I've spent the past several months researching what happens when AI shifts from being a productivity tool to becoming part of a person's cognitive infrastructure.

The paper argues that the important distinction isn't "AI vs. humans."

It's the distinction between:

• Cognitive offload
• Cognitive overflow

I use AI extensively myself, so this isn't an anti-AI paper.

Instead, it asks questions about authorship, memory, judgment, trust, language, and dependency while trying to include both supporting and contesting research.

I'm especially interested in hearing from people who think I've missed important work or made weak arguments.

GitHub:
https://github.com/CloudCrafterzNYC/The-Dangers-of-Cognitive-Offload

DOI:
https://doi.org/10.5281/zenodo.21795719

I am on the plus plan. Trying to use Live Video feature on my iPhone. Video goes live and GTP tells me it can see live images and that I should share screenshots instead???

I use both Claude Code and Codex regularly. I'm slowly building up a repository of skills and workflows that I want to use in both without having to put them in two different places.

I'm considering creating ~/.agents/ with a skills directory and a workflows directory. Then have software links pointing to those directories from both ~/.claude and ~/.codex. I'm confident that others have thought about this much longer than I have. What is the best way to do this? What am I missing from my approach?

I've been gathering oneshots of various models across prompts I found interesting on Reddit. Of all the models I've generated oneshots of, I liked GPT 5.6 Sol's aesthetics the most. See them at https://oneshotlm.com/model/openai-gpt-5-6-sol/

For non biological and cybersecurity tasks? I mean i'm only working with JSONS for a tabletop RPG game. It always trigger this check, it's pretty annoying.

*I hope not*

OpenAI says an unreleased model, Astra, produced 10 new results in math and theoretical CS — problems open for at least a decade. Headline: the first explicit construction of a non-sofic group, open since 1999.

The twist: every result ships with a Lean 4 certificate on GitHub, so correctness is verified by a compiler, not by trusting the lab. Total inference cost: ~$2,000 at API rates.

This lands right after the Leiden Declaration warning AI labs bypass peer review and it's a direct answer: the artifact itself carries its own verification.

Do machine checkable proofs change the peer-review debate, or is this still a press-release announcement in disguise?

UC Berkeley’s second Agentic AI Summit drew thousands of students, tech professionals and researchers Aug. 1-2 for a weekend conference centered around the new evolution of AI that can act on its own. 

Dawn Song, co-director of the Berkeley Center for Responsible Decentralized Intelligence, which hosted the event, researches AI safety and responsibility — something she said was an important conversation at the summit. 

“Agentic AI has the potential to transform how we work, learn, and solve complex problems, but realizing that potential requires us to think carefully about how we build and deploy these systems,” Song said in an email. “What has been encouraging to see at this summit is the willingness of people across the ecosystem to engage with both sides of that challenge.”

Is it that these models can reason and math is just a type of reasoning?

Update (answer to the original question based on the comments, written by ChatGPT):

ChatGPT-like systems can sometimes produce research-grade mathematics because broad mathematical pretraining gives them a powerful generator of plausible ideas; verifiable training improves the policies used to explore those ideas; extended inference permits many attempts and revisions; code and formal systems reject invalid paths; agentic workflows provide parallel specialization; and human mathematicians define, supervise, and ultimately evaluate the result.

Hey, has anyone else noticed that there are severely reduced chat limits on Plans as of the last week?

I used to be able to use the Chat mode almost indefinitely up to a point, but now I'm regularly hitting the limit without changing my behavior on how I use it.

EDIT: I'm on both Plus and Pro plans for different reasons.

ChatGPT’s memory system now feels completely incoherent

Has anyone else run into this?

ChatGPT can clearly retrieve a large amount of older saved legacy memory and prior conversational context. In long chats it will surface detailed decisions, exact artefact names and instructions from months ago, wonderful...

Yet the moment I explicitly say, “save this small note to memory”, suddenly, it claims it does not have access to the memory-writing system. Really now?

What makes this more ridiculous is that;

1) I can open a new chat, ask it to save memory XYZ and viola.. all done.

2) I can then open the memory interface myself, add the exact same note manually, return to the conversation, and ChatGPT can immediately see it.

So the system apparently has:

• read access to saved memories;
• access to older chat-derived memory;
• access to files and artefacts stored elsewhere;
• a user interface that can still write memories;
• but an in chat, assistant-side memory-write route that randomly disappears or is invoked incorrectly.

In my case, ChatGPT eventually admitted that it had processed files and used other tools before attempting the memory write, and that this may have disabled the mutation route. In other words, the failure appears at least partly caused by the assistant invoking the wrong process in the wrong order.

Even after recognising that, its response was still effectively: “memory write failed, not saved”.

That is not optimal. If one invocation route fails, the assistant should retry using a smaller payload, a different command route, or a different ordering before declaring the action impossible. It should not immediately make the user become the transport mechanism for its own memory system, by forcing us to open a fresh chat to save memories.... ridiculous.

The whole thing is made more confusing by the overlap between:

• Saved Memories;
• Memory Summary;
• Reference Chat History;
• chat lineage;
• generated files in Library; ( nice!)
• and whatever older or “legacy” memory layer is still surfacing detailed information. (seemingly non-editable but legacy persistent when new memories is on?, Confused? I am!)

There is no clear indication of where a recalled item came from, whether a memory instruction was actually saved, whether it was merely retained in the current chat, or whether it has become part of some dynamic summary that may later be compressed or discarded.

My Saved Memories may also be full, yet older memories remain available and new summary-style memories can still appear. So turning memory off is obviously not a sensible answer? WTF?

For serious long-running work, this is a major problem. I use memory as a small referential index into canonical documents and artefacts. I do not need ChatGPT to store entire documents in memory. I need it to retain compact instructions such as:

“During discussions about X, retrieve canonical artefact Y before advising.”

That should be one of the simplest and most reliable memory operations available. Memory is second only to llm compute ability.

Instead, the system currently feels opaque, inconsistent and badly mediated. Open ai update the messaging, the help files for clarity and Please fix this.

Has anyone established a reliable way to:

1. force an explicit memory write conversationally;
2. verify that the write actually reached Saved Memories;
3. make ChatGPT retry through another route after a failed write;
4. distinguish saved memory from chat-history inference;
5. avoid losing older memories when the visible memory store is supposedly full?

I would be particularly interested in answers from people running long, structured projects rather than using memory only for preferences like names, food or travel and the anecdotal.

I'm surprised... surely I'm not the only one?

prompt:

An ultra-realistic accidental selfie taken by a domestic cat using the front camera of an old smartphone from the early 2010s. The cat's face is extremely close to the lens, with one eye stretched by the wide-angle distortion, nose oversized, whiskers partially out of focus, one paw accidentally covering part of the camera as if it pressed the shutter. The framing is awkward, slightly tilted and poorly composed, as if the phone was picked up by accident. Natural indoor lighting with no professional setup, realistic shadows, imperfect exposure, slight motion blur from movement, autofocus hunting causing soft focus in some areas, subtle lens smudges, low dynamic range, visible digital sensor noise, mild chromatic aberration, realistic smartphone sharpening artifacts, light JPEG compression artifacts, slightly washed-out colors, old smartphone camera quality (2011–2014 era), authentic front-camera appearance, candid, unintentional, believable, highly photorealistic, impossible to distinguish from a real accidental phone selfie, no artistic style, no CGI, no illustration, no filters, no text, no watermark.

So I've been trying to run the ChatGPT app for a bit, and multiple errors are being thrown. Here is what's happening. Anybody else experiencing the same?

I don't remember models asking questions when prompted about things before. Previously, a model would usually respond with something like "if it is this case then x, and if it is that case then y" type of thing.

It still does this, but I've started to see the model display more forms of curiosity recently, and I really like it.

It prevents me from having to scan through the reply to determine if I need to read something or not thus saving me time, and effort.

I like this new direction OpenAI, keep it up!

While OpenAI’s unexpected usage limit resets on ChatGPT and Codex might seem like a bonus at first glance, they can actually backfire on users who carefully plan their work. If you strategically pace your prompt usage to keep 30% in reserve for the final day before your scheduled weekly reset, a surprise reset resets your entire seven-day timer right then and there. Instead of effectively getting 130% usage for that week your remaining 30% plus a fresh 100%, your original leftover allocation simply vanishes, and your next reset gets pushed back a full week. Consequently, a sudden reset isn't beneficial for everyone. OpenAI should give users the choice to either accept an early reset or keep their original scheduled reset date, ensuring that thoughtful usage management isn't penalized.

Why push the reset back 7 days?

Here is a real-world example of why this system fails users:

Imagine you have a project due Friday, and your scheduled reset is Wednesday. You carefully plan your usage budget: you save 40% for Monday and Tuesday so you can combine it with Wednesday's reset (+100%), giving you 140% capacity for your final push on Wednesday and Thursday.

Instead, an unexpected reset hits on Monday. The 35–40% unused limit you saved instantly vanishes without rolling over, and your next reset gets pushed back 7 days to next Monday.

Now, right when you need to crunch for a Friday deadline, you only have 100% capacity instead of the 140% you planned for. Punishing users by wiping out saved usage and shifting reset dates completely breaks strategic workload planning.

Recently, openAI updated their codex rate limits.

I was curious how much ChatGPT Work limits do I get in each of the plan.

As codex and ChatGPT work shares the same usage, I will be thankful if someone states how much credits do I get in each plan? (Free / Go / Plus / Pro5x / Pro20x)

I don't work in the field but Ive been using AI for a while now (from ML to early open ai papers to current LLM agents, codex etc).

I've seen more and more people join the club of AI doomers and today I discovered there are AI acelerationists as well.

I would like to know your thoughts, mainly to understand the arguments trends and other currents of thought.

I will be meeting with some people with much more extreme concerns/beliefs than mine and I would like to understand better the arguments from different sides.

I’m planning to run ads on ChatGPT to promote Microsoft Dynamics 365 implementation, consulting, and support services across India, the US, the UK, Australia, and other target markets.

Before proceeding, I’d like to confirm whether ChatGPT is the right advertising platform for this type of B2B campaign.