AI has been blamed for more than 1,000 job cuts in Australia in the past few months. AI has been blamed for more than 1,000 job cuts in Australia in the past few months. Will AI take Australian jobs, or is it just an excuse for corporate restructure? More than 1,000 local tech jobs have recently been cut, with companies citing AI productivity gains. But that's not the full story, experts say T eresa Lim has one of the most recognisable voices in Australia.

The A.I. Bubble Is Coming for Your Browser Artificial-intelligence startups, like the makers of the "smart" web browser Dia, are being acquired for vast sums. There's an old business maxim dating to the California gold rush: it's easier to make money selling picks and shovels to aspiring miners than to strike it rich finding gold. Artificial intelligence is in a picks-and-shovels phase right now. If gold, in this metaphor, is artificial general intelligence--a machine smarter than a human--or some version of a digital god, then tech companies are snapping up the tools to create one, including graphics-processing units, data centers, and trained A.I. models. That scramble is why Mark Zuckerberg is paying a twenty-four-year-old A.I. researcher two hundred and fifty million dollars to work at Meta, and why Sam Altman, the C.E.O. of OpenAI, recently said that the company would spend "trillions of dollars" building infrastructure.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. My trouble with the app that runs my life began this May. In 2020, after losing a full-time job, I became a freelancer. The next year, while continuing to freelance, I started my own small business, in the honorable field of podcasting. Scattered Apple Notes could no longer keep track of my affairs.

Programmers spend a significant amount of time reading code during the software development process. This trend is amplified by the emergence of large language models (LLMs) that automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from practitioners' perspectives in this new era. In this paper, we conduct a survey to explore the practitioners' perspectives on code readability in the age of LLMs and investigate the readability of our LLM-based software development agents framework, HULA, by comparing its generated code with human-written code in real-world scenarios. Overall, the findings underscore that (1) readability remains a critical aspect of software development; (2) the readability of our LLM-generated code is comparable to human-written code, fostering the establishment of appropriate trust and driving the broad adoption of our LLM-powered software development platform.

Continuous Integration (CI) build failures could significantly impact the software development process and teams, such as delaying the release of new features and reducing developers' productivity. In this work, we report on an empirical study that investigates CI build failures throughout product development at Atlassian. Our quantitative analysis found that the repository dimension is the key factor influencing CI build failures. In addition, our qualitative survey revealed that Atlassian developers perceive CI build failures as challenging issues in practice. Furthermore, we found that the CI build prediction can not only provide proactive insight into CI build failures but also facilitate the team's decision-making. Our study sheds light on the challenges and expectations involved in integrating CI build prediction tools into the Bitbucket environment, providing valuable insights for enhancing CI processes.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Role requiring'No experience data provided' months of experience in Mountain View

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.