mike ✦ (@trithucflow) profile picture

flowapp

Joined January 2019
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You can now watch your agents work live as Hermes Desktop streams the screen of any bot's screen or session in real time. Watch it drive a browser, type into a terminal, or open a window. Take over anytime to interact with the Bot Screen or type credentials and seamlessly hand back off when you're done.
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After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution
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I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
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mike ✦ retweeted
Introducing Muse, the personal agent that understands your goals and works 24/7 to get things done for you.
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mike ✦ retweeted
Realtime TTS-2 is GA today, now the #1 model on Artificial Analysis and fastest TTS in the world of its class. The research preview got far more usage than expected. That experience went back into research and today's GA model represents the biggest leap in Inworld's history.
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meet @mostik_ai! what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can. everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning? we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched. how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance. we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst WIRED has the first external account of the company and the work: wired.com/story/russian-star… full writeup, the setup, and all the numbers: mostik.ai/read-more
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mike ✦ retweeted
Muse Voice Transcribe is MSL's first real-time audio perception model -- rolling out today. SOTA in streaming speech-to-text, it handles speaker diarization, and endpointing natively in a single model.
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mike ✦ retweeted
我宣布: Grok Bot 吊打一切OpenClaw、Hermes类伪数字员工! OpenClaw/Hermes能干的事,Grok Bot都能干;OpenClaw/Hermes不能干的事,它也能干! 我自己的一些具体的场景,我梳理出来了一些,希望可以给你启发。 详见这个链接 mp.weixin.qq.com/s/Fxdy9y-TT… 评论区也有很多案例。也欢迎你补充。谢谢。
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mike ✦ retweeted
Big news: Qwen3.8-Max-0902 by @Alibaba_Qwen just debuted at #1 overall in the Code Arena: WebDev with 1691 pts! It scores 3 pts above Claude Opus 5 (Max), 17 pts above Kimi K3 (Max), and 22 pts above the previous Qwen3.8-Max. Priced at a blended $5/MToken, Qwen3.8-Max-0902 also claims the highest-scoring position on the Pareto frontier! Stay tuned for a closer look at its Pareto positioning, and for Agent Arena scores coming soon. Its strength carries across every Code Arena: WebDev category: - #1 in Data & Analytics and Consumer Product - #2 in Brand & Marketing, Gaming, and Simulations - #3 in Content Creation Tools and Reference-Based Design Congrats to the @Alibaba_Qwen team on this huge update!
🚀Qwen3.8-Max just got upgraded. Meet Qwen3.8-Max-0902! 2.4T parameters. 1M context tokens. Built for real world complexity. Further post trained on Coding & Cowork, Qwen3.8-Max-0902 now delivers stronger performance across complex enterprise tasks, scientific research, and long horizon workflows. 💰Pricing per 1M tokens: $2 input, $6 output. $0.17 explicit cache hit, $0.25 implicit cache hit. Now live via API on QwenCloud. Come try it! 🙌 API: qwencloud.com/models/qwen3.8…
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mike ✦ retweeted
Here's how to make animated history videos using ai 👇 (save for later)
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mike ✦ retweeted
ios devs, this can be useful 🔥 Goldie automates App Store screenshots. it can run flows, take screenshots, add device frames + text and generate preview videos. works with SwiftUI, UIKit, Flutter, React Native + Kotlin Multiplatform. github.com/kacperkapusciak/g…
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mike ✦ retweeted
Stickers. Stickers? Stickers! Turn any photo, idea, or inside joke into a custom sticker pack with ChatGPT Images—now with transparent backgrounds. Make in ChatGPT, share on iMessage or WhatsApp. BTW— you can create transparent backgrounds for your other images too.
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Game theory pretty much proves that the long game is not a strategy most people can execute at all. That's because it tends to require a negative short-term position in exchange for a real advantage later. Most people cannot commit to this because their threat-detection system reads current loss as an existential failure. And that's vicious short-sightedness. It leads them to optimize for visible progress and sacrifice their real position. The people who constantly win rarely have better information. Instead, they accept that losing now will pay off later. Never quit a game before it starts.
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mike ✦ retweeted
Meet Duolingo for Marketing. Just enter your website URL, and Fastlane will create thousands of accounts and videos promoting your product in seconds. This is honestly crazy.
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mike ✦ retweeted
神了,有人已经开始当Codex的星怒了😂 有人真的把自己接进了 Codex 的 Agent Loop。 而且不是接机械臂,也不是接机器人。 是接了一个……会让人身体有反应的小玩具。 作者让 Codex + GPT-5.5 扮演 RPG 里的「触手怪」,iPhone 摄像头实时观察自己的姿势和反应。 模型看一眼画面,判断你现在是什么状态。 然后直接下指令: ESP32-S3 → 蓝牙设备 → 你。 你一动,摄像头又看到了。 模型再根据你的反应,决定下一步该怎么折腾。 于是整个系统变成了: AI 看你 ↓ AI 判断你 ↓ AI 控制设备 ↓ 你产生反应 ↓ AI 继续看你 ↓ 再调整力度和节奏 最变态的是,这还不是一轮一轮玩的。 作者专门做了异步控制,设备在工作的同时,Codex 还在继续观察新的画面。 也就是说,AI 可以一边弄玩弄你,一边看你的反应,再实时改变下一步。 设备本身原来只能手机 App 控制,作者觉得这显然不够赛博,于是干脆把 BLE 协议逆向了,让 ESP32 直接接管。 用一个 iPhone、一个 ESP32,再加 Codex,硬生生做出了一个: 以自己身体作为环境反馈的具身 Agent(真的具身智能) 别人研究具身智能: 机械臂、机器人、灵巧手。 民间开发者研究具身智能: 先让 AI 学会怎么把自己玩明白。 只能说,AI 进入物理世界之后,人类最先探索的应用方向,果然从来不会让人失望。
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We tested Anthropic's $200/mo Claude plan, which can yield up to $8,000/mo💰️💰️, while OpenAI's $200/month plan could yield up to $14,000/month worth of tokens if you exhausted all the weekly limits on long-horizon tasks. This test was done back in June.
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mike ✦ retweeted
Humans are the minority user of AI Agents burn nearly 5x the tokens people do, up 14x since February Charts of the Week: a16z.news/p/charts-of-the-we…
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mike ✦ retweeted
DeepSeek-V4-Flash-Vision-Exp is now live on the DeepSeek API Platform! 🚀 🔹 This experimental multimodal model matches DeepSeek-V4-Flash on text capabilities—including agents, reasoning, and world knowledge. 🔹 On multimodal agent benchmarks, V4-Flash-Vision-Exp makes a major leap over V4-Flash, bringing multimodal agent performance close to Opus-4.8. Try it with model='deepseek-v4-flash-vision-exp'. DeepSeek Harness 0.1.1 was released today with out-of-the-box support for the new model. 1/n
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mike ✦ retweeted
🎉iOS开发者的福音! Deepseek Harness iOS 模拟器 + 真机自动化插件它来了! 安装👉:dsh plugin --profile web add @zseven-w/dsh-ios@latest 作者高产似母牛🐮 有兴趣的快去体验! 传送门👉 github.com/ZSeven-W/dsh-ios
这是我们的第 4 个 DeepSeek Harness 插件 🥳 iOS 模拟器 + 真机自动化插件 dsh-ios 🐳📱 让 DSH 中的 Agent 直接查看并操作 iOS 模拟器和真机 构建并运行 App,完成点击、滑动、输入等操作 还能检查进程、调用栈和内存泄漏 ⚙️ 内置 21 个 Agent 工具 实时画面显示在侧边栏,支持人机协同操作 🤗 支持 SwiftUI 预览热重载 在 DSH 中就能完成 iOS 开发、调试和自动化 欢迎使用:github.com/ZSeven-W/dsh-ios 演示视频前半段为模拟器,后半段为真机 快速安装见评论区 ⬇️ #deepseek #harness #dsh #plugin #ios #agent
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耗子叔这篇 7 年前《如何超过大多数人》,现在看还是很牛逼。当时看的懵懵懂懂,这些年的成长回头看才能看懂更多。 前半段故意反着写:让一个人沉迷碎片信息、热点和争论,把收藏资料当成学习,把加班和重复劳动当成努力,再给他一些快速成功的幻觉,他自然就很难真正成长。 后半段才是答案:找到更好的一手信息,把零散认知整理成系统知识,再通过实践、犯错、复盘和跟高手切磋,把知识练成真正的技能。最后拼的是自己的天赋、兴趣、习惯,以及能不能长期坚持。 到了 AI 时代再看这篇文章,感触更深了。答案越来越容易获得,但独立判断、系统学习和真正动手,反而更重要。 很怀念耗子叔,RIP。也有点担心哪一天 coolshell 会消失。打算有空让 AI 把整个网站爬一下放到 GitHub 上面开源。 coolshell.cn/articles/19464.…
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