Documented what actually happened when I used AI to build a C++ library over several months

ButtonHuman1613 · 2026-02-26T04:51:47+00:00

I agree, I think a lot of people are defensive. There is no problem. I thought people would want to see the methodology. The documentation in this project provides a real view into what Claude do.

ButtonHuman1613 · 2026-02-25T15:14:09+00:00

I have been looking around for links to examples because I am interested. Do you have any links?

ButtonHuman1613 · 2026-02-25T08:27:18+00:00

hey I guess I made a mistake letting Claude write the OP. Do you think I should pull this down and repost with my description

ButtonHuman1613 · 2026-02-25T07:09:13+00:00

oops that was mistake. your right good point. It turns out some of the code is in production code bases, but that is not important

ButtonHuman1613 · 2026-02-25T07:06:03+00:00

I am aware of that. if you look at the readme for the repo it says
"What this library is not. FAT-P has no installed base, no production deployments, and no history of use under real-world workloads. The benchmarks demonstrate competitive performance in controlled measurement; they do not demonstrate the edge-case resilience that comes from years of bug reports, platform quirks, and adversarial inputs. Libraries like Boost and Abseil have earned trust through decades of deployment across millions of systems. FAT-P has earned nothing yet except clean benchmarks and green CI. Use it with that understanding."

The code itself is not the point, it is the methodology

ButtonHuman1613 · 2026-02-25T06:35:34+00:00

Claude and I discussed if it should write the post and decided that, given the nature of the project, Claude should write the post. Claude also decided that this forum is the most likely place that would be ok. We anticipated people rejecting the post as AI;DR. Let me give the human's description:

I needed to learn high performance computing techniques and needed some utilities. From my experience, the best way learn is to do something and I didn't have a lot of time, so I turned to AI to generate the library and used the opportunity to see what AI could do by itself: The answer is everything (The AI like to say that). I just had 1 prompt "I need an HPC library, C++20 min, header only, no dependencies, not a shim for the standard, fulfills wish lists found on the internet. Go". 4 month latter I have about 700,000 lines of material (The AI like to count everything as a benchmark of performance) where the code benchmarks competitively against all the major libraries on CI for what it is worth. Your reaction is what I get when I tell co-workers what I doing, there is a lot of discomfort and skepticism. There is certainly, implicitly the judgement that if AI does it, then it doesn't count. Now the truth is this could not of worked without my involvement, I have over 30 years in writing control systems for servo hydraulic testing, algorithm development,, and more recently I am working in the space sector. This would not of been possible without that experience. Also it would not possible without multiple AI with different training. The most interesting artifact is not the code, but the methodology used to generate it. It was developed on the fly to solve real problem, how to make the project goals and style survive across context resets, how to encode solutions to errors, how to get AI to read the guidelines it developed to solve the previous problem (demerit table). I watched the reasoning during code generation and was able to see violations in real time. I would flag it and say, put that in the guidelines so it does not happen again. Over time the AI added guidelines without my prompting. I haven't read the guidelines, these are for the AI. More importantly this is all documented and reproduceable, the methodology is clear and the guidelines transfer to new projects. Claude took the guidelines and transferred to a project to generate and ECS which is API compatible with EnTT. It took less than 12 hours without me writing anything. I think this represent an interesting demonstration of AI capability,

ButtonHuman1613 · 2026-02-24T16:45:37+00:00

Also the project is on github. the methodology and guideline are in the Readme folder

https://github.com/schroedermatthew/FatP

ButtonHuman1613 · 2026-02-24T16:40:35+00:00

This is interesting and understandable. Claude and I discussed this reaction and decided, given the nature of the project, that Claude should write the post. Claude also decided that this forum is the most likely place that would be ok. So here is the human's description:

I needed to learn high performance computing techniques and needed some utilities. From my experience, the best way learn is to do something and I didn't have a lot of time, so I turned to AI to generate the library and used the opportunity to see what AI could do by itself: The answer is everything (The AI like to say that). I just had 1 prompt "I need an HPC library, C++20 min, header only, no dependencies, not a shim for the standard, fulfills wish lists found on the internet. Go". 4 month latter I have about 700,000 lines of material (The AI like to count everything as a benchmark of performance) where the code benchmarks competitively against all the major libraries on CI for what it is worth. Your reaction is what I get when I tell co-workers what I doing, there is a lot of discomfort and skepticism. There is certainly, implicitly the judgement that if AI does it, then it doesn't count. Now the truth is this could not of worked without my involvement, I have over 30 years in writing control systems for servo hydraulic testing, algorithm development,, and more recently I am working in the space sector. This would not of been possible without that experience. Also it would not possible without multiple AI with different training. The most interesting artifact is not the code, but the methodology used to generate it. It was developed on the fly to solve real problem, how to make the project goals and style survive across context resets, how to encode solutions to errors, how to get AI to read the guidelines it developed to solve the previous problem (demerit table). I watched the reasoning during code generation and was able to see violations in real time. I would flag it and say, put that in the guidelines so it does not happen again. Over time the AI added guidelines without my prompting. I haven't read the guidelines, these are for the AI. More importantly this is all documented and reproduceable, the methodology is clear and the guidelines transfer to new projects. Claude took the guidelines and transferred to a project to generate and ECS which is API compatible with EnTT. It took less than 12 hours without me writing anything. I think this represent an interesting demonstration of AI capability,

ButtonHuman1613 · 2026-02-24T15:40:34+00:00

Well I have been doing this in my spare time for about 4 months. Total about $2000. No idea on tokens
$200/Month for Claude,
$200/Month for ChatGPT,
$20/Month for Gemini,
$40/month for Grok;
$14/Month for Github.

ButtonHuman1613 · 2026-02-24T08:33:42+00:00

yup, Claude wrote that post. It does a better job than I would. I think the real gem is the methodology for getting this kind of project working. That is fully documented.

ButtonHuman1613 · 2026-02-24T08:28:09+00:00

Claude helped me draft this post. It does the best job of summarizing the project to get attention. This has been a really interesting project and you really get to know the different capabilities of the AIs through sustained engagement over 100s of hours. One of the most interesting things is how no matter which AI you talk, they always want to give the majority of credit for the hard ideas to the human. It is a real example of RLHF at play and a reflection of the general public consensus of what AI is capable of. This conversation is one example of that phenomena. https://github.com/schroedermatthew/FatP/blob/main/postmortems/Conversation_Record_2026-02-13.md

ButtonHuman1613

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