Vibe Coding: From Hell to Heaven in One Insight
Source: Dev.to
Project 1: Slack Application
I decided to build a Slack application.
- Infrastructure: Done in two hours.
- Application code: That’s where hell began.
My approach was simple: describe what I wanted to AI, copy‑paste the generated code, deploy, and ship. It didn’t work. Error after error appeared. I kept pasting the errors back to the AI, receiving new code, redeploying—rinse and repeat. After a week of this back‑and‑forth, I was stuck in what I now call “vibe coding hell,” blindly following AI without understanding the fundamentals.
I stopped, took a breath, and read Slack’s official SDK documentation. I learned:
- What features Slack offers
- How the SDK modules work
- The proper workflow for Slack apps
Armed with that knowledge, I returned to the AI and gave it clear architectural instructions based on my new understanding. The app was completed in three days (including learning time and a complete rewrite when I mis‑interpreted terminology). After that, any new feature took minutes to implement.
Key takeaway: You can’t outsource understanding to AI. Software design and architectural decisions still come from humans. AI is a powerful assistant, but you need domain knowledge to guide it effectively.
Project 2: Inference Platform for LLMs
I wanted to build a complete inference platform to host LLM models and fine‑tuned variants. My knowledge level was minimal—I’d only learned the term “inference” the night before starting.
Timeline: Three days to production‑ready.
| Component | Details |
|---|---|
| Infrastructure | 1 hour via Terraform: complete cloud stack |
| Frontend Web UI | Full‑featured interface |
| Backend Inference Services | Two services hosting different LLM models |
| Automated Training Pipeline | End‑to‑end data processing |
| Performance Optimization | Reduced latency from 28‑30 s to 3‑4 s per query (pure software tuning, no hardware upgrades) |
With my SRE background (system architecture, performance optimization, infrastructure patterns), I could guide the AI effectively. I understood the options the AI presented and made informed decisions about:
- Architecture patterns
- Performance trade‑offs
- Infrastructure design
- System integration
AI drove about 80 % of the implementation, but I drove 100 % of the architectural decisions. This demonstrates the power of combining domain expertise with AI assistance—a true force multiplier.
Emerging Patterns
After these experiences, I’m noticing a shift in how I spend my time:
- Less manual coding – I let AI handle boilerplate.
- More architecture and design – I think about system structure, trade‑offs, and integration.
- Validation and refinement – I review AI‑generated code, ensure quality, performance, and security.
It feels similar to the transition from manually configuring servers to writing Infrastructure as Code: the skill set changes, but the value remains.
Lessons Learned
- Fundamentals first: Skipping the basics leads to failure (Slack app).
- Domain knowledge matters: My SRE background enabled success with the LLM platform.
- AI amplifies, not replaces: It multiplies what you already know.
- Higher‑level problem solving: Engineering is moving toward architecture, integration, and validation, while AI handles repetitive implementation details.
What’s Next?
I’m planning to build a full‑stack application in Rust—a language I’ve never learned. This will test whether the principles I’ve discovered apply across domains. Stay tuned.
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