Thursday — September 11, 2025
A hacker integrated a live LLM into Animal Crossing on a GameCube, researchers developed R-Zero, a self-evolving reasoning LLM framework, and a new platform called ROS-MCP-Server enables connecting large language models with ROS robots using MCP.
News
I replaced Animal Crossing's dialogue with a live LLM by hacking GameCube memory
The author of the text embarked on a project to integrate a 24-year-old Animal Crossing game on a Nintendo GameCube with a cloud-based AI, without modifying the game's code, by creating a "bridge" that allows the game to communicate with the AI. This was achieved by using a technique called Inter-Process Communication (IPC) via shared memory, where the author's external Python script writes data directly into a specific chunk of the GameCube's RAM, acting as a "mailbox" that the game can read from.
TikTok has turned culture into a feedback loop of impulse and machine learning
TikTok has successfully "industrialized human attention" by fusing various experiments in short-form videos and algorithmic feeds into a system that harvests attention through instant learning from micro-behaviors, creating a recommender system that feels perceptive and addictive. The platform's model is now being adopted by other media, reshaping how we consume information, with cultural consumption becoming a form of algorithmic training that rewards hyper-specialization and instant gratification, but at the cost of sustained attention and meaningful engagement.
Defeating Nondeterminism in LLM Inference
Reproducible results are difficult to obtain from large language models, even when using greedy sampling, due to nondeterminism in the inference process. The cause of this nondeterminism is not solely due to floating-point non-associativity and concurrency, but rather a more complex issue that can be addressed by making kernels batch-invariant, which can help achieve truly reproducible results in LLM inference.
API, Claude.ai, and Console services impacted [resolved]
Anthropic's API, Claude.ai, and Console services were impacted by an incident that occurred on September 10, 2025, but the issue has since been resolved. The services were down for a period of time, but a fix was implemented and the services were restored, with monitoring continuing to ensure stability.
The Four Fallacies of Modern AI
The development of Artificial Intelligence has been marked by periods of hype and skepticism, with many exaggerated claims about its potential and limitations. To navigate this noise, computer scientist Melanie Mitchell has identified four foundational fallacies that explain our collective confusion about AI, including the assumption that narrow AI achievements are incremental steps towards human-level intelligence and the tendency to project human cognitive biases onto machines.
Research
R-Zero: Self-Evolving Reasoning LLM from Zero Data
Self-evolving Large Language Models (LLMs) can potentially achieve super-intelligence, but current training methods rely heavily on human-curated tasks and labels, limiting their advancement. R-Zero, a fully autonomous framework, overcomes this by generating its own training data through the interaction of two co-evolving models, resulting in significant improvements in reasoning capabilities across various LLMs.
XML Prompting Revolution: Math Proofs for Guaranteed LLM Stability
Researchers have developed a logic-first approach to XML prompting for large language models, which unifies various techniques to produce parseable and schema-adherent outputs. This approach provides a mathematical framework for guiding language models towards desired outputs, and has been instantiated with context-free grammars for XML schemas, demonstrating its potential for practical deployment in human-AI interaction systems.
Database Entity Recognition with Data Augmentation and Deep Learning
The paper presents a novel approach to Database Entity Recognition (DB-ER) in Natural Language Queries (NLQ), including a new benchmark, data augmentation procedure, and specialized language model. The proposed DB-ER tagger outperforms state-of-the-art models, with significant improvements in precision and recall attributed to data augmentation and fine-tuning of the language model backbone.
The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
The increasing reliance on AI systems and digital tools may lead to the decline of internal memory systems, impairing the development of expertise, critical thinking, and long-term retention. Effective human-AI interaction requires strong internal models, and over-reliance on AI during learning can inhibit the development of intuitive mastery and procedural skills, highlighting the need for balanced education and training policies.
Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation
This study explores the use of Large Language Models (LLMs) and Artificial Intelligence (AI) in automating Android penetration testing, specifically in identifying and executing rooting techniques, and evaluates their efficacy and reliability. The research finds that while LLMs can streamline the exploitation workflow, they require human control to ensure accuracy and ethical application, and provides suggestions for secure and ethical use of AI-enabled exploitation in cybersecurity.
Code
Show HN: Robot MCP Server – Connect Any Language Model and ROS Robots Using MCP
The ROS-MCP-Server is a platform that connects large language models with existing robots, enabling bidirectional AI integration and allowing for natural language commanding of robots without modifying their source code. It supports both ROS1 and ROS2, and provides features such as publishing and subscribing to topics, calling services, and getting and setting parameters, making it a versatile tool for robot control and debugging.
Show HN: AI Rules Manager – Package manager for AI coding assistant rules
The AI Rules Manager (ARM) is a package manager that treats AI rules like code dependencies, allowing for versioned, distributable packages that stay in sync across development environments. ARM helps manage AI rules for tools like Cursor and Amazon Q, providing features like semantic versioning, reproducible installs, and automatic distribution, making it easier to keep rules up-to-date and consistent across projects.
Show HN: A "Codebase" as an MCP Server
Fenic is a PySpark-inspired DataFrame framework designed for building AI and agentic applications, allowing users to transform unstructured and structured data into insights using familiar DataFrame operations enhanced with semantic intelligence. It supports various features, including semantic operators, native unstructured data support, production-ready infrastructure, and a familiar DataFrame API, making it suitable for engineers of all backgrounds to build and deploy AI-powered applications.
Show HN: LLM Creative Story‑Writing Benchmark V3
The LLM Creative Story-Writing Benchmark V3 evaluates large language models' ability to produce engaging fiction while incorporating required elements, with multiple graders scoring stories on an 18-question rubric to assess craft, element integration, and overall story quality. The top-performing models, led by Kimi K2-0905, GPT-5, and Qwen 3 Max Preview, demonstrate strong narrative craft and element integration, with the benchmark providing a detailed breakdown of each model's strengths and weaknesses.
GEPA: System Optimization Through Reflective Text Evolution
GEPA is a framework for optimizing text components, such as AI prompts, code, or instructions, within any system using reflective text evolution. It employs large language models to reflect on system behavior and drive targeted improvements, allowing for the evolution of robust and high-performing variants with minimal evaluations.