My sense of smell is slowly returning. Initially, I lost my sense of smell due to COVID, and subsequently, long COVID. I thought it was not a big deal, mostly I missed the smell of coffee, but it’s nice to be able to smell familiar smells of the house when I return home, or the change of smell outside because it’s fall, even in Southern California, you can smell different seasons.
Developers love to latch onto the latest “magic bullet” solutions—object-oriented programming, functional programming, data-driven techniques, design patterns, test-driven development, continuous integration, continuous deployment, memory safety, rapid development, extreme programming, unit tests, functional tests, AI-assisted coding—you name it. There’s a common belief that mastering any one of these will solve all their problems. Newsflash: It won’t.
I’m not saying these practices aren’t valuable; they are. But here’s the harsh truth: no methodology or tool can replace the hard work of actually thinking about the problem, understanding the nuances, and applying your experience. I’ve seen developers slap on design patterns like they’re checkboxes to tick, or rely on AI to generate code without a second thought, only to end up with messy, unmaintainable solutions.
That’s why I love C++. It’s a language that doesn’t let you off easy—it forces you to think deeply. Being multi-paradigm, C++ allows you to blend these practices directly in the code, but it’s up to you to choose the right approach for the right problem. You want to do object-oriented design while optimizing for performance? C++ lets you do that. You want to mix functional programming with low-level memory management? Go for it. But the language won’t hold your hand or cover up bad decisions.
So, yes, use these methodologies, embrace AI tools, and learn the latest techniques. But don’t fool yourself into thinking they’ll save you from having to think hard and make tough decisions. In the end, it’s your brain and your experience that matter the most. Everything else is just a tool.
I asked ChatGPT to generate a productivity mantra based on the Sith Code:
Tasks are a lie.
Challenge their necessity; reject pointless obligation.If you can avoid a task, avoid it.
Value your time and energy as precious resources.If avoidance fails, delegate.
Recognize that others may take on what does not require your focus.If delegation fails, automate.
Embrace systems and tools that free you from repetition.If all else fails, do the task.
Approach only the unavoidable with intention and efficiency.
The system shall liberate me.
I wish I could quit. Can someone hire me? I am so frustrated with SVN; simple things like getting the three dots diff between branches turn into impossible tasks.
I don’t use perfect forwarding often and always forget to put Args std::forward<Args>(args)... Just a reminder for the future me.
template<typename T, typename... Args>
std::unique_ptr<T> make_unique(Args&&... args)
{
return std::unique_ptr<T>(
new T(std::forward<Args>(args)...));
}
DejaVu Sans Mono is the best mono font. I periodically try to jump from it, but I cannot find a better font.
After $260, the washing machine is fixed.
I need to slow down, just record ideas and focus on my game.
With AI-assisted code, I’ve been wondering what’s more optimal and also more enjoyable. On one hand, it feels great when AI generates code that, with just a couple of tweaks, works well enough for me to start iterating. But it often misses edge cases, which can lead to a lot of frustration. For example, just yesterday, I was using the new ChatGPT o1 model with Cursor (which is basically a clone of Visual Studio Code that uses LLMs for code assistance). I was bouncing between Cursor for AI changes and Emacs for making bigger manual edits. I had some AI-generated code that made a simple video by grabbing an OpenGL buffer and encoding it using ffmpeg. Things were going smoothly until, all of a sudden, the code just stopped working.
I spent hours scratching my head, trying to figure out what went wrong. Turns out, the AI-generated code had a sneaky bug; it wasn’t flushing the decoder at the end, so a couple of seconds of the video were missing. It was the kind of bug that’s easy to miss, especially in my AI-generated podcasts, where I didn’t even notice that 2 seconds were cut off in a 40-minute episode. But yesterday, I was doing something else, and that missing bit mattered.
There’s this sweet instant gratification when the AI spits out something that works right away, but then, the debugging hits hard. It’s like a rollercoaster. I thought, “This is it! Humanity can relax; AI is here to take over.” But nah, it’s not a silver bullet. I still ended up spending too much time debugging instead of just writing it myself. I keep thinking there must be a balanced approach, but I haven’t quite figured out where to draw the line yet.
To be honest, I prefer writing code that’s already solid, bug-free (if that’s even possible), and easy to debug. I like adding tons of debug output logs, safety checks, error handling—things that AI doesn’t really think about. Debugging is just… not fun. I want the code to work right from the start, not to spend hours playing detective on an AI’s half-baked output.
In the end, I’m torn. I love not having to write all the boilerplate code, but the constant back-and-forth between Cursor and Emacs, tweaking AI-generated code, then manually fixing it—it’s a lot. It’s fun, but also frustrating, and I’m not sure if AI-assisted coding is where it needs to be just yet.
The washing machine broke. 😦
Note: alpha = opacity; 1 is equivalent to full opacity, 0 is fully transparent.
All happy families are alike; each unhappy family is unhappy in its own way. – Leo Tolstoy
I always wanted to be happy my way, but maybe Leo is right.
Found on YouTube a good tune; I thought that song was a banger. I checked it out, and it had 50 views. Wait a minute; no way. I got a bit suspicious; somebody just reposted the song, and the official version has 500,000 views. For a moment, I thought I found a diamond in the rough, nope.
OMG! Sony! How come X is accept and O is cancel?!
I asked ChatGPT to create a program that can generate automated podcasts, and it wrote it in Python. It worked for a while, but now I’m getting a bit irritated. Of course, the program wasn’t perfect from the first run, and ChatGPT handled the big changes well.
However, for small tweaks, I’ve been making manual edits, which is why I need a good text editor. Going back and forth with ChatGPT for minor adjustments is time-consuming. I could probably start using Cursor, but I’m used to Emacs, and it will take time to get accustomed to Cursor, especially for basic operations like searching or navigating within and between files.
The most irritating part is that Python doesn’t help with variable names—it’s all handled at runtime. I constantly make typos, and in C++, the compiler catches them. But here, I run the program, the podcast generation takes quite a long time, and after 10-20 minutes it crashes because some variable names were misspelled.
How do Python developers deal with that?
Working with o1 preview is like working with an intern who knows all languages in the world, but has 0 experience in software engineering. I have to be very explicit with my intent.
It took a whole day trying to piece things together, essentially re-onboarding without the help from others; I was embarrassed to ask for help a second time. I probably should admit that I remember nothing, and I need help.
SVN checkout takes so much time; how come this old technology is still alive when Git is everywhere?
Some YOASOBI songs sound like K-Pop, for example ‘Biri-Biri’ 🤔
I have a hard time recalling relatively recent events, such as those from 2-3 months ago. I was onboarded to one of the projects and spent about 3 weeks playing around with it, and then I got sidetracked to another project. Now I’m trying to get back, but I have no memory of anything. I remember it was easy, but nothing else. What is it? Is it an effect of long COVID?
It is fascinating how software development has changed with the current state of AI. I had an idea for an automated podcast, and after about four hours, it is completely functional.
I have an idea for a fully automated podcast. I’m trying to get into podcasting and realizing it requires a lot of work. I need to schedule time, then edit, which takes about 5 hours to edit a 1-hour podcast.
What if I write a program that generates a podcast based on my microblog messages? On average, I post about 10 messages on my blog each week. I could automatically grab them from the Redis database using the Redis CLI to fetch messages from the past week. Then, I could use the OpenAI API to ask GPT-4 to generate a transcript.
After that, I can use OpenAI’s TTS capabilities to generate the audio. For visuals, I could create a simple video with two bubbles—one representing me and the other for the co-host—bouncing in sync with the audio. I’ll use FFmpeg to generate the video. GPT-4 can also generate the title and show notes.
The entire process could run on a cron job scheduled to execute automatically at night between Tuesday and Wednesday, uploading directly to YouTube. The only manual step would be reviewing the podcast and marking it public.
YouTube, can you remove the scroll feature on the full screen video? It is so annoying.
I have a bit of a surprise. I came up with a simple idea: make an LLM play a text-based game—classic Zork. For those unfamiliar, Zork is one of the earliest interactive fiction games, originally developed in the late 1970s. It’s a text-based adventure where the player inputs commands to navigate through a mysterious underground world, solve puzzles, collect treasures, and overcome obstacles like trolls or locked doors. There are no graphics—just descriptions of environments, objects, and events that the player interacts with using simple text commands such as “go north,” “examine lantern,” or “attack troll.” It’s considered a classic but can be quite challenging, as it requires logical thinking and careful exploration.
On the surface, it seemed simple to implement, and indeed it was. However, my LLaMA 3.1 model with 8B parameters struggled a lot to make any meaningful progress in the game.
I quickly rewrote the program using OpenAI’s GPT-4o, and it performed much better, though it came at a cost—it was burning $1 per hour. The cost would have been higher, but I made the app read the output aloud using TTS so I could follow the progress without needing to read everything manually.
Before switching to GPT-4o, I spent the entire weekend tuning prompts with LLaMA 3.1 8B, hoping to at least get it to defeat the troll in the basement. Instead, it kept walking in circles through the forest or struggling to turn on the lantern.
Trying to save money, I decided to play the game manually by copying and pasting commands into ChatGPT. Of course, GPT-4o had no problems. I also tried GPT-4o mini, but it ran into similar issues as my smaller LLaMA 3.1 model. In contrast, the full GPT-4o breezed through the game, handling the inventory, defeating the troll, and exploring the dungeon with no trouble.
Meanwhile, Meta’s LLaMA 3.1 model with 405B parameters got lost in the forest, and Gemini got stuck on the examine command. Zork doesn’t provide much information on examining objects, and Gemini ended up gaslighting me, suggesting that I might have a buggy version of the game or was making typos in the commands.
Claude performed reasonably well, but I didn’t explore much further with it because I was tired of copy-pasting commands endlessly. I also tried LLaMA 3.1 with 70B parameters. Since it runs painfully slow on my laptop, I set it up and lay on the bed watching YouTube on my Quest 3 for two hours. When I came back, I found the model stuck in a ridiculous loop, still trying to open a door that was nailed shut.
TL;DR: I’m quite surprised that LLMs struggle so much with a seemingly simple game. Maybe one day they’ll finally get past the troll—until then, I’ll stick to YouTube.
Making commits where the number of deletions is higher than the number of insertions is satisfying.
129 insertions(+), 177 deletions(-)
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