This paper is under CC 4.0 license. available on arxiv Authors: (1) Jingjing Wang, School of Computing; (2) Joshua Luo, The Westminster Schools; (3) Grace Yang, South Windsor High School; (4) Allen Hong, D.W. Daniel High School; (5) Feng Luo, School of Computing. Table of Links Abstract & Introduction Related Work Methodology Experiment Results Discussion References Abstract This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method’s properties using unique network data from the universe of Facebook’s users and existing network data from a field experiment. : Experimental Design, Spillover Effects, Causal Inference, Cluster Designs. JEL Codes: C10, C14, C31, C54 Keywords 1 Introduction Consider a (large) population of n individuals connected under a single observed network. Researchers are interested in conducting an experiment to estimate the global average treatment effect, i.e., the difference between the average effect of treating all versus none of the individuals in the population. Treating an individual may generate spillovers to her friends in the network. To capture such effects, researchers conduct a cluster experiment. Individuals are first partitioned into clusters. Within a cluster, either all units are assigned to the treatment or all units are assigned to the control group. Finally, researchers estimate treatment effects by taking a difference between the average outcomes of treated and control units (possibly adjusting for baseline covariates). The cluster design does not require modeling the dependence of individual outcomes on neighbors’ assignments, but it requires a choice of clusters and some assumptions on the extend of the spillovers along the network. For example, cluster experiments on online platforms require choosing a partition of the social network, and field experiments require choosing the unit of randomization, such as villages or regions. This raises the question of how many and which clusters to use in experiments. Typical approaches in economic research assume prior knowledge of many independent clusters. There are many settings where this information is not available, and instead units in the population have different degrees of connections.[1] This paper provides an econometric framework to choose when and how to design the clusters in cluster experiments. Different from existing clustering algorithms geared towards community detection, we motivate the choice of the clusters based on the task of estimating global treatment effects. The choice of clustering must balance two competing objectives: the larger the clusters (and the smaller the number of clusters), the smaller the bias of the estimated global effect, but the larger its variance. We introduce an algorithmic procedure – entitled Causal Clustering – to choose the clustering that minimizes a weighted combination of the worst-case bias and variance as a function of the network and clusters. The worst-case approach encodes uncertainty over the dependence of individual outcomes on neighbors’ assignments. We study (i) whether to run a cluster-level instead of individual-level randomization; (ii) how to cluster individuals (and how many clusters to use). We focus on a class of models where spillover effects are small relative to the outcomes’ variance but possibly non-negligible for inference. This is formalized in a novel framework of local asymptotics where individual outcomes depend arbitrarily on neighbors’ treatments, and, as n grows, spillovers from neighbors (and possibly also direct effects) converge to zero, but at an arbitrary slow rate (e.g., slower than n −1/2 ). This framework encodes the researchers’ uncertainty on the presence (and magnitude) of spillover effects by modeling first-order neighbors’ effect local to zero, with the convergence rate capturing the expected magnitude of spillovers.[2] The local asymptotic framework we study is consistent with settings with small (but non-negligible) treatment and spillover effects, typical, for instance, in online experiments [e.g., Karrer et al., 2021]. We characterize optimal clustering as a function of the expected magnitude of the largest spillover effects that the experiment can generate. The largest size of the spillover effects is a key input in our algorithms, whose characterization, in practice, is necessary for the design of the experiment but can be challenging. This parameter can be informed by previous experiments, in the same spirit of minimum detectable effects used in power analysis [e.g. Baird et al., 2018], or using some particular modeling assumptions. We provide guidance to practitioners in Section 6. Our analysis proceeds as follows. First, we provide a formal characterization of the worstcase bias and variance. We show that the worst-case bias is closely related to a particular notion of between-clusters connectedness, defined as the per-individual average number of friends in other clusters. The worst-case variance can potentially be an arbitrary function of within-clusters covariances and between-clusters covariances: individuals in the same cluster have identical assignments and in different clusters may share common neighbors. We show that the variance only depends on the average squared cluster size, up to an asymptotically negligible error. This result formalizes the intuition that a larger number of clusters, with a small variance in cluster size, decreases the variance of the estimator. We draw the implications of these results for choosing between a cluster experiment (for a given clustering) or assigning treatments independently between individuals (i.e., Bernoulli design or completely randomized design). Suppose the magnitude of the spillover effects is smaller than the square root of the number of clusters. In that case, the variance component dominates the bias, and a Bernoulli design is preferred (where a Bernoulli design is a special case of a cluster design with clusters containing a single unit). Vice-versa, a cluster design is preferred if the bias dominates the variance. Intuitively, because our objective trades off the bias and variance of the estimator, whenever the number of clusters is small, it is best to run a Bernoulli design for any value of spillover effects local to zero. On the other hand, if the number of clusters is sufficiently large, and the cluster design appropriately controls the bias of the estimator, a cluster design is preferred. We provide practitioners with a simple decision rule between cluster and Bernoulli designs that only depends on the number of clusters and the expected spillover effects’ magnitude. We then turn to the design of the optimal clustering, where the choice is not whether to run a cluster or Bernoulli design, but rather which clustering to use. The choice of optimal clustering reduces to a novel penalized minimum cut optimization problem, with a penalty that depends on the variation in clusters’ size. The program admits a convenient formulation as a sequence of trace-optimization problems, each solved via off-the-shelf semidefinite programming algorithms. We provide an empirical application using unique network data from the universe of Facebook users. We show that our procedure provides an explicit ranking between different clustering algorithms implemented at scale at Facebook. We also illustrate trade-offs in the choice of the clustering algorithm and properties of the graph. A second application using network data from Cai et al. [2015] illustrates the method’s advantages for choosing clusters in field experiments. We show that the choice of the clusters based on village identity is sub-optimal in this application, because the number of village clusters is too small relative to the optimal clustering. We present an alternative choice of clusters. This paper connects to the literature on experimental design, causal inference with spillover effects, and clustering. Existing methods for experimental designs with spillover effects include cluster and saturation designs, studied in Baird et al. [2018], Basse and Feller [2016], Karrer et al. [2021], Pouget-Abadie [2018], Taylor and Eckles [2018], Viviano [2020b], among others. These papers provide an analysis of the properties of particular designs for a given clustering or clustering algorithm. Different from the current paper, these references either do not study the question of the optimal clustering algorithm for experimental design, or only provide heuristic comparisons of different clustering methods. A particular class of clustering algorithms is ε-net clustering algorithms – which consist of assigning sequentially individuals in the same neighborhood to the same cluster [Eckles et al., 2017, Ugander et al., 2013]. Variants of these algorithms have been recently studied in Leung [2022] for spatial spillovers and Faridani and Niehaus [2022] in more general nonEuclidean spaces. These papers provide an optimal rate of the clusters’ size as a function of how interference decays in space for approximately unbiased estimators. Here, instead, we provide an explicit trade-off and novel characterization of the bias and variance, leveraging local asymptotics. It allows us to characterize the optimal clustering as the solution of a trace-optimization program (different from ε-net clustering). The comparison of a cluster and Bernoulli designs through local asymptotics is also a novel contribution. Additional references of experiments with networks in the absence of cluster experiments are Basse and Airoldi [2018b], and Jagadeesan et al. [2020] for estimating direct instead of global average treatment effects studied here; Kang and Imbens [2016] who study encouragement designs, without focusing on the problem of mean-squared error optimal designs; Viviano [2020a] for experiments on networks using information from a pilot study; Basse and Airoldi [2018a] discuss limitations of design-based causal inference under interference. None of these paper study (optimal) cluster designs. The literature on treatment effects under network interference includes Aronow and Samii [2017], Hudgens and Halloran [2008], Manski [2013], Leung [2020], Athey et al. [2018], Goldsmith-Pinkham and Imbens [2013], S¨avje et al. [2021], Ogburn et al. [2017], Manresa [2013], Li and Wager [2022], Leung [2022], among others. The assumption that an individual depends on first-order degree connections is most closely related to Leung [2020].3 None of the above references study experimental designs. Finally, we relate more broadly to the literature on clustering in economics – see Wooldridge [2003], Leung [2023], Abadie et al. [2017] and references therein – with the difference that here we focus on optimal clustering, rather than taking the clusters as given – and the literature on graph clustering, including Von Luxburg [2007], Newman [2013a,b], Lei [2019], Lei et al. [2020], Li et al. [2022], and references therein. Different from this last set of papers on graph clustering, this paper focuses on treatment effect estimation, instead of community detection. The remainder of the paper is organized as follows: Section 2 presents the setup; Section
3 characterizes the bias and variance and contrast cluster and Bernoulli designs; Section
4 formalizes the optimization problem for the optimal clustering; Section 5 presents two
applications and numerical studies and Section 6 contains recommendations for practice. [1] For example, when using villages as clusters, individuals may interact in the same and nearby villages. See Egger et al. [2022] for an example in cash-transfer programs. [2] The assumption of spillovers within first-order neighbors can be relaxed here by assuming that higher order spillovers are an order of magnitude smaller than first-order spillovers.

Is GPT Powerful Enough to Analyze the Emotions of Memes?: Abstract & Intro

Authors: (1) Davide Viviano, Department of Economics, Harvard University; (2) Lihua Lei, Graduate School of Business, Stanford University; (3) Guido Imbens, Graduate School of Business and Department of Economics, Stanford University; (4) Brian Karrer, FAIR, Meta; (5) Okke Schrijvers, Meta Central Applied Science; (6) Liang Shi, Meta Central Applied Science. Table of Links Abstract & Introduction Setup (When) should you cluster? Choosing the cluster design Empirical illustration and numerical studies Recommendations for practice References A) Notation B) Endogenous peer effects C) Proofs Abstract This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method’s properties using unique network data from the universe of Facebook’s users and existing network data from a field experiment. Experimental Design, Spillover Effects, Causal Inference, Cluster Designs. JEL Codes: C10, C14, C31, C54 Keywords: 1 Introduction Consider a (large) population of n individuals connected under a single observed network. Researchers are interested in conducting an experiment to estimate the global average treatment effect, i.e., the difference between the average effect of treating all versus none of the individuals in the population. Treating an individual may generate spillovers to her friends in the network. To capture such effects, researchers conduct a cluster experiment. Individuals are first partitioned into clusters. Within a cluster, either all units are assigned to the treatment or all units are assigned to the control group. Finally, researchers estimate treatment effects by taking a difference between the average outcomes of treated and control units (possibly adjusting for baseline covariates). The cluster design does not require modeling the dependence of individual outcomes on neighbors’ assignments, but it requires a choice of clusters and some assumptions on the extend of the spillovers along the network. For example, cluster experiments on online platforms require choosing a partition of the social network, and field experiments require choosing the unit of randomization, such as villages or regions. This raises the question of how many and which clusters to use in experiments. Typical approaches in economic research assume prior knowledge of many independent clusters. There are many settings where this information is not available, and instead units in the population have different degrees of connections.[1] This paper provides an econometric framework to choose when and how to design the clusters in cluster experiments. Different from existing clustering algorithms geared towards community detection, we motivate the choice of the clusters based on the task of estimating global treatment effects. The choice of clustering must balance two competing objectives: the larger the clusters (and the smaller the number of clusters), the smaller the bias of the estimated global effect, but the larger its variance. We introduce an algorithmic procedure – entitled Causal Clustering – to choose the clustering that minimizes a weighted combination of the worst-case bias and variance as a function of the network and clusters. The worst-case approach encodes uncertainty over the dependence of individual outcomes on neighbors’ assignments. We study (i) whether to run a cluster-level instead of individual-level randomization; (ii) how to cluster individuals (and how many clusters to use). We focus on a class of models where spillover effects are small relative to the outcomes’ variance but possibly non-negligible for inference. This is formalized in a novel framework of local asymptotics where individual outcomes depend arbitrarily on neighbors’ treatments, and, as n grows, spillovers from neighbors (and possibly also direct effects) converge to zero, but at an arbitrary slow rate (e.g., slower than n −1/2 ). This framework encodes the researchers’ uncertainty on the presence (and magnitude) of spillover effects by modeling first-order neighbors’ effect local to zero, with the convergence rate capturing the expected magnitude of spillovers.[2] The local asymptotic framework we study is consistent with settings with small (but non-negligible) treatment and spillover effects, typical, for instance, in online experiments [e.g., Karrer et al., 2021]. We characterize optimal clustering as a function of the expected magnitude of the largest spillover effects that the experiment can generate. The largest size of the spillover effects is a key input in our algorithms, whose characterization, in practice, is necessary for the design of the experiment but can be challenging. This parameter can be informed by previous experiments, in the same spirit of minimum detectable effects used in power analysis [e.g. Baird et al., 2018], or using some particular modeling assumptions. We provide guidance to practitioners in Section 6. Our analysis proceeds as follows. First, we provide a formal characterization of the worstcase bias and variance. We show that the worst-case bias is closely related to a particular notion of between-clusters connectedness, defined as the per-individual average number of friends in other clusters. The worst-case variance can potentially be an arbitrary function of within-clusters covariances and between-clusters covariances: individuals in the same cluster have identical assignments and in different clusters may share common neighbors. We show that the variance only depends on the average squared cluster size, up to an asymptotically negligible error. This result formalizes the intuition that a larger number of clusters, with a small variance in cluster size, decreases the variance of the estimator. We draw the implications of these results for choosing between a cluster experiment (for a given clustering) or assigning treatments independently between individuals (i.e., Bernoulli design or completely randomized design). Suppose the magnitude of the spillover effects is smaller than the square root of the number of clusters. In that case, the variance component dominates the bias, and a Bernoulli design is preferred (where a Bernoulli design is a special case of a cluster design with clusters containing a single unit). Vice-versa, a cluster design is preferred if the bias dominates the variance. Intuitively, because our objective trades off the bias and variance of the estimator, whenever the number of clusters is small, it is best to run a Bernoulli design for any value of spillover effects local to zero. On the other hand, if the number of clusters is sufficiently large, and the cluster design appropriately controls the bias of the estimator, a cluster design is preferred. We provide practitioners with a simple decision rule between cluster and Bernoulli designs that only depends on the number of clusters and the expected spillover effects’ magnitude. [1] For example, when using villages as clusters, individuals may interact in the same and nearby villages. See Egger et al. [2022] for an example in cash-transfer programs. This paper is under CC 1.0 license. available on arxiv

Causal Clustering: Design of Cluster Experiments Under Network Interference

Startups of The Year 2023 Winners: North America 

I despise Christmas. The city becomes like a pestering showroom, seemingly designed to provoke me. Flaunting what they have: Christmas trees, gifts, loving families gathering around the tree once a year like in the movies. I once had a family too—a beautiful wife and a lovely daughter. But I screwed it up... My wife left and took our daughter with her. They might be living in England now. I know nothing about them. I can't even be angry; after all, I was the one who drove them away. Since then, Christmas for me has been about loneliness. About how people constantly remind me of what I've lost. But this Christmas will be different. This year, I'll have a tree too. I will be the tree. I've picked out a sturdy roof beam. That's where I'll hang myself. I thought it would be easier, but I've realized that this actually requires the most courage. Being suicidal isn't simple. So, I jumped down to the store for a bottle of whiskey, to drink up some bravery one last time. The street was completely deserted. There was no sign of life, except for that one convenience store where I finally managed to get some booze. I paid for it; the cashier wished me a Merry Christmas. I just shrugged and walked out the door. Why not start drinking right now? I opened the bottle and took a big swig. The whiskey scraped its way down my throat, and then became a warmth in my stomach, which felt particularly good on this cold winter night. After a few sips, when my steps began to wobble, I decided to stop for a moment to relieve myself. That's when I noticed the homeless person on the street corner. He wore tattered white clothes, possibly burlap. Maybe he stole it from a nearby nativity scene. The light played a strange game on his body as if he was glowing. Or maybe it was just my eyes playing tricks, which wouldn't be surprising after downing half a bottle of whiskey in about fifteen minutes. Come, buddy, sit with me. — he called out. Why not, after all? I'm not in such a hurry to hang myself that I can't spare a few minutes for a half-crazed man. Want some? — I offered him the whiskey. The homeless man just nodded, then took a sip of the whiskey. That's when it hit me where I recognized this face from. No, not an old friend. The last time I saw him was in the church. A very long time ago, when I still went to such places. It was Jesus himself sitting next to me, looking exactly as he was depicted in the church. I sat there with the most influential man in the world on an empty street, sharing a bottle of whiskey. He seemed so real, but I knew it couldn't be true. For a few moments, I just stared blankly, then he broke the silence. I know what you're thinking. You think all of this isn't real. That you've gone mad and your mind is playing tricks on you. But that's nonsensical. If I'm merely a product of your mind, anything you do to prove the contrary will be unsuccessful. You can't deceive your own mind. I could say I know everything about you, but that wouldn’t prove anything; if I'm just a figment of your imagination, you obviously can't ask a question I wouldn't know the answer to. Was the shopkeeper real? Or even this whiskey? How can you know if anything is real beyond yourself? And God? What about God? — I didn’t really understand his point, but I thought discussing God would be a good start in a conversation with Jesus. If everything is just a creation of your mind, then you are God, right?! If I am God, why is my life so miserable? Because you made it that way. And if I die, does everything end? Death is overrated. If you're a butterfly in your dream and someone steps on you, do you mourn the butterfly? You just wake up. Maybe ponder for a bit about what it was like, then start a new dream. So, I'm sitting here with Jesus himself, and you're suggesting I should commit suicide? Jesus? Why do you call me that? What should I call you? Last time you called me Arnold. Arnold? Yes, Arnold, your personal assistant. Personal assistant? Yes. That's the protocol. If you stray too far off the path, you can choose to start a new life or roll back the current one. I’m a bit confused. Can you explain all this clearly? Oh, sure. In the 2050s, human technology reached the singularity. People continue to live in digital form. Great. So, you're a time traveler? Not at all. I'm just a second-generation AI. Second generation? Yes. Humans are the first generation, and artificial intelligence based on the first generation is the second. So, you're some kind of robot? Now I'm starting to understand the Arnold name. A time-traveling robot from the future. Just like the Terminator... — I said, laughing, because this all started to seem very frivolous. Not exactly. Currently, a computer is running your mind, which includes me and all other people. Essentially, you are just a part of your own larger mind. It's hard to explain in a way that you would understand, but at a higher level, we are all one entity. I see. We are God, experiencing a massive split in consciousness. Something like that. The computer running us orbits the Earth with millions of similar machines. The machines are powered by the sun and arranged in a spherical formation around the planet. Nanomachines continuously transform the Earth so that dumb matter becomes smart matter. So, everything around us isn't real? It depends on how you define reality. Currently, it's 2019. If we reach 2050, humanity will likely achieve singularity and move into simulated worlds. From that perspective, this will be reality; from here, the outer world is. But who knows if the outer world is the ultimate objective reality? This is starting to give me a headache, but I still don't understand why you're here. To offer you the choice. A new life, or a rollback? What's a rollback? Before you created this personality, you set a path that you must follow. Why? Why not just enjoy omnipotence? Because it's boring. If you irrevocably stray from the path, you have the option to return to an earlier point and continue from there. It's like a backup of your mind at a specific state. Since your mind reverts to an earlier state, you won't remember what happened afterward. So that's how fate works? Yes. You feel as if a series of random decisions guide your path. Everything seems to lead in one direction. But that's only because we roll back the bad decisions. If something is meant to happen, it will, and you can't prevent it. If you tried, something random would always interfere. At least that's how you would experience it, since every time you managed to prevent an event, the system would roll back. In a mostly deterministic world, randomness is God's only tool. If the world is deterministic, how can anything happen by chance? It's like quantum mechanics. Exciting things happen when no one is watching. The slice of reality that no one is aware of is God's territory. Here happen the big things that people just shrug off as random. So, you came to ask if I'd rather reincarnate or go back in time to correct my mistakes? You could say that. At that point, I took a big gulp of whiskey. Then I want my family back. That last gulp might have been unnecessary. I started to feel dizzy, and the world became increasingly blurry. Finally, everything melted away… I felt a pressure on my chest. Slowly, I opened my eyes and stretched widely. A fire flickered in the fireplace, and Katy, my little girl, was sitting on my stomach. Daddy, daddy! Santa has come. Let’s open the presents! I clambered out of bed and rubbed my eyes. Under the ceiling-high tree, the presents were already laid out. Annie had taken care of everything, and let me sleep until this little imp jumped on my stomach. By the time I came to my senses, Katy was already unwrapping the gifts, then suddenly stopped for a moment and turned towards me. Daddy, didn’t Santa bring you anything? Oh yes, my little girl, he did. I watched Katy unwrap the presents, and a feeling of otherworldly joy washed over me. This is my gift. I had forgotten how much I adore Christmas. If you are confused, read my very first article on HackerNoon: https://hackernoon.com/our-universe-is-a-massive-neural-network-heres-why-c14j3ze1 I wish you a very merry Christmas!

A Christmas Story (After the Technological Singularity)

Why spend a ridiculous amount of time and resources trying to set up & manage your infrastructure when you can rely on Aptible to do it for you? Use these three steps to deploy a Docker image from your AWS ECR private repository to Aptible. What is Aptible? Aptible is a platform as a service that provides a scalable environment for deploying applications. In this environment, Aptible delivers development teams infrastructure tools such as storage, servers, load balancers, security & compliance, and more. These tools allow development teams to focus on writing code and deploying applications rather than managing infrastructure. Companies can save money and all the resources needed to set up and manage their infrastructure by relying on Aptible. Aptible offers a pay-as-you-go pricing model, allowing companies to only pay for the resources they use. It also gives a free one-month trial to development teams testing the platform for the first time. Using Aptible, you can deploy any application via the user-friendly Aptible dashboard. It also allows other deployment options, including Infrastructure as Code via Terraform and the Aptible CLI. All these platforms ensure that developers can deploy applications in the most comfortable manner. How to deploy a Docker image from AWS ECR to Aptible Project Overview For this project, we'll use a word counter application that counts the number of words in a sentence. This application will be packaged into a Docker image and deployed to a private AWS ECR repository. We'll then deploy this Docker image to Aptible using the Aptible CLI. JavaScript From this overview, we can see that there are three main steps to complete this project: Build a Docker image and push it to AWS ECR Authenticate AWS ECR with Aptible Deploy to Aptible Step 1: Step 2: Step 3: Prerequisites Before you begin this project, you'll need to have the following: An AWS account. If you don't have one, you can create one on the official AWS website. The is installed and configured with your AWS account. Make sure the AWS CLI has appropriate permissions to access AWS ECR. AWS CLI An Aptible account. If you don't have one, sign up on the . Aptible website The Aptible CLI. You can install it using the . Aptible CLI documentation Docker. You can install it . here Once you have created the following accounts and installed the necessary tools, you can begin with step one of this project. Step 1: Build a Docker image and push it to AWS ECR We'll package a JavaScript word counter application into a Docker image for this step. After that, we'll push this Docker image to a private AWS ECR repository. To build the Docker image, follow these steps: Clone the word counter application: $ git clone https://github.com/Aahil13/Word-counter.git Navigate to the word counter application directory: $ cd Word-counter You'll find all the files and folders for this application in this directory. Part of these files is a containing instructions for building the image. Dockerfile From the , build the Docker image: Dockerfile $ docker build -t word-counter . Verify that the Docker image was built: $ docker images --filter reference=word-counter You should see the following output: REPOSITORY     TAG       IMAGE ID       CREATED          SIZE
word-counter   latest    c3d724e0a19e   12 seconds ago   60MB You can view the application locally using the following command: $ docker run -p 80:80 word-counter Navigate to your browser and point to the URL: . http://localhost:80 On your browser, you see the word counter application: To stop the application, press . CTRL+C With this, we have built the Docker image. Now, you'll push this Docker image to a private AWS ECR repository. To push the Docker image to AWS ECR, follow these steps: Using the AWS CLI, log in to AWS ECR: $ aws ecr get-login-password --region <REGION> | docker login --username AWS --password-stdin <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com Replace with your AWS account ID and with the AWS region you want to use. <AWS_ACCOUNT_ID> <REGION> Create the private AWS ECR repository: aws ecr create-repository \
  --repository-name word-counter \
  --image-scanning-configuration scanOnPush=true \
  --region region Tag the Docker image: $ docker tag word-counter:latest <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/word-counter Push the Docker image to AWS ECR: $ docker push <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/word-counter You should see the following output: Using default tag: latest
The push refers to repository [<AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/word-counter]
0342869436ee: Pushed
c1f569e0c435: Pushed
29c9ccae1ae2: Pushed
53bd9605a443: Pushed
c140f535da0a: Pushed
aba51e9dc8b1: Pushed
cc2447e1835a: Pushed With this, you have pushed the Docker image to AWS ECR. Now, we'll move on to step two of this project. Step 2: Authenticate AWS ECR with Aptible In this step, we'll authenticate the AWS ECR repository with Aptible. This authentication will give Aptible access to pull the Docker image from the AWS ECR repository. To authenticate the AWS ECR repository with Aptible, follow these steps: Login to Aptible from the CLI: $ aptible login You should see the following output: Email: <EMAIL>
Password:

Logged in as <EMAIL> Authenticate the AWS ECR repository with Aptible. : In this step, we'll run the same command in Step 1 to log in to AWS ECR. However, the ECR Docker login password will be stored in a variable. This variable will then authenticate the AWS ECR repository with Aptible. Note $ ECR_PASSWORD=$(aws ecr get-login-password --region <REGION>) Replace with the AWS region you want to use. This command will store the ECR Docker login password in the variable. <REGION> ECR_PASSWORD $ echo -n "$ECR_PASSWORD" | docker login --username AWS --password-stdin <AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com Replace with your AWS account ID and with the AWS region you want to use. This command will authenticate the AWS ECR repository with Aptible. <AWS_ACCOUNT_ID> <REGION> : We had to re-authenticate the AWS ECR repository with Aptible because the ECR Docker login password is necessary to deploy an application to Aptible. In Step 1, the password was passed using the flag. This flag ensures that the ECR Docker login password is not stored in the shell history. However, this flag is not supported by Aptible. Therefore, we had to re-authenticate the AWS ECR repository with Aptible, saving the ECR Docker login password in a variable. Note --password-stdin With this, we have authenticated the AWS ECR repository with Aptible. Now, we'll move on to step three of this project. Step 3: Deploy to Aptible This project's final step is deploying the Docker image to Aptible. To do this, we'll be using a single Aptible CLI command. Before executing the command, navigate to the Aptible Dashboard and create an app. You can do this by following these steps: Navigate to the Aptible Dashboard. In the Environments section, select the environment you want to use. Navigate to the Apps tab and click on the New App button. Enter the app name and click on the Create App button. After creating the app, you can deploy the Docker image to Aptible. To deploy the Docker image to Aptible, execute the command below: $ aptible deploy --app "<APP_NAME>" --docker-image "<AWS_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/word-counter" --private-registry-username "AWS" --private-registry-password "$ECR_PASSWORD" Replace with your AWS account ID, with the AWS region you want to use, and with the name of the app you created in the Aptible Dashboard. <AWS_ACCOUNT_ID> <REGION> <APP_NAME> You should see the following output: Hurrah! You have successfully deployed the Docker image to Aptible. You can now view the application on Aptible. Navigate to the Aptible Dashboard and select the app you created. You should see the following output: Although we have finished the deployment, we can't access the application yet. To access the application on your browser, you must create an endpoint. To create an endpoint, follow these steps: On the Apps section, select the app and switch to the Endpoints tab below. word-count Click on the New Endpoint button. Select the configurations as shown below: Click on the Save Endpoint button. It will take a few minutes for the endpoint to be provisioned. Once the endpoint is created, you can access the application using the endpoint URL. From the output, you can see that Aptible provisions a secure endpoint for the application. This endpoint is secured using a TLS certificate from Let's Encrypt. Aptible also provisions a load balancer for the application. This load balancer ensures that the application is highly available. : At the time of this writing, Aptible supports the creation of only one endpoint for a free tier account. If you want to create more endpoints, you must upgrade your account. Note Conclusion As displayed throughout this project, Aptible is an excellent platform for deploying applications. Aptible provided all the infrastructure tools needed for deploying the word counter application. Eliminating the need to set up and manage infrastructure. This hands-on project taught you how to deploy a Docker image from AWS ECR to Aptible. You can now deploy any application from AWS ECR to Aptible using the steps outlined in this project. To learn more about Aptible’s services, you can check out the . Aptible documentation More resources: Using Amazon ECR with the AWS CLI Direct Docker image deployment

How to Deploy a Docker Image From AWS ECR to Aptible in Three Steps

Worlds Within Worlds: The Story of Nuclear Energy, Volume 1 (of 3), by Isaac Asimov is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Volume I, ENERGY: The Energy of the Sun The Energy of the Sun The most serious problem raised by the law of conservation of energy involved the sun. Until 1847, scientists did not question sunlight. The sun radiated vast quantities of energy but that apparently was its nature and was no more to be puzzled over than the fact that the earth rotated on its axis. Once Helmholtz had stated that energy could neither be created nor destroyed, however, he was bound to ask where the sun’s energy came from. It had, to man’s best knowledge, been radiating heat and light, with no perceptible change, throughout the history of civilization and, from what biologists and geologists could deduce, for countless ages earlier. Where, then, did that energy come from? 56 The sun gave the appearance of being a huge globe of fire. Could it actually be that—a large heap of burning fuel, turning chemical energy into heat and light? The sun’s mass was known and its rate of energy production was known. Suppose the sun’s mass were a mixture of hydrogen and oxygen and it were burning at a rate sufficient to produce the energy at the rate it was giving it off. If that were so, all the hydrogen and oxygen in its mass would be consumed in 1500 years. No chemical reaction in the sun could account for its having given us heat and light since the days of the pyramids, let alone since the days of the dinosaurs. Was there some source of energy greater than chemical energy? What about the energy of motion? Helmholtz suggested that meteors might be falling into the sun at a steady rate. The energy of their collisions might then be converted into heat and light and this could keep the sun shining for as long as the supply of meteors held out—even millions of years. This, however, would mean that the sun’s mass would be increasing steadily, and so would the force of its gravitational pull. With the sun’s gravitational field increasing steadily, the length of earth’s year would be decreasing at a measurable rate—but it wasn’t. In 1854 Helmholtz came up with something better. He suggested that the sun was contracting. Its outermost layers were falling inward, and the energy of this fall was converted into heat and light. What’s more, this energy would be obtained without any change in the mass of the sun whatever. Helmholtz calculated that the sun’s contraction over the 6000 years of recorded history would have reduced its diameter only 560 miles—a change that would not have been noticeable to the unaided eye. Since the development of the telescope, two and a half centuries earlier, the decrease in 57diameter would have been only 23 miles and that was not measurable by the best techniques of Helmholtz’s day. Working backward, however, it seemed that 25 million years ago, the sun must have been so large as to fill the earth’s orbit. Clearly the earth could not then have existed. In that case, the maximum age of the earth was only 25 million years. Geologists and biologists found themselves disturbed by this. The slow changes in the earth’s crust and in the evolution of life made it seem very likely that the earth must have been in existence—with the sun delivering heat and light very much in the present fashion—for many hundreds of millions of years. Yet there seemed absolutely no other way of accounting for the sun’s energy supply. Either the law of conservation of energy was wrong (which seemed unlikely), or the painfully collected evidence of geologists and biologists was wrong (which seemed unlikely),—or there was some source of energy greater than any known in the 19th century, whose existence had somehow escaped mankind (which also seemed unlikely). Yet one of those unlikely alternatives would have to be true. And then in 1896 came the discovery of radioactivity. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Isaac Asimov. 2015. Worlds Within Worlds: The Story of Nuclear Energy, Volume 1 (of 3). Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/49819/49819-h/49819-h.htm#c14 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Story of Nuclear Energy, Volume 1 (of 3): Energy - The Energy of the Sun

The Genetic Effects of Radiation by Isaac Asimov is part of HackerNoon’s Book Blog Post Series. The Table of Links for this book can be found here . Radiation - Man-made Radiation Man began to add to the background radiation in the 1890s. In 1895, X rays were discovered and since then have become increasingly useful in medical diagnosis and therapy and in industry. In 1896, radioactivity was discovered and radioactive substances were concentrated in laboratories in order that they might be studied. In 1934, it was found that radioactive forms of nonradioactive elements (radioisotopes) could be formed and their use came to be widespread in universities, hospitals, and industries. [4] Then, in 1945, the nuclear bomb was developed. With the uranium or plutonium fission that produces a nuclear explosion, there is an accompaniment of intense gamma radiation. In addition, a variety of radioisotopes are left behind in the form of the residue (fission fragments) of the fissioning atoms. These fission fragments are distributed widely in the atmosphere. Some rise high into the stratosphere and descend (as fallout) over the succeeding months and years. [5] It is hard to try to estimate how much additional radiation is being absorbed by human beings out of these man-made sources. Fallout is not uniformly spread over the earth but is higher in those latitudes where nuclear bombs have been most frequently tested. Then, too, people in industries and research who are involved with the use of radioisotopes, and people in medical centers who constantly deal with X rays, are likely to get more exposure than others. These adjuncts of modern science and medicine are more common and widespread in technologically advanced countries than elsewhere, and nuclear bombs have most often been exploded in just those latitudes where the advanced countries are to be found. Attempts have been made to work out estimates of this exposure. One estimate, involving a number of technologically advanced countries (including the United States) showed that an average of somewhere between 0.02 and 0.18 rem per year was absorbed, as a result of radiations (usually X rays) used in medical diagnosis and therapy. Occupational exposure added, on the average, not more than 0.003 rem, though the individuals constantly exposed in the course of their work would naturally absorb considerably more than this overall average. Man-made radioactivity in the atmosphere produced this nuclear-emulsion photograph. This radiation source is a fission product produced in a nuclear explosion. The enlargement is 1200 diameters. Compare this with the natural radioactivity depicted on page 28 . On the whole, the highest absorption was found, as was to be expected, in the United States. If these findings are expanded to cover a 30-year period, assuming the absorption will remain the same from year to year, it turns out that the average absorption of man-made radiation in the nations studied varies from 0.6 rem to 5.5 rems per 30 years per individual. Considering the higher figure to be applicable to the United States, it would seem that man-made radiation from all sources is now being absorbed at nearly twice the rate that natural radiation is. To put it another way, Americans are just about tripling their radiation dosage by reason of the human activities that are now adding man-made radiation to the natural supply. By far the major part of this additional dosage is the result of the use of X rays in searching for decayed teeth, broken bones, lung lesions, swallowed objects, and so on. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. ). THE GENETIC EFFECTS OF RADIATION This book is part of the public domain. Asimov, Isaac. (October 13, 2017 . Urbana, Illinois: Project Gutenberg. Retrieved June 2022, from https://www.gutenberg.org/files/55738/55738-h/55738-h.htm#c14 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever.  You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Man-made Radiation

There are a bunch of articles floating about how to make cubes in CSS. There is a surprising of articles on how to make Minecraft chickens in CSS though. As such, I thought I'd spend a couple of hours making a CSS-only Minecraft chicken, and this guide will walk you through how I did that. lack , along with the full code via CodePen: Here is the demo https://codepen.io/smpnjn/pen/mdxzLWo The cool thing about the chicken is that you can give him a top hat, and he can spin. Also, if you click him, he may cluck. Making a CSS-Only Minecraft Chicken When I write articles, sometimes someone will message me to say it's , not Javascript - which makes me wonder if it's actually supposed to be Minecraft. In any case, every Minecraft chicken starts with a CSS cube. Making a CSS cube is relatively straightforward. First, we start with this HTML: JavaScript <div id="chicken">
    <div class="c2">
        <div class="cube head">
            <div class="front"></div>
            <div class="bottom"></div>
            <div class="left"></div>
            <div class="right"></div>
        </div>
    </div>
</div> ... which is essentially just a cube with four side div's inside. The reason it's only got sides rather than sides here is that I like to use CSS elements for the other two. It's also got a parent called here, because CSS starts doing some wacky things if you don't do this (like stacking the blocks in weird ways). four six pseudo .c2 After we've done this, we can start making our cube CSS. All the sides are relatively similar in CSS styling, with the only difference being how we transform them: #chicken {
  transform: rotateX(-15deg) rotateY(20deg);
  transform-style: preserve-3d;
  position: relative;
  left: 16rem;
  top: 16rem;
  transform-origin: 6rem 6rem;
  transform-style: preserve-3d;
  transition: all 0.1s ease-out;
  cursor: pointer;
}
#chicken .c2 {
  position: absolute;
  top: 6rem;
}
.cube.head {
  width: 6rem;
  height: 8rem;
}
.cube > div, .cube:after, .cube:before {
  content: '';
  position: absolute;
  height: 100%;
  width: 100%;
  transform-style: preserve-3d;
  background: white;
}
.cube:before {
  transform: rotateX(-90deg);
  transform-origin: 0 0;
}
.cube:after {
  transform: translateZ(-6rem);
  transform-origin: 0 50%;
}
.cube .left {
  transform: rotateY(90deg);
  transform-origin: 0 0;
  background: #eee;
}
.cube .right {    
  transform-origin: 100% 0;
  background: #eee;
  transform: rotateY(-90deg);
}
.cube .bottom {
  transform: rotateX(90deg);
  transform-origin: 100% 100%;
} I've opted to simplify the cube method here by using . Some people like to use a lot of transforms, but I find is the easiest and requires the least amount of repeating yourself. The only place I need to use is for the back piece, which is above. transform-origin translate transform-origin translate .cube:after That's because we'd need to rotate the back piece around the center point of the cube, and although you can do a 3d , it's just as easy to translate the back piece into place. This does bring with it one of the more annoying things about CSS cubes, which is that we will need to change the back plate translation if we change the size of the cube. Since our first cube is already configured correctly, that's OK - but for other pieces, we need to add in some custom lines of CSS. transform-origin head You'll also notice that we use all over the place. This ensures that the object 3d. If you remove this, the cube won't form, and instead, it'll stay on a single plain. transform-style: preserve-3d; remains Moving our cubes into place After you've figured out one cube - the rest is easy. All of our cubes are , so all you need to do is copy and paste the HTML, update the colors as you see fit, and translate them into position. This is a pretty manual process in CSS, but to be fair, it would be in any language. position: absolute To make the chicken a little more interesting, I gave him a wing animation. To do this, I made two keyframes that simply translate a point. I then applied this to one of the cube parent classes, in this case, and . I added new classes to these called and respectively - since both wings rotate differently. The animation here runs forever. .c12 .c13 flutter anti-flutter The only thing left to do now is to update the , since we need to rotate the wings about a point. transform-origin .flutter {
  animation: flutter 2s infinite;
  transform-origin: 0 0;
}
.flutter-anti {
  animation: flutter-anti 2s infinite;
  transform-origin: 1rem 0;
}
@keyframes flutter {
    0% {
        transform: rotateZ(0deg);
    }
    15% {
        transform: rotateZ(-35deg);
    }
    30% {
        transform: rotateZ(-15deg);
    }
    45% {
        transform: rotateZ(-70deg);
    }
    60% {
        transform: rotateZ(0deg);
    }
}
@keyframes aggressively-flutter {
    0% {
        transform: rotateZ(0deg);
    }
    15% {
        transform: rotateZ(-90deg);
    }
    30% {
        transform: rotateZ(-45deg);
    }
    45% {
        transform: rotateZ(-90deg);
    }
    60% {
        transform: rotateZ(-45deg);
    }
    75% {
        transform: rotateZ(-90deg);
    }
    90% {
        transform: rotateZ(-45deg);
    }
    100% {
        transform: rotateZ(0deg);
    }
} Adding the hat and rotation buttons CSS and HTML are weird. In other languages, there would maybe specific UI elements build in to allow us to trigger CSS classes on click. In HTML, we have to use a checkbox, which is connected to a label, which triggers an animation when clicked. is created up front, using the same cube method as before. We then make a label and checkbox above our chicken HTML: The hat <input type="checkbox" id="rotate" name="rotate" />
<input type="checkbox" id="hat" name="hat" />
<div id="controls">
  <div><label id="rotate-label" for="rotate">Rotate</label></div>
  <div><label id="hat-label" for="hat">Hat</label></div>
</div>
<div id="chicken">
    
     Now we can use to trigger animations if the checkbox is checked. What happens is: sibling selectors The acts as our button. When the user clicks it, it is a specific . For , it is the with the . label for input #hat-label for input name hat So when the user clicks the , it checks the checkbox. That means we can use the CSS selector to add some new CSS. label :checked Since , , and are the hat blocks, we can set them to by default, and then to show should the checkbox be checked: .c14 .c15 .c16 display: none #hat .c14, .c15, .c16 { display: none; }

#hat:checked ~ #chicken .c14, #hat:checked ~ #chicken .c15, #hat:checked ~ #chicken .c16 {
  display: block;
} The final thing to do is rotate, which follows the same principle. Instead, though, we'll trigger an when the user checks the checkbox. We have to also use two animations here - one for unchecked, and one for checking. The reason for this is because CSS can be a little weird if we try to re-run an animation upon unchecking a checkbox: animation #rotate #rotate:checked ~ #chicken {
  animation: rotate 2s 1 forwards;
}
#rotate ~ #chicken {
  animation: anti-rotate 2s 1 forwards;
}

@keyframes rotate {
    0% { 
        transform: rotateX(-15deg) rotateY(20deg);
    }
    100% {
        transform: rotateX(-15deg) rotateY(380deg);
    }
}
@keyframes anti-rotate {
    0% { 
        transform: rotateX(-15deg) rotateY(380deg);
    }
    100% {
        transform: rotateX(-15deg) rotateY(20deg);
    }
} And that leaves us with a very cool, Minecraft chicken. Conclusion Making Minecraft chickens has never been easier with CSS. , so you can play around with it if you want to. I hope you've enjoyed this quick intro to building 3d objects with CSS, and hopefully, you can build your own stuff too. The full code for this demo can be found here Also published . here

A Step-By-Step Guide to Making a CSS Only Minecraft Chicken

Adventures of Huckleberry Finn, by Mark Twain (Samuel Clemens) is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book . here CHAPTER XIV. By and by, when we got up, we turned over the truck the gang had stole off of the wreck, and found boots, and blankets, and clothes, and all sorts of other things, and a lot of books, and a spyglass, and three boxes of seegars. We hadn’t ever been this rich before in neither of our lives. The seegars was prime. We laid off all the afternoon in the woods talking, and me reading the books, and having a general good time. I told Jim all about what happened inside the wreck and at the ferryboat, and I said these kinds of things was adventures; but he said he didn’t want no more adventures. He said that when I went in the texas and he crawled back to get on the raft and found her gone he nearly died, because he judged it was all up with him anyway it could be fixed; for if he didn’t get saved he would get drownded; and if he did get saved, whoever saved him would send him back home so as to get the reward, and then Miss Watson would sell him South, sure. Well, he was right; he was most always right; he had an uncommon level head for a nigger. I read considerable to Jim about kings and dukes and earls and such, and how gaudy they dressed, and how much style they put on, and called each other your majesty, and your grace, and your lordship, and so on, ’stead of mister; and Jim’s eyes bugged out, and he was interested. He says: “I didn’ know dey was so many un um. I hain’t hearn ’bout none un um, skasely, but ole King Sollermun, onless you counts dem kings dat’s in a pack er k’yards. How much do a king git?” “Get?” I says; “why, they get a thousand dollars a month if they want it; they can have just as much as they want; everything belongs to them.” “Ain’’ dat gay? En what dey got to do, Huck?” “They don’t do nothing! Why, how you talk! They just set around.” “No; is dat so?” “Of course it is. They just set around—except, maybe, when there’s a war; then they go to the war. But other times they just lazy around; or go hawking—just hawking and sp—Sh!—d’ you hear a noise?” We skipped out and looked; but it warn’t nothing but the flutter of a steamboat’s wheel away down, coming around the point; so we come back. “Yes,” says I, “and other times, when things is dull, they fuss with the parlyment; and if everybody don’t go just so he whacks their heads off. But mostly they hang round the harem.” “Roun’ de which?” “Harem.” “What’s de harem?” “The place where he keeps his wives. Don’t you know about the harem? Solomon had one; he had about a million wives.” “Why, yes, dat’s so; I—I’d done forgot it. A harem’s a bo’d’n-house, I reck’n. Mos’ likely dey has rackety times in de nussery. En I reck’n de wives quarrels considable; en dat ’crease de racket. Yit dey say Sollermun de wises’ man dat ever live’. I doan’ take no stock in dat. Bekase why: would a wise man want to live in de mids’ er sich a blim-blammin’ all de time? No—’deed he wouldn’t. A wise man ’ud take en buil’ a biler-factry; en den he could shet down de biler-factry when he want to res’.” “Well, but he was the wisest man, anyway; because the widow she told me so, her own self.” “I doan k’yer what de widder say, he warn’t no wise man nuther. He had some er de dad-fetchedes’ ways I ever see. Does you know ’bout dat chile dat he ’uz gwyne to chop in two?” “Yes, the widow told me all about it.” “Well, den! Warn’ dat de beatenes’ notion in de worl’? You jes’ take en look at it a minute. Dah’s de stump, dah—dat’s one er de women; heah’s you—dat’s de yuther one; I’s Sollermun; en dish yer dollar bill’s de chile. Bofe un you claims it. What does I do? Does I shin aroun’ mongs’ de neighbors en fine out which un you de bill do b’long to, en han’ it over to de right one, all safe en soun’, de way dat anybody dat had any gumption would? No; I take en whack de bill in two, en give half un it to you, en de yuther half to de yuther woman. Dat’s de way Sollermun was gwyne to do wid de chile. Now I want to ast you: what’s de use er dat half a bill?—can’t buy noth’n wid it. En what use is a half a chile? I wouldn’ give a dern for a million un um.” “But hang it, Jim, you’ve clean missed the point—blame it, you’ve missed it a thousand mile.” “Who? Me? Go ’long. Doan’ talk to me ’bout yo’ pints. I reck’n I knows sense when I sees it; en dey ain’ no sense in sich doin’s as dat. De ’spute warn’t ’bout a half a chile, de ’spute was ’bout a whole chile; en de man dat think he kin settle a ’spute ’bout a whole chile wid a half a chile doan’ know enough to come in out’n de rain. Doan’ talk to me ’bout Sollermun, Huck, I knows him by de back.” “But I tell you you don’t get the point.” “Blame de point! I reck’n I knows what I knows. En mine you, de real pint is down furder—it’s down deeper. It lays in de way Sollermun was raised. You take a man dat’s got on’y one or two chillen; is dat man gwyne to be waseful o’ chillen? No, he ain’t; he can’t ’ford it. He know how to value ’em. But you take a man dat’s got ’bout five million chillen runnin’ roun’ de house, en it’s diffunt. He as soon chop a chile in two as a cat. Dey’s plenty mo’. A chile er two, mo’ er less, warn’t no consekens to Sollermun, dad fatch him!” I never see such a nigger. If he got a notion in his head once, there warn’t no getting it out again. He was the most down on Solomon of any nigger I ever see. So I went to talking about other kings, and let Solomon slide. I told about Louis Sixteenth that got his head cut off in France long time ago; and about his little boy the dolphin, that would a been a king, but they took and shut him up in jail, and some say he died there. “Po’ little chap.” “But some says he got out and got away, and come to America.” “Dat’s good! But he’ll be pooty lonesome—dey ain’ no kings here, is dey, Huck?” “No.” “Den he cain’t git no situation. What he gwyne to do?” “Well, I don’t know. Some of them gets on the police, and some of them learns people how to talk French.” “Why, Huck, doan’ de French people talk de same way we does?” “No, Jim; you couldn’t understand a word they said—not a single word.” “Well, now, I be ding-busted! How do dat come?” “I don’t know; but it’s so. I got some of their jabber out of a book. S’pose a man was to come to you and say Polly-voo-franzy—what would you think?” “I wouldn’ think nuff’n; I’d take en bust him over de head—dat is, if he warn’t white. I wouldn’t ’low no nigger to call me dat.” “Shucks, it ain’t calling you anything. It’s only saying, do you know how to talk French?” “Well, den, why couldn’t he say it?” “Why, he is a-saying it. That’s a Frenchman’s way of saying it.” “Well, it’s a blame ridicklous way, en I doan’ want to hear no mo’ ’bout it. Dey ain’ no sense in it.” “Looky here, Jim; does a cat talk like we do?” “No, a cat don’t.” “Well, does a cow?” “No, a cow don’t, nuther.” “Does a cat talk like a cow, or a cow talk like a cat?” “No, dey don’t.” “It’s natural and right for ’em to talk different from each other, ain’t it?” “Course.” “And ain’t it natural and right for a cat and a cow to talk different from us?” “Why, mos’ sholy it is.” “Well, then, why ain’t it natural and right for a Frenchman to talk different from us? You answer me that.” “Is a cat a man, Huck?” “No.” “Well, den, dey ain’t no sense in a cat talkin’ like a man. Is a cow a man?—er is a cow a cat?” “No, she ain’t either of them.” “Well, den, she ain’t got no business to talk like either one er the yuther of ’em. Is a Frenchman a man?” “Yes.” “ , den! Dad blame it, why doan’ he talk like a man? You answer me !” Well dat I see it warn’t no use wasting words—you can’t learn a nigger to argue. So I quit. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Twain, Mark,1993. Adventures of Huckleberry Finn. https://www.gutenberg.org/files/76/76-h/76-h.htm#c14 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html Photo by on Kiarash Mansouri Unsplash

Adventures of Huckleberry Finn: Chapter XIV

In this article we are going to explore this huge and passionate world of software development by demystifying what it really is and how can we get started in this field. So, let's start with the most important part: What is Software? Yes, in simple words software is just a set of instructions to make your computer do whatever you want, wow that you know what it is, you may be asking: How can we build Software? If software is set of instructions, then to build it we need a way to write those instructions. We humans communicate with each other by using languages and turns out we also use them to communicate instructions to our computers although we call them programming languages. What is a programming language? In short, a programming language is just like any other language, a structured system with a set of rules that lets us build sentences we can use to communicate instructions to our computer. Just like we have several languages to communicate with each other, we do have several programming languages from which to choose, among them, we have two big categories we need to know: High-level and Low-level programming languages Even if I said we have several programming languages, it turns out that the only language the computer can understand is called which is a and it looks like this: Machine Code Low-level programming language 8B542408 83FA0077 06B80000 0000C383 FA027706 B8010000 00C353BB 
01000000 B9010000 008D0419 83FA0376 078BD989 C14AEBF1 5BC3 Although it may seem exciting to learn and start programming software in that way, it is not the most productive option and indeed it is the most difficult. That is the reason because we have created High-level programming languages which are closer to our understanding and are easier to learn, they look like this: Console.WriteLine( ); 
Console.WriteLine( + DateTime.Now); "Hello World!" "The current time is " As you can see most of the words are easy to understand and is this characteristic that makes possible the development of those pieces of Software we see nowadays that changes almost every day to meet our needs. But if the computers can only understand how can we write instructions in The answers are Compilers: machine code high-level languages? What is a compiler? In the software world, a compiler is basically a translator who takes the code you have written in your language of choice and converts it into machine code, that way computers can run your instructions, awesome right? Languages and compilers are the building blocks to create software and we are going to talk more about them in future posts, but now it’s time to understand that nowadays to build software we need more than building blocks. To build them, we need entire , this can sound scary and complex, but they actually make our lives and jobs easier. ecosystems Development Ecosystem Let's explain in simply words what is a development ecosystem and its components: A structured system with a set of rules that helps us to communicate instructions to our computer. Languages: In essence, it is a piece of software the lets you run your code in the target machine, it is part of the “Compiling Process” that makes your code understandable by the computer. Runtimes: An already working and highly flexible software that lets you personalize it by letting you attach to it your own code so you can make software on top of it. Frameworks: Reusable general-purpose pieces of code that you can use repeatedly to speed up development Libraries: A set of other pieces of ready-to-use software that improves the quality of life of the development process Tools: Hey! where my compiler? The “Compiling process” is more complex today, different compilers can be in separate places, they can fall under the tools category and they can also be part of the runtimes. More on this on future posts. That seems a lot, do I need to install all those things? Fortunately no! Nowadays we have something called or that is an all-in-one package we can install to have the entire ecosystem into our machines. SDK software development kit .NET Ecosystem .NET is one of those ecosystems that is easy to use, multiplatform, open source and with it you can build almost every software you can imagine. Getting started with this ecosystem is something that will take you 10 minutes or less, the official tutorial will guide you through step by step but in short you need to: 1️⃣ Download and install the but make sure you download the version as it is the newest one. .NET SDK .NET Core 2️⃣   Create a folder where you want to store you project. 3️⃣ Open that folder into your Terminal. 4️⃣ Create a new .NET project by executing in your the following command. Terminal dotnet new You will see two new files has been created in the directory you have created: Contains various configurations like what language and what framework you want to use. [Yourfoldername].csproj: Contains the code that will be executed when run. Program.cs: 5️⃣ Run the project by executing in your CMD the following command dotnet run You will see a Hello world! text appearing into your console. Congratulations! you have started in the world of Software development. Conclusion Software development is a great world to start working on and although nowadays there are many concepts and things to learn, if you look carefully at them, you will find they can become easy to understand. Especially because most of them were inspired by the daily things we see every day. Also published at https://medium.com/@barahonajm/introduction-to-software-development-9d3e4a0f0b14

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