The video showcases real-time AI-powered game development using OpenAI models within VS Code, emphasizing workflows and model capabilities.
Main Points:
Introduction to new AI models like GPT-5 Codeex and their improvements.
Using GPT-5 Codeex for coding workflows in VS Code enhances code quality.
Models trained on real-world tasks excel at refactoring, bug detection, and code review.
The importance of planning and breaking down ideas before coding with AI assistance.
Demonstration of building a 2D game prototype with AI-generated levels and mechanics.
The role of prompts, prompt tuning, and model selection in AI-driven development.
UX considerations for AI models, including thinking traces and interaction design.
Managing multiple AI chat sessions and integrating version control for projects.
The potential of AI to automate game testing and level design iteration.
Future features like subtasks, parallel workflows, and better memory management.
Takeaways:
Use structured planning and breaking down tasks to maximize AI productivity.
Experiment with different models and prompt configurations for optimal results.
Incorporate AI-generated code and assets early to validate core game mechanics.
Manage chat sessions and version control to streamline iterative development.
Provide user feedback to improve AI UX, such as auto-expanding or collapsing responses.
Summary
This transcript demonstrates a live “vibe-coding” workflow in VS Code Insiders using GitHub Copilot Chat (Agent mode) with the GPT-5 model family—specifically GPT-5 Codex—to ideate, plan, and implement a small web game (“PinkPuff Quest”, a Jigglypuff-inspired 2D side-scroller). It highlights practical model selection (reasoning vs mini), emergent UX behaviors (thinking traces, to-dos, chat sessions), and a real end-to-end loop: prompt → generate files → run dev server → playtest → iterate → commit → publish to GitHub → delegate issues to coding agent.
Primary technologies/tools: VS Code Insiders, GitHub Copilot Chat (Ask/Edit/Agent modes), GPT-5, GPT-5 Codex, Three.js (orthographic 2D-like), Node.js tooling (npm run dev, npm run build), GitHub Pull Requests extension, Copilot coding agent (cloud), and project hygiene files like .gitignore and copilot-instructions.md.
Detailed Step-by-Step Breakdown
1) Model selection & environment setup inside VS Code
Open VS Code Insiders (they explicitly use insiders build).
Use GitHub Copilot Chat and choose a model:
GPT-5 (reasoning) for ideation + planning
GPT-5 Codex for agentic coding workflows (multi-file edits, refactors, implementation)
Enable/inspect “thinking” output:
In Settings, search thinking style (experimental).
Options shown include behaviors like expanded, collapsed, fixed scrolling, etc.
Motivation: improves perceived responsiveness by showing the model is “thinking” instead of just “working”.
Implementation detail:
They discuss two latency concepts:
Time to first token
Time to first effective token (when user sees something meaningful)
2) Create a game idea doc with a reasoning model
In Copilot Chat, use GPT-5 (reasoning model).
Prompt it to generate an idea doc (game concept, mechanics, audience, tone).
Review the generated idea and prune scope if it’s too big.
Key output types created during planning:
Idea doc (high-level concept: Jigglypuff side-scroller, sleep mechanics, enemies, collectibles)
Next they ask for a simplified MVP.
3) Scope down to an MVP
Ask the model to produce an MVP spec:
Single level
Two enemy types
No custom art (simple geometric placeholders)
No audio
Save that into MVP.md.
MVP details mentioned (examples):
Basic movement: left/right, jump
Signature ability: sleep pulse that converts enemies into temporary safe states/platforms
Simple HUD (lives/collectibles)
Basic level endpoint
4) Decide tech stack & write a technical plan
Ask the model to recommend a stack before generating the technical doc.
Model recommends:
Three.js with an orthographic camera to emulate 2D while using 3D rendering.
Optional fallback: 2D canvas
UI/HUD: HTML overlay (instead of doing HUD in Three.js)
Then ask it to write a technical plan in technical.md.
Pitfall shown: The model “overspecifies” (generates lots of files/tests/architecture).
They respond: “keep it simple” and explicitly avoid unnecessary complexity (e.g., no tests).
5) Switch to GPT-5 Codex and implement the game (“slash implement” style)
Start a new chat for implementation (important workflow point):
Avoid re-sending huge prior chat context.
Provide the markdown docs (MVP.md, technical.md) as the key artifacts.
Use GPT-5 Codex in Agent mode.
It generates many files and scaffolds the game.
Observed behavior of GPT-5 Codex in VS Code:
Less verbose step-by-step narration; more “just do it”.
Takes longer per turn but produces larger coherent chunks.
Often yields fewer “Problems” after edits (claimed benefit vs other models).
6) Run and playtest locally
They repeatedly run typical Node-based dev workflows:
npm run dev (to start dev server)
npm run build (to compile / validate build)
They also used “let’s run it” as a natural-language prompt, but practically it maps to the commands above.
git status-driven agent behavior (Codex invoked it when it detected unexpected state)
Pro Tips
Use reasoning models (GPT-5) for:
ideation
MVP scoping
technical planning
Use agentic coding models (GPT-5 Codex) for:
multi-file implementation
refactoring
code review-like improvements
Start a new chat per feature and feed it the current specs (e.g., MVP.md, technical.md) instead of dragging entire chat history.
Add explicit instruction like “keep it simple” / “no tests” when you want a prototype; otherwise models may over-engineer.
While an agent run is in progress, parallelize safe tasks:
e.g., generate .gitignore in another chat using a mini model.
Keep a “queue” of next tasks in a file like ideas/next.txt if the UI doesn’t support prompt queuing.
Enable “thinking” UI to reduce “it’s slow” feeling and improve steering (you can stop earlier if it’s going off-rails).
Potential Limitations/Warnings
Mid-turn commits break agent assumptions: committing while Codex is still editing can cause the agent to get stuck doing git status reconciliation.
Overspecification risk: planning prompts can lead to too many files/abstractions. Counter with explicit constraints (prototype-level).
UX quirks observed in insiders:
To-do rendering / old to-dos appearing in a new session
Weird overlay/refresh behavior in chat UI
Thinking panels sometimes expand/collapse in undesirable ways
Testing games is hard: they explicitly question how to automate tests for gameplay; most validation is manual playtesting. (Some unit tests are possible for pure logic, but “feel” requires humans.)
Dependency on extensions: some session/agent features require GitHub Pull Requests extension; without it the view may not appear.
Assumption: Standard/Typical Setup (not specified in transcript)
A local environment with:
Node.js + npm
A web build toolchain (likely Vite or similar, implied by npm run dev)
Browser for playtesting
A GitHub-authenticated VS Code environment for:
publishing repo
enabling coding agent delegation
Recommended Follow-Up Resources
Create a project-level instruction file:
copilot-instructions.md (they explicitly intend to generate it)
Include constraints like “prototype first, minimal files, no tests unless asked”
Convert feature ideas into GitHub Issues and delegate to:
GitHub Copilot coding agent (cloud) for background PR creation
Add lightweight “game QA” tooling:
An in-game debug overlay (FPS, collision boxes)
Deterministic level seeds for reproducibility
If you want automation:\
Scripted input playback (keyboard events) + snapshotting game state for regression checks (they discussed this conceptually)
Summary
Below is a reproducible spec → tasks → GitHub issues breakdown based strictly on what’s described in the transcript, plus an implied minimal file tree that matches the “Three.js + HTML HUD + levels data + MVP docs” workflow they built.
Detailed Step-by-Step Breakdown
1) Implied minimal file tree (what you’d expect in the repo)
This is “implied” from the transcript (they mention Three.js, HTML HUD, levels, README, MVP.md, technical.md, .gitignore, and an ideas/next.txt queue). Names may vary, but this is the smallest clean structure consistent with what they described.
pinkpuff-quest/
README.md
MVP.md
technical.md
copilot-instructions.md # they planned to add this
.gitignore
ideas/
next.txt # “queue” of next ideas
public/
index.html # or equivalent entrypoint
src/
main.ts # bootstraps the game (or main.js)
game/
Game.ts # core loop + orchestration
input.ts # keyboard mapping: arrows/A-D, jump, sleep
hud.ts # HTML overlay HUD (lives, cooldown, collectibles)
physics.ts # movement, collisions, coyote-time, jump caps
entities/
Player.ts
Enemy.ts
Pickup.ts
Collectible.ts # “notes/stars”
levels/
index.ts # exports levels list + helpers
LevelTypes.ts # types for platforms/spawns/pickups
level1.ts # “Dreamy Cliffs” / first level
level2.ts
level3.ts
boss.ts # boss arena / boss logic (if implemented)
rendering/
renderer.ts # Three.js renderer/camera setup (orthographic)
sprites.ts # placeholder geometry/materials
styles/
hud.css # minimal HUD styling
2) Spec → tasks decomposition (what to build next, in order)
This maps to the transcript’s evolution:
MVP foundation
One level
Two enemy archetypes
No custom art/audio
Basic movement + jump + sleep pulse
HUD: lives + sleep indicator + collectibles
End gate / win condition
Iteration pass
Fix jump behavior (double jump / jump cap / coyote time tuning)
Ensure collectibles don’t spawn inside geometry
Make end-of-level logic reliable
Content expansion
Add multiple levels
Add checkpoints per level
Add health pickups (hearts)
Add boss mode
Repo hygiene + workflow
.gitignore
copilot-instructions.md
“ideas queue” file
Issues-based workflow so the coding agent can take tasks
Key Technical Details
Core mechanics inferred from transcript
Movement: left/right + jump (they later want double jump)
Checkpoints: restart at checkpoint instead of full restart
Build/run
npm run dev
npm run build
Pro Tips
Cut issues so each one is small + testable by manually playing for 30–60 seconds.
Add “acceptance criteria” that is observable in-game (not just “refactor”).
Avoid committing while an agent is mid-turn; finish turn → then commit.
Potential Limitations/Warnings
File names and exact folder layout may differ from the repo they generated; the tree above is a clean minimal structure consistent with the transcript.
“Boss mode” is underspecified in the transcript; expect at least one design pass before implementing mechanics.
Automated testing is not covered; assume manual playtesting for gameplay behavior.
Recommended Follow-Up Resources
Convert each issue below into GitHub Issues and delegate them to the Copilot coding agent once repo is published.
Keep ideas/next.txt updated and periodically convert it into issues.
Type: Bug
Description: End-of-level / gate progression is inconsistent after introducing multiple levels. Ensure reaching the goal reliably advances to the next level.
Acceptance Criteria:
When the player collides with the level end gate/goal, the level completes within 0.5s.
The next level loads (different platform layout/spawns).
HUD resets only what’s intended (e.g., collectibles per level, not total lives unless designed).
If final level is complete, show a simple “You Win” screen and a restart button.
2) Prevent collectibles (“notes/stars”) from spawning inside geometry
Type: Bug
Description: Collectibles sometimes overlap platforms/walls or appear unreachable. Add spawn validation or adjust placement.
Acceptance Criteria:
On each level load, all collectibles are reachable from at least one walkable platform (basic heuristic is OK).
No collectible intersects any platform collider bounding box.
If a collectible is invalid, it is repositioned automatically to a nearby valid location.
Ignores node_modules/, build output (dist/ or equivalent), logs, OS junk.
Repo status is clean after install/build.
13) Add copilot-instructions.md for the project
Type: Chore
Description: Add a project-level Copilot instructions file to keep changes consistent.
Acceptance Criteria:
Includes: “keep it simple”, no tests unless requested, TypeScript preference, file structure expectations, how to run, coding style, and gameplay constraints.
Mentions not to commit mid-agent turn.
Mentions using existing level data types and spawn validation.
14) Add ideas/next.txt and document the “queue” workflow
Type: Chore
Description: Add a lightweight “next ideas” file and explain the workflow in README.
Acceptance Criteria:
ideas/next.txt exists with a few example next tasks.
README explains how to convert ideas into issues and delegate.