pi-autoresearch is an autonomous experiment loop for optimizing various targets like test speed, bundle size, LLM training, or build times. Inspired by karpathy/autoresearch, it utilizes a skill-extension architecture, allowing domain-agnostic infrastructure paired with domain-specific knowledge. The core workflow involves editing code, committing changes, running experiments, logging results, and either keeping or reverting the changes – a cycle that repeats indefinitely. Key components include a status widget, a detailed dashboard, and configuration options for customizing behavior. It persists experiment data in `autoresearch.jsonl` and session context in `autoresearch.md` for resilience and reproducibility.
A 12-week, 26-lesson curriculum all about Machine Learning, using primarily Scikit-learn and avoiding deep learning.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Trail of Bits announces the open-sourcing of Buttercup, their AI-driven Cyber Reasoning System (CRS) developed for DARPA’s AI Cyber Challenge (AIxCC). The article details how Buttercup works, including its four main components (Orchestration/UI, Vulnerability discovery, Contextual analysis, and Patch generation), provides instructions for getting started, and outlines future development plans.
The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.
SciPhi-AI/R2R is a framework for rapid development and deployment of production-ready RAG pipelines. The framework enables the deployment, customization, extension, autoscaling, and optimization of RAG pipeline systems, making it easier for the OSS community to use them. It includes several code examples and client applications that demonstrate application deployment and interaction. The core abstractions come in the form of ingestion, embedding, RAG, and eval pipelines.
