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AMD CEO LISA SU JUST QUIETLY KILLED $400/MONTH IN OPENAI AND CLAUDE CODE WITH ONE BOX RUNNING A 235B MODEL
amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory
the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb
amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload
a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months
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Anthropic engineer:
"If you're not running agents yet, you're losing hours on work that takes minutes."
In 29 minutes he breaks down, why agents collide and what it takes to make them work as 1 team in Claude.
Watch the full talk, then save the exact setup below 👇
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🚨 China Just Gave The World Access To Quantum Technology
China has reportedly released the world’s first open-source quantum operating system — and it’s free for anyone to download. Until now, quantum computing tools were mostly locked inside private labs and research centers.
This breakthrough could allow developers, students, and researchers around the world to explore powerful quantum technology, helping speed up discoveries in AI, cybersecurity, medicine, and science.
Some experts believe this could change the future of computing forever… and the global tech race may have just entered a completely new era.
Source:
China releases open-source quantum operating system for global developers. Xinhua News Agency.
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1. Dense Models - Slow and Smart
Example: Qwen3.6-27B / Gemma-4-31B
What it means:
- when a prompt is sent
- it gets tokenised (words are mapped to tokens)
- token generation starts
- the 27B means 27 billion parameters
- each of those parameters will be activated
- 27 billion matrix multiplications
- for every token generated
Active parameter counts are positively correlated with intelligence. That's why Gemma-4-31B is able to compete with Mixture of Experts (MoEs) 10 times their size.
2. Mixture of Expert models - Fast and Efficient
Example: Deepseek-V4-Flash / Qwen3.5-397B
What it means:
- when a prompt is sent it's tokenised
- it's sent to a router
- a router was trained to match prompts with experts
- experts are sub-networks of the model
- when found the experts are activated
- tokens are generated with only a fraction of the params
For example: Deepseek-v4-flash has 284 billion params 11x larger than the dense Qwen3.6-27b.
But only 13B of those 284B will activate per token, which is less than half of the size of Qwen3.6-27B
----
Dense Pros:
- Dense models are easier to train
- They tend to be smaller overall
- They can be very smart per token
Dense Cons:
- Competitive dense models are on average slower than their MoE peers.
- Less parameters to train and specialise.
MoE Pros:
- Can be much larger and be trained longer
- Faster token generation
MoE Cons:
- Larger vram requirements
- Harder to train
--------
Lmk if there's anything i'm wrong with or missing
🚨BREAKING: Claude can now research like a Stanford PhD student.
Here are 9 insane Claude prompts that turn 40+ research papers into structured literature reviews, knowledge maps, and research gaps in minutes. (Save this)
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