AI · Video
Theo Retracts Praise for Gemini 4 Carbon Over Severe Response Delays
Theo - t3.gg ·
At a glance
This video is from the Theo (t3.gg) channel. Theo corrects his earlier mild review of Gemini 4 Carbon and covers the response delays he found in real-world use. Gemini 4 Carbon is an AI model from Google.
Key points
- In real-world tests, Gemini 4 Carbon was about five times slower than competing models.
- It takes over 11 seconds on the server before it starts answering a request.
- In multi-step automated tasks, the delays add up and bring workflows to a halt.
- He criticizes its caching system (storing frequently used data in advance) for a low hit rate that raises operating costs.
Why it matters
It is useful for developers deciding whether to adopt AI models for real work and for anyone weighing their costs.
Original source
I was wrong...
Theo - t3.gg · Nov 14, 08:45 KST
Watch on YouTube ↗AI summarized the video content. Check the original for accuracy. Copyright belongs to Theo - t3.gg.
More from this field
- Claude Whiteboards, Sheets and Data: What Works Now
This video tries out Claude's new whiteboard, sheet (spreadsheet), and data features to see which ones are actually useful. It also asks whether there is a reason to move from Google Sheets.
The AI Advantage · Nov 14, 08:57 KST - Amazon Plans Index of Online Product Data Across the Web
Amazon reportedly plans to gather data on products sold anywhere online, including items it does not sell, and show it on its own site, according to Reuters. The project would cover products beyond Amazon's own catalog.
The Verge · Nov 14, 08:49 KST - TSMC Leak Exposes 46,000 Staff Records; Hackers Demand Bitcoin
Personal data of about 46,000 current and former TSMC employees in Taiwan was leaked and offered for sale on underground forums. The sellers threatened to publish it within a week if no buyer appeared.
코인리더스 · Nov 14, 08:32 KST - GS Caltex Connects No-Code AI Models to Production Pipelines
GS Caltex linked AI models that process engineers built without code to its live production workflow. Splitting the work between two teams cut model deployment time sharply.
Amazon AWS Machine Learning blog · Nov 14, 08:22 KST