DEV Community: kenta nishida (nishiken)

Why I Stopped Letting AI Write My Code (May 2026)

kenta nishida (nishiken) — Sun, 31 May 2026 23:12:25 +0000

This article shares why a web engineer holding a Claude Code Max 20x plan has mostly stopped using AI for coding at work. It's not about abandoning AI — it's about narrowing down where I let AI take over.

Why I stopped having AI write my code

My growth pace slowed down

Looking back honestly, I was acquiring knowledge faster before I delegated most coding to AI. Once I drastically cut down on writing code by hand, I quietly gave up a side benefit: the investment in growth that came along for the ride with the act of writing.

In learning theory terms: output (writing) drives retention more strongly than input (reading), as evidenced by the Testing Effect — the cognitive psychology finding that recalling material through output strengthens long-term memory. By losing those writing opportunities, I lost a quietly efficient learning channel.

I became less able to deliver quickly

Without the act of writing, the in-brain cache of implementation details fades, and I more often can't answer questions on the spot.
In practical terms: when a non-dev asks "can we do this?", my answer is more often "let me check" than before. From the non-dev's side, "well, in that wait time I'll just ask the AI myself" becomes a perfectly rational move.

The same pattern shows up in implementation and in conversations with other devs: "wait, how did we do this elsewhere?" now requires actual investigation rather than instant recall.

Sometimes it's faster for a human to write than for AI to write + review

When the implementation pattern is essentially copy-paste-then-tweak, asking AI to do it means I have to verify line by line (with diff) that only the intended spots changed. By contrast, when I copy by hand and then substitute, the work process itself guarantees "what I changed vs. what I left alone." The latter is often the faster path.

So what do I still use AI for?

Work outside my specialty

In domains where I have almost no background, AI can produce a passable, "good-enough-looking" output quickly.
Recently I tried to boost search traffic to a side-project web app and ran SEO measures — with essentially zero SEO background, AI gave me something "SEO-shaped" to ship.
Whether it's actually any good is honestly unclear, but once something is shipped a PDCA loop kicks in and things tend to course-correct toward "appropriate" over time.

Research

I'd rather hit primary technical sources directly than have AI summarize them. But for breadth-first surveys — collecting peripheral information, summarizing trends, listing options — AI is overwhelmingly faster.

People worry about "AI missing data it can't access (X, etc.)" and about "hallucinations." But web-information-gathering already assumes "don't take any single source at face value," so the probability of hitting a bad source via AI ends up as a rounding error, in my personal experience.

Recurring routine work

Things like running the app for manual verification, triaging Dependabot PRs, writing PR descriptions — I've increasingly delegated these to my own custom skills.
For example, instead of "spin up the container, seed the DB, hit it with Postman…", I let AI do all of that and just emit a curl command against localhost:3000. My job is reduced to "paste the curl command and observe the response."
The same curl trail also goes into the verification section of the PR description, and it works well enough. A nice side benefit: because every step is CLI, the reviewer can rerun the verification on their own machine more easily.

My personal heuristic: tasks that are "easy to articulate as a decision procedure and that recur frequently" are good candidates for skill-ification, and they pay future-me forward.

Local-only tools I'm the only user of

As long as I clear the minimum security bar, sloppy implementation is fine here.
I've personally built a few ruby-lsp addon gems for myself, which enable VS Code go-to-definition for Rails DSL methods (scope, delegate, etc.) and CodeLens display of model associations. The vibe is "as long as it runs" — and I haven't written a single line of that code by hand.

These use cases tend to fill up my Claude Code usage anyway, so I'm keeping the Max 20x plan.

Closing thoughts

Things feel settled at the moment, but technology moves so fast that I might be saying the opposite tomorrow. Personally I expect that "coding" itself will eventually get fully abstracted away by AI — and this article, which kind of swims against that current, may quietly disappear one day.

Even so, I'll keep enjoying day-to-day engineering through this turbulent era.

Why I Stopped Letting AI Write My Code (May 2026)

kenta nishida (nishiken) — Sun, 31 May 2026 20:52:43 +0000

Why I stopped having AI write my code

My growth pace slowed down

I became less able to deliver quickly

The same pattern shows up in implementation and in conversations with other devs: "wait, how did we do this elsewhere?" now requires actual investigation rather than instant recall.

Sometimes it's faster for a human to write than for AI to write + review

So what do I still use AI for?

Work outside my specialty

Research

Recurring routine work

My personal heuristic: tasks that are "easy to articulate as a decision procedure and that recur frequently" are good candidates for skill-ification, and they pay future-me forward.

Local-only tools I'm the only user of

These use cases tend to fill up my Claude Code usage anyway, so I'm keeping the Max 20x plan.

Closing thoughts

Even so, I'll keep enjoying day-to-day engineering through this turbulent era.

GitHub Actions による記事自動デプロイ v4

kenta nishida (nishiken) — Sun, 11 Jan 2026 14:47:37 +0000

GitHub Actions による記事自動デプロイのテスト

この記事は、GitHub Actions を使った自動デプロイ機能をテストするために作成されました。

概要

このプロジェクトでは、以下の3つのプラットフォームに対して記事を自動デプロイできます:

Zenn
Qiita
dev.to

仕組み

docs/public/meta/ 配下のYMLファイルを変更
GitHub Actions が変更を検知
各プラットフォームのAPIを使って記事を作成・更新

まとめ

GitHub Actions を使うことで、記事の管理とデプロイを自動化できます。

TypeScriptで始めるブログ自動投稿 - Test Article v1

kenta nishida (nishiken) — Sat, 10 Jan 2026 11:59:06 +0000

TypeScriptで始めるブログ自動投稿

この記事では、TypeScriptを使ってブログ記事を複数のプラットフォームに自動投稿する方法を紹介します。

対象読者

ブログを複数のプラットフォームに投稿している方
記事管理を自動化したい方
TypeScriptに興味がある方

実装方法

GitHub Actionsを使って、Zenn、Qiita、dev.toに自動投稿します。

// サンプルコード
async function publishArticle(platform: string) {
  console.log(`Publishing to ${platform}`);
}

まとめ

自動化により、記事管理が劇的に楽になります。