Worn Machine retweeted
Summer of RTX isn't over yet. ๐
One final shot:
๐ข Custom PCs
๐ข GeForce RTX 50 Series GPUs
๐ขGeForce RTX 50 Series laptops
+ more
Drop #RTXPowersPlay below to enter!
Worn Machine retweeted
MiniMax Code CLI is open now:
github.com/MiniMax-AI/minimaโฆ
Worn Machine retweeted
MiniMax Code is going open source in the next few days.
Built with developers. Now, built in the open.
See you on GitHub.
Worn Machine retweeted
I have conducted an audit of Anthropic's finances.
What I have found is so shocking that I am calling for a Congressional investigation.
Anthropic is not just seeking regulatory capture.
It has built a regulatory capture machine that cannot be turned off.
Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.
It starts with METR.
Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.
He proposes METR for this purpose.
But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.
Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.
And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.
This stock was worth $500 million early last year.
It is worth more than $7.7 billion just ~16 months later.
METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.
Because if Anthropic goes under, many of the organizations that fund METR go under as well.
But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.
The "third-party evaluator" is not "third-party" at all.
The evaluator is on Anthropic's payroll.
If this were the end of it, that's bad.
But that isn't all.
The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.
The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.
They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.
All of these organizations are financially dependent on the same exploding $7 billion money pile.
As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.
Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.
From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.
This itself is an objective fact.
The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.
But the problem also goes in the other direction:
If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.
Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.
Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.
They simply are not organizations independent of Anthropic.
And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.
What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.
The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.
And that feedback loop is winning.
That's what Jacob Coxon is.
China is keeping messaging tight. That is why optimism for AI is so high in China.
America has Anthropic: a massive company pushing anti-AI propaganda at a state level.
Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.
Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.
It is the mirror of the same AI virus that it hypothesizes to consume America.
Anthropic's business model, models itself after the very thing it claims to fear.
Except Anthropic's ideology infects humans, not computers.
Congress must investigate.
Evidence and Github in next post.
Then some supplementary figures.
Worn Machine retweeted
๐ Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient.
๐น Introducing the smallest model in our new architecture family, with native visual understanding.
๐น Designed for greater capability, faster inference, higher throughput, and scaling to larger models.
1/6
Worn Machine retweeted
๐ Hy4 preview is here.
770B, 49B active, 1M context.
Built for productivity.
Open source frontier.
Consistent affordable price.
Use it. Tell us what breaks.
More on Hy blog๏ผhy.tencent.ai/research/hy4-pโฆ
HuggingFace๏ผhuggingface.co/tencent/Hy4-pโฆ
Github๏ผgithub.com/Tencent-Hunyuan/Hโฆ
Worn Machine retweeted
Meet Apodex 1.1: Scaling Agentic Intelligence for Complex Work
Open Source Harness: github.com/ApodexAI/Frontierโฆ
Open Weights: huggingface.co/collections/aโฆ
Weโre excited to introduce Apodex 1.1, our new model family built to scale agentic intelligence for professional work.
๐ง Frontier-level intelligence for complex work
Apodex 1.1 brings frontier-level agentic performance across complex professional work, scientific research, financial analysis, and deep search.
๐ค Asynchronous Agent Team
Apodex 1.1 can break down complex tasks, coordinate multiple agents in parallel, continuously integrate their findings, and let you step in to guide or redirect the work at any time.
๐ฌ Open-source research workbench
Weโre open-sourcing FrontierAgentโa locally deployable research workbench for the Apodex 1.1 family, including asynchronous Agent Team.
Available now:
๐น Apodex 1.1 โ our most capable frontier model, available through the Apodex online workbench
๐น Apodex 1.1 mini โ open-weight model for running complex work locally
๐น FrontierAgent โ open-source, locally deployable research workbench
๐ Live workbench: apodex.ai/
๐ฆพ API platform: platform.apodex.ai/
๐ Paper: apodex.com/pdf/20260824
Worn Machine retweeted
๐ฆ๐ฒ๐ป๐๐ฒ๐ก๐ผ๐๐ฎ ๐จ๐ญ.๐ฑ ๐๐ถ๐๐ฒ โ ๐ฆ๐ต๐ฎ๐ฟ๐ฝ๐ฒ๐ฟ. ๐ ๐ผ๐ฟ๐ฒ ๐๐ผ๐ป๐๐ฟ๐ผ๐น๐น๐ฎ๐ฏ๐น๐ฒ. ๐ ๐ผ๐ฟ๐ฒ ๐๐ผ๐๐-๐๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐. An ๐ผ๐ฝ๐ฒ๐ป-๐๐ผ๐๐ฟ๐ฐ๐ฒ, lightweight native unified multimodal model for visual understanding, generation & editing.
๐๐๐ถ๐น๐ ๐ณ๐ผ๐ฟ ๐ฟ๐ฒ๐ฎ๐น-๐๐ผ๐ฟ๐น๐ฑ ๐๐ถ๐๐๐ฎ๐น ๐ฎ๐ฟ๐๐ถ๐ณ๐ฎ๐ฐ๐ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐๐:
๐น๐ก๐ฎ๐๐ถ๐๐ฒ ๐ฐ๐ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป with better detail, composition & realism
๐น๐๐ผ๐บ๐ฝ๐น๐ฒ๐
๐ถ๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป ๐ณ๐ผ๐น๐น๐ผ๐๐ถ๐ป๐ด across subjects, counts, text, layouts & styles
๐น๐๐ป๐ต๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ต๐ถ๐ป๐ฒ๐๐ฒ/๐๐ป๐ด๐น๐ถ๐๐ต ๐๐ฒ๐
๐ ๐ฟ๐ฒ๐ป๐ฑ๐ฒ๐ฟ๐ถ๐ป๐ด & ๐บ๐๐น๐๐ถ-๐๐ฒ๐
๐ ๐น๐ฎ๐๐ผ๐๐
๐น๐ฃ๐ฟ๐ฒ๐ฐ๐ถ๐๐ฒ ๐ถ๐บ๐ฎ๐ด๐ฒ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด & ๐ฐ๐ผ๐ป๐๐ฟ๐ผ๐น with visual markers, bounding boxes & multi-image references
Just ๐ด๐ parameters โ outperforming same-size models in instruction following and editing preservation, while ๐ฐ๐ผ๐บ๐ฝ๐ฒ๐๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐น๐ฎ๐ฟ๐ด๐ฒ ๐ฐ๐ผ๐บ๐บ๐ฒ๐ฟ๐ฐ๐ถ๐ฎ๐น ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ถ๐ป ๐๐ฒ๐
๐ ๐ฟ๐ฒ๐ป๐ฑ๐ฒ๐ฟ๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐
๐น๐ฎ๐๐ผ๐๐๐.
๐ ๏ธGitHub: github.com/OpenSenseNova/Senโฆ
๐คHF: huggingface.co/collections/sโฆ
๐พDiscord: discord.com/invite/BuTXPHmQuโฆ
๐จSenseNova Studio: unify.light-ai.top/
Worn Machine retweeted
1/ Meet dots3-note preview โ an open-weight multimodal model built for long-horizon agency in real life. ๐
โ 280B total / 16B active โ 512K context โ Text + vision + audio
Built to reason, use tools, learn, and adapt over time.
Worn Machine retweeted
We promised open weights for Qwen3.8. Now, time to meet them! ๐
โก Qwen3.8-27B:
- A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows.
- 262K native context, easily extendable to 1M tokens via YaRN.
- Built for builders. Highly efficient, high-quality, and licensed under Apache 2.0.
๐ The open weights for Qwen3.8-2.4T-A95B (Max-level) have also been released recently.
Whether you're shipping lightweight applications with Qwen3.8-27B locally or building agents with Qwen3.8-2.4T-A95B, they're yours now!
Download, deploy, and build something we haven't imagined yet. ๐๐
- Hugging Face๏ผ
huggingface.co/collections/Qโฆ
- ModelScope๏ผ
modelscope.cn/collections/Qwโฆ
Worn Machine retweeted
Introducing dots3-note preview โ a small but mighty step toward long-horizon agency in real life.
๐น 280B MoE with 16B active parameters, a 512K context window, and multimodal understanding across text, vision, and audio
๐น Introduces TEMPO, a new RL approach for long-horizon agent training through self-critiquing and test-time-scaled value estimation
๐น Built to reason, explore unfamiliar environments, update memory over time, and combine multimodal perception with coding and tool use to solve complex tasks
๐น Open weights on Hugging Face, alongside two open benchmarks for real-life agents: VibeSearchBench and VibeLifeBench
Competitive with much larger models across reasoning, agentic, and multimodal evaluations.
๐ Tech blog: studio.dots.ai/dots/dots3-enโฆ
๐ Model weights: huggingface.co/dots-studio/dโฆ
๐ Github: github.com/studio-dots-ai/doโฆ
Worn Machine retweeted
Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.
Worn Machine retweeted
I believe everyone should have access to superintelligence, and I wrote a long piece about Meta's philosophy and values for building a positive future for everyone. meta.com/thefutureisforeveryโฆ
Worn Machine retweeted
๐ Seeing is just the beginning.
With Qwen-MM-Plugins, turn your favorite agent harness multimodal-native โ read images, videos & documents, edit videos, work with 3D/CAD, and more.
From multimodal models โ multimodal agents. ๐
Watch it in action:
github.com/QwenLM/Qwen-MM-Plโฆ
Worn Machine retweeted
The road to AGI was never meant to be traveled alone.
From Hailuo 01 to H3, we've been incredibly lucky to build alongside so many friends, partners, and fellow builders who believed in the same thing we did.
That's why we open-sourced H3. Because progress moves faster when more people can build on it, and because we want to keep moving forward together.
"To the edge of the sky" has always been more than a destination for us. It's a belief, and maybe a kind of idealism.
To everyone who has been part of this journey: thank you.
Together, toward the edge of the sky, we will keep going until we get there.๐โจ
People who know me well know that both @Hailuo_AI and @MiniMax_AI have been my treasures since day one (especially Hailuo)! Iโve been incredibly lucky to witness and help drive its journey from 0 to scale.
Many of the friends and peers Iโve met along this journey have also become key forces shaping this industry. Thereโs no better feeling than getting to grow and progress together.
Built on countless hours, deep dedication, and collective intelligence, weโve grown alongside the AI creative industry, and it has shaped us in return.
Making it open is also our determination to help drive the ecosystem forward and accelerate technological innovation. As I said earlier: from today on, we wonโt be the only ones here for sure.
And that makes me even more excited for whatโs ahead.
The best is always yet to come. Cheers! ๐ฅ
p1: Hailuo 01 (August 2024)
p2: Hailuo 02 (June 2025)
p3: MiniMax H3 (July 2026)




























