Check out a whole batch of fun stuff today. I read a number of things about AI, and a couple of good pieces about Go programming. And a few other good articles in the mix.
[article] Winning the AI Products Arms Race. How do you build AI products that people want to use? I think this question now applies to people in every industry. Read this post for some useful advice.
[blog] Higher-order functions in Go. I do demo-coding at this point, but I still like learning about more advanced patterns. Here’s a good look at writing functions that accept functions as parameters, or return functions to callers.
[report] Open Source Security and Risk Analysis Report. This is a direct link to the PDF from Synopsys. Some scary stats, as well as guidance to help motivate you to take security more seriously.
[article] Companies can’t stop using open source. You’ll keep using open source software because it adds a lot of value, but to the previous link, don’t forget security!
[article] Legacy Software Systems: How to Live with Aging Software Architecture? If you have “legacy software”—which anyone does who’s been in business for more than a month—then congrats, it means you’ve built something that lasted. This post looks at what it is, and how to approach it.
[docs] Uber Go Style Guide. This offers some terrific guidance on writing good Go code. Nice job from the engineering team at Uber.
[docs] Google Cloud to Azure services comparison. This is in the Azure documentation. It helps users map the services between Google Cloud and Azure.
[youtube-video] Pub/Sub Best Practices: Client Library. Great video with tips on using client libraries with Google Cloud’s messaging engine (Pub/Sub).
[blog] A Quick Introduction to Service Weaver. I mentioned this new OSS framework a couple times, and now I feel like I understand it more. If you want the flexibility to build apps a “normal” way and have them broken into microservices later, check out this intro.
[blog] Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages. Can we build an ML model that understand the top 1000 languages? We’re on our way. Good research here.
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