- On permission issue, not sure I follow the flow that got you there, pls email me more details if you are able too and happy to debug: Lkilpatrick@google.com

- On overall friction for billing: we are working on a new billing experience built right into AI Studio that will make it super easy to add a CC and go build. This will also come along with things like hard billing caps and such. The expected ETA for global rollout is January!

Want to use Google’s gmail, maps, calendar or gemini api? Create a cloud account, create an app, enable the gmail service, create an oauth app, download a json file. Cmon now…

https://fal.ai/models/fal-ai/nano-banana-pro

Without a doubt one essential ingredient will be, “you need a Google Project to do that.” Oh, and it will also definitely require me to Manage My Google Account.

Is that going to need AGI? Or maybe it will always be out of reach of our silicon overlords and require human input.

The only way i use google is via an api key which billing for is arcane to be charitable. How can billions not crack the problem of quickly accepting cash from customers? Surely their ads platform does this?

Maybe that one is just not a good test?

Three sentences that do a great job summing up modern big tech. The new model even manages to [digitally] remove all trash.

I guess if you do that then maybe you don't need the cool sliders anymore?

Anyway - thanks so much for all your hard work on this. A very interesting study!

>> All five of the edits are implemented correctly

This is a GREAT example of the (not so) subtle mistakes AI will make in image generation, or code creation, or your future knee surgery. The model placed the specified items in the eye sockets based on the viewers left/right; when we talk relative in this scenario we usually (always?) mean from the perspective of the target or "owner". Doctors make this mistake too (they typically mark the correct side with a sharpie while the patient is still alert) but I'd be more concerned if we're "outsourcing" decision making without adequate oversight.

https://minimaxir.com/2025/11/nano-banana-prompts/#hello-nan...

https://minimaxir.com/2025/11/nano-banana-prompts/#hello-nan...

My recreations of those pancake batter skulls using Nano Banana Pro: https://simonwillison.net/2025/Nov/20/nano-banana-pro/#tryin...

- Fibonacci magnets: code is correctly indented and the syntax highlighting atleast tries giving variables, numbers, and keywords different colors.

- Make me a Studio Ghibli: actually does style transfer correctly, and does it better than ChatGPT ever did.

- Rendering a webpage from HTML: near-perfect recreation of the HTML, including text layout and element sizing.

That said, there may be regressions where even with prompt engineering, the generated images which are more photorealistic look too good and land back into the uncanny valley. I haven't decided if I'm going to write a follow up blog post yet.

The system prompt hacking trick doesn't work with Nano Banana Pro unfortunately.

> "I...worked on the detailed Nano Banana prompt engineering analysis for months"

Early in four decades of tech innovation I wasted time layering on fixes for clear deficiencies in a snowballing trend's tech offerings. If it's a big enough trend to have well funded competitors, just wait. The concern is likely not unique, and will likely be solved tomorrow.

I realized it's better to learn adaptive/defensive techniques, giving your product resilience to change. Your goal is that when surfing the change waves you can pick a point you like between rock solid and cutting edge and surf there safely.

Invest that "remediate their thing" time in "change resilience" instead – pays dividends from then on. It can be argued your tool is in this camp!

// Getting better at this also helps you with zero days.

GDM folks, get Max on!

I've been using a bespoke Generative Model -> VLM Validator -> LLM Prompt Modifier REPL as part of my benchmarks for a while now so I'd be curious to see how this stacks up. From some preliminary testing (9 pointed star, 5 leaf clover, etc) - NB Pro seems slightly better than NB though it still seems to get them wrong. It's hard to tell what's happening under the covers.

That said, I wonder if text is only good in small chunks (less than a sentence) or if it can properly render full sentences.

https://gemini.google.com/share/c9af8de05628

I did manage to get one image of a piano keyboard where the black keys were correct, but not consistently.

"An infographic explaining how player.html works (from the player.html project on Github). https://github.com/pseudosavant/player.html"

And then it made one formatted for social: "Change it to be an infographic formatted to fit on Instagram as a 1:1 square image."

I’ve found in general that the first generation may not be accurate but a few rolls of the dice and you should have enough to pick a style and format that works, which you can iterate on.

I'm reminded of when the air force decided to create a pilot seat that worked for everyone. They took the average body dimensions of all their recruits and designed a seat to fit the average. It turned out, the seat fit none of their recruits. [1]

I think AI image generation is a lot like this. When you train on all images, you get to this weird sort of average space. AI images look like that, and we recognize it immediately. You can prompt or fine tune image models to get away from this, though -- the features are there it's a matter of getting them out. Lots of people trying stuff like this: https://www.reddit.com/r/StableDiffusion/comments/1euqwhr/re..., the results are nearly impossible to distinguish from real images.

[1] https://www.thestar.com/news/insight/when-u-s-air-force-disc...

If you want something that looks original, you have to come up with a more original prompt. Or we have to find a way to train these models to sample things that are less likely from their distribution? Find a way to mathematically describe what it means to be original.

I bet it will be called "Real Photos" or something like that, and the pictures will be signed by the camera hardware. Then iMessage will put a special border around it or something, so that when people share the photos with other Apple users they can prove that it was a real photo taken with their phone's camera.

You're right that there will existed generated content without these watermarks, but you can bet that all the commercial providers burning $$$$ on state of the art models will gradually coalesce around some means of widespread by-default/non-optional watermarking for content they let the public generate so that they can all avoid drowning in their own filth.

I don't see how it would defeat the cat and mouse game.

Maybe zero knowledge proofs could provide anonymity, or a simple solution is to ship the same keys in every camera model, or let them use anonymous sim-style cards with N-month certificate validity. Not everyone needs to prove the veracity of their photos, but make it cheap enough and most people probably will by default.

Unless the watermark randomly replaces objects in the scene with bananas, these images/videos will still spread like wildfire on platforms like TikTok, where the average netizen's idea of due diligence is checking for a six‑fingered hand... at best.

  have some kind of standardized identifier on them

Image verification has never been easy. People have been airbrushed out of and pasted into photos for over a century; AI just makes it easier and more accessible. Expecting a “click to verify” workflow is unreasonable as it has ever been; only media literacy and a bit of legwork can accomplish this task.

We will always have local models. Eventually the Chinese will release a Nano Banana equivalent as open source.

If social media platforms are required by law to categorize content as AI generated, this means they need to check with the public "AI generation" providers. And since there is no agreed upon (public) standard for imperceptible watermarks hashing that means the content (image, video, audio) in its entirety needs to be uploaded to the various providers to check if it's AI generated.

Yes, it sounds crazy, but that's the plan; imagine every image you post on Facebook/X/Reddit/Whatsapp/whatever gets uploaded to Google / Microsoft / OpenAI / UnnamedGovernmentEntity / etc. to "check if it's AI". That's what the current law in Korea and the upcoming laws in California and EU (for August 2026) require :(

Hell, it might even be possible for some arbitrary photographs to come up with an AI prompt that produces them or something similar enough to be indistinguishable to the human eye, opening up the possibility of "proving" something is fake even when it was actually real.

What you want just can't work, not even from a theoretical or practical standpoint, let alone the other concerns mentioned in this thread.

"Generate a piano, but have the left most key start at middle C, and the notes continue in the standard order up (D, E, F, G, ...) to the right most key"

The above prompt will be wrong, seemingly every time. The model has no understanding of the keys or where they belong, and it is not able to intuit creating something within the actual confines of how piano notes are patterned.

"Generate a piano but color every other D key red"

This also wrong, every time, with seemingly random keys being colored.

I would imagine that a keyboard is difficult to render (to some extent) but I also don't think its particularly interesting since it is a fully standardized object with millions of pictures from all angles in existence to learn from right?

To me, this is terrifying. Major use-cases presented on this page:

  * photo editing / post-processing
  * branding
  * infographics

However, Branding and Infographics are where I have concerns.

Branding - it’s AI art, so it can’t be copyrighted, or are we just going to forget that?

—

Infographics, though. We know that AI frequently hallucinates - and even hallucinates citations themselves, so … how can we generated infographics if they’re magicking into existence the stats used in the infographics themselves?!

It enables smaller teams to put out better quality products

Imagine you're an artist that wants to create a video game but you suck at development. You could leverage AI to get good enough code and have amazing art

On the other side someone who invested their entire skill tree in development can have amazing code and passable art

The more I think about it the more it seems this AI revolution will hurt big companies the most. Most people have no hope of competing with a AAA game studio because they don't have the capital. Maybe this levels the playing field?

Have we felt this way for all other large scale advances in human history?

In a coffee shop this morning I saw a lady drawing tulips with a paper and pencil. It was beautiful, and I let her know... But as I walked away I felt sad that I don't feel that when browsing online anymore- because I remember how impressive it used to feel to see an epic render, or an oil painting, etc... I've been turned cynical.

In the past, I've deliberately stuck a Vision-language model in a REPL with a loop running against generative models to try to have it verify/try again because of this exact issue.

EDIT: Just tested it in Gemini - it either didn't use a VLM to actually look at the finished image or the VLM itself failed.

Output:

  I have finished cross-referencing the image against the user's specific requests. The primary focus was on confirming that the number of points on the star precisely matched the requested nine. I observed a clear visual representation of a gold-colored star with the exact point count that the user specified, confirming a complete and precise match.

  Bog standard star with *TEN POINTS*.

To me the AI revolution is making visual media (and music) catch up with the text-based revolution we've had since the dawn of computing.

Computers accelerated typing and text almost immediately, but we've had really crude tools for images, video, and 3D despite graphics and image processing algorithms.

AI really pushes the envelope here.

I think images/media alone could save AI from "the bubble" as these tools enable everyone to make incredible content if you put the work into it.

Everyone now has the ingredients of Pixar and a music production studio in their hands. You just need to learn the tools and put the hours in and you can make chart-topping songs and Hollywood grade VFX. The models won't get you there by themselves, but using them in conjunction with other tools and understanding as to what makes good art - that can and will do it.

Screw ChatGPT, Claude, Gemini, and the rest. This is the exciting part of AI.

Nonetheless, ask it to “create an infographic on how Google works”. Do you not see any excitement in the result? I think it’s pretty impressive and has a lot of utility.

Last week I was making a birthday card for my son with the old model. The new model is dramatically better - I'm asking for an image in comic book style, prompted with some images of him.

With the previous model, the boy was descriptively similar (e.g. hair colour and style) but looked nothing like him. With this model it's recognisably him.

https://imgur.com/a/h0ncCFN

No, its not the beginning, multiple different watermarking standards, watermark checking systems, and, of course, published countermeasures of various effectiveness for most of them, have been around for a while.

I wouldn’t be surprised if Google shortens the name to NBP in the future, hoping everyone collectively forgets what NB stood for. And then proceeds to enshittify the name to something like Google NBP 18.5 Hangouts Image Editor

However, I don’t think 2D animators should feel too safe about their jobs. While these models are bad at creating sprite sheets in one go, there are ways you can use them to create pretty decent sprite sheets.

For example, I’ve had good results by asking for one frame at a time. Also had good results by providing a sprite sheet of a character jumping, and then an image of a new character, and then asking for the same sprite sheet but with the new character.

Results: https://imgur.com/a/9II0Aip

The white house was the original (random photo from Google). The prompt was "What paint color would look nice? Paint the house."

The most effective fix I have found is that when the model is acting dumb, just turn it off and come back in the few hours to a new chat and try again.

A cluster of launches reinforces the idea that Google is growing and leading in a bunch of areas.

In other words, if it's having so many successes it feels like overload, that's an excellent narrative. It's not like it's going to prevent people from using the tools.

https://killedbygoogle.com/

Even so, this is a real advancement. It's impressive to see existing techniques combined to meaningfully improve on SOTA image generation.

https://imgur.com/a/3PDUIQP

https://imgur.com/a/ENNk68B

Much better than previous attempts. Still has an extra lane with the cars on the right cutting off the cars in the middle. Still has the numbers in the wrong order.

That way you can stick your choice of any number of LLM preprocessors in front of a generic prompt like "mountain dew themed pokemon" and push the responsibility of creating a more detailed prompt upstream.

https://imgur.com/a/s5zfxS5

Note: I'm not particularly impressed with either of the results - this is more a demonstration.

The trouble is that learning fundamentals now is a large trough to go past, just the way grade 3-10 children learn their math fundamentals despite there being calculators. It's no longer "easy mode" in creative careers.

1. Trigger Circle to Search with long holding the home button/bar

2. Select the image

3. Navigate to About this image on the Google search top bar all the way to the right - check if it says "Made by Google AI" - which means it detected the SynthID watermark.

I'd be interested to see how Wan 2.2 First/Last frame handles those images though...

- It requires an AI that actually understands English, I.e. an LLM. Older, diffusion-only models were naturally terrible at that, because they weren’t trained on it.

- It requires the AI to make no mistakes on image rendering, and that’s a high bar. Mistakes in image generation are so common we have memes about it, and for all that hands generally work fine now, the rest of the picture is full of mistakes you can’t tell are mistakes. Entirely impossible with text.

Nano Banana Pro seems to somewhat reliably produce entire pictures without any mistakes at all.

https://deepmind.google/models/synthid/

Recently I also started using image generation models to explore ideas for what changes to make in my paintings. Although generally I don't like the suggestions it makes, sometimes it provides me with creative ideas of techniques that are worth experimenting with.

One way to approach thinking about it is that it's good for exploring permutations in an idea-space.

1) I have a tricep tendon injury and ChatGPT wants me to check my tricep reflex. I have no idea where on the elbow you're supposed to tap to trigger the reflex.

2) I'm measuring my body fat using skin fold calipers. Show me were the measurement sites are.

3) I'm going hiking. Remind me how to identify poison ivy and dangerous snakes.

4) What would I look like with a buzz cut?

https://mordenstar.com/portfolio/gorgonzo

https://mordenstar.com/portfolio/brawny-tortillas

https://mordenstar.com/portfolio/ms-frizzle-lava

but concept art, try-it-on for clothes or paint, stock art, etc

But I wouldn't mind being easily able to make infographics like these, I'd just like to supply the textual and factual content myself.

https://drive.google.com/file/d/1QV3pcW1KfbTRQscavNh6ld9PyqG...

https://drive.google.com/file/d/18AzhM-BUZAfLGoHWl6MQW_UW9ju...

And in AI Studio, you need to connect a paid API key to use it:

https://aistudio.google.com/prompts/new_chat?model=gemini-3-...

> Nano Banana Pro is only available for paid-tier users. Link a paid API key to access higher rate limits, advanced features, and more.

The 2nd take is AI is costing companies so much money, that they need to cut workforce to pay for their AI investments.

I'm inclined to think the latter is represents what's happening more than the former.

I had second thoughts about this comment, but if I stopped typing in the middle of it, I would've had to pay a cancellation fee.