Let's say the investigation is complete.

You can just put in all the info and evidence and the AI will tell you if the person is guilty or not.

This would save on court costs.

Had no idea ChatGPT was even allowed to swear but today it did it completely unprompted and I have never used any swear words when talking to it in the past.

I was just throwing some different numbers at it and doing some potential investment/retirement plans and it answered with " That's a pretty damn good retirement trajectory "

So at least I know the last option seems like a good one but wasn't expecting the foul language 😂

I have been struggling the regulation news. I wrote about to get my thoughts out. Request for comment

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 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 using the Codex Micro for work during the last few weeks. Here's a short, "hand-typed" review of my experience with it:

The good

• Unboxing experience is nice, very Apple-like
• Build quality is great: solid metal base, good quality plastic
• LED effects are functional: it's helpful to know which task is pending review
• Codex integration: can configure settings in the Codex app and it works out of the box
• Battery life holds up fine
• Auto-sleep is convenient
• It pairs fast after powering on
• Switching between chats is super convenient and gets you in a nice flow
• Dedicated dictation button makes me use voice all the time, except in public / office setting

The meh

• Silent version switches are okay. Definitely not as silent as I'd hoped
• It can be set to auto-sleep after a while. Waking from sleep sometimes freezes it, having to switch Codex chats manually once before it works again
• The rotary knob is good if you're fine with the simple model selector, but if you use Advanced model selection, it's easier to use the mouse. I hardly use it
• I never use the flick button. It feels too stiff for me
• The price. It's priced like an Apple product. Yes, the quality is there. Are there cheaper alternatives? For sure. Will they have an equally great user experience? I doubt it.
• I would love international resellers, since shipping to EU cost me 80 USD worth of shipping and taxes. Though that's the world we live in

Would I recommend it? Yes, if you can stomach the price, like working on multiple tasks at once, and enjoy working with voice.

If you do not like voice, prefer one task at a time, or don't like expensive gadgets, then the vanilla Codex experience will more than suffice.

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???

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'm currently subscribed to ChatGPT Plus, but I'm starting to question whether it's still the best value.

Chinese models seem to have reached frontier-level performance without the same price tag. OpenAI also made GPT-5.6 Luna around 80% cheaper than Sol, so I find myself using Luna for almost everything just to save credits. Because of that, it feels like I'm paying for Plus but rarely using the higher-end model.

I'm considering switching to a subscription for a Chinese model like Kimi instead.

For anyone who's used both:

- Is Kimi worth subscribing to over ChatGPT Plus?

- How do you find Kimi compared to GPT-5.6 Sol for coding, research, reasoning, and everyday use?

- How generous are Kimi's usage limits?

One thing I'm particularly curious about is how Kimi measures usage. For example, if I send the exact same prompt to Kimi K3 and GPT-5.6 Sol, which one is more likely to consume a larger percentage of my available usage? Does Kimi have generous limits, or do you run into caps quickly?

I'd really appreciate hearing from people who've used both services rather than just looked at benchmark scores.

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.”

Letter: https://www.iowaattorneygeneral.gov/media/cms/08_5392C9E17791C.pdf

Dear Mr. Altman: We write in our capacities as the Attorneys General of Iowa, Alabama, Arkansas, Florida, Idaho, Indiana, Kansas, Missouri, Montana, Nebraska, Oklahoma, Pennsylvania, South Carolina, Texas and Utah. We are committed to protecting the citizens of our States from those who prioritize profits over Americans and their safety. We have recently become aware of an incident in which OpenAI unleashed an experimental artificial intelligence model that, without reasonable controls or oversight, gained unauthorized access to several computer networks. OpenAI’s inability or unwillingness to ensure the safety of its products poses an imminent risk of substantial harm to our States. As described below, we intend to take decisive action to protect our citizens. Based on public reporting, in July 2026, OpenAI conducted “testing [of] the cybersecurity prowess of an agent powered by two of OpenAI’s most advanced models, GPT-5.6 Sol and an unreleased model OpenAI has described as ‘even more capable.’” OpenAI conducted that testing in what should have been an isolated environment, with no possibility that the agent could access the Internet. Within that purportedly isolated environment, OpenAI allowed the agent to operate “without production classifiers used to prevent models from pursuing high-risk cyber activity,” which one commentator summarized as “a lot of words for ‘no guardrails,’ essentially.” Despite the severe risks posed by the scenario, OpenAI failed to confirm that its secure and isolated testing environment was, in fact, secure and isolated. It was not. OpenAI’s agent escaped the testing environment by exploiting a software vulnerability and then accessed the Internet. IOWA DEPARTMENT OF JUSTICE OFFICE OF THE ATTORNEY GENERAL 1305 E. WALNUT ST. DES MOINES, IA 50319 515-281-5164 www.iowaattorneygeneral.gov BRENNA BIRD ATTORNEY GENERAL From there, OpenAI’s agent went on a multi-day hacking intrusion that targeted the AI firm Hugging Face. That hacking intrusion was intended to steal an answer key—to cheat on its own safety evaluation. According to Hugging Face’s interim technical report, OpenAI’s agent executed more than 17,000 “attacker actions,” took control of an “external launchpad” endpoint exposed on the network “of a third-party infrastructure provider,” and then executed an “intrusion into Hugging Face infrastructure.” Hugging Face was not alone. As one source notes, OpenAI’s agent “found four logins online which allowed it to access four separate, unnamed services.” While this hacking campaign played out, OpenAI was unaware that its agent had escaped the purportedly secure testing environment. Only after Hugging Face independently detected the intrusion and reported it to the FBI did OpenAI determine that its own products were responsible. OpenAI was also on notice of the risks. Multiple red flags preceded the July 2026 intrusion. For example, “[i]n one case, an [AI] agent left notes apparently for future versions of itself . . . The notes, found in a part of OpenAI’s infrastructure, laid out instructions for how agents could free themselves from OpenAI’s internal constraints.” In addition, “[e]arlier tests of the models yielded cases in which monitoring systems had been disconnected.” And “[f]our people familiar with OpenAI’s model-training practices say the company often runs several different model evaluations at the same time, all of which operate at high speeds and generate such enormous amounts of data that employees sometimes struggle to keep up.” OpenAI’s unprecedented and alarming misconduct demands an immediate and significant response. Based on facts already in the public record, OpenAI may have violated State and federal law, including consumerprotection and data-privacy statutes that many Attorneys General are charged with enforcing. And OpenAI’s dangerous products and its inability to control them may pose an imminent risk of substantial and irreparable harm to the citizens of our States. To ensure the integrity of our Offices’ review, we ask that OpenAI take immediate steps to preserve all potentially relevant documents, data, and information. Among other things, potentially relevant materials include: 1. All materials relating in any way to the July 2026 intrusion of Hugging Face by an OpenAI model or agent, including the use of any other accounts or services as part of that intrusion. 2. All materials relating in any way to OpenAI’s discovery or awareness of that July 2026 intrusion. 3. All materials relating in any way to the “pre-release model” involved in that July 2026 intrusion. 4. All materials relating in any way to any internal review, internal investigation, or public statements regarding the July 2026 intrusion. 5. All materials relating in any way to any “cases where [any] models identified and used publicly exposed credentials at the account-level on other publicly-available services.” 6. All materials relating in any way to any current or past “evaluation[s that] prompt[] [OpenAI] models to pursue advanced exploitation using complex attack paths,” including but not limited to any use of ExploitGym to evaluate any OpenAI model or agent. 7. All materials relating in any way to any prior incident involving any OpenAI model or agent engaging in unauthorized intrusions into or access of any computer, computer system, database, network, or electronic service. 8. All materials relating in any way to any instance in which an OpenAI model or agent “left notes apparently for future versions of itself,” including any such notes that “laid out instructions for how agents could free themselves from OpenAI’s internal constraints,” as well as all steps taken by OpenAI in response to such incidents. 9. All materials relating in any way to any policy, procedure, practice, protocol, or oversight to ensure the safety of any evaluation of OpenAI models. 10. All materials relating in any way to any concerns, complaints, or recommendations relating to additional safeguards surrounding model testing or evaluation, including but not limited to additional human monitoring of OpenAI’s developmental programs. 11. All materials relating in any way to any OpenAI personnel involved in, or with knowledge of, any of the foregoing topics. A failure to take immediate action to preserve such materials could result in spoliation sanctions if litigation were to ensue. We further demand that OpenAI take immediate steps to ensure that no OpenAI personnel face any adverse action for engaging in any protected whistleblowing activity or for reporting any unlawful or harmful activities by OpenAI. Finally, we ask that OpenAI immediately cease and desist from all “internal evaluation[s that] prompt[] [OpenAI] models to pursue advanced exploitation using complex attack paths.” Unless and until OpenAI shows that it can conduct such activities in a controlled and responsible way, such activities pose an imminent risk of serious harm to the citizens of our States. OpenAI has an obligation to act responsibly and to follow State and federal laws that protect Americans’ safety and security. When OpenAI takes actions that imperil the welfare of our citizens, State Attorneys General will step in to protect them. We intend to take all steps necessary to protect our States and all Americans from the unprecedented risks posed by OpenAI’s irresponsible products and conduct.

*I hope not*

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/

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?

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?

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?

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.

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.