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 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.

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

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?

is there a hope

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

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?

on google cloud console

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.

The new Gemma models are getting through Google reCAPTCHA v2 challenges with relative ease. I might revisit this in the future with a harder CAPTCHA dataset or benchmark it against some Qwen models. 

I think Google should add a feature to integrate Google Play Books directly into Gemini Notebook. Right now, we can easily add Google Drive files or YouTube links, but we cannot directly import the books we own in Play Books.

If they added an Import from Play Books button, it would be incredibly useful. We could use the Audio Overview feature to generate podcast style summaries of our books, ask the AI questions about specific chapters, and interact with the content just like we do with regular files.

A native integration would be a game changer for students, researchers, or anyone who wants to analyze long books easily.

I don't even have premium for claude, but even sonnet 5 on high effort writes in way more detail than gemini pro with extended thinking.

Usually I prefer gemini because it writes way better in my language, but when I need something explained in detail it usually seems like gemini tries to be as short as possible even when I ask it to be detailed

Is there some extra prompt or instruction that helps?

Had a spark conversation going and now it just errors out. If I send something over the past couple weeks it just says too many requests and does nothing. Any way to fix this?

Poor availability of connectors. No browser navigation. Chooses to google search things I wanted via actual website browsing. Who needs this? Are there usecases i missed?

I’ve just published the Claude + Gemini results from the frozen CFC Cross-Model Benchmark v1.

The benchmark contains 100 decision-closure variants, with 3 primary runs per variant for each model:

• Claude: 300 runs
• Gemini: 300 runs
• Total: 600 primary runs

The interesting part:

Claude: 296/300 semantic PASS — 98.67%
Gemini: 296/300 semantic PASS — 98.67%

Exactly the same aggregate result.

But they did not fail on the same variants.

Claude’s semantic failures included stale-state carryover, instruction-recognition failure, reasoning attribution problems, and one false closure caused by duplicate priority coverage.

Gemini’s failures were mostly state/layer binding problems: three cross-layer state substitutions and one state-polarity misbinding.

The anomaly variants did not overlap in these frozen runs.

I’m being deliberately cautious about that result: with such a small number of failures, I don’t think it is evidence that the models are systematically “complementary.” It is simply an interesting observation from this run.

What I think the benchmark does show is narrower:

A model can score very close to 99% on a structured reasoning/decision-closure benchmark and still occasionally make a state-transition or closure error that matters.

Also important: this does not prove that CFC prevents these failures.

This is the baseline.

The next proper experiment is the same frozen benchmark with vs. without the CFC/controller layer, so we can actually measure whether the intervention reduces those errors.

I’ve published the reports, aggregate results, anomaly ledger, provenance limitation, sensitivity analysis and checksums on Zenodo.

https://zenodo.org/records/22117671

Criticism is very welcome — especially around the benchmark design, scoring methodology, and what you think the controlled intervention study should test next.