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170 points anandchowdhary | 2 comments | | HN request time: 0s | source

Continuous Claude is a CLI wrapper I made that runs Claude Code in an iterative loop with persistent context, automatically driving a PR-based workflow. Each iteration creates a branch, applies a focused code change, generates a commit, opens a PR via GitHub's CLI, waits for required checks and reviews, merges if green, and records state into a shared notes file.

This avoids the typical stateless one-shot pattern of current coding agents and enables multi-step changes without losing intermediate reasoning, test failures, or partial progress.

The tool is useful for tasks that require many small, serial modifications: increasing test coverage, large refactors, dependency upgrades guided by release notes, or framework migrations.

Blog post about this: https://anandchowdhary.com/blog/2025/running-claude-code-in-...

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apapalns ◴[] No.45957654[source]
> codebase with hundreds of thousands of lines of code and go from 0% to 80%+ coverage in the next few weeks

I had a coworker do this with windsurf + manual driving awhile back and it was an absolute mess. Awful tests that were unmaintainable and next to useless (too much mocking, testing that the code “works the way it was written”, etc.). Writing a useful test suite is one of the most important parts of a codebase and requires careful deliberate thought. Without deep understanding of business logic (which takes time and is often lost after the initial devs move on) you’re not gonna get great tests.

To be fair to AI, we hired a “consultant” that also got us this same level of testing so it’s not like there is a high bar out there. It’s just not the kind of problem you can solve in 2 weeks.

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simonw ◴[] No.45958225[source]
I find coding agents can produce very high quality tests if and only if you give them detailed guidance and good starting examples.

Ask a coding agent to build tests for a project that has none and you're likely to get all sorts of messy mocks and tests that exercise internals when really you want them to exercise the top level public API of the project.

Give them just a few starting examples that demonstrate how to create a good testable environment without mocking and test the higher level APIs and they are much less likely to make a catastrophic mess.

You're still going to have to keep an eye on what they're doing and carefully review their work though!

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cortesoft ◴[] No.45958377[source]
> I find coding agents can produce very high quality tests if and only if you give them detailed guidance and good starting examples.

I find this to be true for all AI coding, period. When I have the problem fully solved in my head, and I write the instructions to explicitly and fully describe my solution, the code that is generated works remarkably well. If I am not sure how it should work and give more vague instructions, things don't work so well.

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teaearlgraycold ◴[] No.45961124[source]
Yes, but the act of writing code is an important part of figuring out what you need. So I’m left wondering how much of a prefect the AI can actually help with. To be clear I do use AI for some code gen. But I try to use it less than I see others use it.
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1. cortesoft ◴[] No.45961404[source]
Eh, I think my decades of experience writing my own code was necessary for me to develop the skills to be able to precisely tell the AI what to build, but I don't think I need to (always) write new code to know how to know what I need.

Now, if the thing I am building requires a technology I am not familiar with, I will spend some time reading and writing some simple test code to learn how it works, but once I understand it I can then let the AI build from scratch.

Of course, this does rely on the fact that I have years of coding experience that came prior to AI, and I do wonder how new coders can do it without putting in the work to learn how to build working software without AI before using AI.

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2. teaearlgraycold ◴[] No.45971543[source]
It’s not just about new tech. It’s about new businesses and projects.