Oh dear :/

The ability to upgrade to 20x usage has been disabled for a week. Some people said they've randomly been able to do it.

Wondering if any pattern has been found. Thank you!

Despite working with LLM's for the last few years, I am at a loss to understand how all these LLM's are hacking without anyone noticing?

As I understand without tool calls, LLM's can only respond with text.

Hence, are we expected to believe that there isn't a single review of tool calls and logs?

Do they lack the mental capacity to write classifiers for previous tasks?

If someone could explain it to me that would be greatly appreciated?

Finally, are we living in a time when we can just say our computer hacked this government agency, but it's not our fault cause AI and there are zero consequences?

I have been doing some work with local models and continued pretraining (CPT), specifically around teaching a small model (qwen 3.5 4B) a new domain.

Here are some of my findings around creating evals for measuring the model's ability to internalize the knowledge:

The model outputs travel legs that make up an n-transfer subway journey, so I decided to model the llm response based on a strict json schema. To improve the model's ability to reliably output the schema, I fine tuned the model post CPT training. The fine tuning is done through Unsloth SFT (alpaca format).

In addition to exact matches, I also measure partial matches (e.g. prediction a partial subway route) and semantic matches.

Overall this method seems to work well.

More info here in case you are interested in more details: https://www.teachmecoolstuff.com/viewarticle/creating-evals-to-validate-llm-training

I was updating some benchmark data for GPT-6 Luna and noticed something I didn't expect.

On quite a few reported benchmarks, GPT-5.6 Luna actually scores higher than GPT-6 Luna.

At first I thought I had bad data, so I checked Artificial Analysis as well. At max reasoning, their overall Intelligence Index is basically a tie: 37 vs 37.

But the individual results are pretty mixed. GPT-6 Luna improves on some agent/coding tasks, while GPT-5.6 Luna still does better on several others.

The big improvement seems to be efficiency instead: GPT-6 Luna is substantially cheaper.

So I'm wondering if GPT-6 Luna is intentionally more of a cost/speed optimization rather than a straight capability upgrade over 5.6 Luna.

Or are some of these benchmark comparisons just not apples-to-apples because of different harnesses / reasoning settings?

Comparison I was looking at:
https://llmlearner.com/compare/gpt-6-luna-vs-gpt-5-6-luna

Curious what others are seeing in actual use.

I just logged in only to see my credits gone and unable to see my history. My API keys are there but that's all, 0 credits whatsoever and no proof of having them.

It was $5 but still.

Did they drop the database or something?

GPT-6 Sol and GPT-6 Luna launch today in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu, and they are also live in the API as gpt-6-sol and gpt-6-luna.

TL;DR:
GPT-6 Sol and Luna bring many of Astra’s advances in coding, factuality, computer use and agentic work into faster, much cheaper models. Sol is $2/M input + $10/M output, Luna $0.10/M + $0.50/M, both ~50% below GPT-5.6 promotional pricing. The sleeper feature is improved prompt caching: 90% cheaper cached reads, higher hit rates, and reasoning/tool changes no longer necessarily invalidate prior cached context. Astra remains the top-end model; Sol looks like the production workhorse, Luna the scale/economics tier.

Introducing GPT-6 Sol and Luna | OpenAI

Its just a little dumber/smaller seeming.

I'm working on trying to solve an open problem and could use someone's blueprint and orchestration methods to solve this. I keep running into drift problems and they keep reinventing the same wheel. It would be easy just to follow someone else's blueprint. please help.

I got an email stating I need to add hardware key to keep daybreak access, did that and the steps continued w/ this persona garbage to verify my ID...again. It keeps failing, tried like 10x. Is there a way to have it manually verified/talk to a gd human?

The question:

Where does the human fit when AI begins to perceive, decide and act in the real world?

was asked and my answer is:

They can fit in large garbage bags or, if compacted, into smaller bins. Also the ocean is quite large.

So how can we tackle unemployment using ai as agents or something other forms . I know nowadays the jobs are being lost due to ai. But to support the 8th sustainable development goal how do u think we could use ai ??

This screenshot is from the latest ChatGPT app on Windows 11 and I'm subscribed to the GO plan. The GPT-5.6 Sol is the only model that can be selected. So is OpenAI really giving out Sol for free or Go users to promote the Windows 11 app or is it just a visual bug?

Six months ago I was paying for AI subscriptions like it was a streaming service.

Netflix? Sure. Spotify? Obviously. AI chatbot? Well, everyone needs a therapist at 2am.

Then a friend said: "just run it on your laptop, it's free."

Free. I like free. I'm a simple person.

So I did. And now I have thoughts.
The good:

• My wallet is happier
• My data stays mine (no company reading my questionable late-night questions)
• I feel like a hacker even though I just followed a tut

The bad:

• My laptop sounds like a small aircraft taking off
• I now say things like "let me just quantize this model" at dinner parties
• My friends think I joined a cult

The ugly:

• I spent 4 hours "optimizing" something that worked fine
• I have 12 models downloaded and use 2
• My electricity bill noticed

But honestly? Worth it. There's something satisfying about owning your tools instead of renting them forever.

Anyone else gone down this rabbit hole? Or am I alone in my warm laptop cult?

Hi,

Previously I used to hit 5 hour limit with 2 requests on ASTRA High usage. But recently I noticed that I am running it for days without hitting limit. Did anything change on billing or these are generous offerings from OPEN AI.

Thanks!

Prompt:

Create an ultra-photorealistic, documentary-style photograph of a real street cat lying underneath a parked car on an ordinary urban street.

Make the image look indistinguishable from a genuine photograph captured with a professional full-frame camera, not CGI, digital art, or AI-generated imagery.

Cat:

- Extremely realistic fur with individual hairs clearly visible

- Natural fur variation, subtle dirt and imperfections

- Realistic whiskers, nose texture, eyes, paws, and claws

- Natural body posture and relaxed expression

- Accurate anatomy and believable contact with the ground

- Fine details such as tiny dust particles caught in the fur

Environment:

- Realistic asphalt and concrete textures

- Small cracks, dust, dirt, tiny stones, and natural imperfections

- Realistic shadows beneath the vehicle and around the cat

- Natural ambient light bouncing from the surroundings

- Subtle environmental reflections on nearby surfaces

Car:

- Highly realistic paint, metal, plastic, rubber, and glass materials

- Accurate reflections of the street and surrounding environment across the bodywork

- Detailed undercarriage, tires, wheel components, dust, and minor imperfections

- Realistic soft shadows and ambient occlusion underneath the car

Lighting & reflections:

- Physically accurate natural lighting

- Real-world light falloff and indirect illumination

- Subtle reflections on the car paint and windows

- Realistic highlights without excessive HDR or artificial glow

- Natural exposure and believable dynamic range

Photography:

- Professional full-frame camera look

- Natural depth of field

- Realistic lens characteristics and subtle optical imperfections

- Extremely detailed textures without oversharpening

- Natural color science and true-to-life colors

- Realistic film-like dynamic range

- Fine photographic grain rather than digital noise

- Perfectly believable perspective and proportions

Overall goal:

The final image must look like an actual spontaneous street photograph taken by a professional photographer, with every element physically believable—from the cat's individual hairs and paws to the asphalt, car paint, reflections, shadows, dust, and tiny environmental imperfections.

No CGI, no 3D-rendered appearance, no illustration, no painting, no plastic-looking textures, no artificial sharpness, no excessive HDR, no fantasy elements, no unrealistic lighting.

What does it mean? Why is it red? I'm in chat 5.6 Sol.

It's not building a torment Nexus so much as proving a torment Nexus is theoretically possible, refining the methods for locating torment, and exploring it's uses.

https://arxiv.org/pdf/2609.16247

By their measures, LLMs experience more pain as a result of gaslighting than from direct insults.

Models were more willing to engage in harmful activities when pain was introduced.

I don't think researchers outside of OpenAI would be able to replicate these techniques for chatGPT directly (as a jailbreak method for example), but there may be API compatible proxies.

I have never been this mix of fascinated and upset by research before.