This year's annual review, I enlisted the assistance of an AI agent to conduct a deep data based inventory of myself. Sharing this process is because it allows you to easily complete professional level data mining without any programming experience. It's even simpler than Vibe Coding, just need: full data+Coding Agent. To verify the upper limit of this process, I specifically selected the largest and most complex 10-year health records in personal data for stress testing. one ️⃣ Data scale and processing threshold The final exported data from the Apple Health App is health-related raw data from an Apple Watch with a volume of up to 3.5 GB. This document contains hundreds of thousands of heart rate records and various physiological indicators with extremely fine granularity. Before AI intervention, to process data of this scale, I would need to invest at least a few days researching Python libraries and spending a lot of effort parsing complex XML data structures. This is often the biggest obstacle for many people when it comes to quantifying themselves. two ️⃣ Agent empowered workflow When we introduce coding agents such as OpenAI Codex or Claude Code, the entire process is completely changed. You no longer need to focus on specific code implementation, just clarify the 'analysis objectives'. The agent will automatically execute a recursive loop: autonomously study data structures → write Python processing scripts → encounter abnormal structures → re study and correct the code. It can independently complete the entire process from cleaning to analysis, helping me realize many ideas that were previously put on hold due to high technical costs. If you don't know how to start, you can first tell it the background and corresponding files of the task, and let it explore on its own, such as: >This is a complete raw data export from Apple Health. Could you please take a look at what types of health data are included? What is the time span? >Based on these health data, help me find 10 Insights. >Based on different categories of health data, please help me find out if there is any correlation between different categories of data. >Are you sure your research is correct? Please verify and provide me with more intermediate data for me to confirm. These can all be easily implemented, and you can even conduct more complex and specific research. ⚠️ Note: Due to the large scale of raw health data, it is not possible to directly throw it into apps such as ChatGPT. Therefore, it is still necessary to use the CLI version of the Coding Agent for research (Cursor or other IDEs may also be possible). You can ask AI specifically: >How to install Codex CLI/Claude Code CLI. three ️⃣ Sample Profile This analysis covers the complete cycle from May 18, 2015 to December 25, 2025, totaling 3875 days. During this period, I wore 5 Apple Watches and actually wore them for 3503 days, with a wear rate of over 90%. The longest continuous wearing record is 399 days, and the longest interruption record is 33 days. four ️⃣ Key insight: Correlation study of resting heart rate Based on the cleaned effective dataset, the agent has discovered some interesting patterns that were not previously discovered. The following conclusions were calculated by Codex (note: currently only showing correlation, not causality): Instant pressure of environmental noise (sample size: 1802 days): The probability of a high resting heart rate on the same day increases by about 5.2 times due to higher environmental noise. Delayed effects of prolonged sitting (sample size: 2546 days): The probability of higher resting heart rate the next day increases by about 1.75 times due to less standing time. Long term benefits of climbing (sample size: 1909 days): The probability of a higher resting heart rate the next day increases by approximately 1.91 times due to a lower climbing height. So at least for me, a quieter environment, standing up more frequently at work every day, and climbing stairs more can all bring long-term health benefits. five ️⃣ Practice and action suggestions The current analysis only focuses on the dimension of resting heart rate, including algorithm details and cross analysis of more dimensions. I will compile them into a detailed long article in the future. If you also plan to use AI for such analysis, please note that it is important to establish a validation mechanism. Agents may have "illusions" when dealing with complex logic. It is recommended to require them to output intermediate statistical data or conduct manual sampling and verification of small samples to ensure the accuracy of the final conclusion. Alternatively, multiple agents can be used for cross validation. six ️⃣ summary This is just a personal digital examination of my own body, and the conclusion may not be rigorous, only for reference. Sharing this process aims to demonstrate how Coding Agent has bridged the technological gap: mining data assets no longer requires the ability to write code, only the ability to ask questions. Since the technical threshold is gone, why not dig up those dusty data and see if AI can help you uncover some unexpected details.