LLM training in simple, raw C/CUDA.

Tutorials on tinygrad

Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.

A collection of notebooks/recipes showcasing some fun and effective ways of using Claude. - anthropics/anthropic-cookbook

Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.

An informal capture from the CUDA mode hackathon today.https://github.com/karpathy/llm.c

OpenAI released two major new preview models today: o1-preview and o1-mini (that mini one is not a preview)—previously rumored as having the codename “strawberry”. There’s a lot to understand about …

A blazing-fast semantic search React component. Match by meaning, not just by letters. Search as you type without waiting (no debounce needed). Rank by cosine similarity. - Mihaiii/semantic-autocom...

🚀 https://neetcode.io/ - A better way to prepare for Coding Interviews🧑‍💼 LinkedIn: https://www.linkedin.com/in/navdeep-singh-3aaa14161/🐦 Twitter: https:...

Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.

Practical RAG techniques for engineers: learn production-ready solutions from industry experts to optimize performance, cut costs, and enhance the accuracy and relevance of your applications.

Kong AI Gateway 3.8 includes a new class of intelligent semantic plugins, new advanced load balancing capabilities for LLMs, and the official support for AWS Bedrock and GCP Vertex, in addition to all the other supported LLM providers.

The team behind Artifacts - an innovative new way to interact with Claude - shares how they built this innovative feature in just three months with a distributed team. Exclusive details.

To automatically determine the most important context from your codebase, we've just released v1 of Continue's codebase retrieval feature. You can ask questions like “where do I add a new endpoint to the server?”, “what scripts are used to build this project?”, or “where do I edit the user profile

This week, we discover the different types of bugs that LLM-generated code can provide, and how to mitigate their potential impact.

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When you’re writing in your favorite editor, the AI-like copilot will instantly guess and complete based on what you’ve written in the…

A Comprehensive Overview of Prompt Engineering

Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.

However, there are RAG challenges associated with the process. In this blog, we will explore the key RAG challenges in building LLM applications.

Solving the core challenges of Retrieval-Augmented Generation

Choosing the best reranking model for your RAG-based QA system can be tricky. This blog post simplifies RAG reranking model selection, helping you pick the right one to optimize your system's performance.

Indexing in terms of RAG is the process of organizing a vast amount of text data in a way that allows the RAG system to quickly find the…

For the runtime performance aware developer

Explaining the Intuition of Word2Vec & Implementing it in Python

Cost of AI in 2024: find out how much does AI cost now depending on your use case learn effective strategies to manage and optimize expenses.

Problem: Coding manually is painstakingly slow and inefficient. Solution: Innovative ways to integrate AI during development.

Here's a thread on how to make a RAG that supports PDF, URL, Text Uploads 👇🏻 — akhlas (@akhlas_hussain)

Authors: PromptArmor