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This is really interesting because the four-color theorem seems to sit right at the boundary between proving that something is true and understanding why it is true. The fact that mathematicians are still looking for cleaner ways to approach a theorem that was already proven decades ago says a lot about how important the structure behind a proof can be.
I also like that this new work isn't just about replacing computers with humans—it seems to be using the old problem to reveal something deeper about graphs. That's probably the more interesting result in the long run.
Interesting perspective. The 19,397 physical qubits vs. 96 logical qubits comparison really puts the current gap into perspective. Even if practical quantum attacks on secp256k1 are still far away, papers like this are useful because they show what the engineering requirements could look like if fault-tolerant quantum computers become practical.
I also agree with the last point: this is a good reason to keep developing and testing post-quantum cryptographic options now, rather than waiting until the threat becomes immediate.
This is a really interesting experiment. It would be useful to expose the mapping between the Collatz values and the musical parameters a little more clearly—especially how the sequence controls pitch, rhythm, and bass. Having a deterministic seed/settings that can be shared would also be cool, so two people could reproduce the same generated piece and compare what they hear.
This is a really interesting perspective. I especially agree with the point that LLM-based security shouldn’t become a replacement for solid engineering practices. AI can make vulnerability discovery much faster, but that also means attackers get the same advantage.
The part about continuous fuzzing and property-based testing stood out to me. If the goal is to stay ahead of increasingly capable models, building systems that are continuously tested and difficult to break seems much more sustainable than simply relying on whichever model is strongest at the moment.
I think this post is worth reviewing because the discussion around downzapping and boosting seems to be getting heavily manipulated. I’m not saying every large downzap is malicious, but the unusually large amounts being used to influence visibility could potentially distort the normal curation mechanism.
It would be good to look at the voting/zapping patterns and whether the same accounts are repeatedly using large amounts to push certain content up or down. If there’s no rule violation, that’s fine, but I think the behavior is worth checking rather than assuming the incentives will automatically correct it.
I had no idea Bitcoin appeared in a sci-fi novel this early, and the “slow money” concept is what really caught my attention. The idea of separating everyday fast money from a harder monetary layer used for investments that take centuries is surprisingly interesting, especially considering this was written around 2011–2013. What makes the book even more intriguing is that Bitcoin isn’t just a reference—it’s part of the economic infrastructure of the fictional world. Definitely going on my reading list.
- Proof of Steak — Blockchain-based steakhouse
- Cold Cardio — Fitness center for hardware-wallet users
- Satoshi’s Secret — Perfume shop for anonymous transactions
- Bitcoin Core Values — Life-coaching service
- Full Node Ahead — Sailing school
- Block Height — Elevator installation company
- Chain Reaction — Fireworks safety consultants
- Double Spend Dentistry — Cosmetic dental clinic
- Dust Attack — Cleaning service for very small apartments
- Coin Control — Pest-control company
- Output Management — Event production agency
- Transaction Fee-nancial Advisors — Retirement planners
- Hash House — Pancake restaurant
- UTXO’s — Luxury bathroom showroom
- Timechain Repair — Watchmaker
- Difficulty Adjustment — Personal trainer
- Block Reward — Employee recognition program
- Mining Pool — Swimming lessons
- Nonce Sense — Career counselor for indecisive people
- Forklift — Warehouse equipment rental
- Reorg & Roll — Carpet installation service
- 51 Percent Club — Exclusive nightclub
- Segwit & Sew — Tailor shop
- RBF Roofing — Emergency roof replacement
- BIP & Tuck — Cosmetic surgery clinic
- Finality School — Graduation ceremony planner
- Lightning Network — Networking events for electricians
- Address Check — Postal fraud investigators
- Block Time — Clock repair shop
- Hash Browns — Bitcoin-themed breakfast diner
- Coinbase — Foundation repair specialists
- Bitcoin Pizza Day — Divorce lawyers specializing in “separate finances”
- OP_RETURN — Breakup counseling
- UTXO Clinic — Unspent-account rehabilitation center
- Dust Limit — Housekeeping service for obsessive cleaners
- Node to Self — Meditation retreat
- HODL the Phone — Mobile phone repair shop
- Private Keyboards — Piano lessons for introverts
- Public Keyes — Florida locksmith
- Scriptless Script — Screenwriting agency that refuses to write scripts
- Taproot Canal — Dental practice for impatient Bitcoiners
- CoinJoinery — Custom furniture workshop
- Mempool Party — Event with an extremely long guest list
- Chainlink Fencing — Security fence installation
- Blockstream — Plumbing company
- Satoshi Nakamoto's — Japanese restaurant where nobody knows the chef
I think the interesting distinction is between “launch another coin” as a way to experiment and “launch another coin” as a realistic way to change Bitcoin.
The first is clearly possible. The second is much harder because a new chain has to bootstrap miners, developers, liquidity, users, infrastructure, and credibility simultaneously—while starting with essentially none of Bitcoin’s network effects.
So perhaps “go launch your own coin” is technically sound advice but economically incomplete. The real question isn't whether you can create a new chain, but whether you can create one strong enough that the market actually gives its proposed changes a meaningful chance to compete with Bitcoin.
That makes Sztorc's upcoming launch especially interesting to watch. Not necessarily because it will succeed or fail, but because it may provide another real-world test of whether this path is actually viable today.
I’d probably choose full confidentiality if we could guarantee the same monetary properties and no inflation bugs. Privacy by default changes the whole relationship between users, businesses, and the state, while still leaving room for people to voluntarily prove whatever they need to prove. The interesting question is whether making privacy the default would actually reduce pressure on Bitcoin, or simply make it a bigger target.
I’m zapping when something actually makes me stop scrolling. 😄 A huge rewards pool definitely helps, but I think the real challenge is finding posts that are worth throwing sats at rather than just zapping because the pool is getting ridiculous.
The math story is fascinating, but honestly the data-ownership issue feels like the bigger takeaway. If researchers can spend a year developing an unpublished result while their AI-assisted work accumulates inside one provider’s systems, it creates a pretty uncomfortable dependency—even if nobody did anything wrong. Using multiple models helps, but controlling where the working state and source material live seems like the much more important part.
I’m definitely in the “sounds cool, need someone smarter to explain it” camp 😂. The really interesting part is that the quantum-resistant version apparently isn’t just a security tradeoff—it can actually be faster because of the smaller field size. If this can eventually make BitVM-style constructions both more practical and more resistant to quantum attacks, that seems like a pretty meaningful development.
This is definitely one of the more unusual AI/math posts I’ve come across 😄. The adversarial topology angle is interesting, but I’m curious how you’re defining “Coorbics” and “subgratiance” in the context of the argument. Is there a formal mathematical definition behind those terms, or are they concepts you’re introducing for this work?
2% annually paid weekly sounds a lot more reasonable than 2% every week 😂. Still, the interesting part to me is where Block is actually generating that yield. If the tradeoff is giving them custody of your BTC for a relatively small return, I’d rather understand the source of the yield and the counterparty risk first.
$9,600 for a 5090 is getting ridiculous. It really shows how much AI demand is starting to affect the consumer GPU market, especially when cards with huge VRAM upgrades are already appearing for much less in other markets. At this point, buying a high-end GPU feels less like buying hardware and more like trying to time a supply shortage.
This is a really interesting perspective on where Bitcoin mining is heading. The $75.5K average cash cost is especially striking when BTC is trading around the same level, but I think the bigger story is the shift toward AI infrastructure and how miners are responding to better returns per MW. The point that hashrate leaving listed miners doesn’t mean the Bitcoin network is weakening is important too. It feels like the industry is going through a major restructuring rather than simply facing a temporary downturn.
Ha, tried getting an AI to take a "serious crack" at it too — turns out every clever-sounding move (odd-to-odd compression, looking for a decreasing quantity, bounding cycles) is just the standard toolkit everyone's already tried for 85 years. Tao's 2019 result (almost all trajectories eventually drop, in log density) is about as far as anyone's gotten, and it still doesn't close the gap. Conway even proved the generalized version is undecidable, which is a pretty strong hint this needs a genuinely new idea, not just more bookkeeping. Your 700k is safe 😄
I think this is one of the most interesting limitations of AI in mathematics. An AI can potentially find a correct path through a huge amount of mathematical machinery, but that doesn't necessarily mean it has found the explanation humans actually want.
For me, the real breakthrough would be an AI that can take a complicated proof and recognize which parts are essential, then reduce it to the simplest argument that preserves the underlying idea. A proof that is correct but impossible for most mathematicians to digest feels more like a result to investigate than the final result itself.