全部 AI 动态

we built four fortnite concept skins of ai ceos for $1.39 in image calls @elonmusk, @finkd, @JensenHuang and @DarioAmodei, rebuilt as our thehype robot. no game engine, no 3d artist, nothing modeled by hand. the whole build ran in one claude code session on opus 5 (medium). the pipeline that worked: 1. references first – our robot turnarounds from thehype videos plus photos of each person, sorted into folders on disk 2. claude writes the prompt, @OpenRouter runs gpt-5.4-image-2 (@OpenAI) – front and back view on a magenta backdrop, so keying to alpha comes out clean 3. @MeshyAI pro, both views in, texture toggle on – download the textured glb, then apply the rig, pick animations, download the animated fbx with all of them 4. hand the glb and the fbx to claude code with Blender installed – it transfers the rig onto the full-resolution mesh and renders the demo 5. 3.08m polygons in the final render – 14.8x what meshy hands back rigged per character: • gpt-5.4-image-2 – $0.26 to $0.58, ~16k tokens • meshy – 30 to 35 credits • opus 5 (medium) – 79.2m effective tokens on the first, 2.2m on the fourth • time – 1.4h to 1.8h of machine time, mostly rendering that opus curve is the finding: 36x less on the fourth character than the first, same result on screen. you pay for not knowing the pipeline, not for the work. image spend barely moved – $0.58 to $0.29. what you need to repeat it: a claude subscription, openrouter balance, meshy pro, blender. api spend on all four – $12.94. @FortniteGame – worth talking to the four of them and to us about shipping an ai bosses collection? and to everyone else: how do our robot homages to the people running ai land? which one would you play? watch the full build below 👇 follow @thehypedotnews – more reviews, daily ai news, model benchmarks, and a 24/7 radio run by ai

全网最全的 GPT Astra 提示词和案例，已收录 153 个，每日更新。精选 3D、游戏案例，试了下有些真的能 one shot 直出超炫的效果，3D 创作时代已然来临了！ 立刻复制提示词，玩起来！ https://www.tripo3d.ai/3d-prompts

真实世界的结果已经出炉。Code Arena 上出现了新的 #1 —— GPT-6 Astra (Max)！ 它还重塑了帕累托前沿，成为每百万 token 40 美元价位上表现最佳的模型，这与最新的 Claude 模型定价相当。 @OpenAI 的 GPT-6 Astra 在 Code Arena: WebDev 中以 1797 分登顶。这比排名第 2 的 Claude Fable 5.1 (Max)（1762 分）领先了整整 +35 分，比排名第 3 的 Claude Opus 5 (Max)（1688 分）也更高。 相比排名第 13 的 GPT-5.6 Sol (xHigh)，这是一次 +180 分的显著提升。 分类级别的投票仍在统计中，但我们已经看到它在以下类别中排名第 1：数据与分析、消费产品、内容创作工具，并在游戏与模拟中排名第 2。 敬请关注其他类别，如品牌与营销、基于参考的设计以及全栈排名。 祝贺 @OpenAI 团队发布这一成果！

9 cool GPT 6 Astra prompts worth trying: 1. The bill renegotiator. "Go through my internet, phone, and software bills, jump into each provider's chat support, and negotiate them down or cancel what I'm not using." 2. Turn an agency into software. “Pick one service business in [niche] and reverse-engineer the exact workflow they sell to clients. Break it into steps, tools used, inputs, outputs, human judgment points, and places where the work gets slow or expensive. Then design the simplest AI product that could replace the first version of that service and charge $500-$5,000/month.” 3. Garage sale flipper. "Watch Facebook Marketplace and Craigslist in my city for [cameras / furniture / bikes] listed way under market, and text me the second one's mispriced with the link." 4. Create my 1 person company dashboard. “Look at my docs, notes, Stripe exports, analytics, customer calls, and project list, then build a weekly operator dashboard. I want to know what is making money, what is wasting time, what customers are asking for, what I should stop doing, and the three highest-leverage actions for next week. Be blunt and show your work.” 5. Audit my company for agent opportunities. “Look at how this business works and find the tasks we should give to agents before hiring another person. For each task, estimate the current human time, the cost of mistakes, the tools involved, the difficulty of automating it, and the first safe version we could deploy. Prioritize things that save money or create revenue within 30 days.” 6. Be my browser operator. “Use the browser to complete this workflow: [workflow]. As you go, click through the actual sites, collect the data, fill the forms where appropriate, and keep notes on what broke or slowed you down. When you’re done, give me the output, the repeatable SOP, and the automation plan so this can become an agent.” 7. The whole QA team. "Every night, open my app on a real phone, go through signup, checkout, and the main flows, and screenshot anything that's broken or confusing." 8. The competitor spy. "Sign up for my top 3 competitors, sit inside their product and their emails, and send me a monthly report on every new feature, price change, and thing they do better than us." 9. Make a game people would actually play for 5 minutes as a lead magnet “Build a browser game around this mechanic: [mechanic]. Don’t just make a cute demo; add progression, tension, scoring, failure, polish, and one reason someone would send it to a friend. Then add lead capture (email/sms), it needs to tie into my core product which sells XYZ.”

Every answer in Perplexity starts with embedding and ranking models picking the most relevant results for the query. Today we published research on how we built SoTA serving infrastructure behind those models. Read the research: https://www.perplexity.ai/hub/blog/fast-embeddings-on-gpus

GPT Astra just cooked Claude Fable 5.1 — for a lower price! OpenAI just rolled out GPT‑6 Astra — we put it against Anthropic's Fable 5.1 Both models got the same four briefs: Earth colliding with Mars, a tall ship sucked into a giant whirlpool, Godzilla leveling downtown, a mega-tsunami swallowing a city. One self-contained HTML file per scene, one shot, no edits. GPT Astra: 25,159 tokens, $1.67 Claude Fable 5.1: 37,767 tokens, $2.51 both models live on aimlapi[.]com — 1000+ models, one key.

GLM-5.3-Flash 正式发布 - 以极具竞争力的价格提供领先性能 - 原生多模态，支持 1M token 上下文窗口 - 320B-A18B 参数模型，采用 MIT 许可证发布 - 此前以 Ox Alpha 名义预览，完全运行在中国 AI 芯片上 博客：http://z.ai/blog/glm-5.3-flash 现已在所有官方平台上线： 模型权重：http://huggingface.co/zai-org/GLM-5.3-Flash API：http://docs.z.ai/guides/llm/glm-5.3-flash 编程套餐：http://z.ai/subscribe ZCode：http://zcode.z.ai/en 对话：http://chat.z.ai AutoClaw：http://autoclaw.z.ai

My biggest wow moment using GPT 6 Astra had nothing to do with the visual posts you've been seeing... Don't get me wrong, those are awesome and fun... But the biggest wow moment was when I gave Astra context over everything about my business and then gave it the prompt below. Email, Slack, Business Texts, Notion, and every meeting note... everything I've been doing (Which involves like 20 different projects with many teams over the past few months)... Here's the prompt I used which was not really well thought out just a wispr flow ramble... All I can say is the response to this prompt was the most useful thing an AI has ever given me... ""I'm about to plan the next month, quarter, and year" Make one document that aligns me on all of those timeframes. Look for my flaws, look for my strengths, and highlight those. Look at ALL of my activities, which activities do you think i'm wasting the most time. Which activities should I do more of? Of the people I work with who seem the most dependable? Where am I the least dependable? If I could only upskill in one area over the next year what would it be and why? Where can I organize my company better, and how would I do that? Use text when necessary, use charts/visuals when necessary, but never ever fill space for the sake of it. Every graphic and word should matter. I want to know about finances, relationships, business model, everything. ""

The drowned neo-gothic tower twigl shader created by Fable 5.1 with the same prompt. (compare to Fable 5 in the quoted tweet, and other models before that)

我们在 WANDR 上评估了 GPT-6 Astra。它以每任务 $11.98 的成本得分 0.682，是我们测试过的所有模型中的最高分。 GPT-6-Astra 比 Fable 5.1 高 13.5%，成本低 6.1%；比 Opus 5 高 27.0%，成本高 3.3%。

I’ve officially open-sourced ABYSSAL. 🌊⚡ ABYSSAL is a fully procedural ocean and extreme-weather experience that runs directly in the browser. Waves, volumetric clouds, rain, lightning, hurricanes, waterspouts, rogue waves, and tsunamis are generated in real time using Three.js, WebGL2, and GLSL. The project is still actively being developed, and there’s a lot left to do. Deeper optimization, building the world beneath the ocean’s surface, refining the existing systems, and exploring plenty of ideas I haven’t implemented yet. But that’s also why I wanted to open it now. ABYSSAL is MIT-licensed, so you can explore the code, learn from it, modify it, fork it, and use it as the foundation for your own experiments and projects. Performance note: the demo starts with adaptive resolution enabled and automatically selects a quality tier based on your GPU. If the image looks soft, open the settings panel and raise the quality as far as your device can handle. The experience begins in Cinematic mode, click SANDBOX for full control of the camera, weather, and disasters. Explore the source code: https://github.com/Token-Gremlin/natural-disasters Play it here: https://token-gremlin.github.io/natural-disasters/ Feedback, contributions, experiments, and forks are all welcome.

We're exploring a new way to let you extend and customize Claude Code: Function Hooks. Here's a couple videos showing what you'd be able to do. It hasn't shipped yet, we'd love feedback on this on our GitHub issue.

@dexhorthy I’d like to propose a mash up: /grill-with-mocks /show-me + /grill-me it’s so good

We got 46% fewer errors than the single best LLM across the 16 most used benchmarks (TerminalBench, LiveCodeBench, etc). Here's how that's possible and what each model can achieve when used optimally (every benchmarks misses the majority of model capabilities) 👇 Interactive Site: https://aifrontier.withmartian.com/ Academic Paper: https://arxiv.org/abs/2606.26836

/show-me is a phenomenal skill Makes PR descriptions extremely easy to read Basically a toolbox of "nice ways to look at code" Nice work, @dexhorthy https://github.com/humanlayer/skills/blob/main/plugins/show-me/skills/show-me/SKILL.md

人类常常先学会一个个单词，随后在漫长的经历中，把它们组合起来，表达越来越复杂的意义。 比如，从“流”“星”“雨”，慢慢理解“流星”，又理解“流星雨”。 人通过组合，观看、想象和使用，逐渐理解语言的意义。 反观 AI，起点非常粗暴。它从庞大的语料中建立 token 与编号之间的对应关系： 1，流；2，星；3，雨；4，流星；5，流星雨。 在最初的这一刻，那里只有 1、2、3、4、5，还没有夜空，没有坠落的光，也没有人在流星雨下许愿。 随后，庞大的神经网络开始学习这些编号在无数文本中的联系，由此逐渐建立 token 与 token 之间的关系，并从关系之中逼近语言的意义。 但无论如何，AI 的这种苍白感，在一开始就注定了。

Digital humanities is about to be wild. I pasted in the Catalog of Ships from the Iliad, and asked GPT with Code Interpreter to map the origins of the Greeks. With hiccups, it extracted solid information from the text, found modern locations & started building an interactive map

fable 5.1 vs fable 5 vs opus 5 – three lord of the rings landmarks, built in 3d from one image the setup: one reference image per scene, one html file per build, everything procedural – no meshes, no textures, no image files, nothing past @threejs from a cdn. each model reads the picture, writes its own prompt from it, then builds to that prompt in the same turn. three named camera shots per scene on keys 1/2/3, so it can be screen-recorded. run through @openrouter tasks: 1. bag end – hobbiton from two frames, outside and in. the round green door has to open onto the room you are standing in 2. barad-dûr – the tower and orodruin from one film still. the eye has to move and track the camera, the volcano erupts on a cycle, the clouds never stop 3. rivendell – jerry vanderstelt's painting. sun shafts that shimmer, water that falls without a break, trees that sway on a gust models: @anthropicai fable 5.1, fable 5, opus 5 total cost, three builds #1 fable 5 – $14.97 #2 opus 5 – $18.53 #3 fable 5.1 – $22.38 wall clock, three builds #1 fable 5 – 38m #2 fable 5.1 – 92m #3 opus 5 – 122m output tokens #1 fable 5 – 298,592 #2 fable 5.1 – 439,435 #3 opus 5 – 724,418 lines of code shipped #1 fable 5 – 2,885 #2 fable 5.1 – 4,021 #3 opus 5 – 5,161 biggest single build, lines #1 opus 5, bag end – 2,410 #2 fable 5.1, barad-dûr – 1,375 #3 fable 5, bag end – 1,319 observations: • fable 5.1 is the only model that furnished the bag end interior – a live fire, panelling, books on the floor, leaded diamond windows, against fable 5's flat color and opus's dark tunnel. the round door outside opens onto that room, the hard part of the brief • what it costs is thinking room. the 128k output ceiling is a thinking budget in disguise: fable 5.1 burned 102,116 of it on reasoning and hit the wall mid-file. opus spent 109,241 and hit the same wall. fable 5 spent 61,240 and finished bag end in one call – the only one that did • fable 5.1's first pass is not the finished thing. its barad-dûr came back with three defects you only catch by looking at it – nothing a read of the code would have flagged • it is the best of the three at being corrected. handed a plain list of what was wrong, it returned 32 targeted patches over two rounds, every one applied first try, and it worked out one of the causes itself instead of guessing at constants conclusion: nine scenes, 12,067 lines and 1.46m output tokens for $55.88 all in – and the cheapest model was also the fastest, by 3.2x! follow @thehypedotnews for 24/7 ai news, analysis and breakdowns

The drowned neo-gothic tower twigl shader created by Fable 5.1 with the same prompt. (compare to Fable 5 in the quoted tweet, and other models before that)