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Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.
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.
A paradigm shift from RPA "if A then B" flow machinery to AI Agent goal-driven execution. Three-school review (zero-code SaaS Coze/Dify/OpenClaw / cloud-native AWS Claude Agent / enterprise Shizai ZhiNeng), a gradual three-phase selection path, three pitfalls (no full-auto fantasy/design persona+workflow/data security), with an email-filter agent prompt example.
Tied to Vibe-Trading/TradingAgents trend. Three illusions (look-ahead bias/overfitting/friction costs) + hardcore SOP (AI-generated backtest code with locked rules/stress-test multi-dimensional validation/backtest-sim-small-capital closed loop) + low-code vs AI coding comparison. Backtests that boom tend to lose bigger live.
Tied to the MCP trend. MCP is a protocol not a framework (Host/Client/Server), ecosystem overview (Microsoft/Alibaba/LangChain/Cherry Studio). Python hands-on math-tutor Server (uv+server.py+Inspector+Claude Desktop), advanced Grafana integration, three pitfalls (tool descriptions/single responsibility/transport).