In this episode, we’ll cover how to deploy your FastHTML app that’s integrated with Cloudinary to a production environment. By the end of this tutorial, you’ll know how to prepare your app for deployment, select a hosting platform, and get your web app live for users to access.
Author Archives: Surapong Kanoktipsatharporn
Enhancing Media Management with Cloudinary and FastHTML – ep.4
In this episode, we’ll explore how to enhance media management in your FastHTML app using advanced features of Cloudinary. You’ll learn how to apply image transformations, support video uploads, and implement tagging and search functionalities to manage your media effectively.
Displaying Cloudinary Images in FastHTML – ep.3
In this episode, we’ll learn how to dynamically retrieve and display images from Cloudinary in a FastHTML app. By the end of this tutorial, you’ll be able to create a simple image gallery that fetches images from your Cloudinary account and displays them on your webpage.
Uploading Images to Cloudinary with FastHTML – ep.2
In this episode, we’ll learn how to upload images to Cloudinary directly from a FastHTML app. We’ll build a simple image upload form, handle the form submission, and integrate Cloudinary’s Upload API to store the images.
Introduction to FastHTML and Cloudinary – ep.1
In this episode, we’ll introduce FastHTML and Cloudinary, set up the development environment, and create a basic project structure. By the end of this episode, you’ll have a simple FastHTML app running locally and be ready to integrate Cloudinary for media management.
สอนเขียนโปรแกรม CUDA Programming ในการประมวลแบบขนาน ด้วย Python
เริ่มต้น CUDA Programming วันนี้เราจะมาเรียนรู้เรื่องการประมวลผลแบบขนาน ด้วย CUDA ในภาษา Python โดยเฉพาะอย่างยิ่งในงานการประมวลผลภาพ ที่เราจะได้เห็นการแปลงภาพ RGB เป็นภาพเฉดสีเทา และการคูณเมทริกซ์ ซึ่งเป็นพื้นฐานสำคัญในงานประมวลผลภาพ และความเข้าใจในการทำงานของ CUDA
Unlock the Power of LangChain AI: Introducing Graph Q&A with Knowledge Graphs
LangChain AI has an exciting feature that supports knowledge graph data represented as triplets structures using the RDF framework. This implementation provides two LLM-supported triplets operations: graph extraction and graph Q&A. By default, LangChain uses LLMs such as GPT-3, GPT-4 to extract knowledge triplets from text and store them in a NetworkX directed graph.
Secrets Revealed: How LangChain’s Entity Memory Gives You Tailored Responses!
LangChain is a conversational AI framework that provides memory modules to help bots understand the context of a conversation. One of these modules is the Entity Memory, a more complex type of memory that extracts and summarizes entities from the conversation.
Unlock the Secret to Lifelike Chatbots: Conversational Memory Revealed!
By default, LLMs, Chains and Agents are stateless. They operate independently on each incoming query, without retaining any memory of previous interactions. However, in certain applications like chatbots, it is crucial to remember past conversations in both the short and long term. This is where the Memory feature comes into play.
LangChain: The Secret Sauce to Building Next-Level Language Applications
LangChain is a framework that revolves around large language models (LLMs). It enables the development of applications using LLMs for various purposes like chatbots, generative question-answering, summarization, and more.