Small machines. Big possibilities.
Joined April 2026
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Great to see @elonmusk take the lead here. Hopefully others will follow.
xitter.cf/secscottbessent/status…
It would be interesting to donate it directly to Trump Accounts for the children. Directly goes to kids. No administration drain.
It could also be a model for other companies.
At a $2T valuation that would be $400B to seed the reinvention of Social Security. Only would need a handful of other large AI and tech companies to do the same to match what is in Social Security today with larger growth opportunities.
I think there is no better way to guarantee the public benefits by the companies success.
Eye spy some RAM in my black cars center console.
Do I completely ruin the 45min drive to the airport for my wife by asking about it?
swapped vllm for tensorfold on my two DGX Sparks and it literally doubled
same glm 5.3 flash weights, nothing else touched
> 24 → 50 tok/s single
> 40 → 85 tok/s 4 streams
> automationbench performed better too
@MiaAI_lab @ashxhart you two cooked 🔥
localsonly retweeted
As I said many times before, TensorFold is a real turning point for local AI. Just try it for yourself, we have a recipe for everyone!
All of my TensorFold recipes can be found here:
mia-ai.net/models?for=engine…
TensorFold by @ashxhart is one of our newest Inception startups and has been making waves in the local AI community. It's an open-source inference engine for running models on your own hardware. Finally got around to experiencing it for myself.
I'm running Qwen3.8 Flash Next in NVFP4 on it across my two DGX Sparks, and built a paper reader to put its concurrency to work. It splits an arXiv paper into its sections and reads them all at once, one request per section, then merges the results into a one-page brief tagged back to each source section. Per-request and combined tok/s update live as it runs.
2.1x faster than GLM non tensor fold. Very excited to see local and the DGX keep improving.
We will see if there is any performance difference.
Thanks @MiaAI_lab for the work.
He’s such a good actor.
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models
“I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that.
“With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor.
“You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something.
“That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’
“I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
localsonly retweeted
Artificial Analysis has been given access by Reflection and is independently benchmarking Beam
Early indicators suggest Beam will be one of the most token-efficient open models we've seen for its level of intelligence.
Congratulations @reflection_ai on the announcement!
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
GLM 5.3 on 2 DGX Sparks has been pretty impressive so far.
Not only more accurate than Qwen 3.8 Flash-Next and DeepSeek V4, but so much faster. About 20-30% improvement compared to 3.8 and double on DSV4.
Exited to see some of the speed improvements from TensorFold over the next few weeks.
Running on actual workflows - this is the first local model I have run that would actually work as an overnight worker.
Didn’t think Deepseek v4 would be so far behind 3.8 flash next. It was also substantially slower.
Moving onto 2 DGX models now.
@MiaAI_lab GLM 5.3 for 2DGX is next on the list.
localsonly retweeted
Me: buys a house in 2020
Fed: prints $9 trillion more dollars
Also me: “I’m a real estate genius”
localsonly retweeted
I can tell you this. Right now, there is no development of a single board replacement for the DGX platform. There was (and probably still is) talk about a 2nd Gen. 2028-29 (Vera CPU + Rubin GPU) product tied closely to the RTX Spark, which is an entirely different product.
But in talking to many of the major manufacturers who produce the GB10 offerings, there is NO current roadmap deliverable for a replacement DGX Spark AT THIS TIME. Yet.
Qwen 3.8 Flash-Next putting up some pretty good results for 1 DGX.
Deepseek V4 is still running on my second DGX. It’s working at about half the speed.
Qwen 3.8 Flash-Next on 1 DGX spark scoring just below Fable 5 and above Opus 4.8 on SWE Bench Verified was not my expectation for this run.
The DeepSeek V4 Flash was where I thought this would land.
I got 2 DGX Sparks. DMs are open @ChatGPT @AnthropicAI
Bought a DGX Spark. Then bought a second one.
Never had a straight answer for what the upgrade actually bought me. Not before, not after.
So I'm measuring it myself, one run at a time, on the boxes in front of me.
Super pumped for @every Thesis 2027 but then the calendar hit me.
Wife due early December. Flying for a conference that close feels reckless.
Anyone else pulled this call with a due date? What did you actually do?
There are also a lot of firms just starting to roll out Claude and OpenAI because it took them 6 to 12 months to decide and plan what and how they would do it.
These firms are going to then realize they should have started with open-source models first.
But the person who made the decision will not want to admit they were 6 months behind where the trend was going and will double down on their decision.
This is how most of Corporate America works.
