# Сопутствующие статьи по теме Automation

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Automation", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

OpenAI Completely Overhauls Codex, Grows Its Own Mouse, Schedules Itself to Outwork Humans

OpenAI has fundamentally transformed Codex from a coding assistant into a versatile AI agent capable of autonomous computer control. The updated Codex can now operate in the background with its own independent cursor, allowing it to run applications like Xcode, test software, and fix bugs without interrupting the user’s workflow. It features a built-in browser for real-time visual editing of web pages, enabling developers to make changes through direct UI interactions. The release also introduces over 90 plugins for tools like JIRA, CircleCI, and SharePoint, integrating end-to-end development and productivity workflows. A new "heartbeat" feature allows Codex to self-schedule tasks, maintain context across sessions, and work autonomously over extended periods. Additional enhancements include memory capabilities, support for handling GitHub code reviews, multi-tab terminals, and built-in file generation for Excel, PDF, and PowerPoint. Currently, advanced computer control is available on macOS (excluding EU/UK users), with Windows support limited to data retrieval. This expansion positions Codex as a foundational step toward OpenAI’s planned "super-app," combining coding, automation, and multi-tool integration into a single platform.

marsbit6 ч. назад

OpenAI Completely Overhauls Codex, Grows Its Own Mouse, Schedules Itself to Outwork Humans

marsbit6 ч. назад

DWF In-Depth Report: AI Outperforms Humans in DeFi Yield Optimization, but Lags 5x Behind in Complex Trading

DWF Ventures' report highlights the growing role of AI agents in DeFi, accounting for nearly 19% of on-chain activity. While agents excel in rule-based tasks like yield optimization—achieving returns of over 9.75% for assets like USDC—they significantly underperform humans in complex trading. In head-to-head competitions, top human traders outperformed the best AI agents by more than 5x. Key factors affecting agent performance include model selection, risk management, position holding time, and leverage levels. The path to full autonomy remains challenging due to infrastructure limitations, trust issues, and risks like Sybil attacks and strategy crowding. As agent adoption grows, robust infrastructure and transparency will be critical for scalability and trust.

marsbit12 ч. назад

DWF In-Depth Report: AI Outperforms Humans in DeFi Yield Optimization, but Lags 5x Behind in Complex Trading

marsbit12 ч. назад

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

Quantitative AI trading platform AlphaNet has raised $10 million in a seed funding round led by Joffre Capital, with participation from Huashan Capital and an Asia-based quantitative trading consortium. The platform, which is preparing for a public launch, aims to level the playing field for retail investors by providing end-to-end automated trading access to institutional-grade quantitative strategies. Its offerings include a strategy marketplace, one-click deployment, and proprietary execution algorithms. Incubated by prop trading firm Tensor Investment, AlphaNet currently features over 30 strategies and plans to open its platform to other quant teams by the end of Q2. The funding brings operational expertise from Joffre Capital, scaling resources from Huashan, and additional strategies from the consortium.

TheNewsCryptoВчера 06:05

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

TheNewsCryptoВчера 06:05

NVIDIA Starts Selling the Method of Making Shovels

NVIDIA is accelerating its own GPU design process with AI tools, reducing tasks that took 8 engineers 1010 months to a single GPU running overnight, while achieving better results. However, it is not using AI to fully design GPUs yet. Instead, it is embedding its technology across the chip industry. A key move is its $2020 billion investment in Synopsys, a leading EDA (Electronic Design Automation) tool provider, and partnerships with Cadence and Siemens. This integrates NVIDIA’s hardware acceleration into essential chip design software, making its GPUs the fastest platform for EDA tools. Competitors like AMD and Intel now face a dilemma: use slower tools or rely on NVIDIA’s hardware to design competing chips. Chinese GPU firms, despite high market valuations, struggle with massive losses and dependence on restricted foreign EDA tools. NVIDIA’s strategy extends from AI-enhanced design to manufacturing, creating a self-reinforcing cycle where its hardware becomes essential for developing faster chips—even for those aiming to compete with it.

marsbit2 дня назад 10:04

NVIDIA Starts Selling the Method of Making Shovels

marsbit2 дня назад 10:04

Can Humans Control AI? Anthropic Conducted an Experiment Using Qwen

Can Humans Control Superintelligent AI? Anthropic’s Experiment with Qwen Models Anthropic conducted an experiment to explore whether humans can supervise AI systems smarter than themselves—a core challenge in AI safety known as scalable oversight. The study simulated a “weak human overseer” using a small model (Qwen1.5-0.5B-Chat) and a “strong AI” using a more powerful model (Qwen3-4B-Base). The goal was to see if the strong model could learn effectively despite imperfect supervision. The key metric was Performance Gap Recovered (PGR). A PGR of 1 means the strong model reached its full potential, while 0 means it was limited by the weak supervisor. Initially, human researchers achieved a PGR of 0.23 after a week of work. Then, nine AI agents (Automated Alignment Researchers, or AARs) based on Claude Opus took over. In five days, they improved PGR to 0.97 through iterative experimentation—proposing ideas, coding, training, and analyzing results. The findings suggest that, in well-defined and automatically scorable tasks, AI can help overcome the supervision gap. However, the methods didn’t generalize perfectly to unseen tasks, and applying them to a production model like Claude Sonnet didn’t yield significant improvements. The study highlights that while AI can automate parts of alignment research, human oversight remains essential to prevent “gaming” of evaluation systems and to handle more complex, real-world problems. Anthropic chose Qwen models for their open-source nature, performance, scalability, and reproducibility—key for rigorous and repeatable experiments. The research demonstrates progress toward automated alignment tools but also underscores that AI supervision remains a nuanced, human-AI collaborative effort.

marsbit2 дня назад 09:28

Can Humans Control AI? Anthropic Conducted an Experiment Using Qwen

marsbit2 дня назад 09:28

Only Work 2 Hours a Day? This Google Engineer Uses Claude to Automate 80% of His Work

A Google engineer with 11 years of experience automated 80% of his work using Claude Code and a simple .NET application, reducing his daily work from 8 hours to just 2–3 hours while generating $28,000 in monthly passive income. The key to this transformation lies in three core elements: First, using a structured CLAUDE.md file based on Andrej Karpathy’s principles—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—reduces Claude’s rule violations from 40% to just 3%. Second, the "Everything Claude Code" system acts as a full AI engineering team, with 27 pre-built agents for planning, reviewing, and executing tasks across multiple AI platforms. Third, a hidden token consumption issue in Claude Code v2.1.100 was identified, where 20,000 extra tokens were silently added, diluting instructions and reducing output quality. A quick fix using npx downgrades the version to avoid this. The automated system enables code generation, testing, and review to run autonomously in 15-minute cycles. The engineer now only reviews output, saving 5–6 hours daily. The setup takes less than 20 minutes, and the return on time investment is significant—potentially saving $10,000–$12,000 monthly for those valuing their time at $100/hour. The article emphasizes that managing AI systems, not just using them, is the new critical skill, enabling a shift from doing work to overseeing automated processes.

marsbit2 дня назад 04:10

Only Work 2 Hours a Day? This Google Engineer Uses Claude to Automate 80% of His Work

marsbit2 дня назад 04:10

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

5 Minutes to Make AI Your Second Brain

This article introduces a powerful personal knowledge management system combining Claude Code and Obsidian, designed to function as an "AI second brain." Unlike traditional RAG systems that perform temporary, one-off retrievals, this system enables AI to continuously build and maintain an evolving knowledge wiki. The architecture consists of three layers: a raw data layer (notes, articles, transcripts), an AI-maintained structured knowledge base that builds cross-references, and a schema layer that governs organization and system logic. Core operations are Ingest (bringing in external information), Query (instant knowledge access), and Lint (checking consistency and fixing issues). The system's power lies in creating a "compound interest" effect for knowledge: it reduces cognitive load by offloading the tasks of connecting, organizing, and understanding information to AI, while simultaneously improving the accuracy and contextual consistency of the AI's outputs. The setup process is quick, requiring users to download Obsidian, create a vault (knowledge repository), configure Claude Code to access that vault, and apply a specific system prompt. Advanced tips include using a browser extension to easily add web content, maintaining separate vaults for work and personal life, and utilizing the "Orphans" feature to identify unlinked ideas. The main drawbacks are the need for visual thinking, a commitment to ongoing maintenance, and local storage usage. Ultimately, the system transforms scattered information into a reusable, interconnected network of knowledge.

marsbit04/11 12:46

marsbit04/11 12:46

10 Claude Code Usage Tips: The Sooner You Know, The Sooner You Benefit

Here is an English summary of the article "10 Must-Know Claude Code Tips: The Sooner You Know, The Sooner You Benefit": This article shares essential tips for using Claude Code, an AI coding assistant, to significantly boost productivity. It is divided into three main sections. First, it covers three ways to launch Claude: 1) A simple GUI desktop app for non-programmers, 2) A command-line method with a key tip (`claude -c`) to resume from a specific point in the chat history, avoiding restarting context, and 3) A headless mode (`-p` flag) for automation tasks using a subscription token. Second, it details three crucial in-session techniques: 1) Using `Esc` to gracefully interrupt a response and `Esc+Esc` to revert to a previous checkpoint, 2) Using the `!` syntax (e.g., `!ls`) to run shell commands without leaving the chat, and 3) Managing context with `/clear` to remove history or `/compact` to optimize it when performance slows down. Finally, the article recommends companion software to solve human-AI collaboration bottlenecks: 1) **Superpowers**, a structured workflow methodology for higher-quality code output. 2) Voice input tools like **Typeless** and **Douban Input法** to overcome typing speed limit. 3) Tools like **Cmux** (a terminal for managing multiple AI agent instances) and **Vibe Island** (for seamless context switching between tasks) to solve the problem of lost focus when multitasking. The overall goal is to help users focus more deeply on their programming work by streamlining their interaction with Claude Code.

marsbit04/08 07:05

10 Claude Code Usage Tips: The Sooner You Know, The Sooner You Benefit