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X under Elon Musk continues to decline, both as a real-time news feed, and a free speech app, with the…
Instagram’s adding a new element to Reels which could end up being a disaster, or it could encourage more engagement.…

Are you ready to elevate your podcast game? Wondering how to create a show that will attract a growing audience? In this article, we’ll explore the state of the podcasting industry and how to create a better quality show. Why Podcasting Matters Podcasting has become an increasingly popular medium for creators, businesses, and audiences. Why […]
The post Podcasting: How to Create a Better Show appeared first on Social Media Examiner.
Are you ready to elevate your podcast game? Wondering how to create a show that will attract a growing audience?…
Do you find it difficult to keep up with the latest ML research? Are you overwhelmed with the massive amount of papers about LLMs, vector databases, or RAGs?
In this post, I will show how to build an AI assistant that mines this large amount of information easily. You’ll ask it your questions in natural language and it’ll answer according to relevant papers it finds on Papers With Code.
On the backend side, this assistant will be powered with a Retrieval Augmented Generation (RAG) framework that relies on a scalable serverless vector database, an embedding model from VertexAI, and an LLM from OpenAI.
On the front-end side, this assistant will be integrated into an interactive and easily deployable web application built with Streamlit.
Every step of this process will be detailed below with an accompanying source code that you can reuse and adapt👇.
Ready? Let’s dive in 🔍.
If you’re interested in ML content, detailed tutorials, and practical tips from the industry, follow my newsletter. It’s called The Tech Buffet.
Do you find it difficult to keep up with the latest ML research? Are you overwhelmed with the massive amount of papers about LLMs, vector databases, or RAGs?
In this post, I will show how to build an AI assistant that mines this large amount of information easily. You’ll ask it your questions in natural language and it’ll answer according to relevant papers it finds on Papers With Code.
On the backend side, this assistant will be powered with a Retrieval Augmented Generation (RAG) framework that relies on a scalable serverless vector database, an embedding model from VertexAI, and an LLM from OpenAI.
On the front-end side, this assistant will be integrated into an interactive and easily deployable web application built with Streamlit.
Every step of this process will be detailed below with an accompanying source code that you can reuse and adapt👇.
Ready? Let’s dive in 🔍.
If you’re interested in ML content, detailed tutorials, and practical tips from the industry, follow my newsletter. It’s called The Tech Buffet.
Do you find it difficult to keep up with the latest ML research? Are you overwhelmed with the massive amount…
Dressed in camouflage-style swimming trunks, lounging by the pool of a Hard Rock Hotel in Tenerife, these are the latest…