Peter Wang 🦋

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Peter Wang 🦋

Peter Wang 🦋

@pwang

Chief AI & Co-founder @AnacondaInc; invented @pyscript_dev, @PyData @Bokeh @Datashader. Former physicist. A student of the human condition. bsky: @wang.social

Austin, tx Katılım Ağustos 2007
2.4K Takip Edilen47.8K Takipçiler
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Peter Wang 🦋
Peter Wang 🦋@pwang·
THE MOMENT YOU'VE BEEN WAITING FOR!! Type "=PY(" into Excel, and start executing Python directly in the @msexcel grid! Really excited about our new partnership with @Microsoft to democratize data science, machine learning, and AI to all knowledge workers!
Anaconda@anacondainc

We’re excited to unveil Python in Excel! Get ready for a whole new way to execute advanced analytics capabilities from within Excel 🐍 + 📊 = 💚 Check out the new integration btwn @anacondainc & @msexcel, @Microsoft365 here 👇 bit.ly/3KSblQ6

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rohit
rohit@krishnanrohit·
@NathanpmYoung It is indeed a pithier name, I just didn't like the way it looked when written down actually
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Pratyush Kumar
Pratyush Kumar@pratykumar·
📢 Open-sourcing the Sarvam 30B and 105B models! Trained from scratch with all data, model research and inference optimisation done in-house, these models punch above their weight in most global benchmarks plus excel in Indian languages. Get the weights at Hugging Face and AIKosh. Thanks to the good folks at SGLang for day 0 support, vLLM support coming soon. Links, benchmark scores, examples, and more in our blog - sarvam.ai/blogs/sarvam-3…
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Brendan McCord 🏛️ x 🤖
Brendan McCord 🏛️ x 🤖@Brendan_McCord·
This was our 1st time gathering the @cosmos_inst community in one place. @tylercowen had asked a few months prior on zoom: when's your first event? I stumbled to reply, we'd been so busy we overlooked it. He said you don't really know your people until you get them in a room.
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Peter Wang 🦋 retweetledi
Brendan McCord 🏛️ x 🤖
Brendan McCord 🏛️ x 🤖@Brendan_McCord·
I've had a dream that when my kids were old enough, they'd travel with me and sit in the front row when I gave a talk for Cosmos. As I walked out the door for our 1st Symposium this weekend, I said to @yo_adriane: I'm giving my talk here in Austin, what if they came today? ⬇️
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Aida Baradari
Aida Baradari@aidaxbaradari·
Today, we're introducing Spectre I, the first smart device to stop unwanted audio recordings. We live in a world of always-on listening devices. Smart devices and AI dominate our world in business and private conversations. With Deveillance, you will @be_inaudible.
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Eric Weinstein
Eric Weinstein@EricRWeinstein·
Why are we pretending that AI is being invented by the CEOs and VCs??? When did this particular idiocy take over? I too want to hear everything: @naval @balajis @peterthiel @sama @DavidSacks @pmarca @elonmusk @finkd @DarioAmodei I learn from all of them. But they are not driving this revolution. At age 60 I am watching those still on the command line thankful for homebrew. I’m trying to get an @ollama installation working and my GitHub functional. I’m still spending hours a day in LaTeX on @overleaf and reading the @arxiv. And, as much as I love my friends with their own jets, I think this is happening more on the command line than in the board room. I would be happy to debate any of the names above on the need to stop burying our research community under our business community. But I think many of them would actually agree with me. I don’t know how we got here. I don’t think Sam Altman ever woke up and said “I want to blot out the researchers.” I think we the public did this to ourselves. The actual font line AI researchers need to have a voice comparable to the billionaires. Perhaps louder. Or even, dare I say, much louder. And I am proud to use my voice to elevate theirs. As I try to do across the STEM fields.
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Eric Weinstein
Eric Weinstein@EricRWeinstein·
I love Peter both as a mind and as a friend. But this is dangerously wrong. I am both a word person and a math person. And I am watching our business people become our designated public thinkers. By default.
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Danielle Fong 🔆
Danielle Fong 🔆@DanielleFong·
me too
Dean W. Ball@deanwball

This is precisely the reason that I have been so supportive of open-source over the years. It is an insurance policy against both tyrannical governments and the death throes of the industrial-era nation state, the latter of which I fear we are living through. Absent the oh-so-careful navigation of the narrow path (which I tried), it seemed inevitable that closed-source models would be captured by USG and thus subject to all of USG’s many mood swings, and thus intrinsically unreliable for both businesses and other countries. Alas, I did what I could to help us walk the narrow path, as did countless others. But the odds were always stacked against us. The hybrid open/closed model world was a good future. It would mean that AI is an intrinsically profitable business, favoring Western and especially American capitalistic institutions. Yet the power of the closed labs would be checked by open-weight models that, while behind the cutting-edge, provided balance. This supply chain risk designation will render closed-source models less attractive to many customers worldwide. The regulatory risk is now extremely high. USG cannot unfire the gun that it has fired—or if it can, doing so would take an extreme measure of skill, determination, and sensitivity that I do not think will be forthcoming. Thus the balanced open/closed world seems less likely on the margin, and instead it is likelier that we live in the open-source-dominant world now. This world probably favors China, because they have a political economy more favorable to large-scale subsidy of what is essentially digital public infrastructure. It also probably favors Chinese-style digital and physical surveillance (but made all the more pervasive and capable with advanced AI), since the catastrophic misuse risks of open source are higher. Chinese-style institutions have an overall structural advantage in this future, it seems to me, and Western institutions have a structural disadvantage. You can argue this has always been true, but USG just increased the likelihood of AI futures where the US is at an inherent disadvantage. And this is to say nothing of the damage to the business environment, the AI industry, etc., about which I have spoken earlier. The future is likelier to be more decentralized, more confusing, and more dangerous than it was a few weeks ago. Yet it also may be brighter; it is probably a higher variance future than the steadier transition of the narrow path. Perhaps you like that trade off. Nonetheless, we have probably been knocked off the narrow path, and the odds of a “normal” transition to the era of machine intelligence are now meaningfully lower. Humans are living through moving history once more.

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Peter Wang 🦋
Peter Wang 🦋@pwang·
Yeah this basically unavoidable. And has much weaker legal protection than eg the copyrighted materials that are hoovered up into training of all existing frontier models.
POM@peterom

Deepseek got called out for scraping 150k Claude messages. So I'm releasing 155k of my personal Claude Code messages with Opus 4.5. I'm also open sourcing tooling to help you fetch your data, redact sensitive info & make it discoverable on HF - link below to liberate your data!

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Aakash Gupta
Aakash Gupta@aakashgupta·
The math on this project should mass-humble every AI lab on the planet. 1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output. The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice. Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet. And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.” This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one. We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that. The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.
All day Astronomy@forallcurious

🚨: Scientists mapped 1 mm³ of a human brain ─ less than a grain of rice ─ and a microscopic cosmos appeared.

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Ihtesham Ali
Ihtesham Ali@ihtesham2005·
Holy shit... Tsinghua + UC Berkeley just made video diffusion 18.6× faster. 97% attention sparsity. Same quality as full attention. Actually better in some cases. It's called SLA2, and the core insight is wild: most attention computation in video models is wasted. The standard approach of splitting attention into "sparse" and "linear" branches had a fundamental math error the sparse branch was quietly off by a scaling factor α, forcing the linear branch to compensate for a mistake it shouldn't have to fix. SLA2 corrects this with a learnable router that dynamically decides which computations need full attention and which don't, plus a cleaner formulation that directly matches how attention actually decomposes. Stack quantization-aware training on top and you get low-bit attention that adapts during fine-tuning instead of just getting slapped on at inference time. Results on Wan2.1 (both 1.3B and 14B): 97% sparsity, 18.6× kernel speedup, 4.35× end-to-end latency reduction on the 14B model. The kicker? At 97% sparsity, SLA2 outperforms every baseline at 90% sparsity — including on quality metrics. This is what real efficiency research looks like. Paper: SLA2: Sparse-Linear Attention with Learnable Routing and QAT
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chiefofautism
chiefofautism@chiefofautism·
i mapped the ENTIRE supply chain behind a single ChatGPT query 76 nodes in 13 countries with 10 layers, from a quartz mine in North Carolina to your chat window so i built an interactive map where you can trace every path yourself every time you type a prompt, you are touching brazilian sugarcane that turned into ABF varnish by Ajinomoto in Japan that used to package Nvidia GPUs in Taiwan a single quartz mine in Spruce Pine NC that supplies the ENTIRE semiconductor industry with crucibles, no backup, one landslide and chip production stops globally ASML in the Netherlands, the ONLY company on earth that makes EUV lithography machines, they need Zeiss mirrors polished to less than ONE ATOM of roughness, and TRUMPF lasers from Germany to power them chinese germanium, ukrainian neon gas, chilean copper, australian iron ore all flowing through TSMC fabs that print at 2 nanometers, thats 10 atoms wide this is a PHYSICAL supply chain more fragile than most people realize everyone debates which model is better nobody talks about the quartz mine that all of them depend on
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austinstemcenter
austinstemcenter@austinstemcntr·
A few days ago at Austin STEM Center we literally printed our attitude on the wall — bringing our “Punch You in the Brain” graphic to life, one perfectly placed layer at a time. Follow us for more mind-blowing content! #AustinSTEMCenter #MadeIRL #FromScreenToWall #ASC
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Grigory Sapunov
Grigory Sapunov@che_shr_cat·
1/ Transformers don't count like computers. We assume they have hidden "registers" to track variables. We were wrong. New research by @AnthropicAI reverse-engineered Claude 3.5 Haiku and found it works with 6D helical manifolds. It's geometry, not math. 🧵
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malinvestment.jpeg
malinvestment.jpeg@malinvested·
Of course that's your contention. You're a first-time SaaS bear. You just got finished listening to some podcast, Dario on Dwarkesh, probably. Now you think it’s the end of white collar work and seat-based pricing is screwed. You're gonna be convinced of that til tomorrow when you get to “Something Big is Happening”. Then you’ll install ClawdBot on a Mac Mini, vibe code a dashboard on top of a postgres database and say we’re all just a couple ralph loops away from building a Salesforce competitor. That’s gonna last until next week when you discover context graphs, and then you're gonna be talking about how the systems of record will be disintermediated by an agentic layer and reposting OAI marketing graphics. “Well, as a matter of fact, I won't, because ultimately the application layer is just ….” The application layer is just business logic on top a CRUD database. You got that from Satya’s appearance on the BG2 pod, December 2024, right? Yeah, I saw that too. Were you gonna plagiarize the whole thing for us? Do you have any thoughts of your own on this matter? Or...is that your thing? You get into the replies of anyone posting a SaaS ticker. You watch some podcast and then pawn it off as your own idea just to impress some VCs and embarrass some anon who’s long SaaS? See the sad thing about a guy like you is in a couple years you're gonna start doing some thinking on your own and you're gonna come up with the fact that there are two certainties in life. One: don't do that. And two: you dropped thirty grand on Mac Minis and LLM API calls to come to the same conclusion you could’ve got for free by following a handful of VC accounts.
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Jasper Dekoninck
Jasper Dekoninck@j_dekoninck·
Introducing QED-Nano: a 4B model for mathematical proof writing, competitive with larger models like GPT-OSS-120B. We open-source our entire pipeline, including data, code, and a blog post, hoping that the community can build on these artifacts to create more specialized models.
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vixhaℓ
vixhaℓ@TheVixhal·
Inspired by @karpathy microgpt, I built microgpt.c with fully manual forward and backward propagation. It is about 600 lines of pure C with no external libraries or dependencies, just raw computational power.
vixhaℓ@TheVixhal

x.com/i/article/2022…

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