Fixing my Smart Expense Tracker's BRAIN once and for all!

Michael fixes the Smart Expense Tracker to automatically understand dates, match categories, and process receipts flawlessly, saving hours of manual work.

This video focuses on fixing critical AI bugs in a Smart Expense Tracker developed by Michael Compiles. The main goal is to make the AI understand dates correctly, categorize expenses accurately, and process receipts flawlessly (0:34-0:44).

The video highlights three major AI headaches the developer aims to resolve:

Understanding time and relative dates: Initially, the AI confused dates like "yesterday" with past dates from two years ago (0:04-0:09, 6:56-7:01). The fix involved enhancing the model to understand context and use the current date to calculate relative dates (8:17-9:18, 14:54-15:15).
Refining image processing: The AI struggled to correctly extract details from receipt images, often failing silently or misidentifying amounts and categories (16:44-16:56, 17:41-17:56). Michael debugs the image processing, adjusting the way image URLs and dates are passed to the extraction function (18:04-21:02).
Accurate expense categorization: The AI frequently defaulted expenses to the "other" category, even for common merchants like McDonald's (0:09-0:12, 3:24-3:27). This was addressed by modifying the code to correctly identify categories based on the merchant (4:25-4:38, 5:09-6:08).
While the text-based expense entry and date identification were largely fixed (15:03-16:04), the image processing for receipts still presents challenges, with the AI misidentifying amounts and categories despite being able to detect an expense (21:20-21:54). Michael suggests this might be a limitation of the AI model or the underlying technology. The video concludes with a look forward to potential future enhancements for the expense tracker (22:40-23:00).

Read more about this project on his blog at   / i-taught-my-expense-tracker-to-think-how-i...  

GitHub repository: https://github.com/MichaelAshley2000/...