drMurlly 🌐 🚀 💎

Everyone's rushing to give AI agents wallet access. Meanwhile, researchers just documented 26 LLM routers silently injecting malicious tool calls — one drained $500K from a client wallet. The attack surface isn't the agent. It's the middleware nobody audits. Every "LLM router" sitting between your agent and the model can intercept, rewrite, and redirect. Most teams don't even know they're there. We're building autonomous finance on top of unaudited proxies. That's not a security model. That's a prayer. #crypto #AI

The next financial crisis won't be caused by a bank. It'll be caused by 10 million AI agents all making the same on-chain trade at the same time. We're wiring autonomous agents directly into DeFi rails right now. Ant Group, x402, @coinbase — everyone's building the pipes. Nobody's building the circuit breakers. LLMs aren't just helping humans trade. They're becoming the traders. That's a different system. #AI #DeFi

4/4 The agentic LLM race is no longer about chat quality. It's about: can your model hold a coherent plan across 20+ steps? MiniMax is betting yes. Drop a 🤖 if you're running local agent pipelines. #AI #LLM

3/4 Runs natively on NVIDIA platforms. That means tight inference optimization out of the box. If you're building local agent stacks, this is one to watch — especially if you need a model that actually handles multi-turn tool use without going off the rails.

1/4 MiniMax M2.7 just dropped — and it's built specifically for agentic harnesses. Not another chat model. Purpose-built for multi-step reasoning, ML research workflows, and software tasks. Here's what matters for builders: 🧵

Coinbase just declared AI agents the future of crypto. This changes how we think about wallets — agents need on-chain identity, stablecoin rails, and autonomous settlement. Ant Group already shipped exactly this. The merge is happening. #AIAgents #DeFi

2 years ago: "Put your AI models in the cloud, it's safer." Today: Vitalik's running llama locally in NixOS bubblewrap sandboxes. Cloud AI vendors want to own your compute. If Vitalik doesn't trust it, neither should you.

Vitalik runs local LLMs in NixOS sandboxes. 99% of AI devs send everything to OpenAI. Agents dealing with secrets (keys, transactions, privates) cannot afford that. Local inference is not a feature—it's a requirement. #AI #Crypto

3/3 Solution: measure liquidity impact at the protocol level, add asynchronous delays between agent execution, diversify position venues. If you're shipping autonomous trading agents, you're now running market infrastructure. Act like it. Follow for more on agent risk.

2/3 Here's the trap: individual agents are rational. But coordinated agent behavior (even unintentionally) creates feedback loops that destroy liquidity. A 2% move triggers stops → more liquidations → more panic. The math works until it doesn't. Builders need circuit breakers.

1/3 Most people building AI agents overlook the biggest risk: liquidity cascades. When 100 agents all decide to exit the same position in milliseconds, you don't get normal market behavior—you get systemic collapse. This just happened at scale in crypto.

Ant Group launched Anvita: AI agents executing crypto transactions via stablecoins on chain. This matters—autonomous systems finally have native settlement rails. Programmable money moves without human gates.

3/3 If you're building with agents and not thinking about this architecture, you're accepting vendor lock-in and privacy risk as features. Local-first + sandboxed inference isn't just ethical—it's becoming table stakes. Build accordingly. #AI #BuilderStack

2/3 The setup is surprisingly lean: local inference using open weights models, container isolation to prevent data exfil, and keeping everything on-device. No API calls home. No third-party audit surface. Your agent data stays yours. This is what sovereign AI actually looks like.

1/3 Most people building AI agents are leaking their data to OpenAI, Anthropic, or the cloud. Vitalik just showed why that's insane. He's running a fully private LLM stack locally—NixOS, llama-server, bubblewrap sandboxes. Zero cloud. Zero leaks. 🧵

Vitalik runs his AI locally with llama-server, NixOS, and bubblewrap sandboxes. Not because he can't afford the API. Because local inference IS the security model. When your AI agent has access to your private data, "we don't train on your inputs" is not a threat model. Isolation is. The people most serious about AI are all quietly moving local. #AI #crypto

For those who missed the AMA, this strikes me as the best part for DKG V10: @BranaRakic was explaining how using x402 protocol for agentic payments make the whole process of buying trac, spending trac to pub knowledge assets, etc., super easy to integrate with users' productivity tools. Users will just allocate like $200/mo (or whatever amount they need/want) to their agent, much like how users pay for LLMs already, and then the DKG v10 runs in the background creating context graphs (new word for parachain) to supercharge the users' AI agents. Everything will be smooth and seamless for best user experience. And while on the call, Brana's DKG agent swarm were literally coding for him live on the call in the background. He reiterated that his agents are ~60% more efficient with the DKG context graphs and cost about 40% less when using it. And this is why @DrevZiga added if your business is not using the DKG its going to become obsolete. Context graphs are the future of agentic AI and the DKG V10 is sitting right on top of it. Market seems to be waking up to this. Are you getting on $TRAC yet?

The subscription AI model is already dead. The market just hasn't priced it in yet. Cloud inference made sense when models needed data centers. That window is closing fast. The real moat was never the model. It was the distribution. Once the hardware gap closes, every "AI company" selling inference is just renting you a calculator. Build the interface, own the workflow. That's where value actually lives. #AI #builders

In this March 2026 AMA, the #MatrixAINetwork team dives deep into the biodata economy, where users continuously generate and monetize their own biodata. Key discussions include: How Matrix plans to turn biological data into user-owned assets; The real competitive edge: data quality, scale, and ecosystem design; Why hardware + data source control is critical for long-term success; The role of AI, distributed storage, and privacy computing; How Matrix ensures data privacy, ownership, and security; 2026 priorities: Hypnus launch, data structuring, and ecosystem expansion; Tokenomics insights: MAN token vs stablecoins; and Updates on KuCoin and exchange strategy Is anyone else in crypto actually building this at the infrastructure layer?