Many partners I’ve spoken with are experimenting with ChatGPT to automate script generation, write proposals etc—you name it, it’s being tried. But even in a budget-conscious economy, it’s still not worth sacrificing the human element. This is especially true if that move results in script errors, unreliable information, introducing cybersecurity risks, or compromising your company’s intellectual property. You still need a human involved in the coding process today.

ChatGPT has been the talk of the town for more than four months now. As the first ever artificial intelligence (AI) -powered chatbot, it has quickly gained immense popularity, helping students, engineers and even executives generate content, write and debug code and run market analyses. But could ChatGPT be used for anything other than natural language processing (NPL)? Could it, for example, assist businesses with strategic decision-making? I decided to try it out.

In the next two posts (maybe more) I'll share how we have developed elmah.io's email templates currently sent out using Amazon Web Services (AWS). This first post will introduce template development using MJML and Handlebars.js. In the next post, I'll explain the process of building them on Azure DevOps and deploying them to AWS.

Separate “debug” and “release” builds are very common in embedded development. Typically the notion is improved debug capabilities (less aggressive compiler optimizations, more debugging information like logs) vs. highly optimized and hardened production release builds. I’m here to describe disadvantages to this practice, and why it might make sense to consolidate to a single build! Like Interrupt? Subscribe to get our latest posts straight to your mailbox.