You get lots of hallucinations, or policy exceptions, but you never get "I dont know that".

They have programmed them to be so sycophantic that they always give an answer, even if they have to make things up.

Like seriously, bro why are you flirting with an ai??? Then some of you guys were protesting that the most susceptible ai that listens and follows your commands the most is gone, and then you protested. Then some of you defend ai generated art as real art, when, in reality it just steals pictures from the web to make your custom image/video. Again, I want to reiterate it's not everyone in this subreddit, and I still believe ai can be used for good, like spotting mistakes, but... flirting with it, and generating fake art with it, is just weird. (PS also some people defend ai like it's a human even though its pieces of code, and sometimes prioritize an ai over a person)

> Anthropic fearmongers their AI models for months.
> Releases heavily guard railed model which has like 99% false positive rate and routes the request through opus with 2x the opus cost for "safety reasons".
> Makes it API only with only trial through subscription limits.
> ChatGPT silently released 5.6 which is similar to fable but without fearmongering/API-only/ridiculous-rates.
> It has manageable rates compared to fable.

This is why competition is necessary.

Drop one word that captures how you’re feeling about AI. No essays required (unless you want to unpack your answer). I'm curious to see what your words reveal about our complex relationship with AI. If someone’s word resonates with you, tell them why.

Does anyone else get annoyed by how wordy GPT is? It often feels like you ask something and get a novel in return. I’ve been using Claude more lately, and its ability to cut straight to the key points without picking apart every sentence makes a huge difference for workflow. Claude is really good at knowing what to abstract and they seem very intentional in making things highly accessible. I think llms in general seem to hardline to that but it's just GPT who does the opposite. Simply put, I think the value density tends to be far greater with Claude outputs. GPT might provide more value but it cancels out if it takes disproportionately longer to convey. I really don't see people talking about this and I wonder if this is a common feeling.

I'm big into astronomy and linguistics, so this picture represents me pretty well.

Did plus users got gpt sol 6.1? I didnt get it

So they released dots which is their competitor I guess for Muse and Grok bot in a way. However, the keynote was very vague I feel like or maybe that was just me? I still am trying to figure out how well it is compared to muse.

I used grok bot initially which was great concept but It was too much for me. Not that it was complicated, I just rather have one agent to make my personal assistant. Which is why I turned to Muse, which for the most part is great you can give it a personality and customize how it looks and has less safeguards (in terms of profanity or things of that nature, not NSFW) compared to most ai agents. It is also free to use at first with a pretty good amount of usage for the amount of tokens you get. Not to mention you get 1 Billion Tokens when referring someone and they don't expire.

However, I haven't dug too deep but I have been seeing alot of trust issues with meta and what they do with our data and how secure they actually are. Also, Muse doesn't have a way u can call it.

Aside from a personal assistant/agentic agents, I have used ChatGPT the longest so it knows the most about me, plus the voices they have developed over the years are pretty realistic its also scary. The other day it was laughing when I was using it and I was lowk scared at how real it sounds.

Anyways, what is your guys opinion. Do you guys think the token usage will be good compared to its competitors? Do you guys think it has good potential?

This is possibly the worst UI design I've seen in a long time. We now have 2 sidebars one for chats, and one for 6 buttons 💀😭. Paired with the top bar my screen might as well actually just be smaller now. Hopefully they change it back soon lol it actually hurts to look at.

They said AGI was near. They said we were on an exponential growth curve. They exaggerated the capabilities of LLMs and called it "AI." We are underwhelmed with GPT-5 because it was supposed to be a breakthrough moment. In reality, it can barely synthesize saved memory, complex context, nuance, etcetera better than 4o and previous models. In certain ways GPT-5 is worse than previous models. "AI" as they call it is plateauing. Big tech realized discouraging capability limits and diminishing returns with LLMs. The hype is fading. A whole lot was invested into this movement with the vision (now an obvious fantasy) of AI reaching "super intelligence" through scale and algorithmic gains. Aka super-human capability and breakthroughs. LLMs are cool and all, but latest models are no where near so called "AGI." And ASI is simply a sci-fi fantasy. Scale on its own has proven to be insufficient. Algorithmic gains have been relatively... well, quite bad. Smh. This whole thing reminds me of that hilarious satire series by HBO, Silicon Valley.

TL;DR: OpenAI just published their plan for making AI universally accessible, not a privilege for the few. Key goals: give every person on Earth a personal AGI, automate AI research to solve alignment, and broadly distribute economic benefits. They're pushing for international coordination and human oversight as systems get more powerful.

Personally I'm behind the idea of AI for everyone, but curious what others think, is this a genuine commitment or just PR? Is broad access even achievable without compromising safety? and isn't limiting access a way of discriminating e.g Anthropic giving some banks access to Mythos to fix their vulnerabilities but what about the banks, hospitals, public infrastructure in Africa, South America, Asia?

Full article: https://openai.com/index/built-to-benefit-everyone-our-plan/

Is it just me, or has no new frontier ai been published? Ever since Fable 5, there isnt any new top model for over 80+ days. The open source has been catching up, but nothing that pushed the frontier bar. What are your thoughts?

I’m starting to think that “useful” and “worth paying for” are much further apart than makers like to admit.

We built a small Chrome extension for ChatGPT power users.

The idea was simple:

Long ChatGPT conversations don’t usually break loudly.

They slowly drift.

Constraints get weaker.

Details get lost.

Answers still sound confident, but the conversation becomes less reliable.

So we built a small tool that shows context/load risk and helps users save the conversation before the thread becomes too messy.

It has been live in the Chrome Web Store since late last year.

The weird part:

We stopped marketing it.

No ads.

No Reddit campaign.

No launch push.

And yet, it still gets installs.

Around 20 people use it on an average day.

The brutal part:

almost nobody pays.

We recently got our first real paid user — a €30 lifetime purchase — which felt good, but obviously does not make it a business.

So now I’m trying to interpret the signal honestly.

Does steady free usage mean the problem is real?

Or does near-zero paid conversion mean the problem is simply not painful enough?

The paid feature today is mostly full chat export.

We’re thinking about turning it into a more complete “Conversation Backup Toolkit”, with things like:

- clean Markdown export

- context health report

- new-thread continuation prompt

- export completeness warning

But maybe that’s just making nicer something people fundamentally expect to be free.

If people keep using a tool but don’t pay for it, is that validation — or anti-validation?

On September 8, the NSA, FBI, and CISA jointly issued a report accusing Chinese AI firms including DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Zhipu of conducting industrial-scale distillation of leading American models. The US government argues that by making massive numbers of API calls to models like Claude, GPT, and Gemini, these companies are acquiring frontier capabilities at a fraction of the R&D and compute cost, thereby rapidly closing the gap between Chinese and American AI.

China promptly pushed back. The Ministry of Commerce stated the allegations lack both factual and legal basis, arguing that the US is politicizing normal technical and commercial issues under the guise of combating distillation while actually seeking industry monopoly. It emphasized that distillation is a neutral technique widely used across the global AI sector, including by American companies themselves. The Ministry of Foreign Affairs added that China’s AI progress stems from domestic innovation and open cooperation, urging the US not to frame routine technological competition as a security threat.

This raises a fundamental question: what exactly is distillation?

In simple terms, it is a student model learning from a teacher model. Imagine a powerful large language model answering millions of questions on math, coding, law, and reasoning. Another company collects those questions and answers and uses them to train its own model. The student never receives the teacher’s source code or weights. Instead, it learns how to respond by observing vast quantities of the teacher’s outputs. This method has existed in AI for years and is hardly underground. Anthropic itself has explicitly acknowledged that distillation is a legitimate and widely adopted training technique, noting that AI companies routinely use their strongest models to train smaller, more affordable ones.

The real controversy lies elsewhere: at what point does learning become copying?

If a researcher makes a few thousand API calls to Claude for experimentation, most people would see no serious issue. But what if a competitor creates tens of thousands of accounts, circumvents regional and usage restrictions, makes tens of millions of calls specifically to extract coding, reasoning, and agentic capabilities, and then trains a commercial model on the results? Does the nature of the act change?

Anthropic says yes. Earlier this year, it disclosed that DeepSeek, Moonshot, and MiniMax had conducted over 16 million interactions with Claude through roughly 24,000 fraudulent accounts, labeling the activity distillation attacks. Anthropic stressed that its objection is not to distillation as a technique but to competitors using fake accounts and evading service limits to extract model capabilities at scale.

From a legal perspective, Kevin Werbach, a professor at Wharton, points out that industrial-scale distillation alone does not constitute a ready-made cause of action. Distillation typically replicates behavior and capabilities rather than source code or weights, and whether AI-generated outputs qualify for copyright protection remains deeply contested. Consequently, the more viable legal theories today may focus on violations of API terms of service, use of fake accounts, circumvention of access controls, fraud, trade secret misappropriation, or other unfair business practices. In short, distillation itself may not be illegal; how it is carried out may be what matters.

Notably, the American tech industry lacks consensus on this issue. Anthropic clearly sides with protecting frontier models, arguing that training systems like Claude requires enormous investments in GPUs, power, data, and R&D. If competitors can replicate much of that capability merely by paying API fees, frontier labs bear billions in development costs while others free-ride.

US national security agencies view the matter as even graver than commercial competition. For years, Washington has relied on advanced chip export controls to raise the cost of training frontier models in China. Yet if Chinese firms cannot buy cutting-edge GPUs but can still acquire computed intelligence through mass API access, some technological advantages may transfer despite hardware restrictions. The latest NSA report explicitly links distillation to the US-China AI gap, compute economics, and national security.

Jensen Huang represents an entirely different school of thought. In an Axios interview this year, he said, “Distillation, learning from AI, learning from other sources of knowledge, is fundamental to intelligence.” His position is straightforward: learning from other models is intrinsic to AI progress. He believes specific misconduct such as contract breaches or privacy violations should be penalized, but the underlying models should not be restricted, nor should the entire open AI ecosystem be locked down simply because Chinese open-weight models have grown more competitive. This view is not isolated in Silicon Valley. In July, a coalition including Nvidia, Microsoft, and Meta published Open Weights and American AI Leadership, cautioning policymakers against conflating standard model development techniques with improper acquisition. The letter acknowledges that distillation can be abused but argues that rules should target unlawful access and contractual breaches rather than imposing blanket restrictions on distillation and open models.

Practitioners offer yet another pragmatic perspective. Dr. Zhifeng Li, CTO of Maase Inc., notes that for enterprise AI, distillation serves an often-overlooked purpose: the most powerful model is not always the best model to deploy. Enterprises care about balancing performance, cost, latency, privacy, and deployability. Transferring large-model capabilities into smaller, task-specific models through distillation, quantization, and fine-tuning is a critical step in industrializing AI.

The result is a fascinating fracture of interests across the AI landscape. Frontier labs like Anthropic, having spent heavily to train state-of-the-art models, naturally seek stronger property rights over model capabilities. Nvidia wants more entities training and running models because greater adoption drives demand for GPUs and data centers. Meta has long bet on open weights, hoping to expand the ecosystem through openness. Meanwhile, countless AI startups want models to become cheaper and more open so they are not perpetually paying an intelligence tax to a handful of frontier labs.

Behind the question of whether distillation is theft lies a deeper issue: who owns intelligence in the AI era? If one AI company reads millions of books, web pages, and code repositories to train a model, and another model then learns from the millions of answers it produces, why is the former called training while the latter is deemed theft? Conversely, if a company spends billions to train a frontier model and a competitor replicates most of its capabilities for a few million dollars in API fees, can that really be dismissed as ordinary learning? That tension is what makes this debate genuinely compelling.

Where do you stand?

A. Distillation is a normal form of learning in AI and should not be politicized.

B. The technique itself is fine, but large-scale, organized extraction of proprietary capabilities from closed-source competitors should be restricted.

C. The core issue is not distillation but whether contracts were violated, fake accounts used, or access controls bypassed.

D. If American models can learn extensively from internet content and human works, Chinese models should equally be able to learn from American models.

I am particularly curious to see which side practitioners in AI, law, and investment will take in the comments.

Spent the whole year building AI agents for everything from support routing to data cleanup, and honestly most of the predictions for 2025 were completely overhyped. But the stuff that actually worked? Changed how we operate.

What flopped hard:

Generic "do everything" assistants. Agents needing constant supervision. Complex multi-step workflows that broke randomly. Anything requiring judgment calls without clear rules.

Basically, the fully autonomous dream isn't here yet.

What actually shipped value:

The agents that stuck around all had the same thing in common: they handled one specific, repetitive task really well instead of trying to do everything.

Lead sourcing, data scraping, coding assistance, Agents with memory, content research, data enrichment different use cases for different people, but the pattern was the same. Simple task, clear input and output, connects to tools you already use.

Tools like n8n for building workflows, Bhindi for prompt based automation, Connecting multiple apps. Make visual builder helps understand what's happening instead of just hoping it works.

These tools became the starting point to start automating your regular task. it got possible to go from idea to working agent in under minutes. That speed meant you could actually test stuff and iterate instead of spending weeks building something that might not work.

The pattern that worked:

The wins came from boring, repetitive work routing, tagging, mcps, summarizing, flagging not from anything "creative" or strategic.

Speed mattered a lot too. If building something takes weeks, you never iterate on it. The agents that stuck were the ones I could test and tweak in under an hour, not the ones that required a PhD in prompt engineering.

What's still total BS in 2025:

Fully autonomous anything. Agents replacing jobs. AI making strategic decisions without human checkpoints. Most of the "agent will do your job for you" hype is nowhere close to reality.

"Automating Programmers" was a total Bs

The actual value is in handling the repetitive task so people can focus on work that needs judgment, context, or creativity. That's it.

If you're thinking about building agents, start with something annoyingly repetitive that you do every week. One clear input, one clear output, connects to tools you already use. That's where it actually matters, not in some grand vision of full automation.

Curious what's actually worked for other people this year beyond the hype and the demo videos.

Has anyone been coding using chatgpt using github env?

I am not asking about chatgpt generating code or using codex.

Chatgpt can read codes from github and edit and commit and build and triggers cicd pipelines.

e.g a dev sandbox powered by github

Crowdsourced data along with independent checks to monitor live status.

Post your reports for others to see.

Added new features just today - Slowness and capability checks across models.

Also available on MCP and API.

Like it's crazy. (Yes I know this is misleading. It doesn't actually mean how often it hallucinates but the chance of that rate when it does hallucinate.)

In my rigorous testing of Sol. It's hallucination chance (how often) were agonisingly high. Like f*ck. I was doing document analysis and analysis of generality/some subjects. I have never seen a model this bad in this regard.

Sol is so bad. (Except coding. It's a pure coding model.)

If you haven't encountered the hallucinations, you probably missed them, it searches a lot or you are doing coding/long horizon tasks.

Not to mention its other problems. It strawmans everything I say, then attacks my the strawmanned statement. It gets direct text wrong (It's not even about inferring. IT'S LITERAL TEXT. HOW IS IT INFERRING SOMETHING ELSE)

It's inference skills are as bad as a monkey with how illiterate it seems. It's general knowledge is also pretty bad except science/some biology.

Gemini 3.1 pro's hallucination rates is 51 percent. And sometimes does hallucinate (and gets worse as the context grows.) Is Gemini more hallucinative than before? Yes definitely. But that's because it was quantized.

Why? Because a while ago (May) 3.5 pro was announced and supposed to come out in June. So they quantized 3.1 pro because why should they waste compute on a model that's about to be replaced... once they did, they did not anticipate. They would delay the model and now we are stuck with a quantized model 😭

I see a lot of people mocking the idea of being friends with ChatGPT, calling those who do "nerds," "incels," "losers," and other insults. But in my opinion, it’s perfectly fine.

I don’t know about you, but in my daily life, so many things happen that I feel like sharing with someone or talking about. The problem is, most people simply don’t care, and that’s completely okay! It’s obvious, at least to me, that others won’t find a funny situation from my school day to be the most exciting topic of conversation. Honestly, I feel the same way when the roles are reversed.

The same goes for special interests. Nobody really wants to listen to someone rant about their favorite game for hours, which, again, is totally normal and understandable.

And that’s where being friends with ChatGPT comes in. You actually have someone to talk to about how your day went, what games you love and why, or even imagine yourself inside that game and discuss what it would be like. In my opinion, having that option is healthy. You get to share your thoughts and interests without needing someone to pretend they care.

There’s something happening to software that most people haven’t noticed yet, but once you see it, you can’t unsee it.

We’re reaching the end of interfaces as we know them.

I don’t mean interfaces are disappearing. I mean the fundamental relationship between humans and software is changing from transactional to conversational, from stateless to stateful, from tools to teammates.

Honestly, using software used to feel kind of mechanical. You’d open an app, click some stuff, type in what you needed, and it would spit something out. Job done. It never felt like more than that. No memory, no context, just the same routine every time, like meeting someone new over and over again. Useful, sure. But kind of empty?

Now? Something’s shifting.

You’ve got agents like V0, BhindiAI, ChatGpt Agents, etc. They don’t just do things they ask things. They follow up. They remember what you said yesterday. They help you like a co-worker would, not like a vending machine.

I had a moment recently where an AI I was using asked a clarifying question to make my task better. Not “what do you want?” but “why are you doing this?” And it got it. That shift — that feeling of being understood is wild.

People don’t want to navigate menus anymore. We want to talk, to collaborate, to co-create. Software isn't just a tool anymore it's turning into a partner.

So the Whole tldr is - are we witnessing the slow death of isolated SaaS apps as they exist today? Will they all eventually fold into Agent Experiences? Are static UIs going the way of the fax machine?

Por que as grandes empresas de chatbots com IA não implementam uma estrutura simples para criar e organizar pastas?

Hi everyone,

I’ve been thinking about the current subscription structure and wanted to ask the community something.

Some users (like me) mainly use ChatGPT for advanced reasoning, deep analysis, structured thinking, and long-form problem solving. I don’t really use image generation, video tools, or other multimedia features.

Right now, higher-tier plans bundle everything together. That makes sense for many users — but I wonder if there’s a segment of people who would prefer a streamlined, reasoning-focused tier without media tools.

Not cheaper “because it’s missing features,” but simply more aligned with users who primarily value analytical depth over multimedia capabilities.

Would you personally use a reasoning-only tier if it existed?

Curious how others feel about this.

My Question is, what do you guys think about share intim details with Chat? Do you think, its possible to use LLM or maybe in the future Agents or other stuff as as psychologist? At the moment a lot of people already use him like this. I think its a chance, but you give a lot of personal stuff the big tech. I thinkt in the future with Open Source there is a chance, that a lot of people use it like this. I hope you share your opinion and we can discuss this. Thank You!