Every product or company goes through a similar set of steps on the path to success. In previous posts, we’ve looked at different aspects of going to market — from launching to building and measuring a funnel to pricing — with some ideas about how to measure things and know when you’re off track. In this post, we’ll take a step back and walk through all the stages of GTM that you’ll typically experience. Programming notes: this post is n in a series of indeterminate length on GTM topics mainly for startup people, mainly leadership, mainly coming from non-GTM backgrounds. There’s a list at the end. Going To Market What did you make? Who is it for? How does it make their life better? How do you find them? How do they find you? What will get them to try or buy? How much do they have to spend on you? How will you sell it? How many deals do you need to close or users do you need to acquire in a month, a quarter, a year? What is your (real and desired) sales motion? < — All your answers to these questions are only put to the test and can ultimately (for a while) only be arrived at by going to market. Going to market is the sense mechanism of your business. It’s how you know whether you even have a business and provides the only relevant* input to developing that business. Everyone thinks it’s great, but no one wants to pay for it? That’s not a business. A niche of users in the 100s loves it and will pay a small amount for it, but no one else cares? That’s not a business. A niche of users in the 100s loves it and will pay a very big number for it. That might be a business. Millions use the free version and 10s of 1000s pay for premium features such that revenues are growing faster than costs? That might be a business. * Raising money from VCs is not a substitute for going to market. Someone’s, even an expert’s, opinion of whether you might have a business is not a substitute for the market reacting to your proposed business by giving you money (or growth) or rejecting you. Stages of Going To Market What follows is from a B2B angle. B2C is analogous, but the mechanics can be wildly different depending on whether you’re growth and indirect revenue (like from advertising) predicated or direct revenue (user = customer) predicated. There are many models, but this one is mine. :) Discover Problem There’s something out there that’s broken, someone who’s needs are poorly or not served, or just something that could be so much better if someone just built ___. This is where it all starts. And it’s usually . You have the issue yourself or someone you know does. personal Validate Market Ask some people if it’s true. Talk to experts and specialists and the people who would be the users, influencers, and buyers. Maybe do some research, see if anyone else is already solving the problem and how. Find out how big the market might be, how many users on earth or just in your country might exist. Or not. Just solve a problem that you have on the presumption that you represent a set of users or customers big enough to sustain a business through revenue or data or users to sell to advertisers, eventually. Product Build the thing.* *Once you start, you never get to stop. Product Market Fit [Price Discovery] See if the people you built it for like it! If it does the thing you expected. If they want it. If they’ll — signing up, changing something they do, paying some money — to get it and keep it. overcome some friction At base, this is Product Market Fit*. A core function of PMF in products that are supposed to make money is discovering what price your market will bear, or is competitive, for the value you provide. *Depending on who you talk to, PMF requires GTM Fit [see below]. Network Sales Sell it to people you know, people your staff knows, people your investors know. 1–2 degrees away with warm intros. Some companies only ever do this and build solid businesses this way. Growth in the hockey stick sense happen this way.* does not *A lot of sales and biz dev hiring in ye olden days — and more often today than anyone would admit — is simply buying access to a person’s network and the positive disposition of that network towards them. This is not materially different from an advertiser buying eyeballs. You’re paying for at-bats. Novel Customer Acquisition Novel, because it’s not based on your existing rolodex. Now you’re doing lead gen work that exposes new people to your product and costs real money, either in hard currency or in paying for staff/contractors. This includes: sponsoring events, speaking at events, running ads on podcasts, SEM, buying contact lists and emailing them, hiring SDRs to contact people, running meetups, investing in content marketing for inbound search driven traffic, etc. Natural Market Limit Discovery There is a point early in your company at which you will fall off a cliff of diminishing returns on making sales or acquiring users. This will manifest as lead gen effectiveness failing or increased effort to convince prospects who fit a given profile to give your product a shot or poor conversions rates at the middle and bottom of the funnel regardless of what’s happening at the top or any general stalling of growth that seems impervious to the marketing and sales you’ve tried to date. You are discovering the bounds of your Natural Market*. Natural Markets consist of: Have the problem and know they have it, or so close that if you point at it they know they have it Want to solve it Will pay to solve it Have the authority and responsibility needed to spend their own or their team’s or their company’s money to solve it or the influence over such a party to cause it to happen Who you can reach If this happen, you are on a rocket ship — right product right market right team. In which case, your biggest (only?) risk is success. does not *Natural Market is an idea that I got from Tae Hea Nam of Storm Ventures in a board meeting. It is the single most useful thing I’ve learned from a VC. CAC Ramp Usually you don’t know you’ve hit your Natural Market bounds. It will just seem like you’re experiencing the normal diminishing returns of a particular marketing tactic(s) or marketing strategy or playbook or GTM strategy. Most teams will start with the assumption of a fading marketing tactic and try others. Whether you layer on more tactics, like adding display advertising on top of search ads, or out more resources on an existing tactic, like going from one big event sponsorship every six months to one big one every quarter — this inevitably results in more $$$ per customer acquisition.* *This is separate from the fact that CAC is rising for everyone in general due to the sheer number of products and services vying for people’s attention that all ultimately arrive at a small set of channels to do it. This is a secular trend. Tactical Experimentation A kind of flowering of marketing and sales tactics, including potentially larger GTM bets like sales partnerships, takes place in response to the challenge of rising CACs. In the B2C world, if you’re experiencing outstanding user growth, there’s an infinite appetite on the part of investors to fund that growth. Even with rising CAC. Strategic Instability Usually in parallel, you will start questioning your marketing and sales strategies. Maybe the entire GTM model, including core things like who your customer is and is this even the right product. Example: If you thought you had an inbound, inside sales business, you might start playing around with field marketing and outside sales. GTM Fit Exploration Do you have a GTM model that can be executed and scaled sufficiently to get you from where you are right now to your next stage of growth? Then you have GTM Fit. The hallmark of GTM Fit is having predictability in terms of resources in, like marketing dollars or AE headcount — and results out, like users or logos or dollars. *GTM Fit is an idea from Tae He Nam and Bob Tinker. It’s best summed up in this video . I agree with every last thing Tinker says in this talk and have found it to be true both at startups and when shipping new products at places like IBM. Playbook Scaling When you’ve locked into a GTM that works, you’ll pour resources into it to achieve maximal user or rev growth. Diminishing Returns [Again] Just like running into the limits of your Natural Market, you’ll eventually find the a peak in yield from a playbook. This will tend to look more like a slow decline than falling off a cliff.* The market will saturate, at least for you, as you hit max possible penetration and fight harder for smaller gains. Sales and marketing tactics will become less effective. Etc. *If you’re pouring millions+ of dollars and hundreds+ of people on the fire of a working playbook.. it’s more likely that you’re going to fall off a cliff. Operational Discipline If you have great GTM Fit, you’re scaling incredibly fast and just trying to stay one step ahead of your own success. In this situation, everything you’re doing in marketing and sales is massively inefficient and your funnel is leaking everywhere. It just doesn’t matter because growth is so rapid. When you start to hit post GTM Fit speed bumps, all of a sudden people will start caring about operations, analytics, process, and discipline. This is severely normal. Bloomberg didn’t have a formal marketing function until it was 1B+ in rev. New Relic didn’t have an enterprise sales team until many years after it was a tech household name. Most CFOs, CROs, and CMOs of startups in spitting distance of an IPO could not tell you what their sales efficiency is or what the conversion rates are for their funnel stages with even the slightest bit of confidence or accuracy (ask them!)*. *If you don’t have great GTM Fit, if you’re way early with a product the market is not ready for yet, or if it’s just a grind in the way that enterprise businesses are — then you’ll often end up with a great grasp of what to measure and optimize for and the resultant operational discipline much earlier, because you had to in order to survive. Market Expansion You have gotten all you can get out of a particular GTM, so it’s time to expand who you sell to, what you sell, or both.* New channels, such as distribution deals with a partner or being bundled with another product or being sold by consulting companies into accounts/geographies you don’t have access to or leverage in. New segments, such as going after enterprise where you had only been selling to SMB. New verticals, such as selling into Financial Services where you and only been selling to CPG. New products you build or buy that can be sold into your existing markets and/or give you access to new markets. Companies that you buy, get bought by, or merge with to give you access to new markets, or new products that you can sell to your existing markets or new markets, or that you effectively buy revenue streams or users or eyeballs that you can add to your own. *All attempts at Market Expansion basically start a new cycle of GTM with its own Product cycle. I’ve left that out because depicting interacting loops in a serial text format is damn near impossible. Exit or GTFO Eventually, most people want to Exit.* Your early investors want liquidity. Your employees want liquidity. You want liquidity. You might just be tired and ready to dissociate from the thing you’ve spent years and years building. Exiting is a GTM stage because, regardless of how you exit, you will have to hit specific GTM measures around costs, revenues, and growth to do it. *If you don’t want an Exit, you might have a lifestyle business. Or something bootstrapped you want to do forever. Or you’re going to buy out your investors. Or you’re going to take on some private equity (not necessarily PE) investors who get dividends. Or etc. More power to you. Final Note The road is winding and often non-linear. Posts in this series (and templates) Marketing 101 for Engineers: A Functional Introduction Marketing 102 for Engineers: Roughing Out a Funnel Marketing 201 for Engineers: Messaging & Positioning Marketing 202 for Engineers: Launching Marketing 203 for Engineers: Sales Enablement Marketing 204 for Engineers: Generating Demand Marketing 301 for Engineers: Strategy & Planning Marketing 302 for Engineers: Hiring Marketers Marketing 303 for Engineers: Pricing Frame Marketing 401 for Engineers: Stages of GTM Marketing 402 for Engineers: Diagnostics & Troubleshooting Sales 101 for Engineers: A Functional Introduction PR 101 for Engineers Analyst Relations 101 for Engineers Basic Messaging Template [Google Doc] Basic Funnel Metrics Template [Google Sheet] Basic Launch Timeline Template [Google Doc] Basic Battlecard Template [Google Doc] Detailed Battlecard Template [Google Doc] Basic Marketing Calendar Template [Google Sheet] Basic Marketing Ladders Template [Google Sheet] Reading List and Resources Bob Tinker’s GTM Fit and Talk Slides Tomasz Tunguz Tren Griffin Startup Marketing Playbook First Round Capital: PR & Marketing Magazine First Round Capital: Sales Magazine Max Olson: Advantage Flywheels Go-To-Market Fit: The Missing Link for Enterprise Growth HubSpot: Top 10 Sales Methodologies Image credits [in order] Photo by on 2Photo Pots Unsplash Expensify Value Proposition Diagram from Tom Tunguz Bob Tinker on GTM Fit and Talk Slides Using Price & Demand Curves from Tom Tunguz

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A month ago, I had the privilege to chat with a third-time founder with two successful exits under his belt. The one takeaway he had from all three of his experiences? “Distribution is king.” My initial thought was to disagree - that and are actually more important. But: after mulling it over in my head several times, I began to realize why he said that. product engagement retention Most startups , given sufficient product iteration cycles, can create good solutions to problems that probably engage and retain users well. This is because products (especially software solutions) are getting easier and easier to build over time. most software is built on top of open-sourced packages of code with tens of thousands of libraries available at hand, and no-code solutions get more advanced and easier to use by the day. Most Building software is no longer a skill exclusive to Bay Area specialists, and many companies, like , are tapping into remote workforces across the globe to cut down on costs. Stripe It seems the exact opposite is happening with distribution channels. Due to the rising availability of seed funding, cheap incorporation tools, and the ease of building products, a lot more competition exists. Advertising channels are less effective and more expensive, spam filters more aggressive against cold outreach, and SEO is harder to game than ever before. I occasionally hear about this trend through various sources. In particular, Open View has a on why this is happening. really great article Startups need to have a growth strategy from day one. If I was an investor, I wouldn’t back founders if they haven’t extensively thought about and planned for distribution. Long, long, gone are the days when startups were strictly product teams whose growth strategy was based off of the mantra, “if you build it, the users will come.” Companies like Slack got lucky, and in today’s startup environment, There’s simply too much competition — you can’t leave anything up to chance. there’s no room for luck in distribution strategy. I think even regardless of competition, teams that delay hiring on growth will fall behind on finding product-market fit. Growth is more similar to product than founders think. Like product, growth requires constant iteration cycles to make it better. There’s no one magical feature or thing you can build that will unlock growth. It’s a constant process in learning about and iterating on your target market. The longer you wait to invest heavily in your growth strategy the longer it will take for your startup to find product-market fit (unless you get lucky). What’s the saying — first-time founders focus on product, second-time founders focus on distribution? As a first-time founder now, I think the focus should be both from the start. Questions? Comments? Hit me up on Twitter @wrgoto or subscribe to my newsletter . I love chatting with first-time founders and help out anywhere I can.

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Most website managers create terms of use before the site goes live. These terms generally describe what the site owner expects from users and what users can expect from the website. Terms of use identify what users may post onto or use from the site, and help put users on notice of what the site owner considers to be acceptable behavior. They enable owners to reserve the right to deny access to users who engage in objectionable conduct and to remove content they find offensive or that may subject them to liability. Terms of Use also allows owners to put language up online that may help protect them in the event of a lawsuit. Courts will, in general, enforce online terms and conditions against consumer users, provided they have adequate notice and an opportunity for review. However strict business owners lay out the terms and conditions of their website, there are so many ways in which real users, online bots and website scrappers can violate your terms of use, that it's better to plan for all case scenarios. To best enforce the 'Terms of Use' of your website, pay attention to the following five areas: 1. Browsewrap vs. Clickwrap license There are two types of terms of use online - clickwrap and browsewrap. terms require the user to click a box in agreement with the terms of use. Clickwrap terms are listed on the website, without requiring action of any kind. Consequently, if the user never saw the browsewrap terms, there was no contract formed because there was no 'meeting of the minds,' and the terms do not legally bind the users. Browsewrap While some courts have held a contract unenforceable because users did not have to check a box in the agreement of the terms, courts have also refused clickwraps or click-through licenses, because a contract requires both parties agree to terms. Some clickwraps may not ensure that users had read and agree to terms. Be aware that your terms of use may not hold up in court as a binding legal contract. 2. Prohibit automated and excessive queries Or at least try to, by including language in your terms of use that expresses that such conduct will not be permitted. Courts have generally been unforgiving of users that excessively hit a website, citing that will slow down the system and interfere with other users' activity. If a user is making a high number of queries a day while extracting data from that website, courts have held that it may (and has) constituted trespass to chattels. Violation of this tort law could lead to a court injunction or further legal action. "An excellent tool that allows webmasters to control the volume of malicious traffic on a website is Cloudflare's rate limiting." - said Adan Morales, webmaster of , an online portal for party equipment rentals. "It allows a customer to rate limit, shape, or block traffic based on the rate of requests per client IP address, cookie, authentication token, or other attributes of the request. " Jump for Adan 3. Deter data mining Web scraping is a huge part of business activities online. Depending on the nature of your website, you may what to include a clause in your terms of use prohibiting bots, crawlers, cookies, or any other intrusive scraping device. Make clear what type of access is acceptable, and the amount of information that can be accessed. Data mining access can adversely affect the site owner's system as well, so implement a simple screening box to require users to prove they are human. 4. Make terms prominent Courts have held that a hyperlink to the terms of use in the footer of a website does not constitute proper notice of those terms, which means that they do not bind users of that site. If terms are found to be prominent, then a court would most likely hold the user to the terms because they were on inquiry notice - they could and should have seen them. Reasonable notice and user consent are required to hold terms of use licenses enforceable. "Often, business owners don't consult with a copyright attorney about their website's Terms and Conditions or any other legal matter relating the website because there is a lack of transparency in what attorneys charge." - said Ailene Valdivia, spokesperson for , a project dedicated to research and educate consumers about the cost of legal services. -"Solopreneurs and small start-ups just assume that attorney fees are too high, and are left with placing legal jargon on their website when most of the time they don't even understand what it means." AttorneyFee 5. Rights of copyright and licenses Everything created and published on your website is automatically copyrighted. Nowadays, there are many legal service providers online that can help you with copyright issues and licenses to the content you put out there. You can license any of your rights to users to share and use your work, transfer them, or obtain a Creative Commons license. Let your users know what they can and can't share, copy, use, and reproduce to prevent legal issues.

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When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first. Without considering the data first we will continue to incur high-interest technical debt. Dependencies on data cost more than software dependencies, but are constantly overlooked. "Data is a precious thing and will last longer than the system themselves", Tim Berners.Lee To build a face detection and/or face recognition model it's important to know available data set and data augmentation approaches to be followed for training the model. Typical Face Recognition Engine flow looks as below: Note that there's difference between Face Identification and Face Recognition. Face Verification/Authentication: It is process of comparing face image with claimed identity, basically it is a "One-to-one matching". Face Identification/Recognition: It is process of comparing face image with all the registered persons, which is nothing but a "One-to-many matching". Data Augmentation: Data augmentation provides an effective alternative to compensate the insufficient facial training data. Deep learning strongly relies on large and complex training sets to generalize well in unconstrained settings. Collecting and labeling a large quantity of real samples is widely recognized as laborious, expensive and error-prone. Existing datasets are still lack of variations comparing to the samples in the real world. Data Augmentation for Face Detection Data Set: Horizontal Flip: Flip or mirror a face image so that  left side becomes the right side. Random Cropping: Square patches (If face is on crop boundary keep the overlapped part of the face box if its centre is within the crop patch) Photometric Distortion: Involving random changes to image properties such as color, contrast, and brightness. Data Set for Face Recognition: Let us say we aim at recognising 1M unique faces, it makes sense to split the training process into basic training and Advanced training and accordingly split the data set as well. Data Set should meet following two criterion: It should contain as many photos as possible (at least couple of million) with unconstrained pose, expression, lighting, and exposure. It should be broad(many unique people/faces) and deep(many photos for same person) Data Augmentation for Face Recognition: Geometric Transformation: translation, rotation, reflection, flipping, zoomming, scaling, cropping, padding, perspective transformation, elastic distortion, lens distortion, mirroring, etc. Photometric Transformation: color jittering, grayscaling, filtering, lighting perturbation, noise adding, vignetting, contrast adjustment, random erasing, etc. Advanced Data Augmentation methods to improve accuracy on real time data: Component Transformation: Hair style, make-up, accessory Attribute Transformation: Pose, Expression and Age Implementation examples for these are: Model-based Transformation (2D-models and 3D-models) Generative based Transformation (GANs, VAEs, PixelCNN, Glow, etc)

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Automation has been on the rise over the past few years. Advanced technologies like Artificial Intelligence promote automation in various facets of business operations. Many industries, including retail, agriculture, manufacturing, and customer service, have already moved to artificial intelligence to automate their work. , Artificial Intelligence alone is expected to have a $15.7 trillion economic impact by 2030. According to research by PwC This technology has become trending in workplaces across the globe. As , it can lead to a reduction in the requirement of the manual workforce, leaving people looking for alternative career options. Artificial Intelligence automates most of the menial accounting tasks Automation in the Accounting Industry The accounting industry is also not too far behind when it comes to automation. Many leading accounting software providers like Xero, Intuit, and Sage have already incorporated AI technologies into their software to perform various accounting tasks. With the accounting industry moving towards automation at a fast pace, accountants are a little skeptical about future job prospects in the industry. There is no second thought about the fact that machines can perform accounting tasks such as auditing, expense management, bank reconciliations, etc. in a much faster way and with higher accuracy. But does this mean that it will replace humans in these jobs? showed that accountants – specifically accounting clerks and bookkeepers are most at risk from automation in the next 20 years, the reason being that computer systems and robotics will be able to perform accounting tasks in a more efficient manner. A study by PwC But this is not necessarily true. Even though technology has the upper hand over humans in many ways, there are other aspects of accounting services that it can’t replace. Shedding light on this, Dwayne Bragonier, CITP, a chartered professional accountant and founder of BAI Bragonier and Associates Inc. said, “A big chunk of the manpower required to record a transaction or to accumulate transactions into an aggregate will, of course, be automated.” He goes on to say, “The work that will be automated is not a CPAs responsibility. That work was simply something that needed to be done for CPAs to start their job.” This statement by Bragonier makes a lot of sense. Even though the initial raw data and its aggregation are vital for any business, people would still require an accountant to analyze that data and come with effective plans and policies ultimately required for the growth of their business. Thus, automation will essentially free the CPAs from performing all the tedious data work and will push them to focus on other more important responsibilities and to take up different roles such as that of a business analyst, consultant, and strategist. Time to Prepare for the Future Now is the time for accountants to prepare for the automation era in order to grow in the industry. Here are a few ways how they can keep at par with the changing technology- CPAs should be able to quickly adapt to the changing technology and should be ready to seize new opportunities that are coming up with technological advancement. They should educate themselves with the new technology and look for ways to offer more value to their clients. Accounting is no more limited to just auditing and bookkeeping, and thus CPAs must move ahead of the bookish knowledge and gain experience and expertise in their chosen field as soon as possible. Since everything is becoming automated these days, it has become essential for CPAs to become well versed with the use of laptops and computers to carry out their work. So, it is safe to say that automation will transform accountants and not replace them, and CPAs and accountants will remain in the business till the time they add more value to their clients.

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Robotic Process Automation has generated a lot of buzz across many different industries. As businesses focus on digital innovation, is an attractive proposition. automation of repetitive tasks to increase efficiency while decreasing human errors Robots will not tire, will not get bored, and will perform tasks accurately to help their human counterparts improve productivity and free them up to focus on higher-order tasks. Beyond simple RPA, Intelligent Automation can be achieved by integrating machine learning and artificial intelligence with Robotic Process Automation to achieve automation of repetitive tasks with an additional layer of human-like perception and prediction. The Difference Between RPA and Artificial Intelligence By design, RPA is not meant to replicate human-like intelligence. It is generally designed simply to mimic rudimentary human activities. In other words, RPA does not mimic human behavior, it mimics human actions. Behavior implies making intelligent choices among a spectrum of possible options, whereas action is simply movement or process execution. RPA processes are most often driven by pre-defined business rules that can be narrowly defined, thus RPA has limited abilities to deal with ambiguous or complex environments. , on the other hand, is the simulation of human intelligence by machines which requires a broader spectrum of possible inputs and outcomes. AI is both a mechanism for intelligent decision making and a simulation of human behaviors. Artificial Intelligence Meanwhile, is a necessary stepping stone to Artificial Intelligence, contributing deductive analytics and predictive decisions that increasingly approximate the outcomes that can be expected from humans. Machine Learning The IEEE Standards Association published its in June 2017. In it, IEEE Guide for Terms and Concepts in Intelligent Process Automation Robotic Process Automation is defined as a: “preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.” In other words, But it cannot apply the function of learning to improve itself or adapt its skills to a different set of circumstances, that’s where Machine Learning and Artificial Intelligence are increasingly contributing to building more intelligent systems. RPA is simply a system that can perform a defined set of tasks repeatedly and without fail because it has been specifically programmed for that job. Process-driven vs. Data-driven Intelligent Automation is a term that can be applied to the more sophisticated end of the automation-aided workflow continuum consisting of Robotic Desktop Automation, Robotic Process Automation, Machine Learning, and Artificial Intelligence. Depending on the type of business, companies will often employ one or more types of automation to achieve improved efficiency and effectiveness. As you move along the spectrum from process-driven automation to more adaptable data-driven automation, there are additional costs in the form of training data, technical development, infrastructure, and specialized expertise.  But the potential benefits in terms of additional insights and financial impact can be greatly magnified. To remain competitive and efficient, businesses now must contemplate adding Machine Learning and Artificial Intelligence to traditional RP in order to achieve Intelligent Automation. Intelligent Automation Relies on Data Integrity In the Intelligent Automation framework, training data is a central component on which all else depends. In industries such as autonomous driving and healthcare, where decisions made by AI/ML can have serious repercussions, the accuracy of training data that informs these types of decisions is critical.  As the accuracy of modern AI and Machine Learning models utilizing neural networks and deep learning progresses toward 100%, these engines are working more autonomously than ever to make decisions without human intervention. Small variations or inaccuracies in training data can have dramatic and unintended effects.  Data integrity and accuracy thus become increasingly more important as humans come to rely on the decisions made by intelligent machines for complicated tasks. Accurate ML Models Require Accurate Training Data Data integrity involves starting with representative source data then accurately labeling this data prior to the training, testing, and deployment of machine learning models.  An iterative workflow of data preparation, feature engineering, modeling, and validation is the standard Data Science playbook. Any Data Scientist will explain that the availability of accurately labeled training data is perhaps the most important ingredient in their recipes.  Examples of “dirty” data include missing, biased, and outlier data or simply data sets that are not representative of the future data to be processed in production.  Feature engineering is also an important step in the machine learning process i.e. selecting the data features that are likely to be the most critical in informing the predictive accuracy of a given model. In a neural network, where parameters are stacked one on top of the other, correct identification of key features in each iteration is critical to the success of the model building exercise. Poor training data can cause incorrect features to be selected or weighted, thus leading to models that cannot be generalized to a broader population of production data. For instance, for a model that detects specific organs in MRI images, choosing representative training images from a particular MRI machine then accurately isolating the relevant boundaries of specific regions of interest for each organ will lead to better detection results than simply using photos of those organs from public sources.   Another example can be seen in accounts payable systems using optical character recognition (OCR) to programmatically extract relevant information from invoices. Key fields in each invoice such as “Address”, “Name” and “Total” must be accurately distinguished from the body of different types of invoices in order to create an effective and accurate model.   If these items are labeled incompletely or incorrectly, the accuracy of the resulting model will suffer. The Issue of Bias Current AI and machine learning models differ from human intelligence in part because they depend entirely on their initial training data and usually do not have an automatic and recursive mechanism to absorb and process new data for course correction i.e. continuous retraining. This means that poorly balanced data introduced during training may have the potential to cause unexpected bias over time and can produce unexpected (and sometimes offensive) results. When a significant amount of bias is introduced into a system, it becomes difficult to rely on decisions made by these systems. Good Data Annotation Leads to High-Quality Intelligent RPA Accurate training data is the foundation of most successful data science projects.  BasicAI provides high-quality data annotation services to businesses in many different industries, and this is a central theme heard in the majority of our client conversations. With accurate data annotation, machine learning models and AI models can make increasingly accurate decisions, and when combined with the fundamental processes of RPA businesses can achieve truly Intelligent Automation. To learn more, contact us: sales@basic.ai or visit our website: basic.ai

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Humans have sought interconnectivity with each other since early days, and throughout history there have been specific technological developments that have each spurred human interaction significantly forward. As early as 3500BC, one of the earliest technological developments was written language. Allowing people to interact with one another more decisively regardless of physical distance, written language offered early humans an efficient and accurate system with which to facilitate trade, cultural expression and person communication. The desire to connect even inspired the development of written languages in separate parts of the world simultaneously. As technology advanced, faster and more cohesive ways of communicating were developed. Offering people the ability to speak in real time across virtually any distance, the telephone provided new levels of advanced communication and allowed global industrialization to leap forward with the rapid sharing of ideas. The telephone also paved the way for the medium we use most today — the Internet. From its humble beginnings as a US military project, the Internet now connects numerous aspects of everyday human life, and its capabilities encompass the sharing of voice, text, images and video. It facilitates virtually instant interactions in the social sphere, business, entertainment and commerce. It is also, like its predecessors, setting the stage for the next great technological development in human interconnectivity — the virtual world. What is a virtual world, and how are they changing? Virtual worlds, by definition, are environments that first and foremost are computer-generated. A virtual world, secondly, must have a quality called persistence — meaning that unlike a game booted up from a disk, they exist regardless of whether or not a user is interacting with them. The multiplayer game “Maze Wars” was one of the very first virtual worlds built on the US military’s ARPANET. Evolving over time from environments as simple as chat rooms to interactive simulations like the Sims and Second Life, the virtual worlds of today’s massively multiplayer online role-playing games (MMORPGs) such as Eve Online and World of Warcraft are played by . over fifteen million people The tech that powers these virtual worlds is becoming increasingly advanced. Likewise, the interactive capabilities of today’s virtual worlds are becoming more and more sophisticated. Old technologies such as 2D and even 3D imaging are giving way to 3D virtual reality. Powerful graphics cards are rendering detailed images with minimal lag time. Combinations of wearable hardware and advanced software are making for immersive, realistic simulations that are increasingly indistinguishable from natural life — allowing for intuitive physical interactions rather than using clunky controllers and offering optical views that mirror real fields of vision. Meanwhile, tech companies are working rapidly to expand the capabilities of virtual reality technology further. Something called the “six degrees of freedom” — basically describing every direction in which a human can naturally move — is being increasingly integrated and refined through both hardware and software. All-direction treadmills, tactile tech like gloves and wireless hardware without the hindrance of clumsy cables are offering more freedom of movement. Software that can track eye movements, offer 360-degree views with peripheral vision and map increasingly large physical spaces is advancing. As the tech continues into the future, the augmentation of virtual reality with artificial intelligence may offer even more possibilities. AI, because of its vast computing capability, could cater to multiple users simultaneously. Through its ability to learn, it could develop highly realistic in-world elements, such as virtual characters based on human design. In the same way, AI could adjust a virtual environment automatically based on a user’s interactions or past preferences, providing the virtual world with truly dynamic and interactive spaces that respond organically, in real time, rather than a static predetermined design. Consider how different the world looks today from how it did twenty years ago. While these technologies are still developing, they are well on their way in creating the next iterations of the virtual world. Yet, while the virtual worlds of the past have existed for decades, virtual worlds of the present are wildly popular and those of the future infinitely exciting, all iterations of virtual worlds have failed to integrate real world value — until now. Crypto: The Missing Link As popular as they have been, the reality is that no one can prolongedly interact with the virtual world when they have to go to work. Money makes the world go ‘round — and until the advent of crypto, virtual assets did not hold real world value and real world value didn’t exist in digital form. The integration of cryptography and digital assets means that for the first time digital assets are immutable. Ownership rights are indisputable and no asset can be counterfeited. It’s this same immutability that allows digital assets to hold real world value, allowing them to be exchanged for real world items without question. This is the missing link between the virtual and physical worlds. The introduction of cryptoassets allows the integration of the two. The virtual worlds that many people have enjoyed for so long are no longer limited to pastimes or hobbies. Instead, the virtual world is as financially viable a place to work, trade, buy and sell as any other. Apple, for example, has a store in the virtual world Second Life. Customers can check out the newest iPhone, but not buy it. With the integration of crypto, they can. Every shopper has dealt with the online versus in-person conundrum: it’s easier to buy online, but more informative to buy in-store. The virtual shopping experience offers both. Offering expanded capabilities to many industries, virtual commerce is just one area that could be revolutionized by the integration of crypto. The products and services available for sale and purchase would only be limited by imagination, and businesses could reach millions more customers more efficiently. Likewise, jobs worked in the virtual world would earn real world money and assets acquired in the virtual world could be sold for real world cash or redeemed for real world products. While every previous iteration of the virtual world was missing this essential element, crypto provides for the monetization of the virtual world. What might the virtual world look like? The more time people spend in the virtual world, the more businesses will invest in reaching customers there. Ecommerce will become virtual commerce, advertisers will shift their efforts to the virtual world, and everything from socializing to gaming, entertainment and even work will move online. While many previous virtual worlds were limited in scope, such as gaming, the virtual worlds of today are increasingly integrating with other aspects. Decentraland, for example, is one instance of a virtual world in production today that currently encompasses over 35 different districts. Each with their own focus, some of the largest are geared towards entertainment, gaming and shopping, while others include areas for education, socializing, business and art, with a single entrance point called Genesis City. Esports is a growing arena that is already integrated with virtual reality, meanwhile companies are beginning to offer virtual experiences in travel and sell tickets to semi-virtual spectator sports. The ability to virtually combine two parties in different physical locations offers myriads of new possibilities in socializing, work, education and more. Additionally, the ability to create new experiences in a virtual world has applications not only in entertainment but in practical fields such as training. Emergency respondents and doctors are just a couple examples of professions that could greatly benefit from the ability to practice handling extreme situations without taking real world risk. Just as ecommerce has revolutionized shopping, esports have changed gaming and avatars are becoming the new face of social interaction, the physical world will very likely also be altered by the integration of the virtual one. Evolution The virtual world has a lot of appeal for a lot of reasons. Not only increasing efficiency and expanding opportunities, the virtual world is desirable for another reason that stems from a very basic human desire. Through its advanced capabilities, it allows people to more vividly experience their imagination. Since the Oculus Rift’s first debut in 2016, virtual reality users today total , and virtual reality is estimated to be . As the physical limitations of the natural world are reduced, people can change their appearance, simulate abilities such as flight or magic and build their homes and environments according to their preferences. They can connect with one another in person from across the globe and share with each other essentially without limitation. While humans have been inching closer for thousands of years, the virtual world promises to bring them closer than ever before. From the first written records shared in 3500BC, the virtual world is simply the next step forward in a long line that is the evolution of human interconnectivity. over 170 million worldwide close to a fifty billion dollar industry by 2025

Decentraland

Apple

aslan

What Will Virtual Worlds Look Like By The End of This Decade?

The has become one of the most popular analytical signals of . Conceptually, the IOM analysis provides a statistical distribution of aggregated positions of investors in a crypto-asset relative to the current price. That’s a fancy way to say, that IOM shows you different groups of investors realizing gains and losses in a crypto-asset. The analysis leverages unsupervised learning algorithms against a dataset of all the transactions ever recorded for a specific crypto-token in a given blockchain. The basic explanation of the IOM signal is described in the following short video: In-Out-Money(IOM) signal the IntoTheBlock platform The result of the IOM signal, it’s a very visual representation about the distribution of token holder positions across the price spectrum. I believe that part of the popularity of the IOM signal is that analysts immediately get an intuitive representation of the positions of investors in a crypto-asset. To put this in perspective, compare the following IOM analysis for Bitcoin and Ethereum and see if you don’t get an immediate idea of what asset you would rather invest on today. 😉 Despite the popularity of the IOM signal, I am more interested in many other revelations of the analysis that are not very obvious at first glance. Let’s review a few of my favorites. Objective Support-Resistance Levels The clusters closer to the price level act can be used as a quantifiable factor to estimate levels of support and resistance for a crypto-asset. For instance, the following IOM result for Ethereum shows that the cryptocurrency has a massive level of resistance around the $170 price mark with over 3 million addresses holding positions at those levels. Momentum The momentum factors is one of the key pillars of modern quantitative finance. Conceptually, momentum-drive thesis tend to look for factors that can indicate strong performance of the asset in the near future. Measuring the percentage of address in-the-money over different periods of time can be used as a momentum signals like shown in the following chart. IOM_Momentum_Factor= f({iom1, iom2, …, iomn}, {t1, t2, …, tn} Value It’s hard to come up with quantifiable and consistent measures of value for crypto-assets. However, understanding the percentage of “active addresses in-the-money“ compared to the current price. For instance, a crypto-asset that has a larger number of addresses in-the-money relative to the current price could be more valuable with other crypto-assets with a smaller ratio. IOM_Value_Factor= f(Active_InTheMoney_Addresses, Price) Risk Risk is typically associated with positions that can signal negative conditions in the market. Volatility is considered the cornerstone of risk factors but cryptocurrencies introduce their own flavors. Using the IOM analysis, we can model a risk factor using the number of out-of-the-money addresses relative to the current price. In that context, crypto assets with a larger percentage of out of the money address relative to the current price could be consider riskier. IOM_Risk_Factor= f(Active_OutOfTheMoney_Addresses, Price) Another risk factor could be the ratio between active in_the_money and out_of_the_money addresses. That factor should be linearly proportional to the level of risk in a crypto asset. IOM_Risk_Factor= f(Active_Out_Of_The_Money_ Addresses, Active_In_The_Money_Addresses) Behavioral Finally, the IOM analysis reveals fascinating insights about the behavior of individual investors in a crypto asset. Those insights can be extrapolated to infer behavioral economic signals about the investors in a given crypto assets. Let’s illustrate this using two of the best-known patterns of behavioral economics. Overconfidence In behavioral finance, overconfidence refers to the tendency of investors realizing gains to take more aggressive bets that are not exactly correlated to the state of the market. In the case of the IOM analysis, we can define an overconfidence factor as the number of addresses that are realizing gains and are actively trading. Loss Aversion In behavioral finance, loss aversion refers to people’s tendency to prefer avoiding losses to acquiring equivalent gains. We could estimate a similar factor using the IOM analysis and, specifically the number of out_of_the_money addresses. Essentially an address can be said to be averse to loss if is out_of_the_money and hasn’t traded for some period of time. These are some of the hidden gems we can uncover leveraging the IOM signals in the IntoTheBlock platform. Understanding the statistical distribution of the positions of individual investors opens the door to fascinating insights about crypto assets that are nearly impossible to replicate in other asset classes.

Ethereum

Things I Can Learn From Knowing Who is Making and Loosing Money in Crypto-Assets

Many industries are on the brink of the next technological revolution in record keeping. Ten years after Bitcoin made its splash, we’re seeing many inspired by some of the benefits promised by the technology outside of the money use case: Reduction in risk due to verifiable, immutable records Greater efficiency thanks to shared real-time data Simplification of processes enabled by transactions across trust boundaries However, many are still unsure whether these benefits can be achieved, in part due to the modest speed of adoption of these technologies over the first ten years. In this article I will lay out why we at Digital Asset believe that distributed ledgers and smart contracts are the next big step in the digitization of assets and processes. I will also offer an explanation of why adoption is taking so long to gain momentum, some of the common misconceptions around digitization of assets, and how we are addressing those obstacles within the ecosystem of my . DAML The Evolution of Record Keeping “Record Keeping“ may not have the fashionable ring of “Blockchain”, “Smart Contracts”, or “Tokenization”, but it’s what all of this technology is about. Records of contracts, laws, and ownership, as well as the processes to change these records are foundational to society and industry. Consequently, the technology used for record keeping is constantly evolving, with each new innovation trying to solve problems present in the previous. Physical ownership records, be it in the form of paper-based bearer instruments like promissory notes or cash, or centralized records like land registries, have been around for millenia. They solve the foundational problem of having an agreed and legally enforceable way to ascertain who owns what. Unfortunately these paper-based records and processes are slow, expensive, and error-prone. Even before the advent of computers, special machines were developed to aid with the task of keeping crucial records accurate . The advent of computers in the second half of the 20th century offered an enormous boost to the efficiency of keeping and updating records, leading to the broad digitalization of most asset classes including cash, stocks, bonds, treasury notes, and all manner of certificates. [1] Efficient distribution and communication followed soon after, first through point-to-point digital communication channels, and later the internet. The resulting increase in the efficiency of data processing and communication, as well as the scale of the distribution of data, has brought the original problem of being unable to unequivocally ascertain ownership of an asset at a given point in time back to the forefront. Each record is now copied manyfold across a loose network of computers and record changes trickle through the network in multiple steps. Complex and expensive reconciliation is needed to keep all the copies in sync, and errors are commonplace leading to even more expense on dispute resolution and exception handling. The computer revolution didn’t change the role of institutions in record keeping. Most records, be it bank accounts, insurance policies, health records, or land ownership, have a “master copy” controlled by a single, or small number of entities. These controlling parties are required as a matter of business, and often by law, to keep this data, which represents their clients’ legal title to those digital assets, accurate and safe. Properly implemented distributed ledgers address these problems by granting more parties access to the master copy and codifying the rights of the trusted entities into verifiable actions. This removes the need for reconciliation and reduces the total trust required in the controlling institutions. So why have we not not seen a rapid, wide-spread adoption of tokenization for a broad class of assets? I believe there are several major reasons for this, but also that all of them can be overcome with a shift in mindset and recent technological development. The time is ripe for a change. Tokenization misses the point Tokens are digital bearer instruments. A token should represent a redeemable claim to a security and derives its value from that claim. Holding the token is equivalent to owning a claim to the asset and ownership of the claim should be freely transferable. In these respects, tokens mimic cash: The said [Federal Reserve] notes shall be obligations of the United States [...] They shall be redeemed in lawful money on demand at the Treasury Department of the United States, in the city of Washington, District of Columbia, or at any Federal Reserve bank. Many have offered multisignature as a way to add a layer of legal control on top of a token (ourselves included!) and this approach does have some appeal as a final step in the process of moving and controlling money. However legal frameworks and processes involve far more coordination and authorization than the simple final exercising of private keys. This makes multisig a worthwhile step in some processes but it falls short of fully representing the legal requirements most businesses need to operate under. Specifically multisig may work for bearer assets in some cases but does not work for ownership records. In reality, most assets are not bearer instruments. Ownership records are kept and controlled by regulated entities recognized by law. Ownership kept on that regulated ledger is legally enforceable, and restrictions to ownership and transfers often apply. Assets derive their value from the legally enforceable set of rights they bestow upon their owner. Real estate is a good example of an asset and a bad use-case for tokens. Real estate is not a bearer instrument. If I lose the key to my house, I don’t lose ownership. Similarly, if I turned up to someone else’s property and demanded forfeiture while waving a paper land title, I would likely be arrested for forgery or theft. Real estate ownership is controlled by land registries and only the land registry’s ledger is legally enforceable. In many places, there are restrictions on who may own real estate and to what degree it is fungible and transferable. So if I lost the private key to my real estate token, I would similarly not lose ownership of my house. It seems inconceivable that a judge would evict a local homeowner in favor of a pseudo-anonymous, foreign token holder. It would be especially dubious if that token holder gained control through means outside the court’s jurisdiction (nefarious or otherwise). This begs the question - if your goal is to ‘maximally decentralize’ this type of asset, what protections did your token holder receive for the cost of implementation complexity? The purpose of buying a plot isn’t to get an entry in the land registry, nor does land ownership bestow full control of a cone from the centre of the earth into outer space. It gives the owner a very specific set of rights: the right to build a house, the right to keep strangers off the land, the right to split the land and sell a part on. The former two of which no token will ever do for you. Indeed, for many other types of “ownership”, like securities or media, the term is used to convey an idea of a set of rights, which are actually traded individually: lease-holds or mineral rights on land, or dividends or voting rights on securities. In some areas, this is so intricate that “ownership” has become a virtually meaningless term. Digital Rights Management is one such area and shows what “ownership” of digital assets is really all about: Rights. To reap the benefits of open or shared records, we can’t just record ownership as opaque Tokens. Instead, we must represent the rights that make up ownership in our digital systems, be they centralized or decentralized. This is where smart contracts come in. They aim to encode the specific rights that ownership of an asset grants to the owner, and encode the rules and processes around those rights. Rigid Ledgers The rules set forth by smart contracts do not replace law and regulation. Indeed, real world law, regulation, and contracts are often open to interpretation. Their practical meaning is determined by practice and legal test cases. Having actions take place with absolute inevitability and rules enforced by a disinterested network of nodes does not reflect the subtlety of the real world. Such rigidity also leads to enormous risk as unintended flaws in contracts are set in stone. This isn’t hypothetical risk, either. The famous DAO exploit was caused by a subtle bug in a smart contract and could only be fixed by a majority of all Ethereum users updating their nodes to agree to fork the blockchain and create an alternative reality. [2] Such rigidity is not desirable for most business use-cases. Instead, most smart contracts must be a reflection or encoding of the contracts, laws, and regulations governing the real world. Parties bound by a contract must be able to change, supersede, or tear it up by mutual agreement, just like they would with a real contract. The rules of a contract must be enforced by parties with a stake and interest in the contract, with clear avenues for dispute resolution. While those outside the contract should have no say in it. Poor Privacy Asset issuers and investors have distinct privacy requirements. Investors would like their holdings and transactions to be kept secret as far as possible. Issuers have to know who holds their assets and are responsible for keeping personal information safe. Most blockchains systems are entirely public and fully replicated between all participants. The main privacy safeguard on most chains is the use of pseudonymous addresses to create a relatively weak form of anonymization. For investors, pseudonymity is often an ineffective form of privacy and requires a great deal of care. Since all users can see all transactions, it is possible to infer a lot . The risk of confidentiality being broken and information about trades and transactions becoming public is unacceptable for many businesses. [3] For issuers, anonymity makes it impossible to fulfill their legal requirements on KYC, anti money laundering, or data protection. Indeed, it is not entirely clear whether such systems can comply with data-protection regulation like GDPR, which governs many types of records that could well be kept on distributed ledgers . [4] In other words, the (lack of) privacy afforded by most current platforms is inappropriate for all parties and asset types other than cryptocurrencies which make this tradeoff for the sake of other network properties. But even there, privacy is desired by many, yet hard to achieve. Blockchain does not replace Trust Like record keeping, trust around ownership has evolved greatly throughout history. Taking banking as an example, it all started very simply. The trust relationship was fully centralized, with a client having to trust their bank entirely. Banking clients were fully exposed to their bank burning down, being robbed, or embezzling assets. Such a single point of failure is easy to understand, but highly risky. Such risk is harmful to business and consumers alike, so today’s financial system has developed into a complex system of laws, regulations, and institutions that try to safeguard our assets. The perceived safety provided by the system is what helps our markets work smoothly and efficiently, benefiting society as a whole. However, they add complexity and opacity to a degree where many people no longer fully understand who they implicitly or explicitly trust. Some learnt this the hard way during the 2008/2009 financial crisis. Public blockchain’s innovation here is to simplify by going to the opposite end of the trust spectrum. Trust is now transferred to a democratic majority - the 51%. It’s beautifully effective for applications like cryptocurrencies, where all rights concern only the record itself. A cryptocurrency user can have a coin record in their name, and they can transfer it. Most rights are not like that. The right to build a house on a piece of land does not concern the record of that right. A real world party is needed to guarantee and enforce that right. Typically the guarantors of rights are the same institutions that are entrusted with the record keeping for those rights. Democratizing the record keeping for such records achieves little, which is why there are few truly decentralized applications other than cryptocurrencies. Smart contracts do not replace trust, they make it explicit and transparent. A good system encodes the trust assumptions between all participants of a contract in such a way that all parties understand their rights, obligations, and exposures. There must be a clear understanding of who is ultimately responsible for maintaining the link between the ledger and the real world. The risk of Lock-in Developing distributed applications is largely akin to developing mainframe applications several decades ago. There is little abstraction between the surface language and the system internals. Applications are built for one infrastructure and tied to it forever. Yet Gartner predicts that 90% of today’s platforms will need replacing by 2021 . How could any serious business invest significant money to replace a critical system in the knowledge that they will be locked into a technology and an infrastructure vendor that may be superseded in two years? [5] The ledgers we use need to catch up with traditional application development stacks, which offer clean abstractions between different layers. Only by making applications portable between ledger infrastructures can they be future-proofed and development de-risked to a degree where it becomes viable for wide-spread adoption. : Smart Contracts for Intelligent Risk-Taking DAML DAML is Digital Asset’s answer to all of these issues. It is a high-level smart contract language designed to bring unprecedented productivity to the development of distributed applications for enterprise and beyond. A Concise Language for Records and Rights DAML has a rich data definition language to describe records, as well as the rights parties have according to these records. For example, the below representation of ownership of a piece of land gives the owner the right to request sub-division of the plot. with
    owner : commune : plotId : signatory owner, commune

    controller owner can
      nonconsuming : with
          params : create with
            owner
            commune
            plotId
            params template LandRecord Party Party Text where RequestSubdivision ContractId SubdivisionRequest SubdivisionParameters do SubdivisionRequest Clear Data Control gives Flexibility The above contract is between a land owner and their commune. Should the contract be erroneous, those parties should be able to amend it. DAML has a clean concept of data control through signatories. Owner and commune can jointly make changes to an ownership record. with
    owner : commune : plotId : correctedPlotId : signatory owner, commune

    controller commune can : with
          oldRecordCid : oldRecord <- fetch oldRecordCid
          archive oldRecordCid
          assertMsg (oldRecord == with
              owner
              commune
              plotId )
          create oldRecord with
            plotId = correctedPlotId template LandRecordPlotIdCorrection Party Party Text Text where CorrectRecord ContractId LandRecord ContractId LandRecord do "Old Record does not match correction" LandRecord Using this add-on contract, the commune and land owner may decide to correct a record by mutual agreement, in a completely compositional fashion. “Upgrading” or “correcting” of contracts does not need to be planned for. A clear concept of data ownership makes it work out of the box. This is in stark contrast to public blockchains, which should contain permissionless smart contracts specifically designed to be minimally, and ideally not at all modifiable. Privacy, not anonymity The DAML Ledger model specifies precisely who has the right to see data and follows a small set of principles to derive visibility rules: Whenever a party sees an action it sees all consequences A party sees any action on contracts on which they have a stake A party sees nothing else, unless explicitly made an observer These rules maximize privacy while ensuring that any party ends up with a fully valid view of the ledger, and everyone is guaranteed to be informed of the state of contracts in which they hold a stake. The guide gives a powerful example of sub-transaction privacy using a simple trade model. In DAML it is possible to atomically swap assets guaranteed by two institutions without either institution learning anything more than that the asset they back has been transferred. DAML Quickstart Explicit Trust The right to trigger the land subdivision process shown above cannot be enforced on-ledger. The commune or higher authorities have to be trusted that they will honor the on-ledger agreement and perform their roles in the process to decide whether the request is accepted or rejected. DAML makes this very explicit. No party can ever become a signatory on a contract without their consent. Having the commune as a signatory on LandRecord means they may be an obligable party under the rights encoded in the contract, and have agreed to the terms. The signatories of a contract are those that have to be trusted to uphold the rights described and maintain the on- off-ledger link. Infrastructure Abstraction DAML and its API are to distributed applications as SQL and ODBC are to centralized ones. The clean interface between data and logic specification, the underlying storage infrastructure, and the client application consuming the data makes applications portable without re-engineering. This abstraction makes DAML easier to learn and faster to develop than other smart contract languages and tech stacks. The developer need not concern themselves with the detailed mechanics of the infrastructure they are planning to deploy to and system-level concerns are kept out of the language completely. By learning one language and one API, app developers become able to develop applications for local deployment, clouds, and distributed ledgers. It also de-risks and accelerates the development of distributed applications. Applications can be developed without worrying about which infrastructure is the right one to pick, both for current and future needs. They can be developed and prototyped on entirely different infrastructure than the production deployment. DAML supports a wide range of infrastructures, from local database-backed solutions, to cloud-hosted ledgers, and all the way to fully public blockchains true to the DAML Ledger Model. Header image credit: Randall Munroe. Edited. CC-BY 2.5. Original image . Originally published on https://blog.daml.com/

Stacks

Be Informed

Fetch

Ledgers

Working Towards a Sustainable Ecosystem Beyond Tokenization 

Image credit: Unsplash Nowadays, in the era of information overload, it might still be hard to find practical advice to use for building a business or a product. Right books and ebooks can become a great source of insights, real-life experience to review and analyze, a coach helping in avoiding typical mistakes. Here is the list of such books dedicated to online marketing that every industry professional or entrepreneur should read in 2020. 1. by Daniel Daines-Hutt Content Marketing For Traffic And Sales Daniel Daines-Hutt's "Content Marketing for Traffic and Sales" is perfect for any marketer who feels their content is not doing what it should. It's ideal for you if you feel your content is missing something, and you're not sure why? Everyone else tells you to "write more often," but you go crazy for all the work and small results. Daines-Hutt shows you what content will change things for you and then tells you how you can make the most of that content. 2. by Aleksandr Lashkov Theory and Practice. The Ultimate Guide To Online Content Marketing This book is meant to help you solve practical marketing tasks. Alex Lashkov runs his own content marketing agency in Miami, and has worked with dozens of start-up technology companies over the past ten years. A technology industry expert, he has a wealth of experience in helping technology companies talk to people by using a language that people can understand. This guide focuses on the practical issues that every content creator faces in their daily work. It is a training ground which gives you knowledge about the types of modern content and the tactics that can help drive outreach and engagement. All statements are supported by real-life case studies. 3. by Rich Brooks The Lead Machine The Small Business Guide to Digital Marketing Rich Brooks describes the critical aspects of modern digital marketing and shows you how you can expand them and grow your business. He talks about the different strategies you can use to get visitors to your website and how you can make sure they keep coming back. The complexity around some elements of digital marketing is simplified and readers are presented with a book that simplifies what they need to do. The book provides a step-by-step guide to growing your online business that is perfect for marketers and entrepreneurs in any niche. 4. by Nir Eyal Hooked: How to Build Habit-Forming Products Nir Eyal is an expert in the field of behavioral design, and the book is the result of his ten years of research. Eyal helps you understand not how to get people to buy your product, but to make  a habit of buying your product. Another significant fact about this book is that Product Hunt founder Ryan Hoover helped make the contents even more practical. The author outlines real methods that are used by modern businesses to draw attention to and build a direct connection with the product or service they’re selling. This book is a practical tool for driving your business' retention metrics. 5. By Sean Ellis and Morgan Brown Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success Hacking Growth is a relatively recent book and, more importantly, the ideas it contains are not repackaged from the good old things of Philip Kotler. Both authors have solid practical knowledge, especially since Ellis and Brown are pioneers of "growth hacking." This book will give you practical insights into the multiple distribution models available. With it, you will get a real guide on how to implement "growth hacking" within your company. 6. by Magnus Unemyr, 2018 Data-Driven Marketing with Artificial Intelligence: Harness the Power of Predictive Marketing and Machine AI for Marketing This is quite a unique work by Magnus Unemyr. First, it features insights on applying AI to marketing, which Unemyr gained by interviewing business and marketing execs at companies building top products. Second, it features perfectly structured content, as all products are divided into several categories. These categories include Competitive Intelligence, Predictive Pricing, Ads Strategy, E-commerce, Content Marketing, Lead and Customer Acquisition, Customer Relationships, Segmentation, and Customer Journey. These categories allow you to understand how and what tools can help you in solving particular tasks. 7. by Richard Shotton The Choice Factory: 25 Behavioural Biases That Influence What We Buy Behavioral science explains what really motivates people, rather than what they claim motivates them. The Choice Factory shows how findings from this strong field can easily be applied to marketing. Shotton highlights the complexities of target selection and discusses how marketers should do it. Even if you are not a marketer, this book provides fascinating insights into consumerism and human behavior. It focuses on everyday decisions and examines what drives us to make them and, therefore, what makes us tick. What great marketing books worthy of reading do you know?

Seven of the Best Online Marketing Books to Read in 2020

Personally I am not much of a fan of installing too many libraries in my applications with no good reason behind, especially those ones intended to perform minor common tasks in web development such as an AJAX loader or spinner. But possibly this is a good thing to do in terms of security. External dependencies may come in turn with additional packages, and so on, meaning the vulnerability surface of your application might probably degrade because of that habit. In fact, today's tip is to do with controlling complexity. The states in general terms that a subject should be provided with certain capabilities only if requiring them. principle of least privilege So, why would you a new package when it turns out that it is a piece of cake to just write your own AJAX loading component with React? npm install components/Loading.js loading from '../../images/loading.gif'; , { } from t'; {
  constructor(props) { (props);
  }

  render() { ( .props.loading) { (
        <div className= >
          <img src={loading} alt= />
        </div>
      );
    } .props.children;
  }
}

export ; import import React Component 'reac class Loading extends Component super if this return "text-center" "loading" return this default Loading Now, let's see how our React Loading component is used in a declarative way. Creating a new record, a restaurant for example, is quite a common use case for making an AJAX request. components/private/restaurant/Create.js ApiRestaurantActions from ; { Button, ButtonGroup, Form, FormGroup, Jumbotron } from ; { connect } from ; { FormGroups } from ; Loading from ; React from ; RestaurantState from ; Validation from ; initialState = Object.assign({}, RestaurantState.initial(), { response: [] }); { (props) { (props); .state = initialState; .handleChange = .handleChange.bind( ); .handleClickCancel = .handleClickCancel.bind( ); .handleSubmitForm = .handleSubmitForm.bind( );
  }

  handleChange = e => {
    let record = {... .state.record};
    record[e.target.id] = e.target.value; .setState({record});
  }

  handleClickCancel(e) { .setState({
      ...initialState,
      response: []
    });
    e.preventDefault();
  }

  handleSubmitForm(e) {
    e.preventDefault(); .props.create( .state.record)
      .then((res) => {
        switch (res.status) {
          case : .props.fetchAll(); .setState({
              ...initialState,
              response: []
            }); ;
          case : .setState({ response: res.errors }); ;
        }
      });
  }

  render() { (
      <Jumbotron className= >
        <Form className= onSubmit={ (e) => .handleSubmitForm(e) }>
          <FormGroups {... .state.record} handleChange={ .handleChange} />
          <FormGroup>
            <ButtonGroup>
              <Button color= >Add restaurant</Button>
              <Button color= onClick={ (e) => .handleClickCancel(e) }>Cancel</Button>
            </ButtonGroup>
          </FormGroup>
        </Form>
        <Loading loading={ .props.loading}>
          <Validation messages={ .state.response} />
        </Loading>
      </Jumbotron>
    );
  }
} mapStateToProps = state => { state.ApiRestaurantCreateReducer;
}; mapDispatchToProps = dispatch => { {
    create: (record) => dispatch(ApiRestaurantActions.create(record)),
    fetchAll: () => dispatch(ApiRestaurantActions.fetchAll())
  };
};

export connect(mapStateToProps, mapDispatchToProps)(RestaurantCreate); import '../../../actions/api/RestaurantActions' import 'reactstrap' import 'react-redux' import './FormGroups' import '../../Loading' import 'react' import '../../../states/RestaurantState' import '../../Validation' const . class RestaurantCreate extends React Component constructor super this this this this this this this this this this this this this this this 201 this this break 422 this break return "mt-3" "form" this this this "primary" "secondary" this this this const return const return default This is a particular implementation with Redux dealing with validation and error handling, but you get the point: <Loading loading={this }>
  <Validation messages={this } />
</Loading> .props .loading .state .response An AJAX loader GIF is displayed while waiting for the server's validation results. . AJAX request made when creating a record Figure 1 By the way, the validation component is also a custom one aimed to be used declaratively. , { } from t'; {
  render() { (
      <ul className= >
        { .props.messages.map(function(item, index) { (<li key={index}>{item}</li>)
          })
        }
      </ul>
    );
  }
}

export ; import React Component 'reac class Validation extends Component return "text-danger" this return default Validation For further details on this implementation please visit which is a real-worldish React app where I am currently playing around with Redux and Thunk. programarivm/warthog I hope you liked this idea on writing your own declarative AJAX loading component! Now I'd love to hear from you. What is your opinion about implementing least privilege concepts when writing apps? Please leave a comment below and let me know. Thank you so much for reading.

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A Custom, Declarative AJAX Loader With React

Yes, it looks like the BAT-Train will soon leave the station. For months, I have been , how it is so good, how . preaching how BAT is the number 1 coin much of a monster it is There are 8 reasons for that. 1. Overall, Brave is now at on Android and with iOS and Desktop and we are at least at 60–80 million downloads. This is up 50% from March when they were at and 100% up from January, when they were at 15M downloads on Android and a total of . 2. BAT is the cryptocurrency with the most real-world traction of an estimated for their Brave Browser, very impressive. 3. Their founder was the man who invented Firefox and Javascript. Need any more credibility? 4. Brave users can earn money by just seeing ads from 300,000 publishers including and 200,000 Youtube Publishers including more than 100 with 5. The big thing about this is that users can , simply by browsing and seeing ads. 6. In August BAT did 2 million downloads just on Android. This is absolutely insane: . 7. BAT was the only token listed on last week besides 0x and BNB. That is a lot of legitimacy. 8. As for the business side, we can see even stronger growth. BAT has grown their publishers and BAT holders and (that’s actually 20x now.) 30 million downloads 20 million downloads on Android 5.5 million monthly active users 7 million monthly active users(!) Wikipedia, Hamster, Vimeo, TheGuardian, Vice, Washington Post more than 1 million subscribers. earn $70 and possibly earn $320 per year SOMETHING HUGE IS HAPPENING!!! 1 Million Downloads in 13 days on Android + 9 Points Just In Usage Ranking! Surpassed UC Browser On Both Usage Rank & Store Rank Binance.US by 5x over the last 6 months 15x throughout the last year. Conclusion The market can only hold powerful fundamentals back for so long. Now, the market has become just a little bit favorable and BAT directly breaks out. It will be interesting to see what happens with BAT once the alt season comes at the coins at full force.

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Eight Reasons I Think the BAT Cryptocurrency is About to Soar

by Anne Szustek Talbot, VP of Content, BX3 So you’ve decided to start a business. Good for you. Now here comes the time for reckoning: For it to be a successful enterprise, your company needs customers, if not also funding. One way to help make that happen: Getting the word out about your company. Thought leadership is a chance to show how you, the founder — and by extension, your company and team — are smart. We've outlined how your startup can leverage the same (our trademark thought leadership acronym) as long-established consulting firms. strategies for thought leadership SUCCESS There’s another element to thought leadership beyond telling a great story however. If you or your company’s writing is to accomplish what’s written on the tin; that is, lead people in thoughts, it’s going to have to reach someone. Hosting thought leadership content on your own site is one way to go. Yet that brings us back to square one: If no one knows about your company, no one is going to go to your company’s website. The traditional way to get eyeballs for your thought leadership content is to take the , with the hopes of it landing in a publication with a high readership and/or whose audience stands to have a chance of being interested in the topic. Whatever the scenario might be, the following will hold: Insightful, snappy content is useful. That’s not enough. It also needs to be a topic relevant to the crowd and attention you want to draw. It also needs to be written in a way that the reader can take seriously. public relations route of pitching bylines to third-party publications Image by from free stock photos from www.picjumbo.com Pixabay At present, I’m a corporate content and communications professional who’s written plenty of thought leadership content. I got here by way of being a journalist and editor — one specifically tasked with, including a host of other duties, vetting and giving the green light on submitted thought leadership content and bylines. Do you want your thought leadership content to make it past the cutting room floor? Great. Glad we have that in common. Read on for my crash course on how not just to make your thought leadership content sing, but to put a smile on the face of a potential commissioning editor. Know what you’ll say and how your target audience will receive it As any writer knows, the first thing you’ll need to know is what you’re writing. Jot down a potential list of topics (or bandy some ideas about with your co-workers, including your communications team, if you have one) that invoke passion in you and/or align with your brand. The next step: think about your target audience. Do you want to reap customers of a certain demographic? Read up on or conduct some research on which messages resonate with that cohort. Case in point: a Gen Z thought leader about his generation recently was quoted as saying in Business Insider that . Gen Z responds best to clear statistics Second question: Where does your target audience hang out online? If you’re trying to reach investors, a publication with an asset management bent such as or could be a better bet than trying to go for a broad-ranging daily newspaper. Looking to establish your startup as an innovator in workplace culture? Aim for publications that seek to highlight leaders in this area such as or . A female thought leader looking to reach women aspiring to do the same might aim for legacy publications such as or established new media entities like or . Keep in mind that the best venue to tell your story might not even be a publication in the traditional sense. Your own or your company’s such as or might make the most sense to publish your thought leadership, as you’ll retain full control of the messaging. Institutional Investor Pensions & Investments Entrepreneur Fast Company Glamour Girlboss Refinery29 social channels on third-party platforms LinkedIn Medium How to keep the message on point Once you’ve identified the publications you feel are best suited for your message, take some time to read recent op-eds that have run. You’ll get a feel for the tone their editors seek and what topics and content pass muster. Not that you (or your content writer) should try to ape another person’s writing style quip for quip. Nonetheless, trying to incorporate some of the argument structure, formatting, and length will help buffer your piece against heavy editing — and hence, retain control of the message. What publications are looking for in thought leadership varies as much as the publications themselves. Some might be looking for a contrarian take on a well-hewed topic. Remember: controversy gets clicks. Others might want a first-person account of how you handled a problem. This recent thought leadership piece in by Debbie Sterling, founder of girls’ STEM-focused toy company , outlined how she, and turned her colleagues’ demands into projects that benefit the team. Fast Company GoldieBlox as a Millennial herself, faced the challenges of managing a similarly aged workforce Nonetheless, there are some key commonalities to keep in mind when you want commissioning editors to keep away their proverbial red pens — or keep them from rejecting your piece outright: Stay away from promoting your company’s products or services or why that sector is ripe for innovation. Save that for native advertising. The way thought leadership markets you and your company is by showing how you and your company are forward-thinking, not by saying “why you should buy my stuff.” Promotion: Nothing turns off an editor more than tired corporate-speak such as “we sell solutions” or its even more dreaded offspring, “solutionize.” Remember that the existence of a solution presupposes the existence of a problem. Once your editor’s eyes catch such neologisms, your corporate-speak will become said problem. Jargon: (Not sure if “team player,” “ideate,” or “boil the ocean” fall into the jargon category? Pro tip: if they’re on , then the answer is yes.) ’ Jargon Madness brackets Forbes : Remember that journalism is about identifying the key players and why it happened. How do journalists bring readers into a story? They write a thought-provoking , followed by a that identifies the issues at hand and builds tension. Then work through the rest of the story. If you’re feeling up to it, write a that reflects the rest of the piece and sends off the reader on a pithy, memorable note. Storytelling lede nut graf kicker A thought leadership piece, by its very definition, should inspire new ways of approaching a topic or challenge. Chances are, if the piece does a solid job of that, the reader will want to share the story, either in conversation or via social media. A good editor will be able to identify the stories that have the best chance of doing just that. The best thought leaders (and their editors) know not to submit pieces that will fall short of that standard. If out and in doubt, find the human side of the story you’d like to tell. It’s part of human nature to want to read about other people. (For what it’s worth, financial journalists are told to scrounge up any sort of human aspect to the stories they write, lest they ink a 1,000-word piece of little more than portfolio return statistics.) Will resonate with the reader: Thought leadership is about presenting your best self. Would you go into a presentation with wrinkled clothes or a stained tie? Then avoid blemishes on your work that will distract from the key message (or will get your piece auto-rejected). “Its” and “it’s” are two words with distinct meanings, as are “your” and “you’re.” “That” and “which” are not interchangeable. Iron out any grammatical slips before sending. Grammar: You launched your business to serve the market and your customers, whether they are businesses or consumers. That product or service likely aims to solve a problem and answers a question the consumer might not have even been aware that they had. Think of your thought leadership the same way: Let it answer a question and do a service for the reader. Let your target audience come away with a new impression and paradigm it didn’t know they needed. Your readers might just go and tell someone else about your thoughts and your company. That just might be the best way to solutionize your marketing there is. About the author: Anne Szustek Talbot is a longtime content specialist, with nearly a decade and a half of experience covering news in and communicating on behalf of firms working in the asset management, litigation, financial technology, and tax sectors; as well as broader topics such as emerging markets, foreign exchange, and macroeconomics. Her most recent journalism position was as the online editor at Institutional Investor, where, among other responsibilities, she helmed the publication’s op-ed section and third-party thought leadership content. On the public relations side, she has earned coverage in an array of top-tier outlets including Cheddar, CNBC, Forbes, and The New York Times. Previous roles have taken Anne across borders and industries; her earliest roles were working for the senator from her home state, handling communications work, and as an intern diplomat for the State Department at the US Embassy in Tashkent, Uzbekistan and at the US Consulate General in Istanbul, Turkey, where she would return to launch her media career. Anne leans on her varied background to reach across industries and develop the most effective messaging and publicity campaigns on behalf of BX3 and its clients.

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Don't Sell Solutions: How Your Scrappy Startup Can Tell Stories That Resonate

IMHO, definitely. Here's how I see it: 2012 and 2013 were the bull market. 2014 and 2015 were the bear market. 2016 and 2017 were the bull market. 2018 and 2019 were the bear market. 2020 and 2021 will be the bull market. 2022 and 2023 will be the bear market. 2024 and 2025 will be the bull market. It’s always the same story that oscillates around the Halving in a 4 years cycle when the Bitcoin reward halves and massively increases scarcity of Bitcoin. Adding to that, there are so many Bitcoins lost, which isn’t even priced in the current price of Bitcoin. Just last week, were seized by authorities. Then, we still have another that were lost. another $722M worth of Bitcoin 4 million Bitcoins = $28B There are not many Bitcoins left at all for people or investors to buy. The Halving will amplify that effect even more. 2020 is coming in hot.

Will 2020 be the Year of the Whale for Bitcoin?

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Every year Cryptocurrency Market keeps attracting the attention and trust of masses. Mostly, people divide into two camps: those who trust in crypto, and skeptics, who keep saying that “sweet 2017” will newer happen to cryptocurrencies again. Firstly, let’s summarize what the past 2019 year led to. The Six Main Crypto-Market Changes During 2019: 1. s - the first, and the most important step. 2. 3. 4. T . During the last year total Cryptocurrency Market Cap grew by 54%, namely $126 - $194 bln. 5. 6. - Initial Exchange Offering (IEO) as a new and better alternative. Cryptocurrency Regulation Market consolidation The partial hookup of Crypto and Fintech he total capitalization of all crypto assets has grown Bitcoin capitalization has doubled ICOs are almost dead : The year will start with 5AMLD - Starting from January 10, 2020, the Fifth Anti Money Laundering Directive (5AMLD) takes effect. I have already outlined a more detailed overview of this regulation in my previous . Check it for more information;). - this year predicts the creation of unique products related to the gaming industry and others. Game developers and enthusiasts are increasingly exploring what blockchain can do for their gaming systems and how they can incorporate cryptographic assets into. According to Paul Veradittakit’s prediction for gaming will likely boom in 2020 because of important developments in high-performance tools that allow games to run on previously-rate-limiting blockchain technologies, better architected smart contracts, second-layer solutions, and abstracted infrastructure/digital asset storage that makes it easy for game developers to build digital assets into the gameplay and character experience. Hopefully, we’ll see something mainstream on a platform like Steam or Twitch that really puts the power of blockchain in the context of the average gamer. Despite the fact of crypto being popular for quite a long time, there is still a problem of “how to start” for newbies because most people find Trading hard and complicated process. Since Crypto and Fintech Markets become almost united, this will certainly lead to a jump in new users from the classical financial market. - : many platforms do now provide full supportive instructions of how to register, create the personal wallet, Deposit and even make first operations - Thank God now Crypto won’t seem so mysterious any more! - : due to colossal competition in the market, developers are forced to create unique conditions in order to interest new customers and keep the old ones. - best developers provide users with information about the last market changes. - T different traders use different options depending on the outcome they are aiming at. The details of the order types are outlined in my article. - both new and already existing projects understand that each trader has different opportunities and, unfortunately, not everyone can trade at the pro-trader level, but all users would like to receive the corresponding profit, of course. So, why not to share your money with PRO (Social Trading in other words)? Let’s find out what this term means? According to is a form of investing that allows investors to observe the trading behavior of their peers and expert traders and to follow their investment strategies using or . - probably the most known platform for crypto trading with the help of pro-users. Using these service traders can track and invest in variety of fin.instruments; - the new and promising fast-developing project which aims to be the first cryptocurrency exchange platform that has its own social trading module. The project will release its early version soon; - cryptocurrency exchange promises it’s users access to Сopy Trading this year, but only in case of paying with the exchanges native token; - is also the service, which gives the opportunity to copy some leading cryptocurrency traders and earn success fees; and more. What to expect from 2020? Regulations will continue article The crypto industry will gradually become “wider” and even more specialized Coindesk The trend of “crypto for new users” have already started forming, and will definitely continue developing this year. Best practices for new users: Knowledge Useful unique features Analytics and etc.: he ability to place different types of orders: "The Most Useful Instruments on The Crypto Market" Repeat practices of successful traders: What about me, I am a huge fan of Social and Copy Trading. Why? It has a mass of pros and just little cons, probably I will do an analytical study and make an additional article regarding this option soon because it is worth to study this question better. Wikipedia Social trading Copy Trading Mirror Trading Think you were born a professional trader or investor? If you don’t why to refuse any help? Here are some platforms where you can work with professional cryptocurrency traders: eToro Binaryx EXMO Covesting In 2019 . Doing this developer expand Deposit and Withdrawal Options for their coins. For example, Tether (USDT) - one of the most popular stablecoins support both ERC20 and TRC20 standards. - for most platforms verification will become mandatory due to regulations. - Bitcoin will continue showing recovery signs. Everybody is waiting for . Historically first two halvings had an extremely positive effect on Bitcoin’s price and most BTC-holders hope it would repeat. But, unfortunately, any past events can not guarantee future positive results. many popular coins started the tradition of supporting several protocols Identity verification Proof of Life Bitcoin Halving Still not in crypto? Wonder how to choose a safe and convenient crypto platform? Here is a small instruction, I always insist on six important steps before creating an account: — reputation is always the major point, which defines the brand in general. Reputation — is the second and very important way to identify the right platform, as it’s important to know your money is safe. Security — some exchanges have fees for every transaction, some of them are not. Choose the platform according to the kind of trader you are. Fees — every platform has different methods, make your own analysis and find out what fits you more. Payment Methods — are also important. Pay attention to this part before completing the registration. Verification Requirements and Geographical Restrictions — the best services offer 24/7 help. Support

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What Crypto Novelties Should We Expect in 2020?

So I’m on zero sleep. I decided last night at 3am that it wasn’t worth waiting in bed, hoping to fall asleep anymore, having to wake up at 8am anyway. Being incredibly tired, beyond tired, loopy even, I think, “what to do?” I don’t have much to do to pack before my flight tomorrow, and I’m useless for anything productive...maybe a little ? Factorio I’m seeing zigzagging conveyor belt movement patterns when I close my eyes...I’m trying to use WASD to navigate web pages...my developer-brain, my mostly-left-brained, analytical, logical processes are being eaten alive by this...game? This can’t be right. And yet it is. I found out about it from a software dev on Twitter, who learned about it from another dev, who learned about it from I-don’t-know-who but I bet it was another software developer. Devs seem to be going apeshit over Factorio, and after a delirious, sleep-deprived binge I’m starting to see why. Factorio, for the uninitiated, is ostensibly about building factories but in reality is a metaphor for computer programming (it even has fucking programmable circuits, but we’ll get back to that). Factorio is all about the choices you make in using resources to their limit (or beyond). It’s about refactoring your factory, moving shit around so you can squeeze another 10% out of your iron supply line. It’s about debugging supply line issues to figure out why you’re not building any green science thingies (those are important). It’s about systems design. The choices you make in the beginning are easy. You have free reign to set up a couple coal-powered miners, run supplies back and forth from the iron field to the copper field and back to the coal field (if you don’t have those resources nearby, use the new game map preview, Luke!). You can criss-cross your precious resource tiles with power lines and conveyor belts, creating a completely arbitrary design to solve the problem of getting your early-game resources quickly. To build a factory that truly scales, though, you need to generalize. You need to think ahead, refactor your early designs (i.e. rebuild -- while you still can). You need to act deliberately. You need to fix root causes, not band-aid problems by redirecting belts willy-nilly (the equivalent of writing code to correct the result of a bug, instead of fixing the bug -- guilty ✋). Performance is critical in Factorio. Throughput is everything. As you produce resources, you have to ensure that you’re building not too little, not too much, and it gives you the graphs and numbers to be able to do your homework. I’m getting ready to dig some graph paper out of my old D&D materials and start planning my next factory. As I wind down in my sleep deprivation, and approach...fuck, 6:30PM? My brains have turned to mush. Might as well...work on my factory? I may have a problem.

HackerNoon Tech Stories Archive January 14th, 2020

Marketing 401 for Engineers: Stages of Going to Market

@aneel
Aneel

So You're Planning To Pitch Your Startup W/O a Growth Strategy This Year?

@wrgoto
Will Goto

Do Your Website's 'Terms of Use' Mean Anything?

@alejandro-uria
Alejandro Uria

Data Set and Data Augmentation for Face Detection and Recognition

@Venky
Venkatesh Wadawadagi

How Can CPAs Stay Ahead in the Automation Era?

@sharad_acharya
Sharad

Robotic Process Automation + Machine Learning = Intelligent Automation

@BasicAI
Tyler Schulze

What Will Virtual Worlds Look Like By The End of This Decade?

@megan-caitlin-aslan
aslan

Things I Can Learn From Knowing Who is Making and Loosing Money in Crypto-Assets

@jrodthoughts
Jesus Rodriguez

Working Towards a Sustainable Ecosystem Beyond Tokenization

@anthony-lusardi-da
Anthony Lusardi

Seven of the Best Online Marketing Books to Read in 2020

@MelvinTalk
Danny Wesley

A Custom, Declarative AJAX Loader With React

@Bassaganas
Jordi Bassaganas

Eight Reasons I Think the BAT Cryptocurrency is About to Soar

@mariusk
Marius Kramer

Don't Sell Solutions: How Your Scrappy Startup Can Tell Stories That Resonate

@anne
BX3

Will 2020 be the Year of the Whale for Bitcoin?

@mariusk
Marius Kramer

Entendiendo PyTorch: las bases de las bases para hacer inteligencia artificial

@espejelomar
Omar U. Espejel

What Crypto Novelties Should We Expect in 2020?

@lera.valera
Valeriya

This is Your Brain on Factorio

@austin
Austin Pocus

HackerNoon Tech Stories Archive January 14th, 2020

@aneelAneel

@wrgotoWill Goto

@alejandro-uriaAlejandro Uria

@VenkyVenkatesh Wadawadagi

@sharad_acharyaSharad

@BasicAITyler Schulze

@megan-caitlin-aslanaslan

@jrodthoughtsJesus Rodriguez

@anthony-lusardi-daAnthony Lusardi

@MelvinTalk Danny Wesley

@BassaganasJordi Bassaganas

@mariuskMarius Kramer

@anneBX3

@mariuskMarius Kramer

@espejelomarOmar U. Espejel

@lera.valeraValeriya

@austinAustin Pocus

@aneel
Aneel

@wrgoto
Will Goto

@alejandro-uria
Alejandro Uria

@Venky
Venkatesh Wadawadagi

@sharad_acharya
Sharad

@BasicAI
Tyler Schulze

@megan-caitlin-aslan
aslan

@jrodthoughts
Jesus Rodriguez

@anthony-lusardi-da
Anthony Lusardi

@MelvinTalk
Danny Wesley

@Bassaganas
Jordi Bassaganas

@mariusk
Marius Kramer

@anne
BX3

@mariusk
Marius Kramer

@espejelomar
Omar U. Espejel

@lera.valera
Valeriya

@austin
Austin Pocus