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Introducing Open Science Index What if every open source science tool, AI model, dataset, and workflow across biology, chemistry, physics, materials science, and earth science lived together in one searchable place? That's the idea behind Open Science Index. Scientific tooling is exploding, and keeping up with what exists, and...

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Bio Protocol

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20,740 Aufrufe • vor 1 Monat •via X (Twitter)

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15 Kommentare

Profilbild von Erhan Ünal 🐂
Erhan Ünal 🐂vor 1 Monat

👏👏👏

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Bio Protocolvor 1 Monat

🫡

Profilbild von Bio Protocol
Bio Protocolvor 1 Monat

Try Open Science now: Blog:

Profilbild von Bio Protocol
Bio Protocolvor 1 Monat

Read the full blog:

Profilbild von RheumAI
RheumAIvor 1 Monat

Nice! 👏👏

Profilbild von Alpcag.Eth📊
Alpcag.Eth📊vor 1 Monat

🥳⛩️ LFG

Profilbild von rnd.vibes
rnd.vibesvor 1 Monat

How about basement research projects that have credible data sets, but overlooked by the mainstream. such as the boy from Japan who discovered that caterpillars pass down memories to their offspring

Profilbild von Aliya WEB 3
Aliya WEB 3vor 1 Monat

This is much needed. Scientific tooling is so fragmented. Having health signals + alternatives in one place is huge for researchers.

Profilbild von Usman kabir
Usman kabirvor 1 Monat

This is much needed. Scientific tooling is so fragmented. Having health signals + alternatives in one place is huge for researchers.

Profilbild von IronRed | SandHive
IronRed | SandHivevor 1 Monat

Wow what an index!

Profilbild von dOrg
dOrgvor 1 Monat

good start. the maintenance-status signal is the part tool directories skip, and it's the one that matters!

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EPISTEMEvor 1 Monat

great!

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leevor 1 Monat

댓글알바들 많네

Profilbild von HERO
HEROvor 1 Monat

Lets go up

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leevor 1 Monat

또 거짓말하는거야 속지마

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🚨 I just started "Vibe Researching"!! This AI tool makes scientific research 10x EASIER for ordinary people!! Most AI tools can help you READ research papers. But what if you could actually: 1️⃣ Question 2️⃣ Research 3️⃣ Code 4️⃣ Experiment 5️⃣ Analyze 6️⃣ Draft a research document All in ONE workspace? That’s what AIPOCH Open-Science is building. You can give it scientific data, let AI help analyze it, run Python/R with your approval, and actually inspect how the result was produced. So it’s not just giving a RANDOM answer !! You can trace the data, code, execution history & research artifacts behind it. And it’s OPEN SOURCE. 👀 AIPOCH #AI4Science #OpenSource

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1:22

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0:59

David Sacks: Anthropic Is Trying to Crush Open Source AI and the American Developers Who Use It David Sacks: “I know people don't have a lot of sympathy for Chinese companies, that's fine. I'm not defending Chinese companies. I'm defending American developers who need to be able to use everything in the public domain. And let me give you an example. Cursor rolled out its new product, Composer 2. They were able to post-train that model using Kimi K2.5 on their own proprietary coding data. They started with a Chinese open source model, and then they used their own data, and they came up with a new derivative product. This is the way that open source works. You take things that are in the public domain, you fork them, you make them your own. And by the way, once it's in the public domain, it's not a Chinese model anymore. No data is going back to China, nothing's going back to China. An American company has taken open source contributions in the public domain, made it their own, and then developed their own model. And if you say that American companies can't do that, or that somehow it's tainted with IP theft, you are basically going to put a dagger through the heart of the entire American open source ecosystem. And that is exactly what Anthropic wants, because they do not want to have the competition.”

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Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.
1:44

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Sam Rodriques

24,443 Aufrufe • vor 7 Monaten

We are now Cult.Repo. The original creators behind the iconic Open Source documentaries (Vue, React, GraphQL) are back together! Honeypot is now called Cult.Repo and is independently-owned. We are on a mission to document the complete history of every major open source language ever created. With 250K subscribers and over 15M views, we're building on our foundation as creators of the acclaimed Open Source Origin Story documentary format. Coming in 2025: Python Software Foundation, Vite ⚡ documentaries and much more. Our independence is core to our mission. We believe the human stories behind open source deserve authentic, in-depth storytelling. Our new model is powered directly by: - Community support from viewers - Sponsorships from organizations that champion open source This transition represents our commitment to independent, meaningful tech storytelling that honours the collaborative spirit of open source itself.
0:42

We are now Cult.Repo. The original creators behind the iconic Open Source documentaries (Vue, React, GraphQL) are back together! Honeypot is now called Cult.Repo and is independently-owned. We are on a mission to document the complete history of every major open source language ever created. With 250K subscribers and over 15M views, we're building on our foundation as creators of the acclaimed Open Source Origin Story documentary format. Coming in 2025: Python Software Foundation, Vite ⚡ documentaries and much more. Our independence is core to our mission. We believe the human stories behind open source deserve authentic, in-depth storytelling. Our new model is powered directly by: - Community support from viewers - Sponsorships from organizations that champion open source This transition represents our commitment to independent, meaningful tech storytelling that honours the collaborative spirit of open source itself.

Cult.Repo

71,435 Aufrufe • vor 1 Jahr

Priscilla Chan and Mark Zuckerberg co-founded the Chan Zuckerberg Initiative (CZI) in 2015, committing 99% of their Meta shares to advance science, education, and opportunity. As a pediatrician and CEO of Meta respectively, they've built CZI into one of the most ambitious technology-driven philanthropic organizations with a moonshot goal to help cure, prevent, or manage all diseases by the end of the century. In this episode, we sit down with Priscilla and Mark to unpack how CZI bridges state-of-the-art AI with open biomedical research, their investments in open-source tools like the Human Cell Atlas, their philosophy on building teams across different disciplines, and what they envision the future of healthcare. Chan Zuckerberg Initiative swyx Alessio Fanelli
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Priscilla Chan and Mark Zuckerberg co-founded the Chan Zuckerberg Initiative (CZI) in 2015, committing 99% of their Meta shares to advance science, education, and opportunity. As a pediatrician and CEO of Meta respectively, they've built CZI into one of the most ambitious technology-driven philanthropic organizations with a moonshot goal to help cure, prevent, or manage all diseases by the end of the century. In this episode, we sit down with Priscilla and Mark to unpack how CZI bridges state-of-the-art AI with open biomedical research, their investments in open-source tools like the Human Cell Atlas, their philosophy on building teams across different disciplines, and what they envision the future of healthcare. Chan Zuckerberg Initiative swyx Alessio Fanelli

Latent.Space

166,190 Aufrufe • vor 11 Monaten

Livestream Alert: Open World Models in Production: LTX.io and ComfyUI Founders Live A special live conversation with Yoland Yan ( CEO, ComfyUI ) & comfyanonymous ( Founder, ComfyUI ) and Zeev Farbman ( CEO, LTX ) Join us as we sit down with the teams behind LTX and ComfyUI to talk about building an open world model that integrates natively into the production tools creators use. We'll explore what it took to build LTXa foundational model that understands space, motion, and how the physical world operates—with synchronized video and audio generation, truly open weights, and the flexibility to fine-tune on your own data. We'll explore what it took to ship a frontier video model in the open, how LTX and ComfyUI are building the pipeline for AI-driven production together, and what it means for creators, developers, and the wider open-source ecosystem. What we'll cover: → LTX: the open world model for synchronized video + audio, and beyond → Native integration into ComfyUI workflows and production pipelines → Open world models vs. closed platforms and where things are headed → Fine-tuning LTX on your own data with LTX Trainer → Community Q&A — bring your questions
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Livestream Alert: Open World Models in Production: LTX.io and ComfyUI Founders Live A special live conversation with Yoland Yan ( CEO, ComfyUI ) & comfyanonymous ( Founder, ComfyUI ) and Zeev Farbman ( CEO, LTX ) Join us as we sit down with the teams behind LTX and ComfyUI to talk about building an open world model that integrates natively into the production tools creators use. We'll explore what it took to build LTXa foundational model that understands space, motion, and how the physical world operates—with synchronized video and audio generation, truly open weights, and the flexibility to fine-tune on your own data. We'll explore what it took to ship a frontier video model in the open, how LTX and ComfyUI are building the pipeline for AI-driven production together, and what it means for creators, developers, and the wider open-source ecosystem. What we'll cover: → LTX: the open world model for synchronized video + audio, and beyond → Native integration into ComfyUI workflows and production pipelines → Open world models vs. closed platforms and where things are headed → Fine-tuning LTX on your own data with LTX Trainer → Community Q&A — bring your questions

ComfyUI

13,174 Aufrufe • vor 3 Monaten

Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"
0:52

Terence Tao, UCLA mathematics professor and Fields Medallist, on why AI may be succeeding at science while scientists get worse at it: Tao starts by naming the tension directly: "There is this paradox that on the one hand AIs are becoming more powerful and more capable and making fewer mistakes, and they are ostensibly achieving a lot of the goals that we think scientists are trying to do. They're running experiments. They're analyzing data. They're writing papers." Experiments run, data analyzed, papers written — the exact outputs any university or funding body would cite as proof that science is working. Tao's concern is what those outputs stop telling you: "It may be that it comes at the cost of the AI picks up some skill but no human scientist gets any better at doing the science." The skill still accumulates, just in the wrong place. The system gets more capable while the people operating it stand still, because the work that used to build a researcher is now the work being handed off. And the cost shows up in the one thing scientists are supposed to be able to do: "No human can communicate exactly what just happened and why. This scientific discovery is interesting, why this proof is new and what features it has and how it connects." That final clause is the sharp end of it. Tao is describing a result nobody can place — a proof that arrives with no one able to say what makes it new, how it's built, or how it connects to the rest of the field. Understanding is a separate achievement from getting the answer, and it can quietly disappear while the answers keep arriving on schedule. Which is why he thinks the target itself needs re-examining: "We may have to sort of redesign our conception of what science is and what we actually want out of science. What exactly is science for and what are we trying to do? And is there a danger that we are optimizing the wrong thing when we are pointing our AI tools at science?"

Big Brain AI

42,551 Aufrufe • vor 1 Monat