Eight AI dev tools later, three earned a spot on my dock

Eight AI developer tools arrived in my evaluation queue in the last quarter. I installed all of them, used each one for at least a week on a real project, and three of them are still on my dock. The other five are gone. This article is the candid split, the trade-offs I did not see in the marketing copy, and the framework I now use to evaluate the next round.

The reason I wrote this down is that “AI developer tools” is a category that has gotten noisier every month. Every new tool promises to make you faster. Some of them do. Some of them make you slower because of the prompt-engineering tax, the context-window work, and the constant review of suggestions you did not ask for. The honest answer to “is this tool worth your time” is “for what task, in what workflow, and at what cost to the rest of your day.” That is the lens I want to apply to all eight.

What I tested and what I was looking for

Each of the eight tools went into a real project, not a hello-world demo. The projects ranged from a 4,000-line Node.js service to a small data pipeline to a one-page marketing site. I used each tool for at least one full work day, and the tools that stayed earned their keep by being faster than the alternative for a specific task that comes up more than once a week.

The framework I now use for any new tool in this category is four questions:

• Does the tool replace a specific task I do more than once a week? A tool that helps with a task I do twice a year is not a tool I will remember to open.
• Does the output require heavy editing to ship? If half the suggestions need rewriting before they land in the codebase, the tool has not saved me time, it has moved the editing cost from a blank page to a fix-up pass.
• Does the tool keep me in flow, or does it add a side-channel of attention? A tool that pops notifications, suggests context switches, or breaks my keyboard flow is a net loss even if the suggestions are good.
• Does the tool have a clean escape hatch? When the tool is wrong, can I close the tab and keep going without losing work, paying a downgrade fee, or waiting for an export? Tools that lock you in are the ones I uninstall first.

The three that earned their keep

• CodeRabbit (AI-powered code review). This one stayed. The reason is that the alternative is me, on a Friday afternoon, reading a 600-line pull request for the third time. CodeRabbit catches the same class of issues I would catch on the second pass, but it catches them before I have to do the second pass. The output requires light editing, mostly to delete suggestions that do not apply to our codebase, and the tool stays out of my flow because it runs on the pull request, not in my editor. The honest caveat: it is not free, and the per-PR pricing adds up on a high-merge team. For a solo developer, the math is harder to justify.
• HTTPie (HTTP testing CLI). Not new, not strictly an AI tool, but the 2026 release added AI-assisted query generation that is good enough to be useful without being intrusive. I type the URL and the verb, and it suggests headers and body shapes from a natural-language prompt. The output is a curl-like command I can read, edit, and run. The reason it stayed is that the AI is in the suggestion, not in the loop. I get a useful default and I am still in control. The escape hatch is a clean stdout that I can pipe into anything.
• Treblle (API monitoring). This one surprised me. I expected a 14-day trial and an uninstall. Instead it stayed because the dashboard answers a question I ask every other week: “which endpoint started returning 500s, and when did it start.” The AI insight layer is light, mostly clustering of error patterns, but the underlying monitoring is solid. The reason it earned its keep is the alert quality. I get one Slack ping when a real regression lands, not twenty pings when a load test trips a noisy threshold.

The five that did not earn their keep

These are the tools that look good in a marketing screenshot and disappoint in a real project. I am naming them because the marketing pages will not.

• Bolt (full-stack apps from prompts). The pitch is “describe your app and we generate the code.” The reality is that I spent two hours describing the app, an hour editing the generated code to remove the parts that would not run, and another hour wiring the auth provider it had hardcoded to a different service. The output was a starting point, not a finished thing, and the starting point was not faster than writing the same code from scratch. The escape hatch is also bad. I exported the code, lost the prompt context, and had to re-explain the project to the next tool I tried.
• Trae (release orchestration). This one promised to replace my release script. The release script is a 40-line bash file that does what I need. Trae is a configuration UI on top of an opinionated workflow that does not match how my team ships. The escape hatch is “write a new script,” which is what I would have done anyway. I uninstalled it on day four.
• CodeSnippets (snippet management). The promise is “save and reuse your snippets with AI search.” The reality is that I have a folder of .sh files and a grep command, and the folder has been the right tool for five years. The AI search layer did not improve on grep -r for my snippets, and the editor integration asked for permissions I did not want to grant. Uninstalled on day six.
• Taipy (data-driven app builder). This one is good at what it does, and what it does is not what I needed. I do not build data dashboards often enough to learn a Python-specific GUI framework. The framework itself is well-engineered. The escape hatch is “I still need to write the data layer in a different tool.” Uninstalled on day five.
• Super Color Palette (palette generator). This is a useful tool for the specific task of generating a color palette, and I have nothing bad to say about it. The reason it is on the uninstalled list is that I need a new palette about once a year, and the website loads in five seconds and saves my output as a CSS file. There is no AI feature here that I used, and the value of the tool is unrelated to the AI pitch. Uninstalled because I do not need an installed app for a five-second website visit once a year.

The hidden cost nobody mentions

The biggest cost of any new AI tool is not the subscription. It is the prompt-engineering tax, which is the time you spend learning to talk to the tool in a way that produces useful output. Some tools have a low tax and some have a high tax. CodeRabbit’s tax is one afternoon to learn the codebase review preferences. Bolt’s tax is two afternoons of trial and error before you realize the prompts you wrote were not the prompts the tool wanted.

Every new tool also adds a context-switching tax. Every new tool is a new tab, a new keyboard shortcut, and a new mental model. After three tools, the cost of switching between them starts to outweigh the benefit of any individual one. After eight tools, the cost is the dominant cost. The reason I have three tools, not eight, is that I cannot hold eight mental models in working memory at the same time, and the cost of constantly re-learning the keyboard shortcuts and prompt conventions was the actual bottleneck.

The third cost is the review cost. AI-generated suggestions that land in my codebase have to be reviewed by me before they ship. The review is faster than writing from scratch, but it is not free. For a tool where every suggestion needs a five-minute review, the cost adds up fast. For a tool where most suggestions are good and the few bad ones are obvious, the review is fast enough to be a net win.

Trade-offs

Three honest costs to weigh before adding any new AI developer tool:

• Subscription math. Per-PR pricing, per-month pricing, and per-token pricing all add up differently. A solo developer on a per-PR tool is paying for noise. A team on a per-month tool is paying for predictability. The right pricing model depends on the workload shape, not the team size.
• Lock-in risk. Tools that store your context, your snippets, or your codebase analysis in their own format are expensive to leave. The escape hatch matters more than the feature list, because the feature list changes every quarter and the lock-in compounds over years.
• Flow cost. Every tool you add is a new place where attention can be stolen. The cost is invisible until you measure it. The cheapest measurement is to count the number of times you open the tool’s dashboard in a week. If the answer is “more than ten,” the tool is taking attention you did not budget for.

For a solo developer, the right number of AI tools is probably one or two. For a small team, three or four. For a larger team with shared conventions, the math changes because the prompt-engineering tax can be amortized across the team. My own dock has three. The other five got uninstalled in the first week.

If you are evaluating a new tool in this category, the test I would run is a one-week trial on a real project, with a specific task in mind, and a clear “did this tool save me time on that task” question at the end of the week. If the answer is “I am not sure,” the tool did not earn its keep.

Bottom line

If you only do one thing from this article, pick one tool from the three that earned their keep and try it for a week. The reason one tool is the right starting point is that the prompt-engineering tax is real, and the tax compounds with each tool you add. The reason CodeRabbit, HTTPie, and Treblle are on the list is that they each replace a task I do more than once a week, they each have a clean escape hatch, and they each let me stay in flow while using them.

Three things to keep in mind:

• The framework is the value, not the list. A list of “the best tools” is stale in three months. The four questions at the top of this article are the durable part. Apply them to the next round of tools and you will get a more honest answer than any “top N” listicle.
• The five that did not earn their keep are not bad tools. They are tools that did not match my workflow. The same tools might earn their keep for a different team, a different task mix, or a different scale. The point is not “do not install Bolt.” The point is “install Bolt knowing the use case, the cost, and the escape hatch.”
• The three that earned their keep are not a recommendation. They are what worked for me, on the projects I work on, in the workflows I use. Your projects are different. Re-run the framework on your own data before you adopt any of them.