There were also certain IMO low-value questions that really excited the SO hive-mind. I asked a question about the peculiarities of Python assignment syntax, and earned several dozen points for the question, even though no one really should have written code the way I presented it.
I liked StackOverflow for the first ten years or so of its existence, but I gradually stopped using it then suddenly quit altogether when valid questions were being closed unreasonably. At this point, LLMs with documentation in the context, issue trackers and eve the source code (if available) have surpassed SO. Now my main issue is telling the LLM to crap on my idea rather than wishing it were kinder.
As a Virginian, this is good information to have. I see a lot of ludicrous objections to data centers here (the most ludicrous being water consumption, when most of our data centers have closed-loop systems and regardless the humidity here isn't evaporating water).
I've suspected that the energy regulations and the ruling party's close connection with Dominion Energy (the Governor recently attempted to fire the chair of Virginia Tech's board and replace him with the CEO of Dominion) have had an impact on power use more than data centers themselves.
I ran into a problem at work recently: we are given access to a bunch of models up to a full Claude Opus 4.8, but a monthly budget of 100k tokens. We are also given access to Gemini 3.5 Flash & 3.1 Pro with a daily budget of 50M tokens, but no tool calling. I'd love to hook Claude Code (or Pi) into the Gemini model, but the lack of tool-calling makes it quite difficult. I've been planning out how an intelligent router might be able to use a token-efficient tool-calling model (including a small local open-weights model) to handle the basic tools like reading from the file system or interfacing with MCP servers such that context is gathered, but then send the built up context to the Gemini model where I have a nearly unlimited (for my use cases) token budget.
The design of transformers (including LLMs and multi-modal transformer-based models such as OpenAI's image generators) is to attend to relevant details. OpenAI did this at first without guardrails. In response to public backlash, they bolted on "content filtering," which IMO seems like a very GOFAI approach, and regardless doesn't work very well. It routinely flags innocent prompts, then with crafty prompt hacking will generate these kinds of images.
The design of the model is literally to find patterns and attend to them. The infrastructure and process around an OpenAI model is intended to filter "bad" things (in this case, I agree that the outputs are bad), but is designed to stop some enumerated-ish list of things that aren't allowed, perhaps with some limited "reasoning" about them.
I don't exactly appreciate words being put in my mouth. When did I say it was working perfectly? And we're comparing you, a human with common sense and real intelligence, to a multi-mode LLM?
The transformer was designed to attend to relevant pieces of context and generate new ones that match the pattern. OpenAI in particular was doing that work without guardrails, then attempted to bolt on "content filters," which in my opinion just can't work in a rigorous way. (I think Anthropic's "constitutional" approach is much better though not flawless. And regardless, Claude models don't generate images.)
So, yeah, working as designed. Maybe not as intended, because these things are somewhat resistant to the host's intent when the prompter is hostile.
> The spontaneity isn't that ChapGPT woke up and sent this to the author. The spontaneity is that ChatGPT was asked to restore an image that was attached without filtering it, and when no image was attached, instead of generating an error message, it cobbled together random outputs, some of which included graphic, disturbing imagery.
But that's not what happened. The missing image was described as "graphic" or "violent." If I were to receive an email with that request and a missing attachment, my imagination certainly would not conjure images of butterflies & unicorns. Seems the model is working as designed.
I've been using the OpenSCAD version of this for a while. This new release is a big upgrade! I wish it worked with my preferred CAD, FreeCAD. But this is neat!
> Take AI for instance. The US grid is struggling to keep up with demand, while Chinese one has a lot of headway [1]. Usually, this could be solved by an increase in spending lasting a few years which would make the debt tick up, but that would've been an absolutely fine use of debt since it buys some shiny new infra that will pay dividends for the next 20ish years.
I object. The CCP is much more deeply indebted than the US when taking into account provincial and local governments as well as state-owned enterprises.[0] And of course the US debt is financed in its own currency while Chinese foreign debt is financed in dollars or other currencies.
The problem in the US is regulation. An environmental impact study takes 54 months in the US.[1] The CCP, which has no problem poisoning its people or even launching rockets over inhabited villages, doesn't delay itself at all.[2] I'm glad we don't poison our people or place dangerous industry in places that could harm populated areas, or even perform some prophylactic measures to protect nature, but I'm confident that we could do this in less then a year (less than six months?) and make much faster progress. Even for something like nuclear, the ten years (mostly caused by red tape) are really onerous.
> China is the only one that can run if it comes down to it (unless of course the numbers coming out of China are mega bogus, but for that I don't know enough to have an opinion).
Yes, the common opinion among China watchers is that any number the CCP touches is "mega bogus." They're actually in the midst of something of a financial crisis at the moment because of the high debt.
Having read the blog post and then the comments here, I'm rather astonished. Do we understand our craft so little that our only realistic option is to ban LLMs (so-called AI)? Has everyone forgotten we've been in a software crisis for almost sixty years?[0] Have we so internalized the sweat-of-the-brow we've accumulated for decades that it's now part of the identity of being a programmer, and the only reliable signal of whether a contribution is beneficial?
As far as I can tell, architecture, i.e. sound, precise definitions of exactly what a software artifact must do, is now critical. And with LLMs, it's now feasible to begin implementing such things, though many brownfield projects may be intrinsically unsound in ways that their creators are unaware of. In such a world, contributions simply require a modified proof that the software does what it must do, with perhaps additional claims that the maintainers provide.
> it's more about America transitioning from a high-trust society to a low-trust one.
We're talking about Princeton, here. Trust among elites remains persistently high. In fact, it's likely higher than ever due to assortative mating & geographic sorting. Elites, even students in the Ivies, still have trust of government and elite institutions, which the elite stratum itself runs. Trust between elites and lower strata has declined, where elites and middle- and lower-classes have significant mistrust between each other, and the latter have lower trust within their own strata than in the past.
What's more likely IMO is that 1) the cost of cheating (i.e. the cost of assembling a ripped off assignment multiplied by the risk of being caught) has declined precipitously due to LLMs and 2) elite institutions remain the most ruthlessly competitive in the country and even the world.
Math, physics, and chemistry RLHF freelancing is typically north of $40/hr. Even competence at simply reading & writing English prose earns at least $20/hr. I've never seen an offer for less than that, and I lived off of that kind of work for a month after a layoff in 2024.
That seems like a fair trade considering the freelancer takes on none of the risk and has very little required capital.
I have a nearly total opposite take. I can't tell you how many times I've read a book, a paper or something else and been confused by some ambiguity in the author's prose. Being able to drop the paper (or even the book!) into an LLM to dig into the precise meaning has been an unbelievable boost for me.
Now I can actually get beyond conceptual misunderstanding or even ignorance and get to practice, which is how skills actually develop, in a much more streamlined way.
The key is to use the tool with discipline, by going into it with a few inviolable rules. I have a couple in my list, now: embrace Popperian falsifiability; embrace Bertrand Russell's statement: “Everything is vague to a degree you do not realize till you have tried to make it precise.”
LLMs have become excellent teachers for me as a result.
I built an MCP server to speak WHOIS/RDAP so I could have Claude give me better domain name suggestions that weren't already taken. It can also be used in LLM-enabled applications (provided that the model is "tool calling" and that there's an orchestrator).
In principle, MCP servers can be created for just about any OAuth-protected API. However, you still need to create the server, and this is where the usage I'm talking about shines: when working on the MCP server, an LLM can be quite helpful in getting the right APIs integrated.
The same goes for other development that doesn't need an LLM context built-in. If I wanted to sync two calendars, for instance, I wouldn't build an MCP that speaks CalDav and Exchange and then let it loose (though this so-called agentic workflow is becoming more popular); I'd want to build software with an LLM's help that can speak both protocols by having it generate code to handle whatever OAuth tokens and scopes are necessary and then help me deploy the thing.