As knowledge workers including software engineers shifted to remote work during the pandemic, executives expressed a concern that productivity would suffer as a result. The evidence is mixed on this, but in the software industry particularly, remote work exacerbated many of the challenges that employees already faced. According to a 2021 Garden survey, the majority of developers found slow feedback loops during the software development process to be a source of frustration, second only to difficult communication between teams and functional groups. Seventy-five percent said the time they spend on specific tasks is time wasted, suggesting it could be put to more strategic use. In search of a solution to bolster developer productivity, three former Atlassian employees -- Dylan Etkin, Michael Knighten and Don Brown -- cofounded Sleuth, a tool that integrates with existing software development toolchains to provide insights to measure efficiency.

For patients seeking relief from depression, it can take months to pin down an effective treatment. But brain wave patterns could potentially help to predict how individual patients would respond to an antidepressant before treatment even begins, according to a new study published Feb. 10 in the journal Nature Biotechnology. The study addresses one of psychiatry's fundamental challenges: a lack of tests that can help doctors decide the best treatment options for patients with depression, said study co-author Dr. Madhukar Trivedi, a psychiatry professor at UT Southwestern Medical Center in Dallas. Instead, Trivedi said, providers rely on a trial-and-error process in which patients try out medications on six- to eight-week cycles. This imprecise method contributes to a general perception that antidepressants are ineffective, added Dr. Amit Etkin, study co-author and a professor of psychiatry at Stanford University.

An AI can predict from people's brainwaves whether an antidepressant is likely to help them. The technique may offer a new approach to prescribing medicines for mental illnesses. "We have a central problem in psychiatry because we characterise diseases by their end point, such as what behaviours they cause," says Amit Etkin at Stanford University in California. "You tell me you're depressed, and I don't know any more than that. I don't really know what's going on in the brain and we prescribe medication on very little information."

An AI can predict from people's brainwaves whether an antidepressant is likely to help them. The technique may offer a new approach to prescribing medicines for mental illnesses. "We have a central problem in psychiatry because we characterise diseases by their end point, such as what behaviours they cause," says Amit Etkin at Stanford University in California. "You tell me you're depressed, and I don't know any more than that. I don't really know what's going on in the brain and we prescribe medication on very little information."