101–110 of 131 posts
Had a minor conniption until I saw the year. OpenAI just struggled to close a round. And the New Yorker just published an unflattering profile of Altman [1]. So it would make sense they'd go back to the PR strategy of "stop me from shooting grandma." [1] https://www.newyorker.com/magazine/2026/04/13/sam-altman-may...
This was around the time I trained Transformer-XL (outside of OpenAI) with Ben Mann ( https://yaroslavvb.medium.com/scaling-transformer-xl-to-128-... ) . Originally we wanted to release train and release the weights as a kind of GPT-2.5, but our OpenAI friends pushed us to keep weights closed.
I would be taking a grudge against those "friends" to my grave.
Their concerns weren't completely off base, I think they just over estimated how much it would really matter in the grand scheme.
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Yeah, I find it a bit odd how at the time everyone was pointing and laughing at OpenAI for being obviously wrong about this. Now in 2026, AI slop is very obviously a serious problem - it inundates all platforms and obscures the truth. And people are still saying OpenAI in 2019 were wrong?
I think people today are more focused on how OpenAI released a model "too dangerous to release", not that they were right or wrong, as part of the general trend of criticizing OpenAI for not following any of its stated principles.
My assumption is that it was too expensive to actually release at the time. It wasn't good enough for anybody to pay to use it yet, and it surely was very expensive to run, especially for a (fake, granted) non profit.
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They were more than right. They were correct in an intentional, precise manner. This is what OpenAI actually stated[0]: > Synthetic imagery, audio, and video, imply that technologies are reducing the cost of generating fake content and waging disinformation campaigns. > ‘The public at large will need to become more sceptical of text they find online, just as the ”deep fakes” phenomenon calls for more scepticism about…
Yeah, I find it a bit odd how at the time everyone was pointing and laughing at OpenAI for being obviously wrong about this. Now in 2026, AI slop is very obviously a serious problem - it inundates all platforms and obscures the truth. And people are still saying OpenAI in 2019 were wrong?
It's the same with the Mythos stuff, I appreciate their concern/work on safety, but if it's "too dangerous", it should be unavailable until it is less dangerous.
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> I am now just going to go through the (single file) of code and just fix it myself. That's front page news, in this era.
I understand how laughable that sounds when I say it out loud. But the reality is, when I'm in a state of 'Tell LLM what to do, verify, repeat', it's really hard to sometimes break out of that loop and do manual fixes. Maybe the brain has some advanced optimization where once you're in a loop, roughly staying inside that loop has a lower impedance than starting one. Maybe that's why the flow state feels so magical, i…
My experience is rather that I am annoyed by bullshit really fast, so if the model does not get me something that is really good, or it can at least easily be told what needs to be done to make it exceptional, I tend to use my temper really fast, and get annoyed by the LLM.
With this in mind, I rather have the feeling that you are simply too tolerant with respect to shitty code.
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> it's really hard to sometimes break out of that loop and do manual fixes You're aware of the MIT Media Lab study[0] from last summer regarding LLM usage and eroding critical thinking skills...? [0] Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task June 2025 DOI:10.48550/arXiv.2506.08872
>> it's really hard to sometimes break out of that loop and do manual fixes it's not just an erosion of skills, it can also break the whole LLM toolchain flow. Easy example: Put together some fairly complicated multi-facet program with an LLM. You'll eventually hit a bug that it needs to be coaxed into fixing. In the middle of this bug-fixing conversation go and ahead and fire an editor up and flip a true/false or ch…
Depending on the depth of its misunderstanding it could become a memory note or a readme update. I haven’t had any real issues with that approach.
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I understand how laughable that sounds when I say it out loud. But the reality is, when I'm in a state of 'Tell LLM what to do, verify, repeat', it's really hard to sometimes break out of that loop and do manual fixes. Maybe the brain has some advanced optimization where once you're in a loop, roughly staying inside that loop has a lower impedance than starting one. Maybe that's why the flow state feels so magical, i…
> Maybe the brain …is already damaged by reliance on AI.
People who are using them frequently: you’re delusional if you think your brain is not harmed. I won’t go into great detail because I can’t be bothered and I’m sure this post will be down voted - but - I can share my own experience. Ever since I stopped using them my ability to focus, think hard and hold concepts in my brain and reason about them has increased immensely. Not only that but I re-gained the conditioning of my brain to ‘deal with the pain’ that comes with deep thought - all of that gets lost by spending too much time interacting with llm’s.
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> it's really hard to sometimes break out of that loop and do manual fixes You're aware of the MIT Media Lab study[0] from last summer regarding LLM usage and eroding critical thinking skills...? [0] Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task June 2025 DOI:10.48550/arXiv.2506.08872
>> it's really hard to sometimes break out of that loop and do manual fixes it's not just an erosion of skills, it can also break the whole LLM toolchain flow. Easy example: Put together some fairly complicated multi-facet program with an LLM. You'll eventually hit a bug that it needs to be coaxed into fixing. In the middle of this bug-fixing conversation go and ahead and fire an editor up and flip a true/false or ch…
I also remember half coding and half prompting a few months back, only to be frustrated when my manual changes started to confuse the LLM. Eventually you either have to make every change through prompting, or be ok with throwing away an existing session and add back in the relevant context in a fresh one.
This was around the time I trained Transformer-XL (outside of OpenAI) with Ben Mann ( https://yaroslavvb.medium.com/scaling-transformer-xl-to-128-... ) . Originally we wanted to release train and release the weights as a kind of GPT-2.5, but our OpenAI friends pushed us to keep weights closed.
Much like how they claimed to be all about open source.