OpenAI Codex Best-of-N: Worth Switching Back?

Testing if OpenAI's gpt-5 Codex's Best-of-N feature justifies switching from your other coding agents.

Live production test: building a complex image processing pipeline that touches Next.js frontend and Convex backend. No toy examples. Real code, real problems, real answers.

Plus: The hidden install command Cursor doesn't advertise.

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WHAT YOU'LL ACTUALLY SEE:
- Installing Codex on Cursor (command they don't advertise)
- Best-of-N feature: Run 4 versions, pick the best
- Direct comparison: Codex vs Cursor's native models vs Droid
- Real feature build: Image processing with Next.js + Convex
- The revert/apply workflow that changes everything
- Actual costs and performance at production scale

🛠️ INSTALLATION COMMAND:
`cursor --install-extension openai.chatgpt@0.5.20`

🕒 Key moments
00:00:00 Testing Codex's "best of n" feature for developer workflow
00:01:00 Installing the new Codex VS Code/Cursor plugin manually
00:05:00 Explaining the value of running multiple prompt iterations
00:17:20 Reviewing the initial Codex-generated plan (token usage check)
00:25:30 Resolving stream and technical difficulties
00:36:20 Manual install command for the Codex extension
00:40:40 Introducing the AnimeLeak app and the image problem
00:44:00 Image processing strategy review (Next.js & Convex stack)
00:51:50 Demonstrating the Vercel image loading latency issue
00:57:40 Preparing the project by creating a feature branch in Git Tower
01:06:35 Applying the first wave of changes from the cloud to local repo
01:08:40 First test run fails (troubleshooting Bun package install)
01:12:00 Scolding GPT-5 to use research tools (EXA/Ref)
01:18:25 Demonstrating the Codex "best of n" cloud runs feature
01:25:20 Explaining why the project uses Convex over other databases
01:34:00 Debugging unresolved import errors in Convex (runtime constraints)
01:44:20 Context Pruning: Deciding to start a fresh debug session
01:49:10 Comparison of Cursor and Codex plugin features
01:55:00 Re-prompting the model with the latest errors (context refresh)
02:04:10 The optimal LLM price point ($20/month vs Ultra plans)
02:14:00 Fixing complex bugs related to Node/WebAssembly file handling
02:19:10 Exploring Windsurf IDE as a free Codex alternative (community cheat code)
02:24:20 Explaining the host's full stack: Next.js, Clerk, Convex, Polar.sh
02:28:10 Final successful deployment of initial image pipeline fix
02:35:45 Setting up the Plan Mode analysis with Claude Sonnet 4.5
02:45:00 Full cost breakdown: Vercel vs Cloudflare R2 image pipeline
02:53:15 Final cost analysis: 33% savings by simplifying the process
03:04:20 "Brain on AI": Why complex solutions aren't always best
03:10:50 Claude generates the simplified plan (removes Sharp dependency)
03:14:00 Committing the 33% cost-saving code changes
03:22:30 Final implementation of the simplified, less-code plan
03:25:20 Debugging a gallery display bug (showing unprocessed images)
03:30:15 Finalizing the logic to only show finished images
03:33:45 Finalizing the decision to revert the feature branch
03:37:35 Final thoughts on the image processing cost journey
03:39:35 Explaining his Discord community and 1-on-1 mentorship sessions
TECH STACK IN PRODUCTION:
- Next.js with TypeScript (real app, not tutorial)
- Convex (reactive backend with complex mutations)
- Clerk Authentication
- Tailwind CSS v4 + shadcn/ui
- Image processing pipeline (upload/transform/delete)

THE REAL QUESTIONS ANSWERED:
✓ Does Best-of-N actually improve output quality?
✓ Is the revert/apply workflow worth the learning curve?
✓ Performance difference vs Cursor's built-in models?
✓ When does Codex beat Claude/Cursor/Droid?
✓ Actual API costs for production features?

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ABOUT THE HOST
Ray Fernando is a former Apple engineer with 12+ years of experience, testing AI tools against production reality. Currently shipping apps live from Hawaii!

#Codex #OpenAI #Cursor #AIcoding #BestOfN #ProductionTest #LiveCoding #Convex #AIworkflow #RayFernando

Видео OpenAI Codex Best-of-N: Worth Switching Back? канала Ray Fernando