Autoresearch, an open‑source 630‑line framework by Andrej Karpathy, lets an AI autonomously run 5‑minute training experiments, modify code, and iteratively improve a 0.8 B model by 19 % in eight hours, using the val_bpb metric and a minimal three‑file architecture.
The article details how the AliExpress Campaign Engine (ACE) was built from FY25 foundations to FY26’s four‑pillar system—gateway, workbench, standardization, and intelligent AI—using financial‑system analogies to solve duplicated effort, high maintenance costs, and stability risks across multiple e‑commerce business lines.
The article details a five‑layer architecture for a sustainable local knowledge vault—Inbox, Raw Sources, Extracted Text, Distilled, and Archive—explaining each layer’s purpose, the import workflow from tools like Evernote, and the supporting Python scripts that automate ingestion, extraction, and governance.
OpenAI has launched Workspace Agents, an evolution of GPTs powered by Codex that lets teams describe a workflow in plain language and automatically creates a shared, long‑running AI agent that can access tools, remember context, and operate across Slack, Linear, Google Drive and more.
This article reviews four open‑source infrastructure projects—Xata’s cloud‑native Postgres with copy‑on‑write branching and scale‑to‑zero, Obscura’s lightweight Rust headless browser for AI agents, Browser Harness’s self‑healing harness that lets LLMs fill missing APIs, and KillerPDF’s portable, telemetry‑free PDF editor—each addressing high‑frequency developer pain points.
This article details how Karpathy's AutoResearch methodology was transferred to software development, introducing multi‑agent cross‑review, a five‑dimensional quantitative scoring system, and feedback‑driven iteration to build a fully automatic pipeline that resolves a medium‑complexity GitHub Issue in about ten minutes with a 9.0/10 code‑quality score.
The article details how a growing Qoder product suite prompted the creation of a fully automated, 24‑hour feedback handling pipeline that classifies, clusters, analyses logs, and even generates fix code using Qoder CLI agents, cutting manual effort from 30 minutes per issue to about two minutes while maintaining human code‑review oversight.
The article outlines a multi‑stage AI‑driven automation roadmap that extends from requirement gathering through technical solution generation, code creation, testing, and deployment, detailing challenges such as workflow standardization, knowledge‑base construction, skill reuse, and quality assurance, and presents concrete metrics showing up to 80% efficiency gains.
The article explains how developers with high‑quality large‑model tokens and strong coding skills can capture premium revenue by using AI‑driven CDP and ADB to automate non‑API, labor‑intensive tasks in traditional industries, outlining four high‑margin use cases and a micro‑SaaS commercialization strategy.
This article details a step‑by‑step solution that uses Dify AI workflows, Alibaba Cloud MCP data, and an Enterprise WeChat bot to automatically collect, filter, and deliver government procurement bids, reducing manual search time from days to a few hours while improving data quality and reporting efficiency.
The article explains how rapid AI, low‑code, and edge‑computing autonomy erode the CIO’s traditional gate‑keeping role, illustrates four real‑world cases of bypassed IT oversight, and proposes a four‑stage roadmap for CIOs to shift from custodians to digital orchestrators.
Anthropic’s latest Claude Opus 4.7 model introduces autonomous task execution via Routines, enhanced code review with /ultrareview, higher-resolution visual input, and significant performance gains across knowledge work, vision, and long‑context reasoning, while adding safety guardrails, a new xhigh compute tier, and unchanged pricing.
This guide walks you through installing Hermes Agent, configuring models and memory, creating custom skills, setting up cron jobs, integrating with messaging platforms, troubleshooting common issues, and exploring community resources, providing a complete roadmap from a first conversation to advanced automation and monetization.
Claude Code Routines, launched on April 14, 2026, let developers define prompts, repositories, and connectors so that an AI agent can run scheduled, API‑triggered, or GitHub‑event‑driven tasks entirely on Anthropic’s cloud, automating code reviews, alert triage, documentation sync, and more without manual oversight.
The article analyzes the risks of tying core business systems to a single AI model, breaks down Claude Code's workflow into three engineering layers, and offers practical guidelines for building model‑agnostic, observable, and secure automation pipelines that can survive model changes and cost fluctuations.
The article dissects the karpathy/autoresearch project, explaining its autonomous experiment loop design—single mutable file, fixed 5‑minute budget, read‑only evaluator, and systematic logging/rollback—to show how teams can automate research cycles and continuously improve without manual overhead.
Claude Code's new Routines feature lets AI run saved workflows on Anthropic's cloud, triggered by schedules, GitHub events, or API calls, offering context‑aware automation that surpasses traditional cron with examples like code review, alert handling, and documentation maintenance.
Claude and Cursor released AI‑driven automation tools that automatically investigate Sentry errors, generate fix pull requests, and post results to Slack, removing the need for developers to wake up at night to monitor alerts.
Claude Code introduces Routines, letting AI execute scheduled, API‑triggered, or GitHub‑event tasks on Anthropic’s cloud even when your computer is off, while its desktop client now supports multiple parallel sessions, an integrated terminal, editor, diff viewer, and side‑chat features.
The article explains how a Google engineer leveraged Claude Code together with a lightweight .NET application and a curated CLAUDE.md file based on Karpathy’s principles, plus the Everything‑Claude‑Code repository, to automate roughly 80 % of his development tasks, reduce token waste, and achieve massive time and cost savings.
AutoSOTA, a Tsinghua‑Beijing Zhongguancun Institute project, automates end‑to‑end AI research using a multi‑agent framework, toolkit, and skill set, enabling it to discover 105 significantly improved SOTA models in a week—over 60% with novel architectures and ~10% average performance gains—freeing scientists from repetitive optimization.
This article details 15 powerful, under‑utilized Claude Code capabilities—including mobile apps, cross‑device session transfer, loop/schedule automation, hooks, Chrome extension verification, Git worktrees, batch processing, custom agents, remote dispatch, and voice input—showing how to build a verifiable, isolated, forkable, and schedulable AI‑driven development environment.
DeepScientist V1.5, an open‑source AI system from Westlake University, claims to automate the full research pipeline—from literature review and code debugging to experiment analysis and paper writing—delivering SOTA results in weeks and offering one‑click deployment on Windows, Linux and macOS.
OpenClaw is a powerful AI agent that can automate tasks via natural language, yet many users don’t know how to apply it; this article introduces the open‑source “awesome‑openclaw‑usecases” repository, which organizes over 30 real‑world workflows into six categories—from social media summarization to DevOps and finance automation.
This guide explains how to use Claude Code hooks—PreToolUse and PostToolUse—to run custom commands before or after tool execution, configure them in global or project settings, and implement practical examples such as blocking .env file access, type‑checking TypeScript, and preventing duplicate queries.
Deep Agents, an open‑source framework built on LangChain and LangGraph, provides a ready‑to‑use agent harness with planning, file‑system tools, sandboxed shell access, sub‑agents, automatic context management, and built‑in observability for Python and TypeScript developers seeking a flexible replacement for Claude Code.
This article introduces the community‑driven Awesome OpenClaw Use Cases repository, detailing over 40 real‑world scenarios—from daily Reddit digests and multi‑agent content pipelines to self‑healing home servers and personal CRMs—complete with step‑by‑step instructions, code snippets, and reference links.
This guide walks you through installing the open‑source Feishu CLI, configuring it with Claude Code, and using the resulting SKILL to let an AI agent interact with Feishu messaging, cloud docs, calendars, and other services for automated workflows.
This guide walks you through the open‑source Claude HowTo project, showing how to clone the repository, install slash commands, memory files, skills, sub‑agents, MCP, hooks, plugins and other modules, and explains each feature with concrete examples, usage commands and estimated learning times.
This comprehensive FAQ walks through 40 technical questions about OpenClaw, covering its innovations, architecture, multi‑agent collaboration, memory and context handling, security risks, token‑saving strategies, real‑world use cases, comparisons with other agents, and competitive landscape.
UCSD’s AIBuildAI agent achieved first place on OpenAI’s benchmark by automatically designing, coding, training, and tuning a complete AI model without human engineers, a breakthrough that suggests a shift from tool‑assisted AI creation to fully autonomous AI‑generated AI, raising both efficiency gains and new interpretability challenges.
After tiring manual reviews of Claude Code’s mistakes, I added a simple "完善" rule that lets the model automatically analyze, prioritize, and record its own pitfalls, cutting repeat errors, saving hours each week, and justifying the $400 monthly cost as cognitive relief.
The article introduces four free, open‑source AI projects—Postiz, Nanobrowser, ShortGPT, and MockingBird—detailing their core features, deployment requirements, suitable side‑hustle scenarios, and difficulty levels, and then shows how combining them can automate content creation, data collection, and voice synthesis for low‑cost online income.
This article introduces the open-source "awesome-openclaw-usecases" repository, which curates over 30 practical OpenClaw scenarios across social media, creativity, DevOps, productivity, research, and finance, providing step-by-step instructions and examples to help users quickly adopt AI-driven automation.
In just 48 hours Anthropic released Claude Desktop’s Computer Use feature that lets the AI control mouse, keyboard and apps, and Claude Code’s Auto Mode that lets the AI judge and execute code actions autonomously, both backed by multi‑layer safety mechanisms.
This article explains the 2026 definition of Agentic Workflow, outlines its four core components, presents a five‑step execution loop, shares real‑world productivity data, and provides ready‑to‑use prompts and tool recommendations for instantly applying the concept in the workplace.
OpenClaw is an AI‑driven automation platform that lets users describe tasks in natural language—like extracting PDF data or generating PPTs—and the system’s agents select appropriate skills to execute the work, aiming to simplify repetitive document workflows without coding.
A server alert triggered a 100% CPU usage warning while I was shopping, but by messaging an AI‑powered chatbot from my phone I diagnosed the offending Node.js process, restarted the service, and restored normal performance in under five minutes.
XcodeBuildMCP, an open‑source Sentry project, lets AI agents such as Cursor and ClaudeCode fully automate iOS/macOS development by handling Xcode build management, device selection, log capture and error handling, eliminating the manual verify‑compile‑test loop.
Analyzing 168 OpenClaw projects from TrustMRR, the article reveals a $3.18 million total revenue where 42% earn nothing, two projects capture 57% of earnings, and most participants profit by selling services, tutorials, or token‑based courses rather than improving enterprise efficiency.
The article provides a detailed overview of OpenClaw, an open‑source AI automation framework, and examines nine core ecosystem tools that together simplify skill expansion, deployment, local execution, packaging, and agent configuration for individuals, developers, and enterprise teams.
The unofficial notebooklm-py library provides a Python API and CLI that give programmatic access to Google NotebookLM’s hidden capabilities, enabling batch import of PDFs or videos, automated generation of podcasts, mind‑maps, and other outputs, and integration into AI agents for scalable research workflows.
This guide shows how game operators can use Alibaba Cloud Log Service (SLS) Intelligent Q&A Assistant to automate log queries, root‑cause analysis, and response generation, dramatically reducing ticket handling time and improving player satisfaction.
The article analyzes why static agent skills become brittle as codebases, models, and user needs change, and proposes a closed‑loop architecture that observes executions, learns from failures, automatically suggests improvements, and evaluates changes to keep skills continuously evolvable.
Claude Code can read your project’s code, git history, and structure, then automatically create or update Obsidian notes, generate dev logs, and even produce architecture Canvas files with a single /obsidian command, offering a local‑first, plugin‑free workflow for AI‑driven knowledge management.
Karpathy’s new "karpathy/jobs" visualization scores 342 U.S. occupations on AI exposure using Gemini Flash, revealing that software engineers rank 8‑9, transcriptionists hit 10, while roofers stay near 0, highlighting which jobs may soon be automated.
This guide explains the SKILL.md open standard, its four core characteristics, directory layout, gating and lifecycle controls, testing with the Evals framework, market adoption stats, and future self‑evolving agent capabilities, providing step‑by‑step instructions for creating, installing, and iterating Skills across multiple AI platforms.
This article analyzes OpenClaw's role as a flexible AI‑driven shell, explains why its capabilities are hard to define, and reveals how businesses can profit by turning its programmable digital‑labor potential into concrete services and products.
This article explains how OpenClaw’s Skill system gives AI executable instructions, combines it with a DuckDB analytical instance, and builds a fully automated e‑commerce behavior forecasting pipeline that iteratively trains, validates, and optimizes models to achieve sub‑10% prediction error.
This guide explains how to enhance OpenClaw's stability by configuring a standby agent, backing up configurations, installing an OpenClaw operations skill, and scheduling periodic health checks, providing concrete steps, tool choices, and example commands to achieve 24/7 reliable operation.
The article objectively examines whether the new /loop scheduling command in Claude Code makes the popular OpenClaw “little lobster” redundant for developers who work primarily in the terminal, outlining feature overlap, remaining advantages, and practical limitations.
The gws CLI bundles every Google Workspace API into a single command‑line tool, offering dynamic command generation, structured JSON output, and native MCP support for AI agents, with easy npm installation, step‑by‑step usage examples, OpenClaw integration, and essential tips to avoid common pitfalls.
This guide walks you through eight production‑grade OpenClaw scenarios—including cron‑driven briefings, webhook integrations, MCP data access, multi‑agent roles, browser automation, mobile camera monitoring, custom skill creation, and workspace configuration—providing complete configuration files and command‑line examples for immediate use.
OpenClaw 2026.3.1 merges over 90 pull requests, introduces Claude adaptive reasoning, faster OpenAI responses via WebSocket, extensive Android node permissions, and Feishu table automation with bilingual UI, while its GitHub star count jumps to 252 K, surpassing React.
OpenClaw, the AI‑driven automation platform, is often misused as a mere plugin collection; this article analyzes why, presents over 30 verified real‑world use cases across six categories, offers a three‑step workflow to adapt them, and outlines essential security and architectural principles for safe, effective deployment.
In 2026 the CTO's role has shifted from choosing languages and platforms to translating business goals into strategic technology roadmaps, as AI agents, platform engineering, and FinOps automate most traditional technical decision‑making.
Ralph uses a simple Bash‑loop architecture to launch a fresh AI instance for each PRD task, storing state in Git, progress.txt and prd.json, then iteratively runs quality checks, commits code, and updates documentation, enabling end‑to‑end automated development from specification to production.
Refly is an open‑source Agent Skills Builder that lets you create production‑grade AI workflows in minutes using natural language, offering versioned, reusable skills, runtime intervention, extensive tool integrations, and export options that outperform traditional automation platforms.
CuaBot v1.0 introduces a new open‑source approach that lets AI agents interact with a desktop via independent cursors and sandboxed windows, avoiding full‑screen screenshots and mouse hijacking while supporting multi‑agent parallelism, H.265 video, audio, clipboard sharing, and a CLI built on Xpra and Docker.
The article outlines essential UX testing dimensions for education software—including functional, usability, accessibility, emotional load, and security—while detailing challenges such as multi‑role scenarios, performance demands, and compliance, and proposes agile, AI‑enhanced, cross‑disciplinary approaches and concrete case studies to improve learning outcomes.
Playwriter, an open‑source Chrome extension built on Microsoft Playwright, lets AI bypass login and captcha hurdles by acting as a browser co‑pilot, offering visual‑accessibility tagging to cut token usage and exposing the full Playwright API for advanced automation tasks.
This step‑by‑step tutorial shows how to create a Claude Code Skill that interactively asks for an article, generates short, medium, or detailed structured summaries, saves the result, and includes advanced techniques such as dynamic context injection, sub‑agents, hooks, and version‑controlled best practices.
TuriX‑CUA is an open‑source AI desktop agent that captures screen content, uses multimodal large models to decide actions, and automatically moves the mouse or types, offering cross‑platform support, multi‑model architecture, and detailed setup instructions for Windows and macOS.
The article explains how the Ralph loop, a minimalist bash for‑loop design introduced by Geoffrey Huntley, lets Claude Code repeatedly execute tasks until completion, offering a simpler, more reliable alternative to elaborate multi‑agent orchestrations for quantifiable, verifiable programming work.
This guide explains what Coze Agent Skills are, how they are structured with YAML and Markdown, and provides step‑by‑step instructions and real‑world examples for creating, deploying, and using no‑code AI skills to automate tasks like model scouting, recipe suggestions, and camera settings.
This guide explains how Anthropic Claude’s Agent Skills act as reusable, file‑system‑based knowledge packages that transform a generic AI agent into a domain expert, detailing their structure, on‑demand loading process, best‑practice creation steps, a concrete code‑review skill example, workflow automation, and comparison with MCP.
This guide explains the core principles, progressive disclosure architecture, and practical implementation of Claude Code Skills, showing how five real‑world use cases achieved an average 6,444% ROI by reducing token usage, speeding up tasks, and enabling automated, context‑aware workflows without additional infrastructure.
TuriX-CUA is an open‑source Python framework that lets AI agents understand screen content and perform mouse‑keyboard actions on Windows and macOS, offering high success rates, model hot‑swapping, MCP integration, and step‑by‑step installation for desktop automation tasks.
This article explains Anthropic’s open‑standard Agent Skill, how it serves as a reusable task specification for Claude, walks through creating a skill with metadata, instructions, and advanced Reference/Script features, and compares Skill with MCP to help developers choose the right tool.
This article explains the ReAct paradigm—combining reasoning, action, and observation—to turn large language models into controllable agents, detailing its core concepts, architecture, workflow, code implementation, application scenarios, advantages over other methods, and future research directions.
OpenAI’s Codex now officially supports the open Agent Skills standard, introducing reusable instruction‑script‑resource bundles, progressive disclosure loading, explicit and implicit invocation methods, built‑in system skills, and cross‑tool compatibility including GitHub Copilot, signaling a move toward a unified AI agent ecosystem.
Lalamove’s year‑long P2J project transformed over a thousand legacy PHP interfaces into Java, tackling complexity, stability, and efficiency challenges through standardized technical solutions, AI‑assisted code generation, rigorous testing, and disciplined project management, ultimately delivering faster, higher‑quality services.
This article examines the shortcomings of manual test case creation, explains how large language models (LLMs) can dramatically improve efficiency, coverage, consistency, and knowledge sharing in software testing, outlines the key capabilities required, and presents a detailed end‑to‑end solution with practical steps, evaluation metrics, and future outlook.
This article shares the author's practical experience in using AI to tackle complex repetitive tasks, presenting a reusable methodology that abstracts human actions into a perception‑decision‑execution loop, and demonstrates three automation modes—tool assistant, workflow, and intelligent agent—through real‑world cases in data governance, ticket handling, and baseline operations.
The article surveys five open‑source AI agents—MobiAgent, Mobile‑Agent, Droidrun, AppAgent, and mobile‑use—that let AI perceive screen content and perform cross‑app actions on Android/iOS devices, describing their architectures, example use cases, benchmark results, and GitHub links.
This article introduces five free, open‑source Model Context Protocol (MCP) services that extend AI capabilities—covering web scraping, browser automation, document‑to‑Markdown conversion, real‑time documentation, and structured reasoning—along with concise step‑by‑step setup instructions for each.
The author recounts how an AI‑driven development pipeline initially seemed promising but quickly ran into costly context‑loss issues, leading to a redesign that introduces a single commanding agent and expert sub‑agents, dramatically reducing token costs and improving workflow efficiency.
The article surveys the most popular Model Context Protocol (MCP) Server implementations in 2025, explains their core features, compares them across use‑cases, and provides practical guidance on selecting and quickly deploying the right server to automate repetitive development workflows.
This guide shows how to extract API definitions from a Word table, structure them with a simple fill‑in template, and use an AI prompt to automatically generate a complete Postman Collection, enabling fast, repeatable API testing.
Google is porting roughly 30,000 production software packages to Arm, using an AI‑driven tool called CogniPort to automate the migration and aiming to run them on its Axion chips and x86 processors while improving cost and energy efficiency.
This guide presents an AI‑assisted workflow for Linux disk maintenance, including scripts for rapid diagnosis, risk assessment, personalized cleanup plans, safe log and cache removal, large‑file analysis, interactive cleanup modes, real‑time feedback, automated backups, and a one‑click execution script.
This article explains how an AI‑driven tool dramatically reduces XPath creation costs and improves stability for complex 3D‑canvas web applications by combining prompt engineering, HTML compression, self‑checking, retry mechanisms, and specialized handling for inputs and 3D model selectors.
Claude Code 1.0.123 transforms user‑defined slash commands from manual shortcuts into AI‑controlled tools, enabling Claude to autonomously invoke commands like /lint or /deploy‑staging, which streamlines developer workflows and marks a significant step toward smarter, automated human‑AI collaboration.
This article provides a comprehensive technical analysis of Large Action Models (LAMs), detailing their neuro‑symbolic architecture, core components such as LAMProcessor, NeuroSymbolicLayer, ActionExecutor, and learning modules, and demonstrates how they enable intelligent, end‑to‑end automation of enterprise tasks.
This article examines the evolution of automation from basic scripting to AI‑driven intelligent systems, compares traditional and smart automation across multiple dimensions, and showcases practical implementations using Playwright, MidScene.js, and Chrome bridge mode with code examples for web and mobile testing.
This article systematically compares major open‑source deep‑research agent frameworks—including DeerFlow, SmolAgents, LangChainAI, SkyworkAI, and Researcher—detailing their architectures, best practices, and commercial alternatives, to help developers and users choose the most suitable tool for automated research workflows.
This article explains how the open‑source browser‑use framework combines carefully designed SystemMessage prompts, structured HumanMessage inputs, and LangChain‑driven tool calls to enable large language models to automate complex web tasks such as shopping, CRM updates, résumé processing, and document generation, while providing concrete code examples and best‑practice tips.
The update introduces a YAML‑based cache with precise XPath targeting, dual‑validation and smart fallback, a structured API for extracting booleans, numbers, strings and queries, enhanced replay reports with custom nodes and video export, plus extensive web, Android, and reporting optimizations that dramatically improve performance and reduce report size.
This article explains the differences among performance, load, stress, and stability testing, shares practical platform design, tuning case studies, traffic recording techniques, AI‑driven log analysis, and CI/CD integration to help engineers build robust, scalable systems.
The article shares practical experiences using large language models such as Cursor and DeepSeek in data‑warehouse workflows, covering assisted coding, automated metric extraction, self‑service analysis, documentation generation, their benefits, limitations, and the broader impact on data engineering roles.
The article analyzes how Claude combined with custom MCPs such as ClaudeCommander dramatically reduces the popularity of traditional IDEs by offering automatic codebase exploration, multi‑step task planning, and long‑running automation like video compression, while providing step‑by‑step installation and usage guidance.
Mechanize, a new AI startup founded by Epoch AI co‑founder Tamay Besiroglu, aims to fully automate all white‑collar work and the global economy, targeting a $60 trillion labor market, but faces technical hurdles, investor scrutiny, and widespread criticism over its radical vision.
Midscene.js, the Web Infra team's AI × UI automation framework, adds Instant Actions for faster, more reliable UI operations and a Deep‑Think option that improves element localization by focusing LLM searches, with concrete code examples and model compatibility notes.
This article explores how multimodal large language models (MM‑LLMs) combined with structured prompt engineering can transform frontend regression testing by enabling natural‑language‑driven test case generation, visual verification, and script self‑healing, thereby reducing maintenance costs and improving coverage across dynamic UI scenarios.
The article examines the rapid rise of AI agents like Manus and OpenManus, explains the limitations of cloud‑only models, details the Function Calling mechanism and its pros and cons, introduces the Model Context Protocol (MCP) as a more powerful evolution, and finally describes how AI Agents combine planning, dynamic tool use, memory, and autonomous decision‑making to achieve fully closed‑loop intelligent automation.
This article provides a comprehensive overview of the Model Context Protocol (MCP), explaining its purpose, core components, differences from traditional APIs, detailed architecture, message types, connection lifecycle, error handling, and step‑by‑step instructions for building and using MCP servers to enable AI agents to act on real‑world data and tasks.
The rapid rise of DeepSeek’s code‑generation AI has led to significant layoffs among programmers, prompting a shift in career strategy toward embracing AI tools, focusing on non‑automatable skills, and continuous learning to stay relevant in the evolving software industry.
The article explains OpenAI’s newly released Operator feature that enables AI to simulate human browser actions, outlines its underlying technologies, explores diverse application scenarios such as web automation and virtual assistants, and discusses the challenges and limitations of this breakthrough.
This article explains why GUI agents are needed, defines their multimodal capabilities, walks through a high‑level automation scenario, details the architecture of large‑model‑driven GUI agents, highlights recent open‑source projects, and compares them with traditional RPA solutions.
The author builds a fully AI‑driven pipeline that scrapes JD.com comments about an Elasticsearch book, processes the data through cleaning and preprocessing, indexes it into Elasticsearch, and creates a series of Kibana visualizations, while reflecting on model selection and practical challenges.
