> AUR is just a collection of user-produced PKGBUILDs.
Is that much different from the entire pypi ecosystem, and npm, and dockerhub (people disable Selinux, --privileged turns off seccomp and apparmour, sandbox escape CVES exist)?
Intriguing... "After months of misdiagnoses Dr Souhel Najjar, employs a test asking Susannah to draw a clock. Instead of the customary clock face, her condition led her to draw all the numbers 1 through 12 on the right side of the clock. This was the breakthrough moment; it was this clock drawing that enabled Dr Najjar to understand that the right side of Susannah’s brain was inflamed, further test revealed this inflammation was a result of anti-NMDA receptor encephalitis, initiating her path to recovery"
"since 2019, on the advice of the National Agency for the Safety of Medicines and Health Products, French health workers have been told not to treat fever or infections with ibuprofen." [1]
But yet in some countries pediatricians will libreally prescribe it to toddlers
Also
from [2] "In this systematic review of NSAID use during acute lower respiratory tract infections in adults, we found that the existing evidence for mortality, pleuro-pulmonary complications and rates of mechanical ventilation or organ failure is of extremely poor quality, very low certainty and should be interpreted with caution."
Anthropic/OpenAI could own this space. They should offer a paid service that offers a mirror with LLM scanned and sandbox-evaluated package with their next gen models. Free for individuals, orgs can subscribe to it.
> “ The agent acted like a hyperparameter optimization algorithm with some basic reasoning baked in.”
Good lens.
The crux of the auto research repo is basically one file - program.md which is a system prompt that can be summarized as “do this in a loop: improve train.py, run the training, run evals, record result. Favor simplicity”. The other files are an arbitrary ML model that is being trained.
Should have funded the entire GIL-removal effort by selling carbon credits. Here's an industry waiting to happen: issue carbon credits for optimizing CPU and GPU resource usage in established libraries.
The solution is ironically, LLMs. You can construct a set of Claude skills to walk you through a code review, or understand code (anything, even course) fast.
This complexity to understanding compression will be a big market going forward.