Keep on to blur preview images; turn off to show them clearly
周五软件分享 - DNS延迟测试工具（图一） https://t.co/15fxHkiJ9v - iDescriptor（图二）：Linux/Win管理iPhone的开源工具 https://t.co/Po64UBIprK - SVG.js（图三）：操作网页SVG动画的JS库 https://t.co/RNIYOEeQhC 更多软件 #科技爱好者周刊（第375期）https://t.co/WbJPzuXrLk
Stay Focused, Keep Shipping. Build Early, Build Always. Improve yourself, Write solid/simple/stupid code.
太真实了！ Nalin 吐槽 LinkedIn 上没有一个真正在招人的工作。 这个月重新开始找工作的感受也是，Boss 直聘、猎聘、脉脉上的职位，也是尝试联系后完全没有回应的 😂 当然这里一方面是我自己竞争力不够的问题，不过也有一些客观现象： 1. 发消息后一直都是未读状态，说明大概率职位是没有招聘方/猎头等在关注的 2. 招聘平台互动很低，所以开始做主动职位推送，以招聘方的语气发职位邀请，匹配度很低；偶尔遇到合适的，又回到 1 的状态 3. 中国国内招聘平台，有些是按职位数量收费的，所以即使职位不要了，也不想下架，不然又要新付费上架职位 在这之外，就是另一个问题： 有些职位，挂出来是比较明显的套方案，或者看竞对薪资的，要么对项目细节问的很多，但不问你个人信息；要么对薪资构成问的很细，但其他基本不咋问。
邵猛，中年失业程序员 😂 专注 - Context Engineering, AI Agents. 分享 - AI papers, apps and OSS. ex Microsoft MVP 合作 - 私信/邮箱：shaomeng@outlook.com 📢 公众号/小红书: AI 启蒙小伙伴
谷歌 TPU v6e、AMD MI300X 和英伟达 H100/B200 性能对比出炉！ Artificial Analysis 硬件基准测试显示，英伟达在推理成本上领先：每美元 token 数量是 TPU v6e 的 5 倍、AMD 的 2 倍。 在 30 token/秒的参考速率下，英伟达 H100 每百万 token 成本仅 $1.06，而 MI300X 为 $2.24，TPU v6e 高达 $5.13。 TPU v7 即将发布，性能大跃进，但定价未知。
开始阅读之前，记得点赞、转发或收藏 本Threads内容由人机协同内容引擎发布 https://t.co/Gxsobg3hEN
I strongly condemn dunking on Prime Intellect, they're doing the exact right thing. Post-training Chinese base models to the frontier level is in fact *more important* right now than learning to pretrain our own bases. I basically don't care what PI, Arcee and others can pretrain, though I have reasonable expectations that they'll catch up soon. Compute is abundant in the West and we already see evidence of sufficient pretraining expertise with smaller models (these two + @ZyphraAI, @Dorialexander, @natolambert with Olmo…) in the Western open space; by all accounts it scales. But that's mostly of… geopolitical significance, of what you guys will be allowed to run on your patriotic servers plugged into agentic frameworks. I'm not Western nor Chinese, and contrary to my posting, I don't care terminally about this dimension, it's a purely instrumental issue. Consult the bio: the race is not between the US/West and China, it's between humans and AGIs vs ape power centralization. And Prime Intellect is doing more than anyone to arrest the centralizing drive. Consider and weep: HF is chock full of Celestial gifts that we're too inept to utilize, they just rot there until they become obsolete. Thousands to millions of downloads and nothing to show. Why is Qwen even doing antiquated, very expensive Llama-like dense models in the first place? Mostly because a) Alibaba has a KPI "monthly HF downloads" and b) academics and small labs can't figure out how to finetune modern architectures. Even were the infrastructure more mature and they less technically ngmi, what do they finetune it on? The narrative peak of open source finetuning was Nous-Hermes, and that paradigm was basically just distilling GPT-4, filtering according to "taste" and vague criteria, SFTing over a strong base, and hoping for the best. That angle of attack was scornfully dismissed in advance by OpenAI et al as a non-threatening dead end that rewards hallucinations and style mimicking, and it predictably fizzled out. What next, «RL»? What RL, how RL, what is the signal generator, how does it intersect with downstream tasks? Kimi-K2, an immaculate frontier-level base, has been available to all for many months. DeepSeek-V3, nearly a year now. V2, well over a year. Dozens of models in all sizes, periodically updated with longer context and other boons. And what have we built with all that? Anything that even approaches Chinese in-house Instructs, nevermind contemporary frontier? Hello? Can you point me to these derivatives? It's a complete profanation of the idea of open science. And not even the Chinese bother, they all just train their own models from scratch. I can think of a tiny number of exceptions (eg Rednote making DSV3-VL), but none of them made a big splash. Startups worth billions, whose moat is search or agentic coding and thus large post-training datasets, sneakily use DS/GLM/Qwen in their proprietary products, but they don't share alpha. That's… about it. Enter Prime Intellect. They're solving training. They're solving environment generation. They're thinking in a principled manner about signals that shape general model cognition. They are, in effect, unlocking the immense store of inert value that had been accumulated. For the world, this is so much more than another me-too model. They're scary smart, they have good intentions, they've got a solid roadmap, and they're my friends. I won't stand for pooh-poohing their work, because it serves the Great Common Task. If you don't see it, you don't have a clue of what's really important at this stage.
We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1
I strongly condemn dunking on Prime Intellect, they're doing the exact right thing. Post-training Chinese base models to the frontier level is in fact *more important* right now than learning to pretrain our own bases. I basically don't care what PI, Arcee and others can pretrain, though I have reasonable expectations that they'll catch up soon. Compute is abundant in the West and we already see evidence of sufficient pretraining expertise with smaller models (these two + @ZyphraAI, @Dorialexander, @natolambert with Olmo…) in the Western open space; by all accounts it scales. But that's mostly of… geopolitical significance, of what you guys will be allowed to run on your patriotic servers plugged into agentic frameworks. I'm not Western nor Chinese, and contrary to my posting, I don't care terminally about this dimension, it's a purely instrumental issue. Consult the bio: the race is not between the US/West and China, it's between humans and AGIs vs ape power centralization. And Prime Intellect is doing more than anyone to arrest the centralizing drive. Consider and weep: HF is chock full of Celestial gifts that we're too inept to utilize, they just rot there until they become obsolete. Thousands to millions of downloads and nothing to show. Why is Qwen even doing antiquated, very expensive Llama-like dense models in the first place? Mostly because a) Alibaba has a KPI "monthly HF downloads" and b) academics and small labs can't figure out how to finetune modern architectures. Even were the infrastructure more mature and they less technically ngmi, what do they finetune it on? The narrative peak of open source finetuning was Nous-Hermes, and that paradigm was basically just distilling GPT-4, filtering according to "taste" and vague criteria, SFTing over a strong base, and hoping for the best. That angle of attack was scornfully dismissed in advance by OpenAI et al as a non-threatening dead end that rewards hallucinations and style mimicking, and it predictably fizzled out. What next, «RL»? What RL, how RL, what is the signal generator, how does it intersect with downstream tasks? Kimi-K2, an immaculate frontier-level base, has been available to all for many months. DeepSeek-V3, nearly a year now. V2, well over a year. Dozens of models in all sizes, periodically updated with longer context and other boons. And what have we built with all that? Anything that even approaches Chinese in-house Instructs, nevermind contemporary frontier? Hello? Can you point me to these derivatives? It's a complete profanation of the idea of open science. And not even the Chinese bother, they all just train their own models from scratch. I can think of a tiny number of exceptions (eg Rednote making DSV3-VL), but none of them made a big splash. Startups worth billions, whose moat is search or agentic coding and thus large post-training datasets, sneakily use DS/GLM/Qwen in their proprietary products, but they don't share alpha. That's… about it. Enter Prime Intellect. They're solving training. They're solving environment generation. They're thinking in a principled manner about signals that shape general model cognition. They are, in effect, unlocking the immense store of inert value that had been accumulated. For the world, this is so much more than another me-too model. They're scary smart, they have good intentions, they've got a solid roadmap, and they're my friends. I won't stand for pooh-poohing their work, because it serves the Great Common Task. If you don't see it, you don't have a clue of what's really important at this stage.
We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1
I strongly condemn dunking on Prime Intellect, they're doing the exact right thing. Post-training Chinese base models to the frontier level is in fact *more important* right now than learning to pretrain our own bases. I basically don't care what PI, Arcee and others can pretrain, though I have reasonable expectations that they'll catch up soon. Compute is abundant in the West and we already see evidence of sufficient pretraining expertise with smaller models (these two + @ZyphraAI, @Dorialexander, @natolambert with Olmo…) in the Western open space; by all accounts it scales. But that's mostly of… geopolitical significance, of what you guys will be allowed to run on your patriotic servers plugged into agentic frameworks. I'm not Western nor Chinese, and contrary to my posting, I don't care terminally about this dimension, it's a purely instrumental issue. Consult the bio: the race is not between the US/West and China, it's between humans and AGIs vs ape power centralization. And Prime Intellect is doing more than anyone to arrest the centralizing drive. Consider and weep: HF is chock full of Celestial gifts that we're too inept to utilize, they just rot there until they become obsolete. Thousands to millions of downloads and nothing to show. Why is Qwen even doing antiquated, very expensive Llama-like dense models in the first place? Mostly because a) Alibaba has a KPI "monthly HF downloads" and b) academics and small labs can't figure out how to finetune modern architectures. Even were the infrastructure more mature and they less technically ngmi, what do they finetune it on? The narrative peak of open source finetuning was Nous-Hermes, and that paradigm was basically just distilling GPT-4, filtering according to "taste" and vague criteria, SFTing over a strong base, and hoping for the best. That angle of attack was scornfully dismissed in advance by OpenAI et al as a non-threatening dead end that rewards hallucinations and style mimicking, and it predictably fizzled out. What next, «RL»? What RL, how RL, what is the signal generator, how does it intersect with downstream tasks? Kimi-K2, an immaculate frontier-level base, has been available to all for many months. DeepSeek-V3, nearly a year now. V2, well over a year. Dozens of models in all sizes, periodically updated with longer context and other boons. And what have we built with all that? Anything that even approaches Chinese in-house Instructs, nevermind contemporary frontier? Hello? Can you point me to these derivatives? It's a complete profanation of the idea of open science. And not even the Chinese bother, they all just train their own models from scratch. I can think of a tiny number of exceptions (eg Rednote making DSV3-VL), but none of them made a big splash. Startups worth billions, whose moat is search or agentic coding and thus large post-training datasets, sneakily use DS/GLM/Qwen in their proprietary products, but they don't share alpha. That's… about it. Enter Prime Intellect. They're solving training. They're solving environment generation. They're thinking in a principled manner about signals that shape general model cognition. They are, in effect, unlocking the immense store of inert value that had been accumulated. For the world, this is so much more than another me-too model. They're scary smart, they have good intentions, they've got a solid roadmap, and they're my friends. I won't stand for pooh-poohing their work, because it serves the Great Common Task. If you don't see it, you don't have a clue of what's really important at this stage.
We're in a race. It's not USA vs China but humans and AGIs vs ape power centralization. @deepseek_ai stan #1, 2023–Deep Time «C’est la guerre.» ®1