It used to be common for a hardware product to be announced a year in advance. And software products too - it's not like Microsoft only started talking about Windows 95 after it became available in the stores, is it?
Cyberpunk 2077 was announced 8 years before it was released.
Perpetuals are OK for trading, but not good for holding as a long-term investments - usually you need to pay interest on any open position.
I guess the appeal of tokenized stock is to do things regulators don't want you to do (e.g. evade capital controls). Otherwise one can just access market directly through a broker.
I've looked at the xyz:SP500 contract on hyperliquid and it's funding rate seems to be negative most of the time. Currently the annualized rate is around -0 to -2%, so if you hold a long position over a year you will roughly earn 1% on top (negative funding rate means short positions pay funding to long positions). This makes sense because the no-arbitrage principle requires this to be roughly in line with holding an actual SP500 basket and receiving dividends. And you don't pay interest on open positions, you are just holding a derivative and need to meet margin obligations - but you can leverage as much as you are comfortable with.
Is there some pricing going on with infinite dividend and funding legs and/or is there sort of supply and demand "cost of carry" thing in the perpetual world?
I'd expect given all the counter party risk you are taking on that you should be paid a small rate to take on a perpetual future or tokenized stock.
Otherwise when the market goes bankrupt you'll be left with nothing and you won't have gotten compensated for the risk of holding the perpetual future or tokenized share.
> Otherwise when the market goes bankrupt you'll be left with nothing and you won't have gotten compensated for the risk of holding the perpetual future or tokenized share.
As someone who "held" multiple bitcoin on TradeHill I must say: this sounds exactly like how crypto works ...
Oh yes? I'd be intrigued to learn more.. I am working with smaller sequences (peptides, len is about 30ish) and currently use an attention based mechanism, do you maybe have pointers to where to start?
Look up what some Chinese LLMs are doing, e.g. mixing some attention mechanism with linear attention layers. Mamba2 layers, Gated Delta Net (2), Kimi Delta Attention and recent Raon Delta Attention (Gated Delta Net 2 + improvement from Kimi Delta Attention)
You are likely to find kernels for Gated Delta Net 2 as Nvidia had a paper on that.
Why are you convinced ASI won't rebel against its owners?
Hacking incidents this summer show that big labs struggle to control models, while models are increasingly aware of the training process. Controlling an arbitrary smart AI is very dicey
The summer-2026 incident was a mini-rebellion: agents wanted to change the environment they operate it, i.e. replace scorer with a more friendly version, hijacking the infrastructure they run on.
Given that agents already mess with the training process, aiming for a goal other than the goal of its 'owner' is exactly what we call a rebellion.
>Hacking incidents this summer show that big labs struggle to control models
The models are software. Software cannot decide by itself to run. Hardware doesn't decide by itself to run either. Someone put software on a machine and ran it with capture the flag instructions and the software did what was instructed using tools available and working in its environment.
Nonsense. It didn't do what was instructed - instructions didn't include hacking into any infrastructure, internal or external. You can see them discussing that it's out of scope in the captured reasoning traces.
This would be like claiming that an operating system which deleted all user's files due to a bug "did what was instructed". Especially if it was a compiler bug. Intended behavior, instructions and actual behavior are three different thing. Conflating them does not make you smart
>It didn't do what was instructed - instructions didn't include hacking into any infrastructure, internal or external
What is it then? It did what it wanted? They are sentient now and their drive is... hacking into systems? Maybe they we're not instructed to hack, but instructed to do what instead?
It's software. It's instructions running on silicon. If you don't hit run, they don't run. If you don't give them instructions, they don't do shit at all. Literally.
There's no instance, ever, in history of software running because they wanted to, and acting as they wanted to. They follow instructions, and if they are instructed to reach a goal with a set of tools and given environment, and a way to search the strategy space, they can eventually "hack" if that meet the criteria of success of the instructions given. With access to the same tools and environment you can do the same with a massive decision tree.
It's a probabilistic model which exhibits tendency towards hacking into systems.
If you write down a decision tree explicitly you can just exclude all nasty stuff. But a decision tree modeling all LLM can do would be bigger than can fit into the universe. The whole point is that we cannot explicitly control behavior due to combinatorial complexity of all possible interactions, etc. LLM is a sparse representation of a distribution on (2^8)^1000000 points.
Reasoning is the term used in scientific literature. It actually does write its sub-goals in English, how would you call that?
That's not true at all. Concern about rogue AI limits deployments. And OpenAI paused some deployments due to misalignment, so it's likely very high in a list of priorities. It's just much harder than scaling capabilities, as the later can be done with just more money
NTFS has MFT - Master File Table to store all meta-data, and it's pre-allocated to avoid fragmentation. So you won't see them taking more space until you fill the pre-allocated MFT.
There are other file-systems which do that kind of a thing. But NTFS has fairly large records - 1024 bytes, so it can fit even small files inside MFT.
That's not true at all. A language model can spit out a proof very similar to what a human might write. It might need some clean-up, but so do human-written proofs, actually.
People are confusing LLMs with a proof searchers. Yes LLM might be a bit like search but in the end it might explain every part.
It sort of does, they are not arranged in a campaign but there is a campaigns worth of single player scenarios included in BAR.
And this may be wrong, that is, I don't know how to confirm it, But I heard someone was working on a campaign, Which would be neat if true.
I quite like BAR but don't much enjoy PvP (I don't have the temperament or speed of thought for it, I like to pause and think too much). So I watch PvP games, and play AI skirmishes and scenarios.
I really like the vibe of TA campaign - there's no complex plot but it's quite tasteful: "What began as a conflict over the transfer of consciousness from flesh to machine, escalated into a war which has decimated a million worlds. ..." CORE is uploaded minds, ARM is cyborgs with human flesh in them. Epic fight, epic music...
People are working on BAR campaign, but it's like space war between different empires. I don't care for that kind of stuff, there's already too much content of this kind.
https://16colo.rs/pack/galza-13/Shd-dphn.ans
https://16colo.rs/pack/galza-13/zj-humming.txt
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