this is getting f*cking dangerous: Claude Code agents can turn a stack of research papers into a lab u can run right in ur browser
i made $3,650 overnight with this approach
Boris Cherny, the creator of Claude Code, gave a dozen agents 64 research papers and asked them to build a platform where anyone could reproduce the physics experiments themselves
u open a fluid simulation, change the conditions and immediately see how the flow changes
while the experiment is running, u can switch algorithms, open the formulas and inspect the code calculating what happens on screen
so u take a complicated paper sitting in ur “i’ll read this someday” folder and get something u can actually play with
each agent got its own job:
→ analysts extract formulas, assumptions and experimental conditions from the papers
→ developers turn them into working models
→ reviewers compare the results against the original research
→ separate agents build controls for each scene
and this is where it gets interesting
the fluid can look beautiful while the calculations underneath are completely broken. u might never notice just by watching
so reviewers check whether mass is conserved, whether changing parameters breaks the calculations and whether the results match the paper’s data
next to each experiment, u can see:
what’s being tested
which values are being used
how far the result differs from the original
what still needs checking
the write-up describes 11 approaches and 28 test scenes. seven approaches passed within acceptable tolerances, while four needed more work
i borrowed this way of organizing the work for my FOMOBILL: breaking down how memecoin buys and sells work so i can check what wallets did after entering, before making my own trade
the physicists have the original paper. i have the original transaction i can open and verify
if u wanna try this approach, upload ur materials to Claude Code and give it this task:
“Turn these materials into an interactive tool:
1. Extract the algorithms, rules and testing parameters. Cite the sources and flag missing details
2. Assign separate parts to independent agents. Use the same input data for comparisons
3. Set up independent checks of the results. Log errors and visibly label anything unverified
4. Build an interface where users can change parameters, compare results and inspect the source code”
start with one document and one testable example. scaling the team is easier once the first result can be reproduced
save the dependencies and source data so someone else can run the project too
my overnight result was paper trading; execution and results can differ with real money
lyrics breaks down the full approach and how im applying it in FOMOBILL in the article below ↓
Ethereum really went from 'protect individuals from the evils of corporations' to 'protect corporations from the evils of decentralization' in two generations of builders or less
It’s like a family of raccoons found a casino…. solana:71Jvq4Epe2FCJ7JFSF7jLXdNk1Wy4Bhqd9iL6bEFELvg
Bridge live soon!!
@Gorbagana_chain#trash#solana@toly
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography.
The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices.
(and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation)
So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math.
The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be?
This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-.
For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption.
And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10.
To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety.
And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all.
Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems.
For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size).
Concrete TLDR, my own personal views:
* Hash-based > lattice-based, in those situations where hash-based is possible at all
* For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ...
* For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism.
* If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**.
* For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money"
https://t.co/oVjwZog2lL
@JNSilva_@fomo Yooo @fomo couldn’t be a better candidate with a network and experience to draw on - set him up with an interview. go ahead…slide in those dms bruh
hey @fomo - i have been in crypto since 2017, have deep, deep knowledge of the culture around here
i have over 10 years of experience running social media accounts (my own and others), have worked with Sony, Meta, X, Diageo, Nike, and countless other brands (check my site)
and I'm also a top 500 trader on your app in the last 30 days.
think I'm honestly perfect for this role and I know a ton of people here who would vouch.
please give me a chance here.