YHK - yet another hacker news clone

Argon is trying to solve the "hard" problems like those.

edit: It failed on the second prompt and they disabled it now.

https://one.google.com/about/google-ai-plans/?icid=microsite...

It doesn't show in the desktop UI.

> Gemini 4 Argon (Medium)

  Unavailable · Gemini 4 Argon is currently available to Ultra plan subscribers.

    Antigravity CLI 1.3.4
    [My account email] (Google AI Ultra)
    Gemini 4 Argon (High)
    ~/[working folder]

  > /model
    ⎿  Model set to Gemini 4 Argon (High)

Unique users, maybe. But a simple query that's answered in a paragraph is negligible compared to devs running agents continuously for tens of hours.

Given FelonyBench, do we also have a FantasyBench?

noooooooo

The results are a bit disappointing given Googles scale and training data.

The results are on par with cheaper Chinese open source models to be frank. https://cliphou.se/benchmark/

I'm not entirely sure you're correct. Your claim boils down to "Writing natural human prose" vs "Broad use of complex software and spatial awareness". My gut feeling is that there's more untapped and fruitful applications in the second group than the first - at least in the short term.

Source? Their documentation says it's available on the $20 plan and I can confirm firsthand.

https://one.google.com/intl/en_us/about/google-ai-plans/

$2/million input and $10/million output today. $4/$20 after December 1st.

+1. FWIW, I didn't even get far enough to wonder about the price.

The lead (and only) sentence states "Our next era of frontier intelligence, built to solve your hardest problems" a statement equally true of any new Google or competitor model, hence completely devoid of useful meaning. I then kept scrolling down... and down... and down, giving up after a half dozen screens of big images, huge videos and some customer stories. The first sentence of every text block was equally vague or meaningless so... tv;dr (Too vague, didn't read).

Even the Gemini sub-reddit is laughing: https://www.reddit.com/r/GeminiAI/comments/1x2dr47/the_lates...

It might not be as smart, though my employer gives me access to Claude, so I tried giving the same task to Opus 5.5. Both DS4.1 Flash and Opus5.5 produced nearly identical outputs.

After that, my company switched the entire engineering department to Azure hosted DeepSeek4.1 and costs were cut by 3/4. So far, no one has really noticed the change.

> If it's on HN and is a meme at this point, it will end up in the training set.

> It's kind of fun to imagine that there is an intern in every AI company furiously trying to get nice looking svg pelicans on bicycles.

Google listened and assigned a few interns, I guess, for the task

Modern models can make nearly photorealistic games in threejs. So why do they struggle with these pelicans?

Subtle highlights on the metal frame, sweat beads, jaunty little hat…it has style. At least compared to the rather literal ChatGPT outputs. https://static.simonwillison.net/static/2026/gpt-pelicans-gr...

It's a simple manner of prompt...generate concept art with nanobanana, measure, llm creates svg with text, measure the distance between rasterized svg embedding to nanobanana art....do post-training RL on a 3 million such images and matching embeddings.

I swear this will be a canonical example of Goodhardt's Law in action.

You have to switch to a new benchmark that isn't so easy to run a verifiable rewards pipeline on...Perhaps a longer horizon task where it's easy to mess up midway.

As one point, the high pelican's arm is see-through/messed up.

They do look worlds better than previously, to be fair.

https://en.wikipedia.org/wiki/Pelican#/media/File:Pelikan_Wa...

Give me reverse engineering bench and agentic coding.

Leave making visuals to humans who actually have aesthetic sense unlike whatever made this mess

I note that even these bikes are not actually rideable. For a bike to be stable, the front wheel axle needs to extend forward a little ahead of the fork - the fork must ”bend forwards”. This is what actually keeps the bike upright, not gyroscopic effects or some such. Given LLMs are generally good at basic maths, im surprised they get this wrong.

Maybe a bit less so in this case though. As per the announcements, the model was actually trained for this prompt, so was fed some training data. I can well imagine the data there was just not so good.

wasn't the whole point of pelicans is that labs *wouldn't* train for that?

so now the benchmark is ruined

Also, that first tweet contains a commentary that, for me, explain a lot about the public misconception about how current AI works:

> wait, does this mean you guys are actively training svg generation? i was mostly amazed by it thinking the models doing better at it by itself due to becoming better at reasoning.

No, it's not better reasoning that drives current AI, it's better examples we have been giving them. Whole army of people generating content for the frontier labs.

Also, reasoning improving the out of distribution part, like computer use, tool use etc.

<https://tools.simonwillison.net/svg-render#%3Csvg%20font-fam...> <https://tools.simonwillison.net/svg-render#%3Csvg%20font-fam...>

- I was having trouble embedding the SVGs as links, and ChatGPT recommended Simon Willison's SVG Renderer as a workaround.

Just my 2 cents on the pelicans benchmark: I think it might help if you were a bit more explicit about not taking this one benchmark too seriously. I know you say that at times, but at other times (such as the comment I'm replying to) you elide it which can read as a seriousness about the benchmark.

Argon: https://jonclegg.github.io/pacman-bakeoff/dev/#gemini-4-argo...

Opus 5.5 https://jonclegg.github.io/pacman-bakeoff/dev/#claude-opus-5...

This is the sheet: https://fernandoipar.com/content/music/poly-d/gemini-argon/c... (the annotations on it are by me)

And this is what Argon created: https://fernandoipar.com/content/music/poly-d/gemini-argon/b...

For this simple prompt: https://fernandoipar.com/content/music/poly-d/gemini-argon/p...

I'm honestly impressed.

God knows why the server different things to the same people on different interfaces

They've since scales the rollout to include Pro.

Or else it say, here is the result. (but no it didn't do no research, and tells me, sorry I forgot to search).

It's got balls to lie straight at my face.

They all ramble, they're all allergic to getting to the point. But, Gemini historically has pretty good prose. Even the self-hosted Gemma models have pretty nice, inoffensive prose. I wouldn't want to read a novel written by any of them, but I tend to ask Gemini questions in chat more often than Claude or GPT, because it has good prose and is good at using the web to research.

It looks like you've managed to decode it? Is that legit?

What kind of progress was made with Argon with the Voynich?

But id the progress of decoding it is legit, you should write something properly about it!

Likewise, you look at Artificial Analysis' general index and it cost $8,708 to run with Opus and $2,407 to run with Argon so even if Argon's token price doubles, it won't be the same price as Opus.

Sonnet is cheaper than Opus per-token, but in their coding tests Sonnet burns through enough tokens that it's 7.5% more expensive.

So much depends on how token efficient something is and whether it gets you a good result or not, right?

Plus, if one is using a plan instead of paying token rates, what does my $200 get me from OpenAI, Anthropic, or Google?

It just feels very hard to compare pricing given that the number of tokens used can vary so much. In Artificial Analysis' coding tests, Sonnet used 7.1x more tokens than Sol. Sonnet and Sol might have the same per-token pricing, but there's a huge difference in the number of tokens used.

The only thing I can figure is that Google's consumer-facing free-to-use models are set to "reason as fast as possible with only the most basic plausibility checks." I have to correct them like this every single time.

I think these companies have good models but expose them poorly like others have mentioned

I tell it to "Play Let it go from frozen". It plays it on youtube music. 5 seconds later I tell it again 'Play let it go from frozen" it then just dies saying "I can't play videos in car".

1. I ask it to list out all my old Keep notes that are just throwaway task lists, reminders, etc. to propose deleting them.

2. It comes up with like 4 of them (I know for a fact there are dozens, if not hundreds). I ask it for an exhaustive list.

3. I see its thinking trace briefly show "Refusing to produce an exhaustive list..." and then it says "Yes, that is all of them."

Not sure whether it's lack of intelligence, bad tools, or both, but that was extremely frustrating to see.

If it had exact location access people would be crying that Gemini steals their location data.

I told it to strip screenshots from our old basic explainer video to generate modern 3D video.

Pretty happy with the result. I told it to keep the audio and match the video and it did.

(Chrome, also incognito)

At this point a lot of stuff happens at the product level.

OpenAI and anthropic have distribution and data advantages now that are hard to replicate.

For chat, yes. But switch to code on the same page, and the picture changes. OpenAI takes the lead.

Since then they cut the price but the intelligence (of 6 and 6.1) has gone down to Sonnet 4 levels. I am back in a world where the model is lazy, cuts corners, ignores existing coding standards and context, makes stupid mistakes etc.

I am not sure how those models still rank so high in benchmarks when real-life usage is so obviously bad.

https://www.bridgebench.ai/nerf-bench

https://marginlab.ai/trackers/claude-code/ https://marginlab.ai/trackers/codex/

https://github.com/ninjahawk/livenerf

https://isitnerfed.org/

Related funny https://www.youtube.com/watch?v=DlTNN0gvkLM

?

They had gemini cli, what happened to that? Was fine

Is there anybody in Google having a look at how bad and complicated their payment system is? In all the other AI providers you just sign in, pay, get a key, done. In other clouds like AWS, you add a card, pay little to verify the card the first time, monthly bill, done. In Google it looks like you have to fight them every time for the very thing they should make as easy as possible: opening your wallet.

I would also advise against buying Gemini if you have any other Google services, because you get banned across board if they don't like something. To use Google it's best to first migrate off of Google.

Otherwise great product, I really like and would recommend Gemini. The usage is also very generous, at least Flash models.

i live in French Polynesia, a French territory, i'm a Google AI subscriber and i can't use Google AI Studio or antigravity cli because i'm in a geo restricted area ...

It seems Google has no issue laying out cables across the Pacific Ocean from/to Tahiti, they don't have an issue selling their services and plans there but i can't use AI Studio or antigravity cli because of geo restrictions ...

- Try in Gemini

- Try in Google AI Studio

- Try in Google Antigravity

Will non users of Google products even know what these three things even are?

What you did isn’t wrong, but is unfortunately very similar to what real scammers do. The heuristics behind these anti-abuse efforts are always going to have false positives.

Ended up with Soniox instead, took minutes to get started.

They made using it so hard/unclear that myself and every other dev I knew gave up.

It's clear that they still haven't done any organizational self-reflection.

They now ask for your mobile number during signup. And it wouldn't let me register any more accounts with my number. So frustrating.

"Unavailable · Gemini 4 Argon is currently available to Ultra plan subscribers."

That said, asking Gemini.google.com to walk you through the steps seems to suffice. In fact, one of AIs big perks for me is walking my through kafkaesque systems like Google’s to cut the shit and get to the result

- Then I realized Gemini 3 was active.

- I tried switching models.

- I was presented with a $99/mo subscription offering.

- I scrolled down and clicked to get cheaper options.

- I chose the $19.99 "AI Pro" monthly option and paid.

- In chat I was able to use Argon. Cool. Seems pretty smart.

- I asked Argon if I could use it through opencode with my Google account.

- Argon said yes, but then told me I'd have to use the Gemini CLI as middleman.

- I told it I didn't know there was a CLI for Gemini; I'd just use that.

- It told me how to install gemini-cli.

- I authenticate via Google OAuth inside gemini-cli.

- Gemini-cli rejects my login, says the app is deprecated for subscription users, who must use agy.

- I install agy.

- In agy, I open the model selection menu.

- "Gemini 4 Argon is currently available to Ultra plan subscribers." Odd... Gemini 4 Argon is the model (included in my Pro plan) that told me to do this.

- I discover that Claude Opus 5.5 is included in the subscription. I switch to it and give it a debugging task.

- Within about 5 minutes: "Individual quota reached. Please upgrade your subscription to increase your limits."

- This is dumb. Google stole twenty bucks from me; I want my money back.

- I go to the Gemini web app to cancel my subscription. I paid on my phone, so I have to cancel on my phone.

- The Gemini iOS app has a "manage my subscription" page. I go there to manage my subscription.

- This "manage my subscription" page includes no way of cancelling.

- After a minute of confusion I go to the App Store > Subscriptions > Gemini > Cancel.

- My subscription will end at the end of the month (no refund).

- Then I have to find the complaint form so I can request a refund.

- But I did request one.

Anyway, "frontier intelligence" just wasted about an hour of my life.

but they don't want to share their compute with us. giving those resources to their ad targeting algos is more profitable than renting them to us. so they make it as hard as possible. they tried as hard as they could not to release a new pro model at all, but now that fight has been lost they at least need to make sure we don't use it.

It's extremely inconsistent internally as well.

Look up the slime mold analogy for Google.

What if the answer is "they don't"?

It's also worth noting, Gemini 3.5 Pro never came out.

Having worked there (but only in a small part of it that eventually got absorbed into Gemini), it's some combination of:

* The eye of Sauron (management) can make specific spots (e.g. Gemini) push harder (and attract the motivated people at the company and be selective)

* They hire a lot better than average (Generally from the top 10%, hotter places in Google are more selective)

* They have a promo process that gives career motivated people a way to prove themselves (not always aligned with things being on the right track ofc, and not always non-political, but they definitely do something)

* Redundancy in teams causes them to compete (not necessarily productively)

Of course, there are other downsides not listed here (insane bureaucracy for launches, GDPR/BCID compliance, etc, etc), but a lot of what makes Google possible is the insane amounts of cash Search throws off.

They don't.

If they did, neither OpenAI nor Anthropic would even exist, as the core of what they're all doing was invented by Google engineers and researchers.

And at least Flash 3.7/3.8 were both difficult to persuade that they have wrong knowledge cutoff.

I reported it as a bug to Google, but as all of my reporting to Google, no response ever. This is just stupid!

why? is it not so good on that? or is there something better performance, or better price for same performance?

As an example, I recently had an OCT scan of my retina because I'm a “glaucoma suspect.” I'm a glaucoma suspect because I have an unusually high cup-to-disc ratio, which is a pattern typically seen in glaucomatous patients.

As part of this OCT scan, you get a report back on your RNFL, which is basically a report on the health and thickness of your optic nerve(s) and retina(s). (Dramatically oversimplifying, of course.)

My ophthalmologist struggled to explain my OCT scan results to me and instead sort of shrugged and said, "It's probably because you're a high myope, but we'll keep you coming in once a year.”

But my scans look horrifically bright red — meaning I'm in less than the first percentile of retinal thickness for my age. Obviously concerning enough to do my own research…

ChatGPT 6 Pro and Opus 5.5 have struggled to make any sense of the clinical implications of these scans. In contrast, Argon was the first model to:

1. explain how the OCT machine works

2. detail the physiological mechanism that causes the cupping and thinner RNFL layers in my scenario

3. sum it up into a clinical recommendation (similar to what my ophthalmologist recommended, with the addition of basic supplementation and lifestyle modification changes)

4. provide an interactive visualization of the topography of my eye vs a glaucomatic eye: https://optic-nerve-3d.netlify.app/

I'm blown away.

I've actually spent some effort trying to make them halfway decent placeholders by putting together a skill based on diataxis framework (https://diataxis.fr/) that at least got everything into the right location

just took a pass at rewriting them w/ argon and am very pleased, big improvement

From what I saw online.

Outside of that, behavior is quite good, a bit slow, and definitely different than both GPTs and Claudes -- which is appreciated.

do you have to pay those cheap prices eventually or can you just accrue an unlimited amount of overdue charges?

Astra 6 is significantly better than Google Argon 4 and a lot better than Claude Fable

https://generative-ai.review/2026/10/catchup-openai-astra-6-...

(edited thanks to tekacs)

I really can’t believe how bad the Gemini experience is. I have personal paid workspace account, and meant to have some better AI Google features I think.

So, I connected my Gmail to Gemini. 3.6 Flash kept telling me it doesn’t have any Gmail connnection, and then proceeded to hallucinate instructions for me to enable it, all bogus. At least 3.1 Pro realized that my Gmail is connected and then searched.

...or Gemini 4 is being gradually rolled out and is not available to everyone all at once.

I’ve tried the various alternatives (opencode, pi.dev) but for “have several physical machines, each running an orchestrator and 5-10 agents, and each agent spawning its own subagents to do work in parallel, all of that on a tanky GitHub project boards” level of work only Claude was doing work dependably.

I can have a few orchestrator leads which I talk to and brainstorm, and let them file tickets, organize them and assemble teams of agents to work of the various problems. And all of that with just a smidge of custom tooling on top of Claude Code’s default.

Plus don’t have a budget to spin all of this on cloud runners so I made sure to do as much as I can with local work on several machines I own. Was quite easy to achieve with Claude.

Do you have something similar built on top of a open source harness, or is it from scratch?

We won't know the answer to this until IPO and until each company reveals the economics of their models (training and serving).

Like when Miller in The Expanse talk to phone AI (https://m.youtube.com/watch?v=SC3bpJmGnPo) to trace Anubis flight, notice how the phone doesnt even reply in words.

Imagine if the phone instead replied with: "Thats a great insight, Anubis really goes there", it would be a joke. We are only tolerating it now because everyone is doing it.

Depends on where you draw the line for "near frontier model", but the current leading open-weights model, MiMo-v2.6, has exactly this problem. They released a fix on Hugging Face[1], but as of two days ago the model available via Xiaomi's own API still sticks in reasoning loops.

[1] https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD

This seems to be such a Google thing. Anyone who has used Gemini for long enough has encountered these 'She loves me, She loves me not' spam loops