According to Demis Hassabis on Twitter, many startups are building with Google’s Gemma models, shared during a chat hosted by Garry Tan at a YC community event. As reported by Demis Hassabis, this signals growing developer traction for Gemma’s lightweight open models, which are optimized for on-device and cost-efficient inference. According to Google’s official Gemma documentation, Gemma models are available in sizes like 2B and 7B with permissive licensing, enabling startups to fine-tune for domain tasks while controlling infrastructure costs. As reported by Google, the Gemma stack integrates with popular frameworks such as JAX, PyTorch, and TensorFlow, and supports safety toolkits, boosting time-to-market for early-stage AI apps. Business implications include lower total cost of ownership for inference, faster iteration cycles for vertical copilots, and improved data privacy via edge deployment, according to Google’s Gemma launch materials.

According to AndrewYNg on X, deeplearning.ai launched a short course "Build and Train an LLM with JAX" in partnership with Google, taught by Chris Achard, that guides learners to implement a MiniGPT-style 20-million parameter language model using JAX, Flax/NNX, and a chat UI for inference. As reported by deeplearning.ai, the curriculum covers JAX core primitives—automatic differentiation, JIT compilation, and vectorized execution—plus constructing embeddings and transformer blocks, loading a pretrained MiniGPT checkpoint, and running chat-based inference through a graphical interface. According to AndrewYNg, JAX underpins Google’s advanced models including Gemini and Veo, positioning this course as a practical route for engineers to understand the software layer behind large model training and deployment. For businesses and developers, the course offers hands-on skills for rapid LLM prototyping on accelerators, enabling cost-aware experimentation with compact architectures, reproducible training pipelines in Flax/NNX, and production-aligned inference patterns.

According to DeepLearning.AI on X (Twitter), the organization has launched a short course in collaboration with Google that teaches learners to implement and train a 20M-parameter MiniGPT-style language model from scratch using JAX, the open-source library underpinning Gemini. As reported by DeepLearning.AI, the curriculum covers model architecture design, dataset loading, and end-to-end training workflows in JAX, positioning practitioners to prototype compact LLMs and understand transformer internals. According to DeepLearning.AI, the course highlights practical advantages of JAX—such as function transformations, XLA compilation, and TPU/GPU acceleration—which can reduce training latency and cost for small to mid-scale LLMs. For businesses, this creates opportunities to upskill teams on JAX-based MLOps, accelerate custom domain adaptation with smaller LLMs, and evaluate migration paths for inference and training on Google Cloud TPUs, as reported by DeepLearning.AI.