Time to get back to Assembly, I guess.

But do you trust the CPU?

Well, fuck

I think about this every day.

Dev's what?

Maybe? If you poison the prompt then there's evidence and it can be undone. Poison fragments of the source training data, however, and that's some KT shit right there. Enterprise foundation models cost bonkers money to train and pretty much slurp up all the data on the internet for mostly automated annotation. Stick something in an obscure part of the internet which becomes part of the training and produces the malicious response and it's going to be both hard and expensive to detect or correct.

For the uninitiated: who compiles the compilers?

The very fact that Anthropic is now injecting a kind of watermark into every output, is solid proof that such a Ken Thompson hack is a inevetable risk

https://www.teamten.com/lawrence/writings/coding-machines/

Devs should be "dev managers and executives". Real developers know LLMs are basically just a tool for finding examples and helping with syntax. Sure they're useful, but I'd never let them write code, much less compile it. Who knows what they'd inject into a build.

At work, I use AI for some things. Right now I'm rewriting some legacy spaghetti code that's had a bunch of things hacked into it over the years. I spoke to the person most familiar with the expected behaviour and used AI to combine his info plus the existing code and unit/integration tests into a list of requirements.

I wrote the new code and tests based on the requirements rather than based on the old code. After each commit, I used AI to check for parity between the old and new code, and it keeps a Google Sheet up to date with the progress (which features were fully implemented, and which ones were missing or had gaps). I had AI write some tests cases too - given the list of requirements, write integration tests for them based on the style of a few tests I wrote by hand.

It has some quirks (eg for tests it loves over-mocking even though our skills tell it to mock as little as possible) but it definitely speeds things up.

I use AI for small side projects at work too. Tweaking and adding features I want to shared libraries, internal tools to help our team debug stuff and automate triaging of bug reports (they're all still reviewed by a human), etc.

The entire reason I can trust its code is because I can read it and tweak it myself. I sometimes need to go through a few iterations to get AI code into an acceptable state. AI writing machine code directly, like what's been talked about recently and what this post is referencing, is such a dumb idea.

There's other people at work that use AI for absolutely everything. Writing code, reading code, writing posts in our internal groups, etc. That's something I don't understand. Some people that are all-in on AI produce so much low-quality AI slop.