One of the reasons developers love Rust is its well-developed ecosystem. Clippy, a linter for the Rust code, is one of the main components in this ecosystem. It performs additional checks of the developed code reporting found issues and explaining how to fix them (and sometimes it even can fix them automatically). Its usage may be beneficial for Rust beginners and even professionals. In this article, I describe this tool and explain how to start using it.
Rust has been recently named as the most loved language on Stackoverflow one more time. Currently it is not widely used in professional development, therefore developers often develop small programs to try it out. One obvious choice for these programs are different command line utilities, thus the number of such tools developed in Rust constantly grows. On crates.io, there is even a separate category devoted to these programs. Therefore, I have decided to write an article about the utilities I find useful in order to have their list in one place. I plan to update this article as I try more utils.
In the previous article comparing the JAMstack services of two popular providers, I have mentioned that one of my incentives of moving to Cloudflare was its basic server-side analytics provided even for the free tier users. Extended analytics is available on both Cloudflare and Netlify as a paid option: on Cloudflare you have to subscribe to one of the paid accounts (the cheapest is “Pro” plan that costs 20 US Dollars per month); on Netlify you can either subscribe to “Business” plan for 99 US Dollars per member per month, or you can enable this feature for every your site for just 9 US Dollars a month. If you need an accurate web analytics data, I definitely recommend you choosing one of these options because, as my analysis in this article shows, the client-side analytics solutions (e.g., Google Analytics, Yandex Metrica or Microsoft Clarity) overlook a large portion of visitors’ interactions due to different anti-tracking solutions (e.g., personally I use uBlock Origin plugin for my web-browser). In this article, I show how much data you may overlook.
The core idea of the JAMstack architecture (JavaScript, API and Markup) is to substitute dynamic webpages with pre-rendered from a template static pages that use different services exposed through Javascript API to offer dynamic features. During the last several years, the JAMstack architecture has been becoming more and more popular especially due to the proliferation of serverless paradigm. This architecture also gains its interest among bloggers who appreciate full control over their websites. Seeing the rapid development of JAMstack, several companies have started offering services for this architecture: automated builds, build previews, CDN, caching, SSL certificate provisions, hosting. Using these platforms, it is now quite easy for developer-bloggers to launch a website. To my point of view, these platforms may take a piece of a pie from traditional blogging platforms.
For the last three years, I used Netlify to host my website. I was pretty happy with the services this company provides, especially considering that they were free of charge for me. However, recently Cloudflare has also launched its JAMstack platform called Cloudflare Pages, and I decided to try it myself in order to discover its pros and cons. In this article, I compare the services provided by these companies from a blogger perspective, and share my opinion when each of them should be used.
In the article describing Tmux, as an example I have shown the script that creates an environment for writing content for my Hugo website. However, this script is not very convenient if I need to start writing a new blog post (the action that I do most often): I have to create an environment, then I must create new directory for a post, copy there a template and modify the parameters in the preamble (at least, I have to add the date and the title). Therefore, in order to facilitate this process I have developed a new script used to create an environment for writing new blog posts. In this article, I share this script and explain how it works so that you can adapt my experience in your setup.
Nowadays, it is a quite popular to store semi-structured information using JSON format. Indeed, JSON files have quite simple structure and can be easily read by human beings. JSON syntax allows one to represent complex dependencies in data and avoid data duplication. Moreover, all modern programming languages have libraries that facilitate JSON parsing and storing data into this format. Not surprisingly, JSON is extensively used to return data in Application Programming Interfaces (APIs) .
At the same time, data analysts prefer to deal with structured data represented in the form of series and dataframes. Unfortunately, transforming JSON data into structured format is not that straightforward. Previously, I preferred to develop code to parse manually complex JSON files and create a pandas dataframe from the parsed data. However, recently I have discovered a pandas function called json_normalize that saved me some time in my projects. In this article, I explain how you can start using it in your projects.