any tips based job; serving/waiting, stripping, bartending, etc. gig/service work. freelancer websites that offer escrow, etc. Shopify. Hell, github sponsorships. You don't even need a physical store these days, or a business for that matter. Cashapp even. The list is endless and it's easier than ever.
Now I just need some dirty money to go through the hassle of cleaning
Adobe fixes PDF zero-day security bug that hackers have exploited for months
https://techcrunch.com/2026/04/14/adobe-fixes-pdf-zero-day-security-bug-that-hackers-have-exploited-for-months/
I had claude shit out a site that tracked the recent moon flyby mission and the visual feel of that site is very much like this one, and my first thought when the page loaded was this was an ai project.
Sadly we live in a world where software engineer "stolen valor" now exists, where someone with no or little actual engineering ability will use ai to shit out something and then claim they made it themselves.
Not 100% certain that's happening here, but it can't be a coincidence that this site looks so much like a site I had AI create tracking other things in space, imo
I think of LLMs as being well equipped for handling dynamic data or adapting to unforeseen circumstances well (random code requests, website's ever changing layouts, typos, non-standard formatting in docs, groking out important info, etc), but math problems are be definition a very specific set of instructions to run, so is the overhead and "thinking" aspect of a LLM/AI even needed here? I'm genuinely curious, btw, I'm not asking sarcastically. Can't these math problems just be yanked from some test file and rapid fired directly at a gpu/compute unit?
> Can't these math problems just be yanked from some test file and rapid fired directly at a gpu/compute unit?
Yes this is exactly what I'm doing. I isolated the actual math question, and then sent it to my two servers to process and that's what's taking 10m+ to return. I'm asking them to solve the question and return the full answer along with their steps. I care about correctness so taking time is okay but I can't use 10m per solution.
Nono, parent was asking “They’re bad and inefficient at that, so why have an LLM do math? Why not just use some code and the CPU/GPU that’s already good and efficient at basic math?”
insider trading on events probably wouldn't show any trends, right? These are point in time events (they call them markets), but they are finite and short lived. An insider would be a one and done thing, so it would be pretty hard to spot them or trend any sort of month over month insider scheming imo.
Also...
> We study trading gains and losses on Polymarket, the largest prediction market
This is not a natural thing to say and I fucking hate that it's impossible to know anymore if I'm wasting time replying to an AI/bot or not
Not meant to sound like AI, but most academic journals limit abstracts to 100 words, so they rarely feel natural...
I agree: insiders are hard to study because they are finite and short-lived. We're pretty confident there are insiders out there trading on Polymarket; however, our conclusion is that they don't account for a significant fraction of the total trading gains on the platform.
This is true if the stock market as well. There is insider trading. But that vast, vast majority of profits are made by the market makers (citadel etc).
Is that the case? I would expect long-term investors would make more profits from the stock market than market makers.
If the market-makers are making vastly more than long-term investors (who are making trillions), who is coming in and venting off the multiple trillions to keep the system feeding long-term investors well while market makers gorge themselves?
It's simpler when looking at prediction markets because of bounded payoffs and the zero-sum nature, so these are pure trading gains.
In equity markets, you have both the trading and investment components to account for. Market makers like Citadel don't invest; they aim to exit positions as quickly as possible to minimize risk and capital requirements. Long-term investors commit capital to risky assets and are compensated with a risk premium (expected to be positive, but it can turn out to be negative). Usually, the "cost" of liquidity paid by long-term investors is tiny related to the overall expected returns. In prediction markets, you don't have that.
Yes to put the other comment into different words, vast maj the excess returns (alpha as opposed to beta ), or the “slack” in markets , however you want to think of it, are picked up by market makers.
Difference between prediction markets and stock markets is prediction markets on a flat road and stock market on an uphill road
Some people feel strongly about defining jargon when using it - an article on here [1] the other day about Capture The Flag (CTF) hacking puzzle competitions was full of comments comparing the article didn’t say what CTFs were.
It’s not a useless qualifier. Many readers might not know that Polymarket is the biggest now, and if at some future date it’s not the biggest anymore the statement makes it clear why they studied at this time.
This is a familiar style in abstracts. Weird as it sounds, it’s normal to have some language implying the reader is a hermit living in a cave. If it sounds like something an AI would say, maybe it’s because models have been trained on academic papers?
I agree - you're not going to be an insider on a significant proportion of trades and it would be stupid to use the same account for more than a couple.
Insiders are going to be earning large amounts in single trades, either by betting a lot when it's odds-on or a small amount when it's out the odds (for a large return).
I think it's just bad tense, which I think makes it not AI amusingly.
Except this was a comment on hacker news, not an academic paper... on an article talking about prediction markets.
The context already exists, and there isn't any reason to tack that onto the end of what was said, and it doesn't matter for that sentence or the entire comment.
Just feels like something a agent being overly verbose/descriptive would say.
Another possibility could be that SEO for LLMs is now a thing, and keyword stuffing or model manipulation is going to take subtle things like `We study trading gains and losses on Polymarket, the largest prediction market.` and interpret that as fact, in order to, idk what to call it, trick?, brainwash? the model into internalizing "polymarket is the largest" into its trained dataset and then proceeding to recommend polymarket to people when they ask about prediction markets, even if isn't true anymore at that time.
The comment is the abstract of the paper verbatim. This is one of the authors posting their paper on HN, sharing their data, and answering questions. This not just normal and respectable behaviour, it's really cool.
Use those few weeks to write a proper product description and pitch, because I just read your entire paragraph and am no closer to understanding what you're building.
I have build a proper workflow system for complex work that composes environments from nix flakes (very extended flakes) - it allows you to combines deterministic with agentic steps.
The release process is a workflow in your project that requires to generate reproducible outputs - nix does this for you.
The network basically is a distributed CI system - Multiple parties need to check and build your release. When all parties agree, that the package is correct, the SUI smart contract gives it's go. IKA is a multi party encryption network which implements a novel MCP-2pc algorithm. Your release key is split in half - IKA has one part, you have the other. Only is both parties sign the transaction, your software package gets release.
Every party, even your PC can be compromised, and it is very hard to just release malware in your package. The whole release pipeline needs to succeed ant then you need to sign it with your hardware token.
There are mandatory workflows in the release pipeline that check your software for side loading, comparing your dependency tree to the last version published, etc.
It is the largest project I have ever build and I code since 25 years.
I was just not satisfied with the agentic tools out there, nor with the CI / build infrastructure. Nix is nice, but the way packages are build / signed is just half way to perfect.
We can be lucky that the github hack did not infect the CI infrastructure. Imagine you infect the compilers in github CI to add a sleeper worm that does nothing for a month...
Only if you have multiple parties, building the same software with bootstrapped compilers and every artifact is hash compared, you can be sure it is legit
It's easy to wave a magic wand and have one developer do better than a corporation of tens of thousands. There is a reason I don't use Microsoft products: I can't do it myself and do won't do it for me.
Now I just need some dirty money to go through the hassle of cleaning
reply