I Built a Log Monitoring Script with DeepSeek — Here is What Went Wrong

Short summary

Author used DeepSeek to generate a Python log-monitoring script but encountered hallucinations (missing libraries), incorrect sliding-window logic, and datetime serialization errors. The article walks through exact fixes applied—dependency validation, deque reconstruction, JSON conversion, and better error handling—demonstrating both AI's speed and the real-world engineering required to ship generated code.

• •DeepSeek AI generated a log-monitoring script in 560 tokens but included hallucinated dependencies and logic bugs
• •Author systematically debugged and fixed five categories of errors: imports, sliding-window logic, JSON serialization, colored output fallback, and daemonization
• •Concrete takeaway: AI-generated code accelerates development but requires careful review; includes the exact prompt and cost breakdown

Generated with AI, which can make mistakes.