Not a Number 🎇

这家伙很懒，什么都没有留下。

[阿橡 @oakvale5]

1. 先给员工无限量的Token随便尝试
2. 限制Token额度
3. 员工已经离不开AI，回不到过去
4. 员工自己购买Token，进入自费上班阶段。

是不是跟“毒贩引诱吸毒，产生戒断反应，最后自费吸毒”一个流程😂

[Not a Number 🎇 @ouchxp]

@Lee_Narci @RobinSeun 官方的tps太低了, 试试Fireworks的

[Not a Number 🎇 @ouchxp]

速度快最大的优点就是有即时的反馈，不需要等待，不会打断心流。即使改得不满意也可以立刻尝试其他方案。

[Wang Shuyi @wshuyi]

看了归藏公众号介绍，发现如果 MiMo 模型推理速度提升到接近实时输出，确实很多场景都可以改变原先工作流程，显著提升效率。已提交 MiMo-V2.5-Pro-UltraSpeed 内测申请，但愿能早日通过🙏

[Not a Number 🎇 @ouchxp]

@lbt05 DS主要的问题是不支持多模态, 需要贴图片的时候比较麻烦.

[Not a Number 🎇 @ouchxp]

@lbt05 Firepass没了之后我就换到M3了, 就是慢, 过一阵子也能像2.7一样出Highspeed版本就好了

[Not a Number 🎇 @ouchxp]

@XiaomiMiMo

[Not a Number 🎇 @ouchxp]

Though I don't play Wuwa. I'd admit this is a Great colab!

[Kaito @iKaito_1]

Wuwa 3.4 (Spoiler)

→ Lucy and David finally reunited...OMGGGG 😭😭❤️‍🩹

#WutheringWaves #Edgerunners #Gacha

[OpenRouter @OpenRouter]

New server tool: Advisor

Let smaller models consult a higher-intelligence "advisor" model.

Helps them escape doom loops, and helps you migrate to cheaper models! 🧵

[Rick and Morty @RickandMorty]

Morty, I know Kung Fu

[Xiaomi MiMo @XiaomiMiMo]

🚀 1,000+ TOKENS/S ON A 1T MODEL! 🚀

We are thrilled to release Xiaomi MiMo-V2.5-Pro-UltraSpeed in collaboration with @TileRT_AI , breaking the 1,000 tokens/s output speed on a 1 Trillion parameter model for the FIRST TIME!

Not wafer-scale integration like Cerebras. Not pure on-chip SRAM chips like Groq. We achieve 1,000 tps on a 1T MoE model using just a SINGLE, STANDARD 8-GPGPU NODE.

Read the full technical deep dive：https://t.co/MX0kjHKdKi

Want to experience the future of real-time AI?
👉 Apply for UltraSpeed now: https://t.co/aeWAxyhwVk
⏳ Limited-Time Access: Application-based · Jun 8 – Jun 23 (PDT)
💬 Chat Experience: Completely FREE for a limited time — try the blazing-fast web chat now.
⚡ UltraSpeed API: Just 3x the price for a ~10x boost in output experience.
🤝 Enterprise & Large-Scale Needs: [email protected]

[ModelScope @ModelScope2022]

Nex-N2 is now open source！An agentic model series from Nex AGI built for coding, tool use, deep research, and long-horizon workflows. 🧠🔎

🛠️ https://t.co/p2n2cMdBlR
⚙️ https://t.co/k67uqVgDDP

● Models: Nex-N2-Pro 397B total, 17B active; Nex-N2-mini 35B total, 3B active

● Agentic Thinking: adaptive reasoning depth + coherent reasoning across coding, search, tool calling, and execution

● Efficiency: Nex-N2-mini saves roughly 20% overall token cost vs forced thinking while matching or slightly exceeding task performance

● Open-model lead: 75.3 on Terminal-Bench 2.1, 80.8 on SWE-Bench Verified, 83.7 on BrowseComp, and 1585 on GDPval among listed open baselines

● Deployment: customized SGLang fork, reasoning parser, tool-call parser, Docker image

● License: Apache 2.0

[Not a Number 🎇 @ouchxp]

@MikaStars39 M3什么时候出Highspeed呀？如果M3在速度上能有优势的话，也是一个不错的定位。

[无敌的小白羽大人🍥 @yuerQwQ]

感觉这个对我很有用，

[Ray Wang @wangray]

这是哪个天才想的文案

阿迪怕不是疯了吧哈哈哈哈

[Xiuyu Li @sheriyuo]

Normal Ending: You are just a CS doro.

True Ending: You were the doro agent all along.

[Not a Number 🎇 @ouchxp]

@royxy Fireworks的Firepass是最好的，$50，200+tps，无限token

[Sisyphus Labs @justsisyphus]

ok @q_yeon_gyu_kim 's @AnthropicAI oss support is gone after ultracode copy thing

now it's our turn
ultracode -- which is our 'ultrawork'

but in your codex.

```
npx lazycodex-ai install
```

and $ulw-loop instead of your ultracode copy shit

[Programmer Humor @PR0GRAMMERHUM0R]

breakTheViciousCircle

[𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram]

Just tried out NVIDIA's latest vision language detection model.

Tried with 2 images. As you can see, the first image is where I asked to detect only lentils, and in the other one, I tried to find more than one thing.

Worked really well. I tried it in Hugging Face Spaces.

NVIDIA just dropped LocateAnything-3B, which is new vision-language model that could be a major step forward for embodied AI and robotics.

Instead of generating bounding box coordinates one token at a time, it introduces Parallel Box Decoding, allowing AI systems to locate objects much faster while improving accuracy.

The model was trained on over 138 million language queries and 785 million bounding boxes, enabling everything from object detection and GUI understanding to robotics and autonomous systems

NVIDIA claims up to 2.5x higher throughput, making AI agents faster at seeing, understanding, and interacting with the real world.