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YAML is a well-known language for generating JSON files, but as we have been generating Docker Compose for integration tests, we’ve found Jsonnet to be better. Before we discuss both languages, keep in mind that this piece is strictly to discuss the use of each language when generating JSON. By Colin Mo.

The article walks over:

• YAML pros
• YAML cons
• Jsonnet pros
• Jsonnet cons
• Examples

Jsonnet is a data serialization format and a programming language. As a superset of JSON, Jsonnet can parse any valid JSON file and add extra functionality to it. Jsonnet is specifically designed to easily work with JSON data, especially in large and complex projects, by providing features such as variables, conditionals, and loops.

On of disadvantage of YAML is its lack of support for certain types of data, such as dates and times. While YAML can handle numbers and strings, it does not have built-in support for more complex data types, which can make it more difficult to work with certain types of data. Interesting read!

Deno Fresh WASM is pretty easy to set up, just by adding a single dependency to your project. This lets you write code in Rust, compile it to WASM and then use that generated module in your Deno project. By Rodney Lab.

The article then covers:

• Setting up the project for Rust WASM
• Rust code
• Deno Fresh WASM: Rust source
• Compiling the module
• Using the module

In the post we have had a whistle-stop tour of setting up your first Deno WASM project. In particular, we saw how to use wasmbuild to quickly add a Rust WASM module to your Deno project, how to add console logs in your WASM Rust code, some basic Rust image manipulation. Excellent read with code in GitHub repo provided!

Over the course of the last few months, we at Memgraph have been working on something that we believe could be helpful with classical graph prediction tasks. With our latest newborn query module, you will have the option of performing both label classification and link prediction. By Antonio Filipovic.

The following is explained:

• Graph neural networks
• Temporal graph networks
• Amazon data example
• Exploring an Amazon data network in Memgraph

You probably already know that a graph consists of nodes (vertices) and edges (relationships). Every node can have its feature vector, which essentially describes that node with a vector of numbers. We can look at this feature vector as the representation vector of each node, also called embedding of the node. To avoid getting lost in technical details, graph neural networks work as a message passing system, where each node aggregates feature representations of its 1-hop neighbors.

Plenty of examples and code is provided as well in the article. Good read!

While more and more teams are adopting Kubernetes as their standard container orchestration technology, cost insight is lacking. Teams often don’t know how much they’re spending, where in their organization they are spending, or what is driving their infrastructure cost increases. OpenCost helps alleviate this problem by bringing real-time cost monitoring to Kubernetes workloads with a solution that encompasses both an open specification and an open source project. By Mark Poko, JuanJo Ciarlante.

Further in the article:

• How Grafana Labs uses OpenCost
  • Getting more accurate insight into vCPU and memory cost
  • Monitoring costs of a multi-cluster architecture
• How Grafana Labs will contribute to the OpenCost OSS project
  • Observability
  • Ease of use
  • Performance

To get started with OpenCost and cost observability, you can find the OpenCost code in GitHub and check out the OpenCost documentation. Nice one!

Global energy demand has rapidly increased over the last few years and looks set to continue accelerating at such a pace. With a booming middle class, economic growth, digitization, urbanization, and increased mobility of populations, energy suppliers are in a race to leverage the development of new technologies that can more optimally and sustainably generate, store, and transport energy to consumers. By Rudeon Snell.

The article deals with:

• The rising demand for energy
• Going green—Energy industry innovation abounds
• Key takeaways

As the energy industry eyes a period of unprecedented growth and change, the role of technology will become ever more profound. Good read!

A new PostgreSQL extension is now available in Supabase: pgvector, an open-source vector similarity search. By Greg Richardson.

The exponential progress of AI functionality over the past year has inspired many new real world applications. One specific challenge has been the ability to store and query embeddings at scale.

Further in the article:

• What are embeddings?
• Human language
• How do embeddings work?
• OpenAI embeddings
• Embeddings in practice
• Using PostgreSQL

… and more. Storing embeddings in Postgres opens a world of possibilities. You can combine your search function with telemetry functions, add an user-provided feedback (thumbs up/down), and make your search feel more integrated with your products. Good read!

This article will help you dive into the exciting world of AIOps, and learn why it’s a must-have. We’ll also cover how it works, its use cases, and most importantly, how to get started with Artificial Intelligence in IT Operations as a business. By Brayan Kai Mwanyumba.

This article then explains:

• What is AIOps?
• Is AIOps the same as DevOps?
• Why AIOps is important and why you need it
• Advantages of using AIOps
• How does AIOps work?
• AIOps use cases
• How to get started with AIOps

AI Ops is revolutionizing how organizations manage their IT operations. With its ability to collect and analyze data from various sources in real time, AIOps has become a game changer in the industry. Good read!

This is the journey of author to create serverless solution written in Go which enabled him to be notified about best currency exchange rates for his small business. Nice one!

Having a personal Forex bot is very useful only if it is always up and running. AWS Lambda is a virtually free solution to host a simple program like mine (or even small microservice architectures), so I refactored my code to run as a serverless function. By Loris Occhipinti.

Sometimes, a client may be willing to pay you in its local currency only. Of course, this is a slight inconvenience but, after all, setting up a multicurrency account is usually easy and inexpensive, so it might be best to accept the arrangement and plan to convert the money later.

The strategy for bot was simple: sending a notification every 2 hours with the current rate and the potential gain or loss I could make at that point in time. The most critical part was getting correct, timely data about exchanges: there are myriads of APIs that can help with this,

This is the journey of author to create serverless solution to enable hi to convert currencies at most favorable terms and rates for his small business. Nice one!

Data integration and data ingestion are two IT disciplines that are often confused with one another. Here’s how they differ and the challenges you may encounter. By Aminu Abdullahi.

Data integration combines data from different sources and transforms it into a unified view for easier access and analysis

Further in the article:

• What is data integration?
• ​​What is data ingestion?
• Common challenges of data integration and ingestion
• Data integration and ingestion tools

With the increasing amount of data being produced, businesses need better ways to handle and use the information they collect. Data integration and data ingestion are essential components of a successful data strategy and help organizations make the most of their data assets. Good read!

This blog post will cover Python web scraping packages in terms of their speed, ease of use, and personal investigations. This blog post won’t cover what webscraping is and how parsers work. By Dmitriy Zub.

The article recommendations:

• If you need to scrape data from a dynamic page that doesn’t require clicking, scrolling and similar things but still requires rendering JavaScript, try requests-html. It uses pure XPath as lxml and should be faster than the other two browser automations.

• If you need to do complex page manipulation on the dynamic page, try to use playwright or selenium.

• If you scraping non-dynamic pages (rendered via JavaScript), try selectolax over bs4, lxml or parsel. It’s a lot faster, uses less memory, and has almost identical syntax to parsel or bs4. A hidden gem I would say.

• If you need to use XPath in your parser, try to use either lxml or parsel. parsel is built on top of lxml and translates every CSS query to XPath and can combine (chain) CSS and XPath queries. However, lxml is faster.

Excellent read with charts and code to complement the comparison of each package!