gptcli VS qwik

You must have an account with Open AI. If you don't have one, go here and create one.

For a long time, I was looking for something exciting to work on and this was it. The fact that ChatCraft already supported Speech to Text transcription using Whisper, which is another one of OpenAI's models with unique capabilities, integrating Text-to-Speech would essentially turn our application into something like an Amazon Alexa but with a brain powered the same LLM that ChatGPT uses.

With your API key, you’ll be able to make authenticated HTTP requests to OpenAI. So it’s a good idea to get familiar with the API itself. I’d encourage you to take a brief look through the OpenAI Documentation and become familiar with some concepts. The models are particularly good to understand because they have varying capabilities.

Next, we’ll also need an OpenAI account to have access to their API.

Capable of reasoning:The application can be connected to a large language model like OpenAI, Cohere, or Hugging Face. This enables the application to answer based on the context it is given.

Beyond that, "AI tools" is a pretty broad category. Odds are you're asking about LLMs (perhaps even specifically GPT), in which case a good place to start is with OpenAI and their API. Alternatively, Hugging Face is a good place to find models (not just LLMs, either) and they have tooling to let you interact with them in a consistent way. In these cases, you're not actually developing an AI, per se, but just utilizing it. But when I think of "AI Tools" at this point in time, I'm thinking of tools built on top of LLMs.

Like most things in IT and programming, this imo is also best learned by doing rather than reading a bunch of technical papers. I recommend starting with ChatGPT and testing out varieties of prompts asking it to fill in your knowledge. There are great documentation by both OpenAI and Microsoft Azure on using these models with examples. For open-source models (and transformers in general) HuggingFace is a great resource and you can get started on some smaller models by downloading it and trying right away. Karpathy's nanoGPT video on Youtube is also very useful to get started.

It's just a gui to (most of) what you get through the API. Read the API docs for details of each option: https://platform.openai.com/docs/introduction

For the full list of options, check out the OpenAI API documentation.

If you are interested in an API for ChatGPT in general, you can go look at the documentation or this starter guide for gpt-4 and gpt-3.5-turbo on the openai website. This should then be adapted to the language you are using.

Similarly to Promises/A+, this effort focuses on aligning the JavaScript ecosystem. If this alignment is successful, then a standard could emerge, based on that experience. Several framework authors are collaborating here on a common model which could back their reactivity core. The current draft is based on design input from the authors/maintainers of Angular, Bubble, Ember, FAST, MobX, Preact, Qwik, RxJS, Solid, Starbeam, Svelte, Vue, Wiz, and more…

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

If you want to see the framework that does it right, check out Qwik.

Incredibly small JS / CSS bundles. Only loads what it needs.

https://qwik.dev/

So here at Builder.io, my first task was to ensure that we migrated our site from Next.js to Qwik with a 100% pixel match. We aimed to utilize the power of Qwik to enhance our site's performance to unprecedented levels.

That was the last straw; cumulatively, I had spent more time looking for something to do than actually doing it. But I really wanted to contribute! So a few more months went by, until one day I met an Italian open source maintainer and long-time speaker, Giorgio Boa, who by the way was a guest on our podcast Continuous Delivery, and asked him for advice, saying that I wanted to be part of the OS world. He said he was working on a small library of Qwik components and could help me if I wanted. I gladly accepted, and we found an issue that seemed pretty straightforward. A few days after our conversation, I followed the little README guide to install everything required, and...nothing worked. So, after a few bad words, a lot of doubt about my skills as an engineer, and self pep talks to overcome my shyness about asking for help, I contacted Giorgio again. Even with his help, at first we had some trouble figuring out what was going wrong, but in the end I finally had a working setup.

In the previous post, we got AI generated jokes into our Qwik application from OpenAI API. It worked, but the user experience suffered because we had to wait until the API completed the entire response before updating the client.

In this series, we’ll learn how to integrate OpenAI‘s AI services into an application built with Qwik, a JavaScript framework focused on the concept of resumability (this will be relevant to understand later).