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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.