To reduce an AIGC suspicion rate, the core task is not to turn a paper into casual speech, but to first locate risky sections, then preserve academic facts while strengthening concrete context. This article provides three workflows: segmented Agent rewriting, web-based AI handling, and solidifying rules as a Skill. It is useful for graduation-thesis revision and emphasizes that terminology, data, citations, and section numbers must be manually reviewed, especially for drafts that already have a detection report.
If you are unsure which AI coding subscription to buy, this hands-on cost comparison compares GLM, Minimax, Kimi, Codex, Gemini, and Claude. It explains quota rules, hidden limits, Kimi’s 3× expansion, GLM weekly caps, and recommended combinations for different budgets and usage levels so developers can avoid wasting money.
This SCI paper-polishing prompt uses a roundtable-meeting format and forces the AI to simulate an editorial workflow similar to Nature. It does not rewrite immediately. Instead, a logic architect and reviewers first run three rounds of debate, then a writing specialist outputs two versions, Draft A and Draft B. The process raises information density and reduces the mechanical feel of AI-generated prose, making it suitable for detailed academic-paper revision.
A framework for keeping long-running AI development from drifting, using tasks.md for process, feature_list.json for state, progress.txt for handoff, and reproducible validation bundles with scripts, logs, and screenshots. Worker implements and delivers a bundle; Supervisor runs acceptance and records evidence; after PASS, state and commits are archived.
This article introduces two methods for literature search: when you already have papers, expand the literature pool through CNKI citation networks or Inciteful.xyz; when you only have a topic, build and iteratively refine search queries with CNKI professional search and Web of Science advanced search. It includes operation steps, a query-generation prompt, and an optimization workflow using 5–20 seed papers.
A practical explanation of Spec-Driven Development (SDD): write What/Why and acceptance criteria as a living specification before asking AI to implement How. The article explains why vibe coding easily loses intent, and shows how SpecKit (Constitution → Specify → Plan → Tasks) fits 0→1 development while OpenSpec (proposal → apply → archive) fits 1→n iteration.
This article clarifies six often-confused AI tool concepts: Prompt handles temporary conversation instructions, Skills preserve standard workflows, MCP exposes external tools, Commands create shortcut templates, Agent runs autonomous loops, and Subagent isolates parallel tasks. It compares definitions, use cases, engineering cost, reuse, and decision criteria based on Anthropic documentation and LangChain-style agent concepts.
This article combines AI self-improvement with practical AI prompts and collects six deep self-questioning frameworks suitable for AI products with global memory: a brutal life-truth scanner, HUMAN 3.0 evaluation, ruthless debate partner, letter-writing decompression, legends versus ordinary people questions, and using AI to kill procrastination. It helps you use AI to see yourself more clearly and upgrade your lifestyle.
A step-by-step guide to bypassing the Claude Code extension login check and routing the plugin through Claude Code Router so it can connect to large-model services of your choice.
