Microsoft plans to invest US$ 1 billion from 2026 to 2028 to advance AI in Thailand - w.media
<a href="https://news.google.com/rss/articles/CBMioAFBVV95cUxNYlJscGtUamJVRzVubjA4YkY3c3JwaG9qVVVqX05RMG41bmczWXhKWmZyQ1ZpMUhHa2VpaFNReXBvQ3diT1gtRE42VktacjQ1M1A2VElUaFJDNEhjREJBWWpXbENEdnZ3Z1JUVld6T0lWZW1OOUllLXUxTmFWT3BPWHhFdnkxUjg2WkV1X3NvRWNuT0k4b2lueHRFV0FIbXBt?oc=5" target="_blank">Microsoft plans to invest US$ 1 billion from 2026 to 2028 to advance AI in Thailand</a> <font color="#6f6f6f">w.media</font>
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Per-Layer Embeddings: A simple explanation of the magic behind the small Gemma 4 models
Many of you seem to have liked my recent post "A simple explanation of the key idea behind TurboQuant" . Now I'm really not much of a blogger and I usually like to invest all my available time into developing Heretic, but there is another really cool new development happening with lots of confusion around it, so I decided to make another quick explainer post. You may have noticed that the brand-new Gemma 4 model family includes two small models: gemma-4-E2B and gemma-4-E4B . Yup, that's an "E", not an "A". Those are neither Mixture-of-Experts (MoE) models, nor dense models in the traditional sense. They are something else entirely, something that enables interesting new performance tradeoffs for inference. What's going on? To understand how these models work, and why they are so cool, let'
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