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Disclaimer - Please read the second part of this blog post here before proceeding. It explains the different data-fetching techniques that Next.js supports, and it guides you through the process of statically rendering a Next.js application page that fetches data at build time via the getStaticProps function. If you just want to jump straight into this tutorial, then clone the project repository and install the dependencies. Slow page loading times hurt the user experience. Anytime a user waits longer than a few seconds for a page's content to appear, they usually lose their patience and close out the page in frustration. A significant contributor to slow page loading times is image sizes. The larger an image is, the longer it takes the browser to download and render it to the page. One way to improve the perceived load time of images (and by extension, page) is to initially show a placeholder image to the user. This image should occupy the same space as the intended image to prevent cumulative layout shifting . Additionally, compared to the intended image, this image should be much smaller in size (at most, several KBs) so that it loads instantaneously (within the window of the page's first contentful paint). The placeholder image can be as simple as a single, solid color (e.g., Google Images or Medium) or as advanced as a blurred representation of the image (e.g., Unsplash).

An important chore that gets neglected by developers is writing documentation for their RESTful APIs. Often, this task ends up being assigned lower priority than other tasks, such as building a new feature or modifying an existing feature. Although it delivers no immediate tangible value to end users like features, documentation produces intangible value for developers and their companies. By having detailed information about a RESTful API's endpoints, developers can quickly know how to obtain authorization for protected endpoints, access and interact with certain resources, format the data that's required in a request's body, etc. Ultimately, the value in documentation comes from increased developer productivity and saved development time. The more developers the documentation serves, the more value the documentation produces. Fortunately, you don't have to spend any time or effort to manually build and maintain documentation from scratch. There are open source tools like Swagger that automate the process of designing and generating RESTful API documentation for developers. These tools: Best of all, most of these tools only need, as input, the source code of the RESTful API or a JSON representation of the RESTful API that's compliant with the OpenAPI Specification . Or, these tools can get fed other representations of the RESTful API that are based on alternative API specifications like RAML ( R ESTful A PI M odeling L anguage).

Accessing an e-mail account anywhere in the world on any device requires authenticating yourself to prove the data associated with the account (e.g., e-mail address and inbox messages) actually belongs to you. Often, you must fill out a login form with credentials, such as an e-mail address and password, that uniquely identify your account. When you first create an account, you provide this information in a sign-up form. In some cases, the service sends either a confirmation e-mail or an SMS text message to ensure that you own the supplied e-mail address or phone number. Because it is highly likely that only you know the credentials to your account, authentication prevents unwanted actors from accessing your account and its data. Each time you log into your e-mail account and read your most recent unread messages, you, and like many other end users, don't think about how the service implements authentication to protect/secure your data and hide your activity history. You're busy, and you only want to spend a few minutes in your e-mail inbox before closing it out and resuming your day. For developers, the difficulty in implementing authentication comes from striking a balance between the user experience and the strength of the authentication. For example, a sign up form may prompt the user to enter a password that contains not only alphanumeric characters, but also must meet other requirements such as a minimum password length and containing punctuation marks. Asking for a stronger password decreases the likelihood of a malicious user correctly guessing it, but simultaneously, this password is increasingly more difficult for the user to remember. Keep in mind that poorly designed authentication can easily be bypassed and introduce more vulnerabilities into your application. In most cases, applications implement either session-based or token-based authentication to reliably verify a user's identity and persist authentication for subsequent page visits. Since Go is a popular choice for building server-side applications, Go's ecosystem offers many third-party packages for implementing these solutions into your applications.

The amount of time and effort a developer dedicates towards writing a function depends on the details they choose to focus on: coding conventions, structure, programming style, etc. Suppose a group of developers is presented a high-level prompt to write the same function: given some input, return some output. For example, given a list of numbers, return a sorted list of numbers. The actual implementation of the function is left entirely to the discretion of the developer. A quick, mathematical way to evaluate each developer's implementation of this function, without any additional code, is by its time complexity . Particularly, knowing each implementation's Big-O complexity tells us how it might perform in the worst case scenario, commonly when the size of the input is very large. However, time complexity fails to account for the hardware the function is executed upon, and it does not provide any tangible, quantifiable metrics to base decisions on. Metrics such as operation speed and total execution time assign real numerical values to the performance of a function. By adding benchmarks , developers can leverage these metrics to better inform them on how to improve their code. The Go programming language has a benchmarking utility in its built-in, standard library package testing . To benchmark code in Go, define a function with a name prefixed with Benchmark (followed by a capitalized segment of text) and accepts an argument of struct type B , which contains methods and values for determining the number of iterations to run, running multiple benchmarks in parallel, timing execution times, etc. Example :