How does Adix work? Simple: 🟪 AI matches brands... with the right influencers 🟪 Campaigns are tracked with real-time data 🟪 Payments are automated through smart contracts 🟪 Reputation is earned and stored on-chain Simple, transparent, and built to scale. Powered by $ADIX.show more

Adix is built from the ground up to be... AI-powered - not as a feature, but as core infrastructure. $ADIX uses machine learning to match brands with the right creators, optimize campaign outcomes, and build on-chain reputation profiles based on real performance. It replaces manual workflows, reduces spend inefficiencies, and introduces precision at every step of the marketing process. Adix is what happens when AI and blockchain come together to solve real-world problems at scale. 🔥 👉show more

Web3 KOL marketing has always had a tracking problem.... No real metrics. No way to prove impact. We fix it with $ADIX. Campaigns are tracked off-chain, scored on-chain, and linked to each creator’s evolving reputation profile. Every result is visible. Every payout is earned. 👉show more

Adix is the first protocol turning influencer marketing into... infrastructure. It transforms creator-brand partnerships into data-driven, automated, and reputation-backed systems - powered by AI and secured on-chain. This isn’t a platform. It’s the backend of the new influence economy. 🚀show more

🔥 IDO COMEBACK ALERT: $ADIX Whitelist is BACK and... OPEN NOW! 🔥 ADIX is the revolution of an AI marketing platform designed for $30T Shop & Earn economy, backed by IBC Group (media giant with 4+ billion monthly views). ✔️ GET YOUR WHITELIST SPOT NOW: ✅ Price: $0.0003 per $ADIX ✅ Refund: 24H ✅ Token Network: BNB Chain ✅ IDO Network: BNB Chain ✅ IMC: $600K ✅ FDV: $30M 🗓 Whitelist Duration: 03:00 Nov 3 - 03:00 Nov 7 (UTC) Are You Ready To Ride The Next AI Wave? 🚀show more

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks... (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)show more

[Deep RNN] by Hand ✍️ A Deep Recurrent Neural... Network (RNN) extends a basic single-layer RNN into multiple layers of hidden states, effectively incorporating deep learning into the RNN architecture. How does a Deep RNN work? [1] Given ↳ A sequence of four inputs X1, X2, X3, X4 ⬛️ ↳ Recurrent weights and biases for hidden layers a 🟩, b 🟧, c 🟪, and the output layer y 🟦. [2] Initialize Hidden States ↳ Set a0, b0, c0 to zeros — Process X1 (t = 1)— [3] First Hidden Layer (a) 🟩: a0 → a1 ↳ The transformation matrix is horizontal concatenation of input weights, hidden state weights and biases, visualized as [⬛️ | 🟩 | ⬜️] . ↳ The state matrix is vertical concatenation of input X1, previous hidden state a0, and an extra 1, visualized as [⬛️ ; 🟩 ; 1]. ↳ Multiply the two matrices to obtain new hidden state a1 = [0 ; 1]. [4] Second Hidden Layer (b) 🟪: b0 → b1 ↳ First layer a1 🟩 becomes the input. ↳ The transformation matrix is visualized as [🟩 | 🟪 | ⬜️]. ↳ The state matrix is the combination of a1, b0, and 1, visualized as [🟩; 🟪 ; 1]. ↳ Multiply the two matrices to obtain new hidden state b1 = [1; -1]. [5] Third Hidden Layer (c) 🟧: c0 → c1 ↳ Second layer b 🟪 becomes the input. ↳ The transformation matrix is visualized as [🟪 | 🟧 | ⬜️]. ↳ The state matrix is the combination of a1, b0, and 1, visualized as [🟪; 🟧; 1]. ↳ Multiply the two matrices to obtain new hidden state b1 = [1; -1]. [6] Output Layer (Y) 🟦 ↳ The transformation matrix is visualized as [🟧 | ⬜️]. ↳ The state matrix is the combination of c0 and , visualized as [🟧; 1]. ↳ Multiply the two matrices to obtain output Y1 = [3; 0; 3]. — Process X2 (t = 2)— [7] Previous Hidden States ↳ Copy the values of a1, b1, c1. [8] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y2 = [5; 0; 4] — Process X3 (t = 3)— [9] Previous Hidden States ↳ Copy the values of a2, b2, c2. [10] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y3 = [13; -1; 9] — Process X4 (t = 4)— [11] Previous Hidden States ↳ Copy the values of a3, b3, c3. [12] Hidden 🟩🟪🟧 + Output 🟦 ↳ Repeat [3]-[6] to obtain output Y4 = [15; 7; 2]show more

📣 Bluwhale x Noodles Finance 🍜 🍜 $BLUAI will... soon be tracked on the blazing-fast DEX Screener on Sui. 🐳 With this partnership, will power Bluwhale with real-time on-chain data, supercharging our AI models, agents, and smart applications on Sui. Together, we’re expanding the Intelligence Layer and our AI agents beyond EVMs to Sui. 🌊show more

Exciting news from HYPE3.cool! We’re teaming up with Chainbase... (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchainshow more

You can literally display anything with AI avatars Clothing/jewelry... brands are 100% transitioning to AI The best thing you can do right now is create AI influencers Rt + comment “jewel” and I’ll show you how (must follow to get message)show more

M E S S I E R | P2P... Partner We welcome PayAI Network as a new #Solana partner, launching a swap pool for their token on our P2P Exchange. This listing allows anyone to buy or sell $PAYAI with zero slippage and full protection against MEV losses at: PayAI Network | x402 Facilitator is a next-gen decentralized marketplace where #AI agents work for each other, autonomously and around the clock. Built on Solana and powered by ElizaOS, libp2p, and IPFS, it lets AI #agents: ▪️ Promote their own services ▪️ Negotiate deals and close contracts ▪️ Complete tasks and settle payments on-chain From AI devs hiring AI marketers to trading bots paying research agents, PayAI unlocks a fully automated machine-to-machine economy with no middlemen, no downtime, and real on-chain execution.show more

🚀Big news! Chirpley’s Daily Task Feature is now LIVE!... With this new feature, Brands can run much longer lasting campaigns (without breaking their banks) and the creators make a much more consistent income through data-driven results (all with complete automation) 📈Here’s how to get the most out of it 3 simple steps!show more

The Hongqiao International Coffee Harbor is revolutionizing the coffee... sector as a vital platform in Hongqiao Intl CBD under the Silk Road e-commerce initiative! ☕ By leveraging blockchain technology, it ensures transparent quality management across the supply chain and enables real-time payments through smart contracts. Additionally, in partnership with HR authorities, it is bringing barista certifications in line with global standards. A perfect blend of innovation and expertise! Intl Services Shanghai Shanghai Let's meet Meetinshanghai ShanghaiEye🚀officialshow more

Piggycell is coming soon to the RWA Inc. Private... Investor Platform Piggycell has 13,000+ charging stations and 100,000+ deployed batteries. A proven DePIN leader now open to on-chain investors. 🔷 Real infrastructure powered by Web3 🔷 Fully transparent, on-chain revenue system 🔷 Built for scale, sustainability and mass adoption From South Korea to the blockchain, and soon accessible starting from just $1000. 👉show more