arsu

[go4gordon 🌕 @go4gordon]

"Amaze amaze amaze"

#ArtemisII

[arsu @arsu]

@NotebookLM Also would be cool automatic / recommended tagging and to generate a knowledge graph to navigate different topics. :)

[arsu @arsu]

@NotebookLM I would love to have tags implemented. Also, being able to call other notebooks / folders / tags when I am working on a notebook, so I can build knowledge layers and call them on demand.

[arsu @arsu]

@joshwoodward @GeminiApp Hey Josh,
I hope you can take this one.
It would be cool if we could pin and add a label to individual chat replies in Gemini. It is difficult to locate and retrieve older responses. Cheers!

[arsu @arsu]

@GeminiApp A Walkman, drawn with a BIC pen!
"Generate an ultra-detailed, with detail price description, drawn with bic pen, hyperrealistic exploded technical view of a walkman"

[Jason Crawford @jasoncrawford]

Information overload and the hectic pace of modern life, 1924 edition

[Thomas Bloom @thomasfbloom]

@kevinweil Hi, as the owner/maintainer of https://t.co/69gOJM7Ci7, this is a dramatic misrepresentation. GPT-5 found references, which solved these problems, that I personally was unaware of.

The 'open' status only means I personally am unaware of a paper which solves it.

[Demis Hassabis @demishassabis]

@SebastienBubeck @MarkSellke this is embarrassing

[César de la Fuente @delafuentelab]

For over a century, antibiotics were found by slow, painstaking, serendipitous screening.

Today we share a different path: the first demonstration of deep reinforcement learning (RL) for antibiotic discovery.

I’m thrilled to introduce ApexAmphion—inspired by the systems that mastered Go and StarCraft. We pair a 6.4B-parameter protein language model with PPO in a closed loop to generate → score → optimize antibiotic candidates at digital speed.

In games, RL agents explore vast decision spaces and optimize toward rewards (winning). ApexAmphion brings the same logic to biology.

Instead of chess moves, the agent proposes peptide sequences (candidate antibiotics). Instead of a scoreboard, it receives multi-objective rewards tied to predicted potency (minimum inhibitory concentration, MIC) and developability—properties that make a drug more likely to succeed. Iteration by iteration, the system learns to design better molecules.

Highlights (ground-truth experiments):
• 100/100 designed molecules active in vitro (100% hit rate)
• 99/100 active against ≥2 clinically relevant pathogens (incl. MDR)
• Multi-objective rewards: predicted MIC + key physicochemical properties
• Rapid & steerable: tune potency and developability on demand
• Amphorium: >2 million machine-annotated candidates for follow-up

In other words, our ApexAmphion model has now mastered antibiotic discovery and can design promising candidates within hours, often in a single morning or afternoon. We’re compressing years of trial-and-error into hours-scale loops, expanding the space of antimicrobial discovery, and executing a programmable RL pipeline that moves from games to potential life-saving therapeutics.

Incredible Team work: @Hanqun_CAO, @mdt_torres, Jingjie Zhang, Zijun Gao, @WUFang40615703, Chunbin Gu, @jure, @YejinChoinka, Guangyong Chen, Pheng-Ann Heng.

Paper: https://t.co/jpNhoGRlNb

#ReinforcementLearning #AI #DrugDiscovery #Antibiotics #AMR #ProteinLM #SyntheticBiology #Biotech #ApexAmphion

[AEMET Divulga @AEMET_Divulga]

🧵¿Cómo se hace una predicción meteorológica? ¿Cómo funciona un modelo meteorológico? En este hilo hablaremos del complejo proceso científico y técnico en que se basa el método de elaboración de la predicción moderna del tiempo y que se usa para la predicción de una dana.

[Arturo González-Campos @ArturoGCampos]

Ojo. Estoy descubriendo que hay cuentas aquí que hablan sobre temas de los que no tienen mucha idea, pero lo escriben en plan contundente y, claro, les cuela. No creo que sean muchas. Sigo investigando y os digo.

[SpaceX @SpaceX]

Raptor 3 (sea level variant)

Thrust: 280tf
Specific impulse: 350s
Engine mass: 1525kg
Engine + vehicle-side commodities and hardware mass : 1720kg

[SpaceX @SpaceX]

The world’s most powerful launch vehicle ever developed, powered by 33 Raptor engines, lifting off from Starbase

[elea boa @eleaboa]

Elea Boa elkarlanean sortu da. Hasieratik proiektuan sinetsi eta %100a eman duen jendez inguratuta.

Abestiak Santa Monican jaio, Los Angeleseko Los Feliz auzoan grabatu eta Zarautzen musikari eta lagun finen laguntzaz borobildu eta amaitu genituen.

[elea boa @eleaboa]

3 hilabete ‘Amesten’ kalean dela.

Eskerrik asko abestia entzun, bideoa ikusi, lagunekin partekatu edo babes mezu bat bidali didazuen guztioi.

‘Amesten’ was released 3 months ago.

Thanks to everyone who listened to the song, watched the video, shared it or sent me a message.

[arsu @arsu]

@Dollywaggon @NiallTierney10 @Cold_Brewd @nntaleb Be aware that aluminium is still used on most critical impact prone areas (ie, cabin, wing leading edge and engine intakes)

[Randall Munroe @xkcd]

Gravity https://t.co/ZX4DtNML0l

Today’s comic is interactive! Click the image on https://t.co/vLIvAoAAwv and use arrow keys or tap to fire thrusters.

[Benjamin Hilton @benjamin_hilton]

There's a new competitor to DALL-E out there: Google's Imagen.

I don't have access to Imagen but I do have some of their example images. Let's do some side-by side comparisons.

(left is Imagen, right is DALL-E)

1. "A blue jay standing on a large basket of rainbow macarons."

[Dr Donna Kean @donnaeilidhkean]

I train these clever creatures to save victims trapped in collapsed buildings after earthquakes. We kit them out with a rat backpack, and train them to trigger a switch when they find a victim & come back for a tasty treat 🐀
#herosnotpests #science #weirdjobs #WomenInSTEM