Build your own Artifact Previewer using Local LLM

Backend Engineer ๐ Cloud Native Enthusiast โ
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Backend Engineer ๐ Cloud Native Enthusiast โ
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vLLM is a workhorse to run inference for any LLM under the sun. One of the recent developments in the project is the ability to define thinking_token_budget, basically a request level argument that ca

In a common workflow we create a virtual environment using different tools inside of the project directory. You might have venv, .venv, env and so on. I find it a bit cluttered. My approach is a bit d

Learn how to build your own powerful Large Language Model (LLM) application that can search the web, extract information, and answer your questions โ all LOCALLY! In this tutorial, I'll walk you through the process of creating a web-searching LLM fro...

Having a private PyPI repository can be beneficial in many ways. We can host our internal private packages; from security perspective, it can be a controlled environment for public packages from where all the dependencies are installed in our project...

Large Language Models are great at generating code, but we need to go an extra mile to run them; well not any more.
In this video, we will be building our application that can use both local and hosted large language models (Gemini, ChatGPT, and so on) for generating code, and then we'll build our own sandbox to run that code and preview the output. This is similar to what you can experience with ChatGPT Canvas and Claude Artifact; but in our case, we have complete end-to-end control over the environment, dependencies, and the entire workflow. Go beyond generation โ execute and iterate with confidence! ๐