0G to Exhibit at ETHDenver Conference from February 17 to 21

The varieties of #RDNT #ORDI tracks are worth paying attention to: 1. MEME board: DOGE, SHIB, PEPE, FLOKI, BONK 2. AI sector: AGIX, WLD, FET, AI, NMR 3. Public chain plates: ETH, SOL, MATIC, FTM, ADA, Avax, DOT, Atom 4. Layer 2: OP, ARB, METIS, IMX, MANTA, EGLD, MNT 5. 2024 halving plates: BCH, BSV, ZEC, ZEN, BTG 6. GAMEFI PLATE: SAND, MANA, GALA, ENJ, RON, PIXEL, ACE, ILV, MAGIC 7. Oracle section: TRB, LINK, API3, BAND 8. Payment concept section: MOB, ACH, XLM, CELO0 9. Inscription plates: ORDI, SATS, RATS BTCS 10. Storage plate: AR, FIL, STORJ, BLZ 11. DEFI sector: Aave, COMP, RDNT, CRV, MKR, Avax, DYDX 12. NFT sector: BLUR, DYDX, L0OKS, DEGO 13. Modular plates: TIA, MANTA, DYM, ALT 14. Sports section: CHZ, SANT0S, POR, CITY, 0G# Bitcoin digital currency

Software Engineer: Anchoring AI on Public Blockchains Aids in Establishing a ‘Permanent Provenance Trail' With artificial intelligence (AI) seemingly destined to become central to everyday digital applications and services, anchoring AI models on public blockchains potentially helps to “establish a permanent provenance trail,” asserted Michael Heinrich, CEO of 0G Labs. According to Heinrich, such a provenance trail enables “ex-post or real-time monitoring analysis” to detect any tampering, injection of bias, or use of problematic data during AI model training.#BTC #ContentStar #比特币 #ETH

pov: you bought the dip in zero gravity. 🐸

For many people, blockchain and AI still seem like two concepts hanging in the air, far from reality. It wasn't until I started paying close attention to the progress of 0G Labs, especially as I saw it gradually advancing the mainnet process and attracting infrastructure providers like QuickNode and Thirdweb to participate in ecosystem development, that I realized this is not just a narrative at the vision level, but a serious technical foundation being built. @0G_labs impresses me the most because from the very beginning, it did not position itself as a traditional public chain, but designed its structure around the real needs of AI. It breaks down storage, computation, and data availability into independent modules, like building blocks, allowing developers to freely combine them based on application requirements. The significance of this design will be continuously amplified in practice. In the past, developers often had to passively accept the rules and cost structures set by cloud service providers. Now, they are beginning to gain more initiative, able to deploy and run AI applications on an open network. This change brings not only efficiency improvements but also a shift in power dynamics. My personal biggest feeling is that AI is gradually transforming from products owned by a few companies into infrastructure that anyone can access and use. Perhaps we are still in the early stages, but as more developers enter this ecosystem, true innovation often occurs naturally in such an open environment. That’s also why I continue to follow 0G, because it’s not just building a chain, but trying to redefine what kind of network AI should run on. @Galxe @GalxeQuest @easydotfunX @wallchain

I have always believed that most public blockchains, when initially designed, did not truly consider the existence of AI. Their goal was to handle transactions, not models, data streams, and continuous reasoning. But when AI applications start to go on-chain, this difference becomes very obvious. High throughput, large-scale storage, and real-time computing are no longer just nice-to-have features but fundamental conditions that determine whether an application can exist. It is in this context that I began to reevaluate @0G_labs's architectural approach. 0G Labs is not trying to solve problems simply by increasing TPS; instead, they choose to start from the structure itself. They separate storage, data availability, and computation, allowing each layer to be independently scalable. This has a greater significance in development than it appears on the surface. In the past, developers had to adapt to the limitations of the chain, but now the chain is beginning to adapt to the needs of applications. This change is essentially a transfer of control. Developers are no longer just users of the chain but are gaining greater architectural freedom. The performance of the testnet also gave me a real sense of this for the first time. Running AI applications on-chain is no longer just a narrative in white papers but is gradually approaching a practical engineering reality. Modularization brings scalability but also increases the complexity of system design. The real challenge is not just technical implementation but how to make developers willing to migrate and adopt. In the long run, the value of this evolvable architecture is very clear. Future computing methods will continue to evolve, and modularity means systems can follow changes rather than be rendered obsolete by the times. In my view, 0G is trying to transform blockchain from a ledger with fixed rules into a continuously growing computing infrastructure. If AI is the core of next-generation applications, then such a foundation could become increasingly important. @Galxe @GalxeQuest @easydotfunX @wallchain