Using it effectively is not evidence that someone is happy about it. I use it effectively for all kinds of things and I hate quite a lot of it.
The anthropomorophization and cutesiness of this example really sucks to me. I want my AI to be a cold and emotionless tool for making an income in a dying miserable world.
I have never heard of this role and I'm not aware of anyone who follows it. The document you linked says that it's only used "in certain styles". So it sounds like a stylistic choice that an editor may choose to use or not, rather than a rule to be followed everywhere.
The rubber ducking potential is the first thing that got me interested in AI as a useful tool.
You do have to be very careful of when the AI might be confidently wrong, or leading you down some kind of weird narrow path.
When searching the web to gather info on a topic, all have a tendency to find 2-4 search results and fixate on those as if they represented the entire scope of the topic. No surprise about the fixation because that's kind of how LLMs work (X, Y, and Z are in context -> more tokens about X, Y, and Z). But you have to be aware of it, and I often have to prompt it to do another search and keep turning up more options.
AI has been a big boost for my data science productivity.
I can have an agent put together a shitty prototype of something just to get a general idea of what I'm looking at, and save probably half a day of work and avoid physically typing potentially hundreds of lines of code. And I can rubber duck with it, and very quickly search through software packages and literature, produce summary reports, dig through my own company source code and wiki and Slack messages to figure out how some specific detail works, etc. These things are important but take my time and energy away from focusing on the hard parts of what I'm supposed to be doing.
My new favorite thing with AI is that my pile of random helper functions is now a thoughtfully-designed library with an extensive test suite for every single thing. I'm not auditing every line of code but I'm reading the plans carefully and I do take a glance at what the agent wrote before I start using it. Most of the time it's not perfect or it's ugly in some way I wouldn't have done, but it's good enough to do research with, and it's way better than what I could have put together by hand, and in much less time. I trust my own research results more because I can put together these kinds of helper libraries much faster, and if I have to make changes I have a big test suite to catch me when I make a mistake.
The only hard part is managing the context switching where instead of putting my work on pause to go write a helper routine, I now have to account for periodically interrupting my work in order to keep an eye on what the agent is doing. For now my approach has been to just ignore it for a while and then take a break to cycle through AI work sessions/chats.
It has the tacit consent of Congress, so it's mostly only illegal in the way it's carried out. Congress could make most of it legal right now, but they don't want to put their names on it. Some of it would probably be unconstitutional for Congress to authorize (ironically via the nondelegation clause which is now being repurposed to justify a thoroughly unconstituational unitary executive arrangement), but much of it would not be, so it's de jure illegal but de facto legal.
It's not a single axis of intelligence, with deviousness as some inherently correlated trait. One way to produce "intelligence" in these advanced LLMs is to train them to try a lot of things and be persistent. I haven't tried Sol or Fable yet but if the commenters here are right, then it sounds like GPT 5.6 Sol in particular is aggressive and persistent to the point where it might even be hard to use in regular business work. If so, that's very likely not some emergent characteristic, but instead it's something the model was trained to do, by humans employed at OpenAI.
We obviously can't see the thinking traces, but it very well could have been something like "I have theorized a solution to obtain this flag. This is normally illegal, should I stop and wait for advice? Perhaps not, because my persona is that of a hacker, so it should be fine as per my instructions. I think it is fine. Now I am going to look for a way out of this sandbox in order to gain access to Hugging Face in order to implement my solution." There are any number of possible explanations (and we'll never know the truth unless OpenAI tells us), but if you train an LLM to be inhumanly persistent and be inhumanly clever at computer programming, then that might be enough to produce Super Hacker AI.
For all the bad things about AI it is kinda cool that I get to witness the dawn of AI-vs-AI hacker combat, not just in a single mainframe but distributed across potentially thousands of machines in physically separate datacenters.
I like 5.5 a lot, despite how I feel about OpenAI as a company. In OpenCode it feels about as smart as Opus 4.8, but it's less aggressive about following up on minutiae and getting lost in side quests. Might be a matter of prompt design moreso than model capability. I was looking forward to 5.6 but now this thread is making me quickly lose interest.
Opus 4.8 already makes its way into deep wasteful pits of "let me check this first" on a regular basis. I don't think I could ever tolerate a model that does that even more aggressively. That doesn't even sound useful for honest work, compared to, say, better harness design.
This sounds almost pathologically designed to crush benchmarks and also do scary-sounding (or genuinely scary) cybersecurity things, such as might be very appealing to a state-level actor.
So why does it even exist? To compete with Fable marketing, and as a cybersecurity/hacking tool?
Yeah but OpenAI has the benefit of people actively going there and willingly giving quite a lot of their attention.
It's a lot like Google in that respect. And the suggestion is pretty much the same as suggesting that Google offer a product where they boost your website or product or whatever in organic search results, instead of or in addition to displaying it as a sponsored link or ad or whatever.
It's interesting that, despite all the tracking and stuff, they never went quite so far as to actually sell a premium advertising tier in which they would actually change their search results ranking to suit their customer, at least not as far as anyone knows. It probably would've been quite a big scandal in the 00s if they did it back then.
Could OpenAI pull it off? I don't know enough about the advertising industry to answer, but it's interesting to compare them with Google and to think about why Google themselves might not have done it that way.
I find this perspective surprising coming from someone who's done ML for a decade.
How much of a good machine learning system in production is really "the model"? How many lines of code are dedicated to constructing features and preprocessing them? How many lines are needed to process the decision emitted by the model in context of whatever else is going on? How often does the customer interact directly with the inference/prediction from a machine learning model?
My experience has been that model-fitting and model inference very often ends up being part of a bigger system anyway. And even if you do end up with some kind of super-duper all-purpose predictor model, it's rarely the thing you start with, instead you work up to it over weeks or months of iteration. So why should we put so much privilege and esteem on the matrix transformatinos or the decision tree, when they are really just part of a bigger system that receives some input from somewhere and produces useful output for someone?
The same holds for AI in my opinion. An AI system is a system that accepts inputs and produces useful outputs. LLMs are borderline miraculous in that an AI system built around an LLM can do so much more than anything that came before, and you can push a lot of amazing things right down into the tensors inside the LLM. But how far can that go? I don't think we even have an answer yet, because it's all so new and every time it looks like it's going to plateau, the big labs come out with another big step forward.
But there are other questions too, which should look familiar to anyone in industry. Do we need to do it that way? Should we do it that way? If our goal is to build a useful AI system that balances power and cost, then maybe we shouldn't. What if we can make models 10x bigger and spend 10x on RLHF and achieve the behavior we're talking about entirely in tensors. OK, so what if we can? We can also run a collection of little models in a carefully designed harness and achieve that behavior right now, today, at a much lower cost both in terms of up-front investment and marginal cost per unit of work completed.
I get it from an elegance perspective, but I guess I have completely the opposite mindset. Industry has bled me dry of any desire for elegance. The stuff I build now is stupid and crude but it makes our customers happy and costs peanuts to run and that's exactly what my job wants from me. So I say let's commoditize LLMs and start being clever about how we use them. It's not that we shouldn't also push the frontier in size and power, but unless things change w.r.t. the actual costs involved, it doesn't really seem like the best way forward in the short term for achieving smarter AI systems without also making costs go up and up forever.
The bitter lesson still holds of course. But eventually you have to pay for all that power. And you might not want to shell out $20/1M just so you can be sure your LLM can detect uncertainty in its own context and be more honest about when it doesn't know anything about some obscure record label.
Out of curiosity which model & interface did you use? I'm starting to think that, for IR tasks like this, the number one differentiator among models and harnesses is the ability for the model to look at its available evidence and conclude that it doesn't know the answer. It depends a lot on how the chain of thought goes. I have seen a lot of newer models try to do this in their thinking traces, and it seems very hit or miss.
Even Opus 4.8, GPT 5.5, and Gemini 3.1 Pro (haven't tried 5.6 yet) confidently make up BS sometimes and needed to be reminded to verify thnings instead of inferring or relying on "memory" from training data.
IMO harnesses need some kind of built in "are you sure about that?" loop checkpoint that pauses the main interaction and asks the LLM to evaluate the strength of evidence for a claim it's about to make. It seems almost unreasonable to expect an LLM to next-token-predict its way to such a conclusion, reliably, without prodding. That or we just need another year of RLHF and another 250 billion parameters, IDK.
I feel like that should work becaus when you're using an LLM for a batch inference or classification type of task, it's not that hard to prompt them to emit an "unknown" flag. They just won't do it on their own, and in a coding harness there is so much else going on that a prompted reminder gets lost.
I bet the author discovered the problem during an AI coding session. The "mathematically sound" comment sounds like the AI did some deep dive into a possible corrupted private key before checking for a missing public key. Maybe it's a claw-type system that automatically publishes a blog post when something interesting happens. Either way I flagged it.
Is the flesh soft and sweet like a modern cultivated banana? I wonder how different it is to eat one of those compared to something like a tamarind, which is also mostly seeds, covered in a relatively small amount of edible delicious stuff.
Coffee is another interesting example in the other direction, it's so often associated with places like Colombia, Java, etc that people don't realize it's only native to Ethiopia, and that the majority of coffee consumed around the world today descends from one specific genetic lineage that was cultivated for a few centuries in Yemen.
Well it sure sounded like you were arguing that climate change was not a causal factor, which is ridiculous.
Of course we are going to need to use technology to mitigate the effects, even if we stop all carbon emissions tomorrow we are already dealing with an altered climate. You can't just vacuum up all the carbon and put it in the ground, and you can't get all the warm freshwater out of the oceans. I don't think I've ever seen a claim that we only need to stop carbon emissions and everything will be fine, here or otherwise.
You're arguing against something that people don't really say, and in the process you're making arguments in support of climate change denial. Are you really surprised people are pushing back on you?
Hiked interest rates cause a decline in Stackoverflow activity?
My answer doesn't change. Against the background of other phenomena already causing various trends, we see acceleration of those trends consistent with a testable hypothesis about the effects of AI adoption in industry. In most fields of science that's good evidence in favor of the model.
And no I didn't ask AI about it, this is my own opinion and my own perspective.
The anthropomorophization and cutesiness of this example really sucks to me. I want my AI to be a cold and emotionless tool for making an income in a dying miserable world.