March 22, 2026
Shells, scans, and sticker shock
25 Years of Eggs
25 Years of Eggs: $1.6k to count $1.9k — commenters lose it
TLDR: One man used AI for 14 days to read 11k receipts and count 8,604 eggs, spending $1,591 to confirm $1,972 in egg buys. Commenters roasted the cost, said a human could do it cheaper, argued about inflation, and swapped tool tips—raising questions about AI’s value in everyday tasks.
A man scanned every receipt he’s saved since 2001, then unleashed two AI helpers on 11,345 files to answer one question: how much did he spend on eggs? The bots ran for 14 days and 1.6 billion tokens to find 589 egg receipts, tallying $1,972 for 8,604 eggs. The community reaction? Pure sticker shock. One user summed up the mood with “That’s a lot of eggs!” while another did the math: $2.70 per receipt, and quipped they’d be in “no hurry” to see the 30-year update. A practical crowd piled on: “just hire a human,” complete with a back-of-the-napkin estimate that a person could’ve done it for around $101.
Others loved the nerdy adventure but hated the price, calling it flashy proof that AI still isn’t “there” for everyday tasks. Then came the twist: inflation truthers jumped in, claiming eggs have outpaced CPI (the Consumer Price Index) or that CPI is undercounting prices entirely. Tech wars flared, too. People dunked on old-school OCR (turning images into text) for reading “OAT MILK” as “OATH ILK” and a model looping “TILL YGRT,” while fans cheered Meta’s one-click image tool and Claude reading sideways receipts. A Gemini booster bragged it crushed messy handwriting and Japanese text, igniting a “which bot is boss” showdown. Funniest meme: a family photo filed as a receipt. Verdict: half awe, half “$1,600 for egg math?!”
Key Points
- •Processed 11,345 receipts collected since 2001 to extract egg purchases, identifying 589 egg-related receipts over 14 days using 1.6B tokens.
- •Classical CV methods for segmenting flatbed-scanned receipts (white-on-white) underperformed; best F1 score reached 0.302.
- •Meta’s SAM3 achieved 0.92–0.98 boundary confidence at ~4 seconds per scan, extracting 1,873 receipts from 760 multi-receipt pages.
- •LLM-based OCR via Sonnet and Codex outperformed traditional pipelines for rotated/faded receipts, removing the need for orientation pre-processing.
- •Replacing Tesseract with PaddleOCR-VL improved OCR quality; dynamic slicing of tall receipts (based on aspect ratio) resolved repetition loops.