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Computer Terms in Plain Language: What TTS, STT, API, RAG, and Agent Really Mean

Tue, 12 May 2026 22:15:34 +0800

Computer science has many terms that sound advanced the first time you hear them. But once translated into plain language, many of them describe everyday actions.

For example, when AI can speak, it is called TTS; when AI can listen to you, it is called STT. It sounds like a complex system, but the simple version is “read text aloud” and “write down speech.”

Reference link: https://www.zhihu.com/question/267978646/answer/2035405228460201515

This article strings together several common terms from that angle: keep the terms themselves, but explain them in plain language.

TTS and STT: Converting Between Text and Speech

TTS means Text-to-Speech. It converts a piece of text into playable audio. Navigation announcements, audiobook reading, AI customer service voices, and voice assistants all use this kind of capability.

STT means Speech-to-Text. It does the reverse: it turns spoken audio into text, then passes that text to the next program. Voice input, meeting transcription, automatic subtitles, and smart speakers all rely on STT.

Many voice AI products are basically this pipeline:

STT: convert what you said into text.
LLM: generate a reply from that text.
TTS: read the reply aloud.

So it may feel like a natural conversation, but underneath, several modules are handing work to one another.

OCR: Copying Text Out of Images

OCR means Optical Character Recognition.

In plain language, it copies text out of images. Taking a photo of an invoice, scanning a page from a book, or reading a name and ID number from an identity document are all OCR tasks.

Early OCR was closer to “guess the character shape.” Modern OCR uses deep learning and is more tolerant of messy backgrounds, tilted text, handwriting, and blurry images. But the core question remains simple: what words are in the image?

NLP and LLM: Letting Machines Handle Human Language

NLP means Natural Language Processing. It deals with human language: tokenization, translation, summarization, sentiment analysis, question answering, classification, and more.

LLM means Large Language Model. It can understand and generate text, so many NLP tasks today are handled by LLMs.

Plain-language version:

NLP: make machines process what people say and write.
LLM: a larger text model that can handle many language tasks.

When you ask AI to summarize an article, write an email, polish a title, or explain code, it all belongs to this broad direction.

API and SDK: One Is an Interface, the Other Is a Toolkit

API means Application Programming Interface.

In plain language, someone exposes an entry point for you to call a capability. A weather API takes a city and returns weather; a payment API takes an order and returns a payment result.

SDK means Software Development Kit.

In plain language, the official team packages common code, types, examples, and tools so you can call the API more easily. An API is like the restaurant counter; an SDK is like the ordering app. You can talk to the counter directly, or use the app to make ordering easier.

CRUD: Create, Read, Update, Delete

CRUD means Create, Read, Update, and Delete.

In plain language: add, view, edit, and delete.

Many admin systems, management systems, and database operations revolve around CRUD. User management, article management, order management, and inventory management may look like different businesses, but underneath they are often forms plus create/read/update/delete operations.

That is why programmers say they wrote “another CRUD.” It is not necessarily dismissive; it is simply very common.

Cache: Keep a Copy So You Do Not Recompute Every Time

Cache means caching.

In plain language, keep frequently used things close by so you can grab them directly next time instead of searching, computing, or requesting them again.

Web pages can cache images and scripts; slow database queries can put hot results in Redis; expensive model inference can cache answers to repeated questions.

The hard part of caching is not “keeping a copy,” but “knowing when to update it.” If the data changes but the cache does not, users see stale data. That is the root of many cache problems.

Queue: Line Up Tasks and Process Them Slowly

Queue means a queue.

In plain language: too many things are happening, so put them in line and process them one by one.

For example, after a user uploads a video, transcoding may not finish immediately. The system can put the job into a queue and let a background service process it later. Sending SMS messages, emails, reports, and order callbacks also commonly use queues.

Queues solve the problem of not blocking the current request with every slow task. The user gets a response first, and time-consuming work happens later.

Index: A Table of Contents for the Database

Index means an index.

A database index is like a table of contents in a book. Without it, you may need to scan from the first page to the last page; with it, you can locate content much faster.

But more indexes are not always better. Queries may become faster, while writes and updates may become slower, because the index also needs to be maintained when data changes.

That is why database optimization often starts with indexes. But when creating one, you still need to consider query conditions, sorting fields, data volume, and write frequency.

RPC, REST, and Webhook: How Systems Talk to Each Other

RPC means Remote Procedure Call.

In plain language, it lets you call a function on another machine as if it were a local function.

REST is common in Web APIs. It uses URLs and HTTP methods to describe operations on resources, such as GET /users to query users and POST /orders to create orders.

Webhook is a callback in the opposite direction. Instead of constantly asking “is it done?”, the other side calls your URL when something happens.

Simple memory aid:

RPC: call a remote function.
REST: manage resources with HTTP.
Webhook: notify you when something happens.

CDN means Content Delivery Network.

In plain language, put static resources on nodes closer to users. When users access images, videos, CSS, or JS, they do not always have to reach the origin server.

Load Balancing means load balancing.

In plain language, when traffic is too high, do not make one server carry everything; distribute requests across multiple machines.

One is about being closer to users; the other is about not exhausting one machine. Large websites usually use both.

Docker, Container, and Kubernetes: Package, Run, and Schedule

Docker is a common container tool, and Container means container.

In plain language, package the program together with its runtime environment so it can run similarly on another machine. This reduces “it works on my computer but not on the server” problems.

Kubernetes, often written as K8s, is a container orchestration system.

In plain language, when there are many containers, it decides where they run, how to restart them if they fail, how to route traffic, and how to roll out versions.

If you only have one small service, Docker may be enough. If you have many services, machines, and replicas, K8s becomes more useful.

CI/CD: Automated Build and Deployment

CI means Continuous Integration.

In plain language, whenever code is submitted, the system automatically pulls the code, runs tests, and builds it to catch problems early.

CD can mean Continuous Delivery or Continuous Deployment.

In plain language, after the build passes, the code is delivered to testing or production environments in a more stable and automated way.

It does not solve “how to write code”; it solves “how to ship code with fewer mistakes.”

Serialization: Pack Objects Into a Transmittable Format

Serialization means turning objects inside a program into a format that can be saved or transmitted, such as JSON, XML, or Protobuf.

The reverse, Deserialization, turns those formats back into objects the program can use.

When frontend and backend exchange JSON, or services exchange Protobuf, serialization is involved.

Token, Embedding, and Vector DB: Turning Text Into Forms Models Can Process

In large models, Token usually refers to the basic units that text is split into. It is not necessarily one Chinese character or one English word; it is more like the model’s internal granularity for processing text.

Embedding means an embedding vector.

In plain language, it turns text, images, or other content into a sequence of numbers so models can compare similarity.

Vector DB means vector database.

In plain language, it stores those vectors and can quickly find content with similar meaning.

For example, if you ask “how do I reset my router?”, the system may search the vector database for content about “factory reset,” “forgot Wi-Fi password,” or “admin login failure,” then pass related materials back to the model.

RAG: Search First, Then Answer

RAG means Retrieval-Augmented Generation.

In plain language, before the model answers, it first searches a knowledge base and then answers with those materials.

It addresses the problem that large models may make things up from memory. By connecting enterprise documents, knowledge bases, product manuals, or code snippets, the model can refer to your latest materials instead of relying only on training memory.

A typical flow is:

The user asks a question.
The system turns the question into an Embedding.
It searches related documents in a Vector DB.
It sends the document snippets and the question to an LLM.
The model generates an answer.

So RAG sounds advanced, but the essence is: look up the materials first, then organize the answer.

Agent: An Automated Flow That Can Break Down Tasks

In AI contexts, Agent often means an intelligent agent.

In plain language, it does not just answer one message. It can break a goal into steps, call tools, observe results, and decide the next action.

For example, if you ask it to analyze why tests fail in a repository, a regular chat model may only give suggestions. An Agent may read files, run tests, inspect errors, edit code, and run tests again.

Of course, Agent does not mean guaranteed reliability. It is essentially “model + tool calling + state loop.” Whether it works well depends on tool permissions, task boundaries, error handling, and human confirmation.

Summary

Many computer terms sound impressive because they are wrapped in acronyms, architecture diagrams, and product copy. Once unpacked, many describe very simple actions:

TTS: read text aloud.
STT: write down speech.
OCR: copy text out of images.
API: expose a calling entry point.
SDK: package calling tools.
CRUD: create, read, update, delete.
Cache: keep a copy of common results.
Queue: line tasks up for later processing.
Index: add a table of contents to data.
CDN: put content closer to users.
Load Balancing: distribute requests.
Docker: package the runtime environment.
CI/CD: automate testing and deployment.
Embedding: turn content into numeric vectors.
RAG: search first, then answer.
Agent: let a model use tools step by step.

The terms should be preserved because they make searching, communication, and documentation easier. But you do not need to be intimidated by them. Translate them into plain language first, then return to the technical details; many concepts become much clearer.

Reference

Zhihu answer: https://www.zhihu.com/question/267978646/answer/2035405228460201515

Pixelle-Video: An Open-Source AI Engine for Generating Short Videos From One Topic

Thu, 07 May 2026 20:25:17 +0800

Pixelle-Video is an open-source fully automated short-video generation engine from AIDC-AI. Its goal is direct: the user enters a topic, and the system automatically writes the script, generates AI images or videos, creates voice narration, adds background music, and renders the final video.

This kind of tool is useful for batch short-video creation, knowledge explainers, talking-head content, novel recaps, history and culture videos, and self-media experiments. It is not a single text-to-video model. It is a production pipeline that connects several AI capabilities.

What It Automates

Pixelle-Video’s default flow can be summarized as:

enter a topic or fixed script;
use an LLM to generate narration;
plan scenes and generate images or video clips;
use TTS to create voice narration;
add background music;
apply a video template and render the final result.

The README describes the flow as “script generation → image planning → frame-by-frame processing → video composition.” The modular design is clear: each step can be replaced, tuned, or connected to a custom workflow.

Key Features

The project covers a fairly complete set of capabilities:

AI script writing: automatically generate narration from a topic;
AI image generation: create illustrations for each line or scene;
AI video generation: connect to video generation models such as WAN 2.1;
TTS voice: support Edge-TTS, Index-TTS, and other options;
background music: use built-in BGM or custom music;
multiple aspect ratios: support vertical, horizontal, and other video sizes;
multiple models: connect to GPT, Qwen, DeepSeek, Ollama, and more;
ComfyUI workflows: use built-in workflows or replace image, TTS, and video generation steps.

Recent updates also mention motion transfer, digital-human talking videos, image-to-video pipelines, multilingual TTS voices, RunningHub support, and a Windows all-in-one package. The project is clearly moving beyond a simple script toward a fuller creation tool.

Installation and Launch

Windows users can first look at the official all-in-one package. It is designed to reduce setup friction: no manual Python, uv, or ffmpeg installation is required. After extracting the package, run start.bat, open the web interface, and configure the required APIs and image generation service.

For source installation, the README gives this basic flow:

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git clone https://github.com/AIDC-AI/Pixelle-Video.git
cd Pixelle-Video
uv run streamlit run web/app.py

The source route is suitable for macOS and Linux users, and for anyone who wants to modify templates, workflows, or service configuration. The main prerequisites are uv and ffmpeg.

Configuration Priorities

On first use, the key is not to click “generate” immediately. The important part is connecting the external capabilities properly.

LLM configuration determines script quality. You can choose models such as Qwen, GPT, DeepSeek, or Ollama, then fill in the API Key, Base URL, and model name. If you want to minimize cost, local Ollama is one option. If you want more stable results, a cloud model is usually easier.

Image and video generation configuration determines visual quality. The project supports local ComfyUI and RunningHub. Users who understand ComfyUI can place their own workflows under workflows/ to replace the default image, video, or TTS pipeline.

Template configuration determines the final visual form. The project organizes video templates under templates/, with naming rules for static templates, image templates, and video templates. For creators, this is more practical than generating raw assets only, because the output is a video that can be previewed and downloaded directly.

Who It Is For

Pixelle-Video is especially suitable for three groups:

Short-video creators who want to turn ideas into draft videos quickly.
AIGC tool users who want to connect LLMs, ComfyUI, TTS, and video composition.
Developers and automation users who want to modify templates, workflows, or integrate their own materials and models.

If you only want to make one polished premium video, it may not replace manual editing. But if you want to generate many explainers, talking videos, or science and education videos with a consistent structure, its pipeline approach is valuable.

Things to Note

The ceiling of this kind of tool is determined by multiple links in the chain. A weak script model produces empty content; a weak image model gives scattered visuals; unnatural TTS makes the video feel rough; and a poor template weakens the final result.

So it is better to start with one fixed scenario, such as a “60-second vertical science explainer.” Fix the LLM, visual style, TTS voice, BGM, and template first, then expand to more topics.

The project supports a local free setup, but local setups often require a GPU, ComfyUI configuration, and model files. Users without a local inference environment can reduce setup difficulty by using a cloud LLM plus RunningHub, while keeping an eye on usage cost.

Short Take

Pixelle-Video is interesting not merely because it can “generate a video from one sentence.” Its real value is that it breaks short-video production into replaceable modules: script, visuals, voice, music, templates, and rendering. For ordinary users, it is a low-barrier AI video tool. For developers, it is closer to a hackable short-video automation framework.

If you are studying AI short-video pipelines, or want to connect ComfyUI, TTS, LLMs, and template rendering into a usable product, Pixelle-Video is worth trying and dissecting.