How did OpenAI use Codex to build and release a Sora Android version from scratch in just 28 days with only 4 people? What inspiration can we draw from this? Key Achievements and Background: Rapid Development: In just 4 weeks (October 8th to November 5th), a small engineering team of 4 people worked with Codex to complete the development from prototype to global release. • High-quality delivery: The app topped the Play Store charts immediately after its release, generated over a million videos on its first day, and maintained a 99.9% crash-free rate. • Resource consumption: The entire process consumed approximately 5B tokens. Development Philosophy: Breaking the "Brooks Law" Brooks' Law states that adding manpower to a software project that is already behind schedule will only make it slower. Instead of addressing the tight release deadline by adding manpower, OpenAI exponentially increased individual productivity by equipping each engineer with Codex. How to use Codex efficiently? OpenAI views Codex as a "newly hired senior engineer" and has developed an efficient human-computer collaboration model: 1. Establish context: While Codex is proficient in coding, he lacks understanding of team architecture standards and product intuition. • The team maintains a large number of AGENT.md files in the codebase to clarify coding standards, allowing Codex to "adapt to local customs". 2. Plan first, then code: Instead of directly assigning the task to Codex, let it read the code and generate a "mini design document" or implementation plan first. • Human engineers review and revise the plan before Codex executes it. This avoids directional errors and makes code review easier. 3. A "translator" for cross-platform development: • Use existing iOS codebases as a reference. Codex excels at reading Swift code and "translating" its logic into Android Kotlin code, enabling logic reuse. The team jokingly refers to it as "the cross-platform framework of the future." 4. Distributed project management: Engineers can run multiple Codex sessions simultaneously (e.g., one for writing a player, one for writing a search engine, and one for writing tests). • The engineer's role has shifted from "soloist" to "conductor," primarily responsible for architectural decisions, code review, and system integration. Key Insights and Future Outlook: AI does not diminish the need for rigor; on the contrary, it increases the demands on system design and architecture capabilities. AI can generate code rapidly, but humans must establish the correct "foundation" and "structure." • Shift in focus: The focus of software engineering has shifted from writing boilerplate code to architecture design, user experience, and trade-offs in complex systems. • New Normal: This AI-assisted development cycle has become their default way of working, showcasing a new form of software engineering in the future. OpenAI Original Text
