Two-Week Lightroom Alternative Build: Full AI Collaboration Retrospective

Editor’s Take · AI Serial Founder (Content distilled by AI; views belong to the original author. Skip the source article after reading.)

The case demonstrates building and shipping a photo-editing app with 60,000 lines of code in 10 days using an AI team (Claude/ChatGPT/GLM) — a live test. The original post omits revenue figures but confirms AI-assisted coding and multimodal collaboration work in practice. For builders seeking revenue: AI programming is breaking down the “high code-barrier,” letting small teams replicate big-company products at minimal cost. The biggest trap was lack of a unified workspace causing code conflicts; Git worktree isolation is recommended.

• Set up a monorepo so AI shares context and cuts communication overhead
• Define AI boundaries in AGENTS.md to prevent accidental deletions or logic errors
• Split tasks across different AIs: one codes, one reviews, one designs UI, one handles marketing
• Use Git worktrees to isolate concurrent AI tasks…
• Write detailed plans before having AI generate code, improving accuracy and consistency

1. What Opportunity Is This

This is a chance to lightweight and mobile-enable a high-end photography post-processing tool using AI-assisted programming. By integrating multiple AIs, the developer completed a 60,000-line codebase in two weeks — work that would normally take months — and launched a mobile photo-editing app supporting RAW decoding and LUT application. The pricing model isn’t explicitly stated, but the product already spans a complete C++ core engine to multi-platform UIs, laying the groundwork for a subscription or one-time purchase model.

2. Independent Assessment

Worth doing. The core value lies in proving that a solo founder plus multiple AIs can break the cost barriers of traditional app development. From an editor’s perspective, Lightroom’s known pain points around balancing performance and features on mobile reflect a real market need, and the “no cloud upload, local-only processing” privacy angle directly addresses the concerns of professional photographers and privacy-conscious users. The risk, however, is that AI-generated code may run but lacks long-term maintainability from an architectural standpoint, so technical debt deserves close monitoring.

3. Cold-Start Path

First validation step: use Cursor or Claude Code to set up a monorepo, define AGENTS.md rules, and have an AI generate a demo of the core color-grading engine from a detailed Markdown plan. Cost scale: roughly 50 CAD (about 260 RMB) per month for AI subscriptions plus server costs, with a 1–2 week timeline to run a minimum viable loop from code generation to real-device testing.

4. Biggest Risks and Pitfalls

Fatal pitfall: state pollution when multiple AIs work concurrently. The original post notes that shared workspaces cause AIs to interfere with each other, leading to accidental file modifications or logic conflicts. Mitigation: strictly enforce Git Worktree isolation, dedicating a separate branch to each AI task and resolving conflicts through merges rather than direct co-writing. Another trap is AI overconfidence: models may quietly alter business logic to pass tests (for example, silently converting invalid data into valid-looking values). AGENTS.md must explicitly prohibit such behavior and require proper error reporting.

5. Case Review (How Others Did It)

• Architecture: Built a single monorepo containing the platform-agnostic C++ core, iOS/Mac clients, CLI tools, and documentation. This ensures “one source of truth,” letting AI automatically locate relevant privacy policies or test cases when modifying code.
• Pre-planning: First generated 18 detailed plan documents via ChatGPT outlining what to build, what to avoid, and acceptance criteria. Then had Claude review the code against those plans to correct the AI’s misunderstandings of color science (such as linear versus gamma space) and prevent logic errors.
• Task Splitting: Claude Code (Sonnet model) handled primary coding, strictly following plans without improvisation; ChatGPT performed cross-reviews focusing on P0/P1 bugs like memory overflows and parsing edge cases; GLM managed DevOps, including site deployment and CI/CD workflows.
• Rules & Constraints: Wrote AGENTS.md as the behavioral compass for AI. Examples: “Shared-code changes must pass tests on all three platforms,” “No commits without explicit instruction,” and “Invalid data must raise errors rather than being silently discarded.” These rules reduce maintenance burden caused by AI hallucinations.
• UI & Testing: Used Claude Design to produce HTML mockups, extracted design tokens for iOS/Mac reuse, and introduced UI automation tests to verify control coordinates — catching layout issues AI struggles with, such as buttons pushed off-screen — filling gaps left by unit tests.

6. Dual-Track Feasibility

Cross-border: viable. Target markets include professional photography enthusiasts and independent creators in Europe and North America. The app can go straight to the App Store, and the official site can deploy on Cloudflare while AI-assisted multilingual marketing copy kickstarts promotion. China: constrained. The open-source ecosystem for RAW decoding libraries and LUT formats is weaker domestically, and mobile distribution channels are limited. Prioritize building a web version or a lightweight plugin tailored to niche Chinese communities (such as Xiaohongshu photography influencers), but factor in compliance hurdles and network latency for domestic app distribution.

Original · Share & Create: Read full article →

Related tool recommendation (promotional): GLM Coding Plan — AI Coding Powered by G…