Dysons Sphere enjoyoor Commentary on bitcoin and monetary system
Mempool
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Running Timechain bip448 retweeted
War desensitises you to loss.
That's when taxes are raised.
The 1940s are upon us.
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Running Timechain bip448 retweeted
They'll suck the money out of your equities, your houses, any of your immovable assets.
call it "war time measures"
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Running Timechain bip448 retweeted
"The problem I have with new rules is that there can always be newer rules.
Listen to what we’re saying when we tell a worried developer: “Don’t worry. We have a pretty good relationship with the Administration.”
That’s not fucking good enough. They should be able to ask what the law is and get a straight answer they can rely on for years." - @valkenburgh
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You are here.
63% of voters back wealth tax for mega-rich.
Wealth tax can be:
Tax on accumulated wealth
Higher rates on dividends & capital gains
Mansion tax
Investment income surcharge
Cap tax relief on pension contributions by the mega rich
What will Chancellor do?
mirror.co.uk/news/politics/j…
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Finally they got this scammer. Fuk yeah.
x.com/i/trending/21103360218…
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Chicken-n-egg
Need to show market demand for the thing before implementing changes on Bitcoin. But before the change it's not really the thing. Its all trust-me-bro setups which Bitcoiners DON'T do. So you're stuck with gullible people to bootstrap the thing. So they lie.
Whenever someone say “X on Bitcoin without a soft fork,” make them explain the bridging mechanism in detail. Shielded CSV is an L2 that would absolutely need a soft fork to execute trustlessly on Bitcoin. I’m honestly disappointed in @BitcoinMagazine for this title. They should know better.
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Running Timechain bip448 retweeted
Post-quantum transactions that require only 96 bytes on-chain:
@will_wjk (Imperial College) et al. published a very interesting variant of Shielded CSV, called Compact Shielded CSV.
I've always thought the CSV model is attractive in a PQ setting: the blockchain only stores the nullifier, so miners don't have to verify a signature before it gets into a block. Compact Shielded CSV goes a step further: the on-chain nullifier carries no signature at all, only what they call a "degriefer". Pre-quantum that's more than Shielded CSV's 64 bytes, but post-quantum it's much less.
Unlike Shielded CSV, which operates on top of Bitcoin, Compact is its own blockchain. That matters for the degriefer: a Compact miner who includes a nullifier with an invalid degriefer can't claim the block reward. Shielded CSV could adopt the degriefer, but on Bitcoin there is no such penalty. Anyone who sees your nullifier before it's confirmed can front-run it with bogus copies for ~100 bytes of fees each, and your spending proof then has to show for every one of them that it's invalid. This is something we deliberately avoided in the design of Shielded CSV. With PQ signatures as large as they are, it probably is a reasonable trade-off.
Compact also never discusses reorgs, which are a significant part of the Shielded CSV design. Integrating the degriefer into Shielded CSV would require combining it with the existing reorg handling.
arxiv.org/pdf/2609.32015
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Running Timechain bip448 retweeted
1/ A quantum-safe Bitcoin transaction now costs ~$59.
From $320 to $59 in just a few weeks: an 82% reduction driven by developers and AI agents.
Our $20,000 QSB Autoresearch Challenge with @yukonresearch and @eigenlabs has officially ended 🧵
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financial repression accelerating hard
this week
make other assets, except bonds, highly undesirable
france is the leading country on the planet for crypto kidnappings and wrench attacks
largely because their tax authority leaked the personal data of everyone
now they want to increase by 50,000x the amount of data they collect and hence leak
zero
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Running Timechain bip448 retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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He doesn't even know Bitcoin uses double sha256 everywhere 🤣
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