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The AI Backend * work in progress, please provide feedback so we can improve Just like in 1995 it was obvious that every business needs an internet presence to stay competitive, in 2024 it's obvious that every software needs intelligence to stay competitive. Software products generally have 3 c...

A practical guide to building successful LLM products.

Let's reproduce the GPT-2 (124M) in llm.c (~4,000 lines of C/CUDA) in 90 minutes for $20. The 124M model is the smallest model in the GPT-2 series released by OpenAI in 2019, and is actually quite ...

Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the first blog post in a three-part series explaining some key elements of how LLMs function. This blog post covers pre-training—the process by which LLMs learn to predict the next word—and why it’s so surprisingly powerful.

Join us for a comprehensive survey of techniques designed to unlock the full potential of Language Model Models (LLMs). Explore strategies such as fine-tunin...

Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows - neuml/txtai

Gain valuable insights into essential topics such as LLM training, prompt engineering, concerns, applications, and more.

Proving you're a human on a web flooded with generative AI content

i gave a talk at the recent AI Eng Singapore meetup (https://www.latent.space/p/community, scroll down) about the past 1 year in agents thinking and the bui...

Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail. - ...

Notes about my hobbies and other things I find interesting.

How does something like Perplexity work, and how do we make our own? And having done that, what turned out to be the most interesting or challenging parts?

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. - karpathy/minbpe

llama3 implementation one matrix multiplication at a time - naklecha/llama3-from-scratch

An opensource agent orchestration framework built on top of the latest OpenAI Assistants API. - VRSEN/agency-swarm

Crew AI is not suitable for production use cases. I’ll be going through why I believe this is the case and what you should do instead when building your own ...

Today’s language models are powering ChatGPT and other popular AIs. Learn the differences between LLMs and SLMs in this detailed article.

SingleStore Webinar

Transform yourself from a Python Developer to a Junior NLP Data Scientist with practical projects.

Serverless RAG on AWS — Amazon Bedrock, Amazon Kendra, AWS Lambda, Claude-2, LangChain, and Streamlit.

Lyzr SDKs help you to build all your favorite GenAI SaaS products as enterprise applications in minutes. - LyzrCore/lyzr

The unreasonable effectiveness of embeddingsOr how I learned to stop worrying and love the hallucinations.This week I dived deep into vector databases. My go...

Evaluating intermediate steps (like query rephrasing and retrieved documents) is also super important when trying to make sure RAG works Goo tutorial by on how to do this! — Harrison Chase (@hwchase17)

Learn how to build an agent that can reason over your documents and answer complex questions. Learn from the co-founder and CEO of LlamaIndex

Site Reliability Engineer

sqlite-vec is an new vector search SQLite extension, coming soon!