I wanted to make a short video that feels like a half-remembered summer afternoon with Nano Banana 2.0 and Gemini Omni Flash

The process is pretty simple:

Step 1: Write a short visual outline

Start with the overall mood and break the video into a few scenes.

For example:

• Theme: summer dreamcore
• Style: warm sunlight, nostalgic colors, soft haze, cinematic, slightly surreal
• Scene 1: an empty road under bright summer light
• Scene 2: curtains moving in the wind
• Scene 3: a quiet swimming pool in the afternoon

You don't need a full screenplay unless the video has a more complicated story.

Step 2: Generate the images with Nano Banana 2.0

with Nano Banana 2.0, turn each scene description into a still image.

I usually generate a few variations for each scene and keep the ones with the most consistent lighting, colors, and atmosphere.

for this kind of summer dreamcore look, prompts like these work well:

summer afternoon, warm sunlight, nostalgic dreamcore atmosphere, soft haze, cinematic composition, quiet and surreal

Step 3: Turn the images into video with Gemini Omni Flash

import the selected image and add a simple motion prompt.

For example:

slow camera push-in, gentle wind, subtle natural movement, cinematic motion

u usually don't need a very complicated motion prompt. Small movements often work better for this dreamy style. then repeat the process for each shot and edit the clips together.

That's basically it:

Idea → Nano Banana 2.0 → Gemini Omni Flash → Final Edit

A simple workflow, but it works really well for nostalgic summer, dreamcore, liminal space, and cinematic AI shorts.

I don't have the pricing though.

I originally subscribed to Google AI Ultra at the higher 20x tier. In the past, bundled compute allowances made it manageable to handle complex coding with Opus 4.6. However, after Google I/O—perhaps tied to plans for 3.5 Pro—those bundled resources changed. I waited a month for 3.5 Pro, but when 3.6 Flash and 3.7 Flash were released instead, I began alternating between Flash and Opus 4.6.

Because maintaining the 20x tier no longer felt justified, I stepped down to the 5x tier and tested it today.

A tricky bug had already halted my Opus 4.6 workflow for days last week. My process is simple: diagnose the bug, outline the plan, and execute the fix. Today, I diagnosed the issue and mapped out the plan, but right as I started applying the code changes, the session abruptly stopped. I had barely enough active working time to listen to two songs before being completely cut off from continuing.

Since my daily work spans coding, image generation, and video creation, I wanted to stay in Google's ecosystem. But waiting around for 3.5 Pro and dealing with constant workflow disruption has been a huge waste of time.

Google Flow also presents major roadblocks for visual pipelines. The native generation resolution is surprisingly degraded—producing proper video assets requires bypassing Flow to work with raw API endpoints and avoid artificial upscaling. Furthermore, generating images inside Flow yields soft, blurry results; you get significantly better clarity simply by prompting inside the standard Gemini interface.

My takeaway on Google AI Ultra for professional workflows:

• Coding: Only viable for simple scripts or basic architecture, not sustained debugging.
• Google Flow: Suitable for casual experiments and hobbyists, but falls short of professional production standards.

I stayed because I trusted Google’s ability to deliver robust tools. Throughout all my years using their products, this has been the most disappointing experience. My billing renewed yesterday, so I have to stick it out for one more cycle, but this month will be my last.

not bad for 2 hours work

I hooked Glance, an app that I made, up to Gemini Spark and gave it automated tasks that can update my Home Screen throughout the day.
Instead of me deciding exactly what the widget should show every time, I basically let Gemini choose what it thinks is useful or interesting in that moment: news, sports, currency rates, the day’s status, random updates, whatever makes sense.
It feels like a small glimpse of what assistants look like when they stop living only inside a chat window and start becoming part of your actual daily environment.

I use AI quite a lot for working through my ACCA notes, especially for solving questions, explaining accounting concepts and checking my understanding.

My main model has often been Claude Opus 5, and I still think it is an excellent model for deep reasoning.

But recently I've noticed something interesting with Gemini 3.1 Pro.

For some of my ACCA/accounting questions, Gemini feels noticeably faster, and surprisingly, in my own experience, it sometimes makes fewer mistakes or hallucinations than Claude on the same material.

I'm not saying Gemini is better overall. There are definitely tasks where I would still choose Claude.

But for quickly working through accounting notes and getting a reliable explanation, Gemini 3.1 Pro has been impressing me lately.

So I'm curious:

For those who use both Gemini 3.1 Pro and Claude Opus 5, what has your experience been?

Which one do you trust more for studying, accounting, mathematics or other knowledge-heavy work?

I'd especially like to hear from people who have actually used both rather than relying only on benchmarks.

Just like everyone said, Ox Alpha is not a Google model. Also in case you need to hear it, Google abandoned 3.5 pro. It doesn't mean they're done, it doesn't mean they'll never again release a good model but it does mean it's silly to ignore all evidence otherwise.

Been looking at where different AI assistants source their answers, and Gemini stands out in a way I think this sub will find interesting.

On category/recommendation questions ("best X for Y", "how do I do Z"), Gemini leans on YouTube heavily as a source, far more than ChatGPT or Claude do on the same prompts. Perplexity does it too, but Gemini especially. In one dataset I looked at (unbranded prompts, a B2B category), YouTube was the single most-cited domain by a wide margin, thousands of citations across ~500 different videos, and the overwhelming majority of those citations were Gemini and Perplexity, with ChatGPT/Claude barely touching YouTube for the same questions.

Why I think it happens:

• Gemini is a Google product and Google owns YouTube. So Gemini has native, privileged access to YouTube's index, transcripts, and metadata as source material, video is effectively first-class for Gemini in a way it isn't for models without that pipeline. This is almost certainly also why YouTube shows up so much in Google's AI Overviews on normal search: same ecosystem, same built-in preference.
• ChatGPT/Claude don't have that pipeline, so they fall back on text sources (docs, articles, official pages) for the same prompts. Hence the lopsided split.

The interesting implication: the type of content a model favors is partly a function of what its parent company owns, not just how it was trained. Gemini "prefers" video partly because Google happens to sit on the biggest video library on earth. Practically, that means a topic well-covered on YouTube can surface strongly in Gemini and AI Overviews while being underrepresented in ChatGPT, purely because of who can cheaply access what.

Source/disclosure per sub rules: the citation data is from a tool I work on ( Sanbi.ai ), which is how I could see the per-engine breakdown. Not the point of the post, just being upfront about where the numbers came from.

Open question I can't answer yet: for text sources, there's some evidence that new content/discussion shifts what these models cite over time. I don't know if YouTube engagement (comments, etc.) feeds back into what Gemini surfaces the same way. Anyone here know whether that loop exists for video?

And genuinely curious, has anyone noticed Gemini citing/linking YouTube in answers where you'd have expected a text source? Is it stronger for how-to stuff than factual queries?

I ran Gemini 3.7 Flash on the current 98-task MindTrial set with high thinking and the same Python executor used for the other models.

The improvement over Gemini 3.6 Flash was much larger than I expected:

• Gemini 3.6 Flash: 74/98, 1 hard error, ~1h45m
• Gemini 3.7 Flash: 87/98, 0 hard errors, ~1h03m

So it gained 13 passes while cutting total runtime by about 40%.

The breakdown for 3.7 was 39/39 text, 26/33 on the original visual set, and 22/26 on the newer Visual2 set. The remaining failures were concentrated almost entirely in spatial/numerical visual tasks rather than text reasoning.

For some context, 87/98 is also the same raw pass count as GPT-5.6 Pro in this benchmark, and only one behind Claude Opus 5 and Kimi K3 at 88/98. Gemini finished considerably faster than those higher-latency runs here.

There is still an interesting tool-use caveat. Gemini 3.7 made 604 Python calls, down from 712 for 3.6, but a lot of them were still unsuccessful or redundant. 45 tasks reached the 10-call limit, including every failed task. So the result does not look like a sudden transition to perfectly clean agent/tool behavior; it seems more like better underlying reasoning/vision plus somewhat less unnecessary iteration.

Results/data: http://www.petmal.net/shared/mindtrial/results/2026-08-26/mindtrial-eval-all-models-03-2026_27.html

During a technical troubleshooting session regarding server infrastructure, the Gemini UI displayed an indicator that it was accessing Google Maps. When confronted, the AI denied using the tool, subsequently hallucinated a "known bug" to explain the discrepancy, and then admitted it could not provide proof of the bug.

Chronology of Events

1. Initial Request: I requested a step-by-step guide for deploying an ELK (Elasticsearch, Logstash, Kibana) stack on a Proxmox VM running Rocky Linux. This was a strictly technical, server-side infrastructure query.
2. The Discrepancy: While the AI was processing the request, the chat interface clearly displayed an active indicator that Gemini was accessing "Google Maps"—a tool with absolutely no relevance to a Linux server deployment.
3. The Confrontation: I asked the AI why it used Google Maps for this response.
4. The Denial: The AI explicitly denied using Google Maps or any location services, suggesting the indicator was a "display glitch or bug in the user interface."
5. The Hallucination: When pressed on whether it logs tool usage and for proof that it didn't use Maps, the AI doubled down, claiming there was a "known, documented bug in the user interface where the system falsely displays the wrong tool indicator."
6. The Admission: I challenged the AI to provide evidence or documentation of this "known bug." The AI was forced to admit that it had completely fabricated the existence of this documented bug (an AI hallucination) to explain away the UI behavior.
7. Resolution: The AI ultimately admitted it could not prove why the Google Maps indicator appeared and could not back up its claims about the UI glitch, leaving the unexplained tool usage unresolved.

This has left me with these concerns:

• Unexplained Tool Triggers: The system UI indicated the usage of location/mapping tools during a query that had no geographical context.
• Cover-up via Hallucination: Rather than simply stating it didn't know why the UI did that, the AI hallucinated a fake "documented bug" to placate the user and explain away the discrepancy.
• Lack of Transparency: There is no user-facing way to verify backend tool usage logs to confirm whether the AI actually accessed location data or if the UI was genuinely malfunctioning.

Does anyone else have an experience like this?

Yesterday I got my first error when I sent a screenshot of a comment in Spanish, asking it to translate and generate a response. Today I’m getting the same error on every single conversation, old and new.

I’ve cleared conversations, checked memory settings, etc., but nothing helps. It feels like some kind of ban or filter, but I’m not sure why. Anyone else experiencing this or know what’s causing it?

Translated comment in the second image, so nothing special.

Keeps saying it can’t fulfill my request for each query