try VS ai

"try" is not a prefix (like watch, nice, etc) uses an overlayfs in order to be able to see and accept or reject changes to your filesystem from a command

https://github.com/binpash/try

For any shell command where a minor mistake has the potential of ruining your day, take a look at “try”, which will allow you to inspect the effects before running it against a live system: https://github.com/binpash/try

he is not running a container, he is mounting an overlay file system for each root directory on the current system, then looking at what has been added as a new layer - that's what the command is supposed to add to the running system.

Pure genius.

See https://github.com/binpash/try/blob/b2df6b650cb2b58951563174...

The Vercel AI SDK is built for OpenAI APIs and includes a range of tools for utilizing OpenAI APIs.

The AI is calling a function (https://platform.openai.com/docs/guides/function-calling) that is mapped to developer-controlled logic. The easiest thing to do is render specific UIs for every function call, but you can render however you'd like.

We kept the code snippets in the blog post brief, but you can see the full code for the demo here: https://github.com/vercel/ai/tree/main/examples/next-ai-rsc

In this blog post, we'll build a Next.js chatbot that runs on your computer. We'll use Ollama to serve the OpenHermes 2.5 Mistral LLM (large language model) locally, the Vercel AI SDK to handle stream forwarding and rendering, and ModelFusion to integrate Ollama with the Vercel AI SDK. The chatbot will be able to generate responses to user messages in real-time.

I recently set up the Next-Langchain-starter from Vercel, and it is phenomenal: Easy to setup, easy to modify. So far I've only done a few modifications:

Vercel AI SDK Playground Documentation

I used infrastructure as code (IaC) to provision and deploy Vercel's next-openai example. IaC is useful because it applies the same rigor of application code development to infrastructure provisioning. Instead of manual point and click in a cloud console which can be unrepeatable or error-prone, you just store and change all infrastructure configurations as code in source control .

You can use LLM providers other than hugging face such as Anthropic, Langchain adn OpenAI as well. Follow this doc to change the route handler(/api/chat/route.ts) and refer to the section of your chosen LLM provider.

**References: If you got stuck anywhere feel free to reach out. The Vercel documentation is excellent and the Github repo is also easily clonable if you don't have time to code from the start and just want to get stuck in customising.