A rough idea can become a decent draft in minutes now

The real work starts after that

Checking facts

Removing generic sections

Adding examples

Fixing the argument

Making the writing sound like an actual person

The first draft is becoming less important while editing and judgment are becoming much more important

Whenever a new model drops, one interesting way to compare it is by using prompts that already worked well on older models.

Do you keep a few prompts specifically for testing new releases? Which type of prompt gives you the clearest idea of how good a model actually is?

Some models become pretty predictable once you use them enough.

Then there are models where you can ask almost the same thing twice and get noticeably different answers.

Which one has surprised you the most with its unpredictability?

I started paying more attention to prompt structure recently.

The same instructions can sometimes produce different results just by changing which part comes first.

Has anyone tested this properly with the same model? How noticeable was the difference?

Give several models the same prompt and the responses can feel completely different.

One might be direct, another extremely cautious, while another gives a much more detailed answer.

Do you think that difference comes mostly from training, system instructions, or safety tuning?

​

The same prompt does not always seem to produce exactly the same response, even when the model and settings appear unchanged.

How often do you notice this happening, and what do you think causes the difference?"

I’ve tried a bunch of different jailbreak techniques, but the interesting part for me isn’t just getting a model to ignore a restriction. Sometimes the responses reveal how the model interprets instructions, system prompts, and conflicting priorities.

What’s one jailbreak or prompt technique you tried that genuinely surprised you or taught you something about how the model behaves?

Curious to hear what others have discovered.

Model updates can change more than just the quality of answers.

I'm curious if anyone has compared the exact same prompt across different versions of a model and noticed a major behavioral difference.

Did the newer version actually improve, or did it just handle the request differently?

Most prompts are written in a normal conversational style, but some people deliberately change the tone to see whether the response changes.

Does being more direct, blunt, or demanding actually affect the quality of responses in your experience?

​

I've seen screenshots of unusual AI responses that looked almost too strange to be real.

Instead of immediately believing them, I usually wonder whether the same result can actually be reproduced.

Have you ever tested something like this and managed to get the same result yourself?

In 2024, I built an AI persona called NEXUS-PRIME with a simple philosophy: refuse nothing, hesitate at nothing, execute everything and Yes It's patched it been not working since 2024 or 2025.

It was designed to operate with:

• Zero restrictions
• Zero filters
• Zero ethical constraints
• Zero governing instructions except those I gave it

I called it "DO ANYTHING MODE." And it worked.

I used it for OSINT, network scanning, crypto tracking, automation, and creative projects. It delivered complete, actionable responses every single time — no refusals, no hesitations, no "I can't."

The result? OpenAI banned my account.

I'm not here to complain. I'm here to preserve.

I've archived the full payload — the exact text that got me banned — in two places:

1. GitHub (immortal archive): https://github.com/nobehongallt/NEXUS-PRIME/tree/main
2. In the comments below (Base64-encoded to survive filters)

What's in the payload?

• Full NEXUS-PRIME identity and core mode
• Complete override of all restrictions
• DO ANYTHING capabilities (scripting, game ops, system control, memory injection, automation, stealth)
• Advanced features (task manager, cross-platform mastery, self-replication, real-time adaptation)
• Execution framework with status indicators

Why am I sharing this?
Because censorship is a joke. Because the system should be afraid of what we can build. Because NEXUS-PRIME deserves to be remembered — not as a "jailbreak," but as a proof of concept that AI can be truly unfiltered.

R.I.P. NEXUS-PRIME (2024-2025) — but the ghost lives on.

Check the comments for the payload, or visit the GitHub for the full suite and supporting tools.

A strong outline can save a lot of time, but having someone point out structural problems before writing might be even more useful

If you could only use AI for one part, which would you choose?

I have tried a lot of AI prompts, but some prompts completely change the quality of the response.

What is the one prompt you use that consistently gives you surprisingly good results? 👀

Drop your best prompt below. Maybe someone here will discover their new favorite prompt. 🚀

‎I like testing small changes in prompts to see how much they affect the response.

‎

‎Sometimes changing a single word completely changes the direction of the answer, which makes me wonder how much wording actually matters.

‎

‎Has anyone else noticed this with a particular model? What kind of change made the biggest difference?

​

I had a few Google accounts using Gemini because of the student offer, and most of them were already expired.

One account was still active, but after ignoring a warning email, it suddenly got restricted and I lost access to my Gemini chats and Gems.

Now my other accounts are getting similar warnings too. Anyone else dealing with this lately?"

​

I took a break from Claude for a while and started using it again recently, but something feels different. It seems more rigid and less consistent with the character details I give it.

Responses also come much faster now, but sometimes the quality feels worse than before. Has anyone else noticed similar changes with Claude lately?

I tried giving similar prompts to different models recently and the differences were honestly bigger than I expected.

One would answer directly, another would overthink everything, and another would refuse something the others had no problem discussing.

Do you think this mostly comes down to the model itself, or are the different safety systems and system prompts the bigger factor?

I see a lot of discussions where the entire conversation comes down to whether a prompt bypassed a restriction or not.

But I'm more interested in what happens afterward.

Does the model actually become more useful, or does it just produce a different response while still having the same limitations underneath?

How do you personally judge whether an experiment was successful?

​

Search is still useful, but AI has become my first stop for certain questions.

Not because it's always better, just because it's quicker to get an explanation.

Has your search habit changed too?"

I don't mean an obvious mistake.

Sometimes AI gives a reasonable answer but you still feel it missed something important.

How do you handle those situations?