道义感召力更加鲜明。一是求大同的理念更得人心。构建人类命运共同体理念连续9年写入联大决议，中国同30多个国家达成命运共同体共识，同中亚、东盟、非洲、阿拉伯国家、拉美和加勒比国家等区域命运共同体建设稳步推进。共建“一带一路”成为规模最大、覆盖范围最广的国际合作平台，全球超过3/4的国家和30多个国际组织加入。全球治理倡议一经提出，迅速得到150多个国家和国际组织响应。二是担大义的举措广受支持。中国旗帜鲜明反对霸权霸道霸凌行径，在关税战、贸易战中挺身而出，凝聚起反对单边主义、保护主义的广泛共识。秉持客观公正立场，积极劝和促谈，中国特色热点问题解决之道广受各方赞许。积极为广大发展中国家仗义执言，推动纠正历史不公，壮大全球治理体系中的“南方声音”。三是行大道的信念备受赞誉。习近平外交思想指引中国外交秉持“自信自立、开放包容、公道正义、合作共赢”的方针原则，同周边国家共建和平、安宁、繁荣、美丽、友好“五大家园”，同非洲共逐“六个现代化”，构建中阿“五大合作格局”，推进共建中拉命运共同体“五大工程”，给予建交的最不发达国家100%税目产品零关税待遇，推动落实对53个非洲建交国100%税目产品零关税举措，深得国际道义人心。观察人士认为，中国智慧、中国方案成为指引世界稳定前行的一盏明灯，为促进全球和平发展事业作出重要贡献。
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
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A few weeks ago my friend Harrison (@hktouw) and I did our yearly Tesla FSD cruise around the Bay Area — seven hours of letting the car drive while we talk about whatever comes to mind. This was the first year we never had to take over the wheel, which meant even more time for conversation. We covered AI adoption, investing, and then landed on something that’s been bugging me for a while. Why do we still store credentials in plaintext .env files?
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