I had an interesting conversation with Gemini about the tech industry this morning and it was worth a share.

Convo link below:

https://share.gemini.google/U8OwFMbc0eI7

I specifically have a code review skill that invokes all of them to review a PR then runs a 4 way debate on the validity of all the findings.

Gemini is invoked through the agy CLI, and all are at max thinking.

Gemini has a significantly lower rate of material findings and a much higher rate of outright hallucinations, frequently referencing code and practices that don't actually exist in the repo.

It feels like using AI 18 months ago, back around the Gemini 2.5 pro days.

I resubbed to Gemini ultra @ 200 when Argon was announced, hopeful, but have since dropped it again. Bummer.

No, Google, I don't want my AI making inhaling breath sounds occasionally while it's talking. It's deeply disturbing.

Just imo, but I'm working with some people and see others online constantly complaining about token usage and how they feel scammed by Google.

I'm not here to do an analysis or whatever but I stay on Argon - Medium and I'm doing what I'd consider are fairly large projects and I've never hit my cap. I don't think thinking 'high' is really what most people need when coding their if/else statements.

When I do swap to Argon - high its for my initial planning or document overviews when I finish a large chunk of code and I want to analyze something pretty forward looking or trying to break my program through mutation testing. When actually implementing my things I'm usually on low or medium and I keep my context tight and well formed. I say this because I personally have never hit a limit for usage but I see everyone complaining all day.

If you are using Argon - high and asking it to review your entire repo multiple times over and your detailed prompt of 'fix my project' accidentally takes in too much data then maybe perhaps try looking at being being a bit more delicate with how you're using it. Those prompts of such a massive window and scope should be a bit more rare than I feel like people are using them.

If you put your car into MAXIMUM OVERDRIVE to drive to starbucks then get upset about your gas tank, be a bit more realistic about your task at hand.

Perhaps google should make an auto mode to keep people from using high thinking to ask about a potato salad recipe.

(imo)

I have been using Gemini extensively for my studies. I have found it to be the best suit for my needs. Used to use Chatgpt(premium), Claude(free version as had many accounts) but I found they tend to give wrong answers to my math and other questions. Am currently studying in University and the math questions they ask are super complicated and somehow Gemini gets them right. Like even the Google search one as well. Sometimes I just use that Google search option for a problem and it gives me the correct answer.(it's not that the question is already asked and famous, even if it were chatgpt and other models should have solved it). Now recently I started using Google Ai Studio , even though I have paid subscription I still often find myself using the fre 3.1 Pro model there and the search option. I have made like 8-10 google accounts on my device. So I literally never get shut out from using the models as I just transfer my chat to another one.

What I am trying to say is, Gemini is absolutely crushing the hard math problems without mistakes(I can't remember the last time it doing any mistake, granted I give it resource files that would be helpful to it but I give them to all models). But then when I come online everyone is absolutely shitting on Gemini. What is the reason for it? Is it just some sort of marketing campaign or what.

Hello Reddit.

I'm mostly a user of Qwen3.8 that I find really worth it for most task, alongside Deepseek 4.1 flash for very simple tasks.

But I'd like an AI model for cutting edge cases. Of course there's the classic Claude and ChatGPT but I grew tired of them and overall distruss them (I don't want to have my token capacities halfed for no reason).
I tried Kimi K3(an 3.1) with the AI Pro equivalent subscription and the usage is a joke : 4 hours of text and browser manipulation and it's over.

Is Gemini 4 Argon with AI Pro subscription any better when it come to cost per token ? I want to use it to calculate physics with Godot, handle reverse engineering of DLL and Binary, and write Kernel-lever drivers, and other Cybersecurity tasks

I've been trying to access the Gemini Student Plan for a while, is it like seasonal offer thing?

I use Argon medium for just answer pure text in one prompt it consume 22% usage, how hilarious is it.

Has anyone else received this?

I apologise to copywriters, but I use Gemini to help me write BlueSky posts for my business.

The word "flattains" was prompted. I looked in the dictionary (Google), to ask what it means AND it was a typo. 🤯

How is this possible? Has anyone else had a similar experience with Gemini or a generative AI chatbot?

Wow, didnt expect it would reach to this point where even lyrics get censored

I typically use ChatGPT and Claude for stuff like Roblox game development and app development using 6.1 sol and 5.5 sonnet and (the occasional astra for an in depth prompt) I have a 20 plan for both and feel like the usage is fine. Last night I redeemed a Gemini student plan which seems to be just a normal 20 plan for Gemini ai, but the usage seems to be terrible if you’re doing something slightly deeper than surface level. I gave it resources to make a prompt using detailed car parts using argon, and it took 30% (it was 20k character and it did type it all out in chat) but I don’t think even Astra would spend that much on making a single prompt. I then asked it to clarify something using flash and that jumped me up to 77%! After I said “no way I’m at 77% usage already lol” to Gemini and it took 10% to explain possible causes. Is this a bottleneck of the student plans or is Gemini just terrible with its usage? Has anyone had a similar experience? Also this is not to hate on Gemini I actually think the prompt it made was pretty good but the amount of usage is super interesting.

Gemini 4 Argon frontier pictures images from Google Cloud, October 6th the image claims (Standard Pricing for Gemini 4 Argon is at December 1) With more information with the Gemini 4 Argon Frontier Perfomeance please go to: https://deepmind.google/models/model-cards/gemini-4-argon/ If you want to use the model go to: https://openrouter.ai/google/gemini-4-argon#providers Then press the Try button (You need to Sign in on Sign Up to use the model and you need to still pay there) The Gemini 4 Argon has Surpassed Claude and ChatGPT In Coding and Knowledge work. Gemini 4 Argon is not Available to all users Google LLC. Hasnt said what date for the free version if you dont want to pay You can wait.

What’s the point of having a weekly limit that’s lower than the 5-hour limit? I still have about 60% of my total limit left, but I can’t use it because the weekly limit ran out first! Ideally, they should both run out at the same time.

My dream was that I opened up Google Gemini on my PC, but was surprised to see that the font was now 8-bit and the models list was in the top left of my screen rather than the bottom right.

While Flash 3.8, Pro 3.1, and Argon 4.0 were still there, there were 'new models' in the works.

"Gemini Punch", the fast/lightweight model that was available to use

"Gemini Gold 4.1" was the successor to the Pro/Argon line, but it was grayed out - with a note underneath it written "otw"

Finally, in all lowercase, at the bottom of the list was 'gemini mommy'

Under gemini mommy, it said 'otws', which I understood to mean "on the way soon". It didn't explain the tier of this model, but in my dream state I understood that this was Google's super high-end model that would be #1 in basically every benchmark.

Disclosure upfront: Pluely gives a lifetime Dev Pro license if a public post about it reaches 5,000 views. I may submit this one. These are my real test results, including what didn't work.

I've been experimenting with using Gemini as the answer model inside a live meeting copilot, mainly because I didn't want another monthly AI subscription.

The copilot is Pluely: an always-on-top overlay that transcribes your mic plus system audio and feeds the transcript to your own model/API key — no bot joins the call, and with your own keys there's no Pluely subscription, just your own API usage.

My first setup was GPT-4o-mini for answers and Whisper for transcription, after adding some OpenAI credit. In my harder tests the answers were bad, and the transcription was shaky: wrong-language guesses, technical phrases coming out wrong, and some lines duplicated because mic and system audio picked up the same thing.

Then I switched only the answer model to Gemini 3.5 Flash-Lite, using my own Gemini API key. On the practice questions I tried, the difference was pretty obvious: answers were more relevant, more technical, and much more usable. Those Gemini runs also stayed within the free API limits.

The biggest improvement actually came from the system prompt, not the model switch. I forced every answer into three lines: NEED — what the other person is actually asking for; SAY — one short answer I could say naturally; ASK NEXT — one useful follow-up question. That format helped because it's short enough to read live, mid-conversation, and it never drops the actual request.

The weak point is still transcription. I still saw duplicated segments and mangled technical terms, and simply switching microphones helped more than tweaking any setting. Document attachments were locked in the setup I tested. I'd also like the answer text bigger on screen.

One limit on all of this: so far I've only run it on practice questions, not in a real meeting yet.

Curious whether anyone here uses Gemini Flash-Lite, or another low-latency Gemini model, for live transcription or meeting workflows — and especially what you're using on the speech-to-text side, since that's where my setup still struggles.

TL;DR: Argon is now my favorite model - over Opus 5.5 and Fable 5.1 (and I have two Claude Max 20x accounts.)

Background: I'm a Solutions Architect building rapid PoCs that may evolve into real products/tools/processes. I've found that Argon is excellent at understanding intent, which is especially useful for rapid PoCs/demos that are not intended to be MVP.

So for a bit over a week now, I've been using Argon almost exclusively, building PoCs/demos for evaluation. I've used about 3.8b Argon tokens (input + output) in that time.

I had been using Opus 5.5 for a specific PoC, and yesterday I had Argon review the entire repo. And Argon absolutely nailed it, so I'm just going to post Argon's review summary here verbatim (this project is my own, and may go open source, so I'm not leaking anything - I did make two mild redactions):

High-Level Takeaways

1. The Core PoC Thesis Is Strong (~35–40% of the repo):
   • The core architecture—an isolated Java 8 in-JVM agent driving Swing/AWT and embedded JCEF/JxBrowser views without Thread.sleep, paired with a Java 25 controller that uses AI at authoring/bind time (<mild redaction> session --vision, Recorder, llm-sql) and freezes deterministic, zero-LLM-call scenarios for CI (replay)—is genuinely well-conceived.
   • Specific subsystems like SwingModule.java, Pointer.java, Capture.java, JcefBrowser.java, TargetProbe.java, Grounding.java, and Recorder.java solve real, gnarly desktop automation problems and should be kept and protected.
2. The Opus 5.5 "Adversarial Governance Spiral" (~60–65% of the repo):
   • Across 8 phases, the model generated 810 numbered decisions (D1–D810), 41 owner ballots (B1–B41), 99 contract amendments (A1–A99), 182 security findings (S1–S182), and 43,240 lines of Markdown contracts.
   • By running 5 rounds of "8-lens + sceptic" security reviews, the threat model (docs/design/threat-model.md) escalated into treating your own AI planner, your local MCP client, the web pages inside the app under test, and your local filesystem as hostile attackers trying to run side-channel attacks against your test harness.
   • The clearest example is the currently open <mild redaction - call it a story>wait-match-oracle-gap-151.md (S180–S182), which proposes rewriting sync.await matches with 128-bit SecureRandom hex nonces so a local MCP client cannot use timeout durations as a side-channel oracle to guess a 4-digit staff code—and mandates updating 10 separate Markdown files (R9) for a ~25-line code change.
3. Where Defensive Ceremony Actively Hurts PoC Velocity:
   • Tier (b) llm-sql is harder than writing SQL by hand: AllowListCheck.java fails with SCHEMA_DRIFT if a live DB table has a single column not manually listed in Java (SqlAllowList), and Analyzer.java forces single-column ID projection requiring custom Java ValueLoaders.
   • SQL debugging & expression masking: DataQuery.java withholds SQLException.getMessage() whenever any MaskPolicy is registered (S22) and redacts every cell of any unnamed SQL expression column (D776).
   • Triple-artifact lock-in (.yaml + .plan.json + .bindings.json): Even though <mild redaction> run and ScenarioTests compile .yaml in memory, committing .plan.json (7,027 lines for 880 lines of YAML in samples) and hashing planHash inside .bindings.json means editing a UI step in YAML invalidates bound DB data across every seed/environment.
   • High onboarding tax: The lending sample required 8,266 LOC across a Java 8 agent module and a Java 25 domain pack; even the 0-verb web-host sample required 729 LOC across 9 Java classes.