nirvalite

Vibro entre my battery is low and it's getting dark y la que puede puede

[nirvalite @nirvalite]

El conocimiento es extenso y soy pequeña, la vida me da para sorprenderme de todo lo que conozco y por ende lo que no voy a llegar a conocer:

[nirvalite @nirvalite]

Mame y mame que me invitaran a una fiesta de Halloween para no echarle ganas a mi disfraz

[nirvalite @nirvalite]

@redpanda_0 @lockedoutfun Jajajajaja

[nirvalite @nirvalite]

Mis hermanas están leyendo tochos y pensé en acompañarlas
Hermana @redpanda_0: El idiota de Dostoyevsky, aprox 196k palabras
Hermana @lockedoutfun: El conde de Montecristo de Dumas, aprox 464k palabras
Yop: The shock doctrine, de Klein, 144k palabras
Lo lograremos en 2026?

[nirvalite @nirvalite]

‘Ora. Este es el tipo de gente que piensa:
Si una mujer puede tener a un bebé en 9 meses, pongamos a 2 mujeres y el bebé nace en 4 meses y medio

[Citrini @citrini]

There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused.

The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc).

The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays).

Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false.

This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology.

Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions.

Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible.

This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use.

How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months.

I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.

[𖦹°‧𝓑𝓮𝓬𝓬𝓪 .⋆♱ @chaoticgrrI]

new extremely load bearing post just dropped

[Pope Crave @ClubConcrave]

Pope Leo declared free of cancer, says Vatican

[Sebas V @vvs00]

Samanta Schweblin contó que una vez le preguntaron cómo pudo escribir «Distancia de rescate» sin ser madre. Ella respondió que a los autores de policiales negros no les preguntan cuánta gente mataron para poder escribir sobre el tema.

[u-.5sigma^2 @U5sigma2]

Pretty unbelievable the head of drug discovery at Anthropic is only now realizing the bottleneck in biotech isn’t actually drug discovery

[Merriam-Webster @MerriamWebster]

'Speed' derives from the Old English 'spēd,' which referred to good fortune/success.

This sense of 'speed' lives on in 'Godspeed':

"God spede you" = "God prosper you"

We hope this brings you up to speed.

[nirvalite @nirvalite]

Han escuchado la teoría del Internet muerto? Así se siente mi página de Twitter estos días.
Cualquier cosa, por DM en ig.

[nirvalite @nirvalite]

@lockedoutfun A mi tampoco me da risa cuando me imagino que la gente se lastima ):

[nirvalite @nirvalite]

@controlzetadev Está chidilla

[Ronan Farrow @RonanFarrow]

Grindr is training its AI on your photos, your age, and your taps by default, without asking. I looked into the company’s record with user data, and where they put the off switch.

[nirvalite @nirvalite]

Rezanding por el papa @Pontifex_es aunque soy atea

[The Nobel Prize @NobelPrize]

Curiosity often sparks scientific breakthroughs. For 2026 medicine laureate Peter Hegemann, it began with a question: how could a tiny green alga called Chlamydomonas react so rapidly to light?

The Chlamydomonas algae is famous for its ability to swim towards light. When the alga is stirred into a petri dish, the liquid turns a faint green colour. If one side of the dish is illuminated, the green colour moves towards the light source, as the microscopic algae swim towards it. The algae sense the light with an eyespot, a dot on the cell’s surface that contains a light-capturing molecule, retinal.

Hegemann discovered electrical signals in the light-sensitive eyespot that occurred just half a millisecond after illumination, far faster than the response of the human eye. He could only think of one explanation for this ultrafast response: the process must be much simpler than in a human eye. He proposed that a single protein could both detect light and act as an ion channel, an idea initially met with scepticism.

After years of experiments with his colleague Georg Nagel, Hegemann's hypothesis proved to be correct. Together, they discovered proteins that functioned exactly as he had predicted: ion channels that opened in response to light. This breakthrough laid the foundation for optogenetics, a revolutionary technique that allows scientists to control individual nerve cells with light. What began as a question about a microscopic alga ultimately helped transform the field of neuroscience.

Read more about the newly awarded 2026 Nobel Prize in Physiology or Medicine: https://t.co/UkNVNYb0xU

[はるかぜ @harukaze_illust]

Homero Gómez González🦋🇲🇽
彼が守っていたものを永遠に

[gustavo @biciclotimia]

Mis ex: superadas

Laika, la perrita que fue al espacio: Tema sensible

[katherine @spacecrone]

My favorite Anne Carson moment ever. She gets it

[Eric Topol @EricTopol]

Why do small dogs live longer than large dog species, and what is the biologic basis of differences in lifespan and healthspan?
Epigenetics.
There's a lot in the methylation pattern of dogs to explain this @ScienceMagazine
https://t.co/rxzWd1G63d