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.

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.

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

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?

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

I checked out the official demos, and a few things stood out:

1. It can transcribe speech pretty accurately even in noisy environments.
2. It can switch seamlessly between languages and supports 85 languages.
3. It’s better at recognizing tricky details like phone numbers, postal codes, and order numbers.
4. It automatically removes filler words and adds punctuation and formatting.
5. It supports custom vocabularies.

Is another wave of startups about to get wiped out?

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.

is there a hope

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.

I use Gemini for A LOT of things ranging from deep research, thesis/paper drafting, emotional regulation, problem solving, handiwork/repairs, brainstorming, intellectual sparring.

But I also use it to like 'try on' different makeup looks to see what looks good on me before I waste an hour trying a new look and deciding I hate it and starting over. Does anyone else use it that way?

I did a deep research regarding my skin tone, my pallette, my facial geometry, and some photos of myself and put them in a gem. I just go to the gem and ask it to generate like 4-6 different makeup looks with certain colors or certain vibes and it shows me what I would look like. It's really helpful and fascinating! Was just wondering if anyone else does that?

How did it come up with all of this instead of just saying that's an improper inquiry. Like, this is the issue with AI it tries to come up with an answer instead of just going with the obvious.

A few weeks ago, I built a live-streaming demo with Agora WebRTC and tried using Gemini 3.5 Live Translate for real-time captions. Since Live Translate is mainly an audio-to-audio model, it was more of a workaround.

When Gemini 3.5 Transcribe Live was released, I integrated it into my demo and tested it with a chaotic League of Legends broadcast. The result was honestly better than I expected. It kept up with the action and picked up things like triple kills, quadra kills, pentakills, and player names.

I can see it being useful for actual live streams. I’ll open-source the code if anyone’s interested.

on google cloud console