What I Learned Building a Lightning Network Tipping Bot

I built a simple Lightning Network tipping bot to understand how real Lightning payments behave in practice. The goal was not to build a large production system, but to explore invoice creation, payment flows, and common failure modes that appear when working with Lightning nodes. While the bot itself is small, the learning came from debugging real payment behavior rather than theoretical explanations.The Problem I Was Solving

The bot allows users to send small Lightning tips by generating invoices and attempting payments through a connected Lightning node. On the surface, the flow seemed straightforward: generate an invoice, pay it, and confirm success. However, during testing, I noticed that payments sometimes failed even though invoice generation appeared correct.Initial Wrong Assumptions

My first assumption was that invoice generation guaranteed successful payment if the node was online. I also assumed that failures were mostly network-related or due to insufficient balance. These assumptions turned out to be incomplete and led me to overlook timing-related issues in the payment flow.Understanding Lightning Invoice Expiry

After adding detailed logs and inspecting node responses, I realized that invoice expiry played a much bigger role than expected. Under certain conditions—such as slight delays or retries—the bot was attempting to pay invoices that were already close to expiring or had expired. This caused silent or unclear failures that were difficult to diagnose without careful logging.Debugging and Fixing the Issue

To fix this, I added explicit checks for invoice expiry timestamps before attempting payment. If an invoice was close to expiring, the bot would generate a fresh invoice instead of retrying the old one. I also improved error handling to surface clearer messages when payments failed. After these changes, repeated test payments behaved consistently and failures became predictable and explainable.What I Learned

This project taught me that Lightning payments require careful handling of timing, retries, and error states. Small assumptions can easily break under real conditions, even in simple tools. More importantly, I learned the value of structured debugging: reproducing issues, logging intermediate states, and validating assumptions step by step. These lessons directly apply to working with larger Bitcoin and Lightning codebases where correctness and reliability matter more than speed.