Random

There’s something magical about the terminal. No fancy graphics, no distracting UI elements - just you and the machine, communicating through text.

It’s Fast

No loading spinners. No waiting for the UI to render. Type a command, get a result. The feedback loop is instant.

It’s Composable

Pipe one command into another. Chain tools together. Build complex workflows from simple primitives. Unix philosophy at its finest.

It’s Honest

When something breaks, the terminal tells you exactly what went wrong. No “Something went wrong, please try again later.” Just raw error messages and stack traces.

It Makes You Feel Like a Hacker

Let’s be honest - there’s a certain satisfaction in watching text scroll by while you’re getting work done. It never gets old.

---

Posted while SSH’d into a server at 2 AM, naturally.

I’ve noticed a pattern in my git commits:

09:00 - "Initial commit" (pre-coffee)
09:30 - "Add feature X" (first cup)
10:00 - "Refactor feature X" (second cup)
10:30 - "Actually refactor feature X properly" (third cup)
11:00 - "Add comprehensive test coverage" (peak caffeine)
14:00 - "Fix typo" (post-lunch crash)
15:00 - "Revert 'Actually refactor feature X properly'" (existential doubt)
16:00 - "Fix: previous fix was wrong" (desperation)
17:00 - "WIP: going home" (acceptance)

The optimal blood caffeine level for coding appears to be somewhere between “can see sound” and “questioning life choices.”

Further research needed. For science.

Went to a tech conference. Immediately felt like a fraud.

Everyone’s talking about distributed systems, machine learning pipelines, and infrastructure at scale. I’m over here proud that I finally understood how this works in JavaScript.

Then I talked to someone. Turns out:

• The ML expert doesn’t understand CSS
• The infrastructure wizard can’t center a div
• The distributed systems architect forgot how REST works
• The senior architect googles “how to exit vim” weekly

Nobody knows everything.

We’re all just people who got really good at specific things and are fumbling through the rest.

The trick isn’t knowing everything. It’s:

1. Knowing what you don’t know
2. Knowing how to find out
3. Being honest about both

Still felt like an imposter at the after-party though. Baby steps.

Instead of working on my actual project, I:

1. Rewrote my .zshrc from scratch
2. Added 47 new aliases I’ll never remember
3. Configured Neovim (again)
4. Made my terminal prompt just right
5. Set up a new color scheme
6. Wrote a script to sync everything to GitHub
7. Added a fancy README with screenshots

Time spent: 6 hours Productivity gained: Maybe 30 seconds per day? Satisfaction: Immeasurable

---

Was It Worth It?

Objectively: No. The ROI is terrible.

Emotionally: Absolutely. My terminal looks beautiful and I understand every line of config.

There’s value in understanding your tools deeply. Even if the “productivity gains” are mostly imaginary.

Plus, now I can answer Stack Overflow questions about zsh configuration.

That’s worth something, right?

Right?

I spent three days refactoring code that was already working. Moved files around. Renamed variables. Created abstractions for things that didn’t need abstracting.

The feature still isn’t shipped.

Meanwhile, a colleague pushed a “quick and dirty” solution that solved the actual business problem. Users are happy. It works.

Will they refactor it later? Maybe. But the important thing is: it’s out there, providing value.

---

Shipping beats polish. Every time.

That doesn’t mean write bad code. It means:

• Ship the 80% solution
• Get feedback from real users
• Iterate based on reality, not imagination

The code you’re embarrassed by but is running in production > the perfect code sitting in your local branch.

Everyone’s talking about AI replacing developers. Here’s how I actually use it:

What Works

• Rubber ducking: Explain my problem, get it reflected back differently
• Boilerplate generation: Tests, types, documentation
• API exploration: “How do I do X with this library?”
• Code review: “What edge cases am I missing?”
• Refactoring suggestions: Alternative approaches I hadn’t considered

What Doesn’t Work

• Architecture decisions (lacks context)
• Business logic (doesn’t understand the domain)
• Debugging complex issues (hallucinations make it worse)
• Writing code I don’t understand

The Real Skill

Knowing when to use it and when to think harder yourself.

AI amplifies your abilities. If you don’t understand the fundamentals, it amplifies your confusion.

Here lie the projects I started and never finished:

The Fallen

• TodoApp v47 - Because the world needed another one
• MyBlog (2022) - 3 commits, 0 posts
• CryptoTracker - Died
• FitnessApp - Lasted longer than my gym membership
• AmazingIdea™ - README.md only
• CloneOfPopularApp - “For learning purposes”
• AI-Powered-Everything - Ahead of its time (I tell myself)

Lessons Learned

Each dead project taught me something:

• How to set up a project from scratch
• A new framework or library
• That I need to validate ideas before coding
• That finishing is harder than starting

The One That Lived

This website. Still breathing. Still getting commits.

Maybe the secret is building something you actually use.

Pours one out for the repos