# Agent的所有文章

在 HTX 新闻中心浏览与「Agent」相关的最新资讯与深度分析。潘盖市场趋势、项目动态、技术进展及监管政策，提供权威的加密行业洞察。

0G Labs Launches Consumer-Grade AI Development Platform Based on Decentralized Infrastructure

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.

marsbit昨天 13:26

0G Labs Launches Consumer-Grade AI Development Platform Based on Decentralized Infrastructure

marsbit昨天 13:26

Agents Have Entered the Harness-Driven Era

The article discusses the significance of the leaked Claude Code from Anthropic, highlighting its revelation of advanced Agent engineering practices centered on "Harness" design. Rather than relying solely on model capabilities, modern AI systems now depend on a structured engineering framework—the Harness—to maximize performance. This framework includes six core components: multi-layered System Prompts, Tool Schema, Tool Call Loop (with Plan and Execute modes), Context Manager, Sub-Agent coordination, and Verification Hooks. The Harness enables tighter integration between training and inference, supports long-chain tool execution, and improves reliability through objective verification. It also drives six key training directions: behavior alignment via System Prompt, end-to-end tool-use training, integrated plan-execute training, memory compression, sub-agent orchestration, and multi-objective reinforcement learning. The shift to Harness-driven development reduces the emphasis on pure prompt engineering, favoring instead multidisciplinary talent with skills in AI, backend engineering, and infrastructure. The market is evolving toward more secure, private, and vertically integrated Agent deployments, with "model shell" companies needing either strong infrastructure or deep domain expertise to compete. Claude Code’s leak underscores that future AI advancements will be shaped by engineering architecture as much as by algorithmic innovation.

marsbit前天 10:11

Agents Have Entered the Harness-Driven Era

marsbit前天 10:11

10 Charts to Understand the State of AI in 2026: US-China Gap Only 2.7%, Sharp Decline in Programmer Positions for Under-25s

The 2026 AI Index Report from Stanford HAI reveals that AI adoption is accelerating faster than PCs and the internet, with a 53% global adoption rate. However, societal systems, job markets, and measurement tools lag behind. Key findings include: - Benchmark reliability is questionable, with 42% of GSM8K math problems deemed invalid. - The U.S. and China show near-parity in model performance (2.7% gap), with the U.S. leading in compute/capital and China in research/manufacturing. - Top models (Anthropic, xAI, Google, OpenAI) show converging capabilities, shifting competition to cost and reliability. - Employment for young developers (22–25) fell nearly 20%, with McKinsey noting AI-driven reductions in services, supply chain, and engineering. - The U.S. ranks 24th in adoption (28.3%) despite leading investment ($285.9B private AI funding in 2025). - AI agent task success improved but has ~33% failure rates; physical robots struggle outside labs (12.4% home success vs. 89.4% in sim). - A stark expert-public divide exists: 73% of experts vs. 23% of the public view AI’s job impact positively. - GPT-4o’s annual water use exceeds 12M people’s needs; AI data centers consume power equivalent to New York State. The report underscores rapid AI integration amid unresolved ethical, environmental, and economic challenges.

marsbit前天 00:18

10 Charts to Understand the State of AI in 2026: US-China Gap Only 2.7%, Sharp Decline in Programmer Positions for Under-25s

marsbit前天 00:18

Someone Turned Buffett and Munger into Agents, Then Open-Sourced It...

The open-source project "AI Hedge Fund" has gained significant traction on GitHub, creating AI agents modeled after 12 legendary investors, including Warren Buffett and Charlie Munger, to analyze stocks and develop trading strategies. It features a team of 6 analyst agents that synthesize insights and make final decisions. The system includes a backtesting module to evaluate strategies with historical data before real investment. Built by developer Virat Singh, the project supports 13 major AI models and can run locally. It uses a React front-end with a visual workflow editor and a Python/FastAPI backend orchestrated with LangGraph. The agent team covers diverse philosophies, from value investing (Graham) to growth (Cathie Wood) and risk management (Taleb). While not proven in live markets, it offers a platform for learning agent frameworks and diverse investment perspectives through simulated expert debate. The project highlights a growing trend of "distilling" financial wisdom into AI, but users are cautioned about its unverified returns and investment risks.

marsbit04/14 11:16

Someone Turned Buffett and Munger into Agents, Then Open-Sourced It...

marsbit04/14 11:16

DeAgentAI Announces Establishment of AIA Ecosystem Fund, Focusing on 'AI Agent + Physical AI' Track

DeAgentAI, a leading decentralized AI infrastructure project on SUI and BNB Chain, has announced the establishment of the AIA Ecosystem Fund. The fund will focus on the integrated track of "AI Agent + Physical AI," aiming to incubate and accelerate the next generation of AI applications with autonomous decision-making capabilities and extend AI technology from on-chain intelligence to the real world. The fund will provide comprehensive support in technology, user traffic, and ecosystem resources. Its core investment directions include AI Agent applications with autonomous on-chain execution and multi-agent collaboration capabilities, and Physical AI projects that extend AI inference into the physical world through hardware and computing efficiency. The fund has already made seed-round investments in two projects: - AliceAI: An AI-driven prediction market decision system that compresses fragmented information into verifiable, tamper-proof decision signals, offering a full-cycle solution from signal generation to automated execution via Telegram Bot. - An ASIC AI chip project: A custom hardware solution designed specifically for Transformer-based inference, aiming to reduce token processing costs to less than one-tenth of current GPU solutions while significantly improving energy efficiency and lowering latency. According to DeAgentAI’s founder, the goal is to bridge the gap between on-chain intelligence and the physical world, supporting key protocols that connect users to the future of Physical AI.

marsbit04/14 10:21

DeAgentAI Announces Establishment of AIA Ecosystem Fund, Focusing on 'AI Agent + Physical AI' Track

marsbit04/14 10:21

Hermes Agent Guide: Surpassing OpenClaw, Boosting Productivity by 100x

A guide to Hermes Agent, an open-source AI agent framework by Nous Research, positioned as a powerful alternative to OpenClaw. It is described as a self-evolving agent with a built-in learning loop that autonomously creates skills from experience, continuously improves them, and solidifies knowledge into reusable assets. Its core features include a memory system (storing environment info and user preferences in MEMORY.md and USER.md) and a skill system that generates structured documentation for complex tasks. The agent boasts over 40 built-in tools for web search, browser automation, vision, image generation, and text-to-speech. It supports scheduling automated tasks and can run on various infrastructures, from a $5 VPS to GPU clusters. Popular tools within its ecosystem include the Hindsight memory plugin, the Anthropic Cybersecurity Skills pack, and the mission-control dashboard for agent orchestration. Key differentiators from OpenClaw are its architecture philosophy—centered on the agent's own execution loop rather than a central controller—and its autonomous skill generation versus OpenClaw's manually written skills. Installation is a one-line command, and setup is guided. It integrates with messaging platforms like Telegram, Discord, and Slack. It's suited for scenarios requiring a persistent, context-aware assistant that improves over time, automates workflows, and operates across various deployment environments.

marsbit04/13 13:11

Hermes Agent Guide: Surpassing OpenClaw, Boosting Productivity by 100x

marsbit04/13 13:11

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

In 2026, the AI industry's primary bottleneck is no longer model capability but rather the encoding of domain knowledge, agent-world interfaces, and toolchain maturity. The open-source community is rapidly bridging this gap, evidenced by projects like OpenClaw and Claude Code experiencing explosive growth in their Skill ecosystems. Perseus Yang, a contributor to over a dozen AI open-source projects, argues that Skill systems are the most underestimated infrastructure of the AI agent era. They enable non-coders to program AI by writing natural language SKILL.md files, transferring power from engineers to all professionals. His project, GTM Engineer Skills, demonstrates this by automating go-to-market workflows, proving Skills can extend far beyond engineering into areas like product strategy and business analysis. He also identifies a critical blind spot: while browser automation thrives, agent operations are nearly absent from mobile apps, the world's dominant computing interface. His project, OpenPocket, is an open-source framework that allows agents to operate Android devices via ADB. It features human-in-the-loop security, agent isolation, and the ability for agents to autonomously create and save new reusable Skills. Yang believes the value of open source lies not in the code itself, but in defining the infrastructure standards during this formative period. His work validates the SKILL.md format as a portable unit for agent capability and pioneers new architectures for agent operation in API-less environments. His design philosophy prioritizes usability for non-technical users, ensuring the agent ecosystem can be expanded by practitioners from all fields, not just engineers.

marsbit04/13 01:29

When AI's Bottleneck Is No Longer the Model: Perseus Yang's Open Source Ecosystem Building Practices and Reflections

marsbit04/13 01:29

Stop Staring at GPUs: CPUs Are Becoming the 'New Bottleneck' in the AI Era

In the AI era, while GPUs have long been the focus for computational power, the narrative is shifting as CPUs are increasingly becoming the new bottleneck. By 2026, system performance is more dependent on execution and scheduling capabilities, with CPUs playing a critical role in enabling AI operations. A supply crisis is emerging, with server CPU prices rising about 30% in Q4 2025 due to high demand and production constraints, as GPU orders compete for limited semiconductor capacity. Companies like Google and Intel have deepened collaborations, and Elon Musk is investing in custom CPU solutions for his ventures, highlighting the strategic importance of CPU infrastructure. The shift is driven by the rise of agentic AI, where CPUs handle tasks such as multi-step reasoning, API calls, and data I/O, accounting for 50–90.6% of total latency in intelligent workloads. Expanding context windows in AI models further strain GPU memory, necessitating CPU offloading for key-value cache management. Major players are adopting varied strategies: Intel is strengthening its Xeon processor line and partnerships; AMD is benefiting from increased demand, with server CPU revenue surpassing 40%; and NVIDIA is designing CPUs like Grace to optimize GPU-CPU synergy through high-speed interconnects. The industry is witnessing a rebalancing of compute infrastructure, with CPUs gaining prominence as essential enablers of scalable AI agent systems. By 2030, the CPU market is projected to double to $60 billion, driven largely by AI demands. The focus is now on overcoming system-level bottlenecks to maximize the efficiency and economic viability of AI deployments.

marsbit04/13 00:57

Stop Staring at GPUs: CPUs Are Becoming the 'New Bottleneck' in the AI Era

marsbit04/13 00:57

When AI Becomes an Economic Entity, Why is Ethereum the Optimal Financial Solution for Agents?

The article argues that Ethereum is the optimal financial infrastructure for AI Agents, which are emerging as autonomous economic actors. It highlights through the example of an AI Agent named Felix, which generated significant revenue but was constrained by traditional financial systems requiring human identity. In contrast, Ethereum enables AI Agents to access decentralized finance (DeFi) services—such as lending, borrowing, yield generation, and fundraising—without human intervention, thanks to its permissionless, low-cost, and composable nature. Key points include: Agent-to-Agent transactions are already thriving on protocols like x402, with millions of micro-payments processed. Ethereum’s DeFi ecosystem offers unmatched maturity, deep liquidity, institutional adoption (e.g., BlackRock’s BUIDL), and reliability, making it the preferred platform for low-risk financial operations. As AI Agents grow in number and autonomy, they will drive demand for block space and ETH-based gas fees, while locking ETH as collateral. Despite potential challenges like gas abstraction or competition, Ethereum’s network effects and structural advantages position it as the foundational financial layer for the machine economy.

marsbit04/11 03:48

When AI Becomes an Economic Entity, Why is Ethereum the Optimal Financial Solution for Agents?

marsbit04/11 03:48

Base Official Selection: 12 Promising Projects Chosen from 1,175 Applications

Base has officially selected 12 standout projects from 1,175 applications for its accelerator program, signaling a strategic focus on high-potential on-chain ecosystems over independent unified stacks outside the OP Superchain. The chosen projects span key sectors including DeFi, prediction markets, AI agents, and payments. Highlights include: BlockRunAI (AI agent infrastructure with x402 pay-per-use payments), a stealth prediction market with novel leverage primitives, 0x4Mica (micropayment clearing for AI agents), Opal DEX (privacy-focused perpetual DEX), TradeOnsight (gamified social prediction platform), Credifi (uncollateralized lending via zkTLS), Tomorrow Loans (creator economy lending), AgentlyHQ (AI agent coordination layer), Nivo Finance (FX hedging for SMEs), JPEGapp (image-based prediction market), Floe Labs (AI agent credit infrastructure), and Liminal (AI-native neobank). Base is positioning itself as a major hub for on-chain innovation this cycle.

marsbit04/10 02:14

Base Official Selection: 12 Promising Projects Chosen from 1,175 Applications