Pinned Tweet
Replying to @cgtwts
We’re witnessing the calcification of a two tier society in real time ..and make no mistake, we’re NOT invited because we’re on the menu.
I can’t tell whether Dario is trying to convince others or himself of this verifiable falsehood - it might be both? But either way its ugly, manipulative and reeks of self interest.
Fearing obvious future price/moat erosion Anthropic are clearly choosing to pull up the ladder over innovation. If you weren’t a fan of Open Source before, I urge you to consider supporting it now - no one else is coming to save you.
Ubuntu means “I am because we are” ..definitely feeling that energy right now ✊
I claimed the "rum" handle on @ThePirateFace 🏴☠️
Where Hugging Face AI models never die, and are immortalized as torrents.
Claim yours 👇
pirateface.co/claim?ref=rum
Mark Sunner retweeted
Replying to @PessimistsArc
Right. Dario was already claiming that GPT2 was too dangerous to open source back in 2019.
I made fun of them then.
Everyone should make fun of them now.
Mark Sunner retweeted
Dario wants to pace the frontier. Sam agrees. Elon agrees. Somehow everyone found the brake pedal right as open models started catching up. I run AI on hardware I actually own. If you care who gets to own intelligence, you need to read this.
Mark Sunner retweeted
🚨BREAKING: the stealth model Ox Alpha is Z.ai (Zhipu)
everyone else is guessing it from tokenizer vibes and emoji rates. i made the server say its own name out loud.
sent it one malformed request and it threw a java stack trace naming its own internal class:
com.wd.paas.api.domain.v4.ch….ChatCompletionRequest
paas/v4/chat = Zhipu's documented route /api/paas/v4/chat/completions
thats not a fingerprint guess. thats the server confessing. proven at the serving layer, confidence 0.98.
Mark Sunner retweeted
New production build on my tp=4 GLM-5.2 cluster, and this one is about a lesson I keep relearning: the benchmark config and the config you actually want to live with are two different animals.
My 44.6 tok/s record build is still in the repo, still reproduces, still the fastest published decode on this hardware. But I don't serve it anymore. Here's why. I watched what my agent actually does all day: long agentic coding sessions that grow until they hit the context ceiling, deep prompts, real work. The record config was tuned for a number, not for that job. It kept running my agent out of room.
The new production build trades the sprint for the marathon: context window up 58% to 316K, deep-context decode up 18% at 77% draft acceptance, deep cold prefill at 667 tok/s which is the best I've seen published for a 4x Spark, and real workload throughput identical to the record config. The only thing it gives up is a peak number my agent never produces in practice.
Everyone benchmarks the sprint. Nobody benchmarks the marathon their agent actually runs. Measure your own workload before you copy anyone's config, including mine.
Both builds ship in the repo one launcher command apart, with the full trade table and raw data, because a config is a set of trades and I publish mine.
github.com/joesinvestments/G…
Mark Sunner retweeted
Today we’ve raised $52M Seed and we are announcing the public launch of S2.1 Pro.
>It can clone a voice from 5 seconds of audio
>2x faster than Cartesia & 1/6th the cost of Eleven Labs
>most expressive model with word level control over emotion, intonation, pacing etc
We support frontier AI companies including HeyGen, LiveKit, Retell, Sanas, and OpenArt all run our model in production.
If you're a business and we can't cut your voice AI costs by 50%, we'll give you 1 year of Fish Audio for free.
Book a demo: s.fish.audio/tmapke
To celebrate our first birthday, we'll give you 1 month of S2.1 Pro for free. Like, retweet, and comment “Fish” to get it.
Mark Sunner retweeted
You can now run GLM-5.2 on 3x @NVIDIAAI DGX Sparks at home 🤯
• 248k context
• 15-20 tok/s (content-dependent)
• MTP for max context / DSpark optional
• NVFP4+AQLM hybrid
Still a work in progress — will keep improving over time.
→ github.com/MiaAI-Lab/GLM-5.2…
Mark Sunner retweeted
The first 2.8T open model! Yes, we are pushing forward the frontier open intelligence!🫡
Introducing Kimi K3: Open Frontier Intelligence
🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows
Kimi K3 is now live on on Kimi.com, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.com/blog/kimi-k3
Mark Sunner retweeted
Chinese models are 112x cheaper than Anthropic per million tokens.
Chamath laid it out on CNBC: a "barrel of intelligence" costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from Chinese models.
That is not a pricing quirk. That is the steepest commodity curve any technology has run in recorded history.
Oil took 40 years to compress like this. Semiconductors took 20. AI inference is doing it in months.
The companies sitting at $26 and $56 are not stupid. They're buying time… betting that trust, safety, and enterprise contracts hold the premium long enough for costs to catch up.
What they cannot bet on is the timeline.
Because the $0.50 model is not a demo. I've been inside the labs building it.
Mark Sunner retweeted
Pay Attention !
If you dig into the HF page, there's also Inkling-Small hiding out: 276B params, 12B active, and it actually outperforms the big model on some benchmarks.
Not a formal launch, just quietly sitting there. Easter egg find 🥚
Open weights, Apache 2.0, fine-tune on Tinker.
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
. @NVIDIAAI We've been running GLM-5.2 across 4× DGX Sparks and hit a class of multi-node NCCL deadlocks that appear to affect the broader Spark community. We did a deep root-cause analysis.
The key finding is that stock DGX Spark lacks GPUDirect RDMA, making host-staged collectives 10-100× more susceptible to TP race conditions. Full write-up with evidence and community references here: github.com/marksunner/dgx-sp…
Mark Sunner retweeted
Announcing our $130M Series A to build the Open Superintelligence Stack
Led by Radical Ventures, with NVIDIA, Intel Capital, Dell Capital, and existing investors
Train, deploy, and continuously improve your own models using our stack.
Own your intelligence.
Tony /@Tech2Wild deserves >10x the number of followers he has - if you have a DGX Spark for local AI I’d urge you give him a follow..
I’m Running a Frontier-Level AI Model at Home… GLM 5.2 on 4 DGX Sparks youtu.be/nbHOBvLlypY
Mark Sunner retweeted
Replying to @Web3Twon
Things are moving in a path that increasingly benefit Anthropic and I think its not all a bunch of coincidences.
Mark Sunner retweeted
Major fallout if so. Basically this is the Dario wins timeline. Get your subscription face on pleeb 😶🌫️
CHINA CONSIDERS RESTRICTING OVERSEAS ACCESS TO CUTTING-EDGE AI MODELS
China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and Z.ai, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released.
The discussions reportedly include not only closed-source models but also open-weight models. However, the scope of application is still under debate, and the rules may ultimately apply only to future frontier models.
Officials have also discussed designating the leakage or theft of proprietary AI technologies as a national security crime, with stronger penalties, as well as restricting the types of foreign capital that can invest in Chinese AI startups.
The backdrop is the U.S. move to strengthen export controls on AI models, along with national security concerns over cutting-edge models that could possess advanced cyberattack capabilities.
Chinese authorities are reportedly concerned that advanced U.S. cybersecurity AI models could be used to exploit vulnerabilities in Chinese software.
Since the beginning of this year, China has continued to tighten measures to prevent AI technology from being transferred overseas. Authorities have investigated whether Chinese AI startups that relocated abroad violated export control laws, while also strengthening oversight of overseas transactions involving Chinese investors, technology, data, and national security concerns.
Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China.
Mark Sunner retweeted
With 2 x DGX Sparks you can run Deepseek v4 Flash at 1M context at 40-46 Tok/s. That's the Tweet. github.com/tonyd2wild/deepse…






























