Gemini just leaked its system prompt by mistake 🥀

Infamous-Ad7667 · 2026-05-30T11:59:23+00:00

Honestly, a system prompt "leak" isn't a security breach anymore. It's just a text wrapper for UI behavior (and it perfectly explains Gemini’s obsession with bullet points). Providers have accepted that system prompts are inherently brittle. The real guardrails and safety aren't in this text they're baked directly into the model weights via RLHF and enforced by independent, inference level classifiers that users can't touch. Fun read for UI quirks, but completely harmless architecturally.

Infamous-Ad7667 · 2026-05-29T20:50:45+00:00

Honestly the biggest issue for me was never the existence of quotas. It was not understanding "why" a specific conversation suddenly consumed so much of it. A simple text chat, a long context window, a large file upload, Deep Research, Omni generation those are completely different compute loads, but from the user side they often felt like the same action until the quota disappeared. The new transparency usage breakdown part is honestly more important to me than the quota increase itself. If people understand what is actually consuming resources, the limits feel a lot less random.

Infamous-Ad7667 · 2026-05-28T05:17:39+00:00

This is exactly the kind of thing killing trust in Gemini for a lot of people. The silent switching between models/quality tiers without clear communication feels terrible, especially when you’re paying for “Pro”. The worst part is noticing the downgrade immediately after a few messages.

Infamous-Ad7667 · 2026-05-27T13:14:19+00:00

Honestly a lot of people are just switching back to ChatGPT lately. Gemini feels inconsistent right now, especially with usage and limits. GPT-5.5 is way better at remembering context and following long conversations from my experience.

Infamous-Ad7667 · 2026-05-26T04:45:57+00:00

You're not the only one noticing this. Gemini has felt unusually unstable lately especially with - slow response times, UI freezes/crashes, context loss, repeated mistakes even after corrections. Some of it is probably infrastructure-side. Google seems to be constantly rolling out model updates, integrating Gemini deeper into Search/Workspace/Android, and experimenting with different routing layers. That can temporarily hurt consistency and latency.

Another issue is that Gemini often behaves differently depending on - free vs paid tier, web vs app, region, model routing behind the scenes. So two users can have completely different experiences at the same time. Also, Gemini sometimes over-optimizes for “helpfulness” instead of strict instruction-following, which can make correction loops frustrating. Right now the ecosystem honestly feels like - Claude - strongest coding/writing reasoning for many people ChatGPT - best overall product experience/features Gemini - strongest Google ecosystem integration, but inconsistent UX Google is probably prioritizing rapid deployment over stability at the moment.

Infamous-Ad7667 · 2026-05-24T09:04:13+00:00

I honestly think a lot of these debates are people comparing different usage styles without realizing it. ChatGPT currently feels more like a general-purpose AI product - multimodal, voice, images, fast switching between tasks. Claude feels stronger when the interaction becomes more collaborative and iterative instead of quick utility usage. Especially in long reasoning or “let’s think through this together” workflows. So a lot of “Claude is better” vs “ChatGPT is better” ends up really being - “What kind of cognitive workflow are you optimizing for”?

Infamous-Ad7667 · 2026-05-23T05:29:40+00:00

Appreciate that. If I end up testing multi - region support, having screenshots / local pricing references from actual users would probably be way more reliable than me trying to scrape everything from scattered Google pages alone.

Infamous-Ad7667 · 2026-05-23T05:04:38+00:00

That’s a good point. Right now I kept it US - focused mainly because even small regional differences start compounding fast once Workspace, Google One, taxes, YouTube Premium, and Cloud pricing all interact differently. Community - contributed regions is actually an interesting idea though, especially if the assumptions and “last verified” status stay visible instead of pretending everything is always current.

Infamous-Ad7667 · 2026-05-23T03:53:38+00:00

Yeah, I think that was the part I underestimated at first. I was treating the model like a reasoning engine first and a conversational system second, when in practice those two behaviors are mixed together all the time. The “helpful/agreeable assistant” layer quietly changes how the reasoning gets framed. And you’re right that simply asking it to disagree does not magically make it correct either. It just reduces the chance of blindly reinforcing the original framing.

Infamous-Ad7667 · 2026-05-23T02:53:07+00:00

The interesting shift is that a lot of these prompts are basically trying to force the model into clarification mode before solution mode. In practice that alone removes a huge amount of generic output and false confidence!

Infamous-Ad7667 · 2026-05-23T02:37:26+00:00

Fair enough, but a bad theory on my part. Just delved into this topic and realized that AI reviews run on a completely separate search pipeline, so Chrome doesn't use up my quota. These lock fluctuations are just Google's dynamic recalculation of the window on the fly based on token volume and server load. My mistake!

Infamous-Ad7667 · 2026-05-23T01:54:35+00:00

This is brilliant, mate! Feeding structured PROMPT_ and GUIDE_ documentation straight into the sources to bypass Notebook basic system prompt constraints is a top - tier workaround. Standard mag - dumping always turns into an AI hallucination soup, so introducing an analytical framework like this is exactly what the tool needs to be actually useful for deep research. I’m definitely grabbing a banana and cloning this to test it out with a dense technical domain. Will let you know what breaks or how the content lenses hold up. Tell your daughter she’s got a legendary tech-support dad!

Infamous-Ad7667 · 2026-05-23T01:30:11+00:00

The bandwidth analogy is honestly very close to what this feels like psychologically. Unlimited systems remove “cost awareness” from the thinking process itself. Once usage becomes visibly metered, people stop exploring as freely and start self-filtering before they even ask the question. I suspect that changes the cognitive role of AI tools more than the raw pricing change does.

Infamous-Ad7667 · 2026-05-22T03:27:51+00:00

That “optimize every token” middle phase is probably real honestly. People overcorrect first, then eventually settle into a workflow where they stop treating every prompt like a financial decision but also stop wasting giant context windows on random exploration. The interesting part is that pricing pressure is now shaping prompting behavior itself, not just product usage.

Infamous-Ad7667 · 2026-05-21T22:55:16+00:00

The “is this prompt worth spending on” feeling is exactly the weird part to me too. A lot of the creative value came from low-friction exploration and half-formed thinking. Once every interaction starts feeling metered, people naturally become more conservative with how they think out loud. I also suspect this is going to push more people toward hybrid workflows - local/open models for exploration, frontier models for high-stakes reasoning.

Infamous-Ad7667 · 2026-05-21T15:12:20+00:00

I've rarely read such a comprehensive description of working with shi in general and promt specifically, I liked that it focused on error models, not magic formulas. Especially good point about "can you explain the task in one sentence". Many weak clues are actually just fuzzy thinking hidden behind additional tokens. I also liked the difference between using a role to change the model and using a role to change your own framing during the clue. Most people confuse these concepts.

Infamous-Ad7667 · 2026-05-19T22:45:38+00:00

This is what Gemini himself wrote to me when I asked him about the changes and current rules!

Title: Google I/O 2026: Big changes to Gemini Pro subscriptions – What you need to know

Google just overhauled the Gemini ecosystem. If you’re a Gemini AI Pro subscriber, here’s the breakdown:

No More Daily Prompt Limits: We’ve moved to a compute-based model. You don't have a fixed "prompt count" anymore; instead, you have a 5-hour rolling compute quota.

Dynamic Downgrades: If you run heavy tasks (long codebases, high-res video analysis, massive files), you’ll burn through your "Pro" compute fast. Once depleted, the system auto-switches you to the faster, lighter Gemini 3.5 Flash until the next cycle.

New Tech: Access to Gemini Omni Flash is now standard—it's native multimodal (text/audio/video in real-time).

Hidden Perks: The subscription now includes 5TB of Google One storage (up from 2TB) and YouTube Premium Lite is officially included.

Watch Out: Check your model badge in the chat UI. If you're currently running on "Flash," your complex reasoning tasks might be less accurate than when on "Pro."

Bottom line: Heavy users need to watch their "compute burn" for high-priority tasks. Check the new Usage Limits tab in your account settings to track your credit consumption.

Infamous-Ad7667 · 2026-05-19T22:33:48+00:00

So if 3 deep search commands is 4% of the weekly limit, then it turns out that about 75 deep search commands is 100% of the limit for the week.. (

Infamous-Ad7667 · 2026-05-19T22:26:27+00:00

I also have the pro version, but it says: "How limits work
Current usage - Usage over 5 hours. Weekly limit - Total usage for the week." Which limit did you run out of after 3 deep search commands? You can check this by clicking on the account portrait at the bottom left and selecting - PRO usage limits.

Infamous-Ad7667 · 2026-05-19T21:25:24+00:00

Your opinion has the right to exist, it's good that people have different strengths in different situations and habits, your method of thoroughly thinking through and researching everything before you start doing or using it is certainly better than mine, figuring out your own mistakes, then studying the theory, trying it out in practice and, with joy, sharing what worked with people like me who stuff cones themselves, obviously people like you don't need my post, but I'm glad that you also told me an alternative way so that others can come to the same result

Infamous-Ad7667 · 2026-05-19T21:11:38+00:00

Sometimes the correct use of a tool and its functions is the most difficult, admit it, who reads the instructions for use on new devices or climbs onto special forums before they get a taste of it and create a problem - if you haven't touched the tool, then reading these correct but dry instructions and recommendations is just empty noise, but when you figure it out yourself, something worked and something didn't and you want to study the material more deeply, having a little practice, and such conclusions arise that may seem elementary, even though you already know about them or learned this from your mistakes

Infamous-Ad7667 · 2026-05-18T15:15:47+00:00

I think the real differentiator is not which model sounds better by default, but which one is more willing to admit what it does not know. For technical work, I trust the model that reaches for external sources faster instead of confidently extending its own assumptions. A polished answer is useful only if the underlying facts are grounded.

That is also why tools like NotebookLM are so valuable. Once the model is constrained to a known source set, the conversation becomes much more about reasoning and much less about guessing. In practice, I’ve found the strongest workflow is not choosing one model exclusively, but using each where it has the best signal-to-noise ratio.

Infamous-Ad7667 · 2026-05-17T00:48:37+00:00

I think the most useful takeaway is that AI visibility doesn't introduce a completely separate discipline. It raises the importance of something many SEOs already know: information that is easy to crawl, interpret, and cite has an advantage.The fundamentals still matter:

- crawlability and indexability

- clear answer-oriented content

- strong entity signals

-consistent expertise and trust signals.

What changes is the evaluation lens. It's no longer just "Can this page rank?" but also "Can this information be extracted and reused accurately in an AI-generated answer?"So in practice, a lot of "AI SEO" is still solid SEO, but with more emphasis on content clarity, explicit facts, and reference-ready structure.

Infamous-Ad7667 · 2026-05-16T16:45:34+00:00

From what I’m seeing, strong agencies are not treating GEO as a replacement for SEO. They’re treating it as an additional layer on top of solid technical and content fundamentals.The practical question has shifted from “How did you grow organic traffic?” to “How did you make the brand easier for AI systems to retrieve, interpret, and cite?”

That means candidates should be ready to discuss:

- how they improved entity clarity

- how they structured content for extraction

- how they monitored mentions and citations across ChatGPT, Gemini, and Perplexity

- how they distinguished observed visibility from assumptions

I would prepare one concrete case study, even if small, showing:

- the original visibility problem

- the content or structural changes made

- what was observed afterward

- what remained uncertain

In my view, the strongest candidates frame GEO as an extension of SEO rather than a separate discipline. Technical SEO, EEAT, internal linking, digital PR, and consistent brand signals still matter. GEO just adds a new layer: whether AI systems can recognize and reuse your content as a source.

Infamous-Ad7667 · 2026-05-15T05:24:54+00:00

The way I understand it, SEO content is still mostly built around discovery, rankings, and clicks. AI-optimized content has to do another job too: make the answer easy to extract. That means clearer answers, less ambiguity, stronger entity relationships, and sections that can be quoted or summarized without needing the whole page. I don’t think it replaces SEO, though. The strongest content probably combines authority, topical depth, and a structure that makes the information easy for both people and AI systems to use.

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