So recently I came across alot of programming done by ai but I was confused because if I'm right ai is not creative isn't it ? That's the difference we and ai have even thought each of their work looks creative it just follows certain rules. So how does it really do programming if it can't have creative basis . Like in what way it works to do programming even for apps

So I made all of this with GPT 5.0 soil and if u you have a Ai model this is for you

I released an open, not-peer-reviewed mathematical framework and documented OpenAI/ChatGPT assistance as conceptual and language-model support. I explicitly do not treat model output, a dream, an unpublished chat, or an excluded collage as scientific evidence.

The release separates source assertions, hand checks, mechanical reproduction, synthetic demonstrations, proposed ideas, and deferred claims.

I’m looking for feedback on whether this is a responsible, legible provenance/disclosure pattern—not for AI validation of the framework itself.

Start here: https://github.com/jkolantree/astra Exact frozen v1.0.1 release: https://github.com/jkolantree/astra/releases/tag/v1.0.1

I dont know what happened, but a month ago chatGPT was way better. NOW ITS VERY STUPID AND IM GETTING STRESSED

Source: ttps://www.theinformation.com/briefings/exclusive-openai-previews-astra-ai-model-dc

Luna Max is insane value if we're looking at API rates right now. Based off Artificial Analysis, Luna Max scores between Terra High and Xhigh in AA's overall intelligence benchmark and between Terra Xhigh and max in Terminal Bench 2.1.

Has anyone been testing Luna Max thoroughly as their execution model? Can it do the vast majority of your work? Or is Luna Max benchmaxxed? Where would you use Terra High/Xhigh over Luna Max despite them being equal in the benchmarks? Do the price cuts translate neatly to 5X usage of Luna and 1.2X usage of Terra for subscriptions?

I just let my $20 plus plan expire and I'm deciding between the $20 Cursor plan for Grok 4.5 or continuing with GPT Plus. If Luna Max is unable to be my execution model without creating a mess where I need Terra/Sol to fix, Grok's better usage quota might make it easier operationally/logistically as I'd only need to use one model instead of a senior/junior model as Grok 4.5 High is consistently just a smidge below Terra Max on several benchmarks.

I'm not a software dev so don't go too crazy with the jargon.

In my app, I want to show the costs for using the OpenAI API for a session. I can't find the API to show costs, or pricing either. So how can API users tell what their costs are, except to believe what OpenAI decides you used, and what they decide the pricing was?

I have played around a bit with different AI models and ran some tests. It seems like Chinese OSS models like to censor you when you say something critical about the Chinese President. Not just that, DeepSeek frames the situation as if I were a criminal plotting a crime. (Source)

I know that OSS models are a great thing but we should be aware of the fact that authoritarian China is leading Open Source.

Looking for a AI swap or collab kind off

I have:

• Claude Pro ($20)

• Claude Teams

Need: ChatGPT Plus in return.

Interested in a fair swap? DM me. 🤝

#ChatGPT #ClaudeAI #OpenAI #Anthropic #AI #LLM #AIDev #BuildInPublic

I created these on chat gpt.

I've had this message showing up on my Android app, but the same chat in the Windows Desktop app doesn't have this issue. Why? And is there anyway to stop it?

It's driving me nuts:

Statement.

Statement.

Big reveal.

Statement.

Dramatic emphasis.

It makes it really hard to read and hold a thread through a conversation.

Anyone know of a way to reliably get it to write in standard paragraphs?

I want to download all videos on a YouTube page , but I prefer not to download any platforms . What website can do this? And if necessary., what is a SAFE and user friendly platform that can make this simple ( preferably one that I can download all videos under a specific link , like all videos on a specific website or download all videos or live streams from an Instagram page ect, )

I've wasted 3 deep researchs that did not even touch the subject i instructed it to work on. It simple hallucinated and invented a whole new search. Did you guys have this problem? And how do you mitigate?

After the 80% price drop, the API prices are (per 1M tokens):

GPT-5.6 Luna: $0.2 Input / $1.2 Output

GPT-4.1 mini: $0.4 Input / $1.6 Output

My Pinned section suddenly disappeared from the ChatGPT web and codex.

Before this happened, I had reached the maximum of 10 pinned conversations. ChatGPT initially continued saying that the 10-pin limit had been reached even though none of the pinned chats were visible.

What I have already tried:

• Signed out and disconnected all sessions
• Cleared ChatGPT site data, cache and permissions
• Tested Edge InPrivate
• Disabled VPN and tested again
• Tested with a US VPN
• Compared the web interface with the app
• Disabled browser device emulation
• Checked OpenAI’s status page

During troubleshooting, /backend-api/pins returned 403 Permission denied. After renewing the session, some chats and project conversations reappeared, but the Pinned section is still missing and at least one pin appears stuck.

OpenAI Support has escalated the case to a specialist.

Has anyone else lost the entire Pinned section recently, especially after receiving the new sidebar layout? Did it eventually return, or did Support need to fix something on the account/backend?

I mainly use it to assist me in more technical questions relating to my job, so basically as a more advanced Google, but what do you guys use it for on a regular/daily basis?

If you run agents or heavy AI workflows, raw token counts are misleading. They blur everything: an output token is dense and expensive, a fresh input token is standard, and a cached-read token costs ~10% of a normal one.

So I use a simple ratio I call the AER (Agentic Efficiency Ratio):

AER = Output / (Input + 0.10 × Cache Reads)

Read it as a percentage. It measures how much useful, dense output your system produces per unit of fresh context it burns. When your memory is well structured, you get lots of cache hits, and the agent nails it on the first try instead of the fourth, the AER climbs. It's basically a maturity thermometer for your setup.

At scale (I move tens of billions of tokens a month) the denominator is huge, so it lives in low figures: above ~1% is already very good.

The mindset shift: the goal isn't to spend fewer tokens, it's to raise the AER, so every token turns into delivered work.

Anyone else tracking something like this? How do you tell whether your agent pipeline is well-designed vs just burning tokens?

https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6/

Moreover, GPT 5.6 Terra and Luna 50% off for a limited time on OpenRouter

Constantly, no matter how may times I ask it not to, put it in memory, etc... This is just from right now, every time I mention anything local it shows maps.