LLMs
Google Adds Multimodal Support to Gemini API File Search
Google's Gemini API File Search evolves into a native multimodal RAG engine, integrating Gemini Embedding 2 for unified text and visual data retrieval.
Category
Category: LLMs
AI Research
Google’s Multimodal RAG: File Search Power and Pitfalls
Google's Gemini Embedding 2 unifies text, image, and audio into a single semantic space, revolutionizing multimodal RAG pipelines and reducing engineering overhead.
LLMs
When Failed Startups Sell Corporate Data for AI Training
Unstructured corporate archives and failed company data are becoming the new gold rush for training context-aware LLMs and agentic workflows.
AI Research
DeepMind and NVIDIA Unveil New AI Models and Robotics
Google DeepMind launches Gemini 3.1 Flash and Gemma 4, alongside NVIDIA collaborations to advance agentic workflows and embodied robotics reasoning.
AI Research
Scaling AI Agents: Compute Deals and Efficient Code
Scaling AI agents requires a dual focus on massive compute capacity and non-blocking, efficient code architectures like async/await to handle production workloads.
Google
Gemini Nano in Chrome: AI Features Deployed in Silence
Google Chrome's silent 4GB Gemini Nano model downloads raise critical concerns regarding user consent, privacy laws, and on-device AI governance.
AI Research
Chrome’s Silent 4GB AI Download Raises Ethical Questions
Google Chrome's unannounced 4GB AI model download raises critical ethical concerns regarding user consent and the hidden costs of on-device inference.
LLMs
Unsloth + NVIDIA: VRAM-Efficient Fine-Tuning in Production
Optimize LLM fine-tuning by bypassing VRAM limitations using Unsloth and NVIDIA's custom CUDA kernels for faster, memory-efficient model training.
LLMs
Unsloth and NVIDIA Partner on Faster LLM Fine-Tuning
Unsloth leverages custom CUDA kernels and 4-bit quantization to deliver up to 30x faster LLM fine-tuning with significantly reduced memory overhead.
AI Research
How Unsloth Breaks the VRAM Wall in LLM Fine-Tuning
Unsloth revolutionizes LLM fine-tuning by bypassing the VRAM wall through custom CUDA kernels, enabling long-context training on consumer-grade hardware.