Born out of an internal hackathon, Amazon's Autonomous Threat Analysis system uses a variety of specialized AI agents to detect weaknesses and propose fixes to the company's platforms. As generative AI pushes the speed of software development, it is also enhancing the ability of digital attackers to carry out financially motivated or state-backed hacks. This means that security teams at tech companies have more code than ever to review while dealing with even more pressure from bad actors. On Monday, Amazon will publish details for the first time of an internal system known as Autonomous Threat Analysis (ATA), which the company has been using to help its security teams proactively identify weaknesses in its platforms, perform variant analysis to quickly search for other, similar flaws, and then develop remediations and detection capabilities to plug holes before attackers find them. ATA was born out of an internal Amazon hackathon in August 2024, and security team members say that it has grown into a crucial tool since then.

An ex-Intel CEO's mission to build a Christian AI: 'hasten the coming of Christ's return' The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. I n March, three months after being forced out of his position as the CEO of Intel and sued by shareholders, Patrick Gelsinger took the reins at Gloo, a technology company made for what he calls the "faith ecosystem" - think Salesforce for churches, plus chatbots and AI assistants for automating pastoral work and ministry support. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. The former CEO's career pivot is taking place as the US tech industry returns to the political realm as a major revenue stream.

Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials science, we organized a hackathon -- AIMHack2024 -- in July 2024. In this hackathon, researchers from fields such as materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. Based on the results of the hackathon, this paper presents topics related to (1) conducting AI-assisted software trials, (2) building AI tutors for software, and (3) developing GUI applications for software. While generative AI continues to evolve rapidly, this paper provides an early record of its application in data-driven materials science and highlights strategies for integrating AI into research and education.

At a weekend hackathon in San Francisco, more than 100 coders gathered to test whether they could beat AI--and win a $12,500 cash prize. On a breezy San Francisco afternoon last Saturday, I found myself at a nondescript coworking space filled with shoeless coders. Just over a hundred visitors had crowded into an office building in the Duboce Triangle neighborhood for a showdown that would pit teams armed with AI coding tools against those made up of only humans (all were asked to ditch their shoes at the door). The hackathon was dubbed " Man vs. Machine," and its goal was to test whether AI does help people code faster--and better. Roughly 37 groups were randomly assigned "human" or "AI-supported."

Microscopy is a primary source of information on materials structure and functionality at nanometer and atomic scales. The data generated is often well-structured, enriched with metadata and sample histories, though not always consistent in detail or format. The adoption of Data Management Plans (DMPs) by major funding agencies promotes preservation and access. However, deriving insights remains difficult due to the lack of standardized code ecosystems, benchmarks, and integration strategies. As a result, data usage is inefficient and analysis time is extensive. In addition to post-acquisition analysis, new APIs from major microscope manufacturers enable real-time, ML-based analytics for automated decision-making and ML-agent-controlled microscope operation. Yet, a gap remains between the ML and microscopy communities, limiting the impact of these methods on physics, materials discovery, and optimization. Hackathons help bridge this divide by fostering collaboration between ML researchers and microscopy experts. They encourage the development of novel solutions that apply ML to microscopy, while preparing a future workforce for instrumentation, materials science, and applied ML. This hackathon produced benchmark datasets and digital twins of microscopes to support community growth and standardized workflows. All related code is available at GitHub: https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1

At a company-wide hackathon this month, developers at finance firm Block built a dizzying number of prototype tools including a database debugger, a program for identifying duplicated code, and an app that automates Bitcoin support. The sudden productivity boost was driven by Goose, an artificial intelligence agent developed by Block several months ago that can help with coding and other work like knocking together data visualizations or mocking up new product features. "We've always had really strong hack weeks, but this one was at another level," says Jackie Brosamer, who leads the AI and data platform at Block. "We have tens of ideas that we're looking to bring to production." Goose helped developers at Block to develop a new agent-to-agent communication server at the hackathon.

Palantir, the software company cofounded by Peter Thiel, is part of an effort by Elon Musk's so-called Department of Government Efficiency (DOGE) to build a new "mega API" for accessing Internal Revenue Service records, IRS sources tell WIRED. For the last three days, DOGE and a handful of Palantir representatives, along with dozens of career IRS engineers, have been collaborating to build a single API layer above all IRS databases at an event previously characterized to WIRED as a "hackathon," sources tell WIRED. Palantir representatives have been on-site at the event this week, a source with direct knowledge tells WIRED. APIs are application programming interfaces, which enable different applications to exchange data, and could be used to move IRS data to the cloud and access it there. DOGE has expressed an interest in the API project possibly touching all IRS data, which includes taxpayer names, addresses, social security numbers, tax returns, and employment data.

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large.

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

Meta's push to deploy its artificial intelligence system inside Britain's public sector has taken a step forward after the tech giant awarded development funding to technology aimed at shortening NHS A&E waiting times. Amid rival efforts by Silicon Valley tech companies to work with national and local government, Meta ran its first "hackathon" in Europe asking more than 200 programmers to devise ways to use its Llama AI system in UK public services and, one senior Meta executive said, "focused on the priorities of the Labour party". The event came after it emerged that Palantir, another US tech company, has been lobbying the Ministry of Justice and government ministers including the chancellor, Rachel Reeves. Microsoft also recently agreed a five-year deal with Whitehall departments to supply its AI Copilot technology to civil servants. Meta's hackathon was addressed by Nick Clegg, the former deputy prime minister and now Meta's president of global affairs based in California.