I’m deep in testing O3-Mini, GPT-4o, DeepSeek-R1, and DeepSeek-V3 to figure out which model is truly the best for AI agents. To find the winner, I’m stress-testing them against the most common tasks agents need to handle: 🧠 Instruction Overload: Can it handle rule-heavy tasks without getting confused or hallucinating? 🛠️ Tool Call Hell: Can it handle 5 consecutive tool calls, feeding results from one into the next without breaking? 🔍 Needle in a Haystack: Can it retrieve precise information from large datasets while staying contextually aware? So far, one model is dominating—and I’ll be switching all of my agents over to it going forward. 💡 I’ll share the full results (and the winning model) in my upcoming video—recording tomorrow, dropping Thursday! Stay tuned. Which model do you think will win? Drop your guesses below! 👇

I have some trouble with Chroma DB, specially in the minute 2:11:00 from the LangChain Master Class. I have done all the steps indicated, but when I try to retrieve the documents to do the query "How can I learn more of Langchain?", it retrieves documents that are non sense! I need some help to solve this, because on Stack Overflow some people said that they are just using Chroma library instead of the LangChain wrapper. The only thing I changed from the original code was the import, because it was deprecated and now it is recommended to use `from langchain_chroma import Chroma`. Does anyone has this same problem?

Hi everyone! I'm running into an issue with lesson 13.7, "Vercel Blob Store Upload." I deployed the project to Vercel, and everything works fine locally. When running the app locally, selecting a project takes me to the project details page as expected. However, on the deployed Vercel version, it shows a "not found" page instead. Has anyone encountered this before? Any ideas on what I might be overlooking? Thanks in advance

Remember to check your API_BASE_URL settings in your project variables *but also* go to your Vercel project settings and remember to turn off Vercel Authentication

Hi Fullstack AI Marketing Platform Course participant With 254 messages in our support thread, finding solutions has become challenging. To streamline this process, I propose we adopt a standardised format for issue reporting: Module: [Number and Name] Section: [Number and Name] Operating System: [Windows/Mac/Linux] Issue: [Brief description] Example: Module: 13. Asset Processing NextJS Section: 5. Manage Upload Header Operating System: Windows Issue: Sidebar doesn't stay at bottom of screen Benefits of this approach: 1. Easier searching for similar issues 2. Potential for creating a searchable FAQ database 3. Identifying recurring problems for @Brandon Hancock to address This format will help us all navigate and resolve issues more efficiently. What do you think about implementing this system?"