⚡️ 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

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

🔬 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

⚛️ 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

🧠 Just 10 Minutes With AI Can Make You Give Up More Easily

AI makes us faster.

But what happens when we suddenly have to think without it?

A newly peer-reviewed study involving 1,222 participants suggests something uncomfortable: after just 10–15 minutes of AI-assisted problem-solving, people performed worse on their own — and were more likely to give up.

Researchers conducted three randomized experiments involving mathematics and reading comprehension. Some participants solved problems independently, while others had access to ChatGPT. Initially, the AI-assisted group performed better. But when researchers unexpectedly removed the chatbot, the advantage disappeared. In the first experiment, participants who had used AI correctly solved just 57% of subsequent problems, compared with 73% among those who had worked independently. They also skipped nearly twice as many questions: 20% versus 11%.

The researchers identified an especially interesting pattern. Participants who used AI primarily to obtain complete answers struggled more afterward than those who used it for hints or explanations. That comparison was observational, not a separate randomized experiment, but it suggests an important distinction: AI that helps you think may be very different from AI that thinks for you.

The possible explanation is a psychological shift. When answers arrive instantly, independent effort begins to feel unnecessarily difficult. Researchers call attention to the loss of productive struggle — the mental work through which we develop skills and persistence.

Important caveat: The experiments measured immediate effects during short online tasks. They do not establish permanent cognitive damage or show that regular AI use necessarily makes people less intelligent. Whether these effects persist over weeks or months remains unknown.

But the implication is fascinating.

We may be building machines that make us extraordinarily productive while quietly changing how willing we are to solve problems ourselves.

Perhaps the smartest AI assistant isn’t the one that always gives you the answer.

It’s the one that sometimes makes you figure it out.

Would you prefer an AI that solves everything instantly — or one that occasionally forces you to think?

#AI #ChatGPT #Psychology #Neuroscience #CognitiveScience #ArtificialIntelligence #Science

https://arxiv.org/abs/2604.04721