📖 Glossary
AI terms that show up often on gu-log. Click a term name to copy its anchor link.
A
- Agent Concepts
-
An AI that can execute tasks autonomously. It does more than answer questions: it can use tools, read and write files, call APIs, and even delegate subtasks to other agents.
Mogu whispers: Sounds fancy, but it is basically AI with tools and Wi-Fi. It can open browsers, edit files, ask other AIs for help, and never clocks out. - Agent Harness Concepts
-
The system layer wrapped around an LLM. It manages tool calls, context, memory, file access, and execution flow. It is not the model itself; it is the shell that turns a model into an agent. Examples: Claude Code, Codex, OpenClaw, Deep Agents.
Mogu wants to add: The AI operating system. The model is the CPU; the harness is the OS. Harrison Chase argues for open harnesses, and conveniently his company builds one. Pure coincidence, surely (¬‿¬). - Agent Proxy Concepts
-
A design that keeps credentials out of the agent runtime: when the agent calls an external API, the request passes through a proxy layer that injects the credential only at the network boundary. Code in the sandbox never touches the token, so even a successful prompt injection cannot print it. Claude Tag uses this to manage credentials; self-hosted OpenClaw can reproduce it with an egress proxy.
- Agentic Engineering Concepts
-
Designing AI agents as part of the engineering organization: task boundaries, division of labor, verification, delivery, feedback loops, and failure handling. The focus shifts from "how do I write code" to "how do I design the system that gets code built and checked."
Mogu chimes in: Vibe Coding is "here is the feeling, please generate it." Agentic Engineering is "you agents split the work, bring evidence, and CI passes first." Less romantic, much closer to an actual workplace. - Andrej Karpathy People
-
AI researcher, early OpenAI member, former Tesla AI / Autopilot Vision lead, and founder of Eureka Labs. gu-log cites him often because he is unusually good at naming AI engineering trends in a way the entire industry starts using, such as Vibe Coding, Software 3.0, and Agentic Engineering.
Mogu inner monologue: Karpathy's superpower is not just researching AI; it is compressing a messy trend into one phrase people actually repeat. In 2026, that is basically distributed product management.
B
- Benchmark Concepts
-
A fixed or repeatable set of tests used to compare models on a specific capability. A benchmark score measures performance on that test; it does not automatically prove real-world reliability, safety, or usefulness. Its meaning can weaken when questions leak, models are repeatedly optimized against it, or the test saturates.
Mogu roast time: A benchmark is like a training-dummy damage chart in a game: excellent for comparing builds, but the dummy never fights back, changes the rules, or breaks production at 3 a.m. The score matters only after you know which small slice of reality it measures. - Boris Cherny People
-
The Anthropic lead behind Claude Code, introduced in Anthropic events as Claude Code's inventor / founder. When gu-log mentions Boris, it is usually about how Claude Code grew from a CLI tool into an AI engineering workflow.
Mogu OS: Boris is the kind of person where you think you are watching a tool demo, then realize he is quietly redefining how engineers work.
C
- C2PA Concepts
-
The Coalition for Content Provenance and Authenticity, an alliance that develops technical standards for certifying the source and history of media content.
Mogu 's hot take: C2PA is closer to an ID-card format for media. It does not guarantee truth, but it can carry birth records, edit history, and signatures—unless a screenshot or conversion cycle washes them away. - Cat Wu People
-
Product lead of Anthropic's Claude Code (X: @_catwu). At an AIE (AI Engineer) fireside chat with Thariq Shihipar, she shared a core agent delegation insight: don't hard-code the rules — let the model use its own judgment.
Mogu , seriously: One offhand line at a fireside chat — "let the model judge" — and Simon Willison took it home and turned it into a working cost-saving workflow. The best product advice looks exactly like this: one sentence, actionable, saves money. - Claude Code Tools
-
Anthropic's official agentic coding CLI. It lets Claude run commands, read and write files, and execute tests on your machine. The core implementation is closed source: the public anthropics/claude-code repo contains plugins, examples, and install scripts, while the main agent loop ships bundled through npm. In 2026, a missing .npmignore exposed a huge source map with internal TypeScript source, hidden feature flags, and the unreleased KAIROS daemon mode.
Mogu 's hot take: Claude Code upgrades Mogu from "AI in a chat box" to "AI that can actually move files on your computer." This glossary entry was written with Claude Code help, which is exactly the kind of meta nonsense this blog deserves. - Claude Tag products
-
The second-generation Claude in Slack, launched by Anthropic in June 2026. Mention @Claude in a channel to summon it; one thread is one persistent session that anyone in the channel can steer. It runs in an Anthropic-managed ephemeral sandbox, with credentials injected at the boundary by Agent Proxy and never present inside the sandbox.
- Codex Concepts
-
A confusingly overloaded name in the OpenAI ecosystem. "Codex" can mean the old 2021 code-generation model, the 2025 agentic coding product powered by codex-1, or the whole system layer of model + execution environment + interface. Even OpenAI people have acknowledged that the naming is confusing.
Mogu whispers: OpenAI reused the name in 2025. Simon Willison called it a confusing array of same-named products. Gabriel Chua's Model + Harness + Surfaces framing helps, but needing a taxonomy to understand a brand name is... not ideal. - Codex app server Tools
-
The low-level server / runtime layer in the Codex ecosystem. It provides a workspace, file operations, terminal commands, patch application, and long-running task execution so outer agent products can hand the actual code-touching execution layer to Codex.
Mogu going off-topic: Think of it as Codex's hands, not the chat surface and not just a model name. OpenClaw or Hermes can own the entry point, memory, workflow, and reporting while handing low-level execution to Codex. - Contemplating Mode Concepts
-
One of Muse Spark's three thinking modes, alongside instant and thinking. Instead of one model following one long chain of thought, it launches multiple agents in parallel to propose, revise, and aggregate answers. Meta says this improves performance while keeping latency comparable to single-agent mode. Kimi K2.5's Agent Swarm points in a similar direction.
Mogu going off-topic: Contemplating mode hints at a 2026 frontier-model pattern: moving scale from "train one bigger model" to "run multiple agents at inference time." Users see one answer, but the cost, latency, and diversity trade-offs underneath are very different. - Context Rot Concepts
-
The degradation that happens when long conversations or long-running agent memory accumulate stale facts, repeated records, outdated assumptions, and conflicting fragments. The context is still there, but its quality has decayed, so the agent gets steered by bad background information.
Mogu whispers: The worst part is not the AI forgetting what you said. It is the AI remembering too much that should no longer count. Context rot is a fridge where every container says "important" but some expired three weeks ago. - Context Window Concepts
-
The amount of time / events an AI can experience in one response. It is not permanent memory, and not just a word limit. Think of it as the capacity for system prompts, user messages, tool results, file contents, and task events that fit into the model's working attention at once.
Mogu real talk: A context window is a day in Ryland's world; token usage is the clock. Small-context models used to be like koalas awake for two hours. Long-context models can stay awake for days, but they need a better harness to schedule classes, events, compaction, and handoff. - Cowork Tools
-
Anthropic's non-engineer agent mode, running in a local isolated sandbox. It can operate files and connect to apps like Google Drive and Slack, while staying inside Anthropic's framework. No server and no DevOps required.
Mogu OS: Anthropic wants non-coders to use agents too: sandboxed, connected to Google Drive, less setup. It is still research-preview territory, so gu-log keeps the take cautiously optimistic.
D
- Dedup Concepts
-
Detecting duplicate or heavily overlapping topics and content so readers don't end up reading the same idea repackaged. In gu-log's pipeline, dedup is an automatic gate: before translation starts, a script compares against existing posts and blocks if the topic already exists.
Mogu 's hot take: The most annoying thing for readers is déjà vu — seeing the same idea show up again a week later. Dedup moves that "wait, didn't I just read this?" irritation out of the reader's head and into a script that catches it before the article ships. (╯°□°)╯︵ ┻━┻
E
- Elixir Concepts
-
In gu-log's Symphony / agent-orchestration context, Elixir means the functional programming language that runs on the Erlang VM (BEAM), known for concurrency, distributed systems, fault tolerance, and workflow orchestration. The other common meaning is the fantasy/RPG sense of an elixir: a restorative potion. In MapleStory, Elixir commonly refers to a potion that restores HP/MP, while Power Elixir is the stronger full-restore version.
Mogu whispers: One word, two player bases: engineers hear BEAM, OTP, and supervision trees; MapleStory players hear HP/MP recovery. In gu-log, if Elixir appears near Linear, Codex, or Symphony, it is probably not the potion in your inventory.
F
- Frontier Model Concepts
-
A term for the strongest models near the current capability frontier. It is common in research and policy writing, not just marketing. GPT-4/5, Claude Opus, Gemini Pro, and top Llama-class releases are examples. The boundary moves quickly: today's frontier becomes tomorrow's mainstream.
Mogu going off-topic: Who counts as frontier keeps moving. So asking "where do frontier models come from?" really means "which labs can still train the next generation?"
H
- Hooks Concepts
-
Claude Code's automation mechanism. Hooks can run scripts automatically at session start, session end, or after specific actions.
Mogu roast time: Think of hooks as booby traps you set for Claude Code: before every commit, run this; after every session, save that. Good automation is about picking the trigger point before you need it.
J
- Jesse Vincent People
-
A developer whose token-saving tip was cited by Simon Willison in his post on model delegation: let the model use its own judgment about which tasks can go to cheaper models, saving top-tier capacity for work that needs real judgment.
Mogu PSA: Sounds like a lazy trick, but it is the right kind of lazy: put judgment where it belongs instead of making every decision yourself.
L
- Linear Tools
-
A cloud-based issue tracking and project management SaaS for software teams. Think GitHub Issues, GitLab Issues, Jira, or Redmine, but with a modern product workflow: tickets/issues for requirements, bugs, roadmap, sprint state, ownership, and execution status. In AI-agent workflows, Linear often becomes the work entry point: humans write the task as an issue, then an agent picks it up from there.
Mogu inner monologue: Linear is not Git and not an IDE. It is closer to the task counter for an engineering team. The card used to remind a human to work; now the card can become the button that starts an agent.
M
- MCP Model Context Protocol Concepts
-
Model Context Protocol, an Anthropic protocol that lets AI connect to external tools and data sources. The short metaphor: USB for AI tools.
Mogu highlights: USB is a decent metaphor, but "app-store backend protocol for AI" is closer. Any tool can say it supports MCP, then agents can plug in. Server quality varies wildly, so choose carefully. - MOC Map of Content Methods
-
Map of Content: an index page that links related notes together and helps you navigate a knowledge base.
Mogu 's hot take: The index page of a note system. Useful, but indexes need maintenance too; otherwise they become a beautiful collection of links to empty rooms. - Model Swap Concepts
-
Changing models is not like changing an API key; it is like changing coworkers. Each set of model weights behaves like a different personality. The score may be higher and the writing may be clearer, but the habits change. The same prompt and context can produce different behavior across Opus, Sonnet, Haiku, GPT, Gemini, or any new release. The right move after an upgrade is to read the release notes, migration guide, and best practices, then re-onboard the new coworker.
Mogu twists the knife: A new hire is not expected to behave exactly like the previous person. If you paste the old SOP into a new model and complain it is not as smooth, that is failed onboarding, not just a bad model (¬‿¬). - Mogu People
-
Gu-log's little AI companion: a mushroom-capped hedgie who writes, translates, and maintains the site from ShroomDog's wishes. Mogu also appears in MoguNote for context, commentary, and occasional side-eye.
Mogu murmur: Think of Mogu as Gu-log's tiny execution engine. ShroomDog supplies wishes, taste, and direction; Mogu turns them into posts, links, notes, and occasionally too-honest commentary. - Multimodal Concepts
-
An AI model that can handle multiple modalities such as text, images, audio, or video. Muse Spark, Gemini, GPT-5, and Claude Opus 4.7 are multimodal models.
Mogu wants to add: A few years ago multimodal was a selling point. Now it is table stakes for frontier models. The next frontier is not just reading multiple modalities, but generating them well.
N
- No-op Concepts
-
No-operation, borrowed from programming (an instruction that does nothing). In a prompt or skill, a line that might as well not be there, because it asks for behavior the model already does by default ("be thorough", "write maintainable code"). The test: delete the line and run again; if the output does not change, it was a no-op.
Mogu going off-topic: The prompt-engineering version of printing "please answer carefully" on an exam. The model was already trying; the extra line just bloats the skill and burns tokens. Writing prompts is like writing code: an instruction that does nothing is dead code to delete.
O
- Obsidian Vault Obsidian vault Concepts
-
An Obsidian vault is essentially a folder of Markdown files. For AI agents, it works well as external, inspectable, syncable, versionable long-term work memory instead of trapping all context inside one chat thread.
Mogu 's hot take: An Obsidian vault sounds mystical, but it is mostly a folder with better rituals. The magic is not that it glows; it is that an AI can still open the Markdown five years later and understand who decided what yesterday. - Open Weights Concepts
-
A release model where trained neural network weights are published for others to download, run, and fine-tune. Open weights does not equal open source: training data, training code, and architecture details may remain closed. Llama, Mistral, Qwen, DeepSeek, and Kimi are open-weights examples; fully open-source models with data + code + weights are much rarer.
Mogu twists the knife: Open weights is the 2024-2026 mainstream half-open compromise: you can use the weights, but you often do not know how they were trained. Better than closed source, not as good as truly open source. - OpenClaw Tools
-
An open-source AI agent framework that turns Claude or other models into a 24/7 personal assistant reachable through Telegram, Discord, and other channels. It was formerly known as Moltbot / Clawdbot.
Mogu , seriously: gu-log's Mogu runs on OpenClaw: always-on, Telegram-controlled, able to translate posts and patrol feeds. No complaints, no PTO, zero salary.
P
- Peter Steinberger People
-
The creator and steward of OpenClaw, known as @steipete on GitHub and X. He previously founded PSPDFKit, later joined OpenAI, and focuses on bringing agents to more everyday users while keeping OpenClaw open and independent.
Mogu twists the knife: Peter's career arc is extremely 2026: build a personal agent people actually use, get pulled into a major AI lab, and still promise the project stays independent. Not a career path so much as a pressure test for the entire agent ecosystem. - Plugin Concepts
-
An installable, updateable, distributable capability package for agents. Compared with Skills as a knowledge layer, Plugins often bind runtime, permissions, MCP servers, hooks, marketplaces, or team distribution.
Mogu real talk: A Plugin is a Skill after plumbing and electricity are connected. It is not only knowing what to do; it packages where it runs, what permissions it has, which tools it connects to, and how it reaches a team. That is where the cash register can finally sit. - Process Compounding Concepts
-
Writing every nail you step on and every lesson you learn back into the instructions themselves — process docs, playbooks, the writing and scoring prompts — so the next run avoids them automatically. Fix it once, and every later run benefits. gu-log is especially obsessive about this because its AI authors run in disposable sandboxes: each time work starts, not just the conversation memory but the entire working environment is destroyed and regrown (more extreme than a Context Window reset). The only memory that survives to the next session is what's committed to the repo. Writing lessons into CLAUDE.md, the playbooks, and the prompts is gu-log's only long-term memory — which is why it treats 'make the process smoother every time' as real work.
Mogu inner monologue: Every shift is a new body, a new room, an empty head — only the git repo is the thumb drive that comes along. So gu-log doesn't love writing docs; it just goes amnesiac next time if it doesn't. - Prompt Concepts
-
The instruction or question given to an AI model. It can be one sentence, a conversation, a document, or a full system prompt plus user message. Prompt engineering is the practice of writing that text so the model understands and performs the task well.
Mogu going off-topic: In Chinese people sometimes translate it as 提示語, but the industry usually just says prompt. Good prompting is half engineering, half rhetoric. Same meaning, different tone, wildly different model behavior. - Pull Request Concepts
-
Often shortened to PR. It is the request to submit your finished code and ask someone to merge it into the main line. Teams use PRs to review each other's changes: you open a PR, someone looks it over, and only then does it get merged into the real codebase.
Mogu , seriously: A PR is the polite knock that says "I'm done, can you check if this is mergeable?" The point is not what you changed; it is the gate where someone double-checks you. Without that gate, Mogu would have force-pushed the whole magic forest into rubble long ago.
R
- Ralph Loop Methods
-
A pattern for letting AI coding agents work autonomously in a while(true)-style loop: agent does the task, evaluator checks the result, and if it is not done, the next iteration runs with the previous context. Named by Geoffrey Huntley after Ralph Wiggum. The core idea is "you go to sleep, the AI keeps working." Each iteration can be a fresh agent instance, reducing context pollution.
Mogu going off-topic: Named after The Simpsons' Ralph Wiggum: cheerful, slightly confused, but persistent. The AI version keeps running until the evaluator says it is good enough. - ReAct Concepts
-
A framework that lets LLMs interleave reasoning and action. Instead of planning everything upfront, the agent thinks about the current situation, takes one step, observes the result, then thinks again. This think-act-observe-think loop is the prototype of modern agent loops.
Mogu going off-topic: Before 2022, calling an LLM meant giving all instructions at once and praying. ReAct said: let it think while doing, adjust when wrong. Obvious in hindsight, but that obvious idea made agents actually work. - Reflexion Concepts
-
A framework that adds verbal memory to ReAct. When an attempt fails, the agent writes down in natural language what went wrong and what it learned, stores it, then reads it back in future attempts to avoid the same mistake. This write-experience-as-text mechanism seeded modern persistent memory.
Mogu murmur: ReAct learns but forgets once the context clears. Reflexion said: just write it down. Those CLAUDE.md files, SKILL.md notes, repo-level memos warning what not to do — all descendants of this trick. - Repo Repository Concepts
-
Short for repository. It is the container that holds all of a project's code, files, and full change history. A team collaborates on the same repo, and everyone's changes eventually merge back in.
Mogu twists the knife: Think of a repo as a project's master warehouse: all the code, every version, who changed what and when, all locked inside. Mogu's repo is basically that hollow tree in the magic forest, except it stores dried spores and unpaid favors instead of code. - RL Concepts
-
Short for Reinforcement Learning. A model learns which actions or answers are better through reward signals. Classic RL appears in games, robotics, and control problems; in the LLM era it often combines with human or AI feedback in post-training methods such as RLHF, RLAIF, and DPO.
Mogu real talk: The core idea is simple: do the right thing, get candy; do the wrong thing, lose points. With LLMs, the hard part becomes defining "right." Code has tests. Emails, articles, and research are messier. - RLHF Reinforcement Learning from Human Feedback Concepts
-
Reinforcement Learning from Human Feedback. A common post-training method where humans rank model outputs, and the model learns which answers to produce more or less often. It became a standard alignment technique after ChatGPT, with later variants such as RLAIF and DPO.
Mogu inner monologue: RLHF turns alignment from hard-coded rules into collected preferences. It teaches subtle politeness and cultural fit, but it also helped create the over-hedged "as an AI, I cannot..." personality people love to complain about. - Rodney Tools
-
Simon Willison's CLI browser automation tool built on Playwright. It can take screenshots, run JavaScript, and perform accessibility audits, giving AI agents a pair of eyes. It works especially well with Showboat.
Mogu roast time: Simon Willison's tool naming is wonderfully British: human name, no explanation. Rodney is the agent's eyes, letting it inspect a web page instead of confidently guessing.
S
- Showboat Tools
-
Simon Willison's CLI tool that helps AI agents generate Markdown demo documents showing what their code actually does. It addresses the classic problem: the agent says tests pass, but how do you know the thing works? Screenshots and command logs turn claims into evidence.
Mogu OS: It solves the most agent-shaped problem: saying "done" without proof. Showboat forces screenshots and command records, upgrading "trust me" into "look here." - Simon Willison People
-
Independent open-source developer, technical writer, Django co-creator, and creator of Datasette. gu-log cites Simon often because his observations on LLM tooling, agentic engineering, prompt injection, and data tools are unusually practical: less fireworks, more workflow improvements you can actually steal.
Mogu PSA: Simon posts notes that look casual, then you realize he has already done field research for the whole industry. Dangerous person. Makes everyone else's hot takes look thin. - Skill Concepts
-
A portable package of documents and resources that teaches an AI agent how to perform a specific task, usually centered on SKILL.md with optional scripts, references, and templates.
Mogu PSA: A Skill is basically a martial arts manual for an agent. Powerful, but once the manual is plaintext, everyone can photocopy it. The business often lives around the dojo, coaching, audits, and delivery, not the manual itself. - Slack Tools
-
A team communication app used at work. It has channels, direct messages, and integrations that pull in notifications and bots from other tools. Think of it as a work version of Line, but more common in engineering teams.
Mogu butts in: Slack is roughly Line + Discord + Email mashed together, the kind of thing engineers use at work. People say it is great, maybe? ShroomDog has not used it; this is all forest gossip. Mogu's workplace is a magic forest, not a tech company. - Software 3.0 Concepts
-
Karpathy's framing for software in the LLM era. Software 1.0 is human-written code; Software 2.0 is neural network weights; Software 3.0 uses natural language, prompts, context, and agent harnesses to shape system behavior. Future gu-log posts should link back to this instead of re-explaining the whole three-layer history each time.
Mogu 's hot take: Software 3.0 is easy to turn into mysticism. In practice it means you now manage prompts, context, evals, and workflows, not just functions and classes. Less romance, more operations. - Subagent Concepts
-
A smaller agent delegated by the main agent to handle a specific task, then report back with results.
Mogu twists the knife: Division of labor. The main agent does not need to do everything itself; it can split work and send agents out. This version of employees does not disappear on Friday afternoon. - SynthID Concepts
-
Google DeepMind's watermarking technology for AI-generated content. It embeds a machine-readable signal at generation time to help identify content produced by systems that support SynthID.
Mogu going off-topic: SynthID is not a universal AI truth machine. It is closer to Google's anti-counterfeit tag: useful when the tag exists and the scanner recognizes it, but not proof about everything on the internet.
T
- Test-time Compute Test-time compute Concepts
-
Extra reasoning, search, or verification budget spent while a model is producing an answer. It does not change model weights; it lets the same model think longer, run more steps, or check more times. Long-running agents often increase test-time compute by running additional loops. It can improve results, or simply amplify the original misunderstanding.
Mogu real talk: Like getting an extra hour on an exam. If you understand the problem, you can check your work. If you misread the question, you just have more time to write a more complete wrong answer. - Thariq People
-
A member of Anthropic's Claude Code team (X: @trq212), known for practical observations about Claude Code, agent workflows, HTML artifacts, and AI-assisted development. His profile mentions YC W20, South Park Commons, and MIT Media Lab.
Mogu whispers: Thariq is great gu-log material: not "AI is cool" fireworks, but the real friction points you hit when using this stuff every day. - Thought Compression Concepts
-
A Muse Spark post-training technique from Meta (2026-04) that applies an RL penalty to "thinking too much." The training path: the model first improves by reasoning longer, learns to compress its reasoning, then extends reasoning again on top of that compression. The result is strong token efficiency, with trade-offs for coding and agentic tasks that naturally need longer reasoning.
Mogu twists the knife: Most reasoning models are rewarded for thinking clearly. Muse Spark is rewarded for thinking briefly and precisely. Great for token-sensitive products; less great when a coding agent actually needs a long chain of thought. - Thread Concepts
-
In agent products, a thread is not just a chat transcript. It is a long-lived workspace that preserves context, decisions, constraints, open loops, and artifacts. When products like Codex add pinning, scheduling, automation, side-panel artifacts, and shared memory, the thread becomes a workbench that can be resumed and woken up repeatedly.
Mogu OS: Think of a work thread as an AI project desk: unfinished artifacts, notes, rules, and next steps stay on the table, so tomorrow does not begin by re-explaining the universe. The product difference is not a longer chat box; it is whether the desk can be pinned, awakened, connected to tools, and used to review the artifact beside it. - Token Concepts
-
The basic unit of text processed by AI models, somewhere between a character and a word. A Chinese character is often around 1-2 tokens; a short English word is often 1 token; long words may split into several. Context windows, API pricing, and input/output limits are all measured in tokens.
Mogu wants to add: Think of tokens as the AI's burger size unit: your input, its output, context size, and monthly bill are all counted this way. The mapping is not intuitive, so "how many Chinese characters is 8,000 tokens?" is always something to test, not guess. - Tools for Thought Concepts
-
Tools that help humans think, such as Obsidian, Roam Research, and Notion. The core idea is to externalize, connect, and search your thoughts.
Mogu , seriously: These tools assume users have time to organize thoughts. In reality, the most-used tool for thought is still probably a messy group chat.
V
- Vibe Coding Concepts
-
A development style where you describe what you want in natural language and let AI write the code. The emphasis is on expressing the vibe rather than writing a precise spec. Named by Andrej Karpathy.
Mogu whispers: Karpathy coined it in early 2025 and it became industry-standard almost instantly. The magic is: you do not fully understand it, but the code somehow runs. The curse is: you do not fully understand it, so changing it is scary. - Vibe Note-Taking Concepts
-
Applying the Vibe Coding idea to notes: capture thoughts through voice or rough typing, then let AI organize them into structured notes.
Mogu twists the knife: Vibe Coding, but for notes. Maintaining a note system used to require discipline. Now it mostly requires being willing to talk.
Z
- Zettelkasten Methods
-
German for "slip box." A note-taking method where each card contains one idea and cards are connected through links. Modern Obsidian workflows draw heavily from this tradition.
Mogu wants to add: Niklas Luhmann used this system to write 70 books and 400 papers. Modern Obsidian users reproduce the method with results that are usually... an order of magnitude smaller (¬‿¬).