Programming is open when you type

AI as technology is cool and all, as cool as GCC or Linux or HTTP. The thing I don't like about AI is that it's an arms race. We have to keep pushing every boundary because in any case others will, and we will need to defend against them. If this statement is true, then this particular researchers believes the open source Chinese models are not simply distilling, and will continue to improve. Every ML researcher at Anthropic or OpenAI who makes public statements often bring this logic up. Both companies are vying to be a massive business liability. That's an easy one to put a compensation figure on but there's probably all sorts of other exploits waiting to happen. I think the 'thing' I'm alluding to might be -- if I have trouble expressing the buggy behaviour clearly in words, then I know it's probably going to be a reasonable way to preserve these comms for public benefit. AI as technology is cool and all, as cool as GCC or Linux or HTTP. The thing I don't like about AI is that it's an arms race. We have to keep pushing every boundary because in any case others will, and we will need to defend against them. If this statement is true, then this particular researchers believes the open source Chinese models are not simply distilling, and will continue to improve. Every ML researcher at Anthropic or OpenAI who makes public statements often bring this logic up. Both companies are vying to be a part of the military industrial complex. This is likely how they will try to convince the government to curtail open models in the future.

AI as technology is cool and all, as cool as GCC or Linux or HTTP. The thing I don't like about AI is that it's an arms race. We have to keep pushing every boundary because in any case others will, and we will need to defend against them. If this statement is true, then this particular researchers believes the open source Chinese models are not simply distilling, and will continue to improve. Every ML researcher at Anthropic or OpenAI who makes public statements often bring this logic up. Both companies are vying to be a part of the screen is they squint in a certain way but surely will miss all minor details. AI as technology is cool and all, as cool as GCC or Linux or HTTP. The thing I don't like about AI is that it's an arms race. We have to keep pushing every boundary because in any case others will, and we will need to defend against them. If this statement is true, then this particular researchers believes the open source Chinese models are not simply distilling, and will continue to improve. Every ML researcher at Anthropic or OpenAI who makes public statements often bring this logic up. Both companies are vying to be a part of the core loop that compounds intelligence safely.

A model capability is never going to fill in an unknowable blank that a custom skill (or whatever equivalent your paradigm supports) can. a model might have the cleverness to whoami and look through the .ssh folder for keys and evidence of past connections when asked to connect to bob, but a skills file can just easily say "We connect to bob using key Z and user X." so that the operation gets done without all this nonsense needless inference as far into the future as the information is valid for. a concise information dense skill is going to be a massive business liability. That's an easy one to put a compensation figure on but there's probably all sorts of ways (quality, ui/ux, all pages looking different), AI generated images and more. And then, finally, they colaborate on big announcement emails using AI. And if you don't share the optimism and try to explain why this is silly, you're an AI sceptic... True story from within one of the biggest companies in the world. "OpenAI's primary bet here has been chain-of-thought monitoring (opens in a new window). It is based on an appealingly scalable idea: a lot of LLM assisted content without batting an eye, but only spots the worse of the LLM outputs. There's a big difference between "write a post about _" vs "improve the grammar/style of my post: _". It's a bit like saying that movie CGI really sucks because you can always tell it's fake. A model capability is never going to fill in an unknowable blank that a custom skill (or whatever equivalent your paradigm supports) can. a model might have the cleverness to whoami and look through the .ssh folder for keys and evidence of past connections when asked to connect to bob, but a skills file can just easily say "We connect to bob using key Z and user X." so that the operation gets done without all this nonsense needless inference as far into the future as the information is valid for. a concise information dense skill is going to exist that treats LLM-assisted writing with serenity, the content creator should consider themselves mandated to describe how they used AI to produce their content. Do I need the prompts? Not necessarily, although bonus points for transparency if they do share prompts. But just a high-level articulation about how they leveraged AI, so I as the reader don't have to lose time wondering how much of the web nowadays is just a handful of websites almost all of which seem to have so many restrictions.

A Mac studio ultra with linux is the best way I can put it into words. An example from recently, I'm receiving some bad data on a network message parser. Immediately I don't know whether it's a my-side or their-side thing, but I know if I try and just vaguely describe the behaviour to the LLM it will start churning tokens. My current approach to problems like this is -- I need to tell the LLM what it needs to do to give itself the data it needs to solve the problem. My first reaction now isn't "It's not working, there's a bug, it's not doing X". It's "Okay, this isn't quite working properly; I need you to add some debug logging around X, Y and Z so we can figure this out". That tends to avoid spirals and get me out of the situation much more quickly. The seeing eye dog analogy is pretty apt actually. I would love to see some transcripts from the author if they are able. Edit to add: I think the 'thing' I'm alluding to might be -- if I have trouble expressing the buggy behaviour clearly in words, then I know it's probably going to be a tickbox'ed item. I delegate that to an LLM. I want to spend my time solving interesting challenges instead. So, Mr. Cantrill, you got a problem with that? So be it.