AI & Tech / Personality- & Consciousness Development
Switzerland
Joined November 2023
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Stardust retweeted
1/4
Meet Qwenputer: Qwen3.5-4B with a full RISC-V CPU inside its weights, using the model's own Gated DeltaNet layers as RAM.
It runs C and MicroPython in its forward passes, no external runtime.
On DeepMind math: base 55–90%, base + Python tool 95–100%, Qwenputer 97.5–100%.
Stardust retweeted
Star Trek TNG had a profound impact on me growing up, and Patrick Stewart’s role above all: the secular, rationalist, egalitarian, post-scarcity (whisper it: socialist) vision of the future, a completion of the Radical Enlightenment project married to a sort of Aristotelian sense of virtue, duty, and deep purpose, a Shakespearean, theatrical-literary, humanist forgiveness and celebration of our foibles and fallenness, and Age of Discovery romantic call to adventure and exploration. I remain a believer that this world can yet be built.
Stardust retweeted
We mourn the passing of Patrick Stewart, who inspired countless individuals at NASA (and beyond) to boldly go where no one has gone before.
ALT Image description: A packed, sparkling starfield captured by our Hubble telescope — with strange new worlds potentially awaiting. Credit: ESA/Hubble and NASA, R. Cohen
Stardust retweeted
Introducing a radical new product... (with thanks to Suno, Opus 5.5 and the heroes at audaciousproject.org who've inspired me.) SOUND ON! #aivideo #humanheart
Stardust retweeted
🚨 NVIDIA’s LocateAnything finds objects over 10× faster by changing how AI writes coordinates.
Instead of writing an object’s location piece by piece, it predicts the full box at once.
- Finds objects, buttons and text in images
- Processes 12.7 boxes per second on one H100
- Over 10× faster than Qwen3-VL in NVIDIA’s test.
Code and weights available. Free for non-commercial research.
Stardust retweeted
Sam Altman on automation of AI R&D, RSI, OpenAI's internal model:
1. "We don't know yet how [automated AI R&D] truly is going to work. We know today how to say: find me a 5% compute efficiency win."
(Note: a 5% compute efficiency gain that the model can find automatically is actually a big deal!)
2. "What I don't think anyone knows how to do yet is to say, go discover a new architecture that is as big of a single leap forward as the transformer was. And I think that's where things are heading."
3. "If you apply the same pipeline that we applied to get our models to be so good at math to AI research, it will probably work [for automated AI R&D]."
4. The bottleneck to getting good AI R&D results is shifting to asking the right questions and using things like swarms correctly, but answers to these questions will become much clearer in the next 3 months:
"One of the interesting things that people talk a lot about in OpenAI... is what are the questions that you're supposed to ask of GPT-7? Probably it's not enough to just say, make me RSI. And also, it's probably not enough to say, buy me a 5% compute efficiency win. I think this is one of the most interesting debates going on right now. And the answer is not just what question to ask it, but it's things like agent swarms. How are we going to use these models in a way that, like, actually maximizes what we can get out of them? I think we're going to learn a lot about that in the next 3 months."
5. OpenAI's extreme focus on model efficiency:
"I bet we could redo Navier-Stokes at 1/100 of the cost a year from when it was done."
6. More on OpenAI's internal model:
"There was one weekend when we got a new model... The name of the channel was "model-name-hold-my-beer". And people were just throwing that at the hardest problems they could. And people just didn't think the model was going to do these things. And we had this collective experience of people's hardest research problems from their academic career, the model was just knocking down one after the other, many different fields, it was incredible to watch... it was many of the problems we published, many other things."
7. The other loop is self-replicating data centers:
"Yeah, people talk a lot about RSI as AI that does AI research. There's very little talk about self-replicating data centers, this idea that you can use AI to control robots and run fabs and build turbines or solar panels and build data centers. But I think we have that within grasp."
Stardust retweeted
Last night an AI agent performed brain surgery on a mouse.
We gave it 19 gene therapy vectors in a spreadsheet, a mouse model of epilepsy, access to a motorize surgery rig with cameras and one goal: reduce seizures.
It picked the drug. It picked the brain region. It derived the coordinates from three papers. Then it drove the stereotaxic arms into the brain and injected 250 nanolitres while watching the entry site through the rig camera for bleeding.
A licensed vet gated every irreversible step and held a physical kill switch to stop the AI agent.
Thread on what it decided, what it saw, and the moment it refused to let me override it.
[Writing this on the spot out of excitement, but I've been 18hrs in front of my laptop coding and coordinating this, so keeping it brief and descriptive, more cohesive thoughts on this soon...]
Stardust retweeted
GPT 6.1 Terra is absurdly good at 3D modeling and motion.
I asked it to build an interactive showcase that counts to 5 and explodes a mechanical hand down to every single gear, wire, and panel.
We are living in the build era.
Stardust retweeted
Introducing the Document Parsing Protocol (DPP)
There are hundreds of different OCR models and tools, but they might all parse a document in different ways.
Some errors are very clear-cut (e.g. hallucinating text, or completely misaligning a table), but many errors are much more ambiguous (e.g. how to represent split tables sharing a header). It's an issue that plagues most parse benchmarks too, because they inherently reward a certain style even if other styles might be semantically correct.
We introduce a protocol that proposes a standard way of parsing these documents into a correct format. We've curated a list of 140 rules that cover literally every aspect of doc parsing, for instance:
- tables: borderless whitespace tables, header-only tables, horizontal slicing, side-by-side mini tables
- forms: key-value regions, checkboxes, checklists, ids and labels, fields outside the form,
- charts: standardized chart output, multiple charts, gantt charts, line charts
You can use DPP as an evaluation rubric for LLMs when determining the quality of parsing a document.
It's not a full evaluation benchmark (yet), but an initial evaluation guide, and meant to encourage a discussion on what are the "best" ways to parse a document.
Check it out: parseprotocol.com/guide/form…
Blog: llamaindex.ai/blog/introduci…
Is that a table, or just text that happens to line up?
If a table has two header rows, should the output preserve both?
Parsers make these judgment calls differently. Those differences shape what your agent understands about a document.
We built the Document Parsing Protocol (DPP) to give those decisions a shared rulebook.
DPP covers text, tables, forms, charts, and diagrams, and even includes chemical structures and music notation. Each rule comes with example pages and expected outputs to make it concrete.
The working draft is live, and we’d love your feedback :) Get the Claude skill or ChatGPT reference file, try it on your messiest page, and let us know what comes back.
Try it here: parseprotocol.com/#adopt
Read more: llamaindex.ai/blog/introduci…
Stardust retweeted
Claude's $200/month plan is getting hammered over usage limits again.
Users say their weekly allowance is disappearing far faster than before.
Now there's a GitHub bug report showing Claude Desktop may force MAX reasoning even when High is selected.
That could explain some of the sudden usage spikes.
Stardust retweeted
People are building some seriously cool things with EmbeddingGemma 2 that it’s becoming hard to keep up with!
The community is taking its multimodal capabilities and running with them, building everything from in-browser WebGPU photo search to native Apple Silicon acceleration.
So, here’s a round up of some amazing projects 🧵👇:
Stardust retweeted
Wanted to share a method for information discovery with Codex App I've been using for the past 4 months that has brough me a lot of value, that is not talked about much docs.google.com/document/d/1…
Stardust retweeted
Much requested, and now yours to connect: iCloud Mail and Calendar plugins are available in ChatGPT.
Ask ChatGPT to find that email, catch up on your inbox, check what’s next, or update a calendar event. Phew.
Available on all ChatGPT plans on web, desktop, and iOS.
Stardust retweeted
Unlimited OCR 2 is here, and it's open source. 📖 ✨
Longer documents can mean a lot more attention computation. Our new SR-SWA mechanism keeps that in check by condensing text-visual attention interactions into a fixed-size set of working references.
Here's what it delivers:
- Document parsing at virtually any length, within a finite context window
- Nearly constant per-step decoding latency and memory footprint as documents grow longer
- State-of-the-art performance on MPDocBench and MarathonDocBench, covering documents from dozens to hundreds of pages
Check out the model here ↓
Stardust retweeted
This is Genie 4, our most realistic and interactive world model yet. 🌐
Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.
