Jaka Hudoklin

@MilkRoadAI He is a legend, because of him Chinese companies could train these OSS models, where American companies would never open them. We need more people like him, who face American distopian agenda.

The co-founder of one of America's biggest AI companies just got arrested by the FBI. His name is Wally Liaw and he co-founded Super Micro Computer in 1993. He sat on the board and he personally held $464 million in company stock. And prosecutors say he spent the last two years secretly shipping America's most powerful AI chips straight to China. Not one shipment but a systematic, coordinated operation. The scheme ran through a Southeast Asian shell company. Fake documents, fake buyers, and servers repackaged mid-route to conceal their true destination. When US compliance auditors showed up to inspect the warehouses, the real servers were already gone. They had been replaced with fake "dummy" servers built specifically to fool inspectors. In just three weeks in spring 2025, they shipped $510 million worth of restricted Nvidia hardware. $2.5 billion in banned AI servers delivered to China and here's where it gets darker. This isn't just one rogue executive. A documentary crew already found the underground network months ago, GPU smugglers stripping chips out of banned graphics cards, modifying them in garages, shipping them one by one across borders. A US based buyer was caught in Arizona meeting a contact in a Prius, testing GPUs in a car, with a spare license plate in the trunk. Street-level smugglers, shell companies in Southeast Asia, and now a co-founder with board access and a $464M stake. It's the same black market but just operating at every level simultaneously. The US has spent years trying to cut China off from the chips that power military AI, surveillance, and weapons systems. Liaw and his co-conspirators allegedly made that effort meaningless from the inside. He faces up to 20 years under the Export Control Reform Act plus additional charges for smuggling and defrauding the United States. One of his co-conspirators is still a fugitive and SMCI stock dropped nearly 15% after hours. The company itself says it wasn't named in the indictment. But the co-founder who built it, sat on its board, and ran business development was apparently running something else entirely on the side.

@ns123abc What a legend, without him we might not have oss models like Kimi or GLM

🚨BREAKING: SUPER MICRO CO-FOUNDER ARRESTED FOR SMUGGLING $2.5B IN NVIDIA GPUs TO CHINA >SMCI co-founder Yih-Shyan "Wally" Liaw arrested today >personally holds $464 MILLION in SMCI stock >charged with smuggling BILLIONS in Nvidia servers to china >used a southeast asian shell company to funnel $2.5B in servers to chinese buyers >$510 million worth shipped in just THREE WEEKS in spring 2025 >built thousands of fake dummy servers to fool U.S compliance auditors >caught on surveillance camera using a HAIR DRYER to swap serial number stickers >coordinated the whole thing over encrypted group chats >SMCI down 12% after hours >faces up to 30 years in federal prison ITS SO OVER…

@0xSero @turdsammy @13_percenters It's the same, you can literally make vscode look the same

@turdsammy @13_percenters

I had to open this abomination today, holy fuck. How can anyone use this?????

@ibuildthecloud The same, but you see many people who do, but seems like most of them haven't seen a line of code. I think I will put claw into container (currently is on my dev server yikes), may become my perplexity alternative.

@offlinehacker I'm confused because I don't mix coding and openclaw. I look at openclaw like a personal assistant. Do you use openclaw for coding?

I'm becoming fascinated with OpenClaw.

@ibuildthecloud Yeah it's subpart, I am resorting to opencode's unofficial android app for now, where I can at least see tool calls and file changes github.com/DNGriffin/whis…

@offlinehacker whatsapp. but I'm not so good at "using it" I'm more dissecting the frog to understand the guts. But I didn't think about a dedicated app. I really don't like the integration to existing messaging apps. It's novel, but just a subpar experience.

We are cooking next gen runtime security 👨‍🍳

@rohanpaul_ai Truth to be told we are not sure how this is going to pan out and if that AGI hype will be worth it for better of humanity. How it's currently going is we are in progress of building distopian future, based on how closed and secretive these AGI labs are.

Sam Altman talks about how in 2014, the so-called "elderly" in the field thought the idea of OpenAI is baseless. "In 2014, we started saying, "You know, we should really do something about this." It was incredibly unpopular at the time. We had said we want to make an AGI lab, and all the elders of the field were like, "You're insane. You're a scammer." This is because, at the time, people thought AGI was 100 years away and none of these approaches were going to work. We really said, "Let's just push on this one idea that scaling deep learning seems to matter." We didn't know at the time how beautifully predictable the scaling laws were, but it was at least clear back in those days that if you threw more compute at something, you got better results most of the time. We kind of just decided we were going to push on it as far as we could." --- From 'TreeHacks" YT channel (link in comment)

So everyone building lame "AI" terminal wrappers these days. No wonder AI has "creative" ideas if everyone just copy same shit

@Yuchenj_UW I am wondering when there will be enough enshitification that people will start to care.

Some people at frontier AI labs told me they believe startups are over. OpenAI, Anthropic, Google, xAI will absorb every industry as AGI nears. Coding today, science, medicine, and finance next. Then everything else. If they’re right, that’s a pretty boring end of the world.

@Yuchenj_UW Basically distopia, very grim end indeed.

@UnemployedVip @rohanpaul_ai Doesn't matter, their energy supply can handle 5 years behind

@rohanpaul_ai Man, I didn’t realize China was still 5 years behind TSMC & the US.

🇨🇳 Hua Hong Group, China's 2nd largest chipmaker has developed the capability to manufacture advanced 7nm processors. This is a massive deal for China because strict trade restrictions were supposed to completely block them from building anything this advanced, making this a huge leap for their own domestic hardware independence. This 7nm process makes transistors incredibly small so you can fit more on chips for faster performance. Biren (Chinese GPU designer) is using this line for tape-out, which turns chip designs into physical testing prototypes. Hua Hong is raising $1.1B to upgrade from their older 22nm and 28nm technology. Their next challenge is improving yields, which means increasing the % of chips on wafers that actually work. Globally, 7nm technology is still actually several generations behind industry leaders like TSMC and Samsung, who are already mass-producing much smaller and faster 3nm chips. --- reuters. com/world/asia-pacific/chinas-no-2-chipmaker-readies-7-nm-production-beijing-ramps-up-self-suffiency-2026-03-16/

@futurenomics @aakashgupta @pegobry_en The problem is that people ask AI for relationship advice, which was trained on reddit...

@aakashgupta @pegobry_en Classic example of selection bias If you’re turning to Reddit for relationship advice you’re likely not in a good place

50% of all relationship advice on Reddit is “leave.” 15 years of data, 52 million comments, and the trend line only goes one direction. A researcher filtered r/relationship_advice down to 1,166,592 quality comments and tracked what people actually recommend. In 2010, “End Relationship” sat around 30%. By 2025, it’s approaching 50%. “Communicate” dropped from 22% to 14%. “Compromise” collapsed from 7% to 3%. “Give Space” fell from 25% to 13%. Every category that requires patience lost ground every single year. The one category growing faster than “leave” is “Seek Therapy,” which went from 1% to 6%. The subreddit is slowly learning to say “this is above my pay grade.” Train a model on this dataset and it would absolutely tell people to break up. The training data is 50% “leave” and climbing. The model wouldn’t be broken. It would be accurately reflecting what 52 million commenters actually believe about your relationship. A 50% prior that you should leave, a 14% prior that you should talk about it, and a 6% prior that you need a professional. That’s not LLM psychosis. That’s the median human opinion on your relationship, backed by the largest advice dataset ever assembled.

@DataChaz @karpathy And out you get more research slop 🤦

THIS is the wildest open-source project I’ve seen this month. We were all hyped about @karpathy's autoresearch project automating the experiment loop a few weeks ago. (ICYMI → github.com/karpathy/autor…) But a bunch of folks just took it ten steps further and automated the entire scientific method end-to-end. It's called AutoResearchClaw, and it's fully open-source. You pass it a single CLI command with a raw idea, and it completely takes over 🤯 The 23-stage loop they designed is insane: ✦ First, it handles the literature review. - It searches arXiv and Semantic Scholar for real papers - Cross-references them against DataCite and CrossRef. - No fake papers make it through. ✦ Second, it runs the sandbox. - It generates the code from scratch. - If the code breaks, it self-heals. - You don't have to step in. ✦ Finally, it writes the paper. - It structures 5,000+ words into Introduction, Related Work, Method, and Experiments. - Formats the math, generates the comparison charts, - Then wraps the whole thing in official ICML or ICLR LaTeX templates. You can set it to pause for human approval, or you can just pass the --auto-approve flag and walk away. What it spits out at the end: → Full academic paper draft → Conference-grade .tex files → Verified, hallucination-free citations → All experiment scripts and sandbox results This is what autonomous AI agents actually look like in 2026. Free and open-source. Link to repo in 🧵 ↓

vibe coders who don’t ship anything showing their agent orchestration setup

@_kaitodev @karpathy Assumptions are a bit off here, physical work is going to be replaced by robots in no time

5 minutes ago, @karpathy just dropped karpathy/jobs! he scraped every job in the US economy (342 occupations from BLS), scored each one's AI exposure 0-10 using an LLM, and visualized it as a treemap. if your whole job happens on a screen you're cooked. average score across all jobs is 5.3/10. software devs: 8-9. roofers: 0-1. medical transcriptionists: 10/10 💀 karpathy.ai/jobs

@Linahuaa @KikiwahT Do you understand your "500$ on fancy restaurant, high end clubs,..." sounded a bit cheap. Don't want to be offensive, but that's a bit of a turn off for intelligent person.

@KikiwahT may I ask you- no offense- are you on the spectrum per chance? Genuinely curious about whether that makes the difference

I must be lame. I would love an escape room or museum.

@ChrisLaubAI Why tho?? X is becoming vibe code slop of posts, you don't even stand out anymore, because it's the standard annoying format...

@offlinehacker No

🚨 BREAKING: A Google researcher and a Turing Award winner just published a paper that exposes the real crisis in AI. It's not training. It's inference. And the hardware we're using was never designed for it. The paper is by Xiaoyu Ma and David Patterson. Accepted by IEEE Computer, 2026. No hype. No product launch. Just a cold breakdown of why serving LLMs is fundamentally broken at the hardware level. The core argument is brutal: → GPU FLOPS grew 80X from 2012 to 2022 → Memory bandwidth grew only 17X in that same period → HBM costs per GB are going UP, not down → The Decode phase is memory-bound, not compute-bound → We're building inference on chips designed for training Here's the wildest part: OpenAI lost roughly $5B on $3.7B in revenue. The bottleneck isn't model quality. It's the cost of serving every single token to every single user. Inference is bleeding these companies dry. And five trends are making it worse simultaneously: → MoE models like DeepSeek-V3 with 256 experts exploding memory → Reasoning models generating massive thought chains before answering → Multimodal inputs (image, audio, video) dwarfing text → Long-context windows straining KV caches → RAG pipelines injecting more context per request Their four proposed hardware shifts: → High Bandwidth Flash: 512GB stacks at HBM-level bandwidth, 10X more memory per node → Processing-Near-Memory: logic dies placed next to memory, not on the same chip → 3D Memory-Logic Stacking: vertical connections delivering 2-3X lower power than HBM → Low-Latency Interconnect: fewer hops, in-network compute, SRAM packet buffers Companies that tried SRAM-only chips like Cerebras and Groq already failed and had to add DRAM back. This paper doesn't sell a product. It maps the entire hardware bottleneck and says: the industry is solving the wrong problem. Paper dropped January 2026. Link in the first comment 👇

@Linahuaa @Rage_of_the_Age More desirable as being well paid tech nerd lol?

@Rage_of_the_Age move to Asia and teach English

It's not that hard for high IQ females to date at all. It's the other way around due to >140 IQ men outnumbering women like 2:1 If have 130-150 IQ with no personality, you can go to Goldman or McKinsey and easily find someone smart and handsome. If you're a nerd with 130-150 IQ, you can become a physicist or join an A.I. lab, and it's 80% males who are mostly smarter than you. If you're 130-150 IQ who failed in school and missed the boat hard, you can join a top tier World of Warcraft guild and easily seduce the guild master and get a seat in the loot council