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This article demonstrates how to use Arm64EC in a C++ application. The application you will implement performs the multiplication of two pseudo-randomly generated square matrices. By Dawid Borycki.

With the advent of complex machine learning and artificial intelligence models, you need fast and energy-efficient hardware. For fast, efficient hardware, Arm has introduced an architecture used by most chips in embedded and mobile systems. It has been adopted for laptops, desktops, and servers and enables you to accelerate your applications.

Further in the article:

• App Porting with Arm64EC
• How It Works
• Arm64EC in Action

The Arm64EC ABI enables transparent and direct interoperability with your existing x64 dependencies, meaning you can use x64 and Arm64EC in the same process. This is possible because Arm64EC code is compiled with the same pre-processor architecture definitions as x64 code, and the Arm64EC ABI is compatible with the X64 ABI. Consequently, you can gain performance by speeding up the computation of intense code by employing Arm architecture without porting everything simultaneously to Arm64.

This article described the Arm64EC architecture, then demonstrated how to configure the Windows Dev Kit 2023 for Visual Studio development. It also showed how to use this environment to implement a C++ application and then how to implement it on an Arm64EC device. Finally, it confirmed the performance boost by switching from x64 to Arm64EC on an Arm-powered device. Nice one!

In this article, author has created a serverless Express API and deployed it on AWS, Azure, and GCP to evaluate each platform’s performance. After conducting load tests using K6, I found that Azure and AWS performed comparably well (slightly better for Azure), while GCP lagged due to significant cold starts (up to 5 seconds when speaking to a Database). By Aurélien BETTINI.

While creating an Express API for a personal project, I wondered if it was possible to deploy it on multiple Clouds. Having the flexibility to link my website to whichever platform offers the best service for my project would be advantageous. For example, choosing Azure if I wanted to access Azure OpenAI Service.

The article main parts:

• Why serverless?
• The API
• API deployment
  • Deployment on AWS
  • Deployment on GCP
  • Deployment on Azure
• Database deployment
• Load testing with K6
• Location parameter
• Function parameters
• K6 parameters
• Results (/newsletter API) function + databases

On the deployment side, it was quite fun to deploy the same Serverless application on multiple Clouds. You can definitely transfer your knowledges from one to another. GCP and AWS looks really similar and deploying the app on both was pretty fast. Azure, on the other end, has some different concepts. Good read!

Headless WordPress + GraphQL + SvelteKit as any other stack where you have a backend (data store), a frontend (the website), and a way to connect the two. In this case, the backend is WordPress, the frontend is SvelteKit and the way to connect the two is GraphQL. By Justin Ahinon.

This guide then will explain:

• Why even use WordPress in headless mode?
• How would that work?
• Setting up WordPress
• Creating a type-safe GraphQL client in SvelteKit

Since the visual presentation will be handled by SvelteKit, we will use WordPress to store our data; posts, pages, custom post types, custom fields, etc. There are code examples also included. Good read!

Older but still relevant article dealing with question of technical patterns and how they are filled with myths and mishaps. Quite often, this happens to CQS and CQRS. By Oskar Dudycz.

Command and Query Responsibility Segregation uses the same definition of Commands and Queries that Meyer used and maintains the viewpoint that they should be pure. The fundamental difference is that in CQRS objects are split into two objects, one containing the Commands one containing the Queries.

Greg Young

Some myths on CQRS topic that author will try to explain:

• You need two databases
• You have to use a messaging queue (e.g. RabbitMQ or Kafka)
• It’s hard to apply and complicates the architecture
• You’ll be facing Eventual Consistency
• You need to do Event Sourcing

Nothing prevents the Write Model and the Read Model from having the same structure or using the same tables. Nothing then stops you from using a relational database, even the same table for reading and writing. If you prefer, you can continue to use ORM. CQRS allows you to have different storages for different business cases. You can, for example, have a relational database on the write model and document database on the read model (e.g. Elastic Search for full-text search) or any other combination. The article also provides a great selections of links to further reading on the topic. Nice one!

Secret values such as API keys, passwords, and certificates need to be safely handled throughout the software development process and your app’s runtime. Exposure of secrets can be catastrophic, as unauthorized actors could use the credentials to perform privileged interactions with your services. By James Walker.

Secrets are often encountered when you’re working with Docker containers. It can be challenging to correctly handle container secrets because Docker historically lacked a built-in secrets management system. In this article, you’ll learn how to securely use secrets when you’re building Docker images and starting containers.

Article will cover:

• Why you need Docker secrets management
• Using secrets in Docker
• Using secrets with Docker Compose
• Using Docker Swarm secrets
• Dockerfile secrets when you’re building images
• Best practices for Docker secrets

Secrets are sensitive values such as API tokens and passwords that your application requires. Losing a secret usually empowers bad actors by providing privileged access to your services, making it essential to apply proper treatment to their storage and retrieval. Secrets mustn’t be hardcoded or stored in plaintext as this expands their audience and makes them difficult to rotate. Good read!

In an increasingly sustainability‑conscious world, cloud software, services, and infrastructure are no longer evaluated only in terms of performance, resilience, scalability, and reliability, but also according to their environmental impact. So, considering cloud sustainability means tools impact on the environment needs to be observed. By aster.cloud.

The article then makes a good job explaining:

• Towards sustainable development
• What is cloud sustainability
• The rise of cloud sustainability, tools, communities and awareness
• kube‑green
• CNCF environmental sustainability working group
• Green software foundation

Sustainability is a systemic and universal issue. It needs to be addressed comprehensively, aligning the behaviors of individuals, companies, and institutions with the 17 goals identified in the 2030 Agenda for Sustainable Development. Good read!

Getting featured on the HN front page is high on every Indie Hacker’s bingo card, not to mention “hacking Hacker News.” However, we found a way to consistently get to the front page using a repeatable process that you can use too. By Iron Brands.

The article then describes how authors approached their research for ranking on HN:

• Understanding Hacker News
• Our Strategy: The war room
• Side-effects

Authors then explain how you must provide value. As explained, there are two ways to approach this, but without value, no front page. Stay on top of relevant news in your niche that is also relevant to the HN audience. If you want to recreate authors bot, check Adriaan’s Twitter, or feel free to send me a message. Be the first one to act on the news + add perspective to the news item. Obviously, it does not work all the time. We’ve posted enough stuff that we thought was relevant and spent time on that was just not interesting enough. Interesting read!

With the LangChain library, you can conveniently generate Cypher queries, enabling an efficient retrieval of information from Neo4j. By Tomaz Bratanic.

If you have developed or plan to implement any solution that uses Large Language Models, you have most likely heard of the LangChain library. LangChain library is the most widely known Python library used to develop applications that use LLMs in one or another capabilities. It is designed to be modular, allowing us to use any LLM in any available modules, such as chains, tools, memory, or agents.

Further in the article:

• What is a knowledge graph
• Setting up Neo4j environment
• Knowledge Graph Cypher Search

Graph databases are an excellent tool for retrieving or analyzing the connections between various entities like people and organizations. In this blog post, we looked at a simple shortest path use case, where the number of relationships and the sequence of relationship types is unknown beforehand. These types of queries are virtually impossible in a vector database and could also be quite complicated in a SQL database. Nice one!

Using directives and plugins in Apollo Server 4 to customize the introspection query output. Once you have created your first schema and your GraphQL server is up and running, it makes sense to implement precautions to prevent it from being compromised by bad actors. By Florian Fuchs·

This article will explore excluding fields from an introspection request without disabling the server’s discoverability feature by completely turning off the introspection queries. You will then learn:

• The result of hiding parts of the schema on introspection
• Taking a look at GraphQL schema introspection
• Directives in Graphql can apply custom logic
• Formalizing the @hidden directive
• Why does the directive alone not work?
• Plugins in Apollo server allow you to hook into the request life cycle
• An implementation tells more than 1000 words!

First, this is, by no means, a safeguard for your server. Even if we omit certain fields and types from the introspection response, they are still part of the schema and will be available normally. Secondly, you must ensure that any reference to other definitions you have made must also be hidden. Always double-check if you are not breaking other tools that are relying on your schema by carelessly using the hidden directive 🙌 Good read!

Having the ability to identify clear root cause for incidents in production workloads is valuable, the best way to prevent future incidents is to understand what caused past ones. Of course you can have a good set of load tests but still, incidents in production environments can happen and it’s crucial to have available data that can help to understand what happened so application can be improved and prevent replications. By LuisLopes.

In this article we will learn how to use Application Insights and automatic profiler to find root cause for past incidents and prepare logging platform to ensure meaningful data for analysis. The guide goes over:

• Check configuration
• Isolate relevant data in a concrete scenario
• Finding the latency source
• Other tools
• Checking profiler trace using Windows Performance Analyzer

Having the profiler option enabled in Application Insights is a great way to root cause past events without having to wait for problem to happen again to collect fresh data. Traces can be isolated, verified and changes applied avoiding future incidents. In this article is shown how to isolate relevant data in timeline, locate traces and use them to root cause analysis. Good read!