Nirali Bhavsar’s Post

LazyLLM is a low-code development tool for building multi-agent LLMs(large language models) applications. It assists developers in creating complex AI applications at very low costs and enables continuous iterative optimization. LazyLLM offers a convenient workflow for application building and provides numerous standard processes and tools for various stages of the application development process. Features: - Easy AI Assembly: Assemble AI applications like Lego with built-in modules, no deep knowledge needed. - One-Click Deployment: Deploy complex multi-agent apps easily during POC and package images for Kubernetes in production. - Cross-Platform Compatibility: Seamlessly switch IaaS platforms without code changes, supporting various environments. - Grid Search Optimization: Automate hyperparameter tuning for efficient application optimization. - Efficient Fine-Tuning: Simplify model fine-tuning, focusing on algorithms and data, with minimal engineering effort. GitHub: https://lnkd.in/gkUh97kx https://lnkd.in/gff8N-gA

AI is the bone broth of software.

Week update: Techno Tender got 3x growth in registred users and we discovered that we need to focus on owners of heavy equipment. Weekly: make the app even more aesy focus on owners vNorme got a little stuck cause of some bugs. pipeline rebuilt, pivoted development methods. focusing on making a quick system for testing product hypoteses. Got Machine Learning Specalization for better understanding what we work with. MLOps is planned.

Build a live Object Detection Web Application using a pre-trained YOLOv5 model 🛠️ This article discussed the technologies used, key stages of machine learning, and practical tips for implementing real-time object detection. Requirements? A great attitude 😎 #mycompany https://gag.gl/v5Z3b3

🚀 Transforming Images into Insights with DSPy 📸✨ At Langtrace.ai, we're pushing the boundaries of what's possible with AI-powered applications. Our latest blog dives into how DSPy enables seamless attribute extraction from images, unlocking valuable insights from visual data. Whether you're building LLM-powered apps, optimizing e-commerce catalogs, or enhancing computer vision systems, this guide is packed with practical tips and real-world applications to get you started. 🔗 Read the full blog here: https://lnkd.in/gxBrKbGs

GPT-4 has made it very easy for me to build tools and write code. Something I would not have imagined before. There are a few LLM providers like Anyscale, TogetherAI and Groq that provide API access to the most popular LLMs. And they have generous free usage limits too. TogetherAI provides a good amount of starting credits and Groq has decent free usage limits. While many of us can easily sign-up and get these credits, we end up writing a lot of code to access the different LLMs not to mention different prompt templates for various tasks. To solve for this I created FastIQ - https://lnkd.in/e-8BdxrN FastIQ is a simple web interface I created where you can choose the LLM provider, provide your API key and choose a specific LLM you want to use. I also added a few common tasks that you can use immediately. And the best part, if you want to change something, just copy and paste the source of the webpage into an HTML file and go at it. You can even circulate the HTML file to your friends/colleagues and they can just open the file in their browser and start using it with their own API keys. I used GPT-4 to help me write all the code for the webpage although I asked for code that uses JQuery and TailwindCSS. I just wanted to keep it lightweight and easy to tinker with. As for the providers list, these are just the ones that are popular. You can add the ones that are missing. Adding tasks and prompts is just as easy. Hope you find it useful. Github: https://lnkd.in/gurnr_7i #fastiq

A few customers have contacted me to enquire whether bounding box annotation is more cost-effective than segmentation for large-scale annotation tasks. Their main concern is cost, yet they prefer segmentation since it offers exact annotations for an object. Previously, bounding box annotations were cheaper than polygon annotations. With Labellerr's cutting-edge auto-labeling features such as SAM, active learning, and model-assisted labeling, there is no price difference between the two options. Our customers may complete the segmentation of millions of pictures in just a few weeks with quality control. If anyone is wanting to label a high volume (>500,000) of images with the lowest turnaround time, please DM me or plan a demo call at https://lnkd.in/gRrf-jpU. 

Anyscale Together AI Groq OpenAI if you have API keys of all these providers or just a few of them, you can use a single playground by going to fastiq.netlify.app

In this episode of “Rust in the Cosmos” Part 2 we delve into the challenge of testing software for… ehm … space How can Rust help? Let’s find out 😉

Dear AI Enthusiasts, Machine Learning Lovers and Computer Vision Visionaries, we are Rodina and we would like to present to you our very own project we spent building over the last few weeks, the 🌊 Rodina Flow 🌊 We noticed that although data augmentation and synthesization are crucial for training computer vision models, performing it is laborious and difficult to tune and adapt to different use cases 🤔 This was a problem we could think of a solution for: one app with a simple friendly interface that lets you do all you need 🤯 That is how Rodina Flow was born 😎 Go check it out at https://www.rodina.app/ 🤘 You can currently download executables for both Mac 🍏 and Linux🐧Here are some examples to get you started https://lnkd.in/eMAmVw_K 🚀 We are still in a very early stage of development, so we appreciate any feedback on the current product! 🍀