而這種創傷仍普遍存在，在政治受難者及後代身上更為明顯。劉品佑表示，童年時，住在台北市中心的他，曾被家人帶著經過太陽花運動抗議現場，母親知道後，對此無法諒解，「可能因為家族經驗，她一直不喜歡我談政治、碰政治」。
他指出，美國幾乎能「無限期」維持目前的作戰強度。隨著戰爭持續，可供攻擊的目標清單會逐漸變少——這意味著作戰節奏會逐步放緩。
，推荐阅读新收录的资料获取更多信息
I didn’t take much of an interest in CCC,，推荐阅读新收录的资料获取更多信息
弹出 AI 对话框：在应用侧边打开 AI 对话界面
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.