Rust has leaped to its highest position ever in the monthly Tiobe index of language popularity, scaling to the 13^th spot this month, with placement in the top 10 anticipated in an upcoming edition.

Previously, Rust has never gone higher than 17^th place in the Tiobe Programming Index. Tiobe CEO Paul Jansen attributed Rust’s ascent in the just-released July index to a February 2024 US White House report recommending Rust over C/C+, for safety reasons. He also credited the growing community and ecosystem support for the language. “Rust is finally moving up. After the tailwind of the US government, which recently announced to recommend moving from C/C++ to Rust for security reasons, things are going fast for Rust,” Jansen said. “The community is growing, including the number of third-party libraries and tools. In short, Rust is preparing itself for a top 10 position in the Tiobe index.”

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In a highly-anticipated federal ruling on July 3, US District Court Judge Ada Brown determined that the US Federal Trade Commission (FTC) did not have the authority to issue a nationwide ban of non-compete agreements. Although the judge’s decision was preliminary, employment lawyers watching the case agree that the FTC non-compete move is effectively dead.

Brown, of the US District Court for the Northern District of Texas, said that she would issue a final ruling on Aug. 30, the day before the FTC ban was slated to take effect. But based on the strong wording of her preliminary decision, there seemed little doubt that she would ultimately block the ban. 

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As agents using artificial intelligence have wormed their way into the mainstream for everything from customer service to fixing software code, it’s increasingly important to determine which are the best for a given application, and the criteria to consider when selecting an agent besides its functionality. And that’s where benchmarking comes in.

Benchmarks don’t reflect real-world applications

However, a new research paper, AI Agents That Matter, points out that current agent evaluation and benchmarking processes contain a number of shortcomings that hinder their usefulness in real-world applications. The authors, five Princeton University researchers, note that those shortcomings encourage development of agents that do well in benchmarks, but not in practice, and propose ways to address them.

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Well, that didn’t take long. After all the “this time it’s different” comments about artificial intelligence (We see you, John Chambers!), enterprises are coming to grips with reality. AI isn’t going to take your job. It’s not going to write your code. It’s not going to write all your marketing copy (not unless you’re prepared to hire back the humans to fix it). And, no, it’s nowhere near artificial general intelligence (AGI) and won’t be anytime soon. Possibly never.

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In 2007, some of the programmers at Google looked at their options for writing software and didn’t like what they saw. They needed to manage millions of lines of code that would be constantly storing and shipping data for the world wide web. The code would juggle thousands or maybe millions of connections on networks throughout the globe. The data paths were full of challenges from race cases and concurrency.

The existing programming languages weren’t much help. They were built for games or managing desktops, or many of the other common tasks from a world before the web browser. Their rough edges and failure modes drove coders at Google crazy enough to start asking if there might be a better way. Was there something that could handle the I/O chores in just a few lines with all of the safety and security that Google needed?

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