Queda acentuada de 0G (0G)

0G Labs has launched 0G App, a consumer AI development platform that enables users to build applications using natural language instructions without coding. It combines decentralized infrastructure, privacy-preserving Trusted Execution Environment (TEE) verification, and integrated token and AI agent deployment. The platform aims to bridge the gap between Web2 usability and Web3 infrastructure, allowing users to create, preview, and deploy applications entirely within a unified ecosystem. Key features include support for real-time iteration, hardware-secured AI inference (using Intel TDX and NVIDIA H100/H200), and the use of the GLM-5 74.4B parameter model. Additionally, 0G App includes a "Launcher Hub" for app, agent, and token deployment, with compute resources tied to the $0G token. Applications can be deployed to platforms like Vercel while storing data on 0G’s decentralized storage layer.

"Agents Capture 15 Critical Zero-Day Bugs: 0G Lab's Multi-Agent Framework Automates Debugging in Consensus Protocols" Distributed consensus protocols are notoriously difficult to debug due to complex, intertwined states. A novel framework, Agora, developed by 0G Labs with researchers from NUS, Peking University, and Beijing University of Posts and Telecommunications, tackles this by fusing deep domain expertise with a collaborative multi-agent LLM architecture. Agora moves beyond the limitations of single LLMs and traditional testing like fuzzing. It employs three specialized agents: an Orchestrator for global state, a Strategy agent for generating attack scenarios using distributed systems knowledge, and a TestGen agent that creates executable tests. A core innovation is its efficient "Succinct Memory & Communication" mechanism and a dynamic test harness. This allows the system to translate abstract hypotheses into concrete tests across languages like Go and Rust, run them, capture failures, and refine the approach in a closed loop—all with minimal token overhead. In rigorous evaluations on production-level protocols including Raft, EPaxos, and components from etcd and Sui, Agora discovered 15 previously unknown deep logic bugs (e.g., execution divergence, liveness violations). In stark contrast, powerful standalone LLMs like GPT-5.2 and Claude 4.5 found zero such bugs. Agora achieved this with a high precision of 73.9% and at an average cost of only about $40 per bug found. The framework demonstrates high generalizability. Its decoupled design allows the "Multi-Agent + Hypothesis-Driven Testing" paradigm to be applied to other complex domains like database concurrency control, OS kernels, and Web3 smart contract auditing. By enabling efficient, automated detection of deep logic flaws, Agora points the way for AI-powered security in critical infrastructure, aligning with the growing trends of agentic systems and automated quality control.