Obsidian-Copilot: An Assistant for Writing & Reflecting
Eugene Yan details Obsidian-Copilot, an RAG-based personal AI assistant for writing and reflection from personal journal entries.
Eugene Yan·11 Jun 2023
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Eugene Yan details Obsidian-Copilot, an RAG-based personal AI assistant for writing and reflection from personal journal entries.
Chip Huyen presented a framework for developing a generative AI strategy, addressing common enterprise challenges in adoption.
Hugging Face announced integration of DuckDB, enabling direct SQL analysis on 50,000+ datasets hosted on the Hugging Face Hub.
Hugging Face integrated fastText into its Hub, enabling easier access and sharing of fastText models and embeddings for text classification and representation.
TII's Falcon LLM series is now available on Hugging Face, including optimized versions and integration with the Hugging Face ecosystem.
Hugging Face detailed integrating AI speech recognition models with Unity game engine, enabling real-time voice interaction.
OpenAI launched a grant program to fund AI-powered cybersecurity tools for defenders, focusing on open-source and public goods.
Hugging Face is hosting an Open Source AI Game Jam, encouraging developers to build games using open-source AI models and tools.
Hugging Face released an LLM Inference Container for AWS SageMaker, simplifying model deployment and management for enterprises.
Hugging Face now natively integrates BERTopic, an open-source topic modeling framework, making it easier for users to deploy and share topic models.
OpenAI's non-profit, OpenAI, Inc., launched a grant program offering ten $100,000 awards to fund experiments on democratic AI rule-setting processes.
Hugging Face is integrating its Model Catalog directly into Microsoft Azure, making open-source models more accessible for Azure users.
Hugging Face and IBM announced a partnership to integrate Hugging Face models and open-source capabilities into IBM's watsonx.ai platform.
Intel claims Q8-Chat, an 8-bit quantized LLM, runs efficiently on Xeon, potentially lowering local inference costs.
Hugging Face was selected by France's CNIL for its enhanced support program, indicating increased regulatory engagement with open-source AI platforms.
OpenAI used GPT-4 to generate and score explanations for individual neuron behavior in GPT-2, releasing a dataset of these explanations.
Hugging Face released StarCoder, an open-source LLM specifically for code generation, finetuned on a large dataset of GitHub code.
Hugging Face released a Unity API, enabling developers to integrate Hugging Face models into Unity-based applications.
Hugging Face detailed how to train language models using its Transformers library with TensorFlow on Google's TPUs.
Databricks announced a collaboration with Hugging Face to optimize LLM training and tuning, claiming up to 40% speed improvements.
Hugging Face launched a dedicated Chinese blog and community platform to engage with Chinese AI researchers and developers.
Eugene Yan outlines 9 ML system design patterns, including human-in-the-loop, hard mining, reframing, cascade, data flywheel, and business rules layer.
Hugging Face announced optimization for Transformers on AWS Inferentia2, claiming significant performance and cost improvements for inference.
A small LLM was run on a Raspberry Pico to generate short, creative text formats like headlines and comments, demonstrating local inference.
Hugging Face blog post discusses Substra, an open-source framework for federated learning and privacy-preserving AI.
OpenAI launched a bug bounty program to incentivize researchers to discover and report security vulnerabilities in its models and platforms.
Eugene Yan details architectural design patterns for LLM planning, self-reflection, and hybrid search/recsys methods to improve RAG retrieval.
Snorkel AI and Hugging Face partnered to integrate Snorkel Flow's data labeling and programmatic workflow capabilities with Hugging Face models.
OpenAI published a blog post outlining its general approach to AI safety, focusing on responsible development and deployment.
Hugging Face released a guide and code for training LLaMA models using Reinforcement Learning from Human Feedback (RLHF).