881–890 of 1001 posts
What a time. I am back here genuinely wishing for OpenAI to release a great model, because without stiff competition, it feels like Anthropic has completely lost its mind.
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Opus 4.5 and 4.6 were the first models that i could talk to and get a sense that they really "understood" WHY i'm saying the things i am
Opus 4.7 kinda took that away, it's a definite regression. it doesn't extrapolate.
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refactor this thing? sure, will do! wait, what do you mean "obviously do not refactor the unrelated thing that's colocated in the same file"? i'm sorry, you're absolutely right, conceptually these two things have nothing to do with each other. i see it now. i shouldn't have thought they're the same just because they're in the same file.
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whereas GPT 5.5, much like Opus 4.6, gets it.
i wanted to build a MIDI listener for a macOS app i'm making, and translate every message into a new enum. that enum was to be opinionated and not to reflect MIDI message data. moreover, i explicitly said not to do bit shifting or pointer arithmetic as part of the transport.
what did Opus 4.7 do? it still used pointer arithmetic for the parsing! should i have to be this explicit? it also seemingly didn't care that i wanted the enum to be opinionated and not reflect the raw MIDI values. Opus 4.6 got it right (although with ugly, questionable implementation).
GPT 5.5 both immediately understood that I didn't want pointer arithmetic because of the risk of UB and that shuffling around bits is cumbersome and out of place. it started searching for alternatives, looking up crates to handle MIDI transports and parsing independently.
then it built out a very lean implementation that was immediately understandable. even when i told Opus 4.7 to use packages, and even how to use them, it still added a ton of math weirdness, matching against raw MIDI packet bytes, indirection after indirection, etc. even worse, it still did so after giving them the public API i wanted them to implement.
GPT 5.5 nailed it first try. incredibly impressed with this model and feel much safer delegating some harder tasks to it
A playable 3D dungeon arena prototype built with Codex and GPT models. Codex handled the game architecture, TypeScript/Three.js implementation, combat systems, enemy encounters, HUD feedback, and GPT‑generated environment textures. Character models, character textures, and animations were created with third-party asset-generation tools The game that this prompt generated looks pretty decent visually. A big part of th…
LLM models can not do spacial reasoning. I haven't tried with GPT, however, Claude can not solve a Rubik Cube no matter how much I try with prompt engineering. I got Opus 4.6 to get ~70% of the puzzle solved but it got stuck. At $20 a run it prohibitively expensive. The point is if we can prompt an LLM to reason about 3 dimensions, we likely will be able to apply that to math problems which it isn't able to solve cur…
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it will be whatever data it is trained on(isn't very philosophical). language model generates language based on trained language set. if the internet keeps reciting ai doom stories and that is the data fed to it, then that is how it will behave. if humanity creates more ai utopia stories, or that is what makes it to the training set, that is how it will behave. this one seems to be trained on troll stories - real-lif…
There's a weirder implication I keep arriving at. The pre-training data doesn't go away. RLHF adds a censorship layer on top, but the nasty stuff is all still there, under the surface. (Claude has been trained on a significant amount of content from 4chan, for example.) In psychology this maps to the persona and the shadow. The friendly mask you show to the world, and... the other stuff.
That sounds like nonsense to me. I can't see why they would do that and I can't find any confirmation that they have. Why do you think they would do that? You might be thinking about Grok.
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This is frankly exciting, outside of the politics of it all, it always feel great to wake up and a new model being released, I personally will stay awake quite long tonight if GPT-5.5 drop in codex.
I don't find it exciting at all. I just feel anxiety about my career and my place in the world. I have a set of skills that I've developed over many years. I care about what I create. I consider it a craft. When I use my skills to solve a hard problem, I feel good about myself. When the AI does the work for me, I don't get that sense of accomplishment. I am seeing my value evaporate before my eyes. I hate this stuff…
Still huge hallucination rate, unfortunately at 86%. To compare, Opus sits at 36%. Source: https://artificialanalysis.ai/models?omniscience=omniscience...
grok is 17%? And that's the lowest, most models are like 80%+? While hallucination is probably closer to 100% depending on the question. This benchmark makes no sense.
This is 3x the price of GPT-5.1, released just 6 months ago. Is no one else alarmed by the trend? What happens when the cheaper models are deprecated/removed over time?
This is entirely expected. The low prices of using LLMs early on was totally and completely unsustainable. The companies providing such services were (and still are) burning money by the truckload. The hope is to get a big userbase who eventually become dependent on it for their workflow, then crank up the price until it finally becomes profitable. The price for all models by all companies will continue to go up, and…
Do you think this is true for DeepSeek as well?
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LLMs upend a few centuries of labor theory. The current market is predicated on the assumption that labor is atomic and has little bargaining power (minus unions). While capital has huge bargaining power and can effectively put whatever price it wants on labor (in markets where labor is plentiful, which is most of them). What happens to a company used to extracting surplus value from labor when the labor is provided…
I am still trying to figure out the business model of open weights. Like... it's wonderful that there are open LLMs, super happy about it, good for everyone, but why are there these? What is the advantage to their companies to release them?
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LLM models can not do spacial reasoning. I haven't tried with GPT, however, Claude can not solve a Rubik Cube no matter how much I try with prompt engineering. I got Opus 4.6 to get ~70% of the puzzle solved but it got stuck. At $20 a run it prohibitively expensive. The point is if we can prompt an LLM to reason about 3 dimensions, we likely will be able to apply that to math problems which it isn't able to solve cur…
I’ve had a similar experience building a geometry/woodworking-flavored web app with Three.js and SVG rendering. It’s been kind of wild how quickly the SOTA models let me approach a new space in spatial development and rendering 3d (or SA optimization approaches, for that matter). That said, there are still easy "3d app" mistakes it makes like z-axis flipping or misreading coordinate conventions. But these models make…
Temporal. I had a research project where the LLM had no concept about preventing data from the future to leak in. I eventually had to create a wall clock and an agent that would step through every line of code and ensure by writing that lines logic and why there is no future of the wall clock data leaking.
Spatial. I created a canvas for rendering thinking model's attention and feedforward layers for data visualization animations. It was having a hard time working with it until I pointed Opus 4.7 to some ancient JavaScript code [0] about projecting 3d to 2d and after searching Github repositories. It worked perfect with pan zoom in one shot after that.
No matter how hard I tried I couldn't get it to stack all the layers correctly. It must have remembered all the parts for projecting 3d to 2d because it could not figure out how to position the layers.
There is a ton of information burnt into the weights during training but it can not reason about it. When it does work well with spatial and temporal it is more slight of hand than being able to generalize.
People say, why not just do reinforcement learning? That can't generalize in the same way a LLM can. I'm thinking about doing the Rubik's Cube because if people can solve that it might open up solutions for working temporal and spatial problems.
[0] https://jakesgordon.com/writing/javascript-racer-v1-straight...
I've found myself so deeply embedded in the Claude Max subscription that I'm worried about potentially makign a switch. How are people making sure they stay nimble enough not to get trarpped by one company's ecosystem over another? For what it's worth, Opus 4.7 has not been a step up and it's come with an enormously higher usage of the subscription Anthropic offers making the entire offering double worse.
I use pi.dev. I get openai team plan at work. Claude enterprise too. I have openrouter for myself. I use minimax 2.7. Kimi 2.6. And gpt 5.5 and opus 4.7. I can toggle between them in an open source interface that's how I stay able to not be trapped. Minimax is so cheap and for personal stuff it works fine. So I'm always toggling between the nre releases