The software engineers filed a complaint with Seattle's civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs. Earlier this month, five current Amazon employees publicly urged Seattle City Council to regulate data centers . It was an unprecedented act of advocacy by tech workers, and now three of the staffers say they are under internal investigation for what they understand to be allegedly representing themselves as spokespeople for the company without prior approval. "It's a totally ridiculous claim," says one of the affected employees, Patrick Schloesser. The three software engineers, who work in different divisions of Amazon and all live in Seattle, believe they are being unfairly targeted for expressing their political beliefs.

The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here's how to watch'em all. The FIFA Men's World Cup is almost here, and this one will be the biggest ever. The tournament is hosted by three countries: Mexico, Canada, and the US.

Seattle has passed a year-long moratorium on the construction of new datacenters. The city council voted unanimously in favor of the temporary ban on Tuesday. A major tech hub whose metro area is home to Amazon and Microsoft, Seattle is the largest US city to have passed such a moratorium as the backlash against AI infrastructure grows across the country. Lawmakers have framed the pause as an opportunity to draft regulations specifically targeting the electricity-hungry datacenters being built nationwide to serve the AI sector, and to protect local residents from environmental risks and rising electricity bills. According to Seattle's mayor, Katie Wilson, the moratorium will also let city officials determine whether datacenters are a "good use of urban land", and potentially impose new stipulations on their approval, such as requiring developers to invest in local transit and housing initiatives in exchange for construction permits.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

'A 25-hour story that just about makes sense' Vampire: The Masquerade Bloodlines 2. 'A 25-hour story that just about makes sense' Vampire: The Masquerade Bloodlines 2. Y ou are an ancient and powerful vampire, and you wake up in the basement of some decrepit Seattle building, with no recent memories and a strange sigil on your hand. The first thing you do is feed on the cop who finds you, before smacking his partner into a wall so hard that his blood spatters the brick. A violent fanged rampage ensues, where you beat up and tear apart rival undead and their ghouls while currying the favour of the local court of vampires, and trying to keep your existence hidden from the mortal populace of this sultry city. But this is also a detective story: there's a younger night-stalker sharing your brain, a voice in your head named Fabian, who talks like a 1920s gumshoe (presumably because he once was one). Fabian isn't violent at all; he evidently works with the human police and the vampire underworld, snacking on consenting volunteers' blood and using his mind-delving powers to solve murders.

Tool-augmented large language models (LLMs) are increasingly employed in real-world applications, but tool usage errors still hinder their reliability. We introduce ToolCritic, a diagnostic framework that evaluates and improves LLM behavior in multi-turn, tool-augmented dialogues. ToolCritic detects eight distinct error types specific to tool-calling (e.g., premature invocation, argument misalignment, and misinterpretation of tool outputs) and provides targeted feedback to the main LLM. The main LLM, assumed to have strong reasoning, task understanding and orchestration capabilities, then revises its response based on ToolCritic's feedback. We systematically define these error categories and construct a synthetic dataset to train ToolCritic. Experimental results on the Schema-Guided Dialogue (SGD) dataset demonstrate that ToolCritic improves tool-calling accuracy by up to 13% over baselines, including zero-shot prompting and self-correction techniques. This represents a promising step toward more robust LLM integration with external tools in real-world dialogue applications.

Large Language Models (LLMs) falter in multi-step interactions -- often hallucinating, repeating actions, or misinterpreting user corrections -- due to reliance on linear, unstructured context. This fragility stems from the lack of persistent memory to track evolving goals and task dependencies, undermining trust in autonomous agents. We introduce the Task Memory Engine (TME), a modular memory controller that transforms existing LLMs into robust, revision-aware agents without fine-tuning. TME implements a spatial memory framework that replaces flat context with graph-based structures to support consistent, multi-turn reasoning. Departing from linear concatenation and ReAct-style prompting, TME builds a dynamic task graph -- either a tree or directed acyclic graph (DAG) -- to map user inputs to subtasks, align them with prior context, and enable dependency-tracked revisions. Its Task Representation and Intent Management (TRIM) component models task semantics and user intent to ensure accurate interpretation. Across four multi-turn scenarios-trip planning, cooking, meeting scheduling, and shopping cart editing -- TME eliminates 100% of hallucinations and misinterpretations in three tasks, and reduces hallucinations by 66.7% and misinterpretations by 83.3% across 27 user turns, outperforming ReAct. TME's modular design supports plug-and-play deployment and domain-specific customization, adaptable to both personal assistants and enterprise automation. We release TME's codebase, benchmarks, and components as open-source resources, enabling researchers to develop reliable LLM agents. TME's scalable architecture addresses a critical gap in agent performance across complex, interactive settings.

Initiation, monitoring, and evaluation of development programmes can involve field-based data collection about project activities. This data collection through digital devices may not always be feasible though, for reasons such as unaffordability of smartphones and tablets by field-based cadre, or shortfalls in their training and capacity building. Paper-based data collection has been argued to be more appropriate in several contexts, with automated digitization of the paper forms through OCR (Optical Character Recognition) and OMR (Optical Mark Recognition) techniques. We contribute with providing a large dataset of handwritten digits, and deep learning based models and methods built using this data, that are effective in real-world environments. We demonstrate the deployment of these tools in the context of a maternal and child health and nutrition awareness project, which uses IVR (Interactive Voice Response) systems to provide awareness information to rural women SHG (Self Help Group) members in north India. Paper forms were used to collect phone numbers of the SHG members at scale, which were digitized using the OCR tools developed by us, and used to push almost 4 million phone calls. The data, model, and code have been released in the open-source domain.

Local governments around the world are making consequential decisions on behalf of their constituents, and these constituents are responding with requests, advice, and assessments of their officials at public meetings. So many small meetings cannot be covered by traditional newsrooms at scale. We propose PUBLICSPEAK, a probabilistic framework which can utilize meeting structure, domain knowledge, and linguistic information to discover public remarks in local government meetings. We then use our approach to inspect the issues raised by constituents in 7 cities across the United States. We evaluate our approach on a novel dataset of local government meetings and find that PUBLICSPEAK improves over state-of-the-art by 10% on average, and by up to 40%.

Samantha posted her story on TikTok and shared the scenario on a private Facebook group; many women responded--including her date's wife. Ultimately, as a result of this conversation, Samantha decided to report his profile to Hinge. The next day, the company contacted her to let her know it would be deleting his profile. Mandy and Samantha were pleased with Bumble's and Hinge's swift action to take down the profiles of the men they had matched with--but the experience was indelible. Neither of them plans to use dating apps again.