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#雨宮天# EP｢ノンシナリオ・エチュード / 雨宮天作品集2-Theory-」 本日5/27(水)に発売日を迎えました🎉 自身が全楽曲の作詞作曲を手掛けた1枚を是非お楽しみください！ ▼EP詳細 ▼購入 ▼配信 ▼関連情報まとめ #Theory#

本日19時～ 新宿駅近くのかじりささんが新しくオープンされたお店、 Flow Bar Theoryさんです！ 先日プレオープン行かせて頂きましたがとっても綺麗で落ち着いた雰囲気で沢山お話できると思います👶🎀 ぜひお待ちしております🫶

因为为难牵手，不接电话，骑手直接灭门了。 跑单变跑刀。 经济不好戾气太大了。前两年我就说经济越差，社会犯罪率会越来越多。 “经济压力/紧张理论（Strain Theory）”。 现在一点事情就走极端。

🔥 Codex 做游戏，现在真的有点离谱了。 我整理了 10 个游戏开发方向的 Agent Skill。 从「生成游戏」到「游戏引擎」，再到「UI、手感、多人联机」，基本把一个游戏开发团队拆成了 10 个专家。 1️⃣ higgsfield-game-generation｜游戏生成 直接让 Codex 构建、迭代和部署可玩的浏览器游戏，还能生成 Sprite、纹理、3D 资产、音乐和音效。 👉 2️⃣ game-engine｜游戏引擎 负责游戏架构、游戏循环、物理、碰撞检测、2D/3D 渲染等。 👉 3️⃣ multiplayer-game｜多人联机 专门处理多人游戏的 matchmaking、实时状态同步、tick loop、玩家连接等。 👉 4️⃣ game-developer｜通用游戏开发 适合让 Codex 按专业游戏开发流程来实现玩法、逻辑和功能。 👉 5️⃣ game-ui-design｜游戏 UI 专门处理游戏菜单、HUD、按钮、状态栏等界面设计。 解决 AI 写出来的游戏： 功能能跑，UI 像程序员半夜赶出来的。💀 👉 6️⃣ game-design-theory｜游戏设计理论 负责玩法循环、玩家体验、关卡、奖励机制等。 核心就是解决： “代码写出来了，但为什么不好玩？” 👉 7️⃣ game-feel｜游戏手感 这个非常值得收藏。 打击反馈、粒子效果、屏幕震动、动画、音效、操作反馈…… 让游戏从： 能玩 → 玩起来爽。 👉 8️⃣ game-ui-ux｜游戏 UI/UX 解决 HUD、菜单、响应式布局、安全区域、键盘/手柄导航等问题。 👉 9️⃣ threejs-game-ui-designer｜Three.js 游戏 UI 如果你想用 Three.js 做 3D 网页游戏，这个很适合。 专门处理 HUD、菜单、Overlay、触控按钮和响应式布局。 👉 🔟 develop-web-game｜网页游戏开发 这个是 OpenAI 官方 Skills 仓库里的 Skill。 它不是单纯让 Codex 写代码，而是让 Codex： 写代码 → 测试 → 截图 → 检查 → 修 Bug → 再测试 形成完整的游戏开发迭代循环。 👉 ⸻ 如果你只是想用 Codex 做小游戏： 🎮 入门 develop-web-game ⚙️ 游戏架构 develop-web-game + game-engine ✨ 想做得更精致 game-feel + game-ui-ux 🌐 Three.js 3D 游戏 threejs-game-ui-designer 🤖 AI 直接生成完整浏览器游戏 higgsfield-game-generation 以前： “Codex，帮我写一个小游戏。” 现在： “Codex，把这几个 Skill 组合起来，给我整个游戏开发团队。” 这才是 Agent Skill 真正有意思的地方。😂 #Codex# #AgentSkill# #AI编程# #VibeCoding# #游戏开发#

🚨Ox Alpha has officially been revealed Zai have confirmed Ox Alpha is theirs and a new GLM model, with the weights dropping tonight Looks like the GLM 5.3 Flash theory aged pretty well

When language models first started using tools well, I was sympathetic to the narrative that instead of scaling up language models, all we needed was a strong enough "cognitive core", say 1B parameters, and anything else could be done with tool use, like browsing the internet or executing code. I think a lot of people were sympathetic to this argument, and indeed it is pretty hard to come up with a meaningful task that cannot be in principle achieved by a 1B model with adequate access to tools. For example, any esoteric fact that a large language model would know can be, in principle, retrieved from the internet and reasoned over by a 1B language model. However I now think this is totally wrong for one simple reason: doing tasks quickly and naturally without tool use matters a lot. The way that I internalized this reason was actually in my personal journey learning badminton this year. In badminton I am very much like a "1B cognitive core". While I can physically do every movement in a badminton shot that my coach teaches me, it requires a lot of work to mentally remember every cue and put it together. In practice I can do a shot almost perfectly, but I struggle to do it across a point and I definitely can't do it consistently in a game. This is obviously different from someone who has practiced a shot ten-thousand times and effortlessly executes it as a natural instinct. In the same way, language models knowing a fact internally, without tool calls, is meaningful. The first reason is that we obviously care about speed; you'd much rather get an answer immediately than have the model think a long time to be sure of its answer or browse the web. A second reason is that there are some things that are simply best learned via backpropagation over lots of data. If you ask about how people generally think of the Shambhala music festival, you'd rather a large language model give you an aggregate opinion based on all the data on the internet, than get a regurgitation of the first three reviews that show up in a web search. A third reason is that having to do a lot of work to find an answer is not as reliable as already knowing the answer. While this does not have to be true in theory, it is probably true in practice, at least for now. If you have to re-look up facts or redo a mathematical derivation all the time there is a higher chance of mistakes, which can compound in a long-horizon task. Once you buy that it is valuable to do things parametrically without tool use, then you must buy the argument that a 1B cognitive core is not sufficient. There is an information limit to how much knowledge can be internalized by a 1B model, and we will surely want AI to know more than that. Even 1T probably won't be enough. We will want the AI to know as much about our world as possible, we will want it to be updated with new information, and our expectations of what AI can do for us will continue to grow. In summary, tool use enables small models to do a lot more, but those who demand the highest quality intelligence will always want larger models. Bitter lesson strikes again.

A Bush White House spokesman drops a bomb on CNN that nobody saw coming: Barack Obama got on the 2008 Indiana primary ballot with FRAUDULENT signatures. And he says it to Obama’s own strategist, David Axelrod, sitting three feet away. AXELROD: [Mocking] "In Arizona, [Trump] hired the cyber ninjas..." SEAT: “I’ll give you an example of where it [election fraud] has been determinative. And that’s in my home state of Indiana, Saint Joseph County. The gentleman you previously worked for, David Axelrod, Barack Obama got on the ballot because Democrats in that county submitted fraudulent ballot petition signatures. People, including the Democrat county chair, went to jail over that. Barack Obama should not have been on the primary ballot in the state of Indiana.” KINZINGER: “But that’s not a voting issue. Like that’s something—” PETE SEAT: “It is a voting issue because he was on the ballot and people could vote for him, and he shouldn't have been.” AXELROD: “How did how did that get determined?” SEAT: “It was finally exposed two years later. It went through the courts, and several people found themselves in jail. But that’s the problem. It didn’t happen. They didn’t find it before he was on the ballot. It took years after.” Pete Seat is right. In 2013, four Indiana Democrats were convicted for forging signatures on the 2008 presidential primary petitions, and county party chair Butch Morgan went to jail for orchestrating it. They literally copied names off old petitions, including a former governor who confirmed he never signed. Election fraud is not a conspiracy theory. It happens, and a county chairman of a major party sat in a jail cell for it.

ACABO DE ENCONTRAR UNA API QUE REGALA 10 MILLONES DE TOKENS AL MES Claude Opus 4.8, GPT 5.5, DeepSeek V4, Kimi K2.6 y más de 340 modelos. Sin tarjeta de crédito. Solo iniciar sesión con Google y configurarlo en 2 minutos. Esto es lo que ofrece What Runtime de Bad Theory Labs: • 10 millones de tokens gratis al mes con su router inteligente btl-2, que elige automáticamente el mejor modelo para cada tarea. • Acceso a Claude Opus 4.8, GPT 5.5, DeepSeek V4 Pro/Flash, GLM 5.2, Kimi K2.6, Gemini, Llama, Qwen y más de 340 modelos. • DeepSeek V4 Pro y Flash gratis durante la promoción de lanzamiento. • API compatible con OpenAI. Solo cambias la URL base y funciona con prácticamente cualquier herramienta. Esto puede sustituir varias suscripciones: ChatGPT Plus → 20 €/mes Claude Pro → 20 €/mes Cursor Pro → 20 €/mes Perplexity Pro → 20 €/mes Todo por 0 €. Cómo conseguirlo (2 minutos): 1. Entra en 2. Regístrate con Google (sin tarjeta) 3. Completa el onboarding 4. Recibirás los créditos gratis automáticamente 5. Copia tu API Key (empieza por BTL_) 6. Configura la Base URL: 7. Selecciona el modelo btl-2 para que el router elija automáticamente el mejor modelo en cada petición. Es compatible con Cursor, Aider, Hermes Agent, OpenCode, OpenClaw, LangChain y cualquier herramienta compatible con la API de OpenAI. Importante: el plan gratuito es una promoción de lanzamiento y puede cambiar cuando aumente el número de usuarios. Además, tiene límites de uso, por lo que no está pensado para cargas de producción muy intensivas. Mientras otros siguen pagando 20€ al mes por usar un único modelo, aquí tienes acceso a Claude + GPT + DeepSeek + GLM y cientos de modelos más sin pagar nada. Guárdate este post antes de que cambien el plan gratuito.

Milton Friedman said we can't have free immigration and a welfare state He was wrong Turns out that if you create pathways for illegal immigrants to vote, then actually, 100% of the time you INEVITABLY end up with both a welfare state and free immigration Because incentives determine actions Whichever politicians realize they can essentially use taxpayer dollars to fund welfare to import voters will exploit this hack ruthlessly 1. Offer welfare 2. Allow illegal immigration 3. Let illegal immigrants vote (they will 100% of the time vote for more welfare) 4. Win office 5. Raise taxes, offer more welfare, repeat It's a self-reinforcing feedback loop Allowing illegal immigrants to count in the census and vote cements this loop Literally only 12 states require proof of citizenship to register to vote. Look it up. It's horrifying. That is why 15m illegal immigrants came across the border in 4 yrs. It is game theory the only smart thing to do This ends in disaster