Apurv A.’s Post

How to get started with #GenAI in 5 minutes. #NIM Step 1: Find the latest foundation models at https://nvda.ws/3XSfZoB Step 2: Select a model Step 3: View the Shell or Python calls to generate the API request Step 4: Start building your own custom applications powered by NVIDIA NIM

https://lnkd.in/g-B64KG3 This is my ai based youtube channel focused on tech news updates content. Completely written on python

Otto Engineer can now work autonomously across multiple files, easily writing the #code and tests for utilities and mini libraries! I also opened things up so that ANYONE can sign up and jump right into a chat with Otto Engineer for free (right now, TODAY, unlike Devin 😝) and with zero setup since it runs right in the browser 😎 Try out Otto, the #AI pairing partner, here: https://otto.engineer Try the interactive demo of the video below here: https://lnkd.in/gp8z762P Thanks to those who are cheering me and giving valuable feedback! Max Poshusta Eric Schneider Ryan Tomczik Ben Hapip and others 🙂 Otto is powered by OpenAI and some other incredible technology like Vercel, Neon, and web containers from StackBlitz 😎 And, of course, Otto currently specializes in #TypeScript, but may expand to other languages in the future.

Companies MUST learn from OpenAI before they are all swallowed up in the AI wave of innovation. It took me 30 minutes to build and launch a GPT in the GTP Store: See video here ---> (https://lnkd.in/gPQdBm8f). When I first applied for the OpenAI API key, I just got it. 👉 Other app stores its a nightmare - most approvals are automated biased algorithms that discriminate against people like us. Even today I have been rejected on the LinkedIn API - the reason: "unknown" 👉 Meta - you must supply ID, business documents and DNA sample to be rejected 5 times and approved the 6th time you submit the same documents. 👉 Twitter: Abandoned since they started charging $1000 for the API 😳 Other app stores just get me pulling my hair, they want screenshots, videos, documents, declarations 🤧 Honestly as a developer we spend more time dealing with approvals for apps that actually building apps, which make no sense at all 🤷♀️ and as a black developer, half your apps will be rejected for no reason. It makes more sense to build in safety control measures within the API instead of making people sign useless declarations.

💡 New Tutorial 💡 Are you looking to optimize your Python and Flask applications by dynamically swapping AI models based on user context? Discover how you can switch between AssemblyAI's state-of-the-art Speech-to-Text models, based on application contexts like user email domain, device, zip code, and more. Our Universal-1 model introduced a dual-class tier system, allowing developers to choose between the highest accuracy “Best” tier and the cost-effective “Nano” tier. This tutorial demonstrates how to leverage these tiers with LaunchDarkly for optimal app performance. Take your app development to the next level and check out the tutorial in the below LaunchDarkly post 👇 #AI #MachineLearning #Python #Flask #LaunchDarkly #AssemblyAI #TechTutorial #AppDevelopment

Use multiple OpenAI (speech) models in one request. Using JS and HTML only. Free download. https://lnkd.in/dNtkZM69

Title: Text Classification with Hugging Face Transformers: A FastAPI and Docker Deployment Guide Hey everyone! I'm excited to share with you a project I've been working on: a simple sentiment analysis application using Hugging Face Transformers, FastAPI, and Docker. With this application, you can quickly analyze the sentiment of any text you input, classifying it into two categories: Positive and Negative, along with the associated probability. Features: 1-FastAPI: FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. 2-Hugging Face Transformers: Hugging Face provides state-of-the-art NLP algorithms via Transformers. In this project, I've utilized their pre-trained model for sentiment analysis. 3-Dockerization: With Docker, I've containerized the application, ensuring that it runs consistently across different environments without any hassle. 4- Gradio Interface: Gradio is an easy-to-use library that allows you to create customizable UI components for machine learning models. With Gradio, you can build interactive interfaces for your models with just a few lines of code. How to use the application: 1-Simply visit the deployment link to access the live application. https://lnkd.in/dA2ffC-W 2-Input any text you want to analyze into the provided text box. 3-Click the "Submit" button, and you'll instantly receive the sentiment classification (Positive or Negative) along with the associated probability. But that's not all! I've also made the entire codebase available on my GitHub repository so you can dive into the implementation details, contribute, or use it as a reference for your own projects. https://lnkd.in/dJBQqsXF

I'm playing with my Semantic Kernel samples and GPT-4o and I've noticed that function calling behaves much better. Some of my demos that previously required a planner to properly work, now they can be achieved just using function calling. It's a great news, especially since planners will be deprecated https://lnkd.in/d-N3pcqA #dotnet #semantickernel

🚀 New Blog Alert! 🌟 Are you ready to supercharge your ML models with blazing-fast GPU inference? Our latest blog walks you through deploying a Python ML service using NVIDIA Triton Inference Server. From setting up Docker and preparing your model to measuring performance, we've got you covered! 🔍 Highlights: Difference between CPU and GPU inference Step-by-step guide to deploying with Triton Code snippets to get you started Tips on optimizing and scaling your service 🔧 Learn How To: Set up a Triton Model Repository Run Triton Inference Server in a Docker container Create a Python client for querying models Measure and optimize performance 🚄 We even use a high-speed train analogy to simplify the process! Don't miss out on making your ML service faster and more efficient. 👉 Read the full blog https://zurl.co/NLxD #MachineLearning #GPU #Triton #Python #Docker #AI #ModelServing #PerformanceOptimization #DataScience

🚀 New Blog Alert! 🌟 Are you ready to supercharge your ML models with blazing-fast GPU inference? Our latest blog walks you through deploying a Python ML service using NVIDIA Triton Inference Server. From setting up Docker and preparing your model to measuring performance, we've got you covered! 🔍 Highlights: Difference between CPU and GPU inference Step-by-step guide to deploying with Triton Code snippets to get you started Tips on optimizing and scaling your service 🔧 Learn How To: Set up a Triton Model Repository Run Triton Inference Server in a Docker container Create a Python client for querying models Measure and optimize performance 🚄 We even use a high-speed train analogy to simplify the process! Don't miss out on making your ML service faster and more efficient. 👉 Read the full blog https://zurl.co/NLxD #MachineLearning #GPU #Triton #Python #Docker #AI #ModelServing #PerformanceOptimization #DataScience