NEW: @KrwqCash, built by @IQofficial & @fraxfinance, adopts Chainlink to enhance reserve transparency for KRWQ, the first fully-backed Korean won stablecoin. Chainlink Proof of Reserve is unlocking regulatory readiness for the world’s largest onchain Korean won FX market.

We wrote the definition for AI Coins on @CoinMarketCap. Our vision with $IQ is to build a more intelligent future through #AI and blockchain technology with dapps like IQ.wiki, IQGPT.com, and IQ.social. coinmarketcap.com/academy/gl…

WE FREAKING DID IT!! Thanks to the 600k+ individual donors from 191 countries (and some help from my buddy Austin Russell for the final push) we hit our goal on removing 30M of trash from the ocean! This is a huge W for the turtles and everyone who helped in any way. Thank you!!

$GENSLR @goodgensler spiked over 70% yesterday after the SEC charged Binance with selling unregistered securities and over 200% today after they filed charges against Coinbase. All the attention on @GaryGensler has brought a lot of eyes toward #GENSLR. iq.wiki/wiki/good-gary-gensl…

New Listing🚨 $IQ is now listed on leading South Korean exchange Bithumb. The listing pair: IQ/KRW Time: 19th March 2024 14:00 feed.bithumb.com/notice/1644…

$IQ being the Nvidia of Crypto isn't just a meme lads 🔥 Credits to people featured: @NavV96 @CryptoGrills @FlywheelDeFi @milesdeutscher @IQWIKI Code Smarter with - iqcode.ai/

The BrainDAO treasury now has over $40 Million in assets. This has allowed $IQ to become a top AI token with BrainDAO launching IQGPT.com, the leading AI assistant for blockchain knowledge and IQCode.ai coming next. iq.wiki/blog/pR_1lVqQCBdIn1t…

IQ Code 🧠 is coming 👀 iqcode.ai

IQ GPT has integrated @etherscan! The first AI assistant for crypto can now access Etherscan's data and answer questions on any Ethereum transaction or address. iq.wiki/blog/LA37ILi1kaUk92n…

A Fraxtal In Time - March 1st, 2024 Watch the full video as we celebrated the special launch of Fraxtal, with exclusive Frax panels featuring @samkazemian, @QiDaoProtocol, & @IQWIKI + a peek of @honeycomb's performance 🦾

IQ.wiki, IQGPT.com and the $IQ token were profiled in a an Oxford report on AI x Crypto. The report was authored by @mo_baioumy, an Oxford PhD in AI & Robotics and @ac_crypto, an Oxford Physics alumni. 🧠

ETHDenver Tapes #3 w/ @NavV96 of @IQWIKI Watch the full ETHDenver Tapes #3 w/ Navin to learn how IQ Wiki is revolutionizing AI w/ IQ Code, the first AI for auditing & generating smart contracts + its future plans for Fraxtal 🧠

Wen Korean version of IQ.wiki? 👀

Celebrating my 5-year milestone with @IQWIKI 🎉🥳 Gratitude to the exceptional $IQ team for an incredible journey, where I've gained valuable insights into the industry. Excited for the future and eager to continue expanding my knowledge! Stay tuned for $IQ in 2024 👀🙌🧠

IQ GPT Chatbot has been officially onboarded to @OneAnalog's Discord community!

IQ.wiki Director of Business Development @andycho will be speaking at TEAMZ Web3/AI Summit, Japan's leading crypto conference. $IQ is continuing to expand in Asia and was recently listed on Bithumb and CoinDCX. It is also on South Korea's Upbit.

1. sfrxETH: 3.77% APR @fraxfinance has a dual token system. FrxETH is the liquid ETH stablecoin, while sfrxETH is the token that receives staking rewards. Frax has the highest rated Ethereum Validator Rating according to Rated.Network iq.wiki/wiki/frax-ether-frxe…

BrainDAO is building iqcode.ai, the first AI model for smart contracts. IQ Code is designed to be able to audit code for vulnerabilities helping solve what @VitalikButerin has called Ethereum's biggest technical risk. iq.wiki/blog/6NtLDDgeBqm44iy…

How to track official election results on the ethereum blockchain and EOS blockchain: on.forbes.com/6015H65nN by @DelRayMan

$IQ has been recognized as one of the top #AI tokens by the leading Chinese crypto media @BlockBeatsAsia & @Foresight_News. theblockbeats.info/news/4928…

Day 32 of selfie a day until $NEAR hits ATH with @IQWIKI and @fraxfinance 🔥 Having a blast at the @akashnet event at @consensus2024

Bitkub Thailand's leading exchange has listed $IQ. The IQ token powers IQGPT.com, the AI assistant for crypto trained on IQ.wiki, IQ.social an AI forum for crypto, and the upcoming IQCode.ai, the AI for smart contracts.

$GENSLR is a new memecoin inspired by a pro-crypto Good Gary Gensler in a parallel universe, who is the antithesis of SEC Commissioner #GaryGensler. @goodgensler has seen over $1.6m in volume since its launch yesterday and now has its own wiki! iq.wiki/wiki/good-gary-gensl…

BrainDAO is currently building IQ Code 🧠the first AI for smart contracts. The BrainDAO team has years of experience working with smart contracts since the launch of the $IQ token in 2018. Here's an example of IQ Code finding vulnerabilities in a smart contract.

🎙 IQ Buzz: Episode 12 is Live! 🧠 In this episode, we spotlight IQ’s remarkable achievement of $724M in trading volume across leading platforms like Binance, Upbit, Fraxswap, and Sushiswap. 💹 The IQ team is considering deploying humanoid robots and autonomous vehicles to support tokens launched via IQAI.com and dives into the future with cutting-edge autonomous EVTOLs showcased at CES, including XPENG’s innovative models, which are now available for preorder in China. 🤖 Meanwhile, IQ Chief Brain Navin Vethanayagam engages in an insightful discussion on AI x Crypto at Web3 Global Talks and our BD Team Lead Snow shares expertise during AMA sessions with AstraDex AI. 🌐 Catch all the latest breakthroughs in AI, crypto, and innovation!

The super fun episode was released today with @MickeyMemeCoin and @IQWIKI! Sticky Mickey & IQ GPT with Jonathan Willis, Isaac Wilson, & Navin Vethanayagam charlieshrem.com/sticky-mick…

1/ Today, IQ is unveiling its Agent Tokenization Platform (ATP) – a groundbreaking innovation combining AI & blockchain to simplify DeFi and empower users to create & manage intelligent, autonomous agents. Here’s why it’s a game-changer. 👇 iq.wiki/blog/yj8d9MGd9pNNciA…

Code Smarter with IQ Code - the first AI model for smart contracts 🧠 iqcode.ai/

Welcome aboard @xraders_xyz! We're pleased to onboard Xraders, who provides AI-powered curated insights for informed decisions for Crypto, has integrated the leading AI assistant for crypto and blockchain, IQ GPT Chatbot.

New month, new tokens, new opportunities. It’s time to DYOR and trade on CoinDCX🚀 $IQ: @IQWIKI $HNT: @helium $WAXL: @axelarnetwork $AEVO: @aevoxyz #CoinDCXListing #IQ #HNT #WAXL #AXL #AEVO

Don’t trust, verify: An Overview of Decentralized AI Inference Say you want to run a large language model like Llama2-70B. A model this massive requires more than 140GB of memory, which means you can’t run the raw model on your home machine. What are your options? You might jump to a cloud provider, but you might not be too keen on trusting a single centralized company to handle this workload for you and hoover up all your usage data. Then what you need is decentralized inference, which lets you run ML models without relying on any single provider. (Note: this is about an 8 minute read. 👇) The Trust Problem In a decentralized network, it's not enough to just run a model and trust the output. Let’s say I ask the network to analyze a governance dilemma using Llama2-70B. How do I know it’s not actually using Llama2-13B, giving me worse analysis, and pocketing the difference? In the centralized world, you might trust that companies like OpenAI are doing this honestly because their reputation is at stake (and to some degree, LLM quality is self-evident). But in the decentralized world, honesty is not assumed—it is verified. This is where verifiable inference comes into play. In addition to providing a response to a query, you also prove it ran correctly on the model you asked for. But how? The naive approach would be to run the model as a smart contract on-chain. This would definitely guarantee the output was verified, but this is wildly impractical. GPT-3 represents words with an embedding dimension of 12,288. If you were to do a single matrix multiplication of this size on-chain, it would cost about $10 billion at current gas prices—the computation would fill every block for about a month straight. So, no. We’re going to need a different approach. After observing the landscape, it's clear to me that three main approaches have emerged to tackle verifiable inference: zero-knowledge proofs, optimistic fraud proofs, and cryptoeconomics. Each has its own flavor of security and cost implications. 1. Zero-Knowledge Proofs (ZK ML): Imagine being able to prove you ran a massive model, but the proof is effectively a fixed size regardless of how large the model is. That's what ZK ML promises, through the magic of ZK-SNARKs. While it sounds elegant in principle, compiling a deep neural network into zero-knowledge circuits which can then be proven is extremely difficult. It’s also massively expensive—at minimum, you’re likely looking at 1000x cost for inference and 1000x latency (the time to generate the proof), to say nothing of compiling the model itself into a circuit before any of this can happen. Ultimately that cost has to be passed down to the user, so this will end up very expensive for end users. ["Chapter 5: The Cost of Intelligence" by Modulus bit.ly/3xl8wms] On the other hand, this is the only approach that cryptographically guarantees correctness. With ZK, the model provider can’t cheat no matter how hard they try. But it does so at huge costs, making this impractical for large models for the foreseeable future. Examples: @ezklxyz - ezkl.xyz @ModulusLabs - modulus.xyz @gizatechxyz - gizatech.xyz 2. Optimistic Fraud Proofs (Optimistic ML): The optimistic approach is to trust, but verify. We assume the inference is correct unless proven otherwise. If a node tries to cheat, “watchers” in the network can call it out the cheater and challenge them using a fraud proof. These watchers have to be watching the chain at all times and re-running the inferences on their own models to ensure the outputs are correct. These fraud proofs are Truebit-style interactive challenge-response games, where you repeatedly bisect the model execution trace on-chain until you find the error. ["Truebit - promise that was never delivered" by German Nikolishin - bit.ly/4axdb2W] If this ever actually happens it’s incredibly costly, since these programs are massive and have huge internal states—a single GPT-3 inference costs about 1 petaflop (10^15 floating point operations). But the game theory suggests this should almost never happen (fraud proofs are also notoriously difficult to code correctly, since the code almost never gets hit in production). [GitHub - "AI and Memory Wall" by Amir Gholami bit.ly/4at29fh] The upside is optimistic ML is secure so long as there’s a single honest watcher who’s paying attention. The cost is cheaper than ZK ML, but remember that each watcher in the network is rerunning every query themselves. At equilibrium, this means that if there are 10 watchers, that security cost must be passed on to the user, so they will have to pay more than 10x the inference cost (or however many watchers there are). The downside, as with optimistic rollups generally, is that you have to wait for the challenge period to pass before you’re sure the response is verified. Depending on how that network is parameterized though, you might be waiting minutes rather than days. Examples: @OraProtocol - ora.io @gensynai (although currently underspecified) - gensyn.ai 3. Cryptoeconomics (Cryptoeconomic ML): Here we drop all the fancy techniques and do the simple thing: stake-weighted voting. A user decides how many nodes should run their query, they each reveal their responses, and if there's a discrepancy among responses, the odd one out gets slashed. Standard oracle stuff—it's a more straightforward approach that lets users set their desired security level, balancing cost and trust. If Chainlink were doing ML, this is how they’d do it. The latency here is fast—you just need a commit-reveal from each node. If this is getting written to a blockchain, then technically this can happen in two blocks. ["Commit-Reveal scheme in Solidity" by Srinivas Joshi bit.ly/3xdZcRp] The security however is the weakest. A majority of nodes could rationally choose to collude if they were wily enough. As a user, you have to reason about how much these nodes have at stake and what it would cost them to cheat. That said, using something like Eigenlayer restaking and attributable security, the network could effectively provide insurance in the case of a security failure. ["Introducing Programmable Trust + EigenLayer Roadmap" by Sreeram Kannan piped.video/-aK6VrmK0yk?t=1159] But the nice part of this system is that the user can specify how much security they want. They could choose to have 3 nodes or 5 nodes in their quorum, or every node in the network—or, if they want to YOLO, they could even choose n=1. The cost function here is simple: the user pays for however many nodes they want in their quorum. If you choose 3, you pay 3x the inference cost. The tricky question here: can you make n=1 secure? In a naive implementation, a lone node should cheat every time if no one is checking. But I suspect if you encrypt the queries and do the payments through intents, you might be able to obfuscate to the node that they’re actually the only one responding to this task. In that case you might be able to charge the average user less than 2x inference cost. Ultimately, the cryptoeconomic approach is the simplest, the easiest, and probably the cheapest, but it’s the least sexy and in principle the least secure. But as always, the devil is in the details. Examples: @ritualnet (although currently underspecified) - ritual.net @Atoma_Network - atoma.network Why Verifiable ML is Hard You might wonder why we don’t have all this already? After all, at bottom, machine learning models are just really large computer programs. Proving that programs were executed correctly has long been the bread and butter of blockchains. This is why these three verification approaches mirror the ways that blockchains secure their block space—ZK rollups use ZK proofs, optimistic rollups use fraud proofs, and most L1 blockchains use cryptoeconomics. It’s no surprise that we arrived at basically the same solutions. So what makes this hard when applied to ML? ML is unique because ML computations are generally represented as dense computation graphs that are designed to be run efficiently on GPUs. They are not designed to be proven. So if you want to prove ML computations in a ZK or optimistic environment, they have to be recompiled in a format that makes this possible—which is very complex and expensive. The second fundamental difficulty with ML is nondeterminism. Program verification assumes that the outputs of programs are deterministic. But if you run the same model on different GPU architectures or CUDA versions, you’ll get different outputs. Even if you have to force each node to use the same architecture, you still have the problem of randomness used in algorithms (the noise in diffusion models, or token sampling in LLMs). You can fix that randomness by controlling the RNG seed. But even with all that, you’re still left with the final menacing problem: the nondeterminism inherent in floating point operations. [Wiki: Random number generation bit.ly/3xbJ5nn] Almost all operations in GPUs are done on floating point numbers. Floating points are finicky because they’re not associative—that is, it’s not true that (a + b) + c is always the same as a + (b + c) for floating points. Because GPUs are highly parallelized, the ordering of additions or multiplications might be different on each execution, which can cascade into small differences in output. This is unlikely to affect the output of an LLM given the discrete nature of words, but for an image model, it may result in subtly different pixel values, leading two images to not match perfectly. [Stack Overflow: Is floating-point addition and multiplication associative? bit.ly/4auhxb7] This means you either need to avoid using floating points, which means an enormous blow to performance, or you need to allow some laxity in comparing outputs. Either way, the details are fiddly, and you can’t exactly abstract them away. (This is why, it turns out, the EVM doesn’t support floating point numbers, although some blockchains like NEAR do.) [Ethereum Stack Exchange: Why was support for floating point numbers not natively added to Solidity bit.ly/43DyKge] [GitHub: README WebAssembly on NEAR bit.ly/3TA0BcC] In short, decentralized inference networks are hard because all the details matter, and reality has a surprising amount of detail. In Conclusion Right now blockchains and ML clearly have a lot to say to each other. One is a technology that creates trust, and the other is a technology in sore need of it. While each approach to decentralized inference has its own tradeoffs, I’m very interested in seeing what entrepreneurs do with these tools to build the best network out there. But I did not write this piece to be the last word—I’m thinking about these ideas a lot in real time and having a lot of vibrant debates with people. I’ve always found writing is the best way to test my ideas. If you’re building something in this space, reach out! I’d always love to learn what you’re working on—and if you can prove me wrong, all the better. Thanks to @ilblackdragon, @caseykcaruso, @sreeramkannan, and @cheryldchan for reviewing drafts of this piece. Disclaimer: This article represents the subjective views of the author and are not the views of Dragonfly or its affiliates. Funds managed by Dragonfly may have invested in some of the protocols and cryptocurrencies mentioned herein. This article is not investment advice and should not be used as the basis for any investment or relied upon in evaluating the merits of any investment.

The BrainDAO treasury is now over $30,000,000! iq.braindao.org/dashboard/tr… This comes as BrainDAO is building IQ Code, the first AI for smart contracts. IQ Code 🧠 will be able to audit smart contracts and catch critical bugs. Vitalik said this is the AI use case he is most excited for. #IQistheNvidiaofCrypto

Thank you all for coming out this evening! This was an excellent turnout for an alpha-packed night of all things metaverse! Special thanks to @NavV96, @cherry_nft, & @ethbeezy for speaking tonight and @ZayiRey for hosting our star panel!👏🎉

Meet the brains from BrainDAO building IQ v3 which includes new dapps like IQ Code (first #AI for smart contracts) and IQ.social (AI hub for crypto news). There are also updates to IQ.wiki and the IQ tokenomics. iq.wiki/blog/WCiojw3P3gZ5UsM…

We are leveling up with the help of our new integrations partners: @prom_io @QiDaoProtocol @redstone_defi @ankr @cozypepecoin @ClusterProtocol Together, we're building the future of #AI assistants for #blockchain 🧠🖥️💪

The SEC has announced the approval of the first #Bitcoin ETFs in the US! IQ.wiki dives into the details with the first wiki on Bitcoin ETFs: iq.wiki/wiki/bitcoin-etfs

The Associated Press (@ap) is partnering with @Everipedia to verify the US election results on the blockchain. When AP calls the election they will cryptographically publish the result of the election on the blockchain through our new oracle product. everipedia.org/blog/ap-and-e…

The world's leading crypto AI assistant, IQ GPT Chatbot, has been officially onboarded to @nekotoken_xyz's Discord Community.

#Binance has added support for $IQ isolated margin trading after #IQ saw a record $100 million in volume today. This comes after IQ.wiki and IQGPT.com were recognized in an AI x Crypto report by Oxford Researchers. binance.com/en/support/annou…

Learn more about IQ.wiki, the $IQ token 🧠, and staking in this @CryptoSlate interview. @akibablade interviewed IQ.wiki Chief Brain @NavV96 about how we built IQ.wiki as a hub for crypto knowledge. cryptoslate.com/videos/can-d…

IQ GPT in action answering questions about @Conflux_Network

February was an exciting month for $IQ 🧠! BrainDAO is building iqcode.ai, the first AI for smart contracts. IQ.wiki Korea was launched. 🇰🇷 IQ.wiki and IQGPT.com were profiled in the Oxford report on AI x Crypto by @mo_baioumy, an Oxford PhD in AI & Robotics, and @ac_crypto, an Oxford Physics alumni. IQ gained several new listings and partnerships that were covered by the leading crypto outlets including @OdailyChina, @BlockBeatsAsia, @ChainCatcher_, and @coinness_kr. iq.wiki/blog/Odt5IquBYwD8V89…

The $IQ token has found its place being placed among one of the top #AI tokens on @binance 👌 @IQWIKI tiktok.com/@iq_wiki/video/73…

Introducing BrainDAO 🧠, the first Web 3.0 DAO dedicated to bridging the real-world and the Metaverse! 🚀 Learn about #IQV3 and how $IQ is expanding with @0xPolygon, #OlympusPro, @arbitrum, @fraxfinance, and @chainlink.👇 everipedia.org/blog/iq-v3-br…

We're pleased to announce that the leading AI assistant for Crypto & Blockchain, IQ GPT Chatbot, has been onboarded to @ankr's community.

Welcome to the first chapter of ‘People in Crypto’, where we aim to educate people about famous figures in crypto! Chapter #1: The father of Ethereum, @VitalikButerin

Created my first ⁦@jellyvideochats⁩ to congratulate $Jelly, @iqramband and the team! I'd love to work with Jelly on a launching a tokenized agent that can autonomously reward contributors socially and financially. We built @iqofficial for this. jellyjelly.com/s/303b3cf1-1a…

It's time for a GPT that can talk to memecoins. Let's see who knows #BONK @bonk_inu best IQ GPT, or ChatGPT. 🧠

We're pleased to announce that @mvlchain has integrated IQ GPT Chatbot, the world's leading AI assistant for Crypto & Blockchain.

Discussing with @NavV96, Chief Brain at @IQWIKI about the Intersection of DeFi and AI Agents and how they could Boost Crypto Mass Adoption. Leaving the link to mint the episode in the comments!

CZ is likely coming home tonight! @cz_binance and #Binance have onboarded over 200 million people across the globe🌎 to crypto so we are excited for him to be free again. There have been a lot of rumors but we've used the government sources below. 👇 iq.wiki/wiki/changpeng-zhao/…

BrainDAO is hiring AI Developers. 🧠 The team is scaling to accelerate the development of IQCode.ai, the first AI for smart contracts. The ecosystem of AI products built around the $IQ token is growing quickly. iq.wiki/blog/buCDdPYMTE6CnBH…

AI x Crypto is a hot narrative right now. But what does “AI x Crypto” even mean? Is it all hype, or do these projects have substance? To answer this, we've written a 90-page report. Key ideas are in this 🧵👇 (1/12)

Have you ever wondered why the Wikimedia Foundation wants so much money? It certainly isn’t needed to operate Wikipedia. You can literally fit a copy of the entire text on your phone! So, what’s the money for? Inquiring minds want to know …

New Listing Alert: IQ on #Bitkub! @BitkubOfficial is the #1 licensed crypto exchange in Thailand. This listing marks another step forward in IQ's strategy of expansion in Asia. IQ is a leader in AI and a leader in Asia. 🧠 iq.wiki/blog/jd5yPSf6n_YBPn2…

Everipedia $IQ, the first Web 3.0 encyclopedia, will soon be editable on #Metamask. More news coming soon…

🔥Over 3,000 views! Chief Brain of @IQWIKI discussing IQ's upcoming AI product! @NavV96 piped.video/ppdAZ9xJQak

If your AI is so smart 1) why would it limit itself to trading its own native token quoted against any asset other than usd 2) why would it waste its time building another asset ledger Smart AIs will produce spectacular profits. How? I’m still not sure. The obvious answer is trading crypto but I could see other paths that play out. What I can tell you is that smart AIs will not allow themselves to be shackled by unnecessary constraints

We are thrilled to be BACK with a another Twitter Space with @IQWIKI about the future of AI & crypto! Led by host, Adel - @glozingvirtual, We'll be featured with @NavV96, founder of IQ.wiki. Join us to discuss mind-blowing use cases of AI and Crypto converging! See you soon‼️

The IQ GPT AI Training Contest ends on May 18th! 🔥 Help train IQGPT.com, the AI-powered assistant for blockchain knowledge, and earn IQ tokens. How to enter: iq.wiki/blog/rzrY7LxuhBmBKzc…

285 PEOPLE! Thank you all 💙 To both our guest speaker, @NavV96 from @IQWIKI & listeners, you all rock!🤘🏻🚀 For all future spaces we host without missing out, subscribe to our community here!👇🏻 🎙️TechTalk Space🎙️ 🔗nitter.app/i/communities/16… See U at the next one ✈️ @maxlobo1995 @lubej24 @MrKalnin @mohsinm81516435 @I_am_Lope @sKylandjKm @brunneis @Tom_Stby @dazzledeezy

Binance will be adding an $IQ / $USDT trading pair to replace the previous BUSD pair! #Binance USDT Tether is the largest and most liquid stablecoin. binance.com/en/support/annou…

.@elizaOS/@ai16zdao caught up with @SolanaFndn on GitHub 🫡

IQGPT.com 🧠 handles crypto queries better than ChatGPT.com in both Korean and English. IQ GPT is the world's AI assistant for crypto knowledge. For an English example check the thread below. 👇

Introducing IQ v3: Navigating the Future of Blockchain and #AI 🧠 The $IQ ecosystem is expanding with new dapps like IQ Code and IQ.social. Learn about the enhanced IQ tokenomics and the updates to IQ.wiki and IQ GPT below. iq.wiki/blog/qAzLe9-YqExKPmZ…

Users on IQ GPT are now able to converse and have interactive queries and also categorize searches! iq.wiki/blog/3Hq-2kbvjFQpx7G…

#Binance is running a promotion for #AI tokens including $IQ with over 20,000 USDC in token vouchers. 🧠 IQ is one of the leading AI tokens on Binance, it powers IQGPT.com, the AI agent for blockchain knowledge and the upcoming IQCode.ai.

Everything was leading up to agents. Biggest wave ever.

In our latest interview we speak with @Navv96, Chief Brain of @IQWIKI, on their encyclopedia of everything blockchain & crypto, incorporating ChatGPT into encyclopedia’s, can we trust #AI to be unbiased? and more. 👇 Watch the Full Interview: piped.video/ppdAZ9xJQak