Just observed a key adjustment in the X algorithm distribution mechanism: it is breaking the "language isolation" on the timeline. X has started to automatically translate foreign language content into the language you set, directly pushing it to the timeline. Previously, you not only had to manually click "translate," but the recommendation algorithm usually wouldn't actively push this kind of "unintelligible" content to you. I'm not sure how long this feature has been online, but this is the first time I've clearly perceived it. This means that high-quality content will no longer be limited by language; authors only need to create in their native language, and it will be automatically translated into the reader's native language for distribution. This step by X lowers the barrier for cross-language communication, and achieving this mainly relies on large language models, right (Grok)?

This year's annual review, I asked the AI Agent for assistance and conducted a data-driven deep inventory of myself. I share this process because it requires no programming experience and allows you to easily complete professional-level data mining. It's even simpler than Vibe Coding, requiring just: full data + Coding Agent. To validate the limits of this process, I specifically selected the largest and most complex 10-year health records from my personal data for stress testing. 1️⃣ Data Scale and Processing Threshold The final data exported from the Apple Health App is a raw health data file from the Apple Watch, totaling 3.5 GB in size. This file contains hundreds of thousands of heart rate records and various finely-grained physiological indicators. Before AI intervention, processing data of this scale would require me to invest several days studying Python libraries and spend a lot of effort parsing complex XML data structures. This is often the biggest obstacle many face when it comes to "quantifying oneself." 2️⃣ Agent-Enabled Workflow When we introduce a Coding Agent (like OpenAI Codex or Claude Code), the entire process is completely transformed. You no longer need to focus on specific code implementations; you just need to clarify the "analysis goals." The Agent will automatically execute a recursive loop: autonomously study the data structure → write Python processing scripts → encounter abnormal structures → research and correct the code again. It can independently complete the entire process from cleaning to analysis, helping me realize many ideas that were previously shelved due to high technical costs. If you don't know how to start, you can first tell it the background of the task and the corresponding files, allowing it to explore on its own, for example: > This is a complete raw data export from Apple Health; please take a look at what types of health data it contains? What is the time span? > Based on this health data, help me find 10 insights. > Based on different categories of health data, please help me find if there are correlations between different categories of data. > Can you confirm your research is correct? Please verify and provide me with more intermediate data for my confirmation. All of these can be easily implemented, and you can even conduct more complex and specific research. ⚠️ Note: Because the original health data is quite large, it cannot be directly input into apps like ChatGPT, so you still need to use the CLI version of the Coding Agent for research (Cursor and other IDEs may also work). You can specifically ask AI: > How to install Codex CLI / Claude Code CLI. 3️⃣ Sample Overview This analysis covers the complete period from May 18, 2015, to December 25, 2025, totaling 3875 days. During this time, I wore 5 different Apple Watches, with an actual wearing duration of 3503 days, achieving a wearing rate of over 90%. The longest continuous wearing record was 399 days, and the longest interruption record was 33 days. 4️⃣ Key Insights: Correlation Study of Resting Heart Rate Based on the cleaned effective dataset, the Agent uncovered some interesting patterns that had not been previously discovered. The following conclusions were all calculated by Codex (Note: currently only presenting correlation, not causation): 🔊 Immediate pressure from environmental noise (sample size: 1802 days): Higher environmental noise → The probability of a higher resting heart rate on that day increases by about 5.2 times. 🪑 The lagging effects of prolonged sitting (sample size: 2546 days): Less standing time → The probability of a higher resting heart rate the next day increases by about 1.75 times. 🪜 Long-term benefits of climbing (sample size: 1909 days): Less height climbed → The probability of a higher resting heart rate the next day increases by about 1.91 times. So at least for me, a quieter environment, standing more often while working, and climbing more stairs can bring long-term health benefits. 5️⃣ Practical and Actionable Suggestions The current analysis focuses only on the dimension of resting heart rate, and the algorithm details and more cross-dimensional analyses will be organized into a detailed article later. If you also plan to use AI for such analyses, please note that it is essential to establish a verification mechanism. The Agent may have "hallucinations" when processing complex logic, so it is recommended to ask it to output intermediate statistical data or conduct small sample manual checks to ensure the accuracy of the final conclusions. Alternatively, use multiple Agents for cross-comparison verification. 6️⃣ Summary This is merely my personal digital examination of my body; the conclusions may not be rigorous and are for reference only. Sharing this process aims to demonstrate how the Coding Agent has bridged the technical gap: mining data assets no longer requires coding skills, just the ability to ask questions. Since the technical barrier is gone, why not dig out that dusty data and see what unexpected details AI can help you uncover?

Just achieved a milestone, and this month the Impressions have finally surpassed 10 million. What does this number mean? It's more than double the Impressions I've had in the past 12 months combined. (Of course, this indicates that my data has been poor all along.) If anyone still doubts the authenticity of this number, let's look at another Bookmark data. This month, it has been Bookmarked nearly 10,000 times, which is almost equivalent to the total of the past 24 months combined. Recently, a few of us have gained some new insights into X's recommendation algorithm; some are mystical, while others include random factors. But it can basically be confirmed that: > AI + Vibe Coding + real personal experiences + article structures suitable for social media reading are the factors that this new algorithm will prioritize. Of course, traffic is just one aspect. More importantly, the extensibility of the topics around AI and Vibe Coding is particularly large, and almost any type of product can be explored. In the past two months, I have also launched many products/websites, and the total DAU has surpassed 1,000, but it's still quite low. I still haven't found a profitable model; maybe I need to try a few more to have a chance. But I believe that these previous products have already provided real value to users; otherwise, they wouldn't consistently open a webpage every day. Perhaps this is the charm of Vibe Coding: you no longer write code just for the sake of writing code, but to capture that fleeting idea and turn it into a product that everyone can reach.