Eslam ewida

[Eslam ewida @EwidaEslam]

و الله العظيم the only way out is through

[David Sirota @davidsirota]

Sam Altman is warning the world about Sam Altman, and says we need to stop Sam Altman because Sam Altman can’t stop Sam Altman.

[Eslam ewida @EwidaEslam]

محتاج rewatch

[Eslam ewida @EwidaEslam]

من المسلسلات القليله اللي فضلت تعلى على نفسها كل موسم...

[Nostalgic Cinephile @cinemamonamore]

Jonathan Nolan is a visionary. 15 years ago, he gave us Person of Interest. A show that predicted the real dangers of AI and mass surveillance long before reality caught up. Easily one of TV’s most underrated masterpieces. Forever grateful for this phenomenal series.

[Rey Cantú @reycantu21]

El Barca todos los años en Champions:

[Communard 🌍⚒️🇵🇸 @communard1917]

A very interesing look into a specific dying breed of Toryism. He is so conservative and so culturally supremacist, that it comes back around to being weirdly inclusive? If you move to England, you MUST become english, he holds, but in turn, this means anyone CAN become english

[Daniel @growing_daniel]

best Oscar

[Dario Amodei @DarioAmodei]

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.

It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.

The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans).

More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains.

In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.

Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology.

I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

[Anthropic @AnthropicAI]

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR.

We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use.

Read more: https://t.co/RuEosScSMb

[thi @thiblvd]

fiz um teste de tipagem sanguínea que mostrou que o meu sangue é B-

meu pai é O-
minha mãe é O+

dois pais de sangue O não podem gerar um filho com alelo B...

[LeSaleAvantLe2 @rayaneAlg00]

Je suis arrivé en retard le prof a dit « bah ça tombe bien on va commencer l’exposé par toi »

[Izann @Mr_izann]

Realizing that you have no choice but to accept it and continuing to live

[Eslam ewida @EwidaEslam]

واو

[Matt Bramanti @mattbramanti]

the breakthroughs in CF have been absolutely astonishing

[vittorio @IterIntellectus]

I thought we were slowing down lmfao

[Eslam ewida @EwidaEslam]

@Schizokiiddo 😭😭😭😭

[Jash Dholani @oldbooksguy]

You, a modern biohacker:

No screens after 8, melatonin gummies, eye mask

Meanwhile Napoleon:

[Choblin @choblin29]

🚨wtf. OpenAI says its new internal model it began training just 24 days ago has already resolved more than 100 long-standing open problems across most areas of mathematics.

The pace has surprised even OpenAI's own mathematicians.

An independent group including Edward Witten, Timothy Gowers and Martin Hairer is now helping assess the results and coordinate how they are released.

[bigsmoke 🤔 @big_smoke2112]

the audacity of teeth to stay intact when people die but decay when they're alive.

[Lost Nomad @lost_nomad__]

- Germanic, ‘earn/deserve’ money: a moral weight from working
- American, ‘make’ money: new wealth is created, not a fixed pie
- Latin: ‘win’ money: you gain by taking it from others, zero-sum fixed pie

No comment is even needed on this one