New mothers working in software development are staring down an AI-pilled workplace they barely recognize. As Danielle settled into the rhythms of new motherhood, her profession underwent a drastic reinvention. Danielle, who asked to use her first name to avoid damaging her job prospects, worked as a software developer at a car company in Portland, Oregon. Before she left the workforce in mid-2024, barely anybody used AI to write code; by the time she was ready to return, a year later, it had become the expectation. Once upon a time, she had been drawn to coding for the job security it offered, but AI was threatening to upend that.

Hustlers are cashing in on China's OpenClaw AI craze The AI tool has become the country's latest tech obsession. Feng Qingyang had always hoped to launch his own company, but he never thought this would be how--or that the day would come this fast. Feng, a 27-year-old software engineer based in Beijing, started tinkering with OpenClaw, a popular new open-source AI tool that can take over a device and autonomously complete tasks for a user, in January. He was immediately hooked, and before long he was helping other curious tech workers with less technical proficiency install the AI agent. Feng soon realized this could be a lucrative opportunity. By the end of January, he had set up a page on Xianyu, a secondhand shopping site, advertising "OpenClaw installation support."

Corporate employees said Amazon's race to roll out AI is leading to surveillance, slop and'more work for everyone'. When Dina, a software developer based in New York, joined Amazon two years ago, her job was to write code. The internal AI tool she's expected to use, called Kiro, frequently hallucinates and generates flawed code, she says. Then she has to dig through and correct the sloppy code it creates, or just revert all changes and start again. She says it feels like "trying to AI my way out of a problem that AI caused".

San Francisco's AI startups are pushing workers to grind endlessly, hinting at pressures soon hitting other sectors Not long after the terms "996" and "grindcore" entered the popular lexicon, people started telling me stories about what was happening at startups in San Francisco, ground zero for the artificial intelligence economy. There was the one about the founder who hadn't taken a weekend off in more than six months. The woman who joked that she'd given up her social life to work at a prestigious AI company. Or the employees who had started taking their shoes off in the office because, well, if you were going to be there for at least 12 hours a day, six days a week, wouldn't you rather be wearing slippers? "If you go to a cafe on a Sunday, everyone is working," says Sanju Lokuhitige, the co-founder of Mythril, a pre-seed-stage AI startup, who moved to San Francisco in November to be closer to the action.

A federal jury on Thursday convicted Linwei Ding, 38, of seven counts of economic espionage and seven counts of theft of trade secrets after an 11-day trial in the U.S. District Court in the Northern District of California.

Workers say the firm's'warp-speed' approach fuels pressure, layoffs and rising emissions More than 1,000 Amazon employees have signed an open letter expressing "serious concerns" about AI development, saying that the company's "all-costs justified, warp speed" approach The letter, published on Wednesday, was signed by the Amazon workers anonymously, and comes a month after Amazon announced mass layoff plans as it increases adoption of AI in its operations. Among the signatories are staffers in a range of positions, including engineers, product managers and warehouse associates. Reflecting broader AI concerns across the industry, the letter was also supported by more than 2,400 workers from companies including Meta, Google, Apple and Microsoft . The letter contains a range of demands for Amazon, concerning its impact on the workplace and the environment. Staffers are calling on the company to power all its data centers with clean energy, make sure its AI-powered products and services do not enable "violence, surveillance and mass deportation", and form a working group comprised of non-managers "that will have significant ownership over org-level goals and how or if AI should be used in their orgs, how or if AI-related layoffs or headcount freezes are implemented, and how to mitigate or minimize the collateral effects of AI use, such as environmental impact".

These authors contributed equally to this work. Abstract--Use case modeling employs user-centered scenarios to outline system requirements. These help to achieve consensus among relevant stakeholders. Because the manual creation of use case models is demanding and time-consuming, it is often skipped in practice. This study explores the potential of Large Language Models (LLMs) to assist in this tedious process. The proposed method integrates an open-weight LLM to systematically extract actors and use cases from software requirements with advanced prompt engineering techniques. The method is evaluated using an exploratory study conducted with five professional software engineers, which compares traditional manual modeling to the proposed LLM-based approach. The results show a substantial acceleration, reducing the modeling time by 60%. At the same time, the model quality remains on par . Besides improving the modeling efficiency, the participants indicated that the method provided valuable guidance in the process.

6G brings a paradigm shift towards AI-native wireless systems, necessitating the seamless integration of digital signal processing (DSP) and machine learning (ML) within the software stacks of cellular networks. This transformation brings the life cycle of modern networks closer to AI systems, where models and algorithms are iteratively trained, simulated, and deployed across adjacent environments. In this work, we propose a robust framework that compiles Python-based algorithms into GPU-runnable blobs. The result is a unified approach that ensures efficiency, flexibility, and the highest possible performance on NVIDIA GPUs. As an example of the capabilities of the framework, we demonstrate the efficacy of performing the channel estimation function in the PUSCH receiver through a convolutional neural network (CNN) trained in Python. This is done in a digital twin first, and subsequently in a real-time testbed. Our proposed methodology, realized in the NVIDIA AI Aerial platform, lays the foundation for scalable integration of AI/ML models into next-generation cellular systems, and is essential for realizing the vision of natively intelligent 6G networks.

With almost laughably low power, a monochrome screen, and unique controls, niche-micro console Playdate shouldn't make any sense in a world of modern gaming. Yet, it's near impossible not to love it. When video game developer and publisher Panic launched its own console, Playdate, back in 2022, it upended just about all conventional wisdom when it came to gaming hardware. Coming just two months after Valve's Steam Deck, the micro-handheld was comparably laughably low in power, brandished a tiny monochrome screen, and took a minimalist approach to physical controls, with only a D-pad, two buttons, and a bizarre crank on offer. Even stranger than the crank was that buyers didn't really know what they'd be playing on it--the earliest games were released in a season pass format, with mystery titles drip-fed to players weekly.

Accurately assessing candidate seniority from resumes is a critical yet challenging task, complicated by the prevalence of overstated experience and ambiguous self-presentation. In this study, we investigate the effectiveness of large language models (LLMs), including fine-tuned BERT architectures, for automating seniority classification in resumes. To rigorously evaluate model performance, we introduce a hybrid dataset comprising both real-world resumes and synthetically generated hard examples designed to simulate exaggerated qualifications and understated seniority. Using the dataset, we evaluate the performance of Large Language Models in detecting subtle linguistic cues associated with seniority inflation and implicit expertise. Our findings highlight promising directions for enhancing AI-driven candidate evaluation systems and mitigating bias introduced by self-promotional language. The dataset is available for the research community at https://bit.ly/4mcTovt