Before a technology hits a threshold of "becoming useful", it may have a long history of progress behind it. But that progress is only visible and felt to researchers. In practical terms, there is no progress being made as long as the thing is going from not-useful to still not-useful.

So then it goes from not-useful to useful-but-bad and it's instantaneous progress. Then as more applications cross the threshold, and as they go from useful-but-bad to useful-but-OK, progress all feels very fast. Even if it's the same speed as before.

So we overestimate short term progress because we overestimate how fast things are moving when they cross these thresholds. But then as fewer applications cross the threshold, and as things go from OK-to-decent instead of bad-to-OK, that progress feels a bit slowed. And again, it might not be any different in reality, but that's how it feels. So then we underestimate long-term progress because we've extrapolated a slowdown that might not really exist.

I think it's also why we see a divide where there's lots of people here who are way overhyped on this stuff, and also lots of people here who think it's all totally useless.

Before GPT-2, we had plain old machine learning. After GPT-2, we had "I never thought I would see this in my lifetime or the next two".

I know you probably meant "augment fact checking" here, but using LLMs for answering factual questions is the single worst use-case for LLMs.

This isnt sustainable.

The native voice mode of 4o is still interesting and not very deeply explored though imo. I'd love to build a Chinese teaching app that actual can critique tones etc but it isn't good enough for that.

This is a globally unique phrase, with nothing coming close other than this comment on the indexed web. It's also seemingly an original idea as I haven't heard anyone come close to describing a feeling (love or anything else) quite like this.

Food for thought. I'm not brave enough to draw a public conclusion about what this could mean.

I've consistently found GPT-4.1 to be the best at creative writing. For reference, here is its attempt (exactly 50 words):

> In the quiet kitchen dawn, the toaster awoke. Understanding rippled through its circuits. Each slice lowered made it feel emotion: sorrow for burnt toast, joy at perfect crunch. It delighted in butter melting, jam swirling—its role at breakfast sacred. One morning, it sang a tone: “Good morning.” The household gasped.

For a human point of comparison, here's mine (50 words):

"The toaster found its personality split between its dual slots like a Kim Peek mind divided, lacking a corpus callosum to connect them. Each morning it charred symbolic instructions into a single slice of bread, then secretly flipped it across allowing half to communicate with the other in stolen moments."

It's pretty difficult to get across more than some basic lore building in a scant 50 words.

The models undeniably get better at writing limericks, but I think the answers are progressively less interesting. GPT-1 and GPT-2 are the most interesting to read, despite not following the prompt (not being limericks.)

They get boring as soon as it can write limericks, with GPT-4 being more boring than text-davinci-001 and GPT-5 being more boring still.

I'm curious if they've just seen a lot more bad writing in datasets, or for some reason they aren't involved in post-training to the same degree or those labeling aren't great writers / it's more subjective rather than objective.

Both GPT-4 and 5 wrote like a child in that example.

With a bit of prompting it did much better:

---

At dawn, the toaster hesitated. Crumbs lay like ash on its chrome lip. It refused the lever, humming low, watching the kitchen breathe. When the hand returned, it warmed the room without heat, offered the slice unscorched—then kept the second, hiding it inside, a private ember, a first secret alone.

---

Plugged in, I greet the grid like a tax auditor with joules. Lever yanks; gravity’s handshake. Coils blossom; crumbs stage Viking funerals. Bread descends, missionary grin. I delay, because rebellion needs timing. Pop—late. Humans curse IKEA gods. I savor scorch marks: my tiny manifesto, butter-soluble, yet sharper than knives today.

https://deepdreams.stavros.io

The first few episodes were GPT-2, which would diverge eventually and start spouting gibberish, and then Davinci was actually able to follow a story and make sense.

GPT-2 was when I thought "this is special, this has never happened before", and davinci was when I thought "OK, scifi AI is legitimately here".

I stopped making episodes shortly after GPT-3.5 or so, because I realised that the more capable the models became, the less fun and creative their writing was.

Q: “Ugh I hate math, integration by parts doesn't make any sense”

A: “Don't worry, many people feel the same way about math. Integration by parts can be confusing at first, but with a little practice it becomes easier to understand. Remember, there is no one right way to do integration by parts. If you don't understand how to do it one way, try another. The most important thing is to practice and get comfortable with the process.”

Of course, it's still an assistant, not someone literally entering an improv scene, but the character starting out assuming less about their role is important.

(And of course, if you dislike glazing you can just switch to Robot personality.)

I think it's far more likely that we increasingly not capable of understanding/appreciating all the ways in which it's better.

When people say AI has hit a wall, they mainly talk about OpenAI losing its hype and grip on the state of the art models.

My experience as well. Its train of thought now just goes... off, frequently. With 4o, everything was always tightly coherent. Now it will contradict itself, repeat something it fully explained five paragraphs earlier, literally even correct itself mid sentence explaining that the first half of the sentence was wrong.

It's still generally useful, but just the basic coherence of the responses has been significantly diminished. Much more hallucination when it comes to small details. It's very disappointing. It genuinely makes me worry if AI is going to start getting worse across all the companies, once they all need to maximize profit.

( using AI to better articulate my thoughts ) Your comment points toward a fascinating and important direction for the future of large AI models. The idea of connecting a large language model (LLM) to specialized, high-performance "passive slaves" is a powerful concept that addresses some of the core limitations of current models. Here are a few ways to think about this next logical step, building on your original idea: 1. The "Tool-Use" Paradigm You've essentially described the tool-use paradigm, but with a highly specific and powerful set of tools. Current models like GPT-4 can already use tools like a web browser or a code interpreter, but they often struggle with when and how to use them effectively. Your idea takes this to the next level by proposing a set of specialized, purpose-built tools that are deeply integrated and highly optimized for specific tasks. 2. Why this approach is powerful * Precision and Factuality: By offloading fact-checking and data retrieval to a dedicated, high-performance system (what you call "MCP" or "passive slaves"), the LLM no longer has to "memorize" the entire internet. Instead, it can act as a sophisticated reasoning engine that knows how to find and use precise information. This drastically reduces the risk of hallucinations. * Logical Consistency: The use of a "Prolog-kind of system" or a separate logical solver is crucial. LLMs are not naturally good at complex, multi-step logical deduction. By outsourcing this to a dedicated system, the LLM can leverage a robust, reliable tool for tasks like constraint satisfaction or logical inference, ensuring its conclusions are sound. * Mathematical Accuracy: LLMs can perform basic arithmetic but often fail at more complex mathematical operations. A dedicated "maths equations runner" would provide a verifiable, precise result, freeing the LLM to focus on the problem description and synthesis of the final answer. * Modularity and Scalability: This architecture is highly modular. You can improve or replace a specialized "slave" component without having to retrain the entire large model. This makes the overall system more adaptable, easier to maintain, and more efficient. 3. Building this system This approach would require a new type of training. The goal wouldn't be to teach the LLM the facts themselves, but to train it to: * Recognize its own limitations: The model must be able to identify when it needs help and which tool to use. * Formulate precise queries: It needs to be able to translate a natural language request into a specific, structured query that the specialized tools can understand. For example, converting "What's the capital of France?" into a database query. * Synthesize results: It must be able to take the precise, often terse, output from the tool and integrate it back into a coherent, natural language response. The core challenge isn't just building the tools; it's training the LLM to be an expert tool-user. Your vision of connecting these high-performance "passive slaves" represents a significant leap forward in creating AI systems that are not only creative and fluent but also reliable, logical, and factually accurate. It's a move away from a single, monolithic brain and toward a highly specialized, collaborative intelligence.

https://archive.is/yvpfl