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A detailed guide on using AI Agents to build Research Logs for scientific experiments, covering skeleton structure, daily workflows, pre-experiment thinking standards, code change tracking, and Agent-researcher division of labor.

Learn how to use AI Agents to build a Research Log for scientific experiments, covering structure, daily workflows, pre-experiment thinking, code change tracking, and human-AI division of labor.

Veteran AI practitioner Remy breaks down the leap from chat models to AI agents: how agents work, the three pillars of context, tools, and skills, MCP connections, and hands-on architecture to make you a 100x employee.

A new solo-company paradigm: replace human staff with AI Agent teams to fully automate newsletter research, writing, publishing, and analytics. 27,000 subscribers, monthly cost slashed from $1,500 to $19.

An in-depth look at how AI Agents are disrupting traditional software testing: the core differences between LLMs and Agents, four capability dimensions (planning/memory/tools/skills), and how test engineers can achieve 10x efficiency gains in test case generation.

An in-depth look at how AI Agents disrupt traditional software testing: the core differences between LLMs and Agents, four capability dimensions (planning/memory/tools/skills), and how testers achieve 10x efficiency gains.

A recursive technical proposition: Can we build a "meta-Skill" that auto-transforms any Skill into a Dify workflow? This article dissects the boundary between deterministic orchestration and autonomous Agent decisions.
Text-to-CAD: How AI Agents Are Reshapi…
Explore how the open-source text-to-cad project wraps CAD modeling as AI agent skills, letting engineers generate 3D models from natural language descriptions.

A complete workflow from Google I/O: use Antigravity, Modern Web Guidance, and Chrome DevTools MCP to build Chrome extensions automatically — from prompt to publish.

Explore the key differences between AI Agents and workflows, and how LLMs evolve from reasoning to execution. Covers ReAct, task decomposition, enterprise value, and Python+LangChain development.

Ditch complex workflow nodes. Use Agent Skill packs to dynamically load AI capabilities, build stable intelligent automation, and understand RAG, LLM limits, and Scale Agent plugin setup.

Learn Claude Code from scratch: understand LLMs vs. AI agents, explore a 3-day onboarding path, and discover how testing engineers can use agents to automate test case and script generation.

A deep dive into Loop Engineering: core concepts and hands-on setup including Codebase Harness, shared file systems, triggers, and Loop Contracts to make AI agents run autonomously.

Hands-on test of Claude Code's Workflow mode with 68 concurrent sub-agents. Covers setup, write-review separation, real concurrency results, and token costs.

Deep dive into Claude Code Workflow's multi-Agent auto-orchestration: a real-world PHP to Golang migration running 14 hours with 100+ Agents, covering planning, execution, and Token cost analysis.

Deep dive into Claude Code Dynamic Workflows: enable parallel sub-agent orchestration via three methods for multi-agent collaboration and automated pipelines.

Explore GitHub Copilot CLI custom Agents: transform one-off terminal prompts into reusable, auditable team workflows for environment setup, CI/CD, and more.
Industry InsightsWarp deeply integrates GPT-5.5 to build cross-environment AI coding agents spanning local terminals, cloud deployment, and open-source collaboration. Explore its architecture, open-source strategy, and differentiation from GitHub Copilot.
Tech FrontiersAnthropic open-sources a financial Agent suite with 10 specialized Agents covering Pitchbook creation, earnings analysis, and valuation modeling, integrating 11 data sources via MCP protocol under Apache 2.0 license.
Product ReviewsDeep dive into Warren, a fully autonomous AI coding system. Learn how developer Jim West uses multi-agent collaboration to automate the entire pipeline from intent definition to production deployment.