Having studied sales statistics, machine learning consultants found they could also predict how many people would purchase different types of goods. Thus, they were able to solve several related tasks with a single dataset and get desirable accuracy. Every task needs its own hypothesis. Consider it as a roadmap for the consultants to follow and also as a way for business owners to understand the route. Once results are achieved on a certain hypothesis, there’s a choice to be made. If the results are promising, machine learning engineers should focus on improving those results. A lot of effort is placed here. However, often the first results show that there’s no value in this particular hypothesis with certain data and the approach taken. In this case, either the hypothesis, data, or approach must be changed. This is the most optimal way to run machine learning projects. Accuracy depends on the data and can’t be predicted without running experiments. Yet, keep in mind that you need to keep it cost-effective according to your business goals. Sometimes the effort to get an additional 5% boost can take more time than the efforts spent on the previous 90% — yet hold no tangible value for the business. How Much Accuracy Is Enough? Stage 3: Building The Cycle There are different approaches to manage machine learning projects. The one that we often use is a combination of classic Agile principles with the for data mining and analysis. A typical cycle takes roughly up to 4 week-long sprints. During the first two sprints — Business & Data Understanding — we generate our first hypothesis and form a baseline to build future models and improve results. CRISP-DM model The scheme below presents 2 cycles spanning 8 sprints. With a hypothesis, it’s possible to make raw data actionable. For this, the following steps are needed: • Understand, explore and assess data, describing its format and quantity • Make an exploration report with initial insights and findings • Verify data quality and completeness • Construct an actionable dataset for modeling For , the data was examined, cleaned, and prepared to get baseline sales forecasting for the most popular goods. To get meaningful results, machine learning consultants have to understand which data is relevant to the hypothesis and which data should be used for analysis. This means not all of the data should be analyzed at the same time. the demand forecasting case In the case of multimodal biometric verification, there was a goal to identify the user by comparing photos from a and a selfie. For , it was fine to use an already pre-trained model to get a baseline. OCR-processed driver's license the task of facial recognition However, pictures on the driver’s licenses were often of poor quality. So, most of the datasets with faces were not acceptable. It was crucial to carefully choose each dataset used. In a case like this, it’s very important to collect datasets relevant to the task. How Much Data Is Enough? This is probably the second most complicated question. The first one - how much time it will take to create the end-product? Before the data could be used for machine learning operations, it has to be cleaned and checked. It often leads to data decreasing. After being verified for relevance to the hypothesis and cleaned, it could be applied to get a baseline result. This is the point where the conclusion satisfies the objectives and metrics can be made. With insufficient data, it’s impossible to pass the modeling stage and the model fails to learn. When we understand that we need more data, we can either collect/generate it or move on to a second hypothesis. If the hypothesis fails, we have to go back and start a new cycle with a new hypothesis. This is probably the single most important risk of a machine learning project. Despite this risk, this is the only way that it can function. Unsupervised Machine Learning To Improve Data Quality In the case of analyzing retail data, it was vital to understand how certain types of goods were sold. For this, it was needed to know how many were typically purchased during a certain time period, and even the average duration of warehouse storage. The more goods covered at a time, the less accuracy that can be achieved. However, it was possible to re-train the model for different types of goods, eventually getting better results for each. To illustrate this, when predicting sales with at least 95% accuracy in a monthly cycle, it was needed to have at least a six-month period of historical data. If the hypothesis passes the test, it means that the model works and can be deployed. Before using data to train a model, it's good to think about how to test model accuracy. That's why initial datasets often can be randomly divided into two datasets: one for training and one for testing. Stage 4: Deploying The Model When the model has been verified, it is deployed on a test server to work with real data. It is reasonable to make predictions and monitor their accuracy over short periods. You can't have statistics gathered over 6 months and predict something accurate for the next 12 months; 1 month or even 1 week would be more reasonable. Eventually, if the model successfully achieves your business objectives in the test environment, it can be deployed in production. In the retail case, the demand prediction model was run on a test server and then integrated with the system on the adopter level, using relevant retailer-specific data. This allows the model to generate predictions with an average accuracy of 95%. It is worth noting that changing business processes and new statistics can make models obsolete over time. However, we can always gather the latest data for more relevant studies. Data science and machine learning tools keep advancing, which makes it easier for developers to build and test models. This, in turn, facilitates integration with your existing software products, enables faster deliveries, and reduces your time to market. To Sum It Up Now you know the typical flow of a machine learning project. Hopefully, this article has helped you understand the role of a machine learning consulting company and the value it can bring to your business: to provide assistance in designing a system from requirements to deployment.

Flow

MobiDev

Transform Your Vision Into Software

Machine Learning Consulting: Does Your Business Really Need It?

Ivan Starinin and Alexander Salnikov are two alumni of Moscow’s elite Higher School of Economics that went on to forge a prolific entrepreneurial partnership in the blockchain space. The now seasoned blockchain entrepreneur duo got started by building cryptocurrency trading software and went on to helm a number of projects, including MarginCall, the fastest and most advanced open-source crypto exchange as well as CoinOffering, the first company to offer its shares in the form of blockchain assets. Together, Ivan and Alexander raised the total of over BTC 5,000 for various blockchain companies as a result of successfully performed ICO campaigns. Currently, Starinin and Salnikov are exploring the potential of the decentralized finance (DeFi) sector. With almost a decade of entrepreneurial experience in the blockchain space, you’ve sent waves in two distinct areas of the industry: the cryptocurrency market and virtual infrastructure development. Which of these spheres do you prefer working in? Well, while it might seem like these kinds of projects are distinct from each other on the surface, if you look a bit deeper, they are interconnected and feed off of each other. The growth of the cryptocurrency market contributes greatly to the evolution of the blockchain industry. It’s unfortunate that there are still people who associate virtual currencies with reckless speculation, but that is gradually giving way to recognition of the opportunities inherent in the space. IS: Even though working with cryptocurrencies has served many as a means to enter the blockchain space and get a taste of the benefits of digital money short-term, there are also a select few who stay devoted to the cause and create technological breakthrough solutions. Yes, each emerging coin or token, so long as it brings something of worth to the table, has the potential to make the market function more effectively. Some digital assets fuel blockchain networks while also serving as a store of value in their own right. AS: For instance, a lot of blockchain projects have designed their own native cryptocurrencies in order to lower fees for cross-border payments or to give access to financial services for developing economies. That’s why we are convinced that crypto goes hand-in-hand with blockchain development and both are essential for future technological development. In this context let’s talk about the overall growth of decentralized finance. Taking into consideration all the possible vulnerabilities in DeFi, what is your opinion on the short and long-term prospects of this sector? What comes to mind when hearing talk of DeFi, and specifically concerning possible security issues, is the old Bitcoin mantra of “be your own bank.” While the freedom that comes with that is great, it comes at a price. Ultimately you are responsible for your experience with digital money and security is an issue that has to be reckoned with by everyone involved in this space individually. As for the short-term, the DeFi economy is organized in such a way that higher risks can lead to higher capital gains. IS: However, all of the above are more growth related challenges than inherent flaws. This is reflected by the current investment environment surrounding DeFI. The space is still free and full of potential in terms of technical innovation. There is certainly risk and, perhaps DeFi isn’t for the faint of heart, but, then again, what sector of finance is? AS: Meanwhile, there are safety nets out there, like decentralized insurance, which can help people protect their capital. The beauty of DeFi lies in its transparent nature. As an open system, anyone can audit DeFi, so, the more investors and developers that get involved, the more positive growth and system refinement we are going to see. What other problems exist in the DeFi market and how can these problems be resolved? There is lack of regulation, making the market rather uncertain for those seeking to invest big money. As it happened to blockchain in general and crypto in particular, initial legislation was only created post factum with regulators playing catch up, and the process seems to be repeating itself with DeFi. What is important here is to understand that the lack of regulation at this stage provides opportunities for higher revenue and ROI - when there is no regulation, there is often a substantial  growth potential. IS: I’d also say that there is a vulnerable link between decentralized services and the real world. Let’s go back to decentralized insurance as an example of what I’m talking about. Here, transactions are executed through the participation of a third party which in many cases is a trustee, chosen by vote and in charge of transmitting information to the blockchain. Unfortunately, third party intermediaries are not always honest. A perfect solution for this problem might be creating an independent and self-contained system that runs in accordance with game theory concepts with minimal influence from third parties. There is actually, I believe, quite a lot that could be improved in DeFi via the application of game theory principles. In addition to that, there is a general perception out there that the UX design of DeFi products tends to be far from high-quality. What we need here is detailed “user manual” style instructions - similar to those provided to first telephone users right after it was introduced to the general public in 1876. If you’re well versed in decentralized finance, structure your product so that it is straightforward, user friendly and serves as a practical representation of the technology. AS: Again, this is more indicative of the industry being in its early stages of development than it is of structural problems. What areas of DeFi development do you find the most promising? Before we move on to that, let me just briefly comment on the ongoing debates about whether it’s necessary to upgrade the protocols and interfaces of DeFi projects. My personal stance is that market participants should focus more on the business end of things. Currently, the DeFi community has plenty of technical enthusiasts but very few skilled entrepreneurs capable of introducing solid business models and bringing theoretical fundamentals to reality in the form of innovative products. IS: Once serious revenue flows are established, they will naturally improve both protocols and the interface as well as attract more ambitious developers. So, the most remarkable examples of DeFi businesses deal with lending and deposits, insurance and various investment products and instruments. From this list, those that deal with lending and deposits seem the most promising to us because their success represents a further advancement of the original principles behind Bitcoin and can lead to the construction of an alternative global banking system open to everyone. AS: Not all projects in the industry are intent on establishing a new financial system. What is your take on central bank digital currencies (CBDCs)? To make it more clear, we’d better start with explaining the essence of CBDCs which are a type of a stablecoin. Stablecoins are virtual assets that are pegged to fiat currencies and designed to mimic their value so as to avoid extreme market volatility. There are centralized stablecoins like Tether whose backing by the US dollar is guaranteed by a private company. Now, compare that to what are essentially similar initiatives, but from the likes of the Federal Reserve or the People’s Bank of China. IS: It’s about reputation, and CBDCs supported by big governmental institutions will add credibility to stablecoins, which are still a bit undervalued. We are going to see the effect these projects have on the market in the nearest future, as the accelerated issuance of the digital yuan will force other leading countries to follow suit if they don’t want to be left behind. As for the link with centralization, I wouldn’t put it quite like that. Actually, the recent flourishing of the stablecoin market was the main impetus behind DeFi’s progress, as this class of digital assets serves as the fuel for decentralized finance. Moreover, we don’t know much about the nature of CBDCs and how they will function exactly, but if similar state-backed stablecoins, such as the digital dollar or the digital euro, are launched on public blockchains, there will be an opportunity to make them decentralized, which will significantly increase the level of DeFi market penetration. AS: This could work in a similar fashion to cash, in the sense that the issuance of banknotes is obviously centralized, but their circulation is not. This could also happen with CBDCs, whose supply could be controlled by central banks without compromising their further movements. What are your own plans in decentralized finance? Now the DeFi sector is experiencing an influx of users who are curious about how things will play out here, so we’re preparing to contribute to the mass adoption of the technology by creating a consumer UX-friendly application that simplifies access to DeFi and connects it with real life. AS: We haven’t announced this project yet and aren’t really ready to reveal any details about it, but I can assure you that, with DeFi technology under its hood, our company will be able to compete with the world’s major existing banking services. IS: We’ve touched on regulations in the blockchain space. What countries do you find the most blockchain-friendly now and where do you think entrepreneurs are privy to the best development conditions? We’ve decided to incorporate our company in the US, which may seem surprising with all the blockchain entrepreneurs that have been moving to areas with more amiable legislation, like Malta, or to countries where blockchain regulations aren’t as clearly defined, giving them a lot of room to manoeuvre. But as far as the latter option is concerned, one has to be aware that chances are that your business will hit the top of its growth potential way too quickly. IS: Another country determining the vector of blockchain evolution is China, the US’s main political and economic rival. Between the two we’ve chosen America because we find this option more suitable in view of long-term development. Moreover, the ongoing crisis impacting the US economy has opened up opportunities for growing demand for DeFi businesses. Additionally, this will assist in dealing with the crisis-caused unemployment issue in the United States. AS: What advice do you have for blockchain entrepreneurs and investors as far as dealing with the constantly growing level of competition in this industry? For entrepreneurs, I’d go back to the point about concentrating on the business end of things I made earlier. It’s not that important right now to create a super technically advanced code or an awesome user interface. What is more important is crafting an effective business model and producing a sustainable product that will bring money to your investor. IS: And for investors, now’s the time to get on board with DeFi. The industry is open now in a way that it is unlikely to be for long. Now’s the time to start gathering teams of highly conscious professionals and determining points of entry. If you don’t act quickly, you may miss out on a golden opportunity. AS:

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An Interview With Ivan Starinin And Alexander Salnikov On DeFi Tech

Creating a successful software as a service (SaaS) product is the dream for many entrepreneurial-minded programmers. In the process of launching my own SaaS I discovered that sharing and comparing experiences with other founders is an essential part of this journey, and without this, I probably would never have created it at all. In this article, I’ll share the mental and practical processes that led me to create a SaaS product from scratch, and how I gained my first paying customers. Whether you are thinking about creating a new product or you have already launched, this article can help you compare your own strategies and methods with the ones that worked for me, and possibly adapt them for yourself. I personally dedicate up to five hours per week researching the experiences of other founders. I’m always looking for new ideas and ways to avoid mistakes, and evaluating new strategies that could help me obtain concrete results (that is, improve the product and increase customers’ happiness). For this reason, I decided to work in a completely frank and transparent way and share everything about my path — including what has been working and what has not — with the aim of helping one another through direct and rational discussion. You can follow my progress on my page. IndieHackers The article is divided into seven chronological sections, following every phase of the work I have done: Detecting the problem Quantifying the problem Evaluating competitors and their approach to the problem Developing the first prototype Throwing everything away and starting again Getting the first subscription How to move forward The SaaS product I built is , a real-time monitoring tool which helps software developers to avoid losing customers and money due technical problem in their applications. Inspector Detecting the problem Spending the last 10 years working with software development teams made me realize how complicated it is for developers to handle technical problems which affect applications every day. Development teams have close relationships with their customers, and this is a high risk for companies which produce software, because with problems you realize how fragile this bond really is. Users do not like problems! It seems obvious, but this aspect is constantly underestimated. This is an uncomfortable truth. No one likes to be in trouble, and it is instinctive to minimize the problem. But by denying reality you could annoying the customer even more, to the point where they may even reconsider whether or not they “should” even pay you. Customers do not spend their time reporting problems and application errors. No one cares about helping us resolve bugs. They just leave our application, and it will probably take years before we see them again. Despite this, every team I have worked with used the best-known method of figuring out whether applications were working properly or not: “If an angry customers calls you, the software is not working.” It is not exactly a technological solution… Maybe it seems ridiculous, but beyond the perception tycoons of technology project on our jobs, insiders know that urgency, limited budget, pressing customers, managers, forcing developers to constantly work under pressure, and adopting Band-Aid solutions (to temporarily fix a problem) as a survival strategy. Working this approach for 10 years helped me realize there is clearly a problem. Quantifying the problem At the beginning of 2019 I had just finished some important projects and I was expecting to enjoy a little period of calm. During the last years I have used these moments to look for business opportunities which allow me to put my technical skills to good use with the hope of finding the right conditions to launch my own business idea. I knew from my experience as a developer that an easy and immediate monitoring instrument would be enough to help development teams to stay up-to-date about the performance of applications, instead of relying on customer calls to know when the software was creating problems. On the other hand, I did not need a tool to monitor everything, as everything often means nothing. And I didn’t want it to be complicated — I did not want to spend a month learning how it worked or need to hire expert engineers just for this job. My life had to be made easier than before. It was necessary to have a ready-to-go developer tool. The first step was to understand if there already were solutions trying to solve this problem, so I googled " " and 941,000,000 results appeared: application monitoring Wow. That’s a very huge amount of contents for a problem that probably is huge. But how huge, exactly? Software development team inefficiency is a problem I have always faced directly, but there is a big difference between estimating a job task and quantifying the economic impact of a problem. It is even more difficult on a large scale. This tweet captured my attention: The 50% of developers declare to spend up to 50% of time just to constantly verify that applications are working. Software development is work mostly paid by the time technicians spend working on a project, and if there are periods in which developers spend 50 percent of their time checking that everything is okay, a tool which completely automates this job could be useful enough to buy. So why aren’t they so common to so many developers? Evaluating competitors and their approach to the problem I thought about the two main parameters a company looks at when it has to decide which tools use to increase productivity: (ease of installation and use) Simplicity (I spend x to solve a problem which is worth x+10, so I gain the +10) Efficacy Using these parameters, I spent about a week creating an evaluation sheet of the most well-known monitoring instruments and I placed them in a graphic: After days of putting information together, a look at the graphic was enough to realize where the problem was. Easy instruments do not provide enough value to the majority of developers. More complete instruments, instead, are thought of as being for big organizations, and they need skilled staff who dedicate themselves to their installation, configuration and use, complicating team operations rather than simplifying them. In my vision, the problem is not the monitoring itself but the development team efficiency. For a massive adoption, it would be necessary to have a product which requires a minute for the installation, no configurations and, at the same time, that provides complete and easy information to consult that would allow even medium-size development teams to fix the real-time monitoring problem. And of course, it has to be cool. Developing the first prototype Finally, I decided to try. The last work experience had gone well and I thought that it would not be impossible for me to create this tool. So, I immediately informed my partners that I wanted to build an MVP for the following two or three months. When I explained it to them, it was hard to make them understand the problem because they are not technicians involved at the same level I am. They gave me the okay based 90 percent on trust, and I thank them for this. Over the course of three months I was able to create this prototype: While working on the implementation, I gradually understood the problems of realizing this kind of tool and even problems users would encounter during its use. From a technical point of view, a monitoring product has to be designed to work with huge quantities of data and I also wanted to deal with these data in real-time. I had to spend longer than I predicted for the backend part —in other words, the part which cannot be seen, or the backstage of an in-cloud software — leaving out the graphic interface (as you can see above), which is the part users see and use. Throwing everything away and starting again In the last few years, the dream of launching a product on the market pushed me to constantly study and apply marketing strategies which are particularly adept for SaaS software, to different projects (even the failed ones). I started to write articles for my blog with the aim of publishing them on different websites and social media to collect the first feedback. Although I wrote horrible content in English with writing mistakes because English is not my mother tongue, feedback started to come: I do not understand what I can do with it How can I install it? Why use it rather than XXX? What does it offer which makes it different from any other one? Etc… It was not easy to be objective while looking at developers' responses and comments. could always take advantage and it was really hard for me to understand where the mistake was because I am not a sales agent or a seller, but I am a damn good technician. Emotional reaction Lesson 1 – Selling sucks Thanks to my technical skills on the matter, I did not need to sell. Rather, I just needed to learn how to communicate the problems I faced every day and how I fixed them with my tools. I spent an entire month writing the most important things I knew about the monitoring and application scalability problems and the reasons why I decided to start this project, the difficulties I had been encountering during the development of a product, how I fixed them and moved forward, code examples, technical guides, my best practices, and more. Then I gave everything to , a Canadian copywriter found on Fiverr, who corrected all the content, including the website text, and polished the writing into native-level English. Robin Lesson 2 – Insufficient product The fear of leaving out the user interface turned out to be a well-founded fear. What I did was not enough to create the kind of experience I had in my head, so I had to start again. The advantage of this was that I fixed the majority of the technical problems. It is not easy for an engineer to put themselves in a designer’s shoes. To improve my design sense I attended two courses about the development of graphic interfaces, read three books on design and user experience, and made direct experiments using VueJs as frontend framework. Lesson 3 – Give it a try despite all doubts When we spend our time reading books and watching videos on marketing and business, we always learn common advice which, in practical situations, usually does not work. Consider, for example, the mantra: "If you wait until everything is ready, you will never start your business". ! So true But first experiences push us to emotional reactions that can put us on the defensive. This is a very big mistake, because creating a product which is worthy of being bought thanks to its utility is a process, not a single event. The word "launch" misleads us, so forget about it and concentrate on "creation", the process which, step by step, helps you understand what you need to change and improve to achieve your aim, comparing with the outside world. Inspector is born It took me another two months of working on the project; during these months I decided to recreate the brand from scratch, trying to use the prototype experience not just in terms of product but also in terms of marketing and communication. – Identify issues in your code before users are aware of them, in less than one minute! Inspector We endlessly repeated ourselves that the aim was not the monitoring itself, but to help developers automate their working processes: Without any effort Without wasting time with manual procedures Guaranteeing flexibility when it is necessary Getting the first subscription On July 14, 2019 one of my technical guides was approved to be published on Laravel News, one of the most important websites for the Laravel developers' community, which got the word out about this tool to an extended audience. Within four days, more than 2,000 people visited the Inspector website, almost 100 companies signed up, and the two first users — from Holland and Switzerland — subscribed. I cannot describe the emotion I felt when I received my first notice from Stripe which informed me that I had just received my first payment. When it happened, it was late in the evening. It felt like I was carrying an elephant on my shoulders for seven months and, finally, it went away and let me breathe. A lot of problems cropped up during the following months, and they required time, effort, and money to be fixed. This included things like hacking attacks and overloaded infrastructures, problems that forced me to stay chained to the PC even on Christmas Eve. These are problems which send you crashing back to earth and make you realize things are even more difficult than before because you have something to lose now. With the first subscribers, I knew I had an interesting product and, thanks to the web and the cloud, I had the chance to make the product known to developers from all over the world. So I kept working hard, full-time, every day for months to create and publish technical articles from my experience on as many channels as possible: https://medium.com/inspector https://dev.to/inspector https://valerio.hashnode.dev/ https://www.facebook.com/inspector.realtime https://www.linkedin.com/company/14054676 https://www.indiehackers.com/product/inspector https://www.reddit.com/r/InspectorFans/ https://www.producthunt.com/posts/inspector Now, more than 800 companies and business people have tried Inspector and we have more than 20 active subscriptions now. How to move forward Sharing and comparing with others has been fundamental in my journey to get here, so I plan to keep going this way. After all, I’m aware there are still a lot of things we need to improve and, even worse, problems that at the moment we’re ignoring entirely. This is why we started to tell this story during the most important Italian events where the topic is innovation. Now we are part of the program, the italian startup accelerator made by Paolo Barberis, Jacopo Marello and Alessandro Sordi, three of Dada’s founders who spent 20 years of their lives collaborating to finance and support more than 30 Italian and foreign companies in growth. Hubble Our aim is to introduce ourselves to other managers, business people, and marketing and technology experts in order to elevate the product to the next level and test new instruments and strategies to get Inspector known all over the world. We would like to help software developers to work in a more productive and fun way thanks to smart tools which give them more free time to spend on more valuable activities instead on boring, repetitive, manual checks. Conclusion In this article, I've shared the mental and practical process that led me to create and launch a SaaS product from scratch, and how I gained my first paying customers. Without sharing my journey I would never have created , so thank you for reading this article and I invite you to leave a comment below with any questions, curiosities, or just to tell me what you think about it. Inspector And if you think this article could be useful to others, I would love it if you could share it on your social media!

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How I Designed And Launched A SaaS Product From Zero To The First 10 Customers 

To be sure, blockchain rose to the world stage on the back of Bitcoin's remarkable performance. However, blockchain technology is gaining its significance, especially after the cryptocurrency ecosystem nearly collapsed in 2018. Today, the technology anchors around three primary use cases: staking, custody, and DeFi. Decentralized Finance (DeFi) is particularly interesting because of the waves it is making. DeFi protocols such as Compound are taking the world by storm as more people realize how the ecosystem could revolutionize the global financial ecosystem. Just last month, Compound MakerDAO as the most valuable DeFi lending project. topped Overall, the DeFi ecosystem’s trajectory is steepening as more value is locked in DeFi projects. To put this in context, consider the value locked in DeFi projects in the last 30 days. In the period, the total value locked (TVL) in DeFi jumped 119% to settle at . If this growth cannot convince one to onboard the DeFi ecosystem, then nothing will. upwards $2.279 billion Source: DeFi Pulse A New Era is Unreeling Admittedly, , but most are still in the abstract realm. Few protocols such as MakerDAO and Compound have been able to go live and to attract investors. These software pieces are coming up with solutions to problems that have plagued the legacy financial system for ages. there is a wave of DeFi protocols DeFi lending protocols enable users to access credit cheaply. On the other hand, lenders can earn high-interest rates on their investment. Importantly, the majority – if not all – of the DeFi developers agree that the protocols will right most of the ills of centralized finance (CeFi). There is a need for an alternative to traditional finance from the foregoing, one that serves all participants in good faith.DeFi lending protocols are more popular today because they enable everyone to supply cryptocurrencies and stablecoins and to . This another avenue for holders of the digital currencies to earn income other than trading them on exchanges earn interest on them According to a , DeFi is the fuel that will propel the financial innovations of the future. The innovations already began with the complete transformation of existing commercial products such as lending. This trustless ecosystem enables finance to reach the unbanked and the under-banked in a previously impossible way. report But There is a Problem Grand as the potential of DeFi protocols is, lack of a unified platform to synergize their efficiencies is problematic. One significant advantage of the traditional finance ecosystem – CeFi – is that centralization makes standardization of practices easy. Standardized practices enable service providers to focus more on consumer satisfaction. Logically, it follows that the DeFi ecosystem has an inherent weakness, which is a lack of standardized practices. Maker, Compound, and other DeFi protocols enable a fairer and more affordable finance ecosystem in which all participants have equal access to financial products. These efforts are bearing fruits, as manifested in the TVL the protocols have amassed over the past few months. PlutusDeFi Aims to Save The Day But there is no doubt that the DeFi protocols might fall short of their potential if standardization issues are not dealt with in good time. It is a problem that intends to correct. The DeFi ecosystem is expanding fast, and a myriad of projects are emerging to address specific issues. In more than one instance, numerous DeFi projects focus on a single sector. PlutusDeFi DeFi Pulse, for instance, lists 10 DeFi protocols that offer lending services where the only standard thing is the Ethereum blockchain platform. Seven protocols, including Balancer and Curve Finance, offer decentralized exchange (DEX) services. Again, all these protocols are on-chain Ethereum solutions. PlutusDeFi envisions a scenario where all these DeFi solutions are available on a single platform. Demonstrating what it has to offer, PlutusDeFi has already taken a lending solution called Lend & Earn. Lend & Earn aims to aggregate DeFi lending protocols on a single platform for easy access by users. The solution enables users to select the preferable lending protocol based on desired parameters. Facilitating The Mainstreaming of DeFi Writing on its , PlutusDeFi described the lack of a mechanism with which users of a DeFi platform can financially involve themselves with the entire DeFi ecosystem as “DeFi’s biggest elephant in the room.” One of PlutusDeFi’s objectives is to address this issue, and it plans to utilize a native token called PlutusDeFi token (PLT). blog Regular DeFi protocols' tokens increase with time. Ultimately, this takes value away from the platform, and the platform ends up worthless in the long-term. To correct this, the supply of PlutusDeFi’s tokens reduces with time. Therefore, the long-term value of the ecosystem grows, which makes the project sustainable. If this were DeFi's only impediment to going mainstream, then PlutusDeFi might have steered the ecosystem in the right direction. If PlutusDeFi’s vision materializes, then the DeFi ecosystem is likely to witness a massive inflow of new users, both individual and enterprise. Perhaps the vision is what it will take for DeFi protocols to seize the 21st century. Disclaimer: I have plans to invest in PlutusDeFi, so I can have a vested interest in the project

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What Will It Take For DeFi Protocols To Seize The Future of Loan Payments? 

The question is not, “Does the company have product/market fit or not?”. The more appropriate question is, “What’s the strength of the company’s product/market fit?” This editorial is a continuation of a piece I wrote last year titled, Product/Market Fit as a Service. In the first few weeks of the stay at home orders across the country, I spoke with a number of co-founders and executives who decided to make a pivot to solve very unique problems during the pandemic. Each proposed solution sounded very viable and each leader had big visions of what solving the problem would mean for their business and for humanity. My conversations were a microcosm of what much of the startup world was experiencing. found 36% of startups pivoted their product due to COVID-19 and that 62% of those pivots were moderate to significant. A survey conducted in April 2020 by NfX I checked in with each of them around mid-June (about 3 months later) and each one of their pivots turned out to be a failure. Failure because they scaled sales & marketing investments before product/market fit (PMF) was strong enough. They got excited about the first customer wins, the size of the market, inbound inquiries, and other key performance indicators so they doubled-down. They thought they had product/market fit, and that might have been true. . .to an extent. So this got me thinking. . . Product/market fit is not binary. It’s a spectrum. It’s a continuum. There is a wide range of content on the topic of PMF from preeminent thought leaders such as , , and who talk about “finding product/market fit”, but few describe PMF as a spectrum or how to strengthen it. Slides by is a good start, but strengthening PMF should not just be a focus for early-stage companies, but also for more mature companies because PMF is a continuum. In the pursuit of continuous improvement, there’s always an opportunity to strengthen PMF. Steve Blank Paul Graham Sean Ellis 16–20 Pete Kazanjy This is an important distinction because the way a company operates needs to revolve around the strength of PMF (how and when to adapt product roadmap, who and when to hire, where and how to spend marketing investments, etc). “Finding” early PMF is the responsibility of the CEO/co-founder, but strengthening it needs to be a shared responsibility across functional leaders of the company. What if we could measure the strength of a company’s PMF from a 1–10? If we could, what would we do once we have an answer? How could internal and external stakeholders take action on these insights? Let’s first think about what a typical Enterprise SaaS company that scores a 10 on PMF would look like: An executive buyer takes a meeting the next day after just one cold email Six-figure deals with marquee brands are closing in less than three months The buyer makes a purchase regardless of the price Now, let’s think about what a typical Enterprise SaaS company that scores a 1 on PMF would look like: 3% close rate because of steep competition and losing on price 100% churn rate High CAC Creating the scoring system It’s about creating a matrix with various weighted criteria that calculate a final score. Example of possible metrics to calculate a score: Number of annual agreements paid up-front Percentage of AEs exceeding quota Close rate Average length of the sales cycle to close $100k+ deal Price elasticity CAC NPS describes metrics that should dictate the strength of a company’s PMF. This Linkedin post by a VC You could validate the PMF score by inputting data from dozens of startups that agree to share their metrics anonymously. The crowdsourcing of data will also create benchmarks for each metric. For instance, the industry average for close rate for an Enterprise SaaS company that sells MarTech to retailers. So let’s say through a diagnostics process, we determine a company scores a 7 on the PMF scale and the conclusion states that the company can reach a 10 if they reduce CAC by 20% and improve close rate by 10%. These insights help the executive team determine what to prioritize. The executive team takes these actions because through benchmarking, the score proves that companies that score a 9 or 10 outperform their peers by X and have a Y% success rate of a successful exit. With more data and constant validation, confidence in the insights goes up. Benchmarking metrics across other startups will be the key to pulling this off, but also presents the biggest challenge for a few reasons: Requires a trusted third-party to do the benchmarking Requires the willingness of founders to share their data (even anonymously) Requires a large enough sample size of other companies in the relevant comparison set for rich benchmarking data Additional thoughts about how the score could be used. . . How do companies with stronger PMF outperform their peers with weaker PMF? Could the score be used as a signal to key potential hires and investors? Could execs or founders use the score to make better decisions around hiring or other investments? Do VCs already use a matrix like this that’s more sophisticated? It’s unclear if even large, well-renowned institutions like Y Combinator use benchmarking data this granular. If I can get some more validation, I’d like to invest time and resources into turning this idea into a project and I’m curious if there are organizations or individuals who would help me. Ping me on Linkedin .

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What the Pandemic Taught Me About Product/Market Fit

Top 21 Companies Hiring for full time , Remote Work-from Home Jobs You probably know how hard it is and trying to find clients online or . What's worse is that you don't know who you can trust, and when it comes to exchanging time and money with someone online that lack of trust is frustrating. It prevents a lot of people from trying. In this review, I'll share my opinion on or working from home website for work from home jobs the best sites for remote work job sites for remote work. is particularly useful for parents and young people who can work from home while staying with their family and children. Given the benefits, are a very genuine and high customer base. You will definitely find a buyer looking for your skill in the Remote employment websites offering work from home best freelancing websites. Working from home or remote job will not only give you money but also give you all freedom you won't as living with family, working as per mood, time, and any place not as a fixed small cubical like sitting a place for 9 to 5 jobs. Here is the list of or top 21 websites offering work from home remote job websites: 1. Remote.co First on the list and also in the market. It has several companies more than 150+ and many of them are a big brand so you will get all genuine and trusted work. This is one of the strongest working portals. work from the home job Good payment, easy payout, and versatile choice of payment mode, you don’t have to worry about any payment related problems they also have a good support system so if you have any question feel free to ask them. 2. Flexjobs founder of flex job making this one also so all the above mention things are valid for this one also but its a bit different from above one as it is a job listing website it will list your job and provide you the customer from everywhere it can reach. Unlike remote.io it is a paying website. If you can spend little money than your investment will be going to pay you triple of that so it is worth it. 3. Jobexpreso Since Jobspresso mentions just remote employment, in a job posting you don't have to trudge through contingencies and fine printing in job listing — you'll realize it's really a career from home. Simply pick your interesting work and then find all remote work, or scan by type of occupation. Keep an eye on the place — certain jobs demand that you operate in a specific time zone and state. 4. Stackoverflow If you're a StackOverflow programmer or entrepreneur, it offers for you, plus it reliable and really old website so many businesses can find by searching. the best home jobs or remote work Mainly it is a discussion forum for developers, programmers but it also offers jobs as well from all reputed giants in the tech industry so if you have good programming skills then you will definitely get a high salary remote job. 5. Gun.io Perhaps one of the biggest Electronic work portals. They are linking excellent developers with outstanding businesses. They are genuinely champions within their network for freelancers and remote workers. Development Gun.io is a perfect place to give it your first shot if you decide to take the jump into freelancing jobs. High pay and best jobs will add great benefits to your assets as they are one of the top programming jobs websites. 6. Fiverr If you've ever looked for remote jobs websites, you've seen Fiverr in the chart. This website has lately become more popular thanks to its quick payout and stable payment method, leading to its large consumer base. There are several forms of employment open if you have some talent that you're certainly going to get customer searching for you, it's a must-try platform for remote work. It's a job-based marketplace so strive to make your project inexpensive and appealing so you can get clients to start operating you as an employer. 7. Freelancer As the name implies yes this is one of the best sites industry. Freelancer.com has an overwhelming range of assignments and even anybody who simply wishes to continue with remote work will register on freelancer.com. freelancing websites on the remote job This website is working on the bidding system so that you should have good communication skills to attract the buyer to your ability and show them that you can do the best and keep the price as low as possible. 8. Upwork An online platform that offers anybody the opportunity to earn a salary worthy of living a normal life. Many people have been living in different parts of the world and being unable to make enough for a normal life, Upwork changes that can provide fairly paid jobs for individuals who can deliver good results to their customers. 9. Peopleperhour Peopleperhour is a location-based website it will provide customers according to your region this will not only benefit you in remote working but also increase your customer base locally. If you ever want to stop remote work then you will have enough customers around you to convert this job into a business. Given the choice of its customers to employ location-based freelancers, you can choose to become a freelancer from anywhere. On this website, with more than 330,000 customers, you will definitely get a job that suits your skills and customer requirements. 10. Guru If you are trying to earn money online as a home job, there's no doubt that freelancing is a perfect way to make money online for that Guru is one of the best sites for that and it's one of the reasons I've added this to my list. Guru is young in this industry but day by day with its good management system it getting popular and many customers and freelancers joining it, so get your seat on this one as you will be going to get a good proposal in the future. 11. 99 designs If you're a designer of any kind including posters, models, stickers, designers of any kind then you should look for this the website it's payment scheme is special from others as this is a contest-based website if you win then you'll get charged. Even if you don't succeed, it's worth trying as it strengthens your abilities and brings good things to your portfolio, it's a win-win scenario, in the end, so do it certainly. 12.Truelancer Truelancer is really strong for content writers, creative writers, or any kind of writing jobs which are in demand so if you are one of them then this is a must-try place for you. They also provide other freelance positions, such as animation, illustration, web design, etc. so if you are not content the writer then also you can have a look at this website because trying every option is better then nothing. 13. We work remotely WeWorkRemotely is a subscription-based platform but the subscription is worth as you get high paying jobs there with full support from there service. The platform also has a much larger audience than rivals, due to its good environment and management. WeWorkRemotely pride of being the world's largest work from the home site, with millions of users a month (around 80,000 users a day). And Internet companies like Google and Amazon use this site to hire accordingly. 14. Outsourcely Outsourcely as the name suggests it is a fully open website for remote workers you will get a job with no investment or advertisement like other competitors in this field. All the above mention websites charge some fees like 3% to 10% but this does not charge a single penny from the home workers. If you want high and full payout then you should try this. 15. Appen Appen is also one of the best sites when it comes to the list of best remote working job websites. It is a genuine and very good competitor in this field to increase its customer base rapidly and providing more and more jobs and work to freelancers or remote workers. with more than 50000+ customer base and most of them being buyers you will be going to get a job instantly. 16. Concentrix This is best for a backup if any website doesn't work then go for it or use this in parallel with other remote work giving websites/companies as this website not pay you much but for extra income, it is a good option. The only negative thing about the work is that the salary isn't enough and isn't competitive with other businesses in the region! Nice job environment. Incredible operations. It also gave sufficient space for production. Very strong opportunities and the perfect way to hike successful results. 17. AngelList If you're in the environment looking for jobs from home, this platform can be a fantastic place to continue your remote journey. It contains about 25,000 free placed companies. startup Register yourself on this website and become the part of world-changing and unique startups from all over the world which will not only earn you a living but also make you proud and famous in this tech giants world. 18. Powertofly If you are a woman reading this then this is for you this website's aim is to provide work to every woman who is willing to work safely from there homes. You will find the most common type of remote-jobs, select your skill category, and start working. The best freelancing platforms offering women and independent mothers' jobs from home which are targeting at delivering the best job environment and raising women to strengthen in the male-dominant world. 19. Remoteok Remote ok is like flex job it posts your jobs requirement, not several websites and companies to get you the best customers without worrying about finding clients on your own. This website offers more than 25000 + employment and temporary jobs to your home. Work from home or a freelancer you will going to get a good amount of offers and several options. 20. Skip the drive SkipTheDrive is a customized career board with full and part-time jobs in a broad range of fields including jobs related to technical and technology-related work. For several of the jobs, the site tends to browse certain work boards, including CareerBuilder and ZipRecruiter. However, for telecommuting jobs, SkipTheDrive does a fine job of categorizing and culling. And, unlike FlexJobs and a number of other curated pages, it does not fee entry for work seekers. You don't really need to sign up for a career quest, which ensures you're much less likely to be spammed than if you're searching for jobs on some free platform then you can go for this. 21. Roamingdesk.com Is one of the best job boards we have come across it is easy to post jobs with not must fuss. The Posting of Jobs is free and jobs are keep on the board until the jobs offer is filled. The business is growing fast and they are open to posting most type of remote jobs. You can find the website at www.roamingdesk.com I hope you liked the list of top websites for remote work and work from home and have chosen the best website suitable for you to start your and live a better life while working and enjoying with family. work from the home journey

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COVID-19 has caused schools all around the world to close their doors and send millions of students to start learning from home. This sudden change is bound to cause problems, and parents, teachers, and students are feeling the effects of this change. Parents are concerned about how well their children will react to the new learning environment. Nearly 90% of parents of students that are learning from home are worried that their child may start to fall behind academically and more than 8 out of every 10 parents are struggling to keep their child engaged and paying attention. Parents are not the only ones feeling that students might start to fall behind with their studies. The vast majority of educators are concerned that their students may not be keeping up on key subjects, such as math, science, and English. The pandemic is also putting teachers and students alike in lower spirits, making it harder to be motivated for school each day. A number of tips and tech can help to shape an easier transition from traditional classrooms into remote learning. Distractions from notifications and other pop-ups on devices used by students can hinder their ability to learn and focus by a significant amount. A 2015 study found that cell phone interactions interfered with attention and productivity - even while the phone was off and the participant was not interacting with the device. In order to remove these distractions, you can turn off notifications during class sessions using ‘do not disturb’. Creating a new schedule, even if it is different from your previous one, can help everything flow better as it becomes more normal. Build a schedule around a type of activity, rather than specific plans to add more flexibility. Encourage sleep to reduce stress for both you and your child, and try to master transitions to keep your child engaged while switching from one task to another. Learn more about how to with more tips and tech here: smoothly start your child learning from home

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How to Hack Home Learning

The text you are about to read describes an imaginary dialog between two programmers on their way to discover the of a library called . orgastic pleasures asyncFn asyncFn provides additional methods to eg. or to introduce “ ” for the promises returned by mock functions. This simplifies async unit testing by allowing tests that read chronologically, . jest.fn sinon.spy late resolve like a story From this, we get a of structuring and writing unit tests. new perspective Foreword Be warned that we’ll also cover topics such as , , , and “ ”. They are not the main topic here, but some words are spared for them nonetheless to justify . TDD pair programming evil pairing negation testing a way of thinking Best of luck, . Dear Reader Chapter 1: The premise and the foundation : My name is Fry, and I find it difficult to unit test async-stuff in JavaScript. Fry : Tell me more. Leela : Yup, say I wanted to implement something to this specification: Fry : As a player, I can play a game called Monster Beatdown : I can damage an encountered monster I encounter a monster I choose to attack it the monster loses hit points : I can try flee a monster I encounter a monster I choose to flee the monster eats me I lose : I can win the game by beating a monster until it is knocked out I encounter a monster I attack it until it has no hit points the monster is knocked out I win Feature Scenario Given And Then Scenario Given And Then And Scenario Given When Then And : The kicker here is that in this game, prompting the player for action is . Fry asynchronous : Mm-hmm, I think I get it. Let’s get our hands dirty and see where it lands us. Leela : Here’s an initial test for good measure: Fry gameWithoutDependencies ;

it( , () => { messagePlayerMock = jest.fn(); game = gameWithoutDependencies({ : messagePlayerMock,
  });

  game.encounterMonster();

  expect(messagePlayerMock).toHaveBeenCalledWith( ,
  );
}); import from './monsterBeatdown' 'when a monster is encountered, informs the player of this' const const messagePlayer 'You encounter a monster with 5 hit-points.' Contents of so far: ./monsterBeatdown.js ({ messagePlayer }) => ({ : {
    messagePlayer( );
  },
}); export default encounterMonster => () `You encounter a monster with 5 hit-points.` : Ok, so far so good. I see you chose to the mock-function for messaging the player as an argument for the function we are testing. Crystal. Full steam ahead. Leela inject : Right on. Next up will be my vision for asking the player if they want to attack, and if not, they lose. Fry Chapter 2: The problems emerge gameWithoutDependencies ;

it( , () => { messagePlayerMock = jest.fn(); game = gameWithoutDependencies({ : messagePlayerMock,
  }); game.encounterMonster();

  expect(messagePlayerMock).toHaveBeenCalledWith( ,
  );
});

it( , () => { askPlayerToHitMock = jest.fn(); game = gameWithoutDependencies({ : askPlayerToHitMock,
  }); game.encounterMonster();

  expect(askPlayerToHitMock).toHaveBeenCalledWith( );
});

it( , () => { askPlayerToHitMock = jest.fn( .resolve( )); messagePlayerMock = jest.fn(); game = gameWithoutDependencies({ : askPlayerToHitMock, : messagePlayerMock,
  }); game.encounterMonster();

  expect(messagePlayerMock).toHaveBeenCalledWith( ,
  );
});

it( , () => { askPlayerToHitMock = jest.fn( .resolve( )); messagePlayerMock = jest.fn(); game = gameWithoutDependencies({ : askPlayerToHitMock, : messagePlayerMock,
  }); game.encounterMonster();

  expect(messagePlayerMock).toHaveBeenCalledWith( );
}); import from './monsterBeatdown' 'when a monster is encountered, informs the player of this' async const const messagePlayer await 'You encounter a monster with 5 hit-points.' 'when a monster is encountered, asks player to attack' async const const askPlayerToHit await 'Do you want to attack it?' 'given a monster is encountered, when player chooses to flee, the monster eats the player' async // When asked to attack, choosing false means to flee. const => () Promise false const const askPlayerToHit messagePlayer await 'You chose to flee the monster, but the monster eats you in disappointment.' 'given a monster is encountered, when player chooses to flee, the game is over' async const => () Promise false const const askPlayerToHit messagePlayer await 'Game over.' Contents of so far: ./monsterBeatdown.js ({ messagePlayer = {}, askPlayerToHit = {} }) => ({ : { messagePlayer( );

    messagePlayer( );

    askPlayerToHit( );

    messagePlayer( ,
    );
  },
}); export default => () => () encounterMonster => () // For real life, the implementation below makes little sense. // This is intentional, and is so to prove a point later. // At this point, it suffices that all tests are green 'Game over.' 'You encounter a monster with 5 hit-points.' 'Do you want to attack it?' 'You chose to flee the monster, but the monster eats you in disappointment.' : Hold the phone. I see two problems here. One is the , and the other one is about the test describing occurrences in , making the test harder to read. Let’s start by fixing the first problem Leela duplication non-chronological order . Chapter 3: The clumsy fix : Uh, I was just about to remove the duplication anyway. , right? Fry Red-green-refactor : Right. Leela gameWithoutDependencies ;

describe( , () => { askPlayerToHitMock; messagePlayerMock; game;

  beforeEach( {
    askPlayerToHitMock = jest.fn();
    messagePlayerMock = jest.fn();

    game = gameWithoutDependencies({ : askPlayerToHitMock, : messagePlayerMock,
    });
  });

  it( , () => { game.encounterMonster();

    expect(messagePlayerMock).toHaveBeenCalledWith( ,
    );
  });

  it( , () => { game.encounterMonster();

    expect(askPlayerToHitMock).toHaveBeenCalledWith( ,
    );
  });

  describe( , () => {
    beforeEach( () => { askPlayerToHitMock.mockResolvedValue( ); game.encounterMonster();
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( );
    });
  });
}); import from './monsterBeatdown' 'given a monster is encountered' let let let => () askPlayerToHit messagePlayer 'informs the player of this' async await 'You encounter a monster with 5 hit-points.' 'asks player to attack' async await 'Do you want to attack it?' 'when player chooses to flee' async // When asked to attack, choosing false means to flee. false await 'player is informed of the grim outcome' 'You chose to flee the monster, but the monster eats you in disappointment.' 'the game is over' 'Game over.' Contents of so far: ./monsterBeatdown.js // no changes, as only tests were refactored. : There. Now the duplication for the test setup has been removed, but only to a degree. However, the other problem of test not being written in chronological order prevents us from removing all the duplication. See how we are forced to repeat because needs to know how to behave before it is called. Worse yet, this makes the “describe” dishonest, as it claims to display how " ", but in reality, this is something that happens only later in the test setup, if ever. All this kind of bums me out, and I’ve felt the pain of this getting out of hand as in real-life requirements and features start to pile up. I know many ways to ease the pain a little bit, but nothing I’ve tried has left me comfortable. Fry game.encounterMonster() askPlayerToHitMock given a monster is encountered : I see. Let’s now introduce as an expansion to the normal to tackle all. Behold. Leela asyncFn jest.fn Chapter 4: The beholding of asyncFn gameWithoutDependencies ; asyncFn ;

describe( , () => { askPlayerToHitMock; messagePlayerMock; game;

  beforeEach( { askPlayerToHitMock = asyncFn();
    messagePlayerMock = jest.fn();

    game = gameWithoutDependencies({ : askPlayerToHitMock, : messagePlayerMock,
    });

    game.encounterMonster();
  });

  it( , () => {
    expect(messagePlayerMock).toHaveBeenCalledWith( ,
    );
  });

  it( , () => { expect(askPlayerToHitMock).toHaveBeenCalledWith( ,
    );
  });

  describe( , () => {
    beforeEach( () => { askPlayerToHitMock.resolve( );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( );
    });
  });
}); import from './monsterBeatdown' import from '@asyncFn/jest' 'given a monster is encountered' let let let => () // Note: Before we used jest.fn here instead of asyncFn. askPlayerToHit messagePlayer 'informs the player of this' async 'You encounter a monster with 5 hit-points.' 'asks player to attack' // Note how even if askPlayerToHitMock is now a mock made using asyncFn(), it still is a jest.fn(). This means eg. toHaveBeenCalledWith can be used with it. 'Do you want to attack it?' 'when player chooses to flee' async // Note how choosing false here still means fleeing combat, but here we use asyncFn's .resolve() to control the outcome. And then we await for the consequences of that. await false 'player is informed of the grim outcome' 'You chose to flee the monster, but the monster eats you in disappointment.' 'the game is over' 'Game over.' Contents of so far: ./monsterBeatdown.js // no changes, as only tests were refactored. : Now the duplication is gone, and everything takes place in clean, chronological order. It kind of reads like a story, don’t you think? Leela : Mm-hmm. I see it. I have a good feeling about it. But something is bothering me with the production code. I see that the tests are all green, but clearly, the code does not do anything sensical. It just blows through, merely satisfying the tests. Fry : You got me there. We are, in fact doing something called “evil pairing”. If you want to mold the production code your way, you need to order it from by writing a test first. Let me show you how. Leela the universe very specific Chapter 5: Evil is good and negative is positive gameWithoutDependencies ; asyncFn ;

describe( , () => { askPlayerToHitMock; messagePlayerMock; game;

  beforeEach( {
    askPlayerToHitMock = asyncFn();
    messagePlayerMock = jest.fn();

    game = gameWithoutDependencies({ : askPlayerToHitMock, : messagePlayerMock,
    });

    game.encounterMonster();
  });

  it( , () => {
    expect(messagePlayerMock).toHaveBeenCalledWith( ,
    );
  });

  it( , () => {
    expect(askPlayerToHitMock).toHaveBeenCalledWith( ,
    );
  }); it( , () => {
    expect(messagePlayerMock).not.toHaveBeenCalledWith( ,
    );
  }); it( , () => {
    expect(messagePlayerMock).not.toHaveBeenCalledWith( );
  });

  describe( , () => {
    beforeEach( () => { askPlayerToHitMock.resolve( );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( );
    });
  });

  describe( , () => {
    beforeEach( () => { askPlayerToHitMock.resolve( );
    }); it( , () => {
      expect(messagePlayerMock).not.toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( );
    });
  });
}); import from './monsterBeatdown' import from '@asyncFn/jest' 'given a monster is encountered' let let let => () askPlayerToHit messagePlayer 'informs the player of this' async 'You encounter a monster with 5 hit-points.' 'asks player to attack' 'Do you want to attack it?' // This is the "negation test" referred later in narrative. 'when player has not chosen anything yet, the player is not eaten' 'You chose to flee the monster, but the monster eats you in disappointment.' // This is another negation test. 'when player has not chosen anything yet, the game is not over' 'Game over.' 'when player chooses to flee' async await false 'player is informed of the grim outcome' 'You chose to flee the monster, but the monster eats you in disappointment.' 'the game is over' 'Game over.' 'when player chooses to attack' async await true // Another negation test 'player does not lose' 'You chose to flee the monster, but the monster eats you in disappointment.' 'the game is over' 'Game over.' Contents of so far: ./monsterBeatdown.js ({ messagePlayer, askPlayerToHit }) => ({ : () => {
    messagePlayer( ); playerChoseToAttack = askPlayerToHit( ,
    ); (!playerChoseToAttack) {
      messagePlayer( ,
      );
    }

    messagePlayer( );
  },
}); export default encounterMonster async 'You encounter a monster with 5 hit-points.' const await 'Do you want to attack it?' if 'You chose to flee the monster, but the monster eats you in disappointment.' 'Game over.' : There. I’ve reduced the “evilness” of the code by adding something we call “negation tests”. By writing tests that describe what is not supposed to happen we forced the production code to make a little bit more sense. And if you are wondering where the name “evil pairing” comes from, it comes from playing unit testing ping-pong in such a way that the production code is always written in the most evil way possible, ie. the code is non-sensical for real-life, yet it still satisfies the tests. The only way out of this is to force sense in production through unit tests. Sometimes you can tell coders are evil pairing from the . In general, this “ ” helps produce code that is very robust for the sake of refactoring, and also allows programmers to hone their a little bit more. But I digress. However important this may be, it is slightly off-course. What is relevant is that supports evil pairing as a line of thinking. Let’s motor on. Leela evil laughter evil pairing -mentality TDD-mojo asyncFn Chapter 6: The multiplicity challenge and the enlightenment : For sure. Can we see how the game develops some steps further? Particularly, I’ve been suffering from testing of functions that are called , yet return promises. Fry multiple times : I feel you. My guess is an asyncFn-mock will get called multiple times soon enough, when we start attacking the monster multiple times. Let’s see how that pans out. Leela gameWithoutDependencies ; asyncFn ;

describe( , () => { askPlayerToHitMock; messagePlayerMock; game;

  beforeEach( {
    askPlayerToHitMock = asyncFn();
    messagePlayerMock = jest.fn();

    game = gameWithoutDependencies({ : askPlayerToHitMock, : messagePlayerMock,
    });

    game.encounterMonster();
  });

  it( , () => {
    expect(messagePlayerMock).toHaveBeenCalledWith( ,
    );
  });

  it( , () => {
    expect(askPlayerToHitMock).toHaveBeenCalledWith( ,
    );
  });

  it( , () => {
    expect(messagePlayerMock).not.toHaveBeenCalledWith( );
  });

  describe( , () => {
    beforeEach( () => { askPlayerToHitMock.resolve( );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).not.toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( );
    });
  });

  it( , () => { askPlayerToHitMock.resolve( );

    expect(messagePlayerMock).toHaveBeenCalledWith( ,
    );
  });

  describe( , () => {
    beforeEach( () => { askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( );
    });

    it( , () => {
      expect(messagePlayerMock).not.toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( ,
      );
    });

    it( , () => {
      expect(messagePlayerMock).toHaveBeenCalledWith( );
    });

    it( , () => {
      expect(messagePlayerMock).not.toHaveBeenCalledWith( ,
      );
    });
  });

  it( , () => { askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( ); askPlayerToHitMock.resolve( );

    expect(messagePlayerMock).not.toHaveBeenCalledWith( );
  });
}); import from './monsterBeatdown' import from '@asyncFn/jest' 'given a monster is encountered' let let let => () askPlayerToHit messagePlayer 'informs the player of this' async 'You encounter a monster with 5 hit-points.' 'asks player to attack' 'Do you want to attack it?' 'when player has not chosen anything yet, the game is not lost' 'Game over.' 'when player chooses to flee' async await false 'player is informed of the grim outcome' 'You chose to flee the monster, but the monster eats you in disappointment.' 'the game is not won' 'You knock out the monster. You are a winner.' 'the game is over' 'Game over.' 'when player hits the monster, the monster loses a hit-point' async await true 'You hit the monster. It now has 4 hit-points remaining.' 'when player hits the monster enough times to knock it out' async // Here the monster is attacked multiple times, and therefore, askPlayerToHitMock is called multiple times, and then the test resolves the mock multiple times. // Using asyncFn, the logic remains the same: things that happen are written and observed in chronological order. await true await true await true await true await true 'does not show out-of-place message about no hit-points' 'You hit the monster. It now has 0 hit-points remaining.' 'player is congratulated' 'You knock out the monster. You are a winner.' 'game is won' 'Game over.' 'the game is not lost' 'You chose to flee the monster, but the monster eats you in disappointment.' 'when player hits the monster until it only has 1 hit-point remaining, the game is not over yet' async // Hit the monster only 4 times instead of 5. await true await true await true await true 'Game over.' The final form of : ./monsterBeatdown.js ({ askPlayerToHit, messagePlayer }) => ({ : () => { monsterHitPoints = ;

    messagePlayer( ,
    ); ( askPlayerToHit( )) {
      monsterHitPoints--; (monsterHitPoints === ) { ;
      }

      messagePlayer( ,
      );
    } (monsterHitPoints === ) {
      messagePlayer( );
    } {
      messagePlayer( ,
      );
    }

    messagePlayer( );
  },
}); export default encounterMonster async let 5 `You encounter a monster with hit-points.` ${monsterHitPoints} while await 'Do you want to attack it?' if 0 break `You hit the monster. It now has hit-points remaining.` ${monsterHitPoints} if 0 'You knock out the monster. You are a winner.' else 'You chose to flee the monster, but the monster eats you in disappointment.' 'Game over.' : There. Now the monster gets hit multiple times, all while things still happen in a clear order. Our work is done here. Leela : Color me enlightened. This has changed my view of the world as a programmer and a human being. I shall make sacrifices in your honor and get an asyncFn-tattoo. My grandchildren will know your name. Fry Summary Having witnessed , we’ve seen how the of permitted troublesome tests, with awkward readability, to be refactored orderly as a story, with significantly better readability. the adventure late resolve asyncFn is something that we, as a team, have used for multiple years now. It has categorically changed the way we approach unit testing of asynchronous code in JavaScript. Which we so adore. asyncFn See more about asyncFn for or . jest sinon Who are we? asyncFn is lovingly crafted by at from . Your Pals Team: Igniter Houston Inc. Consulting We are a software development team of friends, with proven tradition in professional excellence. We specialize in holistic without sacrificing . rapid deployments quality Come say hi at , us, or check out the . We just might be available for hiring ;) Gitter email team itself Previously published at https://medium.com/houston-io/how-to-unit-test-asynchronous-code-for-javascript-in-2020-41c124be2552

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Testing Asynchronous JS Code: 2020 Edition

I always tell people that data is not the new oil, instead, it is the new time machine. Today, using data, you can know  how much profit your firm will attain in the next 5-10 years, and you can do a deep analysis of  what could have been the reasons for the success and/or failures in the past decades or so. Observation is key here. A small change in your data can change the decisions your firm takes. Think about it! That's a big challenge: to depend on data for running a business successfully. And that's a risk that has helped pile up profits for everyone. Here's what Data Science has taught me: 1. What You See And What Exists Is Different 🤯 Quite often, this might happen to you, while dealing with data, that the patterns you see with the naked eye, are almost always never, the patterns that actually exist. That's where the statistics come in. So many statisticians are moving towards data science, and it makes perfect sense because . It's just that, it shows a much 🤯 data science is basically based off of statistics deeper relationship between the business ideas and how statistics affect them! 2. Data Science Is Not Data Analysis 🙄 Data Science is a vast ocean, out of which the analysis part of it, is concentrated in a small area only. pertains to checking the data for anomalies and out of it. , on the other hand, corresponds to the and building an understanding of how the answers to these questions can help the business being analyzed. Data Analysis building insights Data Science questions which can be answered using that data Exactly, It can be product analysis as well, but all in all, That's the difference. business analysis. It is the key to a data science role. data science should answer the questions that have kept businesses in the dark. 3. It May Sound Weird, But Machine Learning Is A Pre-requisite For A Data Science Role 😵 Over the previous decades, , using methods such as . Today, for the coming future. 2020 is a perfect example of how the dynamics of the World will be changing in the coming years. Robotics is rising in the industry, day by day, and there are countries where robots are serving in restaurants! We sure have evolved as a species, and this innovation shows that. tons of customer data has been collected Surveys in 2020, data is being collected from machines, to understand their behavior, in order to automate them In layman's language, Data is essential for machine learning. For example, a robot's movements are captured, and then algorithms are run on that data, to predict how often the robot will function the way it is required to. machine learning is nothing but a study/understanding of how machines act and react to user inputs, as well as on their own. Understanding how the machine works, and what the outcomes of these algorithms represent, is also important, because these days, that help us make the predictions (of a robot's expected working, for example). the role of a data scientist is very holistic, and deals with lots of cleaning, wrangling, and mining of data, but at the same time, it also deals with a deep understanding of machine learning algorithms 4. Cloud Is A Powerhouse, And Data Demands It  ‍✈️ Cloud technologies embed a stable and vast powerhouse in themselves, and that has become the sole reason for organizations to migrate towards the cloud, for storage of Big Data, and for running high-performance applications. Nowadays, On such extensive data, it is not easy to run algorithms on the whole data, in one go, on a normal PC. people are working on GBs, TBs, and higher data storage, with tons and tons of data to analyze. A Supercomputer is required for this task. , and this becomes a strong reason to shift towards cloud technologies since they allow data to be stored, fetched and analyzed in packets while being added to the same algorithm, and the results being saved on the cloud as well. You could say that and that mind-blowing! Supercomputers are not practical for organizations the whole infrastructure shifts to a cloud 5. Why Data Is A Time Machine 👨‍💻 👩‍💻 Let's take the Organizations are laying off people? Why? They sure haven't lost all their savings yet, and they sure are earning profits (reduced, of course), but organizations that have shifted online/work-from-home are still making some profits. So, classic example of this year: 2020. why lay off so many people? , and they are planning for it. In a few days, This is a result of the catastrophe that we are facing this year. So, data is helping companies make decisions. Because data helped them predict the coming recession you will see fruits and vegetables in your local market become costly. Gadgets' prices have already begun to soar. And it has been happening so for about a decade now. This is why you see so many emerging Because their decisions were built on a thorough analysis of incoming data, through years. , bit by bit. startups that were built in the last ten years, and they have progressed with the speed of light! The World is changing at a very fast pace, and data is determining these changes Conclusion Data-driven decisions are the need of the hour, as we have seen how the World can change in a second, and we have experienced this, in 2020. , now that we understand how quickly our lives can be altered. Preparation for the future is essential (since the amount of data being collected each year, is also increasing at a rapid rate). We can prepare for the future, in any domain, with the use of the existing data that has been collected over the years, and the data that is incoming Thank you for reading! Happy learning! After spending an enormous amount of time on the internet, and struggling for years, to keep my foot in the right direction, I have understood how helpful guidance can be! Hence, I have decided to contribute to society by keeping all my personal projects and learnings open-source. 😃 I'd be really happy if you could support my writing by getting me some coffee! https://www.buymeacoffee.com/shauryalalwani

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What Data Science Has Taught Me

In early March, 2020, my business partner would send texts daily about how travel was getting crushed. Every day I would check our numbers and still see growth of 100% YOY with minor drops and dips and then growth again.  We were trending with our projected growth. Then, March 15 (the ides of March) hit and our sales plummeted, down -55%. By March 21 sales were down -87.5%.  We were getting crushed. I can share these specific numbers because Stripe has a great UI and it is super simple to grab this data. Stripe makes looking at numbers simple, and it makes setting up a PCI compliant payment processor super easy for dev teams.  Stripe makes themselves sticky. So sticky that they can try to pull what comes next. On March 20, Stripe decided to start holding 25% of all of our funds for each transaction; this is of course before refunds and fees.  Below is the only communication I received. For what Stripe provides with a great UI and simple integration, they take back fully with their awful customer service/ lack of empathy. I found the timing interesting and asked them if I could show them proof (with pictures from our bank account) that we could pay back any refunds.  I reached out online and found that other businesses in our industry, and elsewhere, have had the same issues. It seemed to me like Stripe wasn’t doing well financially and THEY were the ones that were hit hard financially due to Covid-19. Perhaps they invest their client's money into investment accounts? When I was finally able to speak to someone on the phone to go over this it was like talking to a numb robot without a heart. I knew that no matter what I said, things weren’t going to change. This was seemingly a top down decision, “No wiggle room with Stripe Reserves”. Perhaps the reason for this was an internal “we need this money”? Funnily enough, it's been less than a month since Stripe announced that it would be extending their Series G round and raising another $600M. https://stripe.com/newsroom/news/stripe-extends-series-g-funding-round Stripe stated that it was using the funds “to step up product development, global expansion, and strategic initiatives”. Further, Stripe mentions that they are “Providing fast access to funds through Instant Payouts, Stripe Capital, the Stripe Corporate Card, and helping small businesses access government aid.” I am seeing record numbers in sales this year with and refunds are back to our normal percentages and trends, yet Stripe is radio silent.  No news of the reserve being released anytime soon. Has something similar happened to your startup/ small business. Please share so that we can battle this together. Ideally this post can act as a hub where people can voice their complaints against Stripe and perhaps a non-robot with a heart and some empathy will reach out to help solve all of our issues? my company I’m giving Stripe another month and then I will move our company to a different payment processor. If I do that, do I expect to get my reserves back right away? I surely do not!

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Stripe Is Holding 25% of Our Funds, Indefinitely

HackerNoon Tech Stories Archive July 17th, 2020

Decoding APIs for Product Managers

@ujval-bucha
Ujval Bucha

The Drivers of Company Valuation

@karl-sjogren
Karl Sjogren

Migrating Away From Query Builders and ORMs in JavaScript or TypeScript

@aniravi
Ani Ravi

Sergio Mattei: The Beautiful Humans Of Hacker Noon (Noonies Winner for Under 20ish Award, 2019)

@rachelminnlee
Rachel Lee

What is Dwell Time? Does Google Consider this as A Ranking Factor

@rahulsetia007
Setia

Serverless Architecture: Lambda Triggers and Design Patterns [Part 1]

@taavi-rehemagi
Taavi Rehemägi

A Data-Centric Perspective of ChainLink’s Madness

@jrodthoughts
Jesus Rodriguez

Machine Learning Consulting: Does Your Business Really Need It?

@mobidev
MobiDev

An Interview With Ivan Starinin And Alexander Salnikov On DeFi Tech

@adam-stieb
Adam Stieb

How I Designed And Launched A SaaS Product From Zero To The First 10 Customers

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Inspector.dev

What Will It Take For DeFi Protocols To Seize The Future of Loan Payments?

@Sergeenkov
Andrey Sergeenkov

What the Pandemic Taught Me About Product/Market Fit

@rfpurcell
Richard Purcell

Top 21 Companies Hiring for Remote Work

@roamingdesk.com
Roamingdesk.com

How to Hack Home Learning

@brianwallace
Brian Wallace

Testing Asynchronous JS Code: 2020 Edition

@Team: Igniter
Team: Igniter from Houston Inc. Consulting

What Data Science Has Taught Me

@shauryalalwani
Shaurya

Stripe Is Holding 25% of Our Funds, Indefinitely

@pat
Patrick Murray

HackerNoon Tech Stories Archive July 17th, 2020

@ujval-buchaUjval Bucha

@karl-sjogrenKarl Sjogren

@aniraviAni Ravi

@rachelminnleeRachel Lee

@rahulsetia007Setia

@taavi-rehemagiTaavi Rehemägi

@jrodthoughtsJesus Rodriguez

@mobidevMobiDev

@adam-stiebAdam Stieb

@valerio-barberaInspector.dev

@SergeenkovAndrey Sergeenkov

@rfpurcellRichard Purcell

@roamingdesk.comRoamingdesk.com

@brianwallaceBrian Wallace

@Team: IgniterTeam: Igniter from Houston Inc. Consulting

@shauryalalwaniShaurya

@patPatrick Murray