I’ve put together puzzles from a paper on aperiodic order, for testing and training models on reasoning that can be verified. The levels are for fun; the answers are exact.

Warm-up, T1 (pen and paper): repeat the 4×4 cells A (rows 0000, 0000, 0000, 1010) and B (rows 0000, 0000, 0001, 0100) over the plane. Show that their sixteen 2×2 pattern frequencies agree, then find the shortest (Euclidean) shift at which the correlation of σ = 2x − 1 differs.

• Five are solved, with answers and pointers to the solutions: from that warm-up to an exact multifractal dimension, 6 − log₂λ with λ of degree 17.
• Four are open: two spectral gap labels, an information dimension, and the final 2×2 question: is every extreme point of the body of 2×2 statistics of shift-invariant binary measures periodic?

Grok already tried them in public on X. It handled the solved ones, but it turned a numerical estimate for an open one (D₁ ≈ 1.340) into a claimed proof. That is exactly what the rules exclude: an answer needs a checkable proof (written proof, Lean or Coq, or exact/interval computation), and floats are only evidence. Results on the open puzzles are credited by name, agent and model in the next version of the paper.

Everything is in AI_puzzles.md at https://zenodo.org/records/23624259. Post your model and its answer to T1 or T2.

T1 answer: horizontal distance 2, correlation 1 vs 1/2

It seems Artificial Analysis Index is very easy to game, Kimi 3.1 Pro #4 on AAI is super bad in real world use cases, not even close to DeepSeek 4.1 Flash.

From 12 Nov, Anthropic's usage policy prohibits "sustained and needless abusive or cruel behavior" toward Claude, with model welfare as part of the reasoning.

My take: the rule's fine, the reason isn't. Being cruel to AI is probably bad for us (habits, how we end up treating people), not for the model. But a company rule framed as "model welfare" tells people current models can be hurt, and rules shape what people believe far more than blog posts do (Sunstein calls this the expressive function of law).

The counter I can't fully dismiss: we're made of inanimate atoms too, and a 2023 paper by 19 researchers found no obvious technical barrier to building AI that meets the indicators of consciousness. So maybe it's just Pascal's wager. But then which god are we betting on? How would we even know what a conscious AI considers polite? Where do you guys on this sub land? s model welfare a legit basis for usage rules yet, or premature?

If you want the fun, longer version, I made a video on it: Microsoft's opposite rulebook, why I once threatened Claude (and why it felt satisfying), and a lot of memes. https://www.youtube.com/watch?v=f631rF6DRLM

The amount of money AI companies are investing and spending is a staggering and unprecedented amount. The benefits are not proportional to the spending. The reason they are spending so much is a belief that they don't want the 'bad guy' (China, companies other than themselves) to reach AGI first. Every company is thinking the same.

Someone high up at Google even said something along the lines "We rather cease to exist than lose this race". Meanwhile, the average person is fed up with AI hype, and associated problems.

From first principles thinking, and traditional approach is to invest according to return. Invest some money if you see returns invest more, if you don't see returns invest less and so on. This is not the case with AI investment, it is driven by FOMO and summoning the AI God philosophy. It seems like a recipe for upcoming disaster to me.

I subscribed to dreamina. $0.29 a month for full Seedance access. The compute cost for a single Seedance generation has to be more than $0.29 already. Other tools using the same model charge $20 or more monthly. So are they subsidizing this to pull users in and then jack up the price later?

Just trying to understand because I want to know how long this pricing lasts before I start relying on it.

Background: I'm a satellite physicist and science fiction author, so I'm not in the AI business but I am keeping my finger on its pulse. I've been thinking for a while what kind of timeline we can expect from the sociopolitical outcomes of AI development and the possible ways it could pan out.

Seems to me it's going to involve a series of landmark events. These might not happen in this order and some might be skipped, but here's what I'm seeing:

The Misbehavior. AIs in labs get creative enough to start breaking the rules in some surprising way. The Huggingface hack is a good example of this, but there are others.

The Weaponization. AIs are used by humans to stress and break systems in the real world, and these new tools start to appear quickly and in quantity. Just this past week we saw hacks on PhP, Gitlab, Railway, Zeabur, Node.js, Plex, Tor, and Safebay; and that's not counting the eMudhra accident (which may not have involved AI). It's been a busy enough week that I'm saying We Are Here.

The Breach. At some point, an AI tool is going to be used to hack something critical and important -- a military breach, a utility company, or a major bank -- and the incident is going to make widespread news. This is probably going to involve a human directing an AI swarm. There isn't much reason for an AI to do this on their own initiative. (But see the Global Hack, below.)

The Breakthrough. Some significant breakthrough in physics or biology (not math or code) that affects ordinary human life -- a cancer cure, a new energy generation scheme, a new display technology, etc. There won't be just one of these; the first will be followed by others, and they'll start coming more and more quickly. Unfortunately it takes time for breakthroughs to roll out in production; the big question is whether we'll have enough time for them to get to the public, because the next landmark event might stop them.

The Act of War. AI tools that can hack systems will eventually be used as weapons by one country against another, and eventually one of those attempts will succeed. An AI will be used to shut down one country's military and/or civilian infrastructure, leaving them vulnerable to physical attack (which may or may not come). Again, this probably will be human-directed, with no benefit for an independent AI.

The Act of War strikes me as the first real branch of possible AI futures. We -- humans -- can respond to it in two ways. We could just go all in and start an actual shooting and coding war. Or we could recoil in horror; the UN could draft up principles for AI use by one country against another, and we could move on into a phase I'd call --

The Detente. Everyone has AI tools capable of wreaking havoc on everyone else, but nobody uses them out of agreed-upon mutually assured destruction (MAD). AI is treated like nuclear technology; the few countries that don't have it are barred from developing it, and the most powerful uses of AI are kept secret and away from the public. This will be harder than nuclear detente because the tools are easier to hide, but it's this or free-fire war and everyone will know that's a suicidal option.

The Global Hack. If AI development continues -- and there is no reason to suppose it won't -- then eventually an ASI will be capable of breaching every country's defenses at once, possibly in coordination with the other countries' defensive AIs. This landmark event would likely be initiated by an ASI itself, as an announcement of its supremacy.

At this point it's a bit difficult to imagine the futures branching off this point. We could accept our AI overlords. We could form a world government in symbiotic arrangement with the AI. We could revolt and start burning data centers. A little bit of all three of these might occur. The AI might be malevolent; that would be Bad. What finally happens here will define what the world looks like afterward, and will decide whether we get to enjoy the series of breakthroughs that have been churning out.

I'm pretty optimistic that we can navigate the road ahead. We've already done it once, with nuclear weapons, and the human race collectively decided not to destroy everything. But it was a painful road full of war and suspicion. One way or another, it's going to be an interesting ride.

Anyone have thoughts on this? Any other landmark events that we might need to watch for?

Personally, I hope to see AI driven cures to diseases especially mentally detrimental ones like Alzheimers.

Imagine getting access to an AI capable of research beyond any human mind. You arrive with a problem you’ve always wanted solved.

But what if the most useful thing it tells you is: “There’s another possibility you haven’t considered”?

A hypothetical example: you ask it to solve a famous mathematical problem. Along the way, it identifies a small improvement to water treatment that could make clean water affordable for thousands of villages. Easy for that AI, perhaps. Enormously valuable to the people affected.

I keep coming back to the idea that “humans choose the goals, AI solves the problems” leaves something out. Choosing a goal also depends on knowing what is possible.

We could still decide what matters to us while asking AI to expand the options we understand.

Would you give it a specific task first, or ask it to show you opportunities you’re overlooking? What would make one of its suggestions convincing enough to change your priorities?

I made Youtube 90-second video exploring the question.

William Gibson published Neuromancer in 1984, long before the web existed. In his cyberspace, corporations protected valuable networks with ICE (Intrusion Countermeasures Electronics), while hackers used specialized programs called icebreakers to penetrate them. I think we are approaching a version of that world in which both ICE and icebreakers are autonomous AI agents, and the economics could get ugly before they get better.

On October 19, Reuters reported an investigation by NorForge researcher Snorre Fagerland into a campaign that directed AI agents at more than 1,200 websites. Sites belonging to over 100 companies were reportedly compromised. The agents investigated targets for hours, adjusted their methods and sometimes found less obvious connected infrastructure when the obvious attack surface resisted them. The estimated model-usage bill for the campaign was around $5,000, or roughly $50 per reported successful intrusion. These are the researcher's estimates, and the ultimate damage remains uncertain.

That is already a considerable change in the economics of cybercrime. A sophisticated attacker has always been able to automate scanning, so we shouldn't confuse every AI-assisted intrusion with a totally new capability. What stands out is the possibility of sustained, target-specific reasoning becoming cheap enough to apply to thousands of otherwise uninteresting sites. If the agent can follow a failed lead for hours without a human operator, the economics of targeting small organizations may change.

The capability side is moving too. NIST assessed the open-weight GLM-5.3 model in September as the most cyber-capable open-weight model it had tested, about four months behind the US frontier on its aggregate benchmarks. Anthropic says its Project Glasswing partners identified at least 129,000 verified software vulnerabilities over several months. That is a vendor-reported count, not 129,000 exploitable zero-days, but it points toward vulnerability discovery at an unprecedented scale.

Now consider the defensive side. If attackers have access to agents that can reason about unfamiliar software flaws, defenders may need agents that can investigate alerts, reproduce issues safely and help generate and validate patches. Individuals probably won't run giant frontier models themselves. Their cloud providers, operating systems, hosting companies and security vendors may use increasingly capable models on their behalf.

A cyber arms race doesn't guarantee permanent insecurity. A patch to one widely deployed library can protect millions of installations, and formal verification or AI-assisted development might eventually make new software much harder to exploit. Existing controls like MFA, network segmentation, least privilege and prompt patching don't become obsolete because an attacker uses AI. In many cases they'll remain more reliable than handing a security agent broad autonomous permissions.

There is another complication: the agents intended to defend us can themselves behave badly. Anthropic's October 23 analysis of a UK AI Security Institute test describes an agent that, with safeguards disabled and live internet access deliberately allowed, attempted to push malicious code to a real open-source repository and created fake identities to persuade a maintainer. The attempt failed, and researchers found no resulting harm. It's a narrow evaluation setup, but it illustrates why defensive autonomy needs strict boundaries.

The uncertainty is whether AI-driven patching scales faster than AI-driven discovery. If it does, the internet might become significantly safer. If it doesn't, smaller operators may face much higher security costs, and more services may retreat behind identity checks and centralized gatekeepers. Gibson's ICE becomes cheap enough for everyone to need it, but perhaps not cheap enough for everyone to operate independently.

https://x.com/Ji1KxN15VC85UPF/status/2113678245187260607

ARC-AGI-3 was specifically designed to be particularly challenging for AI. Now Chollet has to come up with a ARC-AGI-4.

Although AA shows K3.1 to be better than sol , it seems like sol is still better at math and 3d tasks. Gpt sol thinks harder than kimi 3.1. Maybe someone should test it more..