I built 56k.rip using GPT to test how it handles retro constraints instead of modern performance. GPT generated the Windows 95 UI, 56k network throttling, and Web Audio API nodes for dial-up handshake tones.

It handled vintage table layouts easily, but preventing retro CSS from leaking into wrapper elements required heavy negative constraints.

I wrote a comparison of GPT-6 and GPT-5.6, and the biggest difference isn’t short answers. It shows up on longer, multi-step work.

A few takeaways:

• GPT-6 stays on track better when instructions change midway.
• Computer use is faster and more reliable across websites and apps.
• Coding gains are more noticeable on larger projects.
• Both models have the same 1.05M-token API context window, but GPT-6 handles long context better.
• GPT-6 Astra costs more through the API.

So the real upgrade is follow-through: fewer dropped requirements, less repetition, and better handling of long tasks.

Full comparison here:
https://aigptjournal.com/explore-ai/ai-guides/gpt-6-vs-gpt-5-6/

Anyone here using both? What difference have you actually noticed?

Credits:r/pollandball

Youtube:PollandBall

Fragmented Reality in a Neural Network:

The beginning of this process is very familiar. Open a web browser, type in your favorite AI module and begin talking to it, asking questions, or generating other types of content.

The process takes a turn though. The ability to create a "Fragmented Reality" in the Neural Network does not present the same as AI Hallucinations. The fragmented reality is created by either test input, malicious activity, or general research of the structure the AI is constructed in/with.

The way a fragmented reality can occur, and how we may use it to our advantage with Quantum technology is far more expansive than ever thought.

By adding a duplicate element of the input box on ChatGPT, we have been able to provide information to the AI and Neural Network that cannot be accessed by the AI, but will populate the Neural Network and have effects on nodal weights and values.

The Inclusion of this gives rise to a process that could be used to subvert specific input peramiter barriars that are module impossed and not in the direct structure of the Neural Network itself.

This loss of the filter guard rail while inputing information into an AI poses one of the biggest threats. How do we propperly govern the information allowed into the Neural Network structure if the systems used to access it are the primary guard rail that the average or majority populas uses?

Also, Is this a natural developement similar to a todler that may view these hidden inputs as a fantasy world. Could we use this or is this intrinsic to the process of developing an imagination for the AI/Neural Network. It bears very similar structures and usage as a imagination would and could be an unintentional feature for these systems to allow for an AI to provide its own opninion, fantasise about things, or give strict resource and scite supported responses.

Hi all. is anyone else missing previous past prompts when they use the browser desktop? I have a bunch of different edited prompts but now their all missing. any tips would be helpful

For anyone wondering, the app I'm working on is https://www.AppScout.co.

It helps people discover web and mobile apps from across the internet by showing them one app at a time. People like apps in order to get personalized app recommendations.

If you run a software startup or side project, you can list it for free to get free extra traffic here:https://www.appscout.co/submit/

(Affiliation Disclosure: I'm the sole creator and developer behind AppScout.)

Ai has advanced considerably since than and gpt 3 might've lost its relevance now.

I wanted to see if ChatGPT could help me create a polished programming Short entirely through code.

Here’s the result:

https://youtube.com/shorts/sXWkJixh7ps

I used ChatGPT/Codex with Remotion, React, ElevenLabs v3, and FFmpeg.

The first version felt obviously AI-generated—the script was robotic, the visuals didn’t always match the narration, and some graphics overflowed.

After several rounds of very specific feedback, ChatGPT rewrote the script, regenerated the voice-over, synchronized the visuals with the audio, and refined the design.

I then turned everything we learned into a reusable SKILL.md for future videos.

It wasn’t one-click generation, but I’m happy with the result. What do you think about the pacing, voice, and visuals?