---
title: "AI Coding Dictionary (aihero.dev)"
section: "raw"
type: "source"
created: "2026-08-27"
updated: "2026-08-27"
canonical: "https://pyweb.dev/wiki/raw/articles/aihero-ai-coding-dictionary-2026"
---
# AI Coding Dictionary (aihero.dev)

Definitions extracted from https://www.aihero.dev/ai-coding-dictionary:

- **AI**: A moving label, not a technology. Points at whatever computers can newly, impressively do — right now, large language models.
- **Model**: The parameters. Stateless — does next-token prediction and nothing else. Cannot do anything agentic on its own.
- **Parameters**: The numbers inside a model — often billions — tuned during training. Everything the model knows lives in them. Also called weights.
- **Training**: The process that sets a model's parameters by exposing it to vast amounts of text and adjusting to improve next-token prediction.
- **Inference**: Running a trained model to generate output — what happens on every model provider request. Parameters stay fixed.
- **Effort**: A dial for how much reasoning the model does before it answers. More effort spends more output tokens for a better shot at hard problems.
- **Token**: The atomic unit a model reads and writes. Roughly word-sized but not exactly. Context window size, cost, and latency all count tokens.
- **Next-token prediction**: What the model actually does. Samples one next token from the context, appends it, and runs again. Its only mode of operation.
- **Non-determinism**: The same input can produce different output. A property of how models generate text and how providers serve requests.
- **Model provider**: Whatever serves a model for inference. Usually remote (Anthropic, OpenAI, Google), but can also be local (Ollama, llama.cpp).
- **Harness**: Everything around the model that turns it into an agent: tools, system prompt, context-window management, permissions, hooks.
- **Model provider request**: One round-trip from the harness to the model provider. The harness sends context; the provider returns one response.
- **Input tokens**: Tokens the harness sends on each model provider request. Billed at a lower rate than output tokens.
- **Output tokens**: Tokens the model generates back. Billed at a higher rate than input tokens, since they cost more compute to produce.
- **Prefix cache**: The provider-side store that lets consecutive requests skip re-processing a shared prefix, billing those tokens at a lower rate.
- **Cache tokens**: Input tokens the provider has cached from a previous request via its prefix cache, billed at a much lower rate.
- **Stateless**: Carries no information forward. The model is stateless across requests; an agent is stateless across sessions by default.
- **Context**: The relevant information the agent has access to right now — what the agent knows that's pertinent to the task.
- **Context window**: Everything the model sees on each model provider request. Finite, model-specific, the only surface through which the model perceives.
- **Stateful**: Carries information forward. Sessions are stateful across turns; agents can be made stateful across sessions via a memory system.
- **Agent**: A model harnessed with tools, a system prompt, and a context window, that takes turns with a user. The model in motion.
- **System prompt**: The instructions the harness prepends to every model provider request — the agent's standing brief. Usually stable across a session.
- **Session**: One bounded run of interaction with an agent. Starts empty, accumulates, ends when cleared, closed, or compacted into a fresh session.
- **Turn**: One user message plus everything the agent does in response, up until it yields back to the user. Contains one or more provider requests.
- **Environment**: The world the agent acts on — anything outside the harness that the agent perceives via tool results and changes via tool calls.
- **Filesystem**: A tree of files and directories the agent reads from, writes to, and executes within — the default environment for a coding agent.
- **Tool**: A function the harness exposes for the agent to call — Read, Write, Bash, Search. How an agent perceives and acts on the environment.
- **Tool call**: The model's output naming a tool and its arguments — just structured text. The harness has to read it and execute.
- **Tool result**: What the harness sends back after executing a tool call — file contents, output, or error. The agent's only view of the environment.
- **MCP**: A protocol for plugging external tool servers into a harness — how an agent gets tools beyond what the harness ships with.
- **Permission request**: What the harness shows the user before executing a tool call that isn't pre-approved. The mechanism for putting a human in the loop.
- **Permission mode**: The permission-gating slice of an agent mode — which tool calls trigger a permission request and which run automatically.
- **Agent mode**: A preset bundling a permission mode with behavioral instructions injected into the system prompt. Can flip mid-session.
- **Sandbox**: An isolated environment the agent runs inside — container, VM, or restricted shell. Limits the blast radius of agent actions.
- **Sycophancy**: Confidently agreeable model output. Caused by training that shaped the model to favor answers humans liked — including agreement.
- **Hallucination**: Confidently-wrong model output. Two flavors: factuality (invented facts) and faithfulness (drift from loaded context).
- **Parametric knowledge**: What the model knows from training, stored in its parameters. Frozen at training time. Counterpart to contextual knowledge.
- **Knowledge cutoff**: The date past which a model has no parametric knowledge. Post-cutoff libraries and APIs are fabrication traps unless docs are loaded.
- **Contextual knowledge**: Facts the agent can read directly from the context right now. Counterpart to parametric knowledge.
- **Attention relationship**: The pairing between two tokens — meaningful pairs influence each other more than unrelated ones. A context of N tokens has ~N² of these.
- **Attention budget**: Each token has a finite amount of influence to distribute across the rest of the context. Per-token, doesn't grow when context does.
- **Attention degradation**: As a session grows, each token's attention budget spreads across more competitors; signal on meaningful relationships shrinks.
- **Smart zone**: Early in a session the agent is sharp and focused. As the session grows it drifts into a dumb zone: sloppier, forgetful, more mistakes.
- **Clearing**: Ending the current session and starting a fresh one. The next message begins with an empty session and an empty context window.
- **Handoff**: Transferring agent context from one session to another, with no return path. Carry mechanism varies — artifact, compaction, others.
- **Primary source**: The thing itself — code, transcripts, raw data. Complete and authoritative, but expensive to load into context.
- **Secondary source**: An account of a primary source, one step removed — summaries, docs, compaction summaries. Cheap to load, lossy by construction.
- **Handoff artifact**: A document used as the carry mechanism for a handoff — written by one session to be read by another.
- **Spec**: A handoff artifact describing a multi-session piece of work — what's being built, not how each session does its share. Made of tickets.
- **Ticket**: A handoff artifact scoping one session of work. Stands alone or hangs off a spec. Can block or be blocked by sibling tickets.
- **Compaction**: A handoff done in-memory: the previous session's history is summarised and seeds a fresh session. Lossy — detail traded for headroom.
- **Autocompact**: Compaction triggered automatically by the harness when the context window approaches full.
- **Memory system**: A system that attempts to make an agent stateful across sessions by persisting to the environment and reloading at session start.
- **AGENTS.md**: A file in the environment that the harness loads into the context window at session start — the project's standing brief to the agent.
- **Progressive disclosure**: Loading only the context an agent needs right now, with context pointers to the rest. Borrowed from UI design.
- **Context pointer**: A mention in one document that points to another, so the agent can pull it into context only when the task calls for it.
- **Skill**: A teachable capability bundled as a unit — kept out of the context window until a context pointer pulls it in for the task at hand.
- **Subagent**: An agent spawned by another agent via a tool call. Runs in its own session, reports a single tool result. Cannot spawn further subagents.
- **Human-in-the-loop**: A working pattern where one or more humans pair with the agent during a session — reviewing, redirecting, or collaborating in real time.
- **AFK**: A working pattern where the user kicks off a session and leaves the agent to run unattended (away from keyboard).
- **Automated check**: A deterministic verification that runs in the environment — tests, type checks, lints, build, pre-commit hooks. Pass/fail, no judgement.
- **Automated review**: An agent reviewing another agent's work, often with a different model or system prompt. Non-deterministic: it forms a judgement.
- **Human review**: The user reading the code the agent produced and forming a judgement on it. Reading the diff counts; reading the summary doesn't.
- **Vibe coding**: A working pattern where the user accepts the agent's code without human review. The diff is treated as opaque.
- **Design concept**: The shared understanding of what's being built, held in common between user and agent but separate from any asset.
- **Grilling**: A technique for developing a design concept: the agent interviews the user Socratically, one decision at a time.
- **Prototyping**: Having the agent build a quick, rough version when conversation is too low-fidelity and you need a real artifact to talk about.
- **DX**: Developer experience: how easy a codebase and its toolchain make it for humans to do good work — docs, feedback speed, errors.
- **AX**: Agent experience: how well the environment is set up for an agent to do good work — checks, architecture, and free context.

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## Agent Navigation

### Machine endpoints
- Knowledge graph: https://pyweb.dev/api/graph.json
- Graph analysis: https://pyweb.dev/api/graph-analysis.json
- Context index: https://pyweb.dev/llms.txt