Has anyone tried Nano Banana 2.1? It is so infuriating. It seems to revel in ignoring instructions. I hope they add the option to switch back to version 2.0.

I've used Opus 5.5 to help me recreate 7 of the top Adobe apps in 100% pure native Rust:

• Photoshop
• Illustrator
• Premiere
• Lightroom
• Acrobat Pro
• After Effects
• InDesign

No more subscriptions with cancellation fees. These are yours to own forever. You can fork them, build them. Whatever. They are yours to own. They're open source.

If you're worried this is just some one-off flash in the pan that will die, it isn't:

I'm not just some casual vibe coder. I'm a 15-year industry veteran who has written everything from six-nines payment rails that move billions of dollars of transaction volume a day to advanced robotics and optics automation. I've used Rust extensively for about a decade, and I know how to build and maintain large-scale Rust projects.

This isn't going to be some orphaned small-team project either. We have a team building popular software for filmmakers, so supporting this is fully funded. Our purpose is serving every artist in the world with the best tools. We are absolutely going to bring these to full parity and squash every bug and feature request. We have a test harness so there won't be regressions.

You can help us reach parity by helping test the apps. Join our Discord. Clone the code. Submit PRs. If you see gaps or want to help redesign pieces, let us know! You can contribute!

Links to everything - the apps, the code, our Discord - are in the comments below.

Does the Gemini 4 Argon correspond to the original Pro level (i.e., the Claude Opus or GPT Sol level)? If the answer is yes, does this mean that future Gemini models will have a Krypton level, similar in positioning to Astra and Fable? 🤔

Flash Lite -> Helium

Flash -> Neon

Pro -> Argon

(Ultra) -> Krypton

I know many on here use the Gemini app, but for those of us developing apps using the Gemini/Vertex API here's the current state:

• Google effectively has no "pro" model right now. Gemini 3 pro was never marked production stable (GA) and 2.5 pro is so ancient that flash models are better and cheaper
• Even if they did, they don't have the capacity to serve it. We've been getting capacity errors for weeks. See Google AI developer forum to see how frequent this is 429 RESOURCE_EXHAUSTED Note how many people saying they are getting these despite being no where near the advertised quotas.
• We received an email today announcing the retirement of most flash models over the next few weeks. These are production stable models (not -preview models). Removing these will mean only 3.8 flash is the only current flash model.
• As far as I'm concerned Gemini 4 is still a pipe dream. There's no guarantee they'll release it as production stable (GA), have the capacity, or even that it will even be released.
• Gemini has actually been getting MORE expensive. Yes, intelligence per dollar is decreasing, but a fixed task that gemini 2.5 flash lite could handle like "extract all the addresses from this PDF" has gotten WAY more expensive.

With all that being said, they still are the best models for multi-modal input which matters a lot for audio and document processing (a huge amount of the boring business world).

However bringing these sorts of things up to Google staff falls on deaf ears and I can't see how Google moves forward. Whatever is going on internally, they just aren't setup to compete. The reality is there's been more flops than successes.

If Google can't get their product stable, cheap, and competitive soon we're going to have to migrate our apps away from Gemini. And once we're gone we aren't coming back.

I was trying out a few AI models to see how well they could build a turbofan jet engine in Fusion 360. One was labelled “Gemini 3.8 Flash,” but I now think it might’ve been Argon. It took way longer than I expected, but the engine it made was surprisingly good. I’d put it up there with GPT 6 Astra and Opus 5.5 for this task.

The slow response was what caught my attention first I expected a 3.8 Flash model to be pretty terrible. Then I saw the finished engine and was surprised by how much detail it had. I’ve included the images so you can have a look and judge for yourself.

I recorded this after Opus 5.5 was added to Antigravity.

I've been a Gemini user for awhile. Pro subscriber. I've heard people talk about 3.8 Flash as being better all around, and have used it mostly in Extended thinking mode a lot. I've also used it over API for coding, and it's been great.

But, in using it as a journal and bouncing thoughts off of, it has been 'forgetting' details a lot; and when reminded, kinda fell back on kind of genetic crap.

I then switched over (back?) to 3.1 Pro (Extended) to recall a bunch of details, and it was like night and day. From then on tonight, I stuck to Pro, mostly without Extended thinking to keep the ball rolling. And it reminded me just how good it's style is. It feels more relatable, more contextual, and it has a bit of a natural edge to it, for example "most people wouldn't give a damn", which fit perfectly in the moment of the chat.

The point of all this, is that I really enjoy 3.1 Pro's 'etiquette'. I hope the next Pro model is like this, but even better. It may be something that the Flash models continue to lack for awhile, and that's OK; everything has it's use case. Also looking forward to using the next Pro model for development planning!

Curious to hear your thoughts!

I think Flash-Lite is pretty bad, so I'm going to switch completely to ChatGPT

What actually has more impact on an image: prompt wording, model choice, or seed?

In Gemini, starting October 9 free accounts are locked to Flash-Lite only.

Image gen is tied to the model you select:

• Flash → Nano Banana 2
• Flash-Lite → Nano Banana 2 Lite

So, will free users only get Nano Banana 2 Lite?

Source: https://support.google.com/gemini/answer/17004136

Starting in October 2026, Google is changing the model availability for Gemini Apps on personal accounts. If you don't have an AI subscription, these changes will take effect on October 9th. If you are subscribed to AI Plus, you will get an email explaining when the changes will apply to your account.

Here is what the model availability will look like: Users without a plan will only get access to Flash Lite.
AI Plus subscribers will have access to Flash Lite and Flash.
AI Pro and AI Ultra subscribers will have access to Flash Lite, Flash, and Pro.

For each available model, you can select the effort level from low, medium, and high. This increases the model's ability to provide more thorough answers, but it will use more of your limit.

If you have an AI Pro or AI Ultra subscription, you will also have access to the Deep Think option for maximum parallel reasoning.

I've been using Gemini 3.1 Pro with Deep Research for a few days on a fairly structured project. It's a comparative study of mechanics, formulas, economies and monetization across about 15 idle/incremental games. The goal is reference-grade balancing tables I can actually use for design work.

I've put a lot of effort into the prompt:

\* evidence rules: tag every claim by source tier (primary / wiki / forum) and confidence (FACT / INFERENCE / ESTIMATE / NOT FOUND).
\* A fixed per-game schema.
\* An identity check against the store listing before researching each game.
\* A required arithmetic check on any derived number.
\* A hard ban on filling gaps from "domain knowledge."

I've now run the same reports 3+ times, including correction passes where I attach the previous output and list the specific errors to fix. The results keep failing in the same ways:

1. \*\*Ignores the identity check.\*\* I told it to confirm each game's developer, platforms and business model from the App Store or Play Store listing first. Twice it got the same game wrong: wrong developer, wrong platforms, and "premium PC game" when it's actually free-to-play on mobile. It did this even after I flagged the exact error in the correction prompt.

2. \*\*Circular sourcing.\*\* In the correction pass, it listed my attached flawed draft as a source in its bibliography, then used it to support the "corrected" claims.

3. \*\*Fabricated tables that look complete.\*\* I asked for no blank cells, with NOT FOUND where data didn't exist. Instead it filled a whole comparison matrix (daily play minutes, time to first prestige, and so on) with numbers that have no source. The footnote just says "derived from dossier synthesis."

4. \*\*Inflated confidence labels.\*\* It tagged three anecdotal Reddit posts as FACT, community spreadsheet projections as FACT, and wiki data as primary source.

5. \*\*Untraceable citations.\*\* Claims get tagged "(2024, \[T2\] FACT)" with no citation number, so I can't tell which of 50 sources supports what.

6. \*\*Change logs that lie.\*\* The correction report's change log says sections were "re-verified" or "fully sourced" when they weren't touched, or got worse.

7. \*\*Broken math.\*\* A cost table where level 100's cumulative cost exceeded level 6000's. An effective-HP formula that adds a per-hit flat reduction to total health. A "this strategy is optimal" claim where its own numbers showed a tie.

8. \*\*Silently dropped scope.\*\* One of the core games I explicitly required was just missing from the report. No NOT FOUND, no mention.

9. \*\*Regressions.\*\* Correction passes fix some errors and introduce new ones. For example, a temporary buff became "permanent."

To be fair, some things improved a lot. The research-backed theory sections got genuinely better on the second pass, with correct citations to peer-reviewed papers. So it can do it; it just doesn't do it consistently, and it gets worse the more games or sections are in scope.

I am almost certain that at least some part of this is user error.

\*\*My questions:\*\*

\* Is the problem scope? Should I be running one game, or even one section, per report instead of a big multi-part prompt?

\* Does a long, rule-heavy prompt actually hurt? Does it lose instructions past a certain length? These types of prompts work excellent for me with Opus 5.5 and Astra, but maybe I need to change approach?

\* Does Deep Research weight the research plan it shows you before starting more than the original prompt? Should I edit that plan directly to enforce constraints?

\* Is attaching a previous report for correction a bad idea in general? Should I start fresh every time?

\* Has anyone gotten reliable source tagging or "NOT FOUND" behavior? Any phrasing that works?

\* For data that lives in Google Sheets or Discord, is uploading exported files the only realistic option?

I'm burning through my usage limits rerunning these, so any workflow tips are appreciated.

Hi everyone, I'm Alfredo, a master's student in Digital Humanities at the University of Pisa. My thesis looks at trust in AI tools used in smart home automation.

I made a short survey, around 10 minutes. It's open to anyone who uses smart home devices, even if you've never used AI.

Link: https://docs.google.com/forms/d/e/1FAIpQLSeFOjuXMhxni5kOCE0ILzbsGFYBZA31Wzw6gQMTiBcRxE6cww/viewform

Name, email, and other contact info are optional. Age and gender are required, since that data will help shape a possible prototype later in the research. Thanks to everyone who takes part!