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算力攻击不一定长得像租来的 GPU。 2018 年 9 月 6 日，Zcash 社区论坛那条 ASIC 长帖走到 #27353#/4847。bitcartel（公开署名，亦写作 比特卡尔 / Simon Liu）没有再争论「参数再调一档够不够」，而是把观察压成两句： “So ASICs can be used to attack a network…” “…perhaps it wasn’t someone renting GPUs from NiceHash, but actually Equihash ASICs that were used to attack Zencash and Bitcoin Gold.” 先把攻击面说清楚：ASIC 不只是提速工具，也可以被用来打网络。再补一句当时许多人容易漏掉的可能——表面上像 NiceHash 租 GPU，骨子里也许是 Equihash ASIC 在打 ZenCash 与 Bitcoin Gold。他把「租算力」与「专有矿机」拆开看，提醒社区别把攻击外观误认成攻击本质。 这和同帖里别处谈的「社会契约」「个人立场」不是同一条线。#4847# 盯的是攻击归因：当哈希战争发生时，你凭什么判断对手手里拿的是可租的 GPU，还是已经量产的 Equihash ASIC？归因错了，对策就会跟着错——你会以为关掉租算力市场就够了，却放过真正改变战场形态的那一侧。论坛公开记录里留下的，不是口号，是一次对「攻击长什么样」的纠偏。 把时间线往后接：论坛里把「ASIC 可被用来攻击网络」写进公开讨论，工程上再往前一步，就是让工作量证明本身更难被专有矿机垄断。Scash 协议骨架贴近 Bitcoin，PoW 选 RandomX——文档写明任何人只要有一台电脑就能参与出块、帮助保护网络；RandomX 先在 Monero 网络落地，并成功抑制了专用 ASIC 矿机的发展路径。家用电脑重新成为默认参与者，而不是专有矿机广告里的旁观者。公平挖矿不是口号墙，是让攻击侧更难用「一套专有硬件」改写整条链的参与门槛。 下次再看见「有人在砸链」的传闻，你会先问：那是租来的 GPU，还是已经量产的 ASIC？ #scash# #算力攻击# #EquihashASIC# #RandomX# #家用电脑#

$BTC Finally reached $66,666. 👍 As promised, I want to change someone's holiday and send 0.15 $BTC(~$10,000) to one person by tomorrow. Just like, retweet and comment '$BTC'. Random winner in 24 hours. I will announce as a tweet at 12pm EST Sat

Giving away 1 $BTC to a random person! As promised, I’m sending 1 BTC (~$69,162) to one lucky follower. Like, retweet and comment ‘BTC’ to enter. * If you’re not following, you will be disqualified. Winner announced in 24h! Comments will be locked after 24 hours, the winner will be announced. Good luck everyone!

$BTC Finally reached $66,666. 👍 As promised, I want to change someone's holiday and send 0.15 $BTC(~$10,000) to one person by tomorrow. Just like, retweet and comment '$BTC'. Random winner in 24 hours. I will announce as a tweet at 12pm EST Sat

I think the best case is to make the "monitor" as un-agentic as possible. We don't mind being "monitored" by various random tiny insects we don't even notice, even guard dogs are fine, but we really do mind if it's a globally connected networked system. And the intermediate case, human security guards with no recording equipment, we mind only a medium amount. The best idea I've come up with so far is to make these monitoring devices out of open-source verifiable hardware that has etched on it an open-weight LLM that classifies for a limited number of activities (basically, just violence; sorry, no scanning for jaywalking or not wearing a mask or drugs or unauthorized manufacturing or illegal immigrants, really just violence) and only records and forwards if that's what it sees. The device has no storage, no ability to load new algorithms, this is all it does. Also, a hash of every recording is published onchain (and a device can't record the next one until it sees confirmation of the previous one) so we can all count how much recording is done. Finally, everyone has a legal right to take any such device they see and grab it and dissassemble and verify the hardware themselves. Explicitly fund university clubs, hacker groups, etc to regularly do this.

I'm giving away 0.1 BTC to one random person! As promised, I'm sending 0.1 BTC (~$6,800) to one lucky follower. To participate, like, retweet, and comment 'Okay'. * You will be disqualified if you are not a follower. The winner will be announced within 24 hours! Grab your chance!

Hello guys, tonight I watched a 50 minute podcast with Albert Castellana from GenLayer on The Future of Crypto Compliance, and one minute in it is still stuck in my head. Code is free now, execution is becoming free, so what still has value? According to Albert, it’s judgment. AI already decides what we see on Twitter, whether we get hired, whether we get a loan or insurance, and right now we’re just trusting a few big AI labs with that power Main points: - His previous project lost access to around $150M after keys were lost, and the courts turned out to be too slow and expensive - GenLayer is building a small digital court onchain for decisions that aren’t just yes or no - Five random validators, each with its own AI model, vote on the outcome - Disagree? Appeal, and the validator set doubles each time, up to 1,500 - The first decision takes minutes and costs around 50 cents - It doesn’t replace real courts, it handles small disputes nobody would take to court anyway

My husband stopped initiating sex after I turned him down three times in one week. At first, I thought he was finally understanding that I couldn’t always be in the mood whenever he was. Then I noticed he had stopped touching me altogether. No random kisses in the kitchen. No pulling me closer when we watched television. No morning hugs before leaving for work. I actually preferred it that way until one evening I asked why he was behaving like a stranger. He looked at me and said, “I figured you wanted me to stop making advances.” I laughed. “There’s a difference between not wanting sex and not wanting you to touch me.” He nodded slowly. “That’s the part I’m struggling to understand. Because lately, every time I reach for you, I have to calculate whether you’re going to feel loved or pressured.”

I am actually disgusted by the timing of this drop. And this will get a bit more technical. Let’s do a real technical teardown of this "1,385-sigma Blackwell weight fingerprint" report that OpenAI and Anthropic are breathlessly promoting today, because banking on nobody reading past the headline is how DC narratives get manufactured. ​First, look at the actual CUDA maths: when PyTorch initialises model weights (torch.nn.init) with a fixed PRNG seed, curand/Philox parallelises random number generation across the GPU’s Streaming Multiprocessors (SMs). Because Hopper has 132 SMs and Blackwell has 148 or 152 SMs, the exact same seed lays out initial weights in a different spatial pattern depending on the GPU’s SM count. In huge MoE models, certain low-gradient null-spaces or unrouted slices preserve faint directional traces of those step-0 weights even after training. ​So yes, brute-forcing the seed and SM grid can show that a model’s step_0 weight initialisation script ran on a 148/152-SM Blackwell GPU. Now read what the summary explicitly admits in the fine print: 1. Initialisation is not full training: The report literally states that these signatures show how the weights were created, and "support (but do not prove) the inference that Blackwell also participated in model training". Initialising a model checkpoint takes seconds on a single rented dev node before loading the safetensors onto a standard Hopper cluster. The "2,000 to 3,000 Blackwell GPUs" and "$500 million" cluster numbers do not come from the weight fingerprint and Depue admits in his own thread that he simply "estimated" cluster sizes from release schedules and model details. ​2. Zero geolocation and zero proof of smuggling: The final sentence of the executive summary explicitly admits that the findings "do not establish the physical location of the chips, how they were acquired, or whether export control regulations were violated". Depue himself rented a cloud GB200 instance just to run his verification test. Commercial labs that are not on the Entity List renting bare-metal Blackwell nodes in offshore clouds (Singapore, Japan, or the Middle East) is completely legal under BIS rules and leaves the exact same SM fingerprint. Now ask yourself why a former OpenAI researcher dropped this on November 12, the exact day Jensen Huang was pushed out, with OpenAI and Anthropic immediately stepping in to "independently verify" it. US closed-source labs are watching their paid API moats get eaten alive by free Chinese open-weights models. Conflating a step-0 PRNG initialisation seed with a "$500M smuggled Blackwell cluster" hands DC hawks the exact pretext OpenAI and Anthropic want to regulate global cloud compute and kneecap open-source AI.