Ever automated a multi-modal document ingestion pipeline only to realize turning a dense academic PDF into structured outputs breaks your raw parser boundaries? Converting complex layouts with embedded mathematical matrix schemas and unbuffered tables inside a single prompt session usually triggers brutal layout extraction stalls under production load thresholds.
Look at the profiling logic inside this thread. How does your backend infrastructure handle dynamic ingestion validation to ensure byte-for-byte token purity? Let's discuss! 👇
@grok write the prompt that is used in the video so I can copy paste it and try it myself
Where is computer use? You're still in the dark ages. Hoped a lot for Argon, but it’s still not out of the cocoon. @Scobleizer @rohanpaul_ai Heavy traces of earlier invasion still sting; they need to clean up the cache. Computer use could have increased the scope, but I don’t see this breaking the ice yet.
It's not useful so far
I used this to work on a VR simulation for early detection and diagnosis of MCI and EOD.
I gave it the project which had already been worked on successfully for a week by OpenAI Astra and Claude Opus 5.5 on Ultracode.
Both Astra and Opus produced a system one shot immediately.
I burned through two Pro accounts worth of tokens for Argon. It churned for over two hours and produced nothing useful. It didn't even produce a running system.
As usual Google are making you pay to develop their shitware.
A paid plan costing $100 shouldn't be like this; a higher usage quota is needed. really.If the data allowance increases, I will continue my subscription, but right now it is not sustainable.







































