I am very curious what some of your lint rules look like in practice. In my mind a lot of the AI-isms in my code that I hate are stylistic or a matter of taste, not necessarily something I could write a deterministic rule to check. But I want to hear more. Like, what kind of linters did you create and which were highest impact?
I'm surprised there isn't a simple image classifier in place to filter out images of gore/porn/etc. - I know that there are such output filters for images with copyrighted content. It suggests to me that either the safeguards aren't in place, or this exploit bypasses those safeguards.
That was an amazing talk, thanks for sharing! I could see the writing on the wall as soon as I saw the bucket names were predictable. Bucket squatting + public buckets + time of check/time of use in the CloudFormation service = deploying resources in any AWS account with enough persistence. I'm surprised this existed in AWS for so long without being flagged by AWS Security.
The "> Remove lockfiles from version control" got me as well.
> Reproducible builds sound nice in theory, but velocity matters more than determinism. Think of it as chaos engineering for your dependency tree.
Reproducible builds are nice in practice, too. :) In the Node.js ecosystem, if you have enough dependencies, even obeying semver your dependencies will break your code. Pinning to specific versions is critical.
This is a cool idea. I wanted to do something like this by adding a Lua API to OpenRCT2 that allows you to manipulate and inspect the game world. Then, you could either provide an LLM agent the ability to write and run scripts in the game, or program a more classic AI using the Lua API. This AI would probably perform much better than an LLM - but an interesting experiment nonetheless to see how a language model can fare in a task it was not trained to do.