r/GoogleGeminiAI

A while ago, I used to write short posts about art online.

I didn’t think about art in a very academic way. I just felt that art shouldn’t only belong to rich people, museums, or people with professional training.

Sometimes a song, a painting, or even a simple object in daily life can comfort someone, especially when life feels difficult.

Now AI can generate images so fast, and some of them really do look beautiful.

But this makes me a bit confused.

If everyone can make beautiful images with AI, then maybe beauty itself is not enough anymore.

Maybe the more important question becomes: what is the person trying to say?

Did they have a real feeling behind it?
Did they make a real choice?
Or did they just type a prompt and pick the most impressive result?

I don’t think AI will destroy art. But I do think it may make us rethink what counts as art.

Maybe there will be different levels of AI art in the future. Some will just be decoration. Some will be made for attention. Some may still carry real human experience, even if AI helped make it.

I’m still not sure where the line is.

Can AI art still feel real to you if the human behind it has a strong idea? Or does the use of AI already make it feel less valuable?

I have built my workflow around Gemini Pro and NotebookLM to analyze massive project documentation, complex personal records, and long transcripts. It was incredibly effective.

But with the new compute-used limits, I burn through the quota after analyzing just a few documents or a long dialogue, and I hit a brick wall.

If Google’s strategy was to attract users with a large context window and then severely restrict it to force API upgrades or expensive add-ons, it’s going to backfire. I need deep context memory and stable analysis, but it has to remain accessible. If this isn't restructured fairly, I'll be forced to look for alternatives.

Is anyone else experiencing similar problems because their document workflows are ruined? 😕

https://preview.redd.it/kc0hzoaoxh2h1.png?width=1177&format=png&auto=webp&s=d7ea42d7504acc37a664bc860ce3196531a4e367

I switched to Gemini because of OpenAI's association with the US military (not from the US), but now that they've (Google) introduced usage limits, I'm switching again lol. Who are you guys switching to? Should I switch back to ChatGPT, or move to Grok (jk, I don't support Elon Musk)?

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars.

What I built

phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers):

1. Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes
2. Pulls star and fork events from the last 24 hours per repo
3. Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history)
4. Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%)
5. Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window
6. Files an issue directly on the targeted repo so the maintainer knows what's happening

Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended.

What the pattern actually looks like

It's remarkably consistent. A fake engagement campaign in the raw data:

• 40-200 accounts, all created within the same 1-2 week window
• Zero original repositories, or only forks they never touched
• No bio, no location, no followers, no following
• All of them starring the same repo within a 90-minute window
• The target repo usually has a name implying it's a tool, hack, executor, or generator

Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast.

Notifying the affected repo

When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first.

Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently.

Why I built this

Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected.

It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/

The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users.

All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq.

Repo: https://github.com/tg12/phantomstars

Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process.

Questions welcome on the detection approach, GraphQL batching, or campaign ID stability.

Is anyone else completely disgusted by Gemini's new "compute-based" usage limits? I was honestly more than happy to pay for the subscription because of its flexibility, but what is even the point anymore if you run up against a 5-hour cap within just 2 to 3 complex prompts? You literally cannot finish a single train of thought or coding session without getting choked out by a heavy compute tax, unless you want to pay a fortune to upgrade. But honestly, maybe that’s exactly the point: gatekeep these advanced AI tools so only the privileged can actually afford to utilize them to keep pace. By pricing out the average user and locking real utility behind massive paywalls, they are actively widening the gap—virtually ensuring a future generation of technological indentured servants who stand zero chance of keeping pace with the wealthy.

I’m on the Pro plan and honestly, I’m pissed.

Just one single prompt that includes referencing my NotebookLM + Google Doc already ate 47% of my current usage. One prompt. Not even a long one.

This is the part that makes me the angriest. Gemini’s biggest selling point is supposed to be its ecosystem. It works seamlessly with Google Docs and NotebookLM. That’s literally its strongest feature. But now using that strength is what destroys your quota the fastest? Are you kidding me?

I don’t sit in front of Gemini all day. Most of us don’t. I have a full-time job. The only time I can actually work is after I get home, shower, eat, then I have maybe 2-3 hours before I need to sleep. That’s my only window to finish freelance projects and make extra money.

Before this limit, I could sit down and finish one project in a single sitting. Now? One prompt + document reference = almost half my quota gone. I can only do 2-3 prompts max and then I have to wait 5 hours for it to reset. By the time it resets, it’s already midnight and I have to wake up for my real job the next day. So now I have to split my work across multiple days.

How the hell am I supposed to work like this?

This isn’t just inconvenient. This limit is actively punishing the exact people who want to use Gemini’s ecosystem properly. If Google isn’t ready to let paying users actually work efficiently with their documents, then they shouldn’t be selling the Pro plan at all.

Anyone else in the same boat? Especially freelancers or people with limited evening hours?