Claude Code has been on my radar for a while, but I have always been skeptical about the combo of CLI + vibe coding. I mean, come on, what if it make a mistake and you want to change just a line. So I stay with Cursor until mid of August (little did I know model degrading was happening), and I decided to give Claude Code a go.

It just works

I was surprised by how effective Claude Code approach is compared other coding tools. While Curosr relies on vector indexing and embeddings to search and retrieve code context, Claude Code often just uses basic tools like grep — then feeds the raw matched code directly back into the LLM.

And unexpectedly, this “primitive” approach often feels better than advanced indexing.

Why This Might Work Better

1. Zero Setup vs. Heavy Preprocessing
   Vector indexing and embedding pipelines require time and infrastructure, generating embeddings → storing them → updating them when code changes. Meanwhile, grep is instant and works out of the box.

2. Uncertainty in Embeddings
   Embeddings are fuzzy by design. They can retrieve semantically similar but irrelevant code. That’s a core limitation of LLM-driven search: you add another probabilistic layer before the model even sees the code. grep is deterministic, it finds exactly what matches.

3. Fewer Points of Failure
   Every extra system (embedding model choice, vector DB reliability, sync jobs) adds an extra layer of potential failure.

4. Privacy & Transparency
   With vector search, the code gets processed by another model and stored somewhere. Claude Code’s straightforward approach means no hidden vector stores and fewer unknowns about where your data lives.

The Cost Trade-Off

One obvious drawback: sending large chunks of code directly to the LLM burns tokens. At first, I thought maybe this was a way for Anthropic to charge more, but that’s unlikely. Claude Code comes bundled with the subscription, if the goal were to upsell API usage, they wouldn’t have made it unlimited in the first place.

Still, token usage is the main technical trade-off here, you still reach your limit faster.

Less Is More

This all circles back to an old engineering principle: Less is more.

Some years felt wasted, and I stood still longer than I wanted. But I also earned a master’s in statistics, moved away from a miserable accounting/finance path back into computing, and rode the waves of Big Data and Data Science. I moved to a new city, moved back, moved again. Then COVID arrived and life reshuffled once more.

Looking back, much of the last decade felt like treading water. The good news is I’ve caught the AI wave and something clicked this year. I don’t want to drift anymore. I’m pressing the reset button.

This blog is my reboot.