Ladies and gentlemen, I had a narrow escape last month—almost fell into my own automated trading strategy. It wasn’t a market crash or a technical bug; it was purely because I overlooked those seemingly unnecessary check steps. Every lesson shared today is earned with real money. Don’t be surprised if you wake up tomorrow to find your automated program has converted all ETH into some small altcoin at 3 a.m.—so take a good look now.

**Lesson One: Check the AI’s "Memory Limit"**

Have you ever encountered this situation—letting the automated system analyze "all protocols in the SOL ecosystem over the past three months plus cross-chain liquidity changes," only for it to start talking nonsense after the second month? I have. I later realized the problem lies in the context memory capacity. When the system is loaded with 2048 tokens, it begins to fail, leaking data like a broken bucket.

The solution is simple: don’t expect it to handle everything at once. Break down complex tasks into pipeline steps. Instead of "analyzing the entire ecosystem," do this—first, check Jupiter’s trading volume over the past 90 days; second, compare it with Raydium’s performance during the same period; third, identify the five trading pairs with the biggest differences. Treat the system as a worker, not an all-powerful deity.

**Lesson Two: Keep an Eye on the "Freshness" of Data**

The scariest thing isn’t the system malfunctioning, but thinking it’s using the latest data when it’s actually working with stale overnight data. Once, I asked it to adjust cross-chain timing based on real-time Gas fees, and foolishly waited for the result—only to find it used the average from 24 hours ago. That cost me quite a bit of unnecessary money.