Vibe coding has its pros and cons, and is a polarizing topic in general. While it can make devs significantly faster, it may come with a lot of debugging pain, or worse: private data leaks.
Here are the top 3 things that can go wrong when you use AI to code:
1) Hardcoded Secrets: AI drops API keys and passwords directly into the code. Instant data breach if the repo leaks!
2) Injection Attacks: Generated code often skips input validation, opening the door to SQL Injection (database takeover).
3) Weak Security Logic: AI implements missing access checks or uses weak password hashes (MD5), making systems trivial to hack.
New video just dropped! We’re starting a tutorial series for solo devs who want to deploy faster, with less setup pain. So here’s episode 1: A quick tour of the Doprax UI.
Top performers who use AI will become much bigger, while average performers will kind of stay where they are. The winners will be the ones who freely experiment with AI in their work environment, who ask better questions (= write better prompts, iterate on the prompts).
It comes from Brian Kernighan’s 1972 book A Tutorial Introduction to the Language B, then made famous in The C Programming Language (1978). It symbolized the joy of making a machine “speak” for the first time.
Many platforms bill per hour, even if your container runs for 3 minutes. Doprax bills per second, so you only pay for real usage. It’s fair billing for developers who automate, experiment, and scale smartly.
🔹 Example: Run a container for 5 minutes, not 1 hour? You pay 5 minutes, not 60. That’s 12× less waste instantly.
Efficiency isn’t just in your code. It’s in your billing.
“Bug” in computing comes from actual insects interfering with early hardware. The most famous incident: in 1947, engineers working on the Harvard Mark II relay computer found a real moth stuck in a relay, which caused a malfunction. They taped it into their logbook with the note: “First actual case of bug being found.”
But the word bug was used in engineering before that. Thomas Edison used it in the late 1800s to describe mechanical or electrical faults:
“It’s not the machine’s fault, it’s a bug in the system.”
So the Mark II story didn’t invent the term; it just made it iconic in computing culture.
In short:
- Origin: 19th-century engineering slang for glitches. - Popularized in tech: 1940s, after the literal moth incident. - Meaning today: Any flaw, defect, or unintended behavior in software or hardware.
It’s a perfect metaphor: small, annoying, hard to find, and capable of breaking big systems.
Why so many developers still choose Django in 2025:
Django isn’t “new.” It’s been around since 2005. Yet it still powers startups, SaaS apps, APIs, dashboards, and even AI tools.
Here’s why:
🧩 Batteries included: Auth, admin, ORM, sessions — all ready out of the box. ⚡️ Rapid dev speed: Build a backend in hours, not weeks. 🛡️ Security built-in: Django ships with protection against the most common web attacks. 🔁 Scales with you: Start small, go big — same codebase, same framework.
It’s not about chasing hype — it’s about using what works. And Django still works.
