As my classmate said, I’m currently experiencing a situation of “being forced to code”… The several websites I maintain each have their own frontend, backend databases, and some even have test code and migration code, involving languages ranging from Python commonly used in bioinformatics to JS, TS, HTML, C#, which I rarely used before. In actual work, there isn’t much time to systematically learn each language from scratch, so mastering the most basic debugging methods is crucial. Among them, I find the debugging methods for JavaScript and TypeScript particularly worth recording, as they do have some unique aspects compared to other languages.

Uptime Kuma is an extremely user-friendly open-source monitoring tool that makes monitoring various network services simple and efficient. It supports multiple protocol monitoring including HTTP(s), TCP, Ping, DNS queries, and even simulates real user access through Chrome engine mode to more accurately monitor service stability. Additionally, it supports dozens of notification methods for alerts and can integrate with various applications we use daily. It also offers a unique Push monitoring method, allowing users to extend monitoring functionality through custom code (such as monitoring SSH service availability).

In cancer research, comparing genomic features between tumor samples and organoid models is crucial for validating model reliability. Circos plots provide an intuitive way to visualize detected mutations across the genome, making them commonly used for representing overall detection results of representative samples. While reading the circlize documentation, I came across an example demonstrating paired samples, which I found suitable for showcasing paired primary samples and organoids. I’ve adapted it to create a plot for displaying paired samples. The code is primarily based on the official documentation’s 9.5 Concatenating two genomes

Last year, I actually used Fydetab Duo to compress videos from my travels. At that time, I found a pre-built library on GitHub, but now I can’t find that repository anymore… So this time, I tried to compile it myself…

There are really many modules for rapidly developing data or AI-related applications in Python. I’ve already used Dash, Streamlit, Gradio, NiceGUI, and recently I discovered two more. Just when I needed to develop a simple data dashboard to display company data, I once again recklessly decided to use a new framework - Taipy.

Picking up from where we left off, after successfully compiling the code in the libapp project, the next steps are: modifying the libapp code and applying the changes to the image.

From last year to this year, I played two PC games: Black Myth and WuChang, both downloaded and played on their release days. This would have been hard to imagine five years ago. Back then, I had just finished Monster Hunter: World’s main story using a Windows KVM virtual machine with GPU passthrough. When the Iceborne expansion was released, Proton could already support quite a few older games, but the latest games were difficult to support at launch. I remember it took two to three months after Iceborne’s official PC release before the game could be launched, and there were still some bugs. Moreover, whenever Proton was updated, the game might become unplayable again…

I’ve been using FydeOS/ChromeOS for about two years now. While the system provides a usable terminal app, it’s honestly not that great. For example, during development, I often need to forward multiple ports. Although I can achieve this by entering SSH commands for port forwarding, this requires manually inputting quite a few parameters. Additionally, during port forwarding, I need to keep the SSH login window open. For someone like me who’s particularly obsessive about minimizing the number of open windows, keeping three or four windows open that won’t be used in the foreground is really uncomfortable… So I thought, can I do it myself, with the help of AI, modify the system’s default terminal client, and add quick forwarding functionality like VSCode has?

When people are speechless, they really do laugh - my direct supervisor has resigned again and again and again. Why so many “agains”? Let me draw a timeline to see how many bosses I’ve “jinxed”.

Compared to two years ago at my previous company, I now use AI much more frequently. If the limitation back then was the convenience of the tools themselves, the current limitation seems to be my own knowledge and technical skills rather than AI.