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Microservices and APIs are becoming famous everywhere in the software development space with the increasing need to produce scalable, secure, and flexible applications at faster turnaround times. By Durga Prasad Acharya.

But many people confuse between them, and when it comes to developing a software application, they don’t know what will be suitable for them This article will compare microservices vs API with an aim to put an end to all your confusion so you can decide the best way to build and deploy your application.

The article contains information on:

• What are microservices?
• What is an API?
• Microservices vs API: How do they work?
• Microservices vs API: Benefits of each
• Microservices vs API: What are they used for?
• Microservices vs API: Similarities and differences
• Can microservices and API work together? How?

Microservices and API can work together in an application. Although they can exist separately, using both together in your application can help organizations effectively implement the microservices architecture. Many companies face difficulties deploying microservices architecture when they already have other architectures deployed. In addition, integrating multiple, smaller services and benefitting from them is problematic. Interesting read!

Automation has found its way into almost all industries. Many industries have adopted automation to reduce human efforts and boost productivity to meet customer demands. Recently, automated farming robots are being used for various agricultural practices. They minimize the intensive hard work required for farming and save a lot of time. By Amna Faryad.

As a result, the agriculture industry has adapted advanced approaches to tackle different uncertain situations like reducing human errors and costs. The article then explains:

• Smart agriculture
• Robots for planting
• Irrigation drones
• Harvest robots
• Weeding robots
• Robots for pruning

With the use of these automated farming approaches, it has become easy for farmers to increase their land’s productivity even in the absence of enough labor. Automated farming robots use artificial intelligence technology and computer vision to increase their efficiency for the effective use of available resources. Interesting read!

As robots make their way into a variety of real-world environments, roboticists are trying to ensure that they can efficiently complete a growing number of tasks. For robots that are designed to assist humans in their homes, this includes household chores, such as cleaning, tidying up and cooking. By Ingrid Fadelli , Tech Xplore.

Stir-fry, the cooking style that the team focused on in their recent paper, involves complex bimanual skills that are difficult to teach to robots. To effectively do this, Liu and his colleagues first tried to train a bimanual coordination model known as a “structured-transformer” using human demonstrations.

Researchers at the Idiap Research Institute in Switzerland, the Chinese University of Hong Kong (CUHK) and Wuhan University (WHU) have recently developed a machine learning-based method to specifically teach robots to master stir-fry, the Chinese culinary cooking technique. Their method, presented in a paper published in IEEE Robotics and Automation Letters, combines the use of a transformer-based model and a graph neural network (GNN).

Food preparation and cooking are two crucial activities in the household, and a robot chef that can follow arbitrary recipes and cook automatically would be practical and bring a new interactive entertainment experience. The researchers assessed their model’s performance both in simulations and on a physical two-handed robotic platform, known as the Panda robot. In these tests, their model allowed the robot to successfully and realistically reproduce the motions involved in stir-fry. Interesting read!

It’s common sense that being part of a thriving startup community can bring immense advantages to your own project. Yet, not all startup founders have the opportunity or desire to move to Silicon Valley, New York, London, Beijing, or any of the other big startup hubs. By Abdo Riani.

If you are serious about building a tech startup, it’s a great idea to create a strong connection with at least one investor, mentor, or partner located in an active startup hub.

In this article, we’ll talk about how to find a productive compromise. The article captures:

• Access to knowledge
• Connectedness and access to talent
• Access to capital

Being connected to a thriving ecosystem is a crucial factor for the success of any startup project. It’s hard to be successful in a high-value-added field if you have to start by reinventing the wheel, so access to fellow successful startup founders, mentors, and knowledgeable partners is an advantage that’s hard to compensate for. Good read.

Learning how to install and manage Kubernetes clusters can be a big obstacle between your organization and all the potential magic of a container orchestration platform. You have to consider tasks like provisioning machines, choosing OS and runtime, and setting up networking and security. By Fer De Oliveira @google.

When adopting containers, you should always ask yourself: Does my startup or tech company have the skills, resources, and time to maintain, upgrade, and secure my platform?

The main sections of the article:

• The benefits of embracing managed services
• Always be building towards an open future

Containers and managed services based on open standards are a powerful combination that allow you to take advantage of best-of-breed capabilities on any platform, while simultaneously standardizing skills and processes. As a leader at a startup or tech company, you’re always looking for ways to move faster, work more efficiently, and make the most of the technical talent you have. You want to spend more time on roadmap priorities and spend the minimum amount of resources on maintaining your infrastructure. Good read!

Since automating IAM remediation requires recurring parsing of JSON documents, modulating your parsing function(s) will save you time as you work on each control. If you have controls that pertain to trust policies, you will need a separate function to parse those out since the format differs from that of permission policies. By Cody Bench.

Automating IAM policy remediation is no walk in the park. A development team must:

• Identify the controls to be remediate
• Build a program that pulls down and parses out the roles and policie
• Validate the permission and trust policies against the necessary control
• Perform the required remediation - which may include checking action history
• Rebuild the JSON document
• Deploy the remediated artifacts per your company’s or client’s workflow

Having any way to access the historical data for the roles and/or policies you are remediating is a huge win when implementing least privilege. A stakeholder will need to decide the cutoff for the last time an action was used or a resource was accessed, whether that be a few months or a year prior to remediation. Excellent read!

A guide to mathematical optimization with Google OR-Tools. Linear programming is a technique to optimize any problem with multiple variables and constraints. It’s a simple but powerful tool every data scientist should master. By Maxime Labonne.

Fortunately for us, there is a method that can solve our problem in an optimal way: linear programming (or linear optimization), which is part of the field of operations research (OR). In this article, we’ll use it to find the best numbers of swordsmen, bowmen, and horsemen to build the army with the highest power possible.

You can run the code from this tutorial with the following Google Colab notebook.

The article then describes:

• Solvers
• Variables
• Constraints
• Objective
• Optimize!

In Python, there are different libraries for linear programming such as the multi-purposed SciPy, the beginner-friendly PuLP, the exhaustive Pyomo, and many others. Excellent read!

Open source software is being vilified once again and, in some circles, even considered a national security threat. Open source software risk has been a recurring theme: First it was classified as dangerous because anyone could work on it and then it was called insecure because nobody was in charge. By Josh Bressers.

Let’s consider where open source stands today. It’s running at minimum 80% of the world. Probably more. Some of the most mission-critical applications and services on the planet (and on Mars) are open source. The reality is, open source software isn’t inherently more risky than anything else. It’s simply misunderstood, so it’s easy to pick on.

The article then discusses:

• Myth 1: Open source software is a risk because it isn’t secure
• Myth 2: Open source software is a risk because it isn’t high quality
• Myth 3: Open source software is a risk because you can’t trust the people writing it
• The true risk of open source software

In an era where the use of open source software is only increasing, the true risk in using open source — or any software for that matter – is failing to understand how it works. In the early days of open source, we could only understand our software by creating it. There wasn’t a difference between being an open source user and an open source contributor. Nice one!

Practical quantum computers could soon arrive with profound implications for everything from drug discovery to code-breaking. By Sascha Brodsky.

One of the biggest challenges in quantum computing today relates to how we can make superconductors perform even better.One of the biggest challenges in quantum computing today relates to how we can make superconductors perform even better.

Making practical quantum computers could hinge on finding better ways to use superconducting materials which have no electrical resistance. In a step toward building better quantum machines, researchers at Oak Ridge National Laboratory recently measured the electrical current between an atomically sharp metallic tip and a superconductor. This new method can find linked electrons with extreme precision in a move that could help detect new kinds of superconductors, which have no electrical resistance.

Better superconductors may be key to making practical quantum computers. Michael Biercuk, the CEO of quantum computing company Q-CTRL, said in an email interview that most current quantum computing systems use niobium alloys and aluminum, in which superconductivity was discovered in the 1950s and 1960s.

While we see small advances in each of the indicated technological directions, combining them into a good working device is still elusive. The ‘Holy Grail’ of quantum computing is a device with hundreds of qubits and low error rates. Scientists can’t agree on how they will achieve this goal, but one possible answer is using superconductors. Interesting read!

Probably the biggest reason not to mix refactorings with behavior changes is that it makes it too easy to make a mistake. By Jason Swett.

When you look at the diff between the before and after versions of a piece of code, it’s not always obvious what the implications of that change are going to be. The less obvious the implications are, the more opportunity there is for a bug to slip through.

When you mix refactoring with behavior changes, it’s hard or impossible for a reviewer to tell which is which. It makes a discussion about a code change harder because now the conversation is about two things, not just one thing. This makes for a potentially slow and painful PR review process.

How to approach refactorings instead:

• Set aside my current feature branch
• Create a new branch off of master on which to perform my refactoring
• Merge my refactoring branch to master (and preferably deploy master to production as well)
• Merge or rebase master into my feature branch
• Resume work on my feature branch

If developer deploys a behavior change that was mixed with a refactoring, and then discovers that the deployment introduced a bug, she/he won’t know whether it was the refactoring or the behavior change that was responsible because the two were mixed together. Good read!