I didn’t watch the FIFA World Cup Final!!! As a Ronaldo fan… If my GOAT isn’t on the pitch, I’m just not emotionally available for 90 minutes of football. so I was at home cooking defenders on PES.😂 But while I was playing, something crossed my mind… This laptop doesn’t have to be for PES only.. In about an hour, when I shut it down, it’ll probably spend the next 15 hours doing absolutely nothing. And it’s not just my laptop. Millions of powerful gaming PCs and workstations around the world spend most of their lives sitting idle. So here’s the question that got me curious: ⬇️🧵

CRYNUX IS LIVE ON ROBINHOOD CHAIN! @crynuxio is the open AI compute network powered by idle consumer GPUs, supporting image generation, video generation, LLMs and fine-tuning, with computation verified through vssML. Mainnet went live in June. $CNX is now bridged to @RobinhoodCrypto, expanding its reach to another ecosystem and creating more opportunities for people to discover and participate in Crynux. The total supply remains fixed at 8,617,333,262 CNX, with 71% released only as real AI tasks are completed. Bridging CNX expands access without increasing supply. Contract: 0x0A020A1B61b4315df6E1338BE93b7d986147729E

Quantum DeFi just became the timeline. Leviathan’s Public Testnet is where you can actually touch it. $XNT

CRYNUX AMA RECAP WITH @travladd From verified AI compute to the long-term vision for @crynuxio, here are the key takeaways: VERIFICATION IS ESSENTIAL Connecting GPUs is only part of the challenge. A decentralized network also needs to verify the work being performed, with economic safeguards that make cheating unattractive. We discussed how Crynux approaches this through vssML, combining cryptographic proofs and secret sampling to verify computation without rerunning every task. PERMISSIONLESS PARTICIPATION GPU providers can download the node software and join without an approval process. Developers can access the network through an OpenAI-compatible API and build within familiar workflows. You can contribute compute, build applications or delegate $CNX to a node operator and share in its rewards without running hardware yourself. CNX AT THE CENTER OF THE NETWORK CNX connects task payments, node rewards and staking. Its role is tied to the network’s activity: developers accessing compute, operators supplying it and participants helping secure the system. The discussion explored how broader adoption and more AI workloads could expand the use of CNX throughout the ecosystem. COMPUTE THAT FITS THE WORKLOAD A chatbot needs a fast response; a background research task can wait. We explored how task fees determine queue priority, giving developers flexibility to balance speed and cost around their application’s needs. LOOKING AHEAD: AI AND WEB3 The conversation also explored longer-term possibilities beyond compute, including model tokenization, cross-chain model assets and AI-Fi applications. A strong sense of optimism emerged around how these possibilities could extend the CNX ecosystem from compute infrastructure into a broader network of AI services and assets. Verified compute provides the foundation for a broader ecosystem where developers can build, deploy and bring new AI services to the market. EXPERIENCE BEHIND THE VISION The AMA also touched on the experience behind our work: years across AI infrastructure, blockchain and cryptography, including work at Google and research in computation verification and privacy-preserving machine learning. Thank you to @travladd, @BraverCrypto and everyone who joined the conversation. Catch the full recording below.

AI compute is the new oil and a few hyperscalers own the wells. base:0x9557dd9e241bc9636732623b672b4090af519396 Crynux is flipping that. They let anyone with an NVIDIA GPU or M-series Mac run nodes, stake CNX, and earn from real AI workloads. The twist? Their vssML system randomly verifies 10% of jobs instead of checking everything. Keeps costs down while catching cheaters. Can decentralized GPU networks actually compete with AWS?

Six important things you should know about the @crynuxio project: ➱ $CNX has pumped 32% in the last 24 hrs. ➱ Crynux is permissionless. Anyone with a supported NVIDIA GPU can join without a whitelist, signup, or KYC. ➱ Its biggest technical edge is vssML, which verifies AI workloads through random secret sampling instead of checking every task 3 times. ➱ Around 71% of the remaining CNX supply is allocated to task mining tied to actual completed workloads. ➱ Developers can use Crynux through an OpenAI-compatible API for LLM inference, image generation, VLMs, and fine-tuning. ➱ Crynux is targeting spare consumer and edge GPUs, with the long-term goal of turning them into a permissionless AI compute cloud. The team is working really hard and its still a very early AI project. NFA but highly advise you guys to research about the project 💯

base:0x9557dd9e241bc9636732623b672b4090af519396 is basically the future of DeAI tech. market cap still under $2m. a potential $500m+ market cap token currently floating under $2m as we speak. the market hasn't seen a real utility-driven tech play like this in years. study @crynuxio namaste. 🌹

𝗹𝗲𝘃𝗶𝗮𝘁𝗵𝗮𝗻 𝗽𝘂𝗯𝗹𝗶𝗰 𝘁𝗲𝘀𝘁𝗻𝗲𝘁 𝗶𝘀 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗹𝗶𝘃𝗲!!!💃💃💃 @NeptunePrivacy's Leviathan is now open to the public. i've tested Leviathan first-hand as a Pioneer before the public testnet went live, so i've already had some time inside the wallet and the different apps now that it's public, let me walk you through how to get started. let's go🧵

Yoo guys… ..@crynuxio X Space is happening today Join us for a live AMA with @travladd as we talk about Crynux and what’s next for the project -Sept 30 -11:30 PM GST (8:30 pm WAT) -There’ll be $200 giveaway live on the Space Set your reminder and pull up 👇 twitter.com/i/spaces/1qJVmyO…

Crynux isn’t building another AI model. It’s building the infrastructure that AI needs to run. In simple terms, @crynuxio connects people who need computing power with people whose GPUs have capacity to spare. Around the world, gaming PCs, workstations, and high-performance computers go underused for hours at a time. Meanwhile, developers pay a premium for the compute they need to build AI applications. One side has unused capacity. The other is paying to access more of it. Crynux brings them together through a decentralized network. Instead of relying on one company’s data center, computing power can come from thousands of independent computers around the world. The idea is surprisingly simple: Put idle computing power to work where AI needs it most. $CNX

this is the part of the @NeptunePrivacy journey i’ve been waiting to see. the L1 is already here now leviathan is moving toward becoming the execution layer where privacy gets programmable. ~private smart contracts, ~neptuneswap, ~confidential ai, ~cross-chain interoperability… and a public testnet coming next. leviathan is about to get interesting. ♟️ good morning guys🌹

DECENTRALIZED AI DOESN’T BUILD ITSELF. YET. Until then, there’s always another commit, another build, and another piece of infrastructure to improve. The work continues at @crynuxio.

What makes Crynux truly unique in the DeAI space? Crynux is about more than simply decentralizing compute. We’re tackling a harder problem: How do you make AI computation decentralized and verifiable? With vssML, ZKPs + VRFs, permissionless nodes, and real AI workloads, Crynux is building infrastructure where computation can be independently verified rather than simply trusted. Combine that with multi-GPU support, tensor parallelism, an EVM-compatible chain, and decentralized governance, and you get a DeAI network built for far more than simply providing access to GPUs. Decentralized AI, with verification built into the stack. $CNX

𝗶𝗳 𝘆𝗼𝘂’𝗿𝗲 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴, 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝘀𝘂𝗿𝗲 𝘁𝗵𝗲𝗿𝗲’𝘀 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝗻 𝗶𝘁. this whole “decentralize ai” thing… what does it look like in the next couple of years? ai is blowing up right now, and obviously the demand for compute is moving with it. so while digging deeper into @crynuxio, i decided to explore another project building in a similar direction. @gonka ai gonka is building decentralized ai infrastructure around distributed compute, with a strong focus on making that compute useful for ai inference and training. basically , i just wanted to understand how both projects are approaching the same broader problem. so, i started looking into some of the features that are important when you’re trying to make this work at scale. things like compute allocation, workload handling, gpu requirements, model availability, performance and the developer experience. … i put the breakdown together below because there’s a lot to unpack, and i didn’t want to turn the post into a 2,000 word essay 😂 but one thing became clearer the deeper i went: decentralizing compute is one thing. making all that fragmented compute actually useful for ai is another. and looking at gonka gave me a better lens for understanding what crynux is building and where it fits in this growing decentralized ai landscape. if you’re building ai, the compute layer is becoming too important to ignore. check the breakdown below 👇

𝐍𝐞𝐩𝐭𝐮𝐧𝐞 𝐣𝐮𝐬𝐭 𝐬𝐡𝐨𝐰𝐞𝐝 𝐰𝐡𝐚𝐭 𝐜𝐨𝐦𝐞𝐬 𝐚𝐟𝐭𝐞𝐫 𝐭𝐡𝐞 𝐋𝟏. The base chain is already live. That’s Layer 1. $XNT can already move privately on Neptune, with privacy built into the protocol rather than added as an extra layer. 𝐋𝐞𝐯𝐢𝐚𝐭𝐡𝐚𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐥𝐚𝐲𝐞𝐫. Think of Neptune L1 as the foundation. Leviathan is where that foundation becomes a programmable private environment. Private smart contracts. Private applications. Private swaps. And eventually, Confidential AI. The interesting part is that some of this is already moving beyond the roadmap. Leviathan’s private DEX has entered testing with the Pioneers, while the public testnet is the next major step. 𝐒𝐨 𝐰𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐭𝐡𝐞 𝐭𝐞𝐬𝐭𝐧𝐞𝐭 𝐦𝐚𝐭𝐭𝐞𝐫? It gives people a place to actually use the system. Wallet. Onboarding. Swaps. Bridges. Applications. Instead of reading about the architecture, users can interact with it and see where everything works, where it breaks, and what still needs to be improved. Then the stack expands. → Cross-chain connectivity → More private applications → NeptuneSwap → Confidential AI → Mainnet infrastructure And there’s one part of the roadmap that really caught my attention. 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐤𝐞𝐲 𝐞𝐱𝐜𝐡𝐚𝐧𝐠𝐞. The idea is simple: Two people should be able to exchange value privately without exposing the entire trade through a public order book. If the exchange doesn't complete, the deposited funds can be returned. That is still part of the roadmap, not something I’d describe as finished today. And that distinction matters. Some pieces are live. Some are being tested. Some are still being built. But the direction is clear. @NeptunePrivacy 𝐢𝐬 𝐦𝐨𝐯𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐦𝐨𝐧𝐞𝐲 𝐭𝐨𝐰𝐚𝐫𝐝 𝐩𝐫𝐢𝐯𝐚𝐭𝐞, 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐚𝐛𝐥𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧. And that’s where Leviathan gets interesting.

Privacy is easy to promise,building an ecosystem around it is the hard part that’s why @NeptunePrivacy ’s Leviathan roadmap caught my attention Neptune already has its privacy-first Layer 1 live. “Leviathan” is the next layer: a private execution environment designed to turn that privacy into something applications can actually build around Private smart contracts → NeptuneSwap → Confidential AI → cross-chain interoperability and eventually, a private, permissionless key exchange for P2P swaps the bigger picture is interesting here Privacy stops being just a feature of a transaction and starts becoming part of the execution layer itself that opens the door to programmable applications where sensitive activity doesn’t have to become public by default. Leviathan is still being built, so the real test will be how these pieces perform once they reach the network If you want to see where Neptune is taking private, programmable money, the Leviathan roadmap is the place to start Follow @NeptunePrivacy and dig into what they’re building

you don’t need to own a massive data center or have access to some expensive cloud provider to contribute compute. sounds complicated, right ?? let me explain - @crynuxio is building around a pretty simple idea: if you have suitable hardware, you can contribute that compute to the network instead of relying entirely on a handful of centralized providers. but then comes the interesting question: if anyone can contribute compute, how do you know the work is actually correct ?? that’s where vssML comes in. Crynux isn’t only making compute more accessible, but also working on a way to verify the work happening across the network. imo, that distinction matters. bcos decentralizing the hardware is only half the problem.. you also need a way to trust what that hardware is doing. so you get: ☑︎ permissionless compute ☑︎ verifiable execution and tbh, that’s what makes the idea interesting to me. it’s not only about putting more GPUs on a network. it’s about making distributed compute accessible without losing the verification layer that makes it useful. in conclusion:- if decentralized AI is going to become more than a narrative, the infra has to work in the real world. have a great day ahead, frens :)

The Certifier Who Never Touches the Stone For over a century, diamond buyers have paid six figures for stones they'll never personally inspect. No microscope, no scale of their own just a certificate from a lab they've never visited, graded by someone they've never met. The trade runs entirely on delegated verification: trust a system, not a person, because nobody can check everything themselves. That's the exact bet a CNX staker makes. Stake CNX to a node operator, and you're funding a GPU you'll never see, running tasks you can't watch, producing outputs you can't personally audit. The operator could be running the model honestly or faking results and pocketing rewards for nothing. You have no microscope. You can't check the stone. What you have is vssML. @crynuxio consensus protocol works like a diamond lab's spot-check, automated and unblinking. It secretly re-verifies roughly one in ten submitted tasks the operator never knows which one. Get caught faking output, and the penalty isn't proportional to the crime. It's total: staked tokens slashed, income gone, reputation dead. The math only works because getting caught costs everything, the same way one revoked certification can end a grading lab's career. So the real question isn't "can I trust this node operator?" It's "how often does the system catch liars, and how badly does it punish them?" Once the answer is "constantly, and catastrophically," the identity behind the GPU stops mattering. Crynux doesn't eliminate trust. It makes dishonesty a losing bet. The staker doesn't need to know the person running their task just the system watching them.

One thing about decentralized AI that I didn’t really think about at first: 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗚𝗣𝗨𝘀 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗮𝗿𝘁. The harder question is: 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻? Think about it like this. If I give you my laptop and ask you to run something for me, I can’t see everything happening inside that laptop. I have to trust that you actually did what I asked. That sounds simple when it’s just one person. Now replace that laptop with thousands of independent machines, owned by different people, running AI workloads across a decentralized network. That changes the problem completely. You’re not only asking: ➜ “Where can I get the computing power?” You’re also asking: ➜ “Did the machine actually do the work?” ➜ “Can I verify the result?” ➜ “Can I rely on the network even though I don’t control the hardware?” And this is where Crynux gets interesting to me. 𝗧𝗵𝗲 𝗶𝗱𝗲𝗮 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘁𝗼 𝗽𝘂𝘁 𝗮 𝗯𝘂𝗻𝗰𝗵 𝗼𝗳 𝗚𝗣𝗨𝘀 𝗶𝗻𝘁𝗼 𝗼𝗻𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. The bigger idea is making those machines useful for AI while also dealing with the trust problem that comes with decentralized execution. @crynuxio describes this around three things: • 𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝗹𝗲𝘀𝘀 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 Independent machines can contribute computing resources. • 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 The network is designed around actually running AI workloads, rather than treating AI as just another job. • 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 There is a protocol-level approach to checking that decentralized AI computation can be trusted. And that last part is the one I keep coming back to. Because decentralization sounds great until you ask: 𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗲𝗿𝗲 𝘁𝗼 𝘁𝗿𝘂𝘀𝘁? With a centralized AI provider, you know where the infrastructure sits. With decentralized infrastructure, the whole point is that you don’t have one central machine or organization controlling everything. That gives you openness. But it also creates a new responsibility: 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮 𝘄𝗮𝘆 𝘁𝗼 𝘃𝗲𝗿𝗶𝗳𝘆 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱. Crynux approaches this with vssML, alongside mechanisms such as VRF-based task selection and zero-knowledge attestation. The interesting part isn’t simply that the network uses fancy cryptography. It’s that verification is being treated as part of the actual AI infrastructure. That distinction matters. Because there’s a big difference between: “Here’s a marketplace where you can rent GPUs.” and “Here’s a network where independent hardware can actually participate in AI computation, with mechanisms designed to make that computation verifiable.” That second problem is much harder. And honestly, I think that’s where the conversation around decentralized AI needs to go. We’ve spent a lot of time asking: “Who has the GPUs?” I think the better question is: 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘁𝗵𝗼𝘀𝗲 𝗚𝗣𝗨𝘀 𝗯𝗲𝗹𝗼𝗻𝗴 𝘁𝗼 𝗽𝗲𝗼𝗽𝗹𝗲 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗸𝗻𝗼𝘄? That’s the part of Crynux I find genuinely interesting. 𝗧𝗵𝗲 𝗚𝗣𝗨 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝘄𝗵𝗼𝗹𝗲 𝘀𝘁𝗼𝗿𝘆. The real challenge is turning scattered, independent computing power into AI infrastructure that developers can actually use and trust. And if decentralized AI is going to become something people build serious products on, that trust question can’t be an afterthought.