What we have read in April

By 1 May 2023Blog

What we have read in April

We read a lot here at INFO, and we find it important to share articles within the organisation as inspiration for new ideas, as reminder of good practices, and sometimes just for some good old fun.We wanted to share the best articles we found this month with you.

Have a read, and let us know what you think! Enjoy. 📚


Google keeping us sharp on Cloud Security

Cybersecurity is a critical issue for every organization, especially in the era of cloud computing and digital transformation. But how can boards of directors effectively oversee and guide their organizations’ cybersecurity and risk decisions? In this article, Google shares insights from a new report by the Google Cybersecurity Action Team GCAT, the progress on open source software security, and the new capabilities for data protection and sovereignty.
And we noticed that Google released a second unscheduled update for its Chrome browser recently, fixing more zero-day vulnerabilities. You can also read more about the update here and make sure you keep your software up-to-date !



The Three Types of Requirements on Agile Projects

There three types of requirements on agile projects: known, overlooked, and emergent. This article argues that agile approaches are better suited for dealing with these types of requirements than traditional ones. Known requirements are those that are clear and agreed upon at the start of the project. Overlooked requirements are those that are missed or forgotten during the initial planning. Emergent requirements are those that arise during the development process due to changing needs or feedback. The article explains how agile teams can handle each type of requirement effectively and deliver value to their customers. It highlights the benefits and challenges we also face during agile product development.



How to Use an LLM to Produce Useful Self-Tested Code

This article is a demonstration of how to use an LLM (large language model) to produce useful self-tested code by using two techniques: chain of thought prompting and general knowledge prompting. Through chain of thought prompting, the LLM is primed with an implementation strategy. Then general knowledge prompting is used to get the LLM to fill in “common knowledge”, aka source code. The article shows how this approach can generate code for various tasks, such as sorting an array, finding the median of a list, or calculating the factorial of a number. We found this article fascinating because it shows the potential of LLMs to assist developers in writing code faster and more accurately.



Scaling Up Without Slowing Down

As a product leader in a tech company, you may have heard your founder say something like “When I started the company we were going much faster”. This reflects their frustration with the loss of speed and agility as the company grows from startup to scaleup. But how can you, and we, deal with this situation and influence the founder to adopt a different mindset and approach? That’s what is explored in this article. It identifies the main causes of inertia in scale-ups, and offers some tips on how to communicate and collaborate with founders who are unhappy with the pace of development. There are some examples of successful scale-ups that have maintained their speed while scaling their business.



We like Good software

We also read this article about how to develop good software using some of the best practices in the industry, just what we like to do. It explains four categories that can be used to evaluate the quality of software: functionality, reliability, usability, and maintainability. With some examples it explores how these principles help evaluating products and services.



Practical Applications for Conversational AI

One of my colleagues, Martijn, has written an interesting whitepaper about how we can make actual use of ChatGPTs (or other LLMs) conversational nature. It explains how we can create context for it for our specific situation and business processes. This whitepaper explores how, by using the latest AI tools, we can create intelligent systems that can answer complex questions about our specific business context. It is a very interesting area where we see there is a huge potential for practical business situations, worth a read.



We hope you enjoyed reading these articles as much as we did. If you have any comments or questions, feel free to share them with us.

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