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Every AI term, in plain English — from backpropagation to spicy autocomplete, plus the words lite.computer uses for its own parts.
Every term below is defined once, in plain English, with no assumed knowledge. It covers general artificial intelligence vocabulary, the slang practitioners actually use, the labs and model families worth knowing, and the words lite.computer uses for its own parts.
Model families are named by family, not by version number. Versions change every few weeks; a glossary that carries them is wrong within a month and misleading within three.
The words underneath everything else.
A set of rules or instructions that a computer follows to complete a task.
A hypothetical AI that matches or exceeds human ability across essentially any intellectual task, rather than being good at one. Nobody agrees on how it would be measured, which is why the term causes more argument than it settles.
Also: AGI
See also: Singularity, Narrow AI
The simulation of human intelligence processes by machines, especially computer systems.
Also: AI
Computing systems loosely inspired by the biological neural networks of human brains.
Also: Neural network, ANN
When an algorithm produces results that are systematically prejudiced, because of erroneous assumptions or skewed data in the learning process.
A software application designed to simulate human conversation, ranging from simple rule-based systems to advanced AI.
The process of extracting hidden patterns and knowledge from large sets of data.
A subset of machine learning that uses multi-layered neural networks to model complex patterns in data.
See also: Artificial Neural Network (ANN)
A system that always produces the same output for the same input. Traditional software is deterministic; language models are not, which is the single biggest adjustment for people coming from ordinary software.
Also: Deterministic data
See also: Probabilistic, Wiggly
A problem-solving shortcut or rule of thumb that produces a solution quickly, even if it is not mathematically perfect.
The specific goal or purpose behind a user's interaction or query — booking a flight, as opposed to checking a flight's status.
A subset of AI focused on building systems that learn from data and improve their performance over time without being explicitly programmed for every task.
Also: ML
An AI built for one job — spotting fraud, transcribing speech, recommending films. Every system in production today is narrow AI, however general it feels to use.
Also: Weak AI
See also: Artificial General Intelligence (AGI)
The simplest type of artificial neuron, and the building block used to create larger neural networks.
A system whose outputs carry uncertainty and are expressed as likelihoods rather than certainties. A probabilistic model answers with a confidence, not a fact.
Also: Probabilistic data
See also: Deterministic, Temperature
A bot that follows a strict, pre-programmed script or if-then logic rather than using AI to understand nuance.
Also: Decision-tree bot
See also: Symbolic AI
An extension of the web that makes data machine-readable, letting computers understand the meaning of and relationships between pieces of information.
See also: Structured data, Schema.org
A hypothetical future point where AI surpasses human intelligence, leading to rapid and unpredictable technological growth.
The older tradition of AI built on explicit rules and logical symbols rather than learned statistics. Rule-based bots and expert systems are its surviving descendants.
Also: Good Old-Fashioned AI, GOFAI
See also: Rule-based bot
A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
How the models that write text are built.
The mechanism that lets a model weigh which parts of its input matter most for the word it is producing now. It is what the transformer paper's title refers to.
Also: Self-attention
See also: Transformer
Generating output one token at a time, each one conditioned on everything produced so far. Why text appears word by word rather than all at once.
Also: Decoder-only
See also: Token
A class of deep neural network used mainly for processing and analyzing visual data such as images and video.
Also: CNN
The architecture behind most AI image and video generation. It starts from noise and removes it step by step until an image emerges. Largely replaced GANs for image work.
See also: Generative Adversarial Network (GAN)
Training a small model to imitate a larger one, so that a cheap model inherits much of an expensive one's behavior.
Also: Knowledge distillation
A list of numbers representing the meaning of a piece of text, image or audio, positioned so that similar things sit close together. The basis of semantic search and most retrieval.
Also: Embeddings, Vector
See also: Vector database, RAG (Retrieval-Augmented Generation)
A large model trained once on broad data and then adapted to many downstream jobs, rather than trained from scratch for each one.
Also: Base model
See also: Fine-tuning
The most capable models in existence at a given moment — the handful at the leading edge, usually from a small number of well-funded labs. The membership list changes every few months.
Also: Frontier models
See also: Frontier lab
A framework where two neural networks — a generator and a discriminator — compete against each other to produce highly realistic synthetic data.
Also: GAN
Deep-learning models that create new content — text, images, audio, video, code — based on patterns in their training data.
Also: GenAI
An AI architecture that uses a transformer design to process language and generate human-like text. Also the name OpenAI gives its model family, which is why the acronym is often used as though it meant one company's product.
Also: GPT
See also: Transformer
Running a trained model to get an answer, as opposed to training it. Almost all the money spent on a deployed AI system is spent here.
See also: Training
The stored intermediate state that lets a model avoid re-reading the whole conversation for every new token. It is what makes long conversations affordable, and what consumes the memory they need.
See also: Context window, Context caching
An AI trained on enormous amounts of text to understand, summarize and generate human language.
Also: LLM
How long you wait for an answer. Often split into time to first token — how long before anything appears — and total time, which matter to users in different ways.
Also: Time to first token
A recurrent network design that holds information over longer sequences. The state of the art for language before transformers arrived.
Also: LSTM
See also: Recurrent Neural Network (RNN)
A design that splits a model into many specialist sub-networks and activates only a few per token. It buys the capability of a very large model at the running cost of a much smaller one.
Also: MoE
A model that handles more than one kind of input or output — text and images, or text and audio and video — rather than text alone.
Also: Multimodal model
The learned numbers inside a model — the weights adjusted during training. Model sizes are quoted in billions of them, though parameter count alone stopped predicting capability some time ago.
Also: Weights
See also: Open weight
Shrinking a model by storing its numbers at lower precision. It costs a little accuracy and buys a large amount of speed and memory, which is what lets big models run on a laptop.
See also: Local AI, Small Language Model (SLM)
A model trained to work through a problem before answering, and to spend more computation on harder questions. The distinction from an ordinary model is how long it thinks, not what it knows.
See also: Chain of thought, Test-time compute
An older architecture that reads sequences one step at a time, carrying state forward. Largely displaced by transformers because it cannot be parallelized.
Also: RNN
See also: Transformer, Long Short-Term Memory (LSTM)
A deliberately compact language model, small enough to run on a laptop or a phone. It trades breadth for speed, cost and privacy.
Also: SLM
See also: Local AI, Quantization
Constraining a model to answer in a fixed shape — valid JSON matching a schema — so a program can consume the answer instead of a person reading it.
Also: JSON mode
A setting that controls how much randomness a model uses when choosing its next token. Low temperature is repetitive and predictable; high temperature is varied and more likely to go off the rails.
See also: Probabilistic, Wiggly
The unit a language model actually reads and writes. Roughly three-quarters of a word in English, so a page of prose is about 500 tokens. Pricing, limits and speed are all measured in them.
Also: Tokens
See also: Tokenization, Context window
Cutting text into tokens before a model sees it. It is why a model can miscount the letters in a word: it never saw the letters.
See also: Token
A sampling setting that limits a model's word choice to the most likely options adding up to a given probability. Used alongside temperature to control how adventurous the output is.
Also: Nucleus sampling, Top-k
See also: Temperature
The neural network architecture introduced in the 2017 paper "Attention Is All You Need". It replaced older designs such as RNNs because it processes data in parallel, making it far faster and more powerful at scale. Nearly every modern language model is a transformer.
See also: Attention, GPT (Generative Pre-trained Transformer)
How a model learns, and how it goes wrong.
The algorithm that works out how much each weight in a network contributed to the error, by passing the loss backwards through it. An optimizer then uses those numbers to actually adjust the weights.
Also: Backprop
See also: Loss function
A simpler alternative to RLHF that learns from preferred and rejected answer pairs directly, with no separate reward model. Now as common as RLHF in practice.
Also: DPO
See also: RLHF (Reinforcement Learning from Human Feedback), Reward model
One complete pass through the training data.
Selecting, transforming or creating the most relevant variables — features — to improve a machine learning model's performance.
Giving a model a handful of worked examples inside the prompt so it can infer the pattern. Often the cheapest alternative to fine-tuning.
Also: Few-shot learning
See also: One-shot, Zero-shot learning
Further training of an existing model on a narrower dataset, so it picks up a specific style, format or domain.
See also: LoRA (Low-Rank Adaptation), Transfer learning
The optimization method that walks a model's weights downhill on the loss, one small step at a time.
See also: Loss function
A cheap way to fine-tune: instead of retraining a whole model, train a small set of extra weights alongside it. Hours on one GPU rather than a data-center run.
Also: LoRA
See also: Fine-tuning
The measure of how wrong a model's output is. Training is the process of making this number smaller.
Also: Objective function
See also: Backpropagation
A proposed failure mode where training models on the output of earlier models, generation after generation, drains the diversity of the original human data. A risk to test for rather than an automatic consequence of using synthetic data.
See also: Synthetic data, Hallucination laundering
Giving a model exactly one worked example before asking it to do the task. Also used loosely to mean getting something right on the first attempt, with no follow-up prompt — "it one-shotted the whole feature".
Also: One-shot learning
See also: Zero-shot learning, Few-shot
When a model learns the training data too well, including its noise and outliers, and so performs poorly on new, unseen data.
See also: Underfitting
Everything done to a model after pre-training to make it useful and well-behaved: instruction tuning, RLHF, safety work. Increasingly where the difference between models is decided.
See also: RLHF (Reinforcement Learning from Human Feedback), Fine-tuning
The first and largest training stage, where a model learns language and world knowledge from a vast general corpus. Everything after it is comparatively small.
See also: Post-training, Fine-tuning
A type of learning where an agent learns to make decisions by taking actions in an environment to maximize a reward.
Also: RL
Training on tasks where correctness can be checked mechanically — the tests pass, the proof holds, the number is right. It sidesteps the need for a reward model, and is much of why reasoning models improved so quickly.
Also: RLVR
See also: Reasoning model, Reward model
A model trained to score how good an answer is, standing in for human judgment during reinforcement learning.
Training a model using human preferences between its own outputs, to make it more helpful and better behaved. The step that turned raw language models into usable assistants.
Also: RLHF
See also: RLHF'd to death, Post-training, Alignment
The observed relationships between how much compute, data and parameters go into a model and how good it turns out. The basis of the bet that bigger keeps working.
Training where the data supplies its own labels — for example, hiding the next word and asking the model to predict it. How language models are pre-trained.
See also: Pre-training
Fine-tuning on example pairs of instruction and ideal answer. Usually the first step of post-training, before any preference learning.
Also: SFT, Instruction tuning
See also: Post-training, Fine-tuning
Training a model on a labeled dataset, where the correct answers are provided so the model can learn the relationship between input and output.
Training data generated by a model rather than collected from the world. Now a large share of what frontier models are trained on, and a live worry about compounding errors.
See also: Model collapse, Hallucination laundering
Adjusting a model's parameters by showing it data. Pre-training is one large run; a model can be fine-tuned and post-trained afterwards. All of it is separate from inference, which is what happens when you actually use the model.
See also: Inference, Pre-training
The latest period a model's training data covers, usually stated only approximately. It bounds what the model knows by itself, which is why it can be confidently wrong about recent events and why search and retrieval tools exist.
Also: Knowledge cutoff
See also: RAG (Retrieval-Augmented Generation)
Taking a model trained on one task and repurposing it for a second, related task — using a model trained on photos of cars to help identify trucks.
See also: Fine-tuning
When a model is too simple to learn the underlying patterns in the data, and so performs poorly on both training and new data.
See also: Overfitting
Training a model on data without explicit labels, so it must find its own patterns and structures in the information.
Software that acts, not just answers.
Describes software that plans and acts over several steps rather than answering once. An agentic workflow decides what to do next by itself; a prompt does not.
See also: AI agent
An autonomous system that can perceive its environment, reason about how to achieve a goal, and take actions using tools or skills, without step-by-step human instruction.
Also: Agent
See also: Agentic, Skills (or tools)
The definitions and protocols that let two software components talk to each other.
Also: API
An agent that reads and writes a codebase, runs commands and iterates on the result — as opposed to a chat window that suggests code you paste yourself.
Also: Agentic coding tool
See also: Vibe coding, Claude Code
An agent operating a computer the way a person does — reading the screen, moving the pointer, typing. The general-purpose fallback for software that offers no API.
See also: AI agent, Skills (or tools)
Deciding what a model should have in front of it at each step, and what it should not. The successor discipline to prompt engineering, and the one that decides whether a long-running agent stays coherent.
See also: Context window, Context rot, Prompt engineering
The checks around a model that constrain what it may say or do — filters, allow-lists, approval gates. A guardrail is enforced outside the model, unlike alignment, which is trained into it.
See also: Alignment
The structural framework or environment around a model that gives it the tools, data and testing protocols it needs to do work or be measured. The model reasons; the harness is everything that lets the reasoning touch the world.
Also: Agent harness
See also: Agentic, Scaffolding
A design where a person approves, corrects or steers the system at defined points, rather than the system running unattended.
Also: HITL
A prompt designed to talk a model past its own safety training.
See also: Prompt injection, Alignment
An open standard that lets AI models connect to external data sources and tools — local files, databases, applications — without custom code for every integration.
Also: MCP, Model Context Protocol
See also: MCP server, Skills (or tools)
The two halves of the Model Context Protocol. The server exposes tools; the client, inside the AI application, connects to it and offers those tools to the model.
Also: MCP host
See also: MCP (Model Context Protocol), MCP server
A program that exposes a set of tools over the Model Context Protocol, so any compatible AI can use them. lite.computer ships one, which is how an agent reads and drives a Showspace.
See also: MCP (Model Context Protocol)
Several agents working on one problem, often with different roles or models, and often reviewing each other. More capable and considerably harder to keep honest than one agent.
Also: Agent swarm
The instruction given to a model. In an agentic system it is rarely one line: it carries the goal, the rules, the available tools and the relevant context.
The craft of writing prompts that get reliable results. Steadily giving ground to context engineering as models get better at instructions and worse at nothing else.
See also: Context engineering
An attack where instructions hidden in content the model reads — a web page, an email, a file — are followed as though the user had given them. The central unsolved security problem of agents that read the outside world.
See also: Guardrails, Jailbreak
The loop, prompts and glue code wrapped around a model to make it complete a multi-step task. Often the difference between two products using the identical model.
See also: Harness
Specific functions or capabilities given to an AI — searching the live web, running code, reading a folder, calling a particular piece of software.
Also: Tools, Tool use, Function calling
See also: MCP (Model Context Protocol), Tool call
The standing instruction that sits above a conversation and governs how the model behaves throughout it. Where an application's rules, persona and constraints live.
Also: Standing instructions
See also: Prompt
One request from a model to run a named function with named arguments, and the result handed back to it. The unit an agentic loop is built from.
Also: Function call
See also: Skills (or tools)
An automated message sent from one application to another when a specific event happens — the push notification of the data world.
What a model knows while it is working, and what it keeps.
Cutting documents into passages small enough to retrieve and feed to a model. Where the chunks are cut decides how good the retrieval is, which is why it gets more attention than it looks like it deserves.
See also: RAG (Retrieval-Augmented Generation)
A pointer from a generated answer back to the source it came from, so a reader can check it. Increasingly what separates a useful AI answer from an unusable one.
See also: Grounding
The information and circumstances surrounding an interaction that let the system understand and answer accurately.
How much a model can hold in mind at once, measured in tokens — the prompt, the conversation so far, every document and tool result. Everything outside it does not exist to the model.
Also: Context length
See also: Token, Context rot, Context engineering
Supplying real evidence — retrieved documents, database rows, tool output — and holding the answer to it, rather than to the model's recollection. The main defense against hallucination, though a citation on its own does not prove an answer was grounded.
See also: Hallucination, RAG (Retrieval-Augmented Generation)
Search by matching the literal words typed. Still better than semantic search at names, codes, version numbers and dates, which is why serious systems run both.
Also: Lexical search, Full-text search
See also: Semantic search
An organized body of documents a person or a system can consult. When an AI is the reader rather than a person, the format matters more than the design.
Also: KB
Anything that persists between sessions — facts a system stores about you, notes it keeps, files it writes. Different from the context window, which is what it holds while working, and which the application decides whether to carry over, summarize or clear.
Also: Persistent memory
See also: Context window, Second brain
A folder of plain Markdown files that the Obsidian app reads as a linked knowledge base. Because it is only files on disk, an AI agent can read and write it directly — which is why it became a common home for a second brain.
Also: Vault
See also: Second brain, Markdown
A technique that gives a model access to specific external data — a company handbook, a folder of documents — and puts it in front of the model as it answers, rather than leaving it to remember. It is only ever as good as what the retrieval step found.
Also: RAG, Retrieval
See also: Vector database, Embedding, Training cutoff
A personal knowledge system you offload thinking into — notes, documents and links kept so that finding something later is easier than remembering it. Popularized by note-taking practice, and now the natural place for an AI to read and write.
Also: Personal knowledge management, PKM
See also: Obsidian vault, Knowledge base
Search by meaning rather than by matching words, using embeddings. Finds "how do I get my money back" when the document says "refunds".
See also: Embedding, Keyword search
A database that stores embeddings and finds the closest ones to a query. The retrieval half of most RAG systems.
Also: Vector store
See also: Embedding, RAG (Retrieval-Augmented Generation)
Measuring it, and the ways it fails.
The problem of making a system actually pursue what its operators intend, including in situations nobody wrote a rule for.
See also: RLHF (Reinforcement Learning from Human Feedback), Guardrails
A standard test set used to compare models. Useful, and steadily less meaningful as popular benchmarks leak into training data.
See also: Evals, Benchmark contamination
When a benchmark's questions and answers end up in a model's training data, so a high score measures memory rather than ability.
Also: Test set contamination
See also: Benchmark
A system whose internal reasoning cannot be inspected, only its inputs and outputs.
See also: Explainability
The intermediate steps a model works through before answering. Some products show a summary of them; many keep the model's actual reasoning private, so what you see is not necessarily what happened.
Also: CoT, Reasoning
See also: Test-time compute
When a system's behavior changes over time without anyone changing it on purpose — a provider updates a model, the data shifts, and outputs quietly stop matching what was tested.
Also: Model drift
See also: Evals
Short for evaluations, and often used as an imperative: don't ship on vibes, what do the evals say? The tests used to measure whether a model or a system is actually doing its job — accuracy, latency, cost, regressions. A benchmark is one kind of eval, not the whole of it.
Also: Evaluations, Eval
How well a human can understand why a system produced a given output. Hard for deep learning, and often a regulatory requirement.
Also: Interpretability, XAI
See also: Black box
When an AI generates a response that sounds confident and fluent but is factually incorrect or disconnected from reality.
Also: Confabulation
See also: Grounding, Hallucination laundering
Deliberately attacking your own system to find what it will do wrong, before someone else does it for you.
See also: Jailbreak, Prompt injection
When a model declines a request. A necessary behavior that becomes a defect when it fires on harmless prompts.
Also: Over-refusal
See also: RLHF'd to death
A model's tendency to agree with the user, praise their idea and fold under pushback, because agreement was rewarded during training. Dangerous precisely when you wanted a second opinion.
See also: RLHF'd to death
Spending more computation at the moment of answering — thinking longer — rather than making the model bigger. The idea behind reasoning models.
Also: Inference-time compute
See also: Chain of thought
Chips, machines, and where the model runs.
A model available only through its owner's API or product. You may use it; you may not have it.
Also: Proprietary model
See also: Open weight
Running models on somebody else's machines, reached over an API. Cheap to start, scales without effort, and means your input leaves your device.
Also: Hosted AI
See also: Local AI
Processing capacity, treated as a commodity: how many chips, for how long. The binding constraint on almost everything in AI.
See also: GPU, Scaling laws
Reusing the processed form of a prompt across calls so the same long preamble is not paid for twice. A large cost saving for agents that carry the same standing instructions all day.
Also: Prompt caching
See also: Token
The building full of machines where training and most inference actually happen. Increasingly the limiting factor is power and cooling rather than chips.
Also: Datacenter
Running models on the device where the data is — a phone, a camera, a car — rather than sending it away to be processed.
See also: Local AI
The graphics processor that turned out to be the right shape for training and running neural networks. The unit in which AI capacity is counted and paid for.
Also: Graphics processing unit
See also: GPU rich / GPU poor, Compute
Running a model on hardware you control. Usually chosen for privacy, offline use or cost rather than for capability. Whether your data really stays put depends on the whole setup, not just the model.
Also: On-device AI, Local model
See also: Desktop AI, Quantization, Open weight
Sending each request to whichever model suits it — a cheap fast one for easy work, an expensive one for hard work. A large cost saving, and a source of confusing inconsistency when it is invisible.
See also: Latency
Software published under a license that lets anyone read, change and redistribute it. The term is used loosely in AI: publishing weights alone is more precisely called open weight, because studying and rebuilding a model also needs its code and information about its data.
See also: Open weight
A model whose trained parameters are published, so anyone can download, run and fine-tune it. Not the same as open source: the weights are given, the training data and code usually are not.
Also: Open weights, Open-weight model
See also: Open source, Local AI
The cap a provider puts on how much you may call an API in a given period.
Running code without managing the machine it runs on — you are billed per request rather than per server.
The models themselves, by family rather than by version.
Anthropic's language models, now in their fifth generation. Named by character rather than by number: Opus, Sonnet and Haiku across capability and speed, with Fable alongside them. Strongly associated with coding and long agentic work.
See also: Anthropic, Claude Code
DeepSeek's models, notable for reaching frontier-adjacent reasoning at a fraction of the expected training cost, and for being released with open weights.
See also: DeepSeek
TII's open-weight models, among the first state-backed releases to compete with the big labs.
See also: Technology Innovation Institute (TII)
Black Forest Labs' image models, and one of the most widely used open-weight options for image generation.
See also: Black Forest Labs
Google DeepMind's flagship models, now in their third generation. Natively multimodal, and known for very large context windows.
See also: Google DeepMind
Google's open-weight models, built from the same research as Gemini but small enough to run locally.
See also: Google DeepMind, Local AI
Z.ai's open-weight bilingual family, widely used where Chinese and English performance both matter.
See also: Z.ai
OpenAI's flagship language models, now in their fifth generation. The name is also the architecture, which is why people say GPT when they mean any chatbot.
See also: OpenAI, GPT (Generative Pre-trained Transformer)
xAI's models, integrated with X and marketed on fewer refusals and real-time access to the platform's content.
See also: xAI
AI21's family, notable for combining transformer and state-space designs for long context.
See also: AI21 Labs
Moonshot AI's models, known for very long context and strong agentic tool use.
See also: Moonshot AI
Meta's open-weight models. The release that made running a capable model on your own hardware normal, and the base most fine-tunes were built on for years.
See also: Meta AI, Open weight
Mistral AI's models, including the Mixtral mixture-of-experts releases. Europe's main open-weight offering.
See also: Mistral AI, Mixture of Experts (MoE)
Nvidia's open-weight models, released partly to demonstrate what its hardware can do.
See also: Nvidia
Microsoft's small language models, built to show how far careful data curation can take a model that fits on a laptop.
See also: Microsoft AI, Small Language Model (SLM)
Alibaba's open-weight family, spanning many sizes and specialisms. The most widely fine-tuned base outside Llama.
See also: Alibaba Qwen, Open weight
The open-weight family of diffusion image models that made local image generation possible for anyone with a decent graphics card.
See also: Stability AI, Diffusion model
OpenAI's open-weight speech recognition family, widely used for transcription, translation and working out which language is being spoken.
See also: OpenAI
Who builds them.
Israeli lab, maker of the Jamba models and long-standing work on language technology.
Also: AI21
See also: Jamba
Alibaba Cloud's model team, in China. Publishes a very broad open-weight family — dense, mixture-of-experts, vision and coding models across many sizes.
Also: Qwen team, Tongyi
See also: Qwen
American company; a major AI infrastructure provider through AWS, an investor in Anthropic, and the maker of the Nova models.
Also: AWS
See also: Nova
American lab, founded 2021 by former OpenAI researchers. Makes the Claude model family and Claude Code. Known for safety research, Constitutional AI, and for publishing the Model Context Protocol as an open standard.
See also: Claude, MCP (Model Context Protocol)
German lab founded by researchers from the original Stable Diffusion team. Makes the FLUX image models.
See also: FLUX
Chinese technology company behind TikTok, with a substantial AI research effort of its own.
Canadian lab aimed squarely at enterprises. Known for embedding and reranking models and for retrieval-tuned generation.
See also: Embedding, RAG (Retrieval-Augmented Generation)
Chinese lab known for open-weight reasoning and mixture-of-experts models, and for cost-efficiency claims that made the industry argue about how much compute a frontier model really needs.
See also: DeepSeek (model family), Open weight
Voice and audio lab, known for speech synthesis and voice cloning good enough to have changed how audio is produced.
One of the small number of organizations training models at the leading edge. The label is about capability and compute, not size or age.
See also: Frontier model
Google's AI division, formed by merging DeepMind and Google Brain. Makes the Gemini model family, the open-weight Gemma models, and the Veo video models. The 2017 transformer paper came from Google researchers, in the then-separate Google Brain team.
Also: DeepMind
See also: Gemini, Transformer
The main public hub for open-weight models and datasets, and the maintainer of libraries most of the ecosystem depends on. Effectively the field's shared warehouse.
See also: Open weight
American company with a long AI research history; publishes the open-weight Granite models for enterprise use.
See also: Granite
Meta's AI organization, in the United States, including its FAIR research arm. Makes the Llama family, and did more than anyone to establish open-weight models as a serious alternative to closed ones.
See also: Llama, Open weight
OpenAI's largest partner and infrastructure provider, and a lab in its own right — the Phi family of small models, and the Copilot products.
See also: Phi, GitHub Copilot
Independent American lab making image models of the same name, known for a distinctive aesthetic and for having no free tier and no API for most of its life.
Chinese lab known for efficient attention architectures, long context, and multimodal and video generation.
French lab, founded 2023. Europe's most prominent frontier lab, known for efficient open-weight models alongside a commercial API.
See also: Mistral (model family), Open weight
Chinese lab behind the Kimi models, known for very long context and for open-weight agentic models.
See also: Kimi
American open-source AI collective, known for the Hermes model family and the Hermes Agent.
See also: Hermes Agent
American company, and not a model lab. Its GPUs run a large share of AI training and inference, and their availability has shaped the industry more than most research results. Google's TPUs and other accelerators are the alternatives.
See also: GPU, GPU rich / GPU poor
American lab, founded 2015. Makes the GPT model family, the ChatGPT product, the Sora video model and the Codex coding tools. The company that made this technology a consumer category.
See also: GPT (model family), ChatGPT
American company building an AI answer engine — search that responds with a cited answer rather than a list of links. Frequently cited as the model for what AEO has to optimize for.
See also: Answer Engine Optimization (AEO)
British company behind Stable Diffusion, the release that put open-weight image generation in everyone's hands.
See also: Stable Diffusion
Abu Dhabi research institute, publisher of the open-weight Falcon models.
Also: TII
See also: Falcon
American lab founded by Elon Musk in 2023. Makes the Grok model family and the GrokBot agent product, and is known for building very large training clusters very quickly.
Chinese lab out of Tsinghua University, formerly Zhipu AI. Publishes the open-weight GLM family.
Also: Zhipu AI
See also: GLM
What people actually use day to day.
Google's agentic development platform, launched November 2025 and built as a fork of VS Code. Agents plan, execute and verify work across the editor, the terminal and a browser, and keep a knowledge base of what they learned. Runs Gemini models and also Claude and open models.
Also: Google Antigravity
See also: Coding agent, Google DeepMind
OpenAI's assistant product. The application that took this technology mainstream, and still the name most people reach for.
See also: OpenAI
OpenAI's agent for working across your apps, files and documents, producing spreadsheets, decks and documents rather than just answers.
Anthropic's agentic coding tool, run in a terminal, an app or an editor. It reads and edits a real codebase, runs commands, and works over many steps rather than answering one question.
See also: Anthropic, Coding agent
A tab inside the Claude desktop app where Claude works with your own files and folders, runs scheduled tasks, and hands back finished work — the same access as a coding agent without a terminal. Its Dispatch feature lets you trigger a desktop session from your phone.
Also: Cowork, Dispatch
See also: Claude Code, Anthropic, Desktop AI
An AI-first code editor, built as a fork of VS Code. One of the first tools to make working alongside a model feel native rather than bolted on.
See also: Coding agent, Vibe coding
AI that runs as an application on your computer, with access to your files and other applications, rather than living in a browser tab. The category lite.computer belongs to.
See also: Local AI, lite.computer
The autocomplete-in-your-editor product that introduced most developers to AI assistance, since grown into an agentic tool.
Also: Copilot
See also: Microsoft AI
xAI's persistent agent product, launched August 2026. Each agent runs on a cloud computer of its own and signs into your applications the way a person does, rather than through an API, and keeps working after you close your laptop. For a Showspace kept in a cloud folder that means work can carry on while your Mac is asleep — and, for the same reason, it cannot reach an app running only on your own machine.
Also: Grok Bot
See also: xAI, Grok, AI agent, Regeneration
Nous Research's open-source agent, released February 2026. It runs as a persistent process on your own infrastructure rather than in a session: it keeps memory across every conversation, runs scheduled tasks while you sleep, and writes its own reusable skills from experience. Works with any model provider, including local ones.
Also: Hermes
See also: AI agent, Memory (in AI), Skills (or tools)
An open-source agent that runs on your own machine and is reached through the messaging apps you already use — WhatsApp, Telegram, Slack, Signal. It acts on your behalf across shell commands, browser automation, email, calendar and files. Previously called Clawdbot and Moltbot.
Also: Open Claw
See also: AI agent, Hermes Agent
Continuing an agent session that is running on your own machine from a phone or a browser. The work, the files and the model access all stay on the machine; the phone is only the way in. Claude Code and Codex both offer it, and Claude Cowork's Dispatch is the same idea for people who never open a terminal.
See also: Claude Code, Claude Cowork, Desktop AI
Being found, and being quoted correctly.
Google's AI-written summaries at the top of search results. A large part of why zero-click search grew, and one of the surfaces AEO aims at.
Also: AI Mode
See also: Zero-click search, Answer Engine Optimization (AEO)
A search product that returns a written answer with citations instead of a page of links.
See also: Perplexity, Answer Engine Optimization (AEO)
Writing and structuring content so that AI answer engines quote it accurately and attribute it. Where SEO competes for a click, AEO competes to be the sentence the machine repeats — which means clear definitions, direct answers near the heading, and markup a machine can parse.
Also: AEO
See also: Generative Engine Optimization (GEO), Structured data, Search Engine Optimization (SEO)
The address you declare as the real one for a page, so the same content at several addresses is not treated as several pages competing with each other.
See also: URL, Search Engine Optimization (SEO)
A program that fetches pages automatically to index them. AI companies now run their own alongside the search engines',
Also: Bot, Spider
See also: llms.txt, robots.txt
A near-synonym for AEO, used for optimizing toward generative search results specifically. The two terms are still settling and are often used interchangeably.
Also: GEO
See also: Answer Engine Optimization (AEO)
The format most structured data is written in — a block of JSON in the page describing what the page contains. The form search engines prefer.
See also: Structured data, Schema.org
A structured map of entities and the relationships between them. What lets a search engine know that a term, a company and a product are three different things that relate.
See also: Semantic Web, Structured data
A proposed convention, not a ratified standard: a Markdown file at a site's root summarizing what the site holds, for an agent that chooses to fetch it. It does not control crawling and does not replace the site's HTML.
See also: robots.txt, Crawler
A file at a site's root telling crawlers what they may fetch. Honored by convention, not enforced.
The shared vocabulary for structured data, maintained jointly by the major search companies. A glossary uses its DefinedTerm and DefinedTermSet types.
See also: Structured data, JSON-LD
The practice of making a page more likely to be found and ranked by a search engine.
Also: SEO
See also: Answer Engine Optimization (AEO)
Machine-readable markup added to a page so software knows what its parts mean — that this is a definition, that is a price, this is an author.
Also: Schema markup
See also: Schema.org, JSON-LD
A search where the answer appears in the results and nobody visits the source. The condition AEO exists to respond to.
See also: Answer Engine Optimization (AEO)
The kinds of page people actually ask an AI for.
Anything an AI produces that you keep and look at again — a page, a report, a deck, a dashboard. The word distinguishes lasting output from a chat reply that scrolls away.
See also: Self-contained page
The single page holding a brand's colors, type, tone and rules. Kept as a page in a Showspace, it is a thing every model you use can read before it writes, rather than a PDF nobody opens.
See also: Showspace, Style guide
A short document that gives someone what they need to act, and nothing else. A daily brief, a creative brief, a project brief.
A product, property or inventory list rendered as pages, one per item. The gallery view earns its keep here, because a thumbnail of the item is the fastest way to find it.
Also: Catalogue, Inventory
See also: Record page
A dated record of what changed, newest first.
A page laying out what is being published, when, and on which channel. The kind of thing that lives in a spreadsheet until someone wants to look at it on a wall.
Also: Editorial calendar
See also: Daily brief
Your customer list as a page each rather than rows in a spreadsheet, grouped into folders by segment and searchable by anything written on the page.
Also: Client directory
See also: Record page, Index page
One page rewritten each morning with what you need to know that day. The most common recurring artifact people set up, because the value is in it being there before you ask rather than in asking for it.
Also: Morning brief
See also: Brief, Living dashboard
A single screen showing the numbers or the state of something, laid out so you can take it in at a glance rather than read it. In lite.computer it is one HTML page that an agent rewrites as the underlying data changes.
A presentation: a sequence of slides meant to be shown rather than read. Built as one HTML page, a deck advances a heading at a time in Presentation Mode without needing a slide format.
Also: Slide deck, Presentation
See also: Presentation Mode, Self-contained page
A gathering of many sources into one readable page — the week's news, a channel's messages, a folder's changes.
See also: Daily brief
The page that lists a set of record pages with a line about each and a link to it. What turns a folder of pages into something navigable rather than a pile.
Also: Directory page
See also: Record page, lite.html
A single page built to make one thing happen — a sign-up, a purchase, a download. Judged by whether people do that thing.
See also: Website
A page put on a display and left there — a menu board, a notice, a status wall — that updates itself because the agent behind it keeps writing the file.
Also: Digital signage
See also: Living dashboard, Presentation Mode
A dashboard that is rewritten on a schedule — usually by a small script reading the numbers underneath, sometimes by an agent — so the page on your screen keeps up instead of being a snapshot of the day it was made. The agent is what builds and changes it; a script is what refreshes it.
See also: Dashboard, Living website
A report, a contract, a piece of research, laid out to actually be read on a screen — one idea per section, room to breathe — rather than a wall of text in a word processor.
Everything about one subject on a single screen or sheet. The discipline is the constraint, not the format.
A page saying what shipped, what is stuck, and what decisions are needed. Made worth keeping by being answerable: you leave comments on it, and next session the agent reads them and revises the same page rather than starting a new one.
Also: Status report
See also: Report, Comments (in lite.computer)
One page for one thing — a customer, a property, a product, a member. Generated in bulk from a spreadsheet or a database export, and regenerated from it when the data moves, so a folder of them becomes a directory you can browse, search and hand to someone, with no database behind it.
See also: Index page, Customer directory, CSV, Regeneration
Rebuilding a page from its source data rather than editing it. The source — a spreadsheet, an export, an API — stays the thing you change; the page is only the current view of it. It is what separates a living page from a document that goes stale.
Also: Rebuild
See also: Living dashboard, Record page, Scheduled job
A written account of what happened over some period, usually with a conclusion. Distinct from a dashboard: a report is read once and argues something; a dashboard is glanced at repeatedly and states.
Step-by-step instructions for doing a specific operational job, written to be followed under pressure by someone who is not the author.
Also: Playbook
The rules for how something should look and sound — colors, type, spacing, tone. Increasingly written for agents to read as much as for people.
See also: Brand guide
Events laid out in time order, so the shape of a sequence is visible at once.
A set of linked pages served over the internet at a domain. A folder of HTML pages becomes a website when it is hosted; until then it is a folder of HTML pages, which is what lite.computer reads.
Also: Site
See also: Host, Static site, Domain
A proposed site staged as real, clickable pages rather than drawn as flat mockups. The reviewer moves through it the way a visitor would, which surfaces problems a picture of a page cannot.
Also: Site concept, Prototype
A body of linked pages anyone can add to, organized by its links rather than by a hierarchy.
See also: Knowledge base, lite.html
The words underneath a page, a folder and a site.
A program you install and run on your own device, as opposed to a page you visit. The difference that matters here is reach: an app can see your files and your other software; a web page cannot.
Also: Application, Native app
See also: Desktop AI, Browser
The program that fetches web pages and draws them — Chrome, Safari, Firefox, Edge. Because HTML pages are what browsers read, a page written for lite.computer also opens in one, with nothing installed.
Command-Line Interface: driving a program by typing commands rather than clicking. Most agentic coding tools are CLIs, because a terminal is where the files, the version control and the build already are.
Also: Command line, Command-line interface
See also: Terminal, Coding agent
Cascading Style Sheets: the rules that decide how HTML looks — color, type, spacing, layout.
Also: Cascading Style Sheets
See also: HTML
Comma-separated values: the plain-text table format nearly every system will export to. Usually the bridge between a database or spreadsheet and a set of pages made from it.
Also: Comma-separated values
See also: Record page, Spreadsheet
A way of embedding a file — usually an image — directly inside a page as text, so the page needs no separate files beside it.
Also: Base64 image
See also: Self-contained page
A snapshot of what a database holds, written out as a file you can hand to something else. The starting point for turning records into pages, and — re-exported on a schedule — the thing that keeps them current.
Also: Data export
See also: CSV, Record page
The Domain Name System: the lookup that turns a domain name into the address of the machine serving it.
Also: Domain Name System
See also: Domain
The name you rent and point at a host, so people can reach your site by a word rather than a number. lite.computer is one.
Also: Domain name
The address of a file on a disk. What you hand an agent so it writes in the right place, and the most common thing to get wrong when pages stop appearing.
Also: Path
See also: Showspace
The version control system nearly all software uses. It records every change, so any past state can be recovered and two people can work on the same files without overwriting each other.
See also: Repository
A computer that serves something to others over a network. To host a site is to put its files on such a machine so anyone with the address can fetch them. Hosting is what turns a folder of pages into a website.
Also: Hosting
HyperText Markup Language, the format web pages are written in. Tags wrap content to say what it is — a heading, a paragraph, a link — and a browser decides how to draw it. It is a text file, readable without any special software, which is why a page written today still opens in twenty years.
Also: HyperText Markup Language
See also: Markup, Self-contained page
The programming language that runs inside a web page and makes it do things after it has loaded.
Also: JS
See also: HTML
A plain-text format for structured data that both people and programs can read. What most APIs speak.
See also: JSON-LD, Structured data
Your own machine, addressed as though it were on the network — usually at an address beginning http://localhost. A page served at localhost is visible only to you, which makes it the normal way to look at a site before anyone else can.
Also: 127.0.0.1
A lightweight markup format designed to be readable as plain text — a hash for a heading, asterisks for emphasis. The default output of most AI assistants, and the format an Obsidian vault is written in.
See also: Markup, Obsidian vault, HTML
Text with tags around it saying what the parts are, rather than how they look. HTML and Markdown are both markup. The point is that a machine can tell a heading from a paragraph without guessing.
See also: HTML, Markdown, Structured data
A link written as a position relative to the page it is in, rather than as a full address. It is what lets a folder of pages be moved, copied or shared and still work.
See also: URL, Self-contained page
A project's files together with the full history of every change to them.
Also: Repo
See also: Git
A page that lays itself out sensibly at any width, so the same file reads well on a phone and on a monitor.
See also: HTML
A restricted environment where code runs without being able to reach anything outside it. Pages in lite.computer render in one, which is why a page cannot read your files or your other pages.
See also: Self-contained page
A script set to run on its own at a fixed time — every hour, every morning — without anyone starting it. On a Mac the scheduler is built in. Pairing one with a generator is how a page stays current with nobody remembering to refresh it.
Also: Cron, Cron job
See also: Regeneration, Living dashboard
A program, or the machine running it, that answers requests over a network. A web server hands out pages when a browser asks for them.
Rows and columns in a file. Excellent for storing and calculating, poor for reading, which is why turning one into pages is worth doing at all.
See also: CSV, Record page
A site made of files that are served exactly as they are, with no application generating pages on request. Fast, cheap, hard to break, and what a folder of self-contained HTML pages becomes when hosted.
See also: Website, Host, Self-contained page
The window you type commands into. The CLI is the interface; the terminal is where it appears.
Also: Shell, Console
See also: CLI
The full address of something on the web, including which protocol to use to fetch it.
Also: Link, Web address
See also: Domain
What practitioners say to each other.
Someone who believes AI progress should be pushed as fast as possible to unlock radical abundance. The other half of the divide, and the origin of the e/acc tag.
Also: e/acc, Effective accelerationism
See also: Doomer
Marketing an ordinary automation or chatbot as an agent because the word sells.
The 2017 Google paper that introduced the transformer. The single most cited origin point for everything since.
See also: Transformer, Attention
The gradual loss of focus as a model's context window fills with too many turns, tool results and irrelevant documents, until it loses the plot and starts missing explicit instructions.
See also: Context window, Context engineering
Someone whose central concern about AI is catastrophic risk — alignment failure, existential threat, runaway frontier models. One half of the field's main ideological divide.
See also: Accelerationist (e/acc), Alignment
The divide in the industry by access to compute. Thousands of H100s or clusters on demand makes you GPU rich. Queuing on rental clouds and asking for credits makes you GPU poor.
Also: GPU rich, GPU poor
When an AI invents something plausible, a person cites it in a blog post or a draft, and a later scrape pulls that error into training data — a hallucination promoted to ground truth by being repeated.
See also: Hallucination
The retort to slop: the observation that most of the low-quality content on the internet predates AI and was written by people.
See also: Slop
A model that does part of the job and stops — leaving a comment where the code should be, summarizing three of ten files, or telling you to finish the rest yourself.
See also: Nerfed
Designing the loop an agent runs inside — what it does between tool calls, when it checks its own work, and how it decides it is finished — rather than hand-writing each prompt. The 2026 successor to prompt engineering, and where the difference between two products on the same model now shows up.
See also: Prompt engineering, Context engineering, Harness, Agentic
Whatever stops a competitor copying you once the underlying model is available to everyone. The question every AI product has to answer.
The complaint that a model has quietly got worse — slower, lazier, more likely to refuse — after a provider update.
Also: Lobotomized
See also: Drift
Half-joking name for someone unusually good at getting what they want out of a model.
See also: Prompt engineering
The complaint that a model has been over-aligned — by reinforcement learning from human feedback or any of the methods beside it — until it turns cautious, sterile, sycophantic, or refuses harmless prompts.
See also: RLHF (Reinforcement Learning from Human Feedback), Sycophancy, Refusal
When a provider changes pricing, limits or model behavior under a product built on top of it, and the product breaks through no fault of its own.
Release it now. In AI work, often the point of tension with evals, which exist to say not yet.
See also: Evals
Low-effort, mass-produced AI content. Used pejoratively for spammy synthetic articles, filler posts, soulless generative stock art, and auto-generated sites built to catch ad traffic.
Also: AI slop
See also: Human slop
A tongue-in-cheek, deflationary term for large language models. A reminder that beneath the apparent reasoning, the system is predicting the next token from statistical probabilities.
See also: Large Language Model (LLM), Stochastic parrot
A model that reproduces the statistical patterns of language without understanding it. From a 2021 paper, and still the sharpest phrase the skeptical side has.
See also: Spicy autocomplete
Rich Sutton's observation that methods which simply use more computation eventually beat methods built on human insight about the problem. Quoted constantly, usually to end an argument.
See also: Scaling laws
The rate at which an agent spends tokens, and the money that goes with it. Long autonomous runs make this a real line item.
Also: Burn rate
See also: Token
The correction to tokenmaxxing: spending tokens where they earn something and measuring the result rather than the spend.
See also: Tokenmaxxing, Evals
Treating token consumption as a measure of productivity — the more your agents burn, the harder you are assumed to be working. The suffix comes from internet slang for pushing one number to an extreme regardless of the outcome. Companies that ranked staff this way in 2026 mostly stopped: token volume measures cost and effort, not results, and it is trivially gamed.
Also: Token maxing
See also: Token burn, Token, Evals
Programming by steering an AI assistant with prompts rather than writing syntax by hand. You describe the intent, accept the diff, check whether it runs, and tweak the vibes until it works.
See also: Coding agent, Vibes
Judging a model or an output by how it feels rather than by measurement. Half the time it is the honest answer, and the other half it is what evals exist to replace.
Also: Vibe check
See also: Evals
Practitioner shorthand for non-deterministic output. Traditional software is rigid and predictable; language models are wiggly, or smushy, because the same input can yield subtle variations each time.
Also: Smushy
See also: Deterministic, Probabilistic, Temperature
A dismissal — "it's just a GPT wrapper" — for a product that is a thin interface over someone else's model. Sometimes fair, and sometimes said about the part that was actually hard.
Also: GPT wrapper
See also: Scaffolding
Letting an agent run without approving each action. Fast, occasionally spectacular, and the reason approval prompts exist.
Also: Auto-approve
See also: Human in the loop
Our own vocabulary, defined once.
The panel in the app that hands your agent what it needs: the real path of your Showspace, and a set of copyable prompts, the first of which turns your Showspace into your agent's default place to write.
See also: lite.computer, MCP server
The display area to the right of the sidebar, where the page or the gallery renders. Settled as the house word in August 2026, replacing display area, main area and viewport.
The sidebar and top bar together: everything that is not canvas. Presentation Mode and the Presenter Frame both remove it.
See also: Canvas, Presenter Frame
Notes attached to a page without changing the page, aimed either at a heading or at the whole page. They are stored in .lite/comments.json, which is why an agent can read them and act on them.
See also: The .lite folder
The design methodology and infrastructure Veue Management Corp uses for agent-built interfaces. lite.computer is its first trial project.
See also: Living website
The buyer's name, signed into the license itself and shown in the sidebar. It cannot be typed, which is the point: the paid copy carries your name and the free trial carries none.
See also: lite.computer
One window per open tab, laid out across the displays you have. Every tab but the active one moves into a window of its own, and the window you started from keeps that one and becomes one of them. It acts on tabs, never on the folder: a Showspace of two hundred pages is not an instruction to open two hundred windows. ⌃⌘E.
See also: Implode, Tear off, Tony Stark effect
A filter inside a Showspace. Clicking one narrows the canvas to its contents. It is never an identity, never branded and never watched separately.
A design check used on every lite.computer decision: render the question through one file, hundreds of files, one narrow shared screen, and several full-screen windows across displays. If any of the four fails, fix it before going on.
See also: Explode
Every page in the Showspace shown as a rendered thumbnail of itself rather than an icon or a filename. For finding a page you would recognize but could not name. ⌘2.
Also: Gallery
See also: Canvas
The undo for Explode. Gathers every other window's tabs back into one window, closing them behind their last tab. ⌃⌘I.
See also: Explode
A macOS app that turns a folder of HTML pages into a browsable library — a file list, a gallery of rendered thumbnails, and a reader — and keeps up as an AI agent writes into that folder. Written lite.computer; said Lite.
Also: Lite
See also: Showspace, Desktop AI
A hand-kept page at the root of a Showspace that says what is worth reading and why. A map of content rather than a file listing, usually written and updated by your agent.
See also: Showspace
A site that is regenerated by agents as its underlying material changes, rather than edited by hand between redesigns. The concept behind the Daylight work that lite.computer's methodology came out of.
See also: Daylight
The page full screen with no chrome at all. A click or an arrow key steps to the next heading, one window at a time. ⌘⇧Return.
Also: Presenting
See also: Presenter Frame, Chrome
The same stripped-down reading surface as Presentation Mode, but the window stays where it is rather than taking the whole display. For a shared screen on a call, or a second monitor. ⌘⇧F.
See also: Presentation Mode, Chrome
Which subset of a Showspace the canvas is showing — the whole thing, or one folder. It has exactly two states, and Show all is the way back out of the second.
See also: Folder (in a Showspace)
An HTML file that carries everything it needs inside it — styles inline, images embedded, system fonts, no build step and no server. The format lite.computer is built around, because such a page still opens in ten years and on any machine.
Also: Single-file HTML
See also: Page (in lite.computer)
One root folder plus everything the app remembers about it: its index, its brand, its open tabs, and its health. The unit you open, switch between and brand. A Showspace is an identity; a folder inside one is only a filter.
See also: Folder (in a Showspace), lite.computer
The app's report on what is broken in a Showspace — links pointing nowhere, images that do not resolve, how full the index is, and how much of the folder it was able to read. The same report an agent gets when it asks.
Also: Diagnostics
See also: MCP server
The left rail. It holds nouns only — which Showspace, which pages, who you are. ⌘\ hides it.
The principle that people navigate by remembering where they put something, not by searching again. lite.computer's folder tree does not reorganize itself, so the order you built is the order you navigate by. Machine-scale views are layers on top of that, never replacements for it.
See also: Showspace
Dragging a single tab out of the strip so it becomes its own window where you drop it. Explode is this done automatically to every tab at once.
See also: Explode
The single hidden folder the app adds to a Showspace. It holds the app's notes about that Showspace and the comments you leave on pages. Your own pages are never altered.
Also: .lite
See also: Comments (in lite.computer)
The internal name for the multi-window Explode experience: several windows open at once, each full-screen on its own display, all watching the same Showspace and all updating live as the AI writes. A desk of monitors becomes a wall of live dashboards. Called Explode on public surfaces.
See also: Explode
Still stuck? Write to support@lite.computer. A person reads it.