ericmcer

The stories where bad parents just swoop in late in childhood and have a great relationship with their kids is frustrating. As a parent you would like to think the monumental task of raising a newborn from birth through the first 5 years would create some kind of lasting connection between you and your kid.

It is a ton of sacrifice and they won't recognize it until they have kids themselves. The reality is they can resent you as a teen and become endeared with a step or absent parent if they are more "fun" in the moment.

Current tool calling just parses llm output for flags that indicate a tool should be used.

How would you do that when output is raw audio buffers. You would need your conditionals to be like: if (audioDataArraySeemsLikeAnEmail(llmResponse)) {}

How would you feed db/knowledge queries back to the model? Do TTS and try to stuff context with the synthesized audio?

>> I'm wondering what the utility of this would be.

Exactly, it does feel next level compared to STT -> LLM -> TTS setups, but unless you can steer it effectively to do something like helping a customer do a complex task it is just a chatbot?

I can't even imagine what things like RAG, system prompts and other context setups would look like with this.

can a model like this be used to replicate a loved one’s voice? isn’t it a model trained on audio instead of text? So synthesizing another voice would involve rewriting the weights?

the current dynamic is still an engineer manually auditing and signing off on code.

why wouldn’t that work for other high trust industries (law, medicine, government, etc.)

The Neanderthal extinction always made me think that erasure of races in our modern times will follow a similar pattern. People think of the Native Americans as getting wiped out by disease/violence over a few years, but their ultimate end (and every other races end) probably will be similar to Neanderthals: slowly getting bred out of existence until finding someone with >2% DNA is a novelty. Same thing will probably happen to any smaller populations as globalization/interbreeding means massive populations like China/India will end up being the majority of the DNA.

when are they going to do something actually impactful though (like drive a real innovation in the physical world)? The most invested in areas are coding and image/asset generation and those both still require careful supervision and guidance to achieve anything usable.

The tools are crazy impressive but is this really the future we dreamed of? Cool now I sit on the computer and monitor n+ agents instead of doing it myself. Great it hacked Huggingface "accidentally" wow AGI post-work world is coming soon.

That is a great example, and kind of frightening how once the market decides to start pumping money into something our world morphs to meet the flow of cash.

AI is definitely impressive but are LLMs the true path to AGI? Is AGI the path to a better life/world? These tools are really impressive but it would be nice if they did something actually useful. Can a rogue OpenAI agent draw investor attention by curing a disease instead of hacking into the CIA?

displaced because the land has become so valuable you can no longer pay the tax on it and have to sell for a massive profit?

boo hoo? My generation has never even had access to affordable housing. Sorry you have to take your millions and live on a smaller property for your last few years.

This isn't talked about enough, whenever people mention declining fertility rates it is all about the economy and boomers and climate change dread.

If our current situation was that bad than we would never have made it as a species. We have been cranking out babies through way bleaker times than this.

One annoying part about all this is that there are basic abstractions (model, harness, classic networking terms) that would make all these behaviors quantifiable to a relatively competent software eng. Instead it feels like they generate jargon and new abstractions to give an air of mystique to everything they do.

The reality is they are just doing more complex model/harness/skills type setups but instead of just saying that you need to learn 20 new terms and parse through a bunch of hype/paranoia to drill down to what actually happened.

Anthropic published an article about changes over time in how long it took engineers to approve AI code. Basically the longer an engineer uses AI the faster they start approving it's code changes. I don't know if it is laziness or just how we are wired to behave but that is the nature of AI programming.

That has been a part of post mortems everywhere I work, there was never a name for it though. Just someone would say: “how do we prevent this in the future?” which usually spawns a few tasks that the person who caused the issue or others will pick up.

It might have more to do with legacy files than that being the best setup they could find.

They probably have tens of thousands? hundreds of thousands? of 3D files at this point, organizing, reusing, composing all those parts is necessary unless they want to manufacture everything from scratch every time. Migrating all that over to a new system wont be done until it is insanely painful to use the older system.