Here’s the thing about AI-assisted coding that nobody tells you:
The quantity of code doesn’t matter. Your workflow does.
A Reddit developer recently shared their experience generating 500,000+ lines of code with Claude Code in just 90 days. The post blew up — 556 upvotes, 100+ comments, and a goldmine of battle-tested techniques from developers who’ve figured out how to actually scale with AI.
I’ve compiled the best insights from that thread. No fluff. Just what works.
Key Takeaways (2-Minute Read)
- Monorepo structure is crucial for context management
- SKILL files teach Claude your codebase patterns
- Test-driven development isn’t optional — it’s your safety net
- Vibe reviewing > vibe coding — know where every function lives
- Parallel worktrees can 3-4x your productivity (if you’re ready)
1. Use a Monorepo (This is Non-Negotiable)
Context is everything when working with Claude Code.
Put your frontend, backend, and microservices in ONE repository. This gives Claude the full picture of your app’s architecture — not isolated fragments.
Meta does this. Google does this. There’s a reason.
From the original author: “By monorepo I mean putting the frontend/backend/microservices for the same app in one repo instead of breaking it down further. Please create separate repos for separate apps.”
Why it matters: Claude makes dramatically better decisions when it can see relationships between components. Isolated repos = isolated (and often broken) solutions.
2. Modular Routing = Less Context Pollution
Here’s a mistake I see constantly:
Developers dump all their API routes in one giant file. Then wonder why Claude gets confused.
The fix: categorize API routes by functionality. Separate files for app routes, workflow routes, OAuth routes, etc.
When Claude works on a specific feature, it only gets relevant context — not your entire codebase.
3. Pick a Popular Stack (Seriously)
LLMs perform better on patterns they’ve seen millions of times in training data.
This means:
- React over obscure frontend frameworks
- FastAPI or Django over custom Python setups
- PostgreSQL over that one database your friend swears by
- Established library versions, not bleeding edge
Community insight: “LLMs are less likely to make mistakes when writing code that they’ve already seen in their training data.”
4. Domain-Driven Design Works Incredibly Well
Multiple developers in the thread mentioned this:
u/chintakoro: “I’m surprised how well CC picks up on DDD — really don’t need to tell it how to architect features if your current code structure heavily points the way.”
DDD creates clear boundaries. Clear patterns. Claude recognizes these and replicates them consistently.
One user even combined DDD with extensive CI/CD:
u/imcguyver: “DDD + CI/CD is the key. DDD has a ton of rules that must be followed then enforced. That’s enough to build a complex SaaS app with 1M lines of actively maintained code.”
5. Write SKILL Files for Every Module
This is where things get interesting.
SKILL files are instructional documents that teach Claude your codebase conventions. Think of them as onboarding docs — but for AI.
Example SKILL files from the original author:
- Handler implementation: Where to place files, class structure, registry points
- Test implementation: Mocking system, test file organization, quality standards
- Socket events: Event registration, Pydantic classes, integration patterns
Original author: “I think of skills as commands that are invoked automatically. I just try to categorize all the general things you could do in a codebase as skills and have Claude automatically use them to gain high-quality context.”
6. Add Comments at the Top of Every File
Simple but powerful:
Configure your CLAUDE file to require comments explaining what each generated file does. This helps Claude navigate your codebase autonomously in fresh sessions.
Bonus: you get human-readable documentation as your codebase grows.
7. Give Claude Read-Only Database Access
Set up an MCP that lets Claude query your database (read-only).
This transforms debugging from a back-and-forth conversation into autonomous investigation. Claude can verify data states, identify inconsistencies, and propose solutions — all without risk of accidental modification.
u/wickker: “For the db I ditched the MCP and ask it to use the mariadb docker container directly. Added a skill on how to access it locally.”
8. Run Services in tmux for Log Access
Run your frontend and backend in tmux sessions. Tell Claude (in your CLAUDE file) how to tail logs when needed.
Combined with database access, Claude can now correlate application behavior with data state. Powerful for debugging complex issues.
9. Test-Driven Development is Your Safety Net
This came up repeatedly in the comments:
u/visarga: “You can delete the code, keep the tests and specs, and regenerate it back. The code is not the core, the tests are. Code without tests is garbage, a ticking bomb in your face. Code with tests is solid, no matter if made by hand or AI.”
The setup:
- Unit tests for every feature
- Automatic test runs on every PR (GitHub Actions)
- Testcontainers for isolated database testing
- CI/CD pipelines for code smell detection
u/imcguyver: “LOTS of local and remote CI/CD to check for code smells/code pattern violations. Install cursorbot to review your PRs.”
10. Plan Before You Build
Don’t just dive in.
Spend time iterating on a design doc with Claude before implementation. Once the architecture is solid, use bypass mode for end-to-end implementation.
u/imcguyver: “Task PRDs are 500+ lines of markdown to include the current state, problem statement, solution, implementation details, all files to be modified + why.”
Yes, 500+ lines for a single task. That’s the level of planning that scales.
11. Scale with Parallel Worktrees
Once you’ve mastered everything above, level up with git worktrees.
The original author runs 3-4 worktrees in parallel — multiple agents working on independent feature branches simultaneously.
Warning: This amplifies both productivity AND problems. Get the fundamentals right first.
12. Vibe Reviewing, Not Vibe Coding
This is the most important insight from the entire thread:
Original author: “Above all – don’t forget to properly review the code you generate. Vibe reviewing is a more accurate description of what you should be doing – not vibe coding. You should at minimum know where every function lives and in which file in your codebase.”
What this means:
- You don’t memorize implementation details
- You DO know the architectural skeleton
- You understand common LLM failure patterns
- You selectively deep-dive on risky areas
On reviewing volume: “This requires a careful understanding of the failure points your LLM. I think it’s possible to review several thousands of lines per day by paying selective attention to mistakes that LLMs make and skimming over overall logic.”
Quick Reference
| Area | What to Do |
|---|---|
| Repo Structure | Monorepo + modular routing |
| Tech Stack | Popular frameworks, stable versions |
| Architecture | Domain-Driven Design + CI/CD enforcement |
| Documentation | SKILL files + file-level comments |
| Debugging | Read-only DB access + tmux logs |
| Testing | TDD mandatory, PR-gated tests, CI/CD pipelines |
| Planning | Detailed PRDs (500+ lines) before coding |
| Review | Know every function’s location, focus on LLM failure patterns |
| Scaling | Parallel worktrees (3-4x productivity) |
Bonus: Advanced Techniques from the Comments
Multi-Model Validation
u/isarmstrong: “Let ChatGPT act as a long-term context holder that reviews plans & diffs… Combined with a Chat project full of your RFCs and a copy of the claude plan, it’s a wonderfully pedantic partner that both eliminates model bias and catches stupidly granular details.”
Use one AI to check another. Eliminates blind spots.
Task Categorization
From u/imcguyver’s workflow:
- Easy tasks: Execute immediately
- Medium tasks: Add to inbox.md for batch processing
- Hard tasks: Dedicated PRD before implementation
Pre-Commit Review Command
u/wickker: “For review I set up a slash command which I run before committing. It has the layout of the conventions we follow in our codebase. Recently added subagents to focus on each main aspect.”
Bottom Line
500,000 lines of code means nothing if your workflow is broken.
The developers winning with Claude Code aren’t just generating more code — they’re building systems that make AI-generated code reliable and maintainable.
Monorepo. SKILL files. Tests. Reviews. That’s the formula.
Start with one technique. Master it. Add the next.
Found this useful? I write about AI development tools and workflows. Drop a comment if you want me to dive deeper into any of these techniques.
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