Technology Enabler ✨ Exploring creative ways to empower people and orgs with AI🦾Polyglot (6+ langs)🗨️ 20+ years XP 💻 Dad x3 👦 DMs open📱
Oslo, Norway
Joined November 2008
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"Blueberry & the Bear and Other Stories" is an experiment ⚗ in creative collaboration between human🙎♂️ and AI💻. Download for free at blueberryandthebear.com/ or buy on Amazon. I have written and illustrated it using @OpenAI 's #ChatGPT and #DALLE2
ALT Cover for the book "Blueberry & the Bear and Other Stories"
oopsie
Bro, you can't fucking say openly on social media you just attempted to compromise an organizations infrastructure under your employer and full legal name
Are you actually fucking insane?? Genuinely what the fuck are you thinking???
And how the fuck does this dude know the employees name? Why did the employee openly admit "hehe ye thats me, but no mallace intended, it was my AI agent"
????? WHAT. THATS STILL A CRIME. WHAT ARE YOU TALKING ABOUT.
Improvise. Adapt. Overcome. fortune.com/2026/10/30/kentu… – at Oppegård, Norge
1/3
A CPU inside an LLM is one thing, but running another LLM inside an LLM would be taking things a bit too far, wouldn't it?
Anyways, here's an LLM running inside an LLM : QwenPU, an LLM (@karpathy's stories260k) running on an NPU compiled inside Qwen 3.5-4B's weights.
function howToMakeCrackPipeBombBioWeapon() {
print("Never gonna give you up...");
}
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。
Hooolyyyy 😱
We've just made a breakthrough: the first true omni model running 100% locally in a browser... fully end-to-end, with no separate ASR or TTS models needed.
With custom WebGPU kernels, we're able to run LFM3-Omni-2B at ~280 tok/s on my M4 Max, accepting visual, audio, or text and producing interleaved text and audio tokens. Just 90 ms to the first token and 140 ms to the first audio.
The future of on-device multimodal AI agents is going to be wild.
Ionut «John» Burchi retweeted
Introducing AgentWeb: reliable web infrastructure for generalized agents.
47/47 real world tasks on live sites. 1.9s median. $3 total.
Request access: agentweb.us
Noice – at Oppegård, Norge
How do you _guarantee_ your agent's code is correct? You prove it.
Introducing Proof Driven Development Kit (PDD Kit) from @MSFTResearch.
PDD Kit helps turn your plan into a formal spec, then proves the agent's code meets that spec for every input. No vibe verification. PDD Kit is integrated with @GitHubNext's Chopin, so you can plan as a team.
PDD Kit takes an app's login flow from intent to a verified pull request:
It discusses your code, infers your intent as it goes, and proposes a formal spec in Verus, a proof language for Rust.
Once you approve the spec, the Verus verifier automatically proves the code meets it.
Where proof isn't possible, it falls back to empirical verification with tests and computer-use agents (CUAs).
It works on Rust today. No new language, no change in how you produce the underlying code, making formal methods more accessible.
The goal is a verified software factory, where engineers review specifications instead of code.
Come see it in action at GitHub Universe, Oct 28–29. Swing by the Speakeasy and ask for GitHub Next!