🚀 @quantumcern

Something is growing on @RobinhoodApp 🌱 Early Access is open embervalegame.com

THE US GOVERNMENT JUST MOVED $470 MILLION The US Government just moved $470M of BTC, WBTC and USDT to likely Coinbase Prime deposit addresses. This is Bitcoin that had been seized from both the Bitfinex Hacker and Alameda Research.

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openai or anthropic cracked pub key cryptography didnt they

Arkham @arkham

2 days ago

THE US GOVERNMENT JUST MOVED $470 MILLION The US Government just moved $470M of BTC, WBTC and USDT to likely Coinbase Prime deposit addresses. This is Bitcoin that had been seized from both the Bitfinex Hacker and Alameda Research.

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Introducing ChatGPT for Dishwashing GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains. The Navier–Stokes Millennium Prize Problem is a $1 million challenge about whether the equations describing fluid motion can break down. In this video, I accidentally found a counter-example while trying to optimize my dishwasher. But in reality, OpenAI used 10,000 AI agents to find a proof in 88 hours: openai.com/index/navier-s… Parody of the original GPT-6 Astra ad @OpenAI #chatgpt_Partner #chatgpt Credits: Written and Directed by Joma Co-Writer - Henry Connor Coan IV @coan_henry Production by Dauntlus Studios @dauntlus.studios Producer/Co-Director - Marcus Liew @dauntlus Camera Operator - Amos Lee @amoselijahlee Art Director - Alethea Soo @aletheasoo Art Assistant - Alicia Lim @aliciatkl_ Assistant Editor - GPT-6 Astra Music: Tensions Run High by Soundridemusic youtube.com/watch?v=Ly9H63…

doomsday

In the beginning of a mega bull run the market must climb a wall of worry. 2017 - block size wars 2020 - Covid 2023 - FTX collapse and rising central bank policy rates 2026 - AI encryption FUD Money shall be printed, bags shall pump.

🚀 Meet Qwen-Image-2.1-Turbo — create and edit images in just 8 denoising steps! Open weights now available! Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation architecture. Fewer steps does not mean lower quality: it still generates strong 2K images from text, and supports continued creation through natural-language edits, from adding accessories to changing a scene. Start directly with Diffusers: load QwenImage21Pipeline and the checkpoint’s recommended 8-step sampling schedule is ready to go. 🌐 Prefer an API? Qwen-Image-2.1 Pro and Turbo APIs are now officially live. Choose a hosted API for your application, or use the Turbo weights in your own workflow. 🤖 ModelScope: modelscope.cn/models/Qwen/Qw… 🤗 Hugging Face: huggingface.co/Qwen/Qwen-Imag… 🔗 Pro API: modelstudio.console.alibabacloud.com/ap-southeast-1… 🔗 Turbo API: modelstudio.console.alibabacloud.com/ap-southeast-1… What will you create with 8 steps? Share your results with us!

Qwen @Alibaba_Qwen

3 weeks ago

Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨 A unified model for both generation and editing, delivering top-tier quality in a lightweight package. Highlights: 👀 - Compact & exceptionally fast:

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airdrop fully reverse-engineered! works flawlessly on my framework laptop

this is like when they teased thanos at the end of the avengers

Apple Design @TheAppleDesign

2 days ago

Yes.. that’s OpenAI’s Sam Altman at an Apple Event in 2008

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iPhone Use github.com/zhongerxin/iPh…

Omarchy is now officially on X! Thanks to our friends at @SpaceXAI 🙏

Omarchy @omarchy

4 hours ago

We can fix everything. We can build the perfect machine.

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After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.

Editor went to God Mode 😭❤️‍🔥

Hemres Agnet

you were promised this future

Today, we are announcing our new Gemini agent for your business—a universal cloud agent running 24/7 to get your work done. It arrives knowing your tools, data, and work history; it works across knowledge work, content, and code; and it comes with built-in governance to help your data stay secure and cost stay in control → goo.gle/4j7daun

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" firefly.social/post/x/2107837…

Intelligence 64

Michael Trinh @_mctrinh

21 hours ago

What's coming after superintelligence?

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DEAD INTERNET THEORY IS REAL this AI character's creator made an estimated $10K in just one week. meet Mr. Paul Grayson: → 74.9K followers → 3.2M likes → roughly 26.1M views across 19 videos → 10.6M on his biggest clip he looks like a real-life Peter Griffin - and he's trying to glow up. awkward selfies, 'before' videos and attempts to look cool. a transformation storyline gives people a reason to follow the next episode. here's how to test a similar format: → use ChatGPT Images 2.5 to generate your character from multiple angles → choose existing footage you have permission to edit → use Higgsfield or another character-swap tool to replace the person while keeping the movement → publish short episodes with a consistent personality your first attempt might flop. test several characters. build a network around the ones people watch and share, then explore sponsorships, affiliate offers and paid collaborations. more experiments give you more chances - not guaranteed income. bookmark this. build your first character and give people a reason to come back.

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BuBBliK @k1rallik

4 days ago

you don't need a $299 course to create an AI character. the workflow is free to learn: → design a face → build a character sheet → animate, edit, publish I broke it down using Jean Phil and Derek. tools, costs, no paywall. x.com/i/article/2107…

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The party of “you should stay the way God made you” really loves their botox, lip fillers, and facelifts

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PaulleyTicks @PaulleyTicks

a day ago

Natalie Harp in 5 years, because everything Donald Trump touches, dies.

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