Today's top AI updates

GLM has released its 5.1 open-source LLM, which has reportedly surpassed leading models like Opus 4.6 and GPT-5.4 on the SWE-Bench Pro benchmark. This significant advancement suggests AI is now capable of handling complex software engineering tasks, potentially enabling it to perform work equivalent to a human's full workday.

Anthropic, a prominent AI research company, is launching a new artificial intelligence model specifically designed for cybersecurity applications. This initiative marks their entry or expansion into the critical domain of digital security, aiming to leverage advanced AI capabilities to enhance defense mechanisms.

Firmus, an Nvidia-backed AI data center builder, has achieved a $5.5 billion valuation after raising $1.35 billion in just six months. Specializing in AI infrastructure in Asia, Firmus is known for projects like the 'Southgate' data center. This significant investment highlights the accelerating demand and financial commitment towards foundational AI computing capabilities.

Anthropic has launched a preview of its powerful new AI model, Mythos, specifically tailored for defensive cybersecurity work. This model is currently being utilized by a small, select group of high-profile companies to bolster their security measures. Its introduction signifies a notable advancement in applying sophisticated AI to critical cybersecurity challenges.

Current Large Language Models are limited by their static "train then deploy" paradigm, hindering dynamic adaptation to new information. Test-Time Training (TTT) offers a promising alternative by updating model parameters during inference, but faces significant challenges like architectural incompatibility and computational inefficiency in existing LLM ecosystems. This paper, "In-Place Test-Time Training," aims to address these critical barriers, potentially enabling more adaptive and continuously learning LLMs.

Large vision-language models frequently produce object hallucinations in image descriptions, highlighting a need for improved detection. This paper reveals that common detection strategies relying on coarse-grained attention weights on visual tokens are unreliable. It identifies hidden confounders like token position and object repetition that distort attention trends, leading to phenomena like Simpson's paradox where expected patterns reverse or vanish.

Frontend-VisualQA addresses a critical limitation where AI coding agents generate valid HTML/CSS but fail to visually verify their UI output, leading to broken layouts or off-screen elements. This tool provides "eyes" for agents like Claude Code and Codex, enabling visual testing and QA by allowing them to verify website UIs against natural language claims given a URL. It significantly enhances the reliability and quality of AI-generated front-end code.

Arcee, a small 26-person U.S. startup, has successfully developed a high-performing, massive open-source Large Language Model (LLM). This achievement is notable given their size, and their model is already gaining traction among OpenClaw users. This highlights the potential for smaller entities to contribute significantly to the open-source AI landscape.

This new tool offers a personalized podcast summarizer that learns user preferences over time through custom tags and an AI chat feature. It aims to help users quickly extract the information they want from long podcasts, saving time and improving content consumption efficiency. Each interaction helps the app better understand and customize future summaries.

Uber is significantly expanding its partnership with Amazon Web Services, opting to run more of its core ride-sharing features on Amazon's AI chips. This move strengthens AWS's position in the enterprise AI chip market and is seen as a direct challenge to competitors like Oracle and Google for major cloud contracts.

A developer, drawing from experience at PostHog AI, has created an MVP database specifically for AI agents. This system uses DuckDB and annotated schemas to expose business data, based on the finding that direct SQL access is more effective for agents than other methods. The project has already been benchmarked against 11 different large language models.

This new AI tool allows users to summarize any podcast quickly, learning individual preferences through custom tags and an AI chat feature. It continuously refines summaries to extract the most relevant information, saving time for listeners who often lack the hours needed for long podcasts. The app aims to provide personalized insights efficiently, addressing a common pain point for podcast enthusiasts.

ClawGod is a runtime patch specifically designed for the official Claude Code, rather than being a third-party client. This patch applies directly to the existing Claude Code and is notable for its ability to maintain functionality across any version, even after updates. Its continuous compatibility ensures that users can benefit from its modifications regardless of future changes to the core Claude Code.

Anthropic's Red Team is actively assessing the cybersecurity capabilities of their new AI model, Claude Mythos Preview. This internal evaluation highlights Anthropic's commitment to proactively identifying and addressing potential security vulnerabilities within their advanced AI systems.

Anthropic has released an "Alignment Risk Update" concerning a preview of a new Claude model, codenamed "Mythos." The specific details of this update are redacted, indicating sensitive information regarding the safety and alignment challenges of their advanced AI development.

A developer is struggling to promote their 'vibe-coded' apps, particularly developer tools with real-life use cases, on platforms like Reddit. Unlike two years ago, they now receive harsh comments about their apps being 'vibe coded' or having LLM-generated text, leading to a lack of engagement and website visits. This highlights a shift in community perception and the challenges of marketing new tools in the current tech landscape.

This research introduces the first quantitative, topology-based definition for 'churn flow,' a chaotic regime in vertical two-phase flow, a problem unsolved for over 40 years. It utilizes Euler Characteristic Surfaces (ECS) and Multiple Kernel Learning (MKL) to characterize and unsupervisedly correct flow-regime maps in small-diameter vertical pipes. This novel approach blends temporal alignment and amplitude statistics derived from ECS to provide a robust characterization.

A user has developed a new reinforcement learning algorithm and is seeking best practices for its implementation and benchmarking using PyTorch. They are looking for guidance on code optimization, directory structure, and standard Gym environments to compare their algorithm against existing ones.

The provided information consists solely of the title "Claude Mythos Preview System Card" and its URL from Anthropic. Without access to the actual content of this system card, it is impossible to summarize the details of the Claude Mythos preview, its features, or its potential implications.