A new bill in New York would require disclaimers on AI-generated news content

The NY FAIR News Act, a proposed bill in New York, would mandate clear disclaimers on news content substantially created using generative AI and require human editorial review before publication. The legislation also includes provisions for internal transparency regarding AI usage, technical safeguards to protect confidential source data from AI access, and labor protections to prevent job displacement due to AI adoption.

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How to effectively write quality code with AI

Effective AI-assisted coding requires developers to maintain architectural control through precise documentation and human-authored, property-based tests that prevent the LLM from bypassing validation. Implementing explicit tagging for review status and security-critical functions ensures oversight, while context-specific prompts and strict linting optimize model output. To maintain code quality, developers should minimize complexity to preserve context windows and decompose large tasks into manageable, verifiable units.

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Man who videotaped himself BASE jumping in Yosemite arrested, says it was AI

A California man facing federal charges for illegal BASE jumping in Yosemite attempted to dismiss incriminating video evidence by claiming it was an AI-generated deepfake. Federal investigators countered this "AI defense" using a combination of license plate recognition data and vehicle-tracking logs to place the suspect at the scene. The case underscores the growing challenge of digital provenance and the use of synthetic media claims as a strategy for plausible deniability in legal proceedings.

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Agent Arena – Test How Manipulation-Proof Your AI Agent Is

Agent Arena is a benchmarking tool for evaluating LLM agent resilience against indirect prompt injection via 10 distinct attack vectors, including structural HTML manipulation, visual hiding, and zero-width character encoding. The platform tests how agents handle adversarial instructions hidden in web content, highlighting that vision-based agents bypass DOM-level attacks but face unique visual vulnerabilities. Key findings emphasize the impact of language on model resistance and the importance of pre-processing HTML as a defense layer.

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Generative Pen-Trained Transformer

GPenT is a wall-mounted polargraph plotter controlled by an Arduino Mega and Raspberry Pi 5. The project implements "dcode," a text-to-gcode diffusion model using a 200M parameter transformer to decode VAE-encoded image latents into plotter-ready path commands. Additionally, it utilizes Gemini to procedurally generate art by mapping natural language prompts to structured JSON commands that orchestrate various turtle scripts and generator parameters.

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End-to-End Transformer Acceleration Through Processing-in-Memory Architectures

This work addresses Transformer scaling challenges—specifically high data movement, KV cache memory bottlenecks, and quadratic attention complexity—through a processing-in-memory (PIM) architecture. The proposed solution minimizes off-chip transfers, employs dynamic KV cache compression, and reinterprets attention as an associative memory operation. Evaluations demonstrate significant improvements in energy efficiency and latency over state-of-the-art GPUs and accelerators.

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Reduce Complexity of Matmul to O(N^2logN) by Asymptotically Optimal Quantum Algo

This work introduces a quantum kernel-based matrix multiplication (QKMM) algorithm that leverages quantum computing's parallelism to achieve an asymptotically optimal complexity of $ O(N^2 \log_2 N) $. This significantly outperforms the classical optimal $ O(N^{2.371552}) $, addressing a major efficiency challenge in large-scale machine learning. Simulations confirm its theoretical and practical advantages.

ArXiv

Code Quality Analysis of Translations from C to Rust

This study evaluates C-to-Rust translation quality across rule-based transpilers (C2Rust), LLM-guided transpilers (C2SaferRust), and direct LLM-based translation (TranslationGym). Using GNU coreutils and human baselines, the authors leverage Clippy and GPT-4o to evaluate internal and external quality attributes like maintainability and robustness. Results indicate that while LLM-driven approaches mitigate unsafe patterns, they introduce new systematic trade-offs and fail to match human-level quality, highlighting the need for multi-dimensional evaluation in automated code migration.

ArXiv

LLMs do plan before they genenrate tokens

LLMs exhibit emergent planning by encoding future response attributes—including structure, content, and behavior—within their hidden representations. Probing reveals that these representations predict global properties like length, reasoning steps, and factual consistency before generation. These findings scale with model size and offer potential for improved transparency and generation control.

ArXiv

Adaptive Hashing: Faster Hash Functions with Fewer Collision

Traditional hash tables rely on fixed hash functions that are either distribution-agnostic or prone to failure when key distributions shift. This research proposes online adaptation of hash functions to the current key set, enabling superior bucketing and faster evaluation with minimal overhead. By dynamically tuning to the data distribution, the approach achieves the performance of specialized functions and the robustness of general-purpose ones without changing the hash table API.

ArXiv

Monty: A minimal, secure Python interpreter written in Rust for use by AI

Monty is a minimal, secure Python interpreter written in Rust specifically designed for executing LLM-generated code within AI agents. It provides sub-microsecond startup times and strict host isolation without the overhead of container-based sandboxing. Key technical features include support for type checking, resource usage tracking, and the ability to snapshot and resume interpreter state across process boundaries.

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Artifact Keeper – Open-Source Artifactory/Nexus Alternative in Rust

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Monty: A minimal, secure Python interpreter written in Rust for use by AI

Monty is a minimal, secure Python interpreter written in Rust specifically designed for executing LLM-generated code within AI agents. It provides sub-microsecond startup times and strict host isolation without the overhead of container-based sandboxing. Key technical features include support for type checking, resource usage tracking, and the ability to snapshot and resume interpreter state across process boundaries.

Repo

Noodles – Explore AI-generated codebases through interactive diagrams

Noodles is a codebase visualization tool that uses OpenAI to identify entry points and map execution flows into interactive D2 diagrams. Designed to help developers understand AI-generated code, it scans local directories to generate manifests and provides an interactive overlay for incremental diagram updates and exploration.

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Make Trust Irrelevant: A Gamer's Take on Agentic AI Safety

Current agentic AI safety relies on soft constraints like prompts and alignment, which fail to address the "confused deputy" problem caused by ambient authority. To achieve robust safety, systems must implement a kernel-level control plane that treats agents as untrusted planners and enforces hard, reduce-only, and time-limited capability permits. This architectural shift moves safety from the model layer to the enforcement layer, ensuring that agents cannot escalate privileges or mint their own authority regardless of adversarial input.

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