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KC • September 9, 2026 9:56 AM
To add to @foo’s comment, it’s interesting that Wikipedia (currently without a source) seems a little confabulated on this finding. I’m also not sure what author it’s referring to.
“After inspecting the original 1653 book in the British Library, the author revealed that the two-line numerical cipher does not appear after the 32nd petition. Instead, the text simply concludes with decorative flourishes, Latin phrases, and a closing statement.”
However, the Vals AI blog seems to reference a source copy at Edinburgh: Maitland Club, 1834.
So, am wondering if more historical cryptanalysts will weigh in?
Clive Robinson • September 9, 2026 10:45 AM
@ Bruce, ALL,
With regards,
“It’s good at things that involve lots of searching and testing.”
As far as “Current AI LLM Systems” are concerned all the “big achievements” we have been informed about are achieved with intensive “searching and testing” gone through rapidly has been the foundation.
If we look at these achievements they are somewhat niche and based around a very large problem domain that can be checked with simple rules. With the testing effectively independent and thus easily parallelizable.
This usefulness even though niche was in effect withheld from the public by the AI companies, untill someone got upset and over a weekend churned out “OpenClaw”, and agenetic use of AI rather rapidly hit the big time.
The thing to note is that “searching and testing” has to be easily parallelizable in some way. If not agenetic use of LLMs will not be particularly fast.
It’s something you would have thought would be obvious to anyone, yet even though it is, and has been pointed out, all the AI Hype hooplah has blinded most to it…
Which makes me wonder how much else that is obvious about AI is being missed due to the AI company hype.
One thing to consider is Current AI Agenetic ability whilst useful, and in some cases is very advantageous, is very niche… Which is not something “crazy mad” investors “throwing money into a hole in the water” are going to want to hear as it makes the ROI tiny.
Clive Robinson • September 9, 2026 11:24 AM
@ Bruce, ALL,
With regards your article in The Guardian where you say,
“We think the contrary view is more likely, at least in the short-term. AI models are nowhere near as capable as experienced academic mathematicians.”
I have to disagree.
It will not be “at least in the short-term” it will be effectively for ever. Because that “gap” between “Current AI LLM Systems” and “experienced academic mathematicians” is not going to close appreciatively.
The reason as with any other “force multiplier tool” we’ve created is that it will “free up drudge work” and alow humans to have ability and time to upscale their creativity.
Thus tools like “LEAN”[1] which has taken a lot of the drudge out of “proofs” will take a lot drudge out of hypothesis testing. Enabling a human mind more time to do the creative work of seeing the loose threads that lead onto new and very different hypothesis, that fall well outside the capabilities of the stochastic fuzzing found in LLMs.
I’ve talked about this issue with “Current AI LLM Systems” in the past, in that they can work their way close in to “known Classes” and find new Instances there. But they can not due to the way they work actually find new instants that form new classes that are more than a short distance away from a known instance. Thus the do not take “intuitive but directed leaps” as humans can just “drunkards walks” at best (though they can as tools test those human inspired intuitive leaps).
[1] LEAN is Open Source and under fairly rapid development. It is based on the “Calculus of Inductive Constructions”(Coq)
https://en.wikipedia.org/wiki/Lean_(proof_assistant)
https://en.wikipedia.org/wiki/Calculus_of_constructions
As I’ve mentioned before another area to keep your eye on is other types of logic that can be more amenable to tool use, such as “Computability Logic”(CoL)
https://en.wikipedia.org/wiki/Computability_logic
They are all methods that can be automated into tools thus act as “force multipliers”.
Rontea • September 9, 2026 11:53 AM
What stands out here is the disciplined process: identify a tractable target, leverage the internal structure of the document, and then verify against historical context. Fable 5.1 didn’t just throw compute at the problem—it recognized a pattern humans overlooked.
For cybersecurity and intelligence folks, this is a reminder that many longstanding puzzles are solvable not by brute force, but by patient, structured reasoning. Once automation can focus human attention on the right clue, entire classes of “unsolved” challenges may collapse quickly.
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Sidebar photo of Bruce Schneier by Joe MacInnis.
foo • September 9, 2026 9:08 AM
Not sure what’s going on here:
https://github.com/reticuli-labs/panel-artifacts/blob/main/distich-refutation-2026-09-01/FINDINGS.md