Video Summary - COMO USAR O CURSOR - GUIA DEFINITIVO PARA 2026

Summary (technological concepts & Cursor features)

The video is a “definitive guide” to using Cursor (an AI-assisted coding editor) efficiently in 2026. The speaker argues that most people don’t get good results from AI due to incorrect usage, and that effective workflows depend on how you provide context and structure prompts.

1) Chats and Tabs (core workflow)

Cursor provides an AI chat interface (opened via a Mac shortcut like Command+L).

Key practice: context management

Cursor works better when you provide relevant, focused context.
Too much context can reduce output quality:
- Example given: a 1-page PDF works well, while a 3,000-page PDF performs worse.

Recommendation: use multiple chats in “layers”

Keep chats aligned to specific functionalities.
When switching tasks or context, start a new chat to “reset memory” and improve results.

2) Adding Context to Files/Folders/Docs (“contexts”)

The speaker explains multiple ways to supply context to Cursor:

A context picker modal (opened from within Cursor) can attach:
- files
- folders
- documentation
- terminal output
- chats
- branches (mentioned but not used)
- browsers / links (if a link is provided, Cursor fetches content and includes it)

Practical examples

Add a whole app folder to chat context (via right-click).
Add a specific file (e.g., @config.ts) so Cursor can reference its contents.

Debugging integration

Select terminal error output and send it to the AI for debugging.
Select code and send it for analysis and fixes.

Inline AI (Command+K)

Mentioned as the ability to modify only selected context.
The speaker doesn’t use it, claiming it’s not reliable.

3) Models (quality vs cost, and “reasoning models”)

Cursor lets you choose among many AI models/templates.

General guidance

Newer models are usually smarter, but cost more.

Billing concept

Usage is charged in credits / points against a credit card, including possible on-demand extra charges when you exceed limits.

“Brain” / reasoning model concept

When enabled, it performs a self-reinforcing step-by-step reasoning loop:
- generates → evaluates → improves the answer
This boosts quality but increases cost (more tokens/time).

Models the speaker recommends (with value/cost opinions)

Sonnet 4.5: acceptable for code (~7–8/10), older but “OK”.
Dimi (Flash): better quality (~8–8.5/9) but more expensive.
GPT 5.2: best overall results/value (~9.5–10), strong for both code and plans.
GPT 5.2 Codex: deemed unnecessary because GPT 5.2 already handles code well.
Opus 4.5: best responses and best-practice adherence (~10/10), but most expensive, so less worthwhile than GPT 5.2.

4) Cursor Modes (agent vs plan vs question)

Cursor includes multiple modes:

Agent mode

AI modifies code directly based on your request.
Downside: it may discover missing information mid-way and require refactoring again, which can be less efficient.

Plan mode (highly recommended)

AI generates a markdown plan file describing changes before touching the code.
Benefit: you can review/correct the plan early to reduce wasted tokens/money.
Example workflow:
- Use a cheaper model (e.g., Gemini Flash) for planning.
- Switch to a better model (e.g., GPT 5.2 or Opus) for execution.

Debug mode

Mentioned but skipped by the speaker because they don’t personally use it.

ESC / Question mode

A “turbocharged GPT chat” focused on Q&A with context.
No code changes; often cheaper because it avoids planning/execution steps.
Can reference code sometimes, but primarily answers questions.

5) Executing changes safely: review, accept/undo, and checkpoints

When Cursor applies changes:

You get a per-file change list.
You can view a diff:
- Red = removed code
- Green = added code
You can:
- review files individually
- undo/accept changes
- review all files via the chat area (speaker demonstrates file-by-file)

Restore checkpoints

If you dislike results, you can revert to a previous state (“restore checkpoint”).
Resending from an earlier point creates a new plan/branch.

6) Reusable project context: cursor folders + skills

The speaker recommends creating a Cursor folder inside the project to store reusable context, such as:

best practices (e.g., React best practices)
project-specific “patterns”
files that should always inform the AI

“Skills”

Uses a separate repository/website called Skills that provides prompt bundles for domains/frameworks (e.g., Swift Expert Skills).
Workflow described:
1. copy a skill file
2. paste/run it in the project terminal to create a context folder
3. include that folder in Cursor context when needed

7) Project file as a “project brief”

Cursor can use a single project file describing goals/requirements (positioned like a message to a friend or PM). This is framed as especially important for complex tasks so the AI understands the project “shape.”

8) Practical example: React → Swift app translation

A highlighted case study:

The speaker took a React project with multiple components.
Using Cursor context, they transformed it into a high-performance native Swift iOS app (leveraging iOS ecosystem benefits).

Emphasized strategy

Convert step-by-step (avoid asking for everything at once).
Keep context smaller so the AI performs better.

Result

The iOS app is mostly running.
Payment/auth are still pending but expected to be completed using the same approach.

Key takeaways / “review-style” guidance

Best results come from:
- correct AI usage
- focused context
- multiple chats for different layers
- Plan mode to preview edits before committing
- review diffs + restore checkpoints when fixes are needed
- reusable context via Cursor folders and Skills

Main speakers/sources

Main speaker: “He spoke. M.” (speaker name not fully shown; later referenced as someone who posted tips on Twitter and answered a request from Bruno)
No external video source cited, but the speaker references:
- a Skills website (skills repository)
- general internet resources (public best practices) for context