tl;dr: Newb should use: venv, conda, poetry, Crew's "crewai install" command, docker containers? I'd love to hear what you recommend! @brandon I believe you prefer Conda ... why? [I've installed anaconda and can use that]

Request help for Building a chatbot for Log analysis using LLMs The Motivation is as follows: There are many industrial applications that use various systems (devices). It is these logs that are analysed by the engineers to study the functioning, performance and maintenance. This exercise is resource intensive. But it is unavoidable because there is deep reasoning and logic behind the analysis and is based on the industry specific knowledge and domain expertise and is currently done by humans.For standard devices like our computers, we have system logs, event logs and application logs. For analysing these logs, there are many commercial and open source applications since these logs follow a standard format. However, for proprietary systems like the one I am building for, the logs follow a custom format and these formats may have custom entities and descriptions. Of course, there is a document that has the Business logic and knowledge embedded in the logs that explains these custom logs in detail i.e. events codes, Alarms, descriptions, etc. This coupled with the "How to perform log analysis" document and product design document will help to understand why a device behaved in a certain way. My main goal is to provide a chatbot application that can use an LLM to extract key insights from log files. Essentially, provide a simple user interface where the upper management (Non technical staff) can ask questions and get a response. i.e. 1. When was the last time Device A generated the following events/alarms ? 2. In the last 30 days, how many times did Device B log the following error "<error description> 3. When the <error> was logged by Device A, was Device B in working condition? 4. Why did Device C generate the alarm at <time> on <date>? 5. Generate an error report for Device D in a tabular form with date time and the event with causation and its consequences on other devices? 6. Generate a summary report for the entire system for the month of <month>?

I have a problem: views on my LinkedIn posts with AI-generated images have dropped. How can I prevent Anti-AI Detector tools from detecting that the image is AI-generated? Any solutions or suggestions?

I wanted to give you a heads-up on an exciting new tutorial I’ve got in the works. This video is all about building a full-stack SaaS application that pulls in YouTube comments, processes them with CrewAI enterprise, and generates actionable content ideas. If you’re interested in building full-stack AI applications, this is for you! 🛠️ Here’s What You’ll Learn In this tutorial, I’ll guide you through each part of the process of creating this full-stack application. Here’s a breakdown: 1️⃣ Building the Frontend and Backend: We’ll use Next.js to set up the app and deploy it on Vercel. Plus, you’ll get hands-on experience connecting to a Neon Postgres database to manage data. 2️⃣ Integrating with CrewAI Enterprise: Learn how to harness CrewAI’s enterprise features to analyze YouTube comments, filter out casual messages, and focus on meaningful feedback. You’ll see how to create a system that automates data analysis and transforms raw comments into structured, actionable insights. 3️⃣ Generating Video Titles and Descriptions: We’ll configure CrewAI to turn filtered comments into potential video titles and descriptions. You’ll build a workflow that streamlines idea generation and content planning, using CrewAI’s advanced capabilities. 💡 Why This Tutorial is Worth Your Time This tutorial doesn’t just cover building one app—it teaches you how to apply the synthesize pattern for data processing, a core skill in AI development. Here’s why this matters: ✅ Real-World Adaptability: The synthesize pattern goes beyond YouTube. After learning it, you can apply it to dozens of other applications where large datasets need to be turned into insights. Imagine using this pattern for customer feedback, product analysis, or trend monitoring—there are endless opportunities to build your own AI-powered apps! ✅ Hands-On Full-Stack Skills: Get practical experience with tools like Next.js, Vercel, and Neon, and learn how to bring everything together into a seamless app. 📅 How to Catch the Release

I am working on video summarization task and want to extract relevant static frames from the video. Idea is: user describes his interests and AI summarizes video in text as well as compliments text sumary with relevant images extracted from video. Ex: "how to grill a steak" - images of BBQ, unpacking, spicing, temperature measurement, flares and then final result. "top investment advisers" - face shots of top advisers, snapshots of charts of their performance, etc... Looking for ideas on approaches to accomplish this.