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Lately, it seems as though the path to quantum computing has more milestones than there are miles. Judging by headlines, each week holds another big announcement—an advance in qubit size, or another record-breaking investment. This is Q&A with the former chief architect of Google’s Sycamore, John MartinisBy Dan Garisto.

But if you go back to one of the points of the quantum supremacy experiment—and something I’ve been talking about for a few years now—one of the key requirements is gate errors. I think gate errors are way more important than the number of qubits at this time. It’s nice to show that you can make a lot of qubits, but if you don’t make them well enough, it’s less clear what the advance is. In the long run, if you want to do a complex quantum computation, say with error correction, you need way below 1% gate errors.

I want to drill down on “scale versus quality,” because I think it’s sort of easy for people to understand that 127 qubits is more qubits.

It depends how you want to quantify it, but it’s not a huge factor. It could be a bit better if you had more qubits, but you would maybe have to architect it in a different way. I don’t think it is good for the field to oversell results making people think that you’re almost there. It’s progress, and that’s great, but there still is a long way to go.

Very interesting read! Seems, like we are still some time away from good quantum system.

We know machine learning is so popular in mobile and desktop applications. Therefore we need basic ML skills to follow this trend. By Oguz Kayra.

CreateML is a programming framework, to create apps with ML in IOS, macOS, and IpadOS (Apple Ecosystem). CreateML is facilitating using ML on projects for non-data scientists. ML power can spread many apps.

The article then does a good job explaining:

• Machine learning workflow
• Step by step: Create ML models with CreateML
  • Determine your dataset
  • Import CreateML and foundation modules
  • Create data table with CSV table
  • Separating relevant data from DataTable
  • Dividing DataTable as test and training
  • Creating model and train the model
  • Test your regression
  • Saving machine learning model

Basically, machine learning workflow is simple, get data, train models with these data, test your model, update your model, use this model in a real application.

You can find code and dataset on author’s GitHub account in this Github repository. Good read!

This is a list of recurring anti-patterns that I see when I help folks with their container build pipelines, and suggestions to avoid them or refactor them into something better. By Jérôme Petazzoni.

Many of them are harmless when used separately. But when combined, they can easily compromise your productivity and waste time and resources, as we will see.

The guide then covers the following in depth:

• Big images
  • All-in-one mega images
  • Data sets
• Small images
• Rebuilding common bases
• Building from the root of a giant monorepo
• Not using BuildKit
• Requiring rebuilds for every single change
• Using custom scripts instead of existing tools
• Forcing things to run in containers
• Using overly complex tools
• Conflicting names for scripts and images
• Building with Dockerfiles

BuildKit is a new backend for docker build. It’s a complete rehaul with a ton of new features, including parallel builds, cross-arch builds (e.g. building ARM images on Intel and vice versa), building images in Kubernetes Pods, and much more; while remaining fully compatible with the existing Dockerfile syntax. It’s like switching to a fully electric car: we still drive it with a wheel and two pedals, but internally it is completely different from the old thing.

And final note: don’t treat these recommendations as absolute rules. What author is saying is “hey, careful, if you do this, it can have unexpected consequences; look, here is what I suggest to improve the situation”. Nice one!

Well-architected describes the key concepts, design principles and architecture best practices for designing your cloud workloads. It balances best practices with business goals to determine the optimal outcome. By Gerald Stewart.

Created by AWS Solutions Architects using the lessons Amazon has learnt from running thousands of systems at a massive scale, it enables developers to compare their workloads against the rigorous standards that AWS holds itself against.

The framework covers these pillars:

• Operational Excellence - To be able to monitor and support workloads effectively, to enable continuous improvement and to deliver business value
• Security - Improving your security posture by taking advantage of Cloud technologies to protect your assets and systems
• Reliability - Ensuring your workload can perform it’s intended function correctly and consistently
• Performance Efficiency - Ensuring appropriate resource allocation to enable performant systems and a positive end user experience
• Cost Optimisation - Ensuring you are delivering business value at the lowest possible cost

If I could recommend one topic of learning to a junior engineer or someone looking to up their game designing and building on AWS it would be to read the Well-Architected framework and understand how to apply it.

Well-Architected should be applied both continuously with reviews carried out at regular intervals. Understanding the pillars, core concepts and reasons behind the pillars of the Well-Architected framework can enable you to make better architecture decisions and avoid rewrites or large changes after a full-scale review has been carried out. For deeper insights follow the link to the full article. Good read!

The AWS Cloud Development Kit (CDK in short) is a relatively new framework for defining cloud Infrastructure as Code (IaC). CDK allows to easily define your cloud infrastructure by importing classes (resources) and instantiating objects. The resulting definition can be versioned in GIT, easily shared and used in command-line tools (for example in CI/CD pipelines). By Maurizio Bonani.

The underlying concept is that you can mutate your infrastructure through code, the same way you mutate any other application.

By using CDK you can fully leverage the ecosystem of your favorite programming language. This means that you can use your favorite code editor and get all the niceties such as syntax highlighting, type checking and auto-completion. Also with CDK, your infrastructure can be as testable as any other code you write.

Autor needed to run a periodic task to perform a basic ETL (Extract Transform Load) process on a MySQL database. The article describes the following steps to achieve it:

• What is Fargate
• Terminology
• The code
• Cluster
• The definition
• Containers
• Service

That’s it. In this article we learned how to use CDK to provision a scheduled workload on AWS Fargate. Plenty of examples of the code which you can directly use with CDK to follow the article and deploy the solution. Perfect!

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted scaleable cloud platform. AWS offering over 200 fully featured services from data centers globally. This tutorial will teach you how to create serverless function in Typescript and deploy it to AWS. By Abdul Waqar.

import * as cdk from "@aws-cdk/core";
import * as lambda from "@aws-cdk/aws-lambda";
import * as apigw from "@aws-cdk/aws-apigateway";

export class HelloLambdaStack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string, props?: cdk.StackProps) {
    super(scope, id, props);
    const hello = new lambda.Function(this, "HelloHandler", {
      runtime: lambda.Runtime.NODEJS_10_X,
      code: lambda.Code.fromAsset("lambda"),
      handler: "hello.handler",
    });

  }
}

The article then in brief steps describes:

• What is AWS and CDK?
• Lambda Function
• Amazon API Gateway
• Lets write code for Lambda Function
• Deploy Lambda Function using CDK

Congratulation! You have successfully deployed your first Lambda Function and API Gateway to call Lambda Function on AWS Cloud. Good read!

Let’s be honest: lots of things in web development are harder than they should be. Some days, it can seem as though everything in the frontend world is needlessly over-engineered and convoluted. By Josh Collinsworth.

At a basic level, you could think of Svelte as a frontend user interface (UI) framework akin to React, Vue, etc. Svelte is the newest of the big names in the space, however, and it definitely seems as though it’s learned from the others, in terms of both developer experience and optimization.

The article then covers:

• What is Svelte?
• How is Svelte different?
• Comparing Svelte to React and Vue
• Build over browser
• Authoring Svelte components
• CSS in Svelte
• Props and component communication
• What to know about Svelte

Just about everywhere I’d normally be reaching for a workaround or tripping over a gotcha when using another framework, Svelte was delightfully straightforward.

You will get practical example and detailed comparison between frameworks how and why these examples are implemented. We liked this observation: When you’ve been living in framework land long enough, it’s easy to forget the reason you need a package in the first place is often compatibility with (or the need to work around) the framework itself. Nice one!

According to the Statista, in the Forecast number of mobile users worldwide 2020-2025 report, the number of mobile users worldwide reached 7.1 billion in 2021, and this number is projected to grow. This initiates a new set of use cases for edge devices due to the explosive growth of network-connected entry points. By Dafné Mendoza.

The article main points:

• Edge challenges here, and now
• IoT, 5G, containers and microservices
• How do I start automating edge devices?

Automation is a long-term journey, moreover if you face the complexities that edge environments already pose. However, there are some golden rules to start:

• Standardization. Automation at any scale requires consistency;
• Use cases selection for automation should include simple but business relevant use cases, and allow all stakeholders to participate.
• Keep automation simple. IT infrastructure already has a lot of complexities
• Think about future scale and consumption by choosing the tooling wisely.

The article contains plenty of useful information. Good read!

When designing a product, you often strive to achieve perfection by building a product with advanced features and offerings. You might find that rather than perfection, you only end up wasting time and resources on a product that you’re not completely sure users will use. In other words, it’s a demanding process that doesn’t guarantee success. By Adam Fard.

The article then describes:

• Lean UX vs Waterfall UX vs Agile UX
• Why Lean over Waterfall UX?
• Lean vs Agile
• Lean UX design process

As mentioned earlier, lean UX design is powered by three main concepts: think, make, and check. The thinking stage refers to making assumptions – and later hypotheses – about your product. Assumptions are usually born in workshop sessions that involve the whole team. Processes of the thinking phase include:

• Competitive analysis
• Stakeholder interviews
• Ideation
• Storyboards
• Mood boards
• Value proposition

The aim of the thinking stage is to identify problems and discuss solution pathways. Focus on the “why” and not the “how” to generate assumptions about your product. To learn more follow the link and read the rest of the article. Good read!

NumPy is a valuable and efficient tool for dealing with large amounts of data. NumPy is quick; thus, it’s easy to work with a vast amount of data. Data analysis libraries like NumPy, SciPy, Pandas, and others have exploded in popularity due to the data science revolution. By Sonia Jessica.

# Accessing Range of Elements
# in array using slicing
val_mul=np.array([(3, 4, 2, 5),(3, 6, 2, 4),(1, 5, 2, 6)])
slice_val = val_mul[:2, :2]
print ("First 2 rows and columns of the array\n", slice_val)

NumPy implements multidimensional arrays and matrices as well as other complex data structures. These data structures help to compute arrays and matrices in the most efficient way possible. NumPy allows you to conduct mathematical and logical operations on arrays. Many other prominent Python packages, like pandas and matplotlib, are compatible with Numpy and use it.

The article then describes in some detail:

• What is a NumPy Array?
• Types of Numpy Array
• Accessing element in the Array

There are many code examples in article with explanation. Good read!