Glossary
Every command, flag, file and concept across all 30 courses — in plain words. The same definitions you see when you hover a command inside a lesson.
Base44 6 course ↗
Features
invokeLLM- A built-in Base44 function that runs an AI language model from inside your app, letting you add features like summarization, classification, or a chat assistant without managing any external API keys.
GenerateImage- A built-in Base44 function that creates an image from a text description, so your app can produce cover photos, thumbnails, or marketing visuals on demand.
GenerateSpeech- A built-in Base44 function that converts text into spoken audio and returns a URL to an MP3 file, supporting 30 languages and five voice styles (river, honey, sunny, storm, spark).
SendEmail- A built-in Base44 function that sends an email (such as a confirmation, alert, or notification) to registered users of your app without needing a separate email service account.
ExtractDataFromUploadedFile- A built-in Base44 function that reads an uploaded document (CSV, PDF, or image) and extracts its contents into structured records your app can store and query.
Commands
npm install- A standard command that downloads and adds a JavaScript library from the public npm registry into your project so your code can use it.
Claude Code 34 course ↗
Commands
claude- The command you type in your terminal to start a Claude Code session; running it opens an interactive AI coding assistant in your project folder.
claude -p "query"- Runs Claude Code in print mode — Claude answers the query and exits immediately, making it useful in scripts and automated pipelines.
claude --permission-mode plan- Starts Claude Code in plan mode so it describes what it intends to do before making any file or code changes, letting you review and approve first.
pytest tests/ -q- Runs the automated test suite in the tests/ folder using pytest, with -q (quiet) flag to show only failures rather than every test name.
git diff- A git command that shows exactly which lines have been added or removed in your files since the last saved version.
gh- The official GitHub command-line tool; Claude Code uses it to create pull requests, read comments, and interact with GitHub repositories.
aws- The Amazon Web Services command-line tool; lets you control cloud services such as S3 storage and Lambda functions from the terminal.
Flags
-p- Short form of --print; tells Claude Code to respond to a single query and exit without opening an interactive session.
Concepts
default- The standard permission mode where only read operations run without prompting; file edits and shell commands always ask for your approval.
acceptEdits- A permission mode that auto-approves file edits and common filesystem commands (mkdir, touch, rm, mv, cp, sed) without prompting, while still asking before other shell commands.
plan- A permission mode where Claude reads files and runs shell commands to explore your codebase, but cannot edit source files until you approve its proposed plan.
PreToolUse- A hook event that fires just before Claude runs any tool, letting you inspect or block the action with a shell script before it takes effect.
Bash- Claude Code's built-in shell tool that executes terminal commands on your machine; it is the tool name used in permission rules and hooks.
pandas- A popular Python library for working with tabular data such as spreadsheets and CSV files; used in many data-analysis scripts.
seaborn.histplot- A Python function from the Seaborn visualisation library that draws a histogram (a bar chart showing how values are distributed).
None- Python's built-in value meaning 'nothing' or 'no result'; a function returns None when it has no explicit return value.
ValueError- A standard Python error raised when a function receives an argument of the right type but an unacceptable value, such as a negative number where a positive one is required.
Slash commands
/- Typing a forward slash at the start of your message opens the command menu so you can see and filter all available Claude Code commands.
/help- Shows a list of available commands and a brief description of each.
/clear- Starts a fresh conversation with empty context; the previous conversation stays available via /resume.
/resume- Opens a picker to return to a previous conversation by name or ID, restoring the full message history.
/rewind- Rolls the conversation and code changes back to an earlier point, or summarizes from a selected message, letting you undo a direction that went wrong.
/usage- Shows session cost, plan usage limits, and activity statistics, including a breakdown by skill, subagent, and MCP server on paid plans.
/compact- Summarises the conversation so far to free up context space; you can optionally tell it what to focus on (e.g. /compact Focus on test output).
/context- Displays a visual breakdown of what is filling your context window and shows optimization suggestions and capacity warnings.
/model- Opens a picker to switch the AI model Claude Code uses; the choice is saved as your default for new sessions.
/config- Opens the Settings panel where you can change your theme, model, output style, and other preferences.
/mcp- Manages MCP server connections and OAuth authentication; run with no argument to open the interactive list.
/schedule- Creates or manages routines that run on Anthropic-managed cloud infrastructure on a schedule, even when your computer is off.
/loop- Runs a prompt repeatedly while the session stays open; omit the interval and Claude self-paces between iterations.
/skill-name- The pattern for invoking any custom skill you have created; replace skill-name with the actual name of your SKILL.md file (e.g. /review-pr).
Files & config
.claude/skills/review-pr/SKILL.md- An example skill file path; a SKILL.md placed in .claude/skills/<name>/ defines a reusable workflow Claude loads when you run /<name>.
.claude/commands/*.md- The legacy location for custom command files; any .md file placed here also creates a slash command and continues to work alongside the newer .claude/skills/ layout.
model: haiku- A settings key that pins the AI model to Haiku (a faster, lower-cost Claude model) for a project or session.
Dify 18 course ↗
Concepts
Chatbot- The simplest Dify application type — you give it a model and a prompt, and users interact with it through a back-and-forth chat interface without needing tool calls or a multi-step workflow.
Agent- A Dify application type where the AI can reason, decompose tasks, and call tools on its own without you having to pre-define every step.
Workflow- A Dify application type that chains multiple steps (nodes) together in a visual diagram; it runs once from start to finish, taking input and returning a result through a fixed, repeatable process.
Chatflow- A Dify application type that combines a workflow's structured steps with a conversational chat interface, so each user message triggers the full node chain before a response is returned.
Node- A single building block inside a Dify workflow — each node does one job (call an LLM, run code, retrieve from a knowledge base, etc.) and passes its result to the next node.
RAG- Retrieval-Augmented Generation — a technique where the AI looks up relevant passages from your knowledge base and uses them to write a more accurate, grounded answer.
Chunking- The process of splitting an uploaded document into smaller pieces so each piece can be independently searched and retrieved by the AI.
Embedding- A way of converting text into numbers (vectors) so the system can measure how similar two pieces of text are — used internally when searching a knowledge base.
ReAct- An agent strategy available in Dify that guides the model through explicit Thought → Action → Observation cycles, useful for models that lack native function-calling support.
System Prompt- Hidden instructions you write once that shape how the AI behaves in every conversation — setting its persona, tone, and rules before any user message arrives.
Variable- A named placeholder in a Dify prompt or workflow that gets filled in at run-time — either from user input or from the output of an earlier node.
Tool- A specific action an Agent or workflow node can call — such as searching the web, running a calculation, or querying an API — to get information or take action beyond text generation.
API Key- A secret credential you enter in Dify when connecting a model provider or external service — it authenticates your workspace so the service accepts requests from your application.
Features
LLM Node- A workflow node that sends a prompt to a language model — supporting text, images, and documents — and returns the model's output as a variable the rest of the workflow can use.
Knowledge Base- A repository of documents you upload to Dify so the AI can search and retrieve relevant passages when answering questions, rather than relying only on its training data.
Reranking- A second sorting pass that takes the initial search results from a knowledge base and re-orders them by relevance using a scoring model so the best chunks reach the AI first.
Annotation- A hand-curated question-and-answer pair you save in Dify so the app returns your exact pre-written response whenever a sufficiently similar question appears, bypassing the LLM entirely.
Plugin- A modular component you install in your Dify workspace to extend it with new model providers, tools, or external integrations — usable across all apps in that workspace.
Gemini 18 course ↗
Concepts
Gemini- Google's personal AI assistant that can answer questions, write text, analyze files, generate images and videos, and connect with Google apps like Gmail and Drive.
prompt- The message or question you type to Gemini to tell it what you want it to do.
context window- The maximum amount of text, files, and conversation history Gemini can read and hold at one time — like its working memory for a single session.
multimodal- Gemini's ability to work with multiple types of content at once — text, images, audio, video, and documents — all in the same conversation.
token- A small chunk of text (roughly a word or part of a word) that Gemini uses to measure how much it has read or generated; larger context windows allow more tokens.
Google AI Pro- A paid subscription tier for Gemini that gives 4× higher usage limits and a 1-million-token context window (supporting up to 1,500 pages of text).
Google AI Ultra- The highest Gemini subscription tier, offering the largest usage allowances and exclusive access to features such as Deep Think and enhanced Deep Research visuals.
NotebookLM- A separate Google AI tool focused on document research; Gemini's Notebook feature is integrated with it, so sources and changes sync automatically between both products.
Features
Deep Research- A Gemini feature that automatically searches many sources, spends roughly 5–10 minutes analyzing them, and produces a detailed written report on any topic you ask about.
Deep Think- An advanced reasoning mode exclusive to Google AI Ultra subscribers where Gemini takes extra time to reason through complex or difficult problems before responding.
Gems- Custom AI assistants you build inside Gemini by giving it a name, specific instructions, and optional reference files, so it always behaves like a specialist for a particular task.
Canvas- A side-by-side workspace in Gemini where you can collaboratively create and edit documents, code, slides, apps, and more while chatting with the AI in real time.
Gemini Live- A feature that lets you have a natural, back-and-forth spoken conversation with Gemini using your voice, and optionally share your camera feed so it can see what you see.
Audio Overview- A podcast-style audio conversation that Gemini generates from a document, research report, or notebook so you can listen to the content instead of reading it.
Notebook- A dedicated project space in Gemini where you upload sources (PDFs, Drive files, websites, etc.) and have ongoing conversations that always remember your documents and past discussions.
Connected Apps- Google services (such as Gmail, Drive, Calendar, and Tasks) that you can link to Gemini so it can search your real emails, documents, and events when you ask questions.
Memory- An optional setting where Gemini learns details from your past conversations and uses that context to give more personalized answers in future sessions.
Imagen- Google's AI image-generation model, available inside Gemini, that creates photorealistic or illustrated images from a text description you provide.
Groq 8 course ↗
Files & config
.mp3- A common compressed audio file format; Groq's speech-to-text API accepts .mp3 files for transcription.
.wav- An uncompressed audio file format that stores raw sound data; Groq's speech-to-text API accepts .wav files for transcription.
base_url- A setting in the OpenAI Python client that tells it which server to send requests to; changing it to a Groq or other compatible endpoint lets you swap providers without rewriting your code.
api_key- A secret password-like string you get from an AI provider's dashboard; you pass it when connecting so the service knows who you are and can bill you correctly.
Models
whisper-large-v3- OpenAI's Whisper speech-recognition model (large version 3), hosted on Groq to convert spoken audio into text at high speed; supports formats including mp3, wav, flac, m4a, ogg, and webm.
llama-3.3-70b-versatile- Meta's Llama 3.3 model with 70 billion parameters, available on Groq for general-purpose tasks; runs at ~280 tokens per second and supports a 131,072-token context window.
llama-3.1-8b-instant- Meta's smaller Llama 3.1 model with 8 billion parameters, available on Groq for fast, low-cost responses; runs at ~560 tokens per second with a 131,072-token context window.
Commands
pip install openai- A shell command that downloads and installs the official OpenAI Python library, which you can also use to talk to Groq because Groq's API follows the same format.
Cerebras 24 course ↗
Concepts
Cerebras Inference API- Cerebras's cloud service that lets you send text to an AI model and receive a response, accessed by sending HTTP requests to api.cerebras.ai/v1.
CEREBRAS_API_KEY- A secret password-like string you get from Cerebras that proves your identity every time your code calls the API.
tokens per second- A measure of how fast the AI generates text — one token is roughly one word or word-piece, so 3,000 tokens/second means about 2,000 words generated every second.
Time to First Token- How long you wait from sending your request until the very first word of the answer appears; shorter is better for real-time, interactive applications.
reasoning tokens- Internal thinking text the model generates before its final answer; depending on the reasoning_format setting they may appear in the response or be hidden, but they always count toward your token usage.
JSONL- A plain-text file format where each line is a separate, valid JSON object — used to package many requests into one file for batch processing.
shared endpoint- The standard public API where many customers share the same hardware; easier to get started with but performance may vary under high demand.
OpenAI compatibility- Cerebras's API speaks the same language as OpenAI's API, so code written for OpenAI can be redirected to Cerebras by changing just the base URL and API key.
base_url- The web address your code sends requests to; for Cerebras it is https://api.cerebras.ai/v1, and changing this is how you switch from the OpenAI SDK to Cerebras.
RAG- Retrieval-Augmented Generation — a pattern where your application fetches relevant documents from a database and adds them to the prompt so the model can answer questions about your own data.
agentic workflow- A design where the AI autonomously takes multiple steps — calling tools, browsing data, making decisions — to complete a goal rather than answering a single question.
Models
gpt-oss-120b- OpenAI's 120-billion-parameter open-source model hosted on Cerebras hardware, capable of generating roughly 3,000 tokens per second.
zai-glm-4.7- A Mixture-of-Experts model from Z.ai with approximately 355 billion total parameters (32 billion active per token), available on Cerebras at around 1,000 tokens per second.
Features
streaming- A mode where the model sends its answer word-by-word as it is generated, so you can display text to the user progressively instead of waiting for the full response.
structured outputs- A feature that forces the model's response to match a JSON schema you define, so your code always receives data in a predictable, machine-readable shape.
JSON mode- A looser version of structured outputs that guarantees the response will be valid JSON but does not enforce a specific field structure.
reasoning_effort- An API parameter that tells the model how much thinking to perform before answering — options are low, medium, or high, trading speed for thoroughness.
prompt caching- A feature that reuses the processed results of repeated prompt prefixes — like a shared system prompt — so subsequent calls with the same opening are faster.
prompt_cache_key- An optional routing hint you attach to a request so that related requests (such as turns in the same conversation) are directed to the same cache, reducing latency.
predicted outputs- A feature where you supply the text you expect the model to produce; the model skips regenerating tokens that already match your prediction, speeding up the response.
Batch API- A service for submitting up to 50,000 AI requests at once as a JSONL file, letting them process in the background (guaranteed within 24 hours) and collecting all results when done.
dedicated endpoint- A private, reserved slice of Cerebras infrastructure for your organisation alone, offering guaranteed throughput and advanced features like fine-tuning, custom model weights, and the Priority service tier.
service tier- A setting on each API request that controls processing priority — Priority (dedicated endpoints only) is highest, Default is standard, Auto uses the highest available tier, and Flex is lowest-priority for non-urgent tasks.
tool calling- A feature where you describe external functions (like a search engine or calculator) to the model, and it can request that your code run one of them mid-conversation to gather information.
Hermes 36 course ↗
Commands
hermes setup- First-run wizard — it asks which model provider to use and writes your config file.
hermes setup --portal- Set up Hermes using a managed Nous Portal model, so you skip picking your own provider and key.
hermes --tui- Start Hermes in its modern text UI (a full-screen terminal interface).
hermes --continue- Reopen Hermes where you left off — it reloads your last session.
hermes doctor- Health check — verifies your config, model connection and tools, and reports what is wrong.
hermes model- Choose or switch the model (the "brain") Hermes runs on.
hermes gateway setup- Connect chat apps (Telegram, Signal, Slack…) so you can message your agent.
hermes gateway status- Check whether your connected chat apps are reachable.
hermes sessions list- List your saved conversations.
Slash commands
/skills pending- List skills the agent has staged and is waiting for you to review.
/skills diff- Show exactly what a proposed skill would add or change before you accept it.
/skills approve- Accept a staged skill so the agent can reuse it.
/skills reject- Discard a staged skill you do not want.
Files & config
~/.hermes/config.yaml- Hermes’s main settings file — which provider, model and server it uses. The ~ means your home folder.
~/.hermes/.env- A hidden file holding secrets like API keys, kept out of the main config.
provider: custom- A config setting that points Hermes at any OpenAI-compatible server — used for local models.
base_url- The web address of the model server Hermes talks to (e.g. a local LM Studio server).
OPENAI_API_KEY- The environment variable holding your model key. Local servers ignore it; cloud providers require a real one.
SKILL.md- A saved skill — a short file describing a procedure the agent learned once and can repeat.
config.yaml- Hermes’s settings file (full path ~/.hermes/config.yaml).
Install
curl- A command-line tool that downloads from a web address — here it fetches the install script.
bash- The standard Linux/macOS shell — it runs the downloaded install script.
iex- PowerShell’s Invoke-Expression — runs the text it is given as a command (Windows install).
irm- PowerShell’s Invoke-RestMethod — downloads from a web address (Windows install).
source ~/.bashrc- Reload your shell’s settings so a newly installed command is found without reopening the terminal.
.dmg- A macOS installer file — double-click it to install an app.
.exe- A Windows installer/program file — double-click it to install or run.
Concepts
CLI- Command-line interface — you type commands in a terminal instead of clicking.
TUI- Text user interface — a richer, full-screen terminal app (menus and panels, still keyboard-driven).
LM Studio- A free desktop app that runs AI models on your own computer and exposes an OpenAI-compatible server.
cron- A scheduler for recurring tasks — e.g. "every weekday at 8am".
token- A chunk of text (~¾ of a word) the model reads or writes; context limits are measured in tokens.
context- How much text the model can consider at once. Hermes needs a model with at least ~64k tokens of context.
localhost- Your own computer, as an address — localhost:1234 is a server running on this machine, port 1234.
gateway- The part of Hermes that connects your chat apps (Telegram, Slack…) to the agent.
artifacts- Everything the agent produced or touched in a session — files, links, images — your record of what it did.
Antigravity 18 course ↗
Concepts
Antigravity- Google's standalone desktop application for AI-assisted software development, where one or more AI agents autonomously plan, write, and test code on your behalf.
Antigravity CLI- A command-line version of Antigravity that lets you run agents directly from your terminal without opening the desktop app.
Antigravity SDK- A set of developer tools that lets you embed Antigravity's agent capabilities inside your own programs or infrastructure.
Agent- An AI process that Antigravity launches to carry out a task — it can read files, run code, browse the web, and make changes autonomously.
Artifacts- Structured outputs an agent produces — such as task lists, implementation plans, code diffs, screenshots, and walkthroughs — so you can review what it did before accepting the changes.
Skills- Packaged instruction files that teach an agent how to handle a specific kind of task; you can add them globally or per project.
Review-Driven Development- An Antigravity mode where the agent works independently but pauses at important steps so you can approve or reject significant changes before they are applied.
Project- A folder (or set of folders) you open in Antigravity that defines the workspace boundary — the files, tools, and permissions the agent is allowed to use.
Conversation- A message thread inside a project where you type instructions to the agent and see its replies; one project can contain many conversations.
MCP- Model Context Protocol — a standard way to connect external tools (databases, APIs, services) to an agent so it can use them during a task.
Features
Manager Surface- The part of the Antigravity interface where you can launch, watch, and control several agents running different tasks at the same time.
Editor View- The traditional code-editor side of Antigravity, which supports AI tab-completions and inline commands for developers who prefer a hands-on coding style.
Plan mode- An agent mode where the agent first produces a written implementation plan for you to inspect and approve before it starts making any changes to your code.
Fast mode- An agent mode for small, single-step tasks where the agent acts immediately without stopping to show a plan first.
Slash commands
/browser- A slash command that tells the agent to open a real web browser and carry out a web-based action you describe.
/schedule- A slash command that sets up a task to run automatically at a fixed time or on a repeating schedule, without you having to start it manually.
Models
Gemini 3.5 Flash- The default AI model powering Antigravity agents — Google describes it as four times faster than frontier models while outperforming them on most coding and agentic benchmarks.
Files & config
SKILL.md- The required file inside a Skills package that contains both YAML metadata (so the agent knows when to load the skill) and Markdown instructions (telling it what to do).
Lovable 22 course ↗
Concepts
Build mode- Lovable's implementation mode where agents write code, apply changes across files, and verify results.
Plan mode- Lovable's reasoning mode for exploring ideas, comparing approaches, and reviewing plans without modifying any code.
Prompt- A plain-English instruction you type to tell Lovable what to create, change, fix, or explain in your app.
Prompt queue- A queue of pending prompts you can reorder, edit, copy, or remove before Lovable processes them.
Diff- A side-by-side file change comparison showing exactly which lines were added or removed by Lovable.
Knowledge- Persistent instructions you write once that Lovable follows across all future conversations, available at both workspace and project level.
Lovable Cloud- Lovable's built-in full-stack platform that provides a database, user authentication, file storage, and edge functions without any manual setup.
Secrets- Securely stored credentials (API keys and passwords) that are automatically injected into your edge functions so they never appear in your code.
Credits- The units Lovable uses to measure usage — both Plan mode messages and Build mode runs consume credits from your workspace's shared balance.
Workspace- The top-level container that holds all your projects, team members, billing, and settings for one account or organisation.
Project- A single app you are building inside Lovable, with its own code, chat history, integrations, and settings.
Subagents- Temporary, read-only helper agents Lovable spins up to research, inspect code, and browse the web in parallel before the main agent makes changes.
Edge functions- Serverless TypeScript functions that run on Lovable Cloud and handle backend tasks such as APIs, webhooks, and third-party integrations.
Features
Preview- The live, interactive view of your app shown inside Lovable while you are building, so you can see changes as they happen.
Preview toolbar- An overlay above the preview that lets you select any element, edit text directly, add annotations, and leave comments.
Visible tasks- The step-by-step progress shown in the chat during a build, displaying the current step, files being modified, and tools being used.
History- The chronological log of every change made to your project, letting you revert to any earlier version or save a checkpoint.
Remix- A way to create an independent copy of a project as the starting point for a brand-new project, preserving the original.
Publish- The action that deploys a snapshot of your project to a live URL so others can visit and use your app.
Custom domain- A web address you own (such as myapp.com) that you connect to your published Lovable project instead of the default lovable.app URL.
GitHub integration- A two-way sync between your Lovable project and a GitHub repository so your code is version-controlled and other developers can collaborate on it.
Browser testing- A feature where Lovable controls a real browser to click buttons, fill forms, and capture screenshots to verify your app's end-to-end behaviour.
n8n 20 course ↗
Concepts
Workflow- A saved sequence of connected steps (nodes) that n8n runs automatically to move or transform data between apps.
Node- A single building block in a workflow — each node performs one action, such as sending an email, filtering data, or calling an API.
Trigger node- A special node that sits at the start of a workflow and decides when it runs — for example, on a schedule, when a form is submitted, or when another app sends a signal.
Action node- A node that does something in an external service — such as creating a row in Google Sheets, sending a Slack message, or reading an email.
Core node- A built-in utility node that handles data processing or flow control without connecting to an external service — examples include IF, Filter, Merge, and Code.
Connection- The arrow drawn between two nodes on the canvas that tells n8n to pass data from one node to the next when the workflow runs.
Execution- One complete run of a workflow — n8n records what happened at each node so you can inspect inputs, outputs, and any errors afterward.
Credentials- Securely stored login details (such as API keys or passwords) that let n8n connect to an external service on your behalf without exposing secrets inside the workflow.
Expression- A small piece of JavaScript written inside double curly braces ({{ }}) that lets you pull in data from a previous node or do a quick calculation instead of typing a fixed value.
Item- A single unit of data travelling through a workflow — for example, one email, one spreadsheet row, or one API result.
Data mapping- The act of telling a node where to find its input by dragging a field from a previous node's output onto the current node's input — no code required.
Manual execution- Running a workflow by clicking the Execute button yourself, used for testing before you switch the workflow on for automatic production runs.
Features
Canvas- The visual drag-and-drop workspace inside n8n where you build a workflow by placing and connecting nodes.
Webhook- A URL that n8n creates for you so that an outside app can instantly start your workflow by sending data to that address.
Schedule trigger- A trigger node that starts a workflow automatically at a set time or repeating interval, similar to a calendar alarm.
IF node- A core node that checks a condition and sends each data item down one of two paths — True or False — so different actions can happen depending on the data.
Sub-workflow- A separate workflow that another workflow calls like a reusable function, helping you keep complex automations organised and avoid repeating the same steps.
Error handling- A set of features in n8n — including dedicated error workflows and the Stop And Error node — that let you define what should happen if a node fails instead of silently stopping.
Sticky note- A text annotation you can place anywhere on the canvas to explain what part of a workflow does, without affecting how it runs.
Template- A pre-built workflow shared by the n8n community that you can import and adapt instead of building from scratch.
openclaw 2 course ↗
Commands
npm i -g openclaw- A terminal command that downloads and installs the OpenClaw program on your computer so you can run it from anywhere; `npm i -g` means "install globally via Node Package Manager".
openclaw onboard- The guided first-run setup wizard for OpenClaw that walks you step by step through connecting your AI model, configuring your gateway, linking chat channels (WhatsApp, Telegram, etc.), and optionally installing OpenClaw as a background service.
opencode 27 course ↗
Concepts
opencode- An open-source AI coding agent that runs in your terminal (or as a desktop app or IDE extension) and helps you read, write, and change code by chatting with a large language model.
TUI- Terminal User Interface — the interactive chat screen you see when you launch opencode in your terminal, where you type messages and watch the AI respond.
provider- A company that supplies the AI model opencode talks to (for example Anthropic, OpenAI, or Google); you configure your API key for each provider you want to use.
model- The specific AI brain opencode uses to answer your questions and write code (for example claude-sonnet-4-5 or gpt-4o); you can switch models mid-session.
agent- A named AI assistant profile in opencode with its own set of instructions, permitted tools, and optionally a specific model — for example a 'Plan' agent that cannot edit files.
subagent- A specialist agent that a primary agent can invoke automatically to handle a subtask (like researching external docs); you can also invoke one manually by typing @agentname in your message.
skill- A reusable instruction set stored in a SKILL.md file that agents can load on demand, like a reference manual the AI can consult for a specific topic or workflow.
MCP server- An external tool server that connects to opencode via the Model Context Protocol, giving the AI access to additional capabilities such as searching documentation or querying a database.
session- One continuous conversation with opencode, including all your messages, the AI's replies, and any file changes made during that exchange.
permission- A setting that controls whether opencode can use a particular tool automatically (allow), must ask you first (ask), or is blocked from using it entirely (deny).
LSP- Language Server Protocol — a standard opencode uses to connect to language servers and receive diagnostics (errors, warnings) as feedback that helps the AI detect and fix code issues.
Features
Plan mode- A built-in primary agent (switched to with the Tab key) where opencode is restricted from making file changes, so it analyses and describes how it would approach a task without touching your code.
Build mode- The default primary agent where opencode has full permission to read, write, and edit files in your project.
OpenCode Zen- A paid AI model gateway maintained by the opencode team that offers a curated set of tested and verified models on a pay-as-you-go basis, so you can access quality-checked models without setting up separate provider accounts.
snapshot- A save-point opencode automatically tracks as the AI makes file changes, used by the undo system to restore your code if you want to reverse the AI's edits.
compaction- The process of automatically summarising older parts of a conversation to keep the context window from filling up, helping the AI remain accurate on long tasks.
Files & config
AGENTS.md- A plain-text file you place in your project folder (or home config folder) that gives the AI standing instructions about your project's structure, coding conventions, and quirks.
opencode.json- The main configuration file for opencode where you set your preferred model, provider API keys, tool permissions, and other project-level settings.
Slash commands
/init- A slash command that scans your project and automatically generates an AGENTS.md file with a summary of the codebase structure and build instructions.
/undo- A slash command that removes your last message, the AI's response, and any file changes made during that exchange, letting you rephrase and try again.
/redo- A slash command that re-applies a change you previously undid.
/share- A slash command that generates a shareable public web link to your current conversation so teammates can review the chat without needing opencode installed.
/compact- A slash command that summarises the current conversation to reduce context size, useful when a long session is making responses slower or less focused.
/new- A slash command that starts a fresh session, clearing the conversation history so you can begin a new task with a clean slate.
/models- A slash command that lists all AI models available to you through your configured providers.
/sessions- A slash command that shows your previous conversations so you can switch back to an earlier session and continue where you left off.
/thinking- A slash command that toggles the display of the model's reasoning blocks, letting you see the thinking the AI showed before answering — it controls visibility only, not whether the model reasons.
OpenRouter 12 course ↗
API Endpoints
/api/v1/chat/completions- The OpenRouter API endpoint you send your message to — it receives your request, routes it to the chosen AI model, and returns the reply.
https://openrouter.ai/api/v1/models- A public OpenRouter URL that returns a JSON list of every available model, including its ID, pricing, context length, and supported features.
Flags & Variants
:free- A suffix you append to a model ID (e.g. meta-llama/llama-3.2-3b-instruct:free) to select the no-cost variant of that model; free variants have low daily rate limits and are intended for experimentation.
Models
google/gemma-4-31b-it:free- An example OpenRouter model ID showing the standard format: provider name, a slash, the model name, and the :free variant suffix to use it at no cost.
anthropic/claude-fable-5- An OpenRouter model ID for Anthropic's Claude Fable 5, a high-capability model designed for long-running, autonomous coding and knowledge-work tasks.
API Response
choices[0].message.content- The path inside the API's JSON response where the model's actual text reply is stored — choices is an array, [0] picks the first (usually only) result, and .content holds the message text.
Concepts
POST- An HTTP method that sends data to a server; when calling the OpenRouter API you POST your request (model choice, messages, settings) as a JSON body.
Headers
Authorization: Bearer YOUR_KEY- An HTTP request header that proves your identity to OpenRouter — replace YOUR_KEY with your actual API key so the server knows who is making the request.
Content-Type: application/json- An HTTP request header that tells the server your request body is formatted as JSON, which is required when calling the OpenRouter chat completions endpoint.
Libraries
openai- The name of the official OpenAI Python package; because OpenRouter uses the same API format as OpenAI, you can install this package and point it at OpenRouter instead, saving you from learning a separate library.
Commands
pip install openai- A terminal command that downloads and installs the OpenAI Python package onto your computer so you can use it in your code.
API Request Fields
model- A key in the JSON request body that tells OpenRouter which AI model to use for your request, specified as a provider/model-name string such as google/gemma-4-31b-it:free.
Perplexity 19 course ↗
Concepts
Answer Engine- What Perplexity calls itself — instead of returning a list of links like a search engine, it reads the web in real time and writes a direct answer with cited sources.
Thread- A single conversation in Perplexity where each follow-up question keeps the context from previous turns, so you never have to repeat yourself.
Citations- Numbered source links that appear inline in every Perplexity answer so you can click through and verify the original web page or paper.
Sonar- Perplexity's own family of AI models, optimised for fast, accurate web-grounded answers; the base Sonar model is the default search model, while Sonar Pro and Sonar Deep Research handle progressively more complex tasks.
Perplexity Pro- The paid subscription tier (around $20/month) that unlocks unlimited Pro Search, Deep Research, file uploads, model selection, and API credits.
Perplexity Max- A higher-tier subscription above Pro that adds features like Model Council, priority access to new models, and additional advanced capabilities.
Sonar API- A developer interface that lets programmers embed Perplexity's web-grounded search capability into their own applications, using the same request format as the OpenAI API.
Features
Focus- A filter you apply before searching that tells Perplexity which part of the web to look in — for example Academic restricts results to peer-reviewed papers, Social searches Reddit and forums, and Video pulls from YouTube content.
Pro Search- A deeper search mode that breaks your question into multiple sub-queries, consults more sources, and synthesizes a more thorough answer than the default Quick Search.
Quick Search- The default, faster search mode suited for simple factual questions where a brief answer with a few sources is enough.
Deep Research- An autonomous research mode that spends several minutes performing dozens of searches across hundreds of sources and produces a structured, multi-section report — equivalent to asking a research analyst to investigate a topic for you.
Spaces- Collaborative workspaces inside Perplexity where you can group related threads, write custom instructions that apply to every search, and invite teammates to contribute.
Pages- A publishing feature that turns any Perplexity research thread into a formatted, shareable article with a public URL you can distribute or embed.
Connectors- Integrations that link Perplexity to your external apps — such as Gmail, Slack, Notion, or Google Drive — so it can pull live data from those tools into its answers.
Model Council- A premium feature that runs your query through three AI models simultaneously, then uses a fourth 'chair' model to synthesize their responses into one combined answer — available to Max subscribers.
Scheduled Searches- Automated queries you set up once that Perplexity re-runs on a daily, weekly, or monthly schedule and delivers to you as a notification.
File Upload- A feature that lets you attach PDFs, spreadsheets, images, or documents to a conversation so Perplexity can read and answer questions about their contents; free users get a daily limit, while Pro unlocks unlimited uploads.
Comet- An AI-native web browser made by Perplexity, built on Chromium, with a built-in assistant for summarising pages and automating multi-step browsing tasks, available on desktop and mobile.
Voice Mode- A conversational interface that lets you speak your questions aloud and hear Perplexity's answers spoken back, using a real-time speech model.
v0 20 course ↗
Concepts
Sandbox- An isolated virtual machine that hosts your project files, runs the live preview, and executes commands on your behalf — completely separate from your deployed app, so each chat gets its own environment with no state leaking between them.
Live preview- A real-time view of your running app inside v0's chat, served from the sandbox so what you see matches what your users will experience.
Deployment- Publishing your v0 project to Vercel so it runs at a public URL with automatic HTTPS and global CDN distribution.
Production URL- The single public web address that represents the live version of your project; the URL stays constant across all deployments so your app is always reachable at the same address.
React Server Components- A Next.js feature where parts of your page are rendered on the server before being sent to the browser, improving load speed and search-engine visibility; v0 uses Next.js, which enables these by default.
Server actions- Functions that run on the server rather than in the browser, used in v0's Next.js projects to handle form submissions, database writes, and other backend tasks.
Environment variables- Secret configuration values (like API keys) stored outside your code and managed through your connected Vercel project, so sensitive credentials are not hard-coded or publicly visible.
NEXT_PUBLIC_- A required prefix for environment variable names in Next.js when the value needs to be accessible in the browser; without it, the variable is available on the server only.
Vercel- The cloud hosting platform that v0 is built on; it handles deploying your app, managing your domain, and distributing it globally — and your v0 account is a Vercel account.
Agentic features- Autonomous capabilities where v0 plans and carries out multi-step tasks on its own — such as running terminal commands, searching the web, fixing errors, and calling external integrations — without you directing every step.
Features
Design Mode- A visual editing tool in v0 that lets you click any element in the live preview and adjust its appearance using a style panel or natural-language instructions, without writing code directly.
Connect panel- The v0 interface where you link external services — databases, AI models, and other integrations — to your project.
Versions- A saved snapshot of your project's code created each time v0 updates a code block in response to a message; you can review, compare, or restore any earlier version.
Duplicate- Creating your own copy of a shared v0 chat so you can make changes without affecting the original, with the option to keep it linked to the same Vercel project.
Unlisted- A sharing setting where anyone with the link can view your project, but it will not be indexed by search engines or appear in public galleries.
Templates- Published v0 chats you can fork as a starting point, giving you a working app structure to customise instead of starting from a blank prompt.
MCP integrations- Connections to external tools and data sources (such as databases, APIs, and services like Stripe or Neon) that v0's AI automatically considers when generating responses, allowing it to incorporate real external context.
Figma integration- A v0 feature that lets you attach a Figma link and have v0 convert the design — including layout, colors, and spacing — into working code.
GitHub integration- A v0 connection that automatically commits every code change to a dedicated branch in your GitHub repository and lets you open a pull request to merge it into main.
Custom domain- A web address you own (like myapp.com) that you can attach to your deployed v0 project instead of using the default Vercel URL.
Codex 22 course ↗
Commands
codex- The OpenAI Codex CLI tool — a lightweight AI coding agent you run in your terminal that can read files, write code, and execute commands on your behalf.
codex exec- Runs Codex non-interactively from the command line, streams results to your terminal or a file, and exits when the task is done — useful for scripting or automation.
codex mcp- A Codex subcommand that manages connections to Model Context Protocol servers, which let Codex reach external tools and data sources beyond your local files.
Flags
-i- Short for --image; attaches one or more image files to your prompt so Codex can see screenshots, diagrams, or other visuals when answering.
--image- Attaches one or more image files to your prompt so Codex can see screenshots, diagrams, or other visuals when answering.
-m- Short for --model; lets you choose which AI model Codex uses for a session (e.g., codex -m gpt-5.5).
--json- Makes Codex print its output as newline-delimited JSON events instead of formatted text — useful when another program needs to read the results.
--output-schema- Points Codex to a JSON Schema file; Codex validates its final response against that schema before finishing, ensuring the output has the exact shape your code expects.
-o <path>- Short for --output-last-message; writes the assistant's final reply to a file at the given path, making it easy to pipe results into other scripts.
Slash commands
/model- An interactive slash command that lets you switch the AI model Codex is using mid-session without restarting.
/permissions- An interactive slash command that sets what Codex is allowed to do without asking first — adjusting the approval threshold for the current session.
Files & config
~/.codex/config.toml- The main user-level configuration file for Codex, stored in your home directory, where you set durable defaults like model, MCP servers, and feature flags.
~/.codex/AGENTS.md- A personal instructions file Codex reads before every session — write it in plain English to tell Codex about your preferences, coding style, or recurring context.
.git- A hidden folder Git creates inside every repository to store the project's full version history and configuration — its presence tells tools (including Codex) that the folder is a Git repo.
Concepts
workspace-write- A sandbox mode that lets Codex read and edit files inside your current project folder, but blocks it from writing elsewhere on your computer or accessing the network.
untrusted- An approval policy that lets Codex run commands it recognises as safe automatically but stops and asks you before running anything outside its trusted set.
on-request- An approval policy that lets Codex work freely within its sandbox but pauses to ask for your permission whenever it needs to go beyond those boundaries.
never- An approval policy that lets Codex act fully autonomously without asking for permission — it still respects the sandbox limits, but never pauses for human approval.
npm test- A standard command that runs the automated tests defined for a JavaScript or TypeScript project — if all tests pass, the code behaves as expected.
Models
gpt-5.5- OpenAI's most capable Codex model (as of mid-2026), best for complex coding, research, and multi-step tasks — the recommended default when quality matters most.
gpt-5.4-mini- A faster, lower-cost Codex model suited for simpler or repetitive tasks where speed matters more than maximum reasoning power.
gpt-5.3-codex-spark- A text-only research-preview Codex model built for near-instant response (over 1,000 tokens per second), optimized for real-time coding iteration — available to ChatGPT Pro subscribers.
Claude 1 course ↗
Concepts
<context>- An XML-style tag you wrap around background information in a Claude prompt so Claude can clearly distinguish it from your instructions or question.
Ollama 34 course ↗
Commands
ollama run- Starts an interactive chat session with a model; if the model isn't already downloaded, Ollama downloads it automatically first.
ollama pull- Downloads a model from the Ollama library to your computer without starting a chat session.
ollama list- Shows all models you have downloaded and stored locally on your machine (the canonical short form of this command is ollama ls).
ollama ps- Lists which models are currently loaded in memory and actively running.
ollama stop- Stops a running model, unloading it from memory without removing it from your computer.
ollama rm- Permanently deletes a downloaded model from your computer to free up disk space.
ollama serve- Manually starts the Ollama background server that listens for requests; on most systems this starts automatically at login.
hermes setup- Runs the Hermes Agent interactive setup wizard that walks you through configuring all or part of your Hermes installation.
hermes --tui- Launches Hermes Agent in its terminal user interface mode instead of the classic command-line prompt interface.
systemctl edit ollama.service- Opens the Linux service configuration for Ollama so you can add environment variables (such as OLLAMA_HOST) that apply every time the server starts.
Slash commands
/bye- A slash command you type inside an Ollama chat session to end the conversation and return to your normal terminal prompt (also works as /exit).
Concepts
>>>- The prompt symbol Ollama shows when it is waiting for you to type a message inside an interactive chat session.
openai- A Python library (also the name of the company) that provides a standard way to call AI chat APIs; Ollama supports the same interface so you can use this library with local models.
pip install openai- The terminal command that installs the OpenAI Python library onto your computer so your Python scripts can call AI APIs.
api_key- A secret string that identifies who is making an API request; when using Ollama locally no real key is needed, but the library requires the field to exist.
base_url- A configuration setting that tells an API client where to send its requests; point it to http://localhost:11434/v1 to redirect OpenAI library calls to your local Ollama server.
localhost- A special hostname that always refers to your own computer, so a service at localhost is only reachable from that same machine.
"stream": false- A JSON setting in an API request that tells the model to send its entire response as one message instead of word-by-word as it generates.
ifconfig- A terminal command on Mac and Linux that displays your computer's network addresses, useful for finding the IP address other devices on your network can use to reach you.
ip addr- A terminal command on Linux (modern alternative to ifconfig) that shows all network interfaces and their IP addresses.
ipconfig- A terminal command on Windows that displays your computer's network configuration including its local IP address.
Flags
OLLAMA_HOST- An environment variable that controls which network address Ollama listens on; set it to 0.0.0.0:11434 to allow other computers on your local network to connect.
OLLAMA_KEEP_ALIVE- An environment variable that sets how long a model stays loaded in memory after its last use; the default is 5 minutes, but you can set values like 24h to keep it loaded longer.
Files & config
http://localhost:11434- The default web address where Ollama's API server runs on your own computer, reachable only from that same machine.
/api/chat- Ollama's built-in REST API endpoint for sending chat messages and receiving model responses programmatically.
/v1/- The URL prefix for Ollama's OpenAI-compatible API, which lets software written for OpenAI's API talk to your local Ollama models instead.
~/.hermes/config.yaml- The main configuration file for Hermes Agent, stored in a hidden folder in your home directory, where you set the model provider, base URL, and other non-secret preferences.
~/.hermes/.env- A file in your Hermes Agent folder that stores secret values such as API keys, kept separate from the main config file so credentials are not accidentally shared.
provider: custom- A setting in the Hermes Agent config.yaml that tells Hermes to call a custom OpenAI-compatible endpoint directly, such as your local Ollama server, using the base_url you specify.
[Service]- A section header in a Linux systemd unit file where you place environment variable definitions that apply to the service being configured.
Models
llama3- Meta's Llama 3 open-weight language model, available through Ollama and described as the most capable openly available LLM at the time of its release.
qwen2.5- Alibaba's Qwen 2.5 open-weight language model series, available through Ollama and well-regarded for coding and mathematics tasks.
gemma- Google's open-weight language model, available through Ollama in 2B and 7B parameter sizes.
mistral- Mistral AI's 7B open-weight language model, available through Ollama and distributed under the Apache license.
LM Studio 21 course ↗
Commands
lms- The command-line tool bundled with LM Studio that lets you start the server, download models, and manage everything from a terminal instead of the desktop app.
lms server start- Launches LM Studio's local API server so other programs on your computer (or network) can send it requests and get AI responses back.
lms server stop- Gracefully shuts down the running LM Studio API server, terminating any in-progress requests before stopping.
lms server status- Prints whether the LM Studio API server is currently running and which port it is listening on.
lms --version- Prints the installed version number of the lms command-line tool — the actual documented subcommand is lms version, but both forms report the current CLI version.
llmster- The headless (no desktop window) daemon version of LM Studio designed for servers or machines without a screen — it runs as a standalone background service and can still serve models over the API.
hermes setup- A setup command for the Hermes AI agent that guides you through configuring your model provider, including connecting it to a local LM Studio server.
hermes --tui- Starts Hermes in its text user interface (TUI) mode — a keyboard-driven chat panel that runs entirely inside your terminal with live streaming and modal overlays.
chmod +x start-local-llm.sh- A shell command that marks a script file as executable on Mac or Linux so you can run it directly; without this step the operating system refuses to run the script.
pip install openai- The Python package-manager command that downloads and installs the OpenAI Python library, which LM Studio also accepts because it speaks the same API format.
API & URLs
http://localhost:1234/v1- The full address of LM Studio's OpenAI-compatible API server on your own machine — localhost means 'this computer', 1234 is the default port number, and /v1 is the path prefix for the OpenAI-compatible endpoints.
localhost:1234- The host and port where LM Studio's server listens by default — shorthand for 'your own machine, port 1234'.
localhost- A special hostname that always means 'this computer' — using it ensures the connection never leaves your machine.
http://<your-ip>:1234/v1- The address other devices on your local network use to reach your LM Studio server — replace <your-ip> with your computer's actual network IP address (e.g. 192.168.1.5).
0.0.0.0- A special network address meaning 'listen on all network interfaces' — when LM Studio binds to this (via lms server start --bind 0.0.0.0), devices on your local Wi-Fi or wired network can connect to it, not just your own computer.
Libraries & Code
openai- The name of a Python library originally made for OpenAI's cloud API; LM Studio intentionally speaks the same format, so you can reuse this library to talk to your local models instead.
api_key- A configuration field where you provide a password or token to authenticate with an API; LM Studio does not require a real key by default, but the field must still be present (any placeholder string works).
base_url- A configuration field that tells a client library where the API server lives — for LM Studio you set this to your local server address instead of OpenAI's cloud address.
Files & Config
config.yaml- A plain-text configuration file written in YAML format — tools like Hermes read this file at startup to know which server URL, model, and preferences to use.
~/.hermes/config.yaml- The main configuration file for the Hermes tool, stored in a hidden folder in your home directory — you edit it to point Hermes at your local LM Studio server.
~/.hermes/.env- A hidden environment-variable file inside the Hermes config folder where you can store API keys and secrets separately from the main config file.
Jan 6 course ↗
Concepts
http://localhost:1337- A common way to write the base URL of Jan's built-in API server; Jan's docs show http://127.0.0.1:1337 as the actual default address, and localhost is simply a hostname alias for 127.0.0.1 on most computers.
localhost:1337- The host and port of Jan's local API server (shorthand without the http:// prefix); Jan's docs use 127.0.0.1:1337 as the canonical form, and you can change the port under Settings > Local API Server > Configuration.
model- A field in an API request body that tells the server which AI model to use; when talking to Jan you set this to the model ID shown in Jan's model list.
openai- A Python library originally made for OpenAI's cloud service that can also talk to any OpenAI-compatible server — including Jan's local server — using the same code.
api_key- A string you set in Jan's API server configuration that callers must include in requests; Jan accepts any string you choose, and you can also leave it empty to disable authentication entirely.
Commands
pip install openai- A shell command that downloads and installs the openai Python library onto your computer so you can import it in your scripts.
AnythingLLM 4 course ↗
Commands
@agent- A mention you type in a chat message in AnythingLLM to explicitly start an agent session, giving the conversation access to tools like web search or file reading.
/api/docs- A URL path you open in your browser on a running AnythingLLM instance to see its full interactive API reference, listing every endpoint you can call from code.
Files & config
mintplexlabs/anythingllm- The official Docker Hub image name for AnythingLLM, which you reference when running the app inside a container on your own machine or server.
Concepts
requests- A popular Python library that lets your code send HTTP requests (GET, POST, etc.) to web addresses, commonly used to call REST APIs like AnythingLLM's.
ChatGPT 18 course ↗
Models
o3- OpenAI's most capable reasoning model available in ChatGPT, designed for difficult problems in coding, mathematics, and science that require extended multi-step thinking before responding.
Concepts
prompt- The message or question you type into ChatGPT — it is the instruction that tells the AI what you want it to do.
context window- The total amount of text (measured in tokens) that ChatGPT can read and hold in a single conversation; content beyond this limit may no longer be considered when generating a response.
token- A small chunk of text — roughly three-quarters of a word — that ChatGPT uses internally to read and generate language; token limits determine how long a conversation or document can be.
ChatGPT Plus- A paid subscription tier that gives individual users access to more powerful models, higher usage limits, and features like Deep Research and Advanced Voice.
Features
Custom Instructions- A settings feature that lets you tell ChatGPT persistent preferences — such as your profession or preferred response style — so you don't have to repeat them in every new chat.
Memory- A feature where ChatGPT saves useful facts you share across conversations (like dietary preferences or your job) so future chats feel more personalised; you can view, edit, or delete saved memories at any time.
Projects- A workspace inside ChatGPT that groups related chats, uploaded files, and shared context under one goal, useful for ongoing work that spans multiple sessions.
Canvas- A side-by-side editing workspace that opens automatically for longer writing or coding tasks, letting you directly edit the output and ask ChatGPT to revise specific sections.
GPTs- Custom versions of ChatGPT built with specific instructions, uploaded knowledge, and selected tools for a particular purpose — for example, a GPT focused on cooking advice or legal summaries.
GPT Store- A public directory at chatgpt.com/gpts where anyone can browse and use GPTs created by OpenAI, partners, and the wider community, organised by category.
Deep Research- A ChatGPT tool that autonomously searches the web, reads multiple sources, and compiles a long-form referenced report on a topic you specify.
Advanced Voice- A mode that lets you speak to ChatGPT and hear it respond in a natural-sounding voice, available on the ChatGPT website, iOS, Android, and Windows app.
Data Analysis- A built-in ChatGPT capability that lets you upload spreadsheets or data files and ask questions about the data, create charts, or run calculations — no coding required.
File Uploads- The ability to attach documents such as PDFs, Word files, or spreadsheets to a ChatGPT conversation so the model can read, summarise, or answer questions about their contents.
Image Generation- A ChatGPT capability that creates original images from a text description you provide, or edits an existing image based on your instructions.
Web Search- A ChatGPT tool that looks up current information on the internet in real time, allowing it to answer questions about recent events or facts beyond its training data.
ChatGPT Agent- A feature that lets ChatGPT perform multi-step tasks on your behalf — such as browsing the web, filling forms, or running code — with minimal input from you.
KNIME 20 course ↗
Concepts
Workflow- A collection of connected nodes arranged on the editor canvas that together carry out a complete data analysis from reading in data to producing a result.
Node- A single building block in a KNIME workflow — a colored box that performs one task, such as reading a file, filtering rows, or training a model.
Port- A connection point on the side of a node — input ports (left) receive data and output ports (right) send data to the next node.
Data Port- A port that passes a data table between nodes; shown as a black triangle and can only connect to another data port.
Model Port- A port that passes a trained machine-learning model from one node (e.g. a trainer) to another (e.g. a predictor).
Flow Variable- A named value (like a file path or a number) that travels between nodes through flow-variable ports and can change how a node is configured without editing it by hand.
Node Status- A traffic-light indicator beneath each node: red means not yet configured, yellow means configured and ready, green means successfully executed.
Workspace- The folder on your computer where KNIME stores all your workflows, node settings, and any data the workflow produces.
Columnar Backend- An optional KNIME execution engine that stores table data column-by-column using Apache Arrow, which reduces memory use on large datasets.
RowID- A unique identifier automatically assigned to every row in a KNIME data table, similar to a row number in a spreadsheet.
Execution- The act of running a node or an entire workflow so that it processes its input data and produces output; triggered by pressing F7 or clicking the Execute button.
Features
Node Repository- The searchable panel listing every node available in your KNIME installation; you drag nodes from here onto the workflow canvas.
Workflow Editor- The central canvas where you place and connect nodes to build a workflow.
Component- A reusable, shareable group of nodes packaged as a single custom node with its own configuration dialog; can be published to KNIME Hub for others to use.
Metanode- A group of nodes collapsed into one box purely to keep the canvas tidy; unlike a Component it cannot be shared or given a custom dialog.
KNIME Hub- The online repository where you can store, share, and download KNIME workflows and components; available as a free Community Hub or an enterprise Business Hub.
K-AI- The built-in KNIME AI assistant that can answer questions about the platform (Q&A mode) or automatically extend your workflow by suggesting and adding nodes (Build mode).
Space Explorer- The file-browser panel in KNIME where you navigate and manage your workflows, folders, components, and data files.
Node Monitor- The bottom panel that shows the output table, statistics, or flow variables produced by a selected node after it has been executed.
Workflow Annotation- A free-text box you can place anywhere on the workflow canvas to document what a section of the workflow does; supports basic markdown formatting.
Cursor 2 course ↗
Concepts
sns.clustermap- A function from the seaborn visualisation library that draws a heatmap with rows and columns automatically reordered so that similar values cluster together.
Files & config
utils.py- A Python file named by convention to hold small helper functions shared across multiple scripts in the same project, keeping the main files shorter and easier to read.
GitHub Copilot 13 course ↗
Concepts
.py- The file extension for a Python source code file — any file ending in .py contains Python instructions the computer can run.
def- A Python keyword that marks the start of a new function definition — everything indented beneath it is the code that runs when you call that function.
Counter- A Python built-in class (from the `collections` module) that counts how many times each item appears in a sequence and stores the results as a dictionary.
DataFrame- A table of data provided by the pandas library — it has named columns and numbered rows, similar to a spreadsheet you can manipulate with code.
KeyError- A Python error that occurs when you try to access a dictionary or DataFrame column using a name that does not exist in it.
ValueError- A Python error that occurs when a function receives a value of the right type but an inappropriate content — for example, passing an empty list where data is required.
groupby- A pandas operation that splits a table into groups based on the values in a column (like splitting experiment rows by treatment condition) so you can calculate statistics for each group separately.
.div()- A pandas method that divides every value in a Series or DataFrame by a number or another Series — commonly used to convert raw counts into proportions.
pd.read_csv- A pandas function that reads a CSV (comma-separated values) file from disk and loads it into a DataFrame so you can analyse it with Python.
sns.heatmap(...)- A seaborn function that draws a colour-coded grid where each cell's colour represents a numeric value — useful for spotting patterns across many samples or genes at once.
method- A function that belongs to an object or class — for example, `.mean()` is a method on a pandas DataFrame that calculates the average of a column.
Features
@- In GitHub Copilot Chat (VS Code), typing @ opens a list of chat participants — domain-expert agents such as @workspace or @github — that you can direct your question to for specialised help.
/- In GitHub Copilot Chat, typing / opens a list of slash commands — shortcuts for common tasks like /explain (explain selected code) or /tests (generate unit tests) — so you do not need to write a full prompt.
Windsurf 6 course ↗
Concepts
argparse- A built-in Python module that lets your script accept named options (like --input or --threshold) typed on the command line, so you don't have to hard-code values inside the file.
evalue- Short for "expect value" — a BLAST statistic that measures how likely a sequence match is to be a coincidence; lower values (e.g. 1e-10) mean a more trustworthy biological match.
import csv- A Python statement that loads the built-in csv module so your script can read and write spreadsheet-style files with rows and columns.
NameError- A Python error that fires when your code refers to a variable or function that hasn't been defined yet — usually a typo or a missing assignment.
TypeError- A Python error that fires when an operation is applied to the wrong kind of value — for example, trying to do arithmetic on a piece of text instead of a number.
Flags
--input- A command-line flag passed to a Python script (via argparse) that tells the script which file to read; you type it after the script name, e.g. python filter.py --input data.csv.
Aider 3 course ↗
Flags
--model- An Aider command-line flag that specifies which AI model to use for the main chat session, for example --model claude-3-opus-20240229 to select a specific Claude model.
Slash commands
/undo- An Aider slash command that undoes the last git commit if it was made by Aider, restoring your files to how they were before that change.
/tokens- An Aider slash command that reports how many tokens the current chat context is using, so you can see how much of the model's context window is occupied.
Flowise 22 course ↗
Concepts
Chatflow- A visual builder in Flowise for creating single-agent chatbots and simple LLM workflows by connecting nodes on a drag-and-drop canvas.
Agentflow- Flowise's most powerful visual builder — a superset of both Chatflow and Assistant — that supports multi-agent systems, branching logic, loops, and human-in-the-loop checkpoints.
Assistant- The most beginner-friendly Flowise builder, which lets you create an AI agent that follows instructions, uses tools, and retrieves answers from uploaded files — without wiring individual nodes.
Node- A single building block on the Flowise canvas — each node performs one job (e.g. call an LLM, search a document, run a tool) and connects to other nodes via edges.
Canvas- The visual workspace inside Flowise where you drag, drop, and wire together nodes to design an AI workflow.
LLM- Large Language Model — the AI text engine (such as GPT-4 or Claude) that reads instructions and generates responses inside a Flowise workflow.
Agent- An autonomous AI component that can reason, plan, decide which tools to use, and take actions — unlike a plain LLM node, it makes decisions dynamically.
RAG- Retrieval-Augmented Generation — a technique where the AI fetches relevant passages from your own documents before answering, so responses are grounded in your data rather than general training.
Vector Store- A specialised database that stores text as lists of numbers (vectors) so that semantically similar content can be found quickly, even if the exact words differ.
Embedding- A numerical representation of a piece of text — two embeddings that are close together in number-space mean the texts have similar meaning, enabling similarity search.
Chunk- A small segment of a document created by splitting the original file into pieces before indexing, so that only the most relevant part is retrieved rather than the whole document.
MCP- Model Context Protocol — an industry-standard interface that lets AI agents connect to external tools and data sources through a common, provider-maintained interface.
Features
Tool- A function that an agent can call to interact with the outside world, such as searching the web, running a calculation, or making an HTTP request.
Document Store- A Flowise feature that lets you upload, split, and index your own files so that agents can search and retrieve information from them.
Upsert- The action in Flowise that sends your prepared document chunks into a Vector Store — it adds new entries and updates existing ones without creating duplicates.
Flow State- A runtime key-value store that passes data between nodes within a single Agentflow run, letting distant nodes read values set by earlier nodes.
Human Input node- An Agentflow node that pauses execution and waits for a real person to review, approve, or provide information before the workflow continues.
Memory- A component that stores previous conversation messages so the AI can refer back to earlier exchanges within the same chat session.
Buffer Window Memory- A memory type that keeps only the most recent K conversation turns, discarding older ones, to avoid sending too much history to the LLM.
Streaming- A mode where the AI sends its reply token-by-token in real time as it is generated, rather than waiting until the full response is complete before showing anything.
Retriever- A node that queries a Document Store using semantic similarity to fetch the passages most relevant to the user's question.
Execute Flow node- An Agentflow node that calls another Chatflow or Agentflow as a sub-workflow, letting you reuse or nest existing flows inside a larger one.
Langflow 2 course ↗
Commands
pip- The standard tool for installing Python packages; running `pip install something` downloads and installs a library so your Python code can use it.
uv- A fast Python package and project manager (made by Astral) that works like pip but runs significantly faster, especially when installing many packages at once.
CrewAI 8 course ↗
Commands
pip install crewai- The terminal command that downloads and installs the CrewAI library onto your computer so Python can use it.
crewai create crew- A CrewAI CLI command that generates a ready-to-run project folder with all the starter files your crew needs (config files, crew.py, and a .env template).
crewai run- A CrewAI CLI command that executes your crew or flow, reading the project type automatically from pyproject.toml and running all the agents and tasks you have defined.
Files & config
.env- A hidden text file in your project folder where you store secret settings like API keys so they are not baked into your code.
agents.yaml- A CrewAI configuration file (in YAML format) where you describe each agent's role, goal, and backstory without writing Python code.
tasks.yaml- A CrewAI configuration file (in YAML format) where you describe each task — what it asks for, what output it expects, and which agent handles it.
crew.py- The main Python file generated by CrewAI that wires your agents and tasks together into a runnable crew using the settings in your YAML config files.
Concepts
Crew- The core CrewAI Python class that groups your agents and tasks together and controls how they collaborate to complete a goal.