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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!

This is just a personal journey documentation on how I decided to use my current tech stack. By Camilo.

Over the years I have tried different frameworks, mostly in PHP, like Code Igniter (2010), ProcessWire (2014) and Laravel (2015). They helped me complete different projects with diverse complexity. They are wonderful tools. But sadly most of the jobs I managed to land were using legacy versions of PHP and the codebase and developer experience was spartan to say the least.

I really loved the idea of LiveView and how it wasn’t needed a separate frontend framework to achieve a SPA like experience.

Author the dives into comparison of prons and cons for various frameworks and languages:

• Vapor and Swift
• Masonite and Python
• Springboot and Java
• Adonis.js and JavaScript
• Elixir and Phoenix

Author also looked at the job market for each compared framework to assess his future job opportunities. A functional language took time for him to understand the conventions and workflows. It was like a whole new world. Interesting read!

There’s been massive innovation in the database and backend space for developers building applications with serverless and edge compute. There are new tools, companies, and even programming models that simplify how developers store data. By Lee Robinson.

This post will be an overview of databases that pair well with modern application and compute providers.

This comparison dives into:

• A new programming model
• Trends
• Databases
  • Established serverless solutions
    • Firebase
    • MongoDB
    • MySQL
    • PostgreSQL
  • Rising serverless database solutions
    • Fauna
    • Convex
    • Grafbase
    • Neon
    • Supabase
  • Stateful backends and other solutions

While there are new companies creating serverless-first storage solutions, a new programming model is required for workloads to be compatible with serverless compute and modern runtimes. Interesting one!

The Go Execution Tracer (aka runtime/trace) was designed to achieve low enough overhead to be usable on “a server in production serving live traffic”. This is achieved by writing events into per-P buffers, using RDTSC for timestamps, and encoding into a relatively efficient binary format. By Felix Geisendörfer.

Stack unwinding (aka stack walking) is part of the process for taking a stack trace. It involves iterating over all stack frames and collecting the return addresses (program counters) in each frame. It may also involve expanding this list if some of the program counters are part of inlined function calls.

Source: https://blog.felixge.de/reducing-gos-execution-tracer-overhead-with-frame-pointer-unwinding/

So why is stack unwinding so expensive in Go? The short answer is because Go uses a form of asynchronous unwinding tables called gopclntab that require a relatively expensive lookup in order to traverse the stack frames. The gnarly details of this mechanism can be found in the gentraceback function. Follow the link to the full article to learn more!

Microservices have been quite popular in the Java ecosystem ever since Spring Boot and Spring Cloud made them easy to build and deploy. Things have gotten even easier in recent years with the proliferation of new Java frameworks built specifically for microservices: MicroProfile, Micronaut, Quarkus, and Helidon. By Matt Raible.

The tutorial contains info about:

• Java microservices with Spring Boot and JHipster
• A quick introduction to Module Federation
• Why should Java developers care?
• Micro frontends in action with JHipster
• Micro frontend options: Angular, React, and Vue
• Build Java microservices with Spring Boot and WebFlux
• Switch identity providers

… and more. This tutorial explained how to use micro frontends within a Java microservices architecture. Author likes how micro frontends allow each microservice application to be self-contained and deployable, independent of the other microservices. It’s also pretty neat how JHipster generates Docker and Kubernetes configuration for you. Great read!