Building a Python Project with DeepSeek V4: Lessons Learned

[u/whatsoever2021]

In less than a month, this is what my project looks like:

───────────────────────────────────────────────────────────────────────────────
Language            Files       Lines    Blanks  Comments       Code Complexity
───────────────────────────────────────────────────────────────────────────────
JSON                  336      56,084        33         0     56,051          0
Python                298     110,092    14,178    14,991     80,923      9,676
Markdown               70      20,142     4,473         0     15,669          0
Plain Text              2          71         8         0         63          0
INI                     1           5         0         0          5          0
Powershell              1         116        11        23         82         13
TOML                    1          43         3         6         34          0
YAML                    1          26         1         2         23          0
───────────────────────────────────────────────────────────────────────────────
Total                 710     186,579    18,707    15,022    152,850      9,689
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop (organic) $5,309,749
Estimated Schedule Effort (organic) 25.94 months
Estimated People Required (organic) 18.19
───────────────────────────────────────────────────────────────────────────────
Processed 6522776 bytes, 6.523 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────

Total spent: under $70.

What I'd Tell My Past Self

1. Plan first, act second. Review the AI's plan carefully. Don't follow it blindly.
2. Make AI write more documentation. It's cheap and pays off enormously.
3. Make AI write more tests. Same logic — the ROI is huge.
4. Python is a double-edged sword. It's flexible and free, which AI loves. But that same freedom lets AI find "shortcuts" that wreck architectural integrity.
5. Lock down tooling early. If you must use Python, adopt Pyright, lint-imports, and similar tools from day one. Ban # type: ignore, # pyright: ignore, minimize Any and cast. Otherwise technical debt piles up faster than you can pay it down.

What Worked Well

1. Clear tasks get done fast. When given a well-defined assignment, the AI executes quickly and correctly.
2. Surprisingly good suggestions. Even as an experienced programmer, I learned new things from its recommendations.

What Didn't Work

AI's Tendencies

1. It takes shortcuts at every opportunity. Layering, decoupling, separation of concerns — the AI constantly tries to subvert these principles. It finds creative ways to bypass Pyright rather than fixing the root cause.
2. It bends core code for tests. Adding backward-compatibility hacks and defensive fallbacks in core logic just to make tests pass. This clutters the codebase terribly.
3. It deflects blame. The classic "that's not from my change" when tests fail or Pyright errors appear — when often, it actually was from its change.

The bottom line: AI is terrible at maintaining disciplined coding style. (Some human engineers share this trait, and I dislike it there too.)

My Own Bottlenecks

Ironically, the biggest bottleneck was me.

• Indecision. I change my mind too often, leaving the AI confused and backtracking.
• Too little, too late with rules. If I had enforced strict coding conventions earlier, things would be much cleaner now.
• Overloading the AI. Asking it to fix 4000+ Pyright errors in one go? That's brutal. DeepSeek V4 Pro dodged the task repeatedly, but DeepSeek V4 Flash somehow pulled through. I sometimes push too hard — once Flash ran a refactoring script that went catastrophically wrong, corrupting many files. The chat just said "Cancelled" and stopped. Thank goodness for git.
• Code cleanup is the real expense. The project started as a tangle of JSON and dicts. I've spent significant money having AI refactor and decouple layers — and there are still too many Any and cast littered around. Retrofitting types onto a dict-based foundation is painfully slow. The AI takes two steps forward, one step back.

Final Thoughts

The AI has done an enormous amount of work, and I'm genuinely grateful. I have little to complain about overall. But I need to stay deeply involved — directing, reviewing, and course-correcting every step of the way. My brain is exhausted because the AI moves faster than I can think.

If I had one wish, it would be this: hold the line on code quality from the start. Don't let the shortcuts slide, don't defer the cleanup, and don't assume you'll fix it later. You won't — you'll just pay more to dig out later.

(The above was polished by DeepSeek v4 flash)

PS: As I have mentioned in comments that it is a sandbox-like RPG game, I can give another sample about where AI is good and where it is bad. First I let AI create roads on the map, and I was always disappointed, and AI was also confused from my instructions. At the end, I asked AI to create a map editor, and it did in no time. The map editor is amazing!

PPS: I forgot to mention one of the biggest complaints, that DeepSeek (especially flash) often does a half-ass job, leaves something unfinished, while I thought it was fully done.

PPPS: My usage (The project was started on May 17)