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Science that matters: AI, space, biotech, physics, future tech — explained sharply
Mostrar más📈 Análisis del canal de Telegram Science in telegram
El canal Science in telegram (@science) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 120 135 suscriptores, ocupando la posición 102 en la categoría Hechos y el puesto 175 en la región EEUU.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 120 135 suscriptores.
Según los últimos datos del 08 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -392, y en las últimas 24 horas de 18, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.55%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.09% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 5 465 visualizaciones. En el primer día suele acumular 2 507 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 49.
- Intereses temáticos: El contenido se centra en temas clave como medicine, cell, researcher, scientist, u.s.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Science that matters: AI, space, biotech, physics, future tech — explained sharply”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 09 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Hechos.
120 135
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 08 octubre | +54 | |||
| 07 octubre | +14 | |||
| 06 octubre | +10 | |||
| 05 octubre | +7 | |||
| 04 octubre | +20 | |||
| 03 octubre | +5 | |||
| 02 octubre | +8 | |||
| 01 octubre | +6 |
Publicaciones del Canal
| 2 | Sin texto... | 2 608 |
| 3 |  Premios del sorteo: 500 estrellas se distribuirán entre 5 ganadores. | 2 485 |
| 4 | ⚛️ Scientists Have Built the World’s First Nuclear Clocks. They Could Change How We Measure Time.
For decades, the world’s most precise clocks have measured time using electrons moving between quantum energy levels inside atoms.
Now, two independent teams in Vienna and Beijing have demonstrated something physicists have pursued for more than 20 years:
Clocks that measure time using the atomic nucleus itself.
And the difference could be revolutionary.
Atomic nuclei are more than 10,000 times smaller than atoms and are generally much less sensitive to external electromagnetic disturbances.
That makes nuclear transitions exceptionally promising as stable frequency references.
The challenge? Almost every nuclear transition requires enormous energies, far beyond what conventional lasers can provide.
But one isotope is special: thorium-229.
Its nucleus has an unusually low-energy excited state that can be accessed using ultraviolet laser light at approximately 148 nanometers.
Researchers embedded thorium-229 nuclei inside tiny calcium fluoride crystals and developed lasers capable of detecting their nuclear transitions.
Then came the breakthrough.
They used the nuclear transitions themselves to automatically stabilize the laser frequency.
The result: two independently demonstrated, functioning nuclear clocks.
The European system operated continuously for approximately 24 hours, achieving fractional frequency instability approaching 10⁻¹⁵ after a day of averaging.
That’s an extraordinarily small fluctuation — although today’s best optical atomic clocks are still substantially more stable.
But the most exciting application might have nothing to do with telling time.
One team has already used its nuclear clock to search for dark matter.
Some theories predict that ultralight dark matter could cause tiny oscillations in fundamental physical constants, subtly changing how atomic nuclei behave.
By comparing nuclear and atomic clocks, physicists can search for these otherwise invisible effects.
The experiment found no dark matter signal, but it established new constraints on certain theoretical models.
And this is only the beginning.
Future nuclear clocks could potentially improve satellite navigation, measure gravitational effects with extraordinary precision and test whether the fundamental constants of physics really remain constant.
For thousands of years, humanity has built better clocks to understand time.
Now we’re building clocks that might help us understand the universe itself.
#Physics #QuantumPhysics #NuclearPhysics #AtomicClocks #DarkMatter #QuantumTechnology #Science
https://www.nature.com/articles/s41586-026-11084-4 | 2 385 |
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| 10 | Sin texto... | 4 146 |
| 11 | 🔬 An AI Scientist Just Made a Discovery by Running Its Own Lab Experiments
We’ve seen AI write scientific papers.
We’ve seen it predict proteins and search enormous biological databases.
This is different.
Researchers built a closed-loop AI scientist connected to a physical laboratory. It can generate a hypothesis, design an experiment, turn that experiment into instructions for laboratory automation, analyze the resulting data — and then decide what to investigate next.
The system was given knowledge about Saccharomyces cerevisiae — ordinary baker’s yeast — including roughly 60,000 known biological relationships involving its metabolism, physiology and phenotype.
From these, it generated 1,933 testable hypotheses about how different compounds might affect yeast growth under stress.
Then came the important part:
the hypotheses met reality.
The system selected experiments and controls, converted them into machine-readable laboratory procedures and analyzed the resulting biological data. Some predictions worked.
Others failed.
And one failure produced the most interesting result.
The AI initially predicted that glutamate might protect yeast from formic-acid stress.
The experiment contradicted it.
Instead of simply recording “wrong,” the system analyzed the new metabolomic data, searched for another explanation and identified aminoadipate, a molecule involved in lysine metabolism, as a candidate.
It formulated a new hypothesis.
The lab tested it.
And aminoadipate did improve yeast growth under formic-acid stress — by about 7% for each millimolar increase in the experiment. The researchers report this as a previously unknown protective interaction.
There is an important caveat.
This was not a completely autonomous robot scientist. Humans defined the research domain and safety boundaries, moved some physical samples between instruments and supplied the overall experimental infrastructure. The biological questions were also relatively narrow yeast-metabolism problems — not Nobel-level discoveries.
But something important has happened.
AI has already become very good at generating hypotheses from existing information.
Now the loop can close:
Hypothesis → physical experiment → unexpected result → new hypothesis → new experiment.
That is no longer just AI analyzing science.
It is AI participating in the scientific method.
What happens when systems like this can run 10,000 experiments while a human scientist sleeps?
#AI #Science #Biology #Robotics #Biotechnology #Automation #Research
https://doi.org/10.1098/rsif.2026.0043 | 5 349 |
| 12 | Sin texto... | 5 403 |
| 13 |  Sin texto... | 4 181 |
| 14 | Meanwhile, something interesting is happening on Telegram: @gadget is officially up for auction.
Yes — the actual @gadget username.
Telegram usernames can be turned into blockchain-based collectibles and traded through Fragment. Whoever wins the auction gets control of the handle and can assign it to a Telegram account, channel, group or bot.
And @gadget is exactly the kind of digital property that could be valuable: short, memorable, universally understandable and sitting right in the middle of the global tech industry.
It’s a strange new category of internet real estate — not a domain name, not quite an NFT, but a piece of identity infrastructure inside a platform used by more than a billion people.
Let’s see what the market thinks @gadget is worth.
https://fragment.com/username/gadget | 4 609 |
| 15 | ⚡️ GPT-6 Astra deciphered a letter to a Napoleonic marshal that nobody had been able to read for 217 years
Researcher Carter Church used GPT-6 Astra to decipher an encrypted letter to Marshal Auguste de Marmont, one of Napoleon's generals. The letter is dated March 1809, and the model took about six hours to do the whole job.
The first step was simply to extract readable text from a poor scan of the manuscript. Astra recognised 1,300 cipher characters, among which there turned out to be 155 distinct signs.
The model then found a published partial key by French cryptology historian Daniel Tant: 33 letters covering roughly 435 characters. For the remaining signs it wrote a simulated-annealing solver, and then checked the result against historical correspondence and corrected the document's date.
Inside was a military briefing from Eugène de Beauharnais's headquarters: troop positions and Austrian movements on the eve of Austria's invasion in April 1809. Marmont is told not to fear "a few detachments or a gathering of rabble." It also turned up the ending of a sentence that breaks off in Napoleon's memoirs, published in 1865.
The solution was reviewed by Satoshi Tomokiyo, who runs the historical ciphers site Cryptiana, and the cipher is now listed there as solved.
https://runtimewire.com/article/gpt-6-astra-marmont-cipher-carter-church
https://x.com/Machinelearrn/status/2105593763582107849 | 5 016 |
| 16 | Sin texto... | 5 217 |
| 17 | 🌌 The Ingredients for Planets Were Spreading Through Space Just 500 Million Years After the Big Bang
The newborn universe started simple.
After the Big Bang, almost everything was hydrogen and helium. Carbon, oxygen, silicon and nearly every other element needed to build planets — and eventually us — had to be manufactured later inside stars.
Astronomers expected that process to take time.
JWST has now shown that it happened remarkably fast.
Researchers analyzed nearly 30 hours of Webb observations of three galaxies seen as they existed roughly 500–700 million years after the Big Bang.
They found unmistakable chemical fingerprints of carbon, oxygen and silicon in gas associated with the galaxies.
But the really interesting part was where that gas was going.
The absorption signatures were blueshifted by roughly 50–250 km/s, indicating that metal-enriched material was moving outward from the galaxies — consistent with powerful galactic winds carrying newly forged elements into surrounding space.
That means an entire cosmic recycling system was already operating while the universe was only about 3% of its present age.
Stars formed.
They forged heavier elements.
Stellar winds and explosions returned those elements to their galaxies.
And galaxies began spraying them outward, chemically transforming the surrounding universe.
Remarkably, the chemical fingerprints look similar to those seen around galaxies billions of years later.
The result may also help solve another mystery.
Astronomers have spent decades searching for Population III stars — the hypothetical first generation of stars, made almost entirely from pristine hydrogen and helium.
None has ever been conclusively found.
If early galaxies contaminated their surroundings with heavier elements this quickly, the window in which truly pristine stars could form may simply have been much shorter than expected.
Important caveat: the result comes from only three unusually bright early galaxies. We don’t yet know whether such rapid enrichment was universal across the young cosmos.
Still, the implication is striking.
Only half a billion years after the Big Bang, the universe had already started distributing the carbon in our bodies, the oxygen in our water and the silicon beneath our feet.
Cosmic chemistry apparently wasted very little time.
#JWST #Space #Astronomy #Cosmology #BigBang #EarlyUniverse #Science
https://www.nature.com/articles/s41550-026-02988-2 | 4 939 |
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