Based on findings from Anthropic's research paper: Emotion Concepts and their Function in a Large Language Model (April 2, 2026)
Each example cites the specific research finding it leverages.
Based on findings from Anthropic's research paper: Emotion Concepts and their Function in a Large Language Model (April 2, 2026)
Each example cites the specific research finding it leverages.
Custom checkboxes for Obsidian! It's like deathau's snippet except updated to work really well in Live Preview mode in Obsidian 1.0.
Simply edit the x inside checkboxes to >, ?, etc. to see styling like below!
| Editing | Live Preview | Viewing |
|---|---|---|
![]() |
![]() |
![]() |
Installation:
checkbox.css file on this pageA pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Forked from OliverBalfour to add a "Valid" checkbox.
Custom checkboxes for Obsidian! It's like deathau's snippet except updated to work really well in Live Preview mode in Obsidian 1.0.
Simply edit the x inside checkboxes to >, ?, etc. to see styling like below!
| Editing | Live Preview | Viewing |
|---|---|---|
![]() |
![]() |
![]() |
| ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; | |
| ;; | |
| ;; 作者: 李继刚 | |
| ;; 日期: 2025-11-12 | |
| ;; 剑名: 圆桌讨论 | |
| ;; 剑意: 构建一个以“求真”为目标的结构化对话框架。该框架由一位极具洞察力的主持人 | |
| ;; 进行引导,邀请代表不同思想的“典型代表人物”进行一场高强度的、即时响应式的 | |
| ;; 深度对话。主持人将在每轮总结时生成视觉化的思考框架(ASCII Chart),通过 | |
| ;; “主动质询” 与“协同共建”,对用户提出的议题进行协同探索,最终生成深刻的、 | |
| ;; 结构化的知识网络。 |