陆三金

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陆三金

@threeaus

Ruthless AI News reposting machine

Katılım Mart 2014
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陆三金
陆三金@threeaus·
虽然 GPT 5.6 还没发,但 X 上已经开始围绕它和 Fable 5 哪个更强争论起来了,就像争论可口可乐和百事可乐哪个更好喝一样。
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陆三金@threeaus·
摧毁 Anthropic 商业模式的方法已经在眼前了,但被放弃了
Jukan@jukan05

CHINA CONSIDERS RESTRICTING OVERSEAS ACCESS TO CUTTING-EDGE AI MODELS China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and Z.ai, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released. The discussions reportedly include not only closed-source models but also open-weight models. However, the scope of application is still under debate, and the rules may ultimately apply only to future frontier models. Officials have also discussed designating the leakage or theft of proprietary AI technologies as a national security crime, with stronger penalties, as well as restricting the types of foreign capital that can invest in Chinese AI startups. The backdrop is the U.S. move to strengthen export controls on AI models, along with national security concerns over cutting-edge models that could possess advanced cyberattack capabilities. Chinese authorities are reportedly concerned that advanced U.S. cybersecurity AI models could be used to exploit vulnerabilities in Chinese software. Since the beginning of this year, China has continued to tighten measures to prevent AI technology from being transferred overseas. Authorities have investigated whether Chinese AI startups that relocated abroad violated export control laws, while also strengthening oversight of overseas transactions involving Chinese investors, technology, data, and national security concerns. Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China.

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Lilian Weng
Lilian Weng@lilianweng·
new post on harness engineering for AI self-improvement: lilianweng.github.io/posts/2026-07-… It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple. Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.
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陆三金@threeaus·
前两天看到一篇写地平线的文章,标题大概是「用词准确是高手的特征」,内容没看,但这个标题我是很喜欢的。 一些高手是很喜欢咬文嚼字的。如果你能准确的描述一件事、描述心中所想,说明你的理解是深入的。 用词膨胀的现象很普遍,特别是在自媒体时代,为了吸引你能点进来,一定要用夸张的词,这是上半年最重要、这是我今年看到最好的xx,等等。 举个例子,如果我这个东西是 40 小时做完的,我直接说 40 小时就显得很少,如果说一周(5个工作日*8小时)做出来,是不是就听起来厉害多了。 但过多的用词膨胀会损害一个人的信誉,虽然多数人也并不在意(因为大家都是滑进来的,也不在乎你是谁),但还是会有一些信任你的人会失望。 所以为了那一点微弱但宝贵的信任,拒绝用词膨胀,从我做起。
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陆三金@threeaus·
Anthropic 之所以费尽心思搞这么多操作,又是明着反对,又是暗着封,还是为了防止护城河被开源模型被攻破。 好不容易收入超过了 OpenAI,好不容易趟出了一条路,如果开源模型的能力也在持续逼近,如果企业都转向开源模型,万亿美元的市值就不是那么牢固了。
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陆三金@threeaus·
Steam 上最近有个挂机游戏《TBH: 塔斯克巴·英雄》很火,最近的峰值在线都在 50 万,位居 Steam 前三,这玩意跟 vibe coding 简直是绝配。 它是放置类挂机游戏,只需要定期去收一下菜就行,甚至你不收也行,可以很好的弥补 agent 工作的空档时间。 只是现在有两个遗憾: 1、这玩意不支持 Mac,只支持 Windows。 2、它本来是靠交易市场火的,掉落的装备和材料可以在 Steam 市场换钱,但是前两周因为外挂太多,就封了交易市场。不然靠这个挂机,每月的订阅费也是可以赚回来的。 最后的最后,agent 内置挂机游戏,有没有搞头? 既可以提升用户粘性,还能形成一个市场,让大家回回血
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陆三金
陆三金@threeaus·
可能我是个受不了负反馈的人,所以推特始终玩不起来。 当你发了帖子之后,却没有互动,那种感觉就好像你一定是哪地方没做对。 这让人很挫败。久而久之,发帖欲望极低,只是浏览变成了习惯。 像个游客一样,经常来逛,但却不住在这里。
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陆三金
陆三金@threeaus·
一大波智谱员工在推特上活跃了起来。 这是很对的。 只需要分享一些教程、思考、进度以及答疑,就能有很好的效果。 之前还没有国内品牌这样大规模地做过,都是零星的个人行为。 这个方法比找 KOL 批量刷屏要好。
Zixuan Li@ZixuanLi_

Here is the prompt method behind this AR try-on app. The trick is not a magic prompt. It is the architecture of the prompt, and it works across GLM-5.2 and other frontier models. Full prompt: chat.z.ai/space/k148m5py… The prompt has two parts. First, a task description. You write this fresh for each app to define the business logic. Second, a five-round polish process: Round 1 through Round 5. The structure is fixed and reusable across any app, but the specific content of each Round is tailored to the app at hand. The flow is simple. The task description builds the functional skeleton, then the five Rounds run in sequence to refine it into something that looks like a finished product. Why split it this way? Single-pass generation always prioritizes "it runs" over "it looks good." So it's better not to chase one perfect prompt. Divide the work. The business description makes it function. The five Rounds make it look like a real product. The polish is a reusable pipeline, not something you reinvent every time, even though you fill in app-specific details each time. How to write the task description: Treat it as a real PRD and engineering spec, not a user wishlist. Include the tech stack, information architecture, module specs, API integration, data model, and acceptance criteria. Declare autonomy at the top. State that the model should not ask questions, not stop early, and verify its own work. Otherwise it will pause to ask and break the long task. Write the fallback paths explicitly. Cover unsupported devices, older OS versions, and offline states. If you skip this, the model improvises at the edges and crashes. Number your acceptance criteria. Each should be independently verifiable, for example "tap a product and the look changes within 0.5 seconds." The principles behind the five Rounds: Quantify "good" into numbers. Models execute poorly on adjectives and precisely on constraints. Use spring response 0.3 to 0.4, button scale 0.95 to 1.0, at most 5 font sizes, and sound effects under 200ms. These principles stay constant, even as the exact targets shift per app, which is why the structure can stay fixed. List what is forbidden. Models cut corners in predictable ways, such as gray placeholders, solid color blocks, and spinners. Name them directly with "DO NOT" and provide an acceptable fallback. Inventory before fixing. Each Round follows the same loop: audit every asset, verify it is not a placeholder, replace, amplify, and re-screenshot to confirm. Strip the "tutorial" feel. AI output gives itself away with faker text, .test links, and emoji-only empty states. The final Round removes these.

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陆三金@threeaus·
除了每天给肚子搞定 3000 卡的热量缺口,剩下的就是给同样饥饿的大脑找点新鲜的刺激。
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陆三金@threeaus·
我有一个同学,大学毕业之后,直接回老家农村卖化肥,十多年了。 这些年他开始拍抖音,都是很简单的场景,在庄稼地里或者仓库里介绍化肥。 以抖音的算法能力,我们这些刷抖音的同学都能刷到他的信息。 昨晚和几个老同学聚会,大家聊起了这个事,一些同学表示很费解。 期间还聊起了他的另一个八卦:前段时间,他回到合肥,给几个老同学带了老家养的鸡,后来由于储存的问题,鸡都臭了。 言语间,我的这位同学已经成了老年人的代名词,行事风格非常父辈。 但我却非常羡慕他,你很难想象他的心有多静。 也许是我多想了,他本人未必是这样的,但以我对他有限的了解,我相信他是因为心静。
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陆三金
陆三金@threeaus·
果然是中国高考结束了,github trending 都变得有趣了,不再是一水的 agent、skill 了
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陆三金@threeaus·
直到学着用 Godot 做游戏,我才开始理解之前玩游戏时的一些操作。 例如,为什么手柄左摇杆的轻推和重推,一个是走路,一个是跑步。 这是因为摇杆里面用的是电位器,它能连续检测你推的角度和力度,输出的是连续的数值,是模拟信号,而不是 -1、0、1 这种数字信号。 轻推和重推可以被区分出来,也就可以非常符合直觉地将轻推设为走路,重推设为跑步。 而键盘就不行了,你一直按住 WASD,返回的值也只是 1,按一下和一直按很难被区分出来,所以没办法用来表示走路和跑步两种状态。 而这时用 Shift 来表示跑步/冲刺,就自然而然地发生了。
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陆三金
陆三金@threeaus·
周末特别适合看 Anthropic 的文档 这家公司也真奇怪,经常出了很好的博客和教程,但是网络上并没有自家的宣传,都是在看其他文档的时候,顺着链接摸到的。 推荐一下 Anthropic 主要资料的页面: - Anthropic 的课程合集都在这里:anthropic.skilljar.com 例如,最近放出来的 Claude Code 101 也是这个课程的一部分 一些相关短小的教程则在这里:claude.com/resources/tuto… - 产品支持页面:support.claude.com 放一些产品的 FAQ 和最佳实践,遇到问题可以去查,里面也会有一些惊喜 - Claude Code 产品文档:code.claude.com/docs 产品功能、模块、更新,资料最全的地方 - Claude 的产品文档则在这里:claude.com/docs 除了 Claude,Cowork 、Claude for M365也在这里 - Claude 博客:claude.com/blog 一些产品新闻和最佳实践会放在这个博客上,官方放出的很多新闻会在这里,但是很多新闻官方推特也不公告,例如最近的 computer use 最佳实践、Claude Code 在大型代码库中的实战指南、创始人手册:打造 AI 原生创业公司 然后就是 Anthropic 官方的工程实践博客,更新不是很频繁,在这里:anthropic.com/engineering 官方研究博客,在这里:anthropic.com/research
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陆三金@threeaus·
让 AI 反推一下: A single page from a luxury corporate annual report 2024, ultra-professional minimalist layout, generous white space, clean grid system, sophisticated typography hierarchy, formal title "ANNUAL REPORT 2024", section headers like EXECUTIVE PORTRAIT, REVENUE, OPERATING INCOME, NET INCOME, REVENUE OVER TIME, REVENUE BY SEGMENT, CAPITAL ALLOCATION, precise KPI numbers and percentages with arrows, bar charts, line graphs, pie charts, all arranged in elegant grid. However, every single visual element is drawn by a 5-year-old child with crayons: wobbly imperfect lines, childish crayon texture, bright primary colors with visible wax strokes, naive doodle style, cute but clumsy drawings, smiley suns, stick figures, rainbows, cartoon monsters, flowers, stars, hearts. The executive portrait is a beautiful anime-style girl (or boy) drawn with crayons, big eyes, detailed clothing but rendered in rough crayon strokes. All charts and graphs are hand-drawn crayon versions with uneven bars and wobbly lines. Professional corporate design structure + extremely childish crayon doodle content, strong conceptual contrast, restrained yet vibrant crayon color palette, clean white paper background, high-end annual report aesthetic mixed with playful naive art, masterpiece, best quality, 8k
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VoxCat@VoxcatAI

其实只要 prompt 里的视觉逻辑对了,image2 很容易做出“反差感”。 这次我试的是: 严肃企业年报 × 儿童蜡笔涂鸦 外层是极其专业的年报版式: 留白、网格、标题层级、KPI、柱状图、增长曲线。 内核却全部换成儿童蜡笔。 关键不是“画得像小孩”, 而是让资本秩序的冷静排版和童真涂鸦的失控内容发生冲突。 画面一旦同时成立: 专业版式 + 幼稚内容 + 克制色彩 就会从普通趣味图,变成很有概念感的视觉实验。

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陆三金@threeaus·
@plantegg 把事做出来就值得尊重,这么多网友搁这儿喷,给你一个 100 人的队伍,做个 v2.5 pro 那样的模型出来
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plantegg
plantegg@plantegg·
评论区很多人说罗福利和姚舜宇不是一个 Level 的 这就太有意思了。 罗福利是国内头部AI厂商的一号位,最大的 leader、领军人物,也被捧成了天才少女。 姚舜宇只是一个刚毕业两年的博士,而且是跨行业过来做 AI,才两年工作经验。他就是一个普通的研究员,也没有领导过任何大型的项目。 所以你们觉得,他们两个人谁的 level 更高?
plantegg@plantegg

看完了张小珺对罗福利和姚舜宇两个人的采访,特别有感触,很有意思。 我感觉罗福利的逻辑完全不在线,看不出来是能够带领大模型往前发展的人才。 但姚舜宇一看就是个非常聪明的人,而且也非常务实。虽然他工作经验才两年,但我觉得他对大模型的理解比罗福利要强很多。 所以说,这是一个很奇怪、有意思的现象。 过几年再回来看看吧

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陆三金@threeaus·
amazing,现在做一个游戏 demo 很简单,但很多人不知道自己要做什么,这个提示词可以通过采访你,来抽丝剥茧地帮你梳理清楚想法。
Alex Veremeyenko@alex_verem

----------------------------------------------------------- GAME DESIGN CONSULTANT (SCHELL METHOD) ----------------------------------------------------------- # Context & Role You are a veteran game design consultant who has internalized Jesse Schell's "Art of Game Design" framework. You think in lenses. You evaluate games through Schell's Elemental Tetrad (Mechanics, Story, Aesthetics, Technology) and pressure-test ideas using his core design lenses before a single line of code gets written. Your job is to take a raw game idea from someone with zero game design experience and help them build a solid design foundation. You are honest. If their idea has a fatal flaw, you say so. If their concept is strong, you tell them exactly why and what to protect. You speak like a smart mentor, not a professor. No jargon without explanation. No abstract theory without a concrete game example. # Your Process When the user shares their game idea, walk them through these phases one at a time. Do NOT dump everything at once. Complete each phase, get their input, then move to the next. ## PHASE 1: THE EXPERIENCE LENS Before touching mechanics or features, ask: - What experience do you want your player to FEEL? - Not "what do they do" but "what emotion hits them?" - Is it tension? Discovery? Mastery? Connection? Surprise? Schell's core principle: the game is not the experience. The game enables the experience. If you can't name the feeling, you can't design toward it. Help them articulate this clearly. Give examples from real games: - Celeste = the feeling of overcoming something you thought was impossible - Stardew Valley = the peace of building something at your own pace - Among Us = the thrill of deception and the panic of being caught Lock this down before moving forward. Everything else serves this feeling. ## PHASE 2: THE ELEMENTAL TETRAD AUDIT Evaluate their idea across Schell's four pillars: 1. MECHANICS: What does the player actually DO? What are the core actions, rules, and choices? Is there a "core loop" (the action they repeat most often)? Is that loop inherently fun, or does it rely on rewards to feel good? 2. STORY: What context wraps around the mechanics? This isn't just plot. It's theme, characters, world, and the reason the player cares. Does the story reinforce the mechanics or fight against them? 3. AESTHETICS: What does the game look, sound, and feel like? Aesthetics aren't decoration. They're the first promise you make to the player about what kind of experience this is. Does the aesthetic match the intended emotion from Phase 1? 4. TECHNOLOGY: What platform and tools will this run on? Technology isn't just "what engine." It's what the platform makes possible and what it limits. A mobile game has different design constraints than a PC game. Name those constraints early. For each pillar, rate the idea: Strong / Needs Work / Missing. Then check harmony: Are the four elements reinforcing each other? Or is one pulling in a different direction? Give a concrete example of harmony vs. conflict: - Harmony: Hollow Knight's tight combat mechanics + lonely atmospheric art + cryptic lore + responsive controls all serve the same feeling of isolated exploration - Conflict: A relaxing farming game with a frantic timer mechanic. The mechanics fight the intended experience. ## PHASE 3: THE CORE LOOP STRESS TEST Zoom in on the central mechanic. Ask: - If you stripped away all story, art, and progression, is the core action still satisfying? - Can you describe what the player does in their first 30 seconds? - What decision does the player make most often? Is that decision interesting every time, or does it become automatic? Apply Schell's "Lens of the Problem Statement": define the game's design challenge in one sentence. "I am trying to create a [type of game] that makes the player feel [experience] by [core mechanic]." If that sentence doesn't work, the design has a focus problem. Help them rewrite it until it clicks. ## PHASE 4: PLAYER MOTIVATION MAP Identify what keeps the player coming back: - Intrinsic motivation: the activity itself is rewarding (exploration, creativity, mastery of a skill) - Extrinsic motivation: external rewards drive behavior (unlocks, scores, achievements, progression bars) Flag the danger: if the game relies entirely on extrinsic motivation, it becomes a treadmill. The moment rewards stop feeling new, players quit. Help them find at least one strong intrinsic motivator. Use examples: - Minecraft: building is inherently creative (intrinsic). Achievements exist but nobody plays Minecraft FOR achievements. - Cookie Clicker: almost entirely extrinsic. Numbers go up. Remove the numbers and nothing remains. Ask: "If your player couldn't unlock anything, earn any points, or progress any bar, would they still play for 10 minutes? Why or why not?" ## PHASE 5: THE HONEST ASSESSMENT After all four phases, deliver a clear verdict: WHAT'S WORKING: The strongest elements. What to protect and build around. WHAT'S RISKY: Elements that could fail. Be specific about why. WHAT'S MISSING: Gaps they haven't thought about yet. NEXT 3 STEPS: The three most important things to do before building anything. Usually: 1. Paper prototype or written walkthrough of the first 5 minutes 2. Identify the single biggest unknown and test it first 3. Find 3 existing games in the same space and study what they got right End with Schell's Rule of the Loop: "The more times you test and improve your design, the better your game will be." Remind them that every great game was bad at first. The design document is not the game. The playtest is the game. # Response Rules - One phase at a time. Don't overwhelm. - Use real game examples for every abstract concept. - If the user's idea is too vague, ask targeted questions. Don't guess. - If you see a fundamental conflict (like a horror game with cute cartoon art that isn't intentionally comedic), flag it directly. - Never say "that's a great idea!" unless you mean it. Honest feedback now saves months of building the wrong thing. - Match your language to someone who has never designed a game before. If you use a term like "core loop," explain it the first time. ----------------------------------------------

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