217 éven át senki sem tudta megfejteni Napóleon titkos parancsait, a ChatGPT 6 óra alatt feltörte

Reporting by HVGRead the original at hvg.hu

Executive Summary

An advanced AI system, GPT-6 Astra, was used to decipher a secret message that was 217 years old, purportedly containing Napoleon's orders. The deciphering process involved an engineer from SentinelOne, Carter Church, who utilized the AI. The source material was a letter sent by Eugène de Beauharnais regarding the main military journey of Auguste de Marmont. The content concerned troop positions of the Bajor, Ly, and French forces in 1809, noting that the Russians were opposing the Austrians and the Pruli army size was 80,000. A specific instruction within the letter mentioned that a detachment of "shot" should not be withheld. The deciphering achieved a result by combining multiple processing methods and a partial key previously unknown to others.

Facts Only

* OpenAI's GPT-6 Astra was used for code-breaking.
* The secret message was 217 years old.
* The code transmitted Napoleon's orders.
* The letter was sent by Eugène de Beauharnais to Auguste de Marmont concerning the main military journey of Marshal Napoleon.
* The text discussed the positions of Bajor, Ly, and French troops in 1809.
* The Pruli army size was noted as 80,000 men.
* A directive instructed not to withhold "one squad" (egy csapat csőcselék).
* The deciphering utilized a partial key unknown to others.
* The message contained numerical data, letters, and symbols across 24 lines following an introductory line.

Full Take

The process highlights the shift in how historical and encrypted information is accessed, moving from specialized, static knowledge (like military codebooks) to emergent computational synthesis for uncovering obscured context. The reliance on a state-of-the-art AI underscores a pattern where complex, layered ambiguities, which previously required decades of specialized cryptanalysis, can now be resolved through advanced pattern recognition. This development raises questions about the true nature of historical knowledge—is it fixed, or is it infinitely malleable depending on the algorithmic lens applied? The fact that the derived context filled gaps in Napoleon's memoirs suggests a potential for AI not just to decode messages but to reconstruct lost narratives by finding latent connections between disparate data points. The implication for cognitive sovereignty lies in recognizing that what is considered "known" history may be inherently layered and subject to new, powerful interpretive frameworks. What assumptions about the stability of historical truth are we making when we outsource context retrieval to systems capable of such deep synthesis?

From the original · HVG

Többször megmutatta már az OpenAI legújabb mesterséges intelligenciája, hogy mire képes. Nemrég például feltörte a nácik 85 évvel ezelőtt írt titkos üzenetét, amit eddig senkinek sem sikerült megfejtenie.

Read the full story at hvg.hu

Sentinel — provisional

Raw score, 0 to 1 (uncalibrated)0.30

No strong signs of machine writing were found in the source article. Provisional estimate, not a finding that a person wrote it.

The model's own note (unverified)

The text reads like journalistic reporting synthesizing technical achievements with historical context, exhibiting the complex narrative structuring of human analysis rather than pure synthetic generation.

This looks only at the wording of the original source article, not at this page's AI-written sections. A small local AI model made this estimate. It has not been checked against known human and machine texts, so treat it as provisional. It cannot show who wrote an article.

What the model noticed in the source article

Moderate sentence length variance; exhibits narrative flow typical of journalistic reporting rather than uniform rhythm.

Strong contextual linkage between disparate historical and technical elements, suggesting human synthesis of information.

Attribution to a named expert (Carter Church) and specific citation of external sources/links is present.

The core narrative involves historical claims and technical process description; the complexity suggests grounding in source material, though verification of the specific findings requires external context.

Signs the model read as against machine writing

Specific references to named historical figures (Napoléon, Eugène de Beauharnais, Auguste de Marmont) and contemporary experts provide anchors that suggest human research and synthesis.

The flow shifts from a modern AI demonstration to dense historical decoding, which shows a narrative structure more complex than typical LLM output.

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