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548 points kmelve | 1 comments | | HN request time: 0s | source
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rhubarbtree ◴[] No.45112846[source]
Does anyone have a link to a video that uses Claude Code to produce clean robust code that solves a non trivial problem (ie not tic tac toe or a landing page) more quickly than a human programmer can write? I don’t want a “demo”, I want a livestream from an independent programmer unaffiliated with any AI company and thus not incentivised to hype.

I want the code to have subsequently been deployed in production and demonstrably robust, without additional work outside of the livestream.

The livestream should include code review, test creation, testing, PR creation.

It should not be on a greenfield project, because nearly all coding is not.

I want to use Claude and I want to be more productive, but my experience to date is that for writing code beyond autocomplete AI is not good enough and leads to low quality code that can’t be maintained, or else requires so much hand holding that it is actually less efficient than a good programmer.

There are lots of incentives for marketing at the grassroots level. I am totally open to changing my mind but I need evidence.

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MontyCarloHall ◴[] No.45114454[source]
Forget a livestream, I want to hear from maintainers of complex, actively developed, and widely used open-source projects (e.g. ffmpeg, curl, openssh, sqlite). Highly capable coding LLMs have been out for long enough that if they do indeed have meaningful impact on writing non-trivial, non-greenfield/boilerplate code, it ought to be clearly apparent in an uptick of positive contributions to projects like these.
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brookst ◴[] No.45114495[source]
So what percentage of human programmers, in the entire world, do you think contribute to meaningful projects like those?
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1. MontyCarloHall ◴[] No.45114578{3}[source]
I picked these specific projects because they are a) mature, b) complex, and as a result c) unlikely to have development needs for lots of new boilerplate code.

I would estimate the majority of developers spend most of their time on problems encompassing all three of these, even if their software is not as meaningful/widely used as the previous examples. Everyone knows that LLMs are fantastic at generating greenfield boilerplate very quickly. They are an invaluable rapid prototyping/MVP generation tool, and that in itself is hugely useful.

But that's not where developers spend most of their time. They spend it maintaining complicated, mature codebases, and the utility of LLMs is much less proven for that use case. This utility would be most easily measured in contributions to open-source projects, since all commits are public and maintainers have no monetary incentive to misrepresent the impact of AI [0, 1, 2, ...].

[0] https://www.businessinsider.com/anthropic-ceo-ai-90-percent-...

[1] https://www.cnbc.com/2025/06/26/ai-salesforce-benioff.html

[2] https://www.cnbc.com/2025/04/29/satya-nadella-says-as-much-a...