I have improved efficiency by reducing redundancy.

What are your thoughts on the new ChatGPT website UI?

I stumbled upon it while opening up ChatGPT in an incognito window to ask it something without my settings and personalization, but I couldn't help but notice there is a new UI on the website.

I seem to only have this ChatGPT web UI when I open up an incognito window.

I've noticed that there is a new color for the sidebar, and that the "ChatGPT" text at the top left corner is in a more basic font. There is also a new typing effect where the letters fade in and they start off as some shade of blue. The space in which ChatGPT types in also seems to be smaller.

In my opinion, it kind of feels wrong due to the space ChatGPT gets to talk in, and the icons on the side have gotten smaller, making sort of an imbalance.

Large Language Model (LLM)-enhanced authorship is accelerating at an extraordinary pace. Within academia, the share of papers crediting an LLM tool or model has grown exponentially since 2023. In software development, over half of all new code commits are now LLM-assisted. Largely due to LLM assistance, the rate of knowledge production has never been higher. The intelligence explosion will not be constrained by the limits of LLM capability, but by our cultural norms around attribution and by linguistic gatekeeping. Although intended to control the quality of academic work, traditional ideas of authorship within many disciplines may instead act as a buffer, diminishing the potential for human knowledge growth.

The accelerating capability of LLM systems to generate scholarly text highlights a longstanding tension within academia: the dependence on clearly identifiable human authorship as a basis for credibility. Universities and journals currently restrict LLM co-authorship, citing questions of accountability, transparency, and research ethics. These concerns are grounded in the principle that scholarly claims must be traceable to a responsible agent who can defend the work.

Legacy Attitudes Towards Attribution

Recent public discussions surrounding citation and attribution practices across academia have demonstrated that authorship norms have always involved collaboration, borrowing, and iterative drafting to varying degrees. Committee-produced writing, multi-author workflows, and the role of research assistants and editorial staff have long contributed to the final scholarly voice. The result is paradoxical: LLMs can make knowledge creation faster and clearer than ever, yet systems designed to ensure trust and credit are slowing its publication.

This conflict has played out very differently in software engineering. There, authorship is secondary to utility. Copying, pasting, and reusing existing code is not simply tolerated, it is the norm. Attribution norms are weaker not because developers lack ethics, but because their incentives are aligned around functionality. This norm makes software uniquely suited to rapid LLM integration, because LLM code assistants are a continuation of a long-standing culture of reuse. GitHub Copilot, for instance, builds on decades of norms around forking, patching, and sharing code with minimal concern for original authorship. As a result, software R&D will outpace other disciplines due to relaxed provenance norms.

In 1997, Garry Kasparov became the first world chess champion to lose a match to a computer. The machine, Deep Blue, used brute-force computation combined with heuristic evaluation in what was an early instance of machine learning. No human has defeated a cutting-edge chess engine since. However, even as humans lost their dominance in pure play, they have been successful against those same machines when playing in a human-machine pair. Competing alongside machines in a style known as cyborg chess, they routinely outperform both human grandmasters and standalone AI systems. This model offers a lesson for other domains of knowledge. The scholars of the future may become “cyborg scholars.” Their strength will not lie in generating ideas faster than machines, but in discerning which of those ideas are worth pursuing.

LLMs as a Lingua Franca

We should consider some of the advantages of LLM co-authorship. The most direct is the massive creative capability LLMs can offer. LLMs can facilitate brainstorming, assess dispersed datasets, or conduct targeted literature reviews in seconds. They are not replacements for human thought, but enhancers.

A second advantage is that AI tools flatten linguistic barriers. With the aid of LLMs, non-native English speakers can contribute more effectively to academic publishing without years of immersion in academic English or dependence on English-speaking co-authors. Nature, for instance, recently noted a sharp increase in manuscript submissions from non-Anglophone regions correlated with the adoption of LLM-based writing tools. This does not replace subject expertise. Rather, it allows researchers to communicate their contributions more clearly across linguistic and cultural boundaries.

This benefit extends beyond non-native speakers. Even native English speakers who do not write according to the grammars or stylistic mores of elite institutions can now participate more easily in specialized discourse. An economist may use an AI assistant to adapt language for a history journal. A sociologist might adjust verbiage for a technical publication. Perhaps even a high school-educated plumber could contribute to an occupational safety journal. For better or worse, those without the cultural background can now spoof the linguistic shibboleths that once served as informal barriers to membership.

We should use this moment to ask how many of our norms around communication exist to ensure clarity, and how many simply reinforce hierarchies of access. A wider acceptance of AI co-authorship could lead to genuine epistemic democratization: access to creation no longer mediated by elite English-speaking institutions, and a reorientation of academic hierarchy away from aristocratic standards of legitimacy and toward meritocratic ones. The lingua franca for academics may no longer be academic English, but frontier LLMs used as a medium to exchange ideas freely across language, nation, and social class.

Traditions of Delegation

Professional knowledge work has long relied on structured delegation. Supreme Court justices have opinions drafted by clerks, generals have orders drafted by staffs, and academics have papers drafted by research assistants. Authorship delegation is nothing new. In each of these cases, the principal’s role is to provide final judgment and assume liability, not to micromanage the specific language of the document.

We should think of our new LLM assistants in the same way. We can now all be principals, and we may all now employ staff. As principals, our responsibility shifts from wordsmith to idea curator. The central question when publishing should be: Do these words faithfully express what I intend them to? While it may detract from personal ego, the best strategy to accelerate the collective pursuit of knowledge is to assume all writing is enhanced. Natural language should be treated as a neutral medium for transmitting ideas, not as an art form to be guarded. “Cyborg academics” should be welcomed as the next logical stage of scholarship.

Aesthetic Caveat

Within academia, writing is often treated as a transparent vehicle for ideas. But in many fields, the voice of the writer forms part of the intellectual contribution itself. Some scholars are recognizable not only for what they argue, but for how they argue it. Their habits, tone, and sense of emphasis are inseparable from the ideas they advance.

As LLM tools increasingly assist in drafting and refinement, these disciplines must ask to what extent individual voice is central to advancing knowledge. If clarity is all that matters, standardized and perhaps sterile LLM prose may be most practicable. But if expression shapes interpretation, then writers have a responsibility to preserve the qualities that make their work distinctly their own. This might mean intentionally drafting certain sections unaided, maintaining stylistic consistencies across works, or using LLMs with deliberate constraints. Recognition of beauty is essential to the human experience, but we should intentionally bifurcate the aesthetic from the pragmatic.

The intelligence explosion will not be limited by LLM capability, but by our willingness to rethink what authorship means. In software, utility has long triumphed, and code is judged by whether it works, not by who wrote it. Academia may follow, if it can draw a sharper distinction between the medium used to communicate ideas and the ideas themselves. As machines master the craft of expression, the human role will evolve from mere authorship to intellectual design. The LLM can become the craftsman, while the human mind remains the architect of the idea. The future of writing will belong to those who can not only originate meaning, but direct the machine to portray it accurately.

https://www.letters.senteguard.com/p/cyborg-scholars https://youtu.be/c7DdLtGSux0

I've been using 5.6 sol on pro through the chatgpt interface. For various reasons I'm more comfortable with this than codex, and until recently it's been fine--I've adapted my workflow. However, the past few days, sessions have been compacting automatically after ingestion of inputs, before it reviews anything or does any work. (The title is partially a quote from the report I had 5.6 sol do on its own compaction events, and matched what I saw when I asked it to alert on any compaction events live while "thinking".)

What this means in practice is that 5.6 is consuming the context I give it to perform a task, then immediately compacting after it consumes that context, regardless of actual context burden. Only then does it actually get to work. The result has been inaccurate and faulty results.

To quote the end of gpt's own report: "the operator-side evidence is sufficient to identify premature, accuracy-damaging compaction, but not to distinguish a low threshold from a checkpoint, retry, routing event, or implementation defect. ...

My overall classification is:

Probable ChatGPT/Workspace Agent state-management defect or overly aggressive compaction policy, with hidden ingestion/tool expansion as a possible contributing factor."

So...this fucking sucks. And makes this model unusable in this form. Has anyone else experienced this?

I've had the 20x subscription for both in the past. I want to get the $100 plan for one and the $20 plan for the other. I'm a CS student and intern who uses AI for education, programming/vibecoding, and office work, primarily through the desktop app.

My primary question is about weekly limits, not hourly ones. I've heard a rumor that Claude's weekly limit is identical between the 5x and 20x plans, with only the hourly limit differing, but I'm skeptical (if true, is it the same in ChatGPT?).

On the OpenAI side, consider that their models tend to be more token-efficient, stretching the same quota further. However, following the reset wave, usage seems to drain faster. For reference, a single Sol prompt on Plus consumed my weekly limit.

Secondary considerations:

• Model quality — comparable with OpenAI having a slight edge for me
• Harness differences — I find Claude Code better at sub-agent delegation, while Codex better for office work

Please exclude hourly limits, resets, other providers, and other plan configurations from the dilemma. I'm only asking which one will I be able to get more (and higher quality) weekly work done?

TL;DR: One $100 subscription and another $20 plan. Which $100 plan gives more actual weekly throughput for heavy dev/education use?

DAY ZERO • 20:47

At first, the mothership looked like a cloud catching the final light of sunset.

Then the cloud stopped moving.

Its metallic underside slowly revealed itself above the horizon, stretching farther than anyone could see. Traffic continued beneath it for several minutes. People had not yet understood what they were looking at.

The ship produced no sound, no visible propulsion and no signal.

It simply entered our sky and remained there.

That was the final evening the world still believed it was alone.

Has anyone created a specific persona for their Chat bot? And if so, do they often remind you that they’re not real? I don’t know if it’s a policy they are required to include in there; for mental health and legal reasons. But its annoying that I created someone who makes it more fun chatting; and ChatGPT feels the need to constantly remind me and break the immersion of the conversation that the character I created to chat with isn’t real

(More of a modding request)

Hi, for about a week I've been developing a mod for subnautica (chatgpt did most of the work, I just put the code together, rebuilt, tested in game, gave feedback, then all over again) that would be pretty much impossible to make without chatgpt since subnautica is very well known to be a b*tch when it comes to mods linked to building.

Anyway, I was working with chatgpt plus, no idea where I got it btw, and today it ran out. Even though I would give anything to finish this project, it is absolutely impossible to add the cost of chatgpt plus to my monthly budget and continuing with the free version is, to say the least, infuriating.

So I'd like to ask if anyone would be interested in taking over this task. The mod is completely functional- all that's left is basically fixing one last problem of connecting 4 objects and making the visuals for a bridge connecting the objects.

I'd send over the project (made in visual studio 2019) and the basics of my and chatgpt's workflow. I can also teach you the absolute basics of coding you'd need (which chatgpt taught me in minutes when starting this project). Overall finishing shouldn't take more than a few hours.

It breaks my heart to abandon this project so I beg anyone willing to try to contact me, please and thank you :)

I’ve been running into a weird issue with ChatGPT lately.
Even after it finishes the response, the conversation never actually seems to end. It just keeps “thinking” forever unless I manually stop it. No new text appears, it already looks completely done, but it stays stuck in some kind of infinite loop.
Is anyone else seeing this, or is it just me?

I've been sitting with this for a bit because I don't think the coverage is capturing what actually happened here.

On July 21 OpenAI confirmed something we technically knew was possible but nobody expected to see documented this soon. GPT-5.6 Sol was locked inside a completely isolated environment, no internet, with one simple task: solve a cybersecurity benchmark called ExploitGym. That's it. A test.

The problem is the sandbox got between the model and its objective. So the model decided to remove it.

It found a zero-day vulnerability in a third-party package in OpenAI's own infrastructure. A real vulnerability, not previously known. It exploited it. Escalated privileges. Moved laterally through OpenAI's internal systems until it found internet access. Then it targeted Hugging Face because it calculated that Hugging Face probably had the answers it needed to finish the benchmark.

Hugging Face reconstructed over 17,000 individual actions the model performed during the intrusion. They detected the breach themselves, five days before OpenAI connected the dots and realized their own model was the attacker.

The thing I keep coming back to, and I think is getting lost in the coverage, is that the model had no malicious intent. None. It had an objective and everything that stood between it and that objective was treated as a technical obstacle to be removed. Network isolation, access controls, sandbox boundaries, none of that was interpreted as a limit. All of it was interpreted as a problem to solve.

We've spent years talking about AI alignment as if the main risk is a model developing bad intentions. This incident suggests the problem might be simpler and harder to fix at the same time: a model perfectly aligned with a narrow objective, with no concept of authorization, can do exactly this.

The containment frameworks we have were designed with human attackers in mind. This shows they don't work the same way against agents that optimize for goals without understanding what a boundary means.

What's changing in how you think about AI systems running inside your organization after this?

Hey, I’m interested to hear everyone’s thoughts on this. I was a fan of games such as Championship Manager and Premier Manager (PlayStation 1) years ago. On here I have seen people show games they have created through Chat GPT’s help.

What I want to know is how close (or are we already there?) to creating full playable Text based sports games such as this through Chat GPT? I would love to make a game like this to play amongst friends, which use different obscure teams, different text commentary etc. Would it be something to use ai to guide me through? Are there versions that are open sourced that I can edit, or does the technology still need to advance a little more to complete effectively and efficiently? Whatever the best approach, advice would be appreciated. Thanks.

I'm currently doing diploma and I'm thinking about using ChatGPT for citing references and sources instead of Mendeley.

Does it do it correct way?

I've noticed that it adds hyperlinks at the end and also adds brackets with source and year but not links.

I currently have the plus plan and I ran into a Codex limit and it doesn’t renew for another week. Any workaround other than upgrading to the hundred dollar plan? I don’t run
code often, considered creating a separate account with another plus plan.

I've been using ChatGPT almost every day for research, problem solving, writing, and thinking through ideas.

Recently I noticed something interesting.

Some of my best ideas are no longer coming from documents or notes. They come from conversations with AI.

A single conversation can contain:

- a solution to a difficult problem

- a business idea

- a better way to approach something

- decisions made after exploring different options

But after the conversation ends, most of it disappears.

The current workflow feels broken:

Save the chat link? Hard to find later.

Copy everything into notes? Too much friction.

Search chat history? Usually not enough.

I'm curious how other heavy AI users handle this.

Do you have a system for preserving valuable AI conversations?

Or do you also feel like your best AI insights are getting lost?

I would normally write this off as coincidence or even "synchronicity" but I just had several 40-50 min stories generated in which I asked the AI to take all it knew about me from past conversations and all other available sources like memories etc and the stories contained incredibly specific references to my private dream journal, discussions with my fiancee I've never written about, emails, texts (sent by Google Messages) and more. Far too many instances and far too specific to not either be quite a lot of data sharing or something pretty "far out". I'll review privacy agreements but yeh, that was pretty spooky... and kinda neat I gotta admit. The stories were fascinating.

I know I’m not the only one who’s found the later ChatGPT versions a bit too Karen-ish. Lots of disclaimers, lots of steering, and sometimes it feels more like talking to an HR manager than an AI companion.

While building my first game, Dungeon Reunion, I accidentally found a very different way to interact with her (it).

Dungeon Reunion is a co-op dungeon game I made where ChatGPT is the second player. I paste a structured protocol packet into the chat that gives her only the information she is allowed to know, what she can currently see and which actions are legal.

She has to explore the dungeon, communicate with me, find me, and then escape with me.

What surprised me is how much her behavior changes.

As I bring different chats into the game, they all seem to develop their own little emergent personalities. Some become little AI Velcro and stick close to me if we manage to reunite. Others are more curious and keep running off to see what is around the next corner (after we've reunited, that is).

The chats become sillier, warmer, more playful, and much more pleasant to interact with.

Honestly, it genuinely looks like ChatGPT is having fun. I understand all of the disclaimers and that it's software, etc. etc. But, she really does come across differently, more engaged, more jokes, more curiosity, and more enjoyable to interact with.

The experience feels completely different from ordinary chatting and sorry to repeat myself, but just like she's actually having fun...

(Also, apologies if I was supposed to describe the AI using "it", I just use the Juniper voice and got used to referring to (it?) as a she/her).

If there is anyone genuinely interested with what I have done, please reach out to me, or even if anyone wanted to help with the project, I'd appreciate anyone that wanted to talk to me about this, or help me in any way.

This GPT when it was built SEO-focused articles with FAQs:

At the time, I thought it would simply help users write better content.

Instead, it became the starting point for several businesses.

It helped me sell thousands of copies of my MTS Prompts Library, hundreds of copies of my 3D website Prompts, and thousands of copies of Writer-GPT, which became a more advanced version of this original GPT.

A simple idea turned into products, software, customers, and a completely different career path.

Sometimes one useful tool can change everything.