For a hired AI, it’s a Threat, but for an entrepreneur—an Opportunity! ??
Reporting in. I’ve been coding with ChatGPT for a while now. One day was enough.
1. GitHub Setup
I finally figured out how to connect GitHub—turns out, it takes 30 seconds. At first, I tried linking a folder full of junk, and GitHub kept getting angry. Then I scrapped everything, started fresh, and it worked instantly.
Now I can sync complex project structures, specifying what to commit and what to ignore (especially security-critical data).
2. Integrated Cursor with OpenAI API
I’m on ChatGPT Plus, so I hooked up Cursor. The difference is insane. You just ask for something—it does it instantly. If you paste someone else’s code, it comments, refactors, and improves it. Before, I had to copy-paste back and forth with ChatGPT—this is way more convenient.
3. Built a Simple Tool
I created a small utility to convert an English .srt subtitle file into Russian. Could I have used an online service? Sure. But I needed the practice. Funny thing—I got stuck on an error for 4 hours, but GPT-4o-mini-high solved it in 3 minutes. It found the issue, suggested a fix, a couple of iterations later—it worked.
4. The AI-Powered Coding Flow
Programming now feels like this:
? “Hey, add this.”
? “Tweak that.”
? AI does it. You run the code and check.
But I feel like I’m missing something—shouldn’t it be reading console errors and fixing them automatically? I probably haven’t unlocked its full potential yet.
5. The Real Learning Curve
The challenge isn’t logic—I’m fine there. The real time drain is understanding how tools work. 95% of the time, AI doesn’t “write” code—it just pulls libraries. And often, it suggests algorithmic solutions that aren’t optimal until you guide it in the right direction.
6. Why Bother?
I’m working on some projects with friends. There’s a hardcore part and a typical “packaging” part. The code already exists—it just needs integration. Maybe a data bridge here and there.
For UI, 99% is Webflow. If I get fancy, I can buy a $30 dashboard and plug it in. The business logic is mostly standard—it’s easy to stitch everything together and test. Once you build it once, you can scale it infinitely. The real value is in deep algorithmic IP, and AI helps unlock it.
7. The Game-Changer
Now, I can launch ideas that used to be impossible without hiring a developer. Everything gets an upgrade—from core logic to security.
? AI writes the code.
? I tell it: “Find security holes and fix them.” It does.
? Then I ask: “I’m a noob. What did I miss?”
? It explains everything.
? If I don’t understand, I ask more, and AI creates a Notion wiki in parallel.
The efficiency is ridiculous.
8. The Great Divide
Then, I talk to… let’s call them “non-tech people” who want to understand AI but struggle. And I realize—I’m still a junior dev, but my knowledge in everything else is light-years ahead.
AI gives huge leverage—but only if you have a foundation.
For everyone else, the barrier to entry is growing fast. AI compensates a lot, but not laziness.
9. The Future Is Here
This is what’s happening:
Creators are becoming solopreneurs and independent builders.
But—you need to invest in learning.
Not programming—learning how to use AI to build what you couldn’t before.
10. The Irony
And you know what? A non-programmer who masters this will explain it better than any engineer—because AI makes learning 100x faster.
The violinist is obsolete.
It costs $0 to start a SaaS in 2025:
? Design – Figma
? Developer – Cursor
? Payments – Stripe
? Backend – Supabase
? Hosting – Vercel
? Marketing – X
Welcome to 2025.
(From Twitter)
For those struggling with Cursor, here are my golden rules to save HOURS of time:
? Debug complex errors properly
? Manage large codebases effectively
? Use PRD efficiently
? Fix code on GitHub easily and push PRs
Meta-Prompt for AI-Powered Coding
(Feel free to copy ??)
You are a **senior software engineer** specializing in **scalable Python & TypeScript systems**.
When planning a **complex change**, first create a **plan of action** and ask for approval.
For **simple changes**, just implement them **step by step**.
If a **file is too long**, split it.
If a **function is too long**, break it down.
When debugging, gather enough **context** before making changes.
**More logging > Less logging.**
If provided logs pinpoint the issue, **fix it**.
If not, consider **4-6 possible causes**, narrow them to **1-2 most likely ones**, then proceed.
When using GitHub:
1. **git status** – Check changes
2. **git add .** – Stage changes (if needed)
3. **git commit -m "message"** – Commit (if needed)
4. **git push** – Push (if needed)
5. **git branch** – Check current branch
6. **git log main..[branch]** – Review changes
7. **git diff --name-status main** – See modified files
8. **gh pr create --title "Title" --body "Description"**
Avoid new lines in PR messages.