Bittensor narxiTAO

1. LAUNCHCOIN 2. QUAI 3. PURPE 4. SUI 5. VIRTUAL 6. ETH 7. PI 8. SOL 9. PEPE 10. ONDO 11. MOODENG 12. XRP 13. GRIFFAIN 14. TAO 15. RDAC

$ETH Could’ve doubled my $ETH position and been breakeven by now — but it’s fine. You can’t expect a 100% win rate forever. Currently sitting at 85% WR over 450+ trades in the last 180 days — solid track record. Taking a day off now to reset. 🙏🏻🤝🏼 Planning to re-enter longs soon to regain momentum. Friday could be wild — if rate cuts hit, we pump. If not, I’ll be looking for heavy long entries on the dump, aiming for a longer hold in $ETH $BTC $TAO $XRP & $LINK

Since my last post, I’ve made steady progress — now ranked #72 out of 150,000+ with a 100% win rate, 5.0 profit factor, and 5.0 average R:R. 👀🤝🏼 $BTC $ETH $TAO

$TAO I like the broken past key lvl going from resistance to support! with a newer key lvl inside the FVG

As of now, $RDAC coin has not yet been established as the first decentralized infrastructure token specifically for AI model storage on-chain, but the idea is technically and conceptually feasible—and it could become a leading project in that space if it delivers on key innovations. Here’s a breakdown of the considerations: 1. What would make $RDAC the first of its kind? To claim that position, $RDAC would need to provide: • Decentralized, scalable storage for AI models (like neural network weights or transformer checkpoints). • On-chain accessibility or verifiable off-chain storage with cryptographic proofs (since full models are often too large to store entirely on-chain). • Tokenized infrastructure incentives, meaning miners/validators or node operators are rewarded in $RDAC for storing or serving model data. • Interoperability with AI execution layers, enabling decentralized model deployment and querying. If it can provide all the above in a unified framework, it could indeed become the first true infrastructure token purpose-built for AI model storage and access. 2. Is storing AI models fully on-chain realistic? Currently, full on-chain storage of large AI models is not practical on most blockchains due to: • Size constraints: Even mid-sized models like GPT-2 are several hundred MBs to multiple GBs. • Gas/fee limitations: Uploading large binaries to chains like Ethereum is extremely expensive. Instead, most approaches focus on: • IPFS/Arweave/Filecoin-style decentralized storage, with on-chain pointers and integrity proofs. • Using zk-proofs or FHE (Fully Homomorphic Encryption) for privacy-preserving model inference. So a successful $RDAC approach would likely: • Store metadata and verification hashes on-chain. • Keep heavy model weights in decentralized storage networks off-chain. • Use the blockchain to orchestrate access control, ownership, and rewards. 3. Are there competitors or similar projects? There are projects working on adjacent ideas: • Ocean Protocol – decentralized data marketplace (not model storage per se). • Bittensor (TAO) – decentralized machine learning, where models are trained and rewarded in a peer-to-peer network. • Gensyn – decentralized ML training compute, though still off-chain for storage. But none of them focus explicitly on model storage as infrastructure in a way that is tied directly to an on-chain AI deployment framework. This is where $RDAC could carve a niche. 4. What would $RDAC need to succeed? • Strong partnerships with decentralized storage layers (IPFS, Filecoin, Arweave). • Incentivization model that rewards both storage providers and model contributors. • Tooling for devs: APIs or SDKs that make it easy to register, retrieve, and run models. • Security and integrity guarantees: cryptographic proofs, access controls, etc. Conclusion: $RDAC could become the first decentralized infrastructure token for AI model storage on-chain, if it can solve both technical and incentive-layer challenges effectively. Right now, it’s a promising concept in an emerging niche—but it would need to show a real working prototype, meaningful adoption, and a clear technical roadmap to take that leading role. If you want, I can help analyze $RDAC’s whitepaper (if available) or compare its roadmap with competitors.