個人開発で4つのAIを使って感じた、任せる場所の違い A developer details hands-on experience with four AI tools in solo projects, identifying t…

As AI coding assistants proliferate, developers increasingly face a subtler decision than whether to use AI at all: which AI to reach for at each step of a project. A recent post on Zenn approaches this question from the ground up, drawing on a solo developer's hands-on experience with four different AI tools across personal projects. The core argument is that the largest productivity gains come not from picking a single "best" assistant, but from deliberately matching each task to the tool best suited to it.

The central premise is straightforward yet easy to overlook. Rather than treating AI assistants as interchangeable, the author reports that each tool has a distinct sweet spot, a domain where it consistently performs better than the others, and that consciously routing work accordingly reduces friction and rework. In practice, this means noticing when a task calls for tight editor integration, when it calls for broad reasoning over a whole codebase, and when it simply calls for fast, low-latency completion of boilerplate.

Although the piece is a personal account rather than a benchmark study, the distinctions it draws map onto the way the current tool landscape has segmented. Editor-native assistants such as Cursor are built around agentic editing inside the IDE, where the model can read the open project, propose multi-file changes, and iterate against the developer's actual code. This makes them well suited to structural refactoring or feature work that touches several files at once. Chat-oriented models, by contrast, tend to shine when the task is exploratory, such as reasoning through an architecture decision, drafting an approach before any code is written, or explaining an unfamiliar library. Lightweight inline completion, meanwhile, remains valuable for the repetitive typing that fills much of a working day.

Part of what drives these differences is technical. Tools vary in how much project context they can ingest, how they retrieve relevant files, and whether they operate as passive suggesters or as more autonomous agents that plan and execute a sequence of edits. Larger context windows and better retrieval let a model reason across a wider slice of a repository, which helps with tasks that depend on understanding existing conventions. Latency and cost also matter: a fast, cheap model is preferable for constant autocomplete, while a slower, more capable model may be worth the wait for a complex change. The author's conclusion, that the "where to delegate" question is as important as the "whether to delegate" one, appears to reflect these underlying tradeoffs rather than any single tool being superior.

The broader industry context helps explain why this kind of tool-switching has become practical. The market now includes editor-first products like Cursor and Windsurf, terminal and agent-style tools such as Claude Code and various command-line coding agents, GitHub's Copilot ecosystem, and general assistants like ChatGPT, Claude, and Gemini that many developers keep open in a browser. Emerging standards such as the Model Context Protocol, which aims to give assistants a common way to connect to external tools and data sources, are likely to blur some of these boundaries over time by letting different front ends tap similar capabilities. For now, though, meaningful differences in workflow, integration, and default behavior remain.

For individual developers, the takeaway is less about which four tools the author happened to use and more about the habit of thinking in terms of task fit. Building a mental model of each assistant's strengths, and being willing to move a task from one to another when results stall, is a low-cost way to improve output. It is worth noting the limits of any such account: experiences are shaped by the specific projects, languages, and subscription tiers involved, and the tools themselves change rapidly, so conclusions drawn today may shift within months.

Read as a field report rather than a definitive ranking, the post is a useful reminder that the skill of working with AI is increasingly about orchestration. As the number of capable assistants grows, knowing where to place each task may prove as valuable as knowing how to prompt any one of them well.