1 link tagged with all of: local models + efficiency + simon willison + supply chain security + gemma
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This article covers highlights from a podcast conversation about recent advancements in AI models, particularly Google's new vision-capable LLMs. It discusses technical features like parameter efficiency and multi-modal capabilities, as well as ongoing challenges in running local models effectively.
- Google released Gemma 4 reasoning models (2B–31B params), with the E2B/E4B variants using Per-Layer Embeddings to boost on-device efficiency without growing total parameter count, and all versions handle text, images, and audio.
- Willison's hands-on testing found the smaller Gemma models worked well but the largest 31B model repeatedly errored out.
- A supply chain attack hit the Axios HTTP client via a malicious npm dependency, underscoring open-source package security risks.
- Willison argues efficient code will dominate AI deployment due to economic incentives, based on his experience running these models locally versus via Google's AI Studio API.
gemma
simon willison
local models
supply chain security
efficiency