David Schmotz, Sahar Abdelnabi, and Maksym Andriushchenko. Agent Skills Enable a New Class of Realistic and Trivially Simple Prompt Injections. 2025. URL https://arxiv.org/abs/2510.26328.
该平台表示此功能与播客具有高度契合性，听众既能借此发掘心仪的新节目，也能"深入探索特定文化话题"。Spotify补充说明，算法会综合用户指令、个人收听记录以及"当下热点事件"来生成播放列表。
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The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat. The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc. A bit more context on this project is here in this tweet.
To withdraw cash, a user inserted a magnetic card that contained an account