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

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.

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

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

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.

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)

Google please 🙏 stop this "trail" ai pro bullshit and give gemini 4 argon and claude opus 5.5 & sonnet 5.5 to non paid pro users through offers

I never thought a company like Google would ever do something like this like they had everything best engineers, gpu, tpu, data like all of it why do they have to scam people like that by marketing pro plan even though it's not the actual pro one they should have mentioned that it's trail version that you will get discrimination later, if model isn't available on the harness it totally worst for me since I don't use web app anymore

P.S They are also cancelling student ai pro plan and forcing refunds

I have the Gemini Pro version (had it free from being a student). Basically Argon 4 can only be used for approximately 3 or 5 requests before reaching my 5 hour limit.

This is just insane to me. I also use Claude and I can ask Sonnet or Opus 5.5 on extra or max effort way more than Argon 4.

So is the usage for Argon 4 that bad or did Google cap the people with the student Pro version specifically ?

If it's like that for eveyone then it's just incomprehensible.

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.

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.

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Im curious. not using 3.8 flash anymore. it's garbage. sticking with codex rn but i want to try argon in case it happens to be good.

Edit : All this happened inside Antigravity

So recently I noticed that, I asked it to do a specific task in a new conversation but then it didn't like yeh results so I aborted it, so in one of my old conversations it started which was not even relevant to that, it started executing it?

I never experienced this before? Does anyone know about this? Or am I the only one who realised this now?

I upgraded to Argon. I’m not concerned about usage limits, as I can partition how I use the model, although being forced to do so because of restrictive limits is ridiculous in itself.

My greater concern is the extent to which guardrails are degrading the quality of responses. Much of my research involves reasoned analysis, theological inquiry and nuanced examination of case studies. Instead of engaging with the substance of my prompts, the model 90% of the time misinterprets the subject matter as a personal crisis and responds with patronising, unsolicited advice about stepping away or touching grass.

The problem is not simply that these false positives occur. When I clarify the context, the model often answers the original question perfectly well. This raises a legitimate question about the effectiveness of the initial safeguards and creates an unnecessary UX problem: why should I have to prompt twice to receive an answer the model was capable of providing in the first place?

This is not a matter of poor prompt quality if the original prompt was already sufficiently clear. The model should be capable of distinguishing analytical discussion, theological reasoning and third-party case studies from genuine expressions of personal distress, rather than treating sensitive subject matter as evidence of a crisis in itself.

I would rather have smaller usage limits on Argon than have its reasoning constrained by excessive false positives. Unfortunately, AI Studios has presented similar problems.

I hope this is addressed sooner rather than later. Safety measures should be context-sensitive, not a substitute for contextual understanding. if anyone at Google is reading this, I'm dissapointed, I hope you're at least trying behind the scenes

Most self-hosted AI integrations rely on pay-per-token API keys (OpenAI, Google AI Studio, Vertex), burning credits on a metered billing meter for every single message and tool call.

Meanwhile, many of us already have a Gemini Pro Free or Paid subscription (or use the included web/mobile quota) with access to massive context windows and reasoning—completely unused by our homelabs.

Google Gemini natively supports the Model Context Protocol (MCP), letting you connect homelab tools directly into the official Gemini web and mobile apps for zero extra API costs.

The catch? Gemini requires a strict, public OAuth 2.1 (RFC 8414 Discovery + PKCE) server and expects a single HTTPS endpoint. It cannot communicate with multiple unauthenticated local Docker microservices.

Nothing in the open-source ecosystem existed to bridge multiple homelab services into Gemini's OAuth requirements, so I built the Universal MCP Gateway.

[ Google Gemini Web / Mobile ] (Zero API Metering • Uses Included Sub)

| (Public HTTPS • Strict WebAuthn / Passkey Only)

v

+----------------------------------------------+

| Universal MCP Gateway (Hub) |

| |- OAuth 2.1 Server (Discovery + PKCE) |

| |- Dynamic Multi-App Port Routing (3000+) |

| |- FIDO2 / WebAuthn Biometric Passkeys |

| |- Composite Tool Execution Engine |

+----------------------┬-----------------------+

| (Internal LAN / Docker Network)

+-----------------+------------------+

v v v

[ OpenGym MCP ] [ HealthBridge ] [ Home Assistant ]

Key Features

• Zero API Bills (No Metered Token Fees): Connects directly into the official Gemini Web and Mobile apps. Your existing subscription handles all the reasoning and tool calls without burning API credits.
• Hub-and-Spoke Aggregator: Consolidates multiple backend microservices (HTTP-MCP) into a single unified catalog. Namespaces tools automatically (gym_*, health_*, home_*) and handles dynamic JSON-RPC routing across your internal Docker network.
• Composite Tools Engine: Chain multiple microservices into one high-level tool from the UI. Supports parallel execution (Promise.allSettled), sequential step pipelining ({{steps.step1.id}}), and custom JS payload transforms. (Example: Querying workout database and nutrition tracker simultaneously in a single 3ms call).
• Strict FIDO2 / WebAuthn Passkeys: Zero password attack surface on the public internet. The /oauth/authorize consent screen requires a 1-click biometric tap using Touch ID, Windows Hello, YubiKey, Bitwarden, or 1Password. Apex domain scoping (domain.com) means a single enrolled passkey works across all your app subdomains.
• Dynamic Multi-App Port Isolation: Run multiple isolated app profiles from a single container on ports 3000, 3002–3010 (e.g. "Personal Coach" vs. "Smart Home") with scoped tool whitelists and JWT claims preventing cross-app token reuse.
• Private Self-Hosted WebUI: Dark-mode dashboard (runs locally on port 8080, unexposed to WAN) with an interactive tool playground, live SSE traffic drawer, secret masking, and real-time spoke health pings with latency metrics.
• Ultra-Lightweight (~14 MB RAM): Built on pure Node.js 22 built-ins with only ONE external production dependency (@simplewebauthn/server). Boots in <100ms inside an unprivileged Docker LXC on Proxmox.

I'm still polishing it but if you're interested, I can make a public repo for everyone to check out.

🥹🥹🥹🥹

Why? Is this the latest update to the subscription plans?

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

noticed the two assistants i use have opposite personalities. gemini pushes back and lists what could go wrong, which is annoying when i already decided but useful when i have not. the other one just runs with whatever i say. lately i pitch risky plans to gemini first to get the objections, then take the survivors to the eager one to actually build. which one do you reach for when you want a sanity check versus speed?

Am I just stupid? I can't believe "skills" are replacing gems soon. I have to manually disable whatever monster the chat creates every time I want to use a new fresh chat! I am using a new chat button to have a new clear chat, not use past prompts... I have to constantly go deactivate these stupid skills, and I need to use constantly new gems for work so it seems unavoidable. Like, one case = one gem. Is there ANY way to make it work the way like it used to as in, new chat = no using of skills without having to deactivate them constantly? The name "skill" is so vague I cannot find anything on the internet about it.