People using google’s models: am I holding it wrong or are the guardrails really overtuned?
I had the dubious pleasure of testing gemini of late and I kept running into refusals. How do I transfer a sim number from one provider to another? No. What should I consider when making backups on ntfs less prone to data loss and more bitrot resistant? No. Evaluate this piece of code? No.
I’m not sure if it’s cold feet from the mythos situation or what, but it reminds me of the dark days where you couldn’t use ai for much of anything. But then I go to chatgpt 5.5 and it does mostly everything I want outside of the usual cybersecurity boogeyman that you run into now and then.
I've always found all versions of gemini to be (for a lack of a better word) lazy.
I guess it's economic wrt. token use, but it often either refused for absurd safety reasons, or other weird stuff like responding that an LLM like itself wasn't a suitable tool for the job, and very quickly gives up.
Claude is on the other end of the spectrum, which makes it more noticeable when switching between them.
Interesting. I have the Google AI Pro plan and use Gemini several times each day and I don't remember the last time I got a refusal. I wonder what criteria go into that, like maybe how they rate your Google account?
The context window size is also very small if you use Gemini in the app. It starts forget quite fast. In my opinion Gemini on app is useless additionally to the guardrails.
I'm outside the US, use Gemini models quite a bit, and I've never run into any refusals of any kind. I'm using them for a fairly wide range of things, I'm sure at least as risqué as asking how to transfer a sim. As a matter of fact I actually asked it's advice on how to transfer banking apps and auth apps from one phone about 3 weeks ago and got decent answers.
It's more dependent on the specific country they are in (and I don't know the specifics). But Google is large enough to have lawyers for every country, and Google is in a never ending whirlwind of national lawsuits/fines, so you end up at the mercy of whatever the lawyers for your country think will not piss off regulators. The EU (and individual states) have pretty heavy AI regulations, and Google even just got fined for an AI overview being incorrect.
It also could just be which way the wind was blowing for OP, the models are stochastic to some degree, but there is no shortage of complaints from (mostly euro) users getting stonewalled.
Computer use is a great idea. It gets the job done when nothing else will.
If you're a person trying to get their job done at a big company, but half your job is in 1-2 proprietary tools or is stuck behind an API you can't program against, computer use can allow you, a non-techie, to do your job more efficiently.
I think it's an awesome way to circumvent gate keepers and the IT department to let people accomplish their goals.
I think there's a sweet spot- a lot of the time you're probably better off with "reverse engineer this web page and build me an API or personalized chrome extension to meet my needs".
I have an agent doing price checks for me for an item on a certain website. Instead of blasting through a zillion tokens processing the DOM over and over, it loaded the page once and figured out how to download a json with the price.
How are folks using “computer use” to click things on intranet portals that are behind an SSO?
Even this OP example shows visitors a url and enter this search term… that is port of useless.
How can I automate things behind an SSO wall? Even if it means I manually authorize it once and watch it do things on its own..
Yeah, it's not that computer use is the most theoretically optimal paradigm, but there's a reasonable case that given the constraints of modern software systems and how they're built, that it's the most realistically optimal paradigm.
The “correct”, elegant way for AI to interact with existing software would take decades and billions of dollars to build. Someone would have to do the hard work of building new APIs, solving decades of accessibility issues, etc.
Or you can show an AI screenshots and ask it where to click.
I disagree if your application is networked. Most SaaS is built on RESTful APIs that can be converted trivially into interfaces / contracts for tool use.
So you can either wait for every application to do that, or at least make it possible for an LLM to do it… or you can make the LLM use a computer interface that works with every application by definition.
Spreadsheet is such a terrible idea. It may look like a valid tool, but ain't no way it's delightful to users. Most of the time people need a database instead. Eventually there'll be an iPhone moment for this.
UI QA only works well if your model plausibly matches the average user behavior and/or real-world edge cases. These models are far from that, and they are much less random than you'd like them to be for fuzzing (mode collapse).
> Computer use is such a terrible idea. It's slow, insecure, error prone, expensive.
And yet having an agent able yo use a computer on your behalf is really useful.
Recently I gave a Nix OS vm to my hermes agent and it has been a good experience. I don't really care if destroy the machine I can just rollback to an earlier version, and for any meaningful data he creates for me I make sure he creates a repo, commit and pushes to my private Gitea instance.
Sure, I don't want an agent watching MY screen. That's why I gave him his own environment, and pretty quickly he discovered that you can open chrome and make it render to a framebuffer, this way he is able to 'view' the website. And apparently with this he is able to bypass a lot of 'anti-bot' measures.
Imagine you have a pretty exotic task you need to complete that involves converting a video file from one format to another.
You can use ChatGPT or something similar and the best you will get is either a script you can run on you machine that does what you need or he may decide to render a new video.
If you have something like OpenwebUI you could configure a MCP that converts videos and allow the model to use this MCP to do your task. This should work, but is quite a lot of work for something you'll ever do once.
But if the agent has it's own environment he can decide to install ffmpg, execute the transformation and serve you the file you want.
In reality there is no new capabilities with this approach, but things get a lot more comfortable.
There's still no MCP support in the Gemini app, which is very useful to get various pieces of info as a user just via chatting. For example I recently wanted to get an Airbnb and wanted to filter by specific criteria including house image analysis and Gemini couldn't do it so I had to do it in Codex.
Yeah, it seems like this is the biggest missing feature from the Gemini ecosystem.
If I can't connect MCP, there's really no selling point for me to use Gemini from my watch, car, smart speaker, etc. If I'm already bound to using my own front end, then I'm only evaluating Gemini as a model/API, at which point it has many competitors that may be cheaper or better fit for the task.
I'm fairly convinced Claude's strongest point is the app. AI users aren't anywhere near as mature or smart as youtube/hn would have folks believe. The claude app is amazing for bridging that gap.
This is why I don't always use the official Gemini Web app. Lately I've found that it's more useful to utilize a CLI. I'm looking forward to the day they add MCP in the web.
The graph has Gemini 3.5 Flash matching Sonnet 4.6, losing to Opus 4.8, and slightly behind GPT-5.5 by 0.3 points... That's not that much of a hands-down loss for Gemini for this specific workload benchmark.
Methodology: All Gemini scores are pass @1 except where otherwise noted. "Single attempt" settings allow no
majority voting or parallel test-time compute. All of the results are all run with the Gemini API for the model-id
gemini-3.5-flash with default sampling settings unless indicated otherwise below. To reduce variance, we
average over multiple trials for smaller benchmarks.
All the results for non-Gemini models are sourced from providers' self reported numbers unless otherwise mentioned below. For Claude Opus 4.7 , Sonnet 4.6, and GPT-5.5 we default to reporting maximum
thinking/reasoning settings available, but when reported results are not available we use best available reasoning
results.
It's honest - people who know what they are looking at will take speed and token costs into account. I don't use Gemini 3.5 for coding, but I use it as something in between a search engine and agent.
It seems to do it just fine when in desktop applications using Qt, fwiw., it leverages all the standard Qt GUI testing stuff (and if you have the money you can just integrate Squish which has LLM support now).
Google said June, and all its model updates seem to be on Tuesdays, Wednesdays or Thursdays. So unless the release is slipping, either tomorrow or Tuesday.
I had the dubious pleasure of testing gemini of late and I kept running into refusals. How do I transfer a sim number from one provider to another? No. What should I consider when making backups on ntfs less prone to data loss and more bitrot resistant? No. Evaluate this piece of code? No.
I’m not sure if it’s cold feet from the mythos situation or what, but it reminds me of the dark days where you couldn’t use ai for much of anything. But then I go to chatgpt 5.5 and it does mostly everything I want outside of the usual cybersecurity boogeyman that you run into now and then.
I guess it's economic wrt. token use, but it often either refused for absurd safety reasons, or other weird stuff like responding that an LLM like itself wasn't a suitable tool for the job, and very quickly gives up.
Claude is on the other end of the spectrum, which makes it more noticeable when switching between them.
It also could just be which way the wind was blowing for OP, the models are stochastic to some degree, but there is no shortage of complaints from (mostly euro) users getting stonewalled.
I guess if you're trying to get people to tokenmaxx it may look like a valid strategy, but ain't no way this will be delightful to users.
I think it's a symptom of just not understanding how LLMs should interface with the OS because we're still in their early days.
Eventually there'll be an iPhone moment for the ergonomics of LLM usage outside of coding
If you're a person trying to get their job done at a big company, but half your job is in 1-2 proprietary tools or is stuck behind an API you can't program against, computer use can allow you, a non-techie, to do your job more efficiently.
I think it's an awesome way to circumvent gate keepers and the IT department to let people accomplish their goals.
I have an agent doing price checks for me for an item on a certain website. Instead of blasting through a zillion tokens processing the DOM over and over, it loaded the page once and figured out how to download a json with the price.
How can I automate things behind an SSO wall? Even if it means I manually authorize it once and watch it do things on its own..
Even then, an AI writing AHK scripts likely outperforms.
Or you can show an AI screenshots and ask it where to click.
Then you get a nice textual world that fits the LLM without having to rewrite every application to have a fullblown HTTP server.
Meanwhile, the entire world economy:
Spreadsheets are fucking glorious, powerful, clever, amazing and delightful, in my view.
And yet having an agent able yo use a computer on your behalf is really useful.
Recently I gave a Nix OS vm to my hermes agent and it has been a good experience. I don't really care if destroy the machine I can just rollback to an earlier version, and for any meaningful data he creates for me I make sure he creates a repo, commit and pushes to my private Gitea instance.
It is, but there's no need for it to be viewing your screen, browsing websites and watching ads.
That stuff is for humans, not for LLMs.
I honestly cannot think of a single use case
Imagine you have a pretty exotic task you need to complete that involves converting a video file from one format to another.
You can use ChatGPT or something similar and the best you will get is either a script you can run on you machine that does what you need or he may decide to render a new video.
If you have something like OpenwebUI you could configure a MCP that converts videos and allow the model to use this MCP to do your task. This should work, but is quite a lot of work for something you'll ever do once.
But if the agent has it's own environment he can decide to install ffmpg, execute the transformation and serve you the file you want.
In reality there is no new capabilities with this approach, but things get a lot more comfortable.
If I can't connect MCP, there's really no selling point for me to use Gemini from my watch, car, smart speaker, etc. If I'm already bound to using my own front end, then I'm only evaluating Gemini as a model/API, at which point it has many competitors that may be cheaper or better fit for the task.
The Gemini apps suck.
The methodology used:
https://deepmind.google/models/evals-methodology/gemini-3-5-...
Methodology: All Gemini scores are pass @1 except where otherwise noted. "Single attempt" settings allow no majority voting or parallel test-time compute. All of the results are all run with the Gemini API for the model-id gemini-3.5-flash with default sampling settings unless indicated otherwise below. To reduce variance, we average over multiple trials for smaller benchmarks.
All the results for non-Gemini models are sourced from providers' self reported numbers unless otherwise mentioned below. For Claude Opus 4.7 , Sonnet 4.6, and GPT-5.5 we default to reporting maximum thinking/reasoning settings available, but when reported results are not available we use best available reasoning results.
It’s something cheap enough you’d put out in front of your customers, and Opus is expensive enough you wouldn’t.