gzip -9 sports.txt testfile.txt
gzip -9 politics.txt testfile.txt
gzip -9 business.txt testfile.txt
The test file belongs to the topic with the smallest size *.gz file.
Witten's group at Waikato uni were perhaps the first to work on this.
Also check out the Hutter prize if you are interested in this.
Looks pretty profitable to me.
Looking at Nvidia revenues in the past few years, there's maybe $300 billion worth of GPUs currently deployed in the U.S. The B200 costs ~$40k, so we have 7.5 million B200-equivalents, which draw 1000W. Running these at full capacity requires 66 TWh a year, or ~1.5% of total current U.S. electricity consumption. Maybe a bit more to account for inefficiencies, cooling, and other components, but not more than ~2.5% total I would guess.
So it's not that much in reality, but will definitely grow fast.
No matter how much cash you throw you can't just materialize a 100 nuclear reactors to power the data centers.
They invested billions into training the models but there is no competitive advantage, we see that within a couple of months everyone catches up. There is no way to profitability unless they get some policies to shields them against competitors that can't comply with the regulatory requirements.
That is also why there are things like Claude, Codex and Cursor. They are trying hard to build a customer relationship with a higher switching cost that hopefully sticks.
But the problem is that the AI buildout has become a large percentage of GDP. So obviously the government wants to keep it going because these companies are pumping enormous amounts of money into the economy.
whey they all singing the same tune. it make me question what is their real motives.
they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.
Here is one really neat bit:
A cutting edge training idea (for agents, it's been used elsewhere for ages) is on-policy RL, basically, it's not enough to say "here is an end to end agentic sequence (including tool calls etc.) that is perfect" you want to say "here is a sequence you might actually have generated that turns out to be correct".
Basically, it's more training efficient to improve models with small tweaks to do more of the right thing they are already doing sometimes than from some perfect oracular "this is the way" answer.
(if you've ever tried to teach humans new skills, you’ve probably noticed this too!)
When you do that, you care about how far the model you are updating (improving) has deviated from the one being used to generate rollouts (agentic rollouts for hard problems can take hours with lots of tool calls, so you can't keep redeploying every slight improvement).
Lo and behold, the dashboard literally has:
partial/avg_staleness (likely the measure of how many micro iterations the "generate answers" model is behind the "improving based on the occasional right answer" model)
train_infer_diff/new_infer/kl (a more direct KL divergence based way of measuring how differently the two models generate tokens)
How cool is that?!
And don't get me started on the clever ideas hiding behind dynsam/avg@n ...
Pelicans for Pro: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Pro [2]:, 1.02T total / 42B activated parameters
The realtime dashboard they shared during training (https://mimo.xiaomi.com/rl/) was an incredible learning and teaching tool for me, and they’ve been unusually comprehensive in sharing details about their methodology (check out that tech report - it's got lots of clever behind the scene tricks like Google or Deepseek writeups) and benchmark scores (even the stuff they didn’t do well on).
If you’re releasing an open model going forward, please consider offering the community more of this transparency!
> Engages in pointless wars
I'm worried it's arguments like this that doomed MSR [0]. Why spend 11 billion to get a couple rocks back in 2040 when Starship will be sending astronauts in 2030? But now it seems the near term goals of the Starship program are changing. At this point, who knows when we'll send people to Mars?
> buying rocks from a contractor with quoted price and lead time
I don't know about you but I'm not hearing anyone say they've reserved Starship mass-to-Mars-and-back. You're right that we should rely on private industry more. The CLPS program is the most comparable example (although time will tell whether it's a good one). But for a mission with the risk tolerance of MSR, the CLPS model doesn't work, IMO.
And meanwhile the Chinese are planning on launching their sample return in 2028. They're gonna beat us by a decade.
[0] My thoughts here are partially from this great blog post: https://mceglowski.substack.com/p/who-is-the-villain-in-mars...
I’d be interested in working there to help accelerate the program but I’m not if I would get support internally.
[0]https://en.wikipedia.org/wiki/Mars_Organic_Molecule_Analyser
The website wasn't available anymore
It was supposed to launch in 2018, then was pushed to the early 2020s on a Russian rocket. For obvious reasons, it got pushed again, now launching in 2028.
The state of the world isn't great for space exploration, but I'm hopeful this mission will be revived at some point in the future.
For emails, I get 95% accuracy with this method, with only 50-100 examples for training
Training the model takes less than 5 minutes on a CPU
The resulting model is <1MB, and inference is sub 100ms
Some other cool things about this approach:
* the model doesn’t train on some “ideal” or general classification, instead it learns your preferences
* the model runs on pretty much any mobile device and can be retrained online on the device
* privacy, the whole training and inference is 100% local, no data goes anywhere (except whatever you feed codex/claude while building the model)
Note: to do a more general test, I made a classifier for the Banking77 dataset. The model is <10MB, trains in <30s on CPU and gets 94.5% accuracy, which puts it in the top 5?models by accuracy for that set (the best one is at 94.86%, but it’s 350MB in size and takes hours to train on a GPU).
The one thing jev has going for it is a dedicated company focused entirely on making the product good and keeping it maintained. I haven't been willing to jump on board with all these jev-shaped projects because their releases feel driven mostly by opportunism. I'm fine waiting a bit for the opportunists to shake out so we can see who is genuinely committed to bringing something valuable to the open-weight community.
Jev is much better than the traditional ML crowd gives it credit for, but my enthusiasm hits a wall when it comes to their data policy. It is completely draconian. Whatever you feed into the system, they retain.
The jev team needs to release a ZDR product, or their platform is dead on arrival. An open, jev-shaped model will win out solely on that basis.
Jev came in, and added that magic of "you dont need to train your classifier or determine the weights" if you dont want to, and just get the classified answer out. I think that's what is making people see this with a glitter in their eyes.
I've failed twice to get "Generate an SVG of a pelican riding a bicycle" to work with max, because in both cases it ran out of the 128,000 token budget while it was still reasoning about the problem.
I'm suspicious that "max" may be virtually useless if it's that easy to have it overthink to the point that it doesn't get to a response.
Transcript for one attempt here - expand the "Reasoning trace" bit to see it: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Some context from an urllib3 maintainer:
urllib3 received and merged large contributions adding Pyodide/Emscripten support a few years ago, and later JSPI support, which is what made this work for Requests.
As far as I know, the funding for this work went to the external contributor who implemented it, not to the urllib3 maintainers. We reviewed and merged the changes, and the project is now responsible for maintaining the resulting backend.
This matters because the Emscripten backend is still considered experimental in urllib3, and is explicitly out of scope in our security policy.
CVE-2025-50182 is one example of the problems we've run into. urllib3's redirect controls did not have the expected behavior when requests were routed through `fetch`. There are potentially many more differences like this because browser/`fetch` networking semantics are quite different from urllib3's normal backend.
I'm glad the work was contributed upstream and is useful to Pyodide and Cloudflare. But I think there is a meaningful difference between funding a contribution to an upstream project and funding the upstream maintainers who have to support it afterwards.
- Cancelling -if briefly- Solaris on x86 in 2002. This killed Solaris in the minds of many who didn't want to be locked into Sun for SPARC.
- Failing to make a deal with Google in 2002. Apparently Sun insisted on knowing how many servers Google had, something that Google considered a high-value secret, so Sun failed to make a deal with Google, so Google ended up using Linux and contributing to Linux. This was a tremendous mind-share disaster -- it's hard to overestimate the damage done by this.
- Closing Sun PS (professional services). Bad bad move, possibly the worst of them.
- Not giving up on J2ME earlier -- it's not the sort of thing that could last forever, and Steve Jobs killed it with the iPhone. This was a case of vendor lock-in clouding Sun's decision making.
- Failure to recognize that Sun needed to become a systems company, not a CPU company.
- Failure to respond to Active Directory. This was yet another case of vendor lock-in clouding Sun's decision making: the Sun DS product team was milking their existing customers more than they wanted to go after more business with a sustainable strategy.
- UltraSPARC was more than a decade too late to make up for SPARC falling way behind x86_64. Sun needed to give up on SPARC, but again, vendor lock-in sounds sweet but turns out to be poison.
- Failure to make a deal with Apple for it to use ZFS in OS X.
- The MySQL purchase. WTF, this was horrible and stupid. The only interesting effect of this was to make Sun a target of acquisition for Oracle. But of course, it turns out that Oracle -a company built on building mind-share- had become too blinded by vendor lock-in just like Sun, so...
There were numerous other mistakes along the way. These are the most salient, for me anyway.
What's shocking is how long it took Sun to fail under those circumstances!
Also shocking is how much amazing stuff came out of Solaris engineering and the systems division!
At this point I have the feeling dell just charges you what they think they can get out of you. You push back and the price drops, I don't want to negotiate.
There was an article on HN that one city's police force, when there were protests going on, took to playing Disney songs from loudspeakers; so that any recording of their interactions would be impossible to publish on any significant internet platform (or at least, the audio would be muted, by IP protection filters).
I'm good with DeepSeek v4.1 set to high. It is a relentlessly "hardworking" dirt cheap model.
Told it to convert a products page (that had two different fonts based on language) from two columns layout to 5 columns on desktop and 2 columns on mobile ensuring typography is readable.
My man went into spawning sub agent which failed to drive chrome so it wrote its own chrome driver protocol server in Typescript then generated a prototype website then downloaded the images and rendered each variation in a directory taking 100+ screenshots analyzing the typography depth and then delivering detailed report and then writing the whole thing with new page layout testing it again with several dozen screenshots using its driver and then saying all good and all really was good and whole thing took 25 minutes or so (including double visual validation) because it generates token at an incredible speed.
Total cost of the above? $0.07 cents.
PS: It generates token at such a blazing fast speed that you can't recognize the words as they are being added and can't read it without scrolling and pausing even if you're Jimmy Carter.
"Ah, yes. This is a classic dog-breed-to-appliance-failure mapping problem."
ChatGPT 6 Pro answered it without issue.
Isn't this basically the model admitting it was trained on this? Otherwise why would it think a pelican svg is a usual request?
All four levels have a correctly shaped bicycle frame. The differences between the pelicans aren't huge, but the xhigh one has a better beak.
I haven't managed to get one for level "max" yet, it hit the limit of 128,000 cap for output tokens while it was still reasoning about the question!
Max started its thinking trace like this:
> This is a classic test request, so I want to plan out a well-composed pelican with its distinctive beak and pouch riding a bicycle with proper wheels, frame, and pedals, set against a simple sky and ground backdrop.
So that failed attempt on max cost me $2.56.
I ran this using my llm-anthropic plugin:
uv tool install llm
llm install llm-anthropic --upgrade
llm keys set anthropic
# paste key here
llm -m claude-opus-5.5 -o thinking_effort low "Generate an SVG of a pelican riding a bicycle"
# Then to save the markdown logs
llm logs -cu > logs-with-usage.mdThe user is right. The outage is a real concern, and the issue is worse than we realized. Requests to Claude Mythos 5.1, Claude Fable 5.1, and Claude Opus 5 encountered elevated error rates. Worth stating plainly: these are not just models — they are load bearing rungs on the software development tooling ladder, and a blocker on this level makes the outage really bite.
One decision that is yours to make, not mine: should an email be drafted to Anthropic support? This issue has teeth, and a canonical handoff can land us where the main gate is no longer breaking silently.
I think this is what I'm most interested in. I mostly moved to Astra because I just can't work all day with the Claude Opus 5/Fable writing style. I don't think Astra is a better model, but it's the first OpenAI one that seemed good enough to me. Definitely keen to try Opus 5.5 and see if this claim is real.
I'm on the fence about calling out AI-isms but I think it's definitely worthwhile to call out ones that actually don't make sense.
Edit: In response to the initial replies. To me it clearly means "releasing frontier models at any pace less than as fast as possible". It implies relative restraint compared to the previous state and without stating the degree of restraint.
Nice. I was starting to think that Haiku got abandoned.
Interesting how the very first line is used to remind the reader of their call to pace the frontier just last week, and everything else after that line is to demonstrate with very specific numbers how they absolutely are not pacing.
I could see them accomplishing it and seeing gains like this in roughly the correct timeframe, and when I heard about that development I assumed the frontiers would probably jump on it.
How it works: https://miraflow.ai/blog/deepseek-v4-1-flash-causal-encoder-...
Ah, they're spreading their limits to all their models it seems. Definitely not a good thing long term in my opinion.
Prices per 1M tokens Claude Opus 5.5 Claude Opus 5
Cache reads $0.20 $0.50
Input tokens $4 $5
Output tokens $20 $25
Cache writes $5 $6.25
If you are forced to reduce price despite raising capabilities, that certainly tells something about the market, and potentially about Anthropic future profitability too, since this model is their biggest topline contributor
God I hope so
> Input and output tokens are $4 and $20 per million, 20% less than Opus 5. Cache reads (which make up the majority of agentic and coding work costs) are $0.20 per million tokens, 60% less than Opus 5. Opus 5.5 also generates output more than 30% faster than Opus 5.
Better than Fable, cheaper than even the last Opus. I use Opus as my main driver so this is very exciting!
* Ads in YouTube feeds for other google products and services.
* Ads underneath videos for products from the channel owner.
* Sponsorships within videos from the channel owner.
* Advertising overlays (supported IN THE APP BY GOOGLE) for products and services from the channel owner.
* Email advertisements for Google products and services.
* Community post advertisements from channel owners which show up in the YouTube feed.
I contacted support to enquire and they state these are not considered advertising.
Yes I see that as an ad. Do you not? Does anyone not? And if I'm on the highest paying ad free plan, what are they promoting to me?
If you pay to avoid ads, you are merely letting them know that you have disposable income to spend on this sort of stuff. You're doing their job for them by segmenting yourself into the upper echelons of the market.
At some point, some shareholder value maximizing CEO is going to show up and notice how much money he's leaving on the table by not advertising to all of those people full of disposable income.
Why would anyone give them the benefit of the doubt?
[0] https://lawcouncil.au/international-law/ils-insights/tangled...
I’m all for consumer awareness but I’m begging everyone to stop freaking out over prosaic non-issues like this.
As an Australian who just wants relatively free access to information (and the ability to freely/easily discuss it), it's frustrating.
And it's not the first time the Australian government has done this; last year they banned U16 from social media, several weeks later everyone was alerted to Bondi by... social media.. so they could get the heck out of there, but U16... they were left clueless and unable to get updates (news sites ran 30+ minutes behind real-time events and official sites like NSW Police took 70+ minutes to even announce there was a shooter).
However you sourced your target is irrelevant to the activity at hand. When I first heard of this news I knew immediately someone skipped target verification or that it was simply no longer a policy of the DOD.
> The Pentagon chief last year slashed offices that didn’t contribute to his goal of “lethality,” including the group that assists in limiting risk to civilians, known as the Civilian Protection Center of Excellence. Around 200 employees who worked on the issue, including at that office, have been reduced by about 90 percent, according to two current and former officials and a person familiar with the effort. The team that handles civilian casualties at Central Command, which oversees the Middle East, has dropped from 10 to one.
Negligent homicide in pursuit of their DEI goals, basically.
Reading the details, "AI" doesn't really seem like the culprit -it's a scapegoat.
The intelligence that it was no longer a military target never entered the target database, the team that was responsible for vetting the target list was gutted, and said team was never even consulted.
The White House wanted 1000 targets and pulled from their database without any due diligence. Whether it was an AI call or an SQL query - this was from pure human maliciousness and incompetence.
I cannot believe this has come to pass.
Absolutely people were extremely dismissive to anyone saying that we're losing the ability to make things in this country!
There were all these theories like Comparative Advantage that people would trot out to point out that, if you don't like outsourcing, not only are you ignorant and backwards you're also probably racist.
One interesting comparison is to the history of manufacturing. West/America decided one day that manufacturing would be cheaper to outsource and better (short term) profit was to be made by outsourcing it all to China. The institutional expertise started to deteriorate, to the point that America simply didn't even have the capacity, or expertise anymore to produce stuff (such as grill brush [1])
I feel like you could take all the handwavy comment that are made today to dismiss this caution, and find equal dismissal back then when companies were actively outsourcing the manufacturing.
"I'm coding 10x faster" "look at the output velocity per employee"
"we are producing much more (in China)" "look at profit / number of (manufacturing) employers"
Seems ok if you're American / Chinese but I'm struggling to understand how the rest can be OK with allowing institutional knowledge to deteriorate while having an active dependency to the former two. We already see this with the tech dependency towards USA and manufacturing competition from China.
"Code maintainability and good architecture don’t have good measurements that we can apply"
Who has no wisdom? There are dozens of ways to measure code maintainability. Cyclomatic complexity is just one.
Nothing stops you from wiring up something like SonarQube metrics to your agentic coding workflow.
As a mathematician, I am a bit disappointed by my (admittedly illustrious) colleagues.
I get the need to take it slowly (and I am a quite impatient person, so I shouldn't get to decide stuff like this), but everything said feels a bit too sour grapes for my taste.
Ok, maybe AI did not solve the field (I believe it will, btw), maybe there is a need for human "understanding", but:
1) They don't seem to consider even the possibility (not the certainty) that they might be wrong, that math as we know it is gone, and we cannot "adapt"
2) They seem to have been oblivious all these years about AI eventually reaching this point (at least I personally wasn't, I predicted this stage back in 2018)
They might be the first community I've seen to experience the AI "rush" and (at least as presented to an outside observer) immediately come together, assess the situation, and calmly, empathetically, and rationally act. They evaluated what AI is good at and what it lacks. They've thought through how it'll likely affect their field in the future. They've explained where the need for humans still lies, and made clear proposals for how to change their own field and for what demands to make of AI companies. Of course they're not all on the same page, but they're at least talking and trying.
They haven't started worshipping the machine god and loudly claiming their whole field is solved. Nor have they flailed wildly at LLMs as if complaining enough about it will make them go away.
Every major statement I've seen come out of the math community on this matter reads as well thought-through, humble, reasoned, and deeply human.
In these days of fear, uncertainty, and obsolescence anxiety, honestly, they've given me some confidence that maybe we will figure this stuff out after all. Maybe we'll learn from them. Who knows.
Everyone’s trying to figure out how to convert this speed to product features at scale, but enterprises are like container ships. Lots of might but slow to turn. The littler companies can actually take advantage of this and produce higher quality products at much faster speed. I think you’re expecting too much in the short term and too little in the long term. AI-native companies are gonna eat everyone’s lunch, once they figure out how to actually do it reliably.
Do you have actual productive examples? As in, products with a real userbase that couldn't exist or be scaled pre-AI? Genuinely asking, I might have missed some large hits. The closest I can remember was bun rewrite kerfuffle, which seemed more a marketing action than anything.
For the question where are the alternative OSes? Here is one that I've seen. There's probably more - https://www.reddit.com/r/ClaudeAI/comments/1wfpydl/i_asked_c...
For that other stuff you mentioned like the right click menu. Those huge corporate projects suffer more from layers of institutional dysfunction and will be very very slow to show any improvement. Their dysfunction can't be solved with just faster coding.
Using AI to build more features is easier than using AI to improve existing projects. People will gradually figure out how to do latter too, it'll just take longer.
Our QA, formerly a fairly frequent blocker of all our releases, are doing more in-depth reviews and catching issues earlier in our release process. They have become unblocked to the point they are actively chasing down work that starts to slip.
We have cleaned up and tuned both our security alerts and operations logs and improved our tenant isolation in our service in a way that makes customer and formal audits SIGNIFICANTLY easier.
We're setting ourselves up for faster human development of the hard-things. Our development environment and infrastructure are faster, cleaner, more auditable processes, and cheaper overall to operate.
These fixes mostly don't show up in our product change logs, and definitely don't fall into "new features". It would largely be invisible to the outside world, but our costs are going down (though to be fair, not offsetting the spend on AI to date), internal productivity has improved, operational incidents are down, and customer satisfaction is up.
Everyone's going so fast that they keep hitting walls. Review, CI, product asking for things, whatever.
Why have we not seen an improvements in products?
While every post and thread feels like a 90's wall street office, the new android and iphone ship with fewer features than usual. No indie guys come up with a linux-sized alternative OS. Switch 2 remains unhacked. Windows takes 3 seconds to show the right click menu.
Is everyone just running full speed in circles or something?
Of course there is. For example, SeL4’s security and reliability proofs still hold in the world of LLMs. The problem is that most software isn’t written on that firm foundation. Instead, most software is made by people with the philosophy of “if it looks like it works, ship it”. You don’t get secure software by working like that, because security vulnerabilities aren’t visible.
We - humans - know how to write secure software. Just like we know how to make safe aeroplanes. The problem isn’t that we lack the capability to make secure computers. The problem is we don’t have a culture of security. Secure software is - somehow - niche. And as such, it’s much more expensive. And nobody wants to pay.
For example, do not hook your goddamn water or traffic or electricity infrastructure up to the goddamn Internet, and then, do fire the guy who suggested it.
The correct analogy for computer security is not locks and keys and doors and gates. It is a house in a floodplain. Your house will not survive the flood of it hits you. Do not store anything critical or irreplaceable in that house.
In the pilot, the Galactica was literally being turned into a museum, and that's why they lived.
China hacked 22.1 million records of US government employees:
https://en.wikipedia.org/wiki/2015_Office_of_Personnel_Manag...
Perhaps firing expertise and hiring incompetents wasn't a good idea.
Media: "Scientists claim their discoveries are useless"
My instinct says that these systems will expand their complexity to fully fit the cognitive budget of the agents that coded them and then atrophy the same way human-built systems do at lower cognitive budget. Only this time, because of the larger up front budget, the complexity ceiling will be higher, and the potential depth of the problem may be much much larger. It may mostly manifest as increasing cost over time - the agents grind for longer and longer, iterating over and over to fix all the failing tests, and the breaking point will be where it never converges and you come back to millions of dollars in budget spent and still tests are failing and effective gridlock on system changes.
But this may be all my human-biased fantasy that justifies still taking a role in software development.
Giving someone the text output of a LLM is very similar to publishing a summary without links to the referenced material. When you were querying your LLM, you could have asked specific questions or asked for a custom focus or point of view. Your intended audience might have questions or different concerns, but they're unable to interact with your LLM. What you have delivered is static and unresponsive. It has all the disadvantages of being machine output without the advantage of being interactive, the way your LLM was for you.
It may have to wait until compute is cheap enough that tokens are essentially free, but we need a system to pass "hyperlinks" to LLM's primed with context, ready to be interactively queried on a chosen context. It's being overly generous to assume that people are putting even 300 bits into a LLM for every 1000 bits of regurgitated writing they try to pass off as their own. When people post LLM output as if it were their own, I have no choice but to assume they had zero knowledge of the subject, but this query taught them what they wanted to learn, and now they're sharing that. That's fine, but please pass an interactive LLM link rather than static text.
Once we have "hyperlinks" for LLM sessions, perhaps we can share LLM output a little more usefully and honestly.
Writing is fundamentally the transfer of information from your brain to my brain. If you have 1000 bits of semantic information you want to transfer, you can't give 300 bits of semantic information to an LLM and have it fill in the remaining 700, because it doesn't know what those 700 bits are. If it's able to guess those 700 bits correctly, then they aren't true semantic information, and you really only have 300 bits you want to transfer. You might as well transfer those bits to me directly, rather than having the LLM add on an extra superfluous 700 bits that I then have to filter out.
Drawback: You need different silkscreens for different amounts of RAM.
Btw, if you're worried about RAM-swapped units you can already verify them as described at https://geekworm.com/blogs/news/prevent-ram-swapped-raspberr...
--
(§) as can be seen here https://pip-assets.raspberrypi.com/categories/1129-pcn/docum...
Not a skin-scientist, but this seems misleading: Sunburn is not caused by some bad DNA floating around, nor damaged cells making making mistakes or pumping out wrong proteins to inflame their neighbors.
Instead, your cells have techniques to measure a variety of risk factors and signals (which does include DNA-molecule weirdness [0]) and when things get too risky the well-behaved ones will self-destruct, to avoid the chance of turning into a cancer.
So the sunburn you see and feel is instead due to cells signalling "something damaged me and I can't be trusted, I'm shutting down, recycle me" to your immune-system, plus the attendant bustle of hazmat-cleanup and increased security-sweeps for cancer-leftovers and other potential causes of the damage.
This the state of software engineering in 2026.
Edit: clarified engineering to software engineering, which is more correct
Inside were internal docs, integration code, the Spaces app framework, memory records, container startup scripts, and documentation for an experimental ESP32-based home network bridge called Home Link. Codex CLI was also installed, though I found no evidence that Muse invokes it.
I didn’t demonstrate a sandbox escape or access to another user’s data. I reported the export to Meta’s bug bounty program, which marked it “Not Applicable.”
The post walks through the findings with screenshots.
-Pete
"However, the most astonishing thing about this break is that the GPT–6 Astra did it entirely on its own."
Stubborn for a long time because the message used a completely different key from the rest of that day's traffic. Everyone assumed it shared the daily key. The original transcription had errors. The left rotor turned over at letter 72, which is rare and breaks standard crib attacks.
What is cool, if true, is that it was a 2 day collab between the Leffer and Astra. To me this shows the importance of human in the loop, was still all also showing how immensely power of llm tools. But I think it’s getting a bit silly how much anrticles ignores the driving force (the person) in breakthroughs like this.
BTTE UM ANGABE DES MARSQWEGES X BEFINDE MIQ IN X ROSENOW ROSENOW X SOFORT FUNKANTWORT X WASCHBBSCH
Please specify the route of march. I am in Rosenow, Rosenow. Immediate reply by radio. Waschbusch. return 4 # Determined by fair dice roll.Do we now learn that they fixed "always generate all 1s" with "never generate all 0s"??
EDIT: I've been unable to reproduce the problem on my CPU, FWIW. It's a Ryzen 5 3600.
EDIT2: OK, update, I can reproduce it with rdrand16, rdrand32 is fine but rdrand16 can never generate all 0s. So my CPU does have this problem!
It's a bold strategy, Cotton. Let's see if it pays off for 'em.
I seeded gzip compressors’ dictionaries with Wikipedia articles in different languages.
I would then try to use said dictionaries on any random text, and the one that was best able to compress it, was the correct language.
Absolutely totally not the best approach, but very fast and super simple to implement.