asaiacai

What you described is exactly my sentiment haha :)

cool to see a really grounded analysis of the "security" and LLMs. I use agents in on the day-to-day for generating implementation but this just furthers my belief that humans and especially human reviewers remain critical for the sustainability of software systems.

also, lol at "The bots are dumb - they want to please you line". LLMs have pretty much ruined technical collaboration between contributors. I get tilted every time an discussion has "but my claude said this..."

This is really cool work! I'm curious like what do you see as the biggest lever for speeding up TTS models or from a technical perspective that this was a promising direction in the first place to push on. If I were to guess, some distillation but I'm certain there are probably TTS model aware architectural changes that just make inference wayyyy faster?

we're only calling it an "incident" now i see. smh

this list has a lot of false positives.

lmao

it's the perfect drug. You don't know how to code something up. Ask AI to implement it. It's broken? Ask AI to fix it for you. Will people become unable to fix things without it?

have you guys tested this against any resource/batch managers in k8s (i.e. kueue, volcano, apache yunikorn)? seems like this is a good fit for people who have already have a large cluster. does it handle autoscaling environments well in your experience?

its cool to see the iterative improvements to your model laid out, but for everything that workedm i imagine there were at least a million other things you also tried but didnt work out. whats your process of trying these different techniques/architectures? do you just wait for one experiment to finish and visually inspect the results everytime. seems hard since these take a while to train. how do you shorten the feedback loop in this space?