A case study in testing with 100+ Claude agents in parallel
1–10 of 57 posts
Re: A case study in testing with 100+ Claude agents in parallel
#2Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of.
Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
Re: A case study in testing with 100+ Claude agents in parallel
#3Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of. Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
There are no secrets when you are using AI providers. They track all interactions b/c that's valuable information for improving their models.
Re: A case study in testing with 100+ Claude agents in parallel
#4Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of. Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
There are no secrets when you are using AI providers. They track all interactions b/c that's valuable information for improving their models.
I'm talking about sharing things publicly that you are trying to claim as your own
Re: A case study in testing with 100+ Claude agents in parallel
#5Earlier quoted context omitted.
There are no secrets when you are using AI providers. They track all interactions b/c that's valuable information for improving their models.
I'm talking about sharing things publicly that you are trying to claim as your own
It doesn't matter. If someone has the same idea then they can use AI the same way you did to recreate it. Keeping it a secret benefits no one other than the AI providers b/c now they can charge money for giving someone else "your" code. The AI providers don't care about license restrictions so it's the perfect way to launder code. If you want credit for something then you'll have to claim it publicly b/c the AI providers sure as hell are not going to give you any credit.
Re: A case study in testing with 100+ Claude agents in parallel
#6Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of. Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
There are no secrets when you are using AI providers. They track all interactions b/c that's valuable information for improving their models.
Re: A case study in testing with 100+ Claude agents in parallel
#7Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of. Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
You think people open sourced things mostly because of license obligations?
Re: A case study in testing with 100+ Claude agents in parallel
#8Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of. Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
There is no such court ruling.
Re: A case study in testing with 100+ Claude agents in parallel
#9Curious how people and companies like this are approaching matters of intellectual property now that the courts have ruled that basically no part of AI generated content or code is copyrightable and is therefore impossible to claim ownership of. Are people just not going to open source anything anymore since licenses don't matter? Might as well just keep the code secret, right?
It was always a bit weird how heavily software companies leaned on copyright, and I think you could basically replicate the same intuitions and dynamics on top of trade secret law if you had to. KFC didn't go out of business when a Chicago Tribune reporter found what's most likely the secret recipe.
I'm also not sure that the current precedent on the matter is _quite_ as strong as you're thinking. The high-profile case you're most likely thinking of was from a guy Stephen Thaler, who was seeking not just to claim copyright on AI-generated content but to specify the AI as the sole author. (IIUC, he planned to still own the copyright on the theory that it was a work-for-hire.)
Re: A case study in testing with 100+ Claude agents in parallel
#10the thing that actually burns token budget at scale isn't the agent count itself—it's understanding the cost model of orchestrating them. 100 agents running in parallel is fine if they're short-lived queries. but once you start running them on a schedule (hourly checks, overnight batch work), the math changes fast.
each agent run against a real codebase probably spends 20-50k tokens just on context: repo structure, relevant files, recent changes. multiply that by 100 agents running every hour across 10-20 repos, and you're already hitting millions of tokens a day before any actual work happens. add in re-runs for failures or retries, and the cost curve gets steep quickly.
the harder problem is observability. with one agent you can read logs and understand what went wrong. with 100 agents you need aggregation, pattern detection, alerting on the common failure modes. if 3 agents fail silently but identically, was that a real issue or just rate limiting? if 40 agents all timeout at the same step, was it a dependency problem or infrastructure saturation? at scale you're debugging distributions, not individual runs.
also helps to be ruthless about concurrency. the async pattern isn't "run as many as possible at once"—it's "run exactly as many as the API and your budget can support without making the failure modes harder to diagnose." for claude api work that's usually smaller than people expect.