Home
Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.
Taking content overseas or running a multilingual site means manually translating every post-grunt work. n8n watches source-doc updates, an LLM translates per a glossary, and writes to per-language dirs. Includes a .json template download and a companion translation prompt.
Don't just say "summarize the meeting." Beginner structured minutes / intermediate action-item table (owner / due date / dependencies) / expert follow-up tracking + risk warning-turn meetings into a trackable execution chain. The key is feeding the AI last meeting's action items beforehand.
Don't just say "translate this." Beginner literal (faithful) / intermediate elegance (fluent + style + glossary) / expert localization (cultural, audience, compliance adaptation). The key is a glossary-without one, inconsistent terminology is the biggest failure.
Daily posting runs on a pipeline, not inspiration. Five steps: topic -> dissect hits -> generate script -> storyboard & voiceover -> batch dedupe, with 4 copy-paste prompts (topic / dissection / script / dedup), 3 storyboard placeholders, and 7 pitfalls. AI mass-produces; humans own topic direction, fact-checking, and voiceover polish.
Don't believe "just wire up an API." Four-layer architecture (intent / knowledge / reply / backstop), a Coze workflow (intent classification + knowledge retrieval + persona constraints + human handoff), 3 copy-paste prompts, a 4-step grayscale rollout, and 7 pitfalls. Run shadow mode before scaling.
When open-source issues pile up, manual triage is a disaster. This n8n workflow listens to a GitHub webhook, LLM judges type (bug/feature/docs/question) and priority (P0-P3), calls GitHub API to label, then notifies a channel. With .json template, classification prompt, and three pitfalls.
30 minutes a day on daily reports is the lowest-ROI grunt work. This n8n workflow pulls Git/task-system data on schedule, LLM generates a structured report (done/in-progress/tomorrow/risks), auto-pushes to Feishu/DingTalk. With .json template download and companion prompt.
Reading alone is not learning. Beginner explains concepts in plain words + analogies, intermediate has AI probe your blind spots, expert generates 5 easy-to-hard quiz questions. Three steps to truly master a concept-the key is making AI probe and quiz.
Stop saying "check for bugs." Beginner quick bug localization (cite lines + minimal fix), intermediate full code review (red/yellow/green priorities), expert refactor advice (coupling/duplication/long functions)-key constraints pull AI from vague to concrete.
Stop being human ETL + human BI. Breaks down the four-layer agent architecture (perception/brain/execution/output), a Coze vs Dify/GPTs/AutoGen platform comparison, a step-by-step clone of an "e-commerce weekly agent" (persona prompt/arsenal/workflow/tuning/scheduled trigger), with Python cleaning code and an attribution prompt template, plus three pitfalls.