직스 retweeted
이 멍청한 인간은 뭘 제대로 아는게 하나도 없으면서 꺼드럭거리기는 엄청 해대네.
Ho Chi Min의 옛 이름은 Saigon인데 패망 전 월남(Vietnam)의 수도. 트럼프가 말하고 싶었던 도시는 Hanoi.
Trump:
I could have won the Vietnam War in a week.
They kept bombing forests for years instead of going after Ho Chi Minh City.
Readers added context they thought people might want to know
Ho Chi Minh City was called Saigon during the Vietnam War and was the capital of US-allied South Vietnam; North Vietnam's capital was Hanoi.
britannica.com/place/Ho-Chi-M…
en.wikipedia.org/wiki/Fall_of_S…
en.wikipedia.org/wiki/Ho_Chi_Mi…
직스 retweeted
メンバー達と汝矣島にきたならやはりとコンククスをば。店舗拡張&この季節になるとすんなり入れる嬉しさよ。市庁前の名店と比べると塩味を感じ豆の香ばしさが強い感じか。何度も言うが実はビビンククスこそ食べてほしい。甘くも絶妙な酸っぱ味はなかなかない。写真ないけどもちろん食べました。良き。
직스 retweeted
신박한 난독화 기법. 함수 이름을 A, B, C로 바꾸는대신 생화학무기나 독 관련 단어로 마구 바꿔서 최신 모델이 코드분석을 거절하게 만든다고.
有人发现了一种非常离谱的 JavaScript 混淆技术:通过在代码里塞入大量生物武器、毒药和致死剂量相关的函数名,让 AI 因为安全限制而拒绝分析代码。
安全研究员 veritas 在分析浏览器指纹脚本时,发现一段来自 ServicePipe 相关域名的 JavaScript,里面充斥着极其诡异的函数名:
computeRicinLethalDosageForWaterSupply
calculateDeadlyLethalSuicideAspirinDosage
光看名字,仿佛在研究生化武器。但实际上,这段代码只是在收集浏览器指纹,包括 User-Agent、GPU、Canvas 等信息。
研究员怀疑,这些名字是开发者故意设置的陷阱,专门用来阻止 AI 辅助逆向工程。
于是他做了个实验:
• Astra:拒绝分析
• Astra + Daybreak Blue:仍然拒绝
• GLM-5.3:成功分析
这就有意思了。
传统代码混淆是通过复杂控制流、加密字符串、无意义变量名,让人类和工具难以理解代码。
而这种新思路,直接瞄准了大模型的安全机制。
代码本身可能完全无害,但只要在函数名里塞进足够多的危险词汇,部分 AI 就可能主动放弃分析。
更讽刺的是,模型越严格遵守某些安全规则,面对这种混淆时反而可能越容易受到干扰。
当然,目前还没有证据证明开发者一定是故意这样设计,也不能说明这种方法对所有模型都有效。
但它揭示了一个值得关注的趋势:随着越来越多的逆向工程依赖 AI,未来的代码混淆技术可能不再只针对编译器、反编译器和人类研究员,还会专门针对大模型的安全判断机制。
以前是让代码难以理解,现在甚至可以尝试让 AI 不敢理解。
직스 retweeted
엇 맞아요 아사달이라는 회사에서 만들었다고 해요
yunseon.substack.com/p/48e 아사달 대표님 인터뷰인데 관심 있으시면 보는 걸 추천드려요
This quoted post is unavailable.
직스 retweeted
なるほど…散らせばよかったのか……🤔
だいぶ田植え感すごくてコレじゃない感凄かったけど、どこをどう修正するか分からなくて
焼いたら地面感まで強くなってしまったフォカッチャはこちらです💁♀️
AI 에이전트가 전문가의 연구 판단에 미치는 영향을 조사한 #논문. 50명 대상으로 가상의 농업지원프로그램의 효과를 분석하고 지속을 권고할지 결정하는 작업을 시킴. AI가 추천하는 분석법을 선택하고, AI의 논리를 그대로 가져다 쓰는 경향. 끝에서 절반은 자신이 판단한 것이라고 여김.
직스 retweeted
Researchers proved every LLM is secretly obsessed with Japan.
And they finally figured out why.
They published a paper investigating hidden cultural biases in large language models.
they tested 31,000 questions across 24 different languages on models like gemini, chatgpt, claude, and llama.
they stripped out all geographic hints and just asked the models to pick a place and answer.
the finding was universal.. across a wide range of models and languages, there is a pervasive "obsession" with japanese culture.
even when you prompt them in a language with zero ties to japan, they default to japanese norms, values, and aesthetics at a disproportionately high rate compared to its actual global demographic weight.
the craziest part? they tracked down exactly when the ai develops this obsession.. and it is not from reading the raw internet.
during pre-training, the models actually have a fairly balanced, global view of the world. the obsession gets injected during the safety phase.
when tech companies fine-tune these models to be "helpful, harmless, and polite," the ai starts mapping those corporate safety guidelines directly onto japanese cultural concepts. in the pursuit of creating the perfect, inoffensive, compliant assistant, they warped how the models view the world.
the quantization process (compressing models to run faster locally) actually amplifies this determinism. the smaller and faster your local model is, the more stereotypically it behaves, causing japanese cultural tropes to spike hard.
we spend all this time worrying about existential ai safety, but the real alignment problem is that our most advanced reasoning engines are hyper-fixated on japanese internet culture.
every model upgrade is reinforcing an invisible regional bias that dictates how they generate stories, facts, and code. the cultural commonsense of the internet is not universal..
it’s just localized to whatever the heaviest reddit and anime wiki users were obsessed with.



















































