RyanLee

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RyanLee

RyanLee

@RyanLeeMiniMax

Head of DevRel @MiniMax_AI. Building @MiniMaxAgent and @Hailuo_AI. Find me: https://t.co/zWZQ8cPPBN

SH/SF Katılım Mayıs 2025
243 Takip Edilen8.5K Takipçiler
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RyanLee
RyanLee@RyanLeeMiniMax·
I’m incredibly excited to share this: MiniMax has just closed a new $2B funding round. 🚀 At the same time, our CEO, IO, shared three long-term commitments with the team: • No salary until we achieve AGI. • Over the next four years, he will dedicate shares equivalent to 4% of the company’s total equity from his personal holdings to reward employees who are building MiniMax for the long term. • Another 1% will be committed to supporting the open-source community. The funding is exciting. But what excites me even more is what it represents: a long-term commitment to AGI, to our people, and to the open-source ecosystem. We’re living through one of the most exciting moments in the history of AI, and we’re just getting started. If you’re passionate about frontier AI, open source, and building the future, we’d love to build with you. Intelligence with Everyone. 🚀
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RyanLee
RyanLee@RyanLeeMiniMax·
@li9292 嗯 我就问问是谁做的 😄 做的不错
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李韭二
李韭二@li9292·
@RyanLeeMiniMax ryan 你好,你可以仔细看下我的原图,保留了原作者的水印,同时我在评论区里贴出了其他作者的开源项目的GitHub链接,而且在评论和主贴中,我都没有声称是我做的,不过我确实在做另外一个具体Lottie的方案,还在研究中
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李韭二
李韭二@li9292·
假如你还在问什么是loop Engineering.一张图看懂
GIF
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RyanLee
RyanLee@RyanLeeMiniMax·
@akshay_pachaar is the most top master of GIF on X
Akshay 🚀@akshay_pachaar

the four types of agent loops. loop engineering keeps getting talked about as one thing. it's actually a choice between four structures, and each one fits a different kind of task. it means designing the system that steers the agent, instead of steering it yourself move by move. that system always answers two questions. what starts a run, and what decides the work is done. in a hand-run session you answer both yourself, every single time. each loop type moves more of that into the system. here's each type, what triggers it, and when to reach for it. 1) turn-based. triggered by a user prompt. the agent gathers context, acts, and checks its work inside a single turn, then a human reviews the output and writes the next prompt. use this when requirements are still forming and every output changes what you'd ask for next. 2) goal-based. triggered by a /goal command carrying success criteria and a budget, like "get the homepage Lighthouse score to 90, stop after 5 tries." when the agent tries to stop, an evaluator model checks whether the goal is met, and a no sends it back to work. use this when the outcome is measurable but the path there isn't worth your attention. 3) time-based. triggered by a clock. an interval fires, the agent runs a fixed prompt like "check the PR, fix CI," then waits for the next tick. /loop runs on your machine, /schedule moves it to the cloud so it survives a closed laptop. use this for recurring work where the task is known in advance and only the timing repeats. 4) proactive. triggered by an event or schedule with no human present. a routine watches a channel, and when something needs handling it spawns a workflow with a triage agent, a fix agent, and a reviewer that adversarially judges the work before the task closes. use this for standing responsibilities where you can't predict what will come in, only that something will. each type hands off one more job than the last. turn-based keeps both with the human, goal-based automates the checking, time-based automates the trigger, and proactive automates both while deciding the workflow shape at runtime. so the mapping question isn't which loop is most advanced. it's whether your task is exploratory, measurable, recurring, or standing. the more you hand off, the less you babysit. I wrote the full breakdown on loop engineering. the article is quoted below.

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RyanLee
RyanLee@RyanLeeMiniMax·
@deedydas Thanks, Deedy, where have these public revenue data?
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Deedy
Deedy@deedydas·
Every single AI startup with $500M+ revenue run rate (excluding the big 3 labs): Lovable - $500M ElevenLabs - $500M Perplexity - $500M Manus - $500M* Cognition - $500M* Kling AI - $500M Crusoe - $500M* Midjourney - $500M* Higgsfield - $500M Lightning AI - $500M+ Replit - $525M* Baseten - $600M* Lambda - $760M* Fireworks - $800M* Together AI - $1B Surge AI - $1.4B* Scale - $2B* Mercor - $2B** Cursor - $4B 22 total companies (including big labs) *estimated / unconfirmed **gross marketplace volume, not net revenue
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Chris von Csefalvay 🔜 CVPR26
@RyanLeeMiniMax To clarify, are you specifically looking for RL datasets for coding? We do it for phys, but if you’re after coding, I don’t want to waste your time. (Good drills on being so proactive about this btw. Awesome example for all labs.)
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RyanLee
RyanLee@RyanLeeMiniMax·
every mail will reply individually within 3 business days
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Fireworks AI
Fireworks AI@FireworksAI_HQ·
Long-context sparse attention has a catch: data-dependent block selection wrecks memory access kills speed. Our @MiniMax_AI M3 kernel on Blackwell answers it. KV-stationary, each block read once, ~980 TFLOP/s on a B200. See the breakdown here → fireworks.ai/blog/kernel-op…
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Aaliya
Aaliya@aaliya_va·
Three years ago, he was one more engineer in Chinese big tech. Today, an entire AI lab speaks through him. In 2023, Ryan Lee made the bet that defines every great tech career: He left the comfort of established internet giants for MiniMax , a young Shanghai AI startup most of the world hadn't heard of. Then he went to work: >@RyanLeeMiniMax helped build Xingye, the AI companion app that became a cornerstone of MiniMax's consumer business >He led the early development of @MiniMax_AI Agent, laying the foundation for its Agent 2.0 leap >He kept writing code the whole time , streaming pipelines, login flows, developer tools. A Hong Kong University of Science and Technology graduate who never stopped being an engineer, even after his title changed His defining project: orchestrating the complete open source release of MiniMax's M2.5 model. Strategy, process, community , all of it ran through him. Over 10,000 developer applications built on day one. Today @MiniMax_AI: 212 million users across 200+ countries. A Hong Kong stock exchange listing in January and this week, a fresh $2 billion raised to chase AGI. When the company announced that historic round, guess whose voice they chose to deliver it to the world? The engineer who joined 3 years ago. @RyanLeeMiniMax never chased a personal brand. He built products, shipped code and earned the trust of developers one honest interaction at a time until his credibility became one of his company's most valuable assets. Titles are given. Voices are earned. Join something young. Build like it's yours. The rest follows. Congrats to @MiniMax_AI and congrats to @RyanLeeMiniMax
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RyanLee
RyanLee@RyanLeeMiniMax·
I’m incredibly excited to share this: MiniMax has just closed a new $2B funding round. 🚀 At the same time, our CEO, IO, shared three long-term commitments with the team: • No salary until we achieve AGI. • Over the next four years, he will dedicate shares equivalent to 4% of the company’s total equity from his personal holdings to reward employees who are building MiniMax for the long term. • Another 1% will be committed to supporting the open-source community. The funding is exciting. But what excites me even more is what it represents: a long-term commitment to AGI, to our people, and to the open-source ecosystem. We’re living through one of the most exciting moments in the history of AI, and we’re just getting started. If you’re passionate about frontier AI, open source, and building the future, we’d love to build with you. Intelligence with Everyone. 🚀
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RyanLee
RyanLee@RyanLeeMiniMax·
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RyanLee
RyanLee@RyanLeeMiniMax·
On the M3 license — thanks for the feedback on M2.7 🫡 You told us prior-approval-for-any-commercial-use was too much. We listened. M3: - Non-commercial: fully free - Commercial for individuals or companies under $20M/yr revenue: just need to give us a heads up (api@minimax.io) and label “Build with MiniMax” - Companies with higher revenue: please contact us for commercial license 😀
RyanLee tweet media
MiniMax (official)@MiniMax_AI

MiniMax M3, Open-Weight, Now On Hugging Face Weights: huggingface.co/MiniMaxAI/Mini… MiniMax Sparse Attention: huggingface.co/papers/2606.13…

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Unsloth AI
Unsloth AI@UnslothAI·
MiniMax M3 can now be run locally!🔥 MiniMax-M3 is a new 428B (23B active) open model with 1M context that performs on par with Gemini 3.1 Pro. Run Dynamic 2-bit GGUF on 138GB RAM/VRAM or 3-bit on 165GB. GGUF: huggingface.co/unsloth/MiniMa… Guide: unsloth.ai/docs/models/mi…
Unsloth AI tweet media
MiniMax (official)@MiniMax_AI

MiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters Weights: huggingface.co/MiniMaxAI/Mini… MiniMax Sparse Attention: huggingface.co/papers/2606.13…

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RyanLee
RyanLee@RyanLeeMiniMax·
@MiniMax_AI hope OSS guys enjoy this license
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vLLM
vLLM@vllm_project·
Day-0 goes beyond inference: NeMo RL from @NVIDIAAI also supports MiniMax M3 on day 0, with vLLM powering rollout generation. 💡 A reference GRPO recipe is ready, so you can start post-training M3 for your own agentic workflows right away. Branch: github.com/NVIDIA-NeMo/RL… Recipe: github.com/NVIDIA-NeMo/RL…
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vLLM
vLLM@vllm_project·
🎉 Congrats to @MiniMax_AI on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at @MiniMax_AI, @NVIDIAAI, @AIatAMD, and @inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗 vllm.ai/blog/2026-06-1…
MiniMax (official)@MiniMax_AI

MiniMax M3, Open-Weight, Now On Hugging Face , with only ~428B parameters and ~23B activated parameters Weights: huggingface.co/MiniMaxAI/Mini… MiniMax Sparse Attention: huggingface.co/papers/2606.13…

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