Over the past few months, a sizable fraction of my developer peers have taken to AI tools in their programming workflows. When I was beckoned from under a rock by the light of day, I was taken aback by this rising wave of vibe coding.

Some of my peers claim AI tools to be phenomenal for frontend technologies like React and NextJS. The selling point? Context aware autocompletes and agent mode.

Context aware autocompletes happen when the model watches your code so it can suggest autocompletes while you code.

Agent mode involves an “autonomous agent” based on a prompt that can manipulate files, run commands and essentially carve out a project.

This unfortunately has become the hot new thing that is vibe coding.

The contradicting self

Surprisingly, when I ask my friends about how LLMs improve their code, it is often simple errors that one would have known had they read the documentation for once. This is what LLMs really provide us, an artificial sense of competence.

Here’s what my friend @noobscience had to say:

The place where it helps me the most is simple errors. Like, let’s say I forgot to await a promise, it pretty much finds that out.

A good first question is “what is the scope of a Copilot?”

Is the purpose simply to write boilerplate code and provide fixes for stupid mistakes?

Most reasonable developers would not want to surrender the steering wheel completely to the LLM because of the unpredictable nature of LLMs. Good luck trying to fix bugs with little to no clue about how your own code works.

A cheap knock-off

A deeper question concerns the tooling used during development.

When most people admit that they use LLMs to catch small mistakes and fix them predictably, what they actually mean is that these models serve as cheap, less predictable knock-offs of more robust, deterministic and less resource hungry language tooling.

Developers needing LLMs assistance is a symptom of the compiler or language tooling being subpar. That they are not good enough to catch low hanging errors.

In fact, quite a few languages provide fantastic tooling and make programming feel incredibly pleasant. Some good examples include gopls for the Go programming language and rust-analyzer for Rust.

To conclude, I think what developers really want are crude, simple yet battle tested language tooling written by humans. We don’t want big blobs that take three nukes’ worth of energy to train and yet require GPUs to run locally.