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.

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?

This year, DeepSeek has made LLMs popular again. Although after ChatGPT, Copilot and Cursor have been advertising everywhere, I never actually tried them… So this year, I… tried Cline, which is indeed good, but also matches my preconception - not that convenient. Especially since I need something that can be used directly in the command line and quickly integrated into scripts to batch complete some simple coding initialization tasks. That’s how I found aider…

Object-oriented programming is a common paradigm in programming. In my actual work, using object-oriented programming mainly serves two purposes: reducing duplicate code through inheritance features, and encapsulating frequently used data into objects to avoid excessive, repetitive, and nested parameter passing.

After getting used to Python, developing scripts in R can feel quite painful. Putting aside the often-discussed issues with unclear error tracing, when scripts become slightly more complex and need to be split into multiple files, I realized R’s import mechanism is also quite frustrating… Fortunately, there’s the box package that allows module imports similar to Python’s logic.

Actually, I’ve known about singularity for quite some time. As a container specifically designed for HPC (High Performance Computing), it’s always been something I wanted to try out. However, just like other NGS technologies outside of Illumina, singularity hasn’t gained much traction and it seems even harder to compete with Kubernetes, which is widely adopted by most cloud vendors… Of course, this doesn’t really matter to me right now… We’re still at least three or five years away from going cloud-based, so using it locally makes perfect sense.

Today, I needed to deploy an RStudio server for someone else. Here are the steps and key points of the deployment.

Recently, out of… well, let’s say a bit of dissatisfaction, I sold my previously purchased Lenovo Chrome Duet2, but then I bought a Pixelbook 2017… Now that I have this device, I still want to tinker with it for a while, trying to find a way to use this Pixelbook 2017 as a replacement for my work computer for general office tasks. In the process of practising, the main issue wasn’t resolved, but I did come up with a few practical by-products… writing Hexo blogs using Codespace is one of them.

This title is a bit too nested… but the fact is, I suddenly felt capable! I finally have a chance to become a full-stack developer! To showcase my capabilities enhanced by chatGPT, I decided to develop a tool that uses chatGPT to read papers (I’ve also considered using it for meta-analysis).