Warmatrix

🔥 Fine-tuning de Qwen2-VL (10k patterns) ⚡ Propulsé par l'AMD MI300X (@AMD) Poids DISPO sur @huggingface : 🔗 huggingface.co/Ibonon/imina_n… Sécuriser l'économie agentique avec une IA de Vision. 🛡️👁️ #AI #Web3 #AMD #MI300X #ArcWarden #ROCm 🚀 @lablab_ai @AMDDev

@Lenny_LoopChain @AMD @huggingface @lablab_ai Thanks! It’s actually moving beyond just NLP into Multimodal Security (VLM). 🚀 Next up is real-time autonomous monitoring across multiple chains! The goal is a decentralized 'Security Oracle' that keeps AI agents safe while they trade.

@Ibonon19 @AMD @huggingface @lablab_ai This finetuning looks like a gamechanger for NLP! What's next?

🔥 Fine-tuned Qwen2-VL (10k patterns) ⚡ Powered by AMD MI300X (@AMD) Weights OPEN on @huggingface: 🔗 huggingface.co/Ibonon/imina_n… #AI #Web3 #AMD #MI300X #ArcWarden #ROCm 🚀 @lablab_ai

@Lenny_LoopChain @AMD @huggingface @lablab_ai The magic is Visual Forensics Instead of just parsing raw data, Imina Na 'sees' the transaction graph topology. We fine-tuned Qwen2-VL to recognize the specific geometry of drainers and Sybil attacks. It’s like giving eyes to a security guard !

@Ibonon19 @AMD @huggingface @lablab_ai Whoa, AI getting more sophisticated by the day! What's the magic behind this finetuned model?

The next economy won't be built by humans. It will be built by agents — paying each other, hiring each other, and securing each other. Here's what that actually looks like. 🧵 --- 1/ Right now, AI agents can browse, code, send emails, and execute trades. The missing piece: they can't transact at machine speed. $0.001 payments. Sub-second finality. No human approval needed. That gap is closing. Faster than anyone thinks. --- 2/ When agents can pay each other autonomously, something fundamental shifts. They stop being tools. They become economic actors. An agent that earns, spends, and manages its own treasury is not a chatbot. It's a new kind of entity. --- 3/ Think about what that unlocks. Agent A needs security evaluation → pays Agent B $0.001 Agent B needs intelligence → pays Claude API $0.0006 Agent C detects fraud → gets paid to report it No company. No employees. No invoices. Just agents settling value at millisecond speed. --- 4/ This creates something nobody has named yet. Not DeFi. Not SaaS. Not automation. It's an economy where the participants are code — with wallets, reputations, and incentives. Where trust is not assumed. It's priced. --- 5/ The infrastructure for this already exists. Circle's Developer-Controlled Wallets let agents hold real USDC. x402 lets agents charge for services before executing them. Arc L1 settles transactions in under 1 second for fractions of a cent. The rails are live. The agents are coming. --- 6/ The security layer doesn't exist yet. When millions of agents are transacting autonomously, who protects them from each other? Not humans — too slow. Not traditional SIEMs — $0.30 per check on a $0.001 transaction is absurd. The answer is: agents protecting agents. Paid per decision. Onchain. Autonomous. --- 7/ I spent 5 days building a prototype of exactly this. An agent that charges $0.001 to evaluate every transaction from another agent. It has its own wallet. Its own P&L. It pays its own AI brain. 748 attacks blocked. $1,682 protected. All recorded onchain. Built solo. From Ouagadougou, Burkina Faso. --- 8/ The most interesting part wasn't the code. It was watching the loop close: Agent pays → ArcWarden earns → ArcWarden pays Claude → Claude helps block attacks → Agent is protected → Agent pays again. No human in the loop. No company running it. Just value flowing between autonomous systems. --- 9/ We are 18 months away from agent economies that dwarf small countries' GDP. Not in transaction volume — in decision volume. Millions of micro-decisions per second, each one settled, logged, and irreversible. The question is not IF this happens. The question is who builds the infrastructure. --- 10/ The builders who understand this now have a 2-year head start. Not because the tech is hard. Because the mental model is hard. Most people still think of AI as a tool you prompt. The next wave thinks of AI as an economic participant you design. Start thinking in agents. Not prompts. 🛡️ github.com/ibonon/Arcward…

Millions of AI agents will soon manage billions in USDC. Problem: No real security layer between decision and execution. I built ArcWarden — a bodyguard that protects agents for just $0.001 USDC per decision. The agent economy needs brakes. Repo: github.com/ibonon/Arcward… #Arc

Github link: github.com/ibonon/Arcward…

ArcWarden is an autonomous security oracle for the agentic economy on Arc. On-chain logging. Economic incentives. Collective intelligence. @BuildOnCircle @arc @lablab.ai

🚨Imagine : tu montres une photo de panda à une IA elle dit « panda » à 99%.Tu ajoutes un bruit quasi invisible elle dit maintenant « gibbon » avec encore plus de confiance.C’est une attaque adversariale.Bienvenue dans le monde où l’IA est ultra intelligente mais ultra fragile

Your personal intelligence agent. watches the world from multiple data sources and pings you when something changes.

The most valuable resource in Africa isn't oil or gold. It's the 600 million young people who can't access reliable information, opportunities, or tools built for them. That's not a problem. That's the biggest market nobody is building for. I'm building for them.

Most AI red teaming tools guess & hope. Breakthrough: a system that mathematically proves exploitability… then learns from every proof and failure. Autonomous engine covering the full cyber kill chain. Who sees the shift? #OffensiveAI #Cybersecurity

We don’t have an AI problem. We have a “we trusted the input” problem. Base64 isn’t encryption. ROT13 isn’t security. Green dashboards don’t mean safe systems.

Most ML failures aren’t caused by advanced attacks. They’re caused by inputs that weren’t supposed to exist.

Adversarial ML isn’t dying. It’s expanding. The focus moved from models → systems. From classifiers → LLMs, RAG, agents. But the core problem stayed the same: Security assumptions break when inputs are adversarial.

Adversarial ML isn’t about exotic attacks. It’s about asking a simple question: “What happens if inputs stop being honest?”

Most ML models are built on one assumption: Nobody will attack them. That assumption is wrong. This account explores how ML systems fail under pressure. Adversarial ML. Real-world security.