Triplebyte Blog

When it comes to coding assessments, there’s no “holy grail” of programming languages. The best choice for one programmer may legitimately be the worst for another. There’s no universally applicable rules of thumb and about a million factors to consider. Here's how to balance these factors and optimize for your specific circumstances and skills.

11 insights from a novel, open data set of Shark Tank tech pitches—plus a chance for readers to show off their data-visualization skills for a chance to win a $500 Amazon gift card!

Machine learning and artificial intelligence are growing more and more integral across almost every part of software. The way that’s translating to the developer job market is through a spike in demand for devs with a grasp in AI and ML tools.

Algorithms have a steep learning curve — so the story goes. This isn’t because algorithms are inherently hard. They’re just couched in stuffy, academic language that’s akin to humans trying to make sense of assembly code.

Thousands of engineers and tech companies interact with the Triplebyte platform on a daily basis. As this economic downturn has progressed, we’ve heard issues engineers and tech companies are facing. From the tough situations, like engineers losing their jobs due to layoffs, to some silver linings, like healthcare and logistics startups seeing unexpected growth, there are tons of changes happening in tech. This week, we launched three product features to help engineers find jobs, and a new program to help students whose summer internships were impacted.

With Git being used everywhere in software engineering, so is the negatively-framed git-blame command, used in the system to track down who merely contributed a line of code — buggy or not. But this isn't about sensitivity (or oversensitivity). Studies show that dealing in shame-inflicting words like blame can stifle productivity and innovation among workers.

Superhuman CTO Conrad Irwin talks about how game design makes software better, how it’s different from gamification, and what skills engineers should learn to build it into their projects.

Academic algorithms tests on interviews are often considered a pointless hurdle, a relic of a bygone era of traditional testing techniques that are no longer relevant. And with the rise of bootcamps and other resources, an ever-increasing number of engineers lack a traditional computer science (CS) background. So why do companies rely on them so heavily? Here are a few reasons that might surprise you.