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Authors share the second report in our series of findings from the Gender Balance in Computing research programme, which they’ve been running as part of the National Centre for Computing Education and with various partners. By Katharine Childs.

In this research programme, funded by the Department for Education in England, we aim to identify ways to encourage more female learners to engage with Computing and choose to study it further.

The study main points are:

• Computing education through a collaborative lens
• Researching collaborative learning in the primary computing classroom
• What can we learn about pair programming from the study?
• Find out more about pair programming
• Collaboration in our research

The evaluators also gathered qualitative data by running teacher and learner interviews and conducting lesson observations, and they were pleased that this data provided some rich insights into the benefits of using a pair programming approach in the primary classroom, and gave some promising indications of possible benefits for female learners in particular. Good read!

The standard academic literature is most useful for the extreme frontend (parsing) and the extreme backend (SSA, instruction selection and code generation), but the middle-end is ignored. This is fine if you want to learn how to build, e.g., the next LLVM: a fat backend with a very thin frontend. By Fernando Borretti.

Imagine a compiler as a 2D grid. The rows are the stages of compilation: from parsing, through the middleend, to the backend. The columns are the different facets of the programming language being implemented. For a smaller language like C, the different facets are: global variable declarations, enum declarations, type declarations, function declarations, and nothing else. More featureful languages add extra facets: interface and class definitions in C++, trait and impl definition in Rust, etc.

This article contains some of the lessons I learned writing the compiler for Austral, a new systems programming language with linear types that I’ve been working on for a while. The first few sections are high-level, the rest most specific to using OCaml to write a compiler.

The article then dives into:

• Implementation strategies
• Just use OCaml
• Just use Menhir
• What tests are useful
• Compilation model
• Code organization
• Bootstrapping issues
• The environment
• Identifying declarations
• Errors
• Remarks

There are, broadly, two ways to implement a compiler. In the waterfall strategy, each stage is implemented one after the other: the complete parser, then the complete semantic analysis pass, then the complete type checking pass, and on to the backend stages. Excellent read!

Get, add and remove JSON objects from an array. By Rob Tomlin.

CREATE INDEX idx_purchases_name
 ON public.purchases
 ((items_purchased ->> 'name'));

In this article, author will work with a jsonb column containing an array of objects and explore how to:

• Get all objects in the array
• Get a specific object from the array by position in the array. When the position is known and when we have to discover it
• Add a json object to the array
• Remove a specific object from the array
• Indexing including indexing a key in the JSONB column

PostgreSQL has many functions and operators for JSON data. Links to further reading also provided in the article. Nice one!

Today, Node.js is widely used in web and mobile applications, for it fills the gap between front-end and back-end applications. Unfortunately, increased usage of Node.js has opened new doors for attackers to exploit misconfigurations and vulnerabilities. By Lahiru Hewawasam.

The article main content is split into:

• Validate user inputs
• Using a reverse proxy to add a layer of security
• Managing application secrets
• Using HTTP response headers
• Server-side logging and monitoring
• Using security linters that capture vulnerabilities in code

Code linters help developers identify various issues in the code before compiling. They can detect the most common issues and force developers to follow best practices.Good read!

Did you know that you can abort an API request in JavaScript? By aborting, I mean canceling a request before it is completed (before you get a response or before the request gets to the server). By Dillion Megida.

There are many reasons why you may want to do this. One of them is to prevent a request from being sent to the server if the user has already navigated away from the page the request was made.

The article the focuses on:

• The AbortController interface
• Aborting API requests in fetch
• Aborting API requests in axios

Even for new methods of making API requests, by accepting a signal property in their request options object, you can abort requests using the abort() method. Good read!

One of the most significant benefits of serverless is that the development teams can fully own and manage all of their infrastructure themself. Keeping all of that infrastructure in the same CloudFormation stack can sometimes be troublesome however, and there are many reasons why you might consider having parts of it in a separate stack. By Sebastian Bille.

services:
  infrastructure:
    path: infrastructure

  application:
    path: application

A common approach is to keep stateful infrastructure, such as your DynamoDB tables or S3 buckets, in one stack, and stateless infrastructure, such as your Lambda functions or API gateways, in another.

This post will show you how you can use Serverless Compose to easily separate your stateful infrastructure into a separate stack and deploy it without the hassle! Serverless Compose is a feature of the Serverless Framework that simplifies orchestration and configuration when you have multiple related CloudFormation stacks. Apart from providing a simple configuration format for orchestrating and defining the services and their respective dependencies, it also provides CLI tooling to interact with those services once deployed, for example to stream the logs of those services directly to your stdout at the same time. Good read!

In reinforcement learning, self-learning agent learns how to interact with the environment and solve a problem within it. In this article, we present complete guide to reinforcemen learning and one type of it Q-Learning (which with the help of deep learning become Deep Q-Learning). We learn about the inspiration behind this type of learning and implement it with Python, TensorFlow and TensorFlow Agents. By Nikola M. Zivkovic.

In supervised learning, an agent learns how to map certain inputs to some output. The agent learns how to do that because during the learning process it is provided with training inputs and labeled expected outputs for those inputs. Using this approach we are able to solve many types of problems, mostly the ones which are classification and regression problems in nature. This is an important type of learning and it is mostly used commercial approach today.

Here are the topics that author covers in this article:

• Introduction to Reinforcement Learning
• Q-Learning Intuition
• Q-Learning implementation with Python
• Deep-Q Learning Intuition
• Deep-Q Learning implementation with TensorFlow

In this article we explored Deep Q-Learning. This is the first type of reinforcement learning that utilize neural networks. Plenty of code examples in Python in the article. Good read!

Today’s workplace is more digitized than ever before – especially in tech. Work-from-home adjustments and nearshore outsourcing has surged in the last two years, requiring the modern manager to adapt quickly. By Steve Taplin.

Working remotely hasn’t changed the core of good management practice but has inspired reevaluating priorities and updating traditional methods. These 5 top tips will help you navigate this new digital workspace with grace and poise:

• Focus on the destination
• Preserve the workday
• Keep it personal (within reason)
• Follow up consistently
• Build a culture of innovation

Modern hybrid workspaces and digital offices offer opportunities never before seen in business. With skill, the right team can deliver exceptional project results from traditional offices, homes and nearshore without compromising the standard workday. Good read!

As one of the most common data structures, the linked list has to be one of the simplest in concept; yet still very powerful. In this post we will be looking at what linked lists are; what types of linked lists there are; when to use a linked list; and whether or why a linked list might be better than an array. By Doeke Norg.

A linked list is a data structure; a way of organizing data that can be used in a particular way. In its most basic form a linked list is a single node that holds a value, as well as an (optional) reference to another single node.

The article main parts are:

• What are Linked lists?
• Types of lists
• Strengths of (singly) linked lists
• Weaknesses of (singly) linked lists
• Circular linked lists
• When to use linked lists

A Singly Linked List is the most basic linked list. With this type every node has a single pointer to the next node. This makes it uni-directional, as you can only traverse from the head to the last node.

The goal of this post is to introduce the linked list as a data structure. I think it is useful for any programmer to know and recognize these basic data structures. Because by recognizing it in code, it can help you optimize your code; making it faster, and more readable. Excellent read!

Version control is an essential concept in software development. Version control tools, such as Git, are used to manage and track source codes. Developers and organizations alike value source code because it is the backbone of their product(s), from which they earn revenue. By Oghenefejiro Esosuota.

Machine learning and artificial intelligence offer an edge to businesses and developers over their competitors, motivating the competition to push to build the best model as it provides a business advantage. As developers optimize for speed and accuracy, minimizing errors also becomes a priority. Model versioning is a great tool for all of those purposes.

The article then walks over:

• What is model versioning?
• Benefits of model versioning
• Best practices for model versioning
• Limitations
• Tools for model versioning

Model versioning is a valuable part of the development process for machine learning projects because of the need for collaboration, tracking changing codes, and monitoring the model’s performance over time. Good read!