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The Verge · AI

Switch 2 exploited by old games and AI to achieve jailbreaking; a modder showcases a custom character for “Mario Kart World”

According to The Verge, well-known modder Sammy Hajhamid (online alias pepsipu) claims to have successfully jailbroken the Nintendo Switch 2. His tool, ‘ns2jb,’ was developed with assistance from OpenAI Codex and exploited a vulnerability triggered by an older game. A demonstration video shows “Mario Kart World” running a modified version that includes unofficial characters such as Cinnamoroll, the red Angry Birds, Peter Griffin, and Minecraft’s Steve.

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AWS Machine Learning Blog

Rescale leverages the AWS AI-DLC framework and Strands Agents to rapidly build an AI test traffic-splitting proxy.

Rescale, in collaboration with AWS engineers and leveraging the AI-Driven Development Lifecycle (AI-DLC) methodology, the Strands Agents framework, and Amazon Bedrock (Claude Opus 4.8), completed within five days a production-ready AI test-routing agent designed to automatically classify and route nighttime end-to-end test failures, and rolled out this human-AI collaborative development paradigm across the organization.

OfficialTutorials
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Microsoft Research

Flint-interactive: An interactive data visualization programming language for AI agents

Microsoft Research has unveiled Flint-interactive, a domain-specific language for building highly expressive, semantically rich interactive data visualizations. Its compiler automatically maps user interactions—such as clicks and brush selections—to semantic events and uses high-level operators to drive coordinated updates across multiple elements, including charts, tables, and text, significantly reducing the cost and development complexity of creating reliable interactive visualizations with AI agents.

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Ars Technica · AI

Hackers exploited a critical PHP2Shell vulnerability four days in advance, and Amazon has confirmed it has been widely weaponized in the wild.

Amazon disclosed that the PHP2Shell vulnerability series, identified by its AI tools, had already been actively probed and exploited by hackers four days before the official public disclosure; currently, this vulnerability is being “widely exploited in the wild,” and governments and enterprises worldwide are urgently hardening their systems.

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Hugging Face 官方博客

Microsoft and Hugging Face have open-sourced the ThinkingBox framework: using the database’s final state as a deterministic reward signal to advance post-training reinforcement learning for agents.

Microsoft and Hugging Face have jointly launched the open-source framework ThinkingBox, which generates binary 0/1 rewards by comparing the LLM agent’s final database state after execution with the expected state, for reinforcement learning-based fine-tuning. The accompanying benchmark, ThinkingBox-Bench, includes 507 real-world backend tasks, none of which were used during training. After 50 steps of GRPO fine-tuning, Qwen3.8-27B achieved a 9.08-percentage-point improvement in pass@1 on the held-out test set, with Observed 20/20 nearly tripling; however, pass@20 declined in some domains, reflecting a trade-off between breadth and stability.

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Hugging Face 官方博客

OlmoEarth v1.3 Released: A High-Performance Open-Source Satellite Embedding Model for the Global Surface

The Allen Institute for AI has released OlmoEarth v1.3, offering planet-wide satellite embedding vectors at 10-meter resolution in both 64-dimensional and 128-dimensional formats, along with the interactive exploration tool Embeddings Explorer. It supports zero-code change detection, similar-region retrieval, and lightweight classification modeling.

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AWS Machine Learning Blog

Deep Insight: How AWS’s Open-Source Multi-Agent System Addresses Five Major Production-Grade Challenges

AWS Solutions Architecture Team has launched Deep Insight, an open-source multi-agent system designed for automated CSV data analysis and natural-language report generation. It systematically addresses five common challenges in production environments—agent orchestration, role-based model selection, stable operation, secure code execution, and observability—and has been validated in real-world scenarios such as LG Electronics and the re:Invent 2025 TechRecon.

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TechCrunch · AI

Recursive releases Flint: a lightweight, AI-driven programming model that can run locally.

AI startup Recursive has unveiled Flint, an open-source coding model that it claims was developed primarily using its in-house AI research system, Eureka Machine. With just 40 billion parameters and only 2.5 billion activations in practice, the model can run offline on a standard laptop—such as an M4 Pro MacBook—at an inference speed of 131 tokens per second, outperforming most models of comparable size on benchmarks like SWE-Bench.

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Ars Technica · AI

OpenAI's latest GPT-6 series models will be available on Google Cloud and the Antigravity platform.

OpenAI’s cutting-edge GPT-6 and 6.1 series models (Astra, Sol, Luna) will be launched on Google Cloud Model Garden in the coming weeks and integrated into Google’s agent-driven programming tool, Antigravity; this move makes Google the first mainstream cloud provider to offer all three major closed-source large models—Gemini, Claude, and GPT.

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TechCrunch · AI

PHP ‘Doomsday Vulnerability’ Exposed: OpenAI and Wiz Use AI to Discover a High-Risk Flaw Spanning 20 Years

The U.S. National Security Agency (NSA) issued an urgent alert: OpenAI and Google’s Wiz team, working together using AI tools, have discovered two critical vulnerabilities affecting all PHP versions 5 and above (since 2004). These vulnerabilities could allow remote exploitation of the server’s file system or database. While there are currently no reports of real-world exploitation, AI has significantly lowered the barrier to attack, making it imperative for tens of millions of websites worldwide to urgently upgrade to PHP 8.5.12.

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TechCrunch · AI

Blacksmith has closed a $66 million Series C funding round, achieving a $1 billion valuation and joining the ranks of unicorns.

AI-powered continuous integration (CI) platform Blacksmith announced a $66 million Series C funding round led by Sequoia Capital, bringing the company’s valuation to $1 billion; within two years of its founding, its valuation has grown by more than 16 times, its ARR has increased 17-fold, and it has executed over 90 million CI jobs.

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Latent Space

Claude Mods: The first implementation of an attention interface, a human-programmable agent interaction layer

Claude Mods is Anthropic’s programmable plugin system for Claude Code, representing an early form of the “Attention Interface”—it shifts the traditional machine-oriented agent harness toward a customizable interaction layer centered on human attention. Users can write Mods in TypeScript/JS or have Claude generate them automatically using natural language, thereby determining when to be interrupted, what content to see, and what decisions to make.

Individual
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Latent Space

Claude Mods: A programmable attention interface that reshapes the collaboration paradigm between humans and AI agents

Claude Mods is a programmable plugin system for developers launched by Anthropic, marking a shift of the ‘agent harness’ from a machine-side interface to a human-side interface—namely, the ‘Attention Interface’. It enables users to customize Claude Code’s behavior, rendering, and interaction logic via TypeScript/JavaScript, thereby facilitating co-design workflows between humans and AI. This mechanism has already been applied to built-in features (such as You Should Know) and supports users in building plugins themselves or having them generated with Claude’s assistance.

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NVIDIA Blog

NVIDIA’s European cloud partners are accelerating the expansion of sovereign, open, and secure AI infrastructure.

NVIDIA announced that its cloud partners across Europe, the Nordics, Africa, and Central Asia are scaling up regional AI computing infrastructure based on the NVIDIA DSX platform, focusing on sovereign computing (local deployment), open model support (such as Mistral Large 4 and the Nemotron series), and AI agent security boundaries (via the OpenShell reference architecture), covering use cases including training, inference, physics‑based AI, and retail agents.

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Latent Space

Tenex Builds an AI-Native SDLC: Agent-Driven Development Practices for Fortune 2000 Companies

Tenex, an AI-native software company founded 18 months ago, leverages output‑driven compensation, the MetaHarness framework, and Opus 5.5–powered AI agent workflows to serve roughly half of the Fortune 2000 companies. Its model focuses on process standardization, revaluing non‑coding capabilities, and addressing the last‑mile challenges of enterprise‑level AI deployment.

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Latent Space

Tenex builds an AI-native SDLC: outcome-based pricing, agent-driven development, and the last mile of enterprise AI deployment.

Tenex is an AI-native software delivery company established 18 months ago (as of 2026), with a team of 75 employees, serving roughly half of the Fortune 2000 companies and a comparable number of mid-sized enterprises. Its core practices include: a dynamic engineer compensation system anchored in story points; a quality‑assurance mechanism led by AI strategists; the MetaHarness framework, which embeds context directly within Git; and a capability realignment focused on “high‑value, non‑coding tasks.” Meanwhile, it has been observed that enterprise AI adoption remains concentrated on general‑purpose LLM deployments and engineering‑side agents, with backend office‑type production‑grade AI agents achieving only about 5% penetration.

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Hugging Face 官方博客

VocabX: Reduces the inference cost of domain-specific small language models by 77% through cost-weighted vocabulary specialization.

Dharma-AI has introduced the VocabX method, which reconfigures the vocabulary of language models for specific tasks—such as automated scoring of Portuguese essays—by optimizing for cost rather than word frequency. Without modifying the model architecture, parameters, or training pipeline, empirical results show a 77% reduction in inference costs, while the evaluation score (bench score) improved from 0.703 to 0.810.

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Hugging Face 官方博客

TorchNeuron System-Level Performance Analysis (Part 1): End-to-End Profiling Based on the AWS Trainium Chip

This article is the first in a series of technical blog posts jointly published by Hugging Face and Amazon, systematically introducing how to use TorchNeuron on AWS Trainium AI accelerator chips for PyTorch model inference performance analysis. It covers the principles of Neuron backend integration, methods for configuring torch.profiler, the use of NeuronConfig and NeuronProfiler tools, and visualized tracing with Neuron Explorer. Additionally, using RMSNorm as an example, it compares the device‑time differences among three implementations: plain PyTorch, the built-in torch.rms_norm fusion, and torch.compile’s automatic fusion.

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Hugging Face 官方博客

NVIDIA releases Kumo Forecast: a multivariate forecasting model that integrates pre-trained time-series representations, historical pattern retrieval, and interpretability.

NVIDIA has launched the open-source time-series forecasting model Kumo Forecast (formerly NV-Tesseract Forecasting), which is built on the MOMENT-1-large Transformer architecture. It supports both direct prediction and dual-path inference via Domain-Aware Representation and Retrieval (DARR), and includes a built-in interpretability framework that requires no modifications to the model structure.

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Hugging Face 官方博客

NVIDIA releases Kumo Relational: the first foundation model for structured data that outperforms supervised learning without any training.

NVIDIA has launched Kumo Relational, a pre-trained relational foundation model that can perform predictive tasks—such as churn prediction, fraud detection, and demand forecasting—directly on enterprise‑grade relational databases, without the need for task fine-tuning, feature engineering, or table flattening. In 41 benchmark tests spanning 15 databases, it outperformed supervised models like XGBoost across the board, and on the SAP SALT enterprise benchmark, its MRR metric surpassed AutoGluon by 8%. The model supports databases with up to 500 billion rows and enables zero‑code predictive queries via Predictive Query Language (PQL).

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