liboai VS NATS

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

1.OpenAI API

OpenAI Account – make an OpenAI Account here

Their big advantage is that they can quickly deal with data they’ve never seen before and scale. NVIDIA and Open AI are currently leading providers of such technologies. Read more.

You need an API key https://openai.com/api

To run ChatGPT, you need access to OpenAI’s API3, which provides a cloud-based platform for interacting with various models, including ChatGPT. You can sign up for an account and request access to the API here: https://openai.com/api/

To get started, go to OpenAI and create an account. After that, click on your profile on the top right corner, and select view API keys.

As far as I know the ChatGPT API isn’t public yet. However you can use the GPT3 version from OpenAI which do have an API you can connect to. The Davinci-003 text model is pretty damn good. https://openai.com/api/ for more info

GPT-3: GPT-3 is an advanced language generation model developed by OpenAI. It is capable of generating high-quality human-like text with a high degree of coherence and coherence. The model has been trained on a vast corpus of text, making it capable of generating text across a range of topics. GPT-3 can be used for a variety of content creation tasks such as blog posts, articles, and social media updates. One of the most significant advantages of using GPT-3 is that it can save a significant amount of time when creating content. The model can generate text much faster than a human writer, and the output can be used as a starting point for further refinement.

Several message brokers, such as NATS and database queues, are not supported by OpenTelemetry (OTel) SDKs. This article will guide you on how to use context propagation explicitly with these message queues.

https://nats.io/ (Tracker removed)

> Connective Technology for Adaptive Edge & Distributed Systems

> An Introduction to NATS - The first screencast

I guess I don't need to know what it is

During our interview, we referred to NATS quite a few times! If you want to learn more about it, Sebastian suggests this tutorial series.

Pueue dumps the state of the queue to the disk as JSON every time the state changes, so when you have a lot of queued jobs this results in considerable disk io. I actually changed it to compress the state file via zstd which helped quite a bit but then eventually just moved on to running NATS [1] locally.

[1] https://nats.io/

Imagine you have an AI-powered personal alerting chat assistant that interacts using up-to-date data. Whether it's a big move in the stock market that affects your investments, any significant change on your shared SharePoint documents, or discounts on Amazon you were waiting for, the application is designed to keep you informed and alert you about any significant changes based on the criteria you set in advance using your natural language. In this post, we will learn how to build a full-stack event-driven weather alert chat application in Python using pretty cool tools: Streamlit, NATS, and OpenAI. The app can collect real-time weather information, understand your criteria for alerts using AI, and deliver these alerts to the user interface.

Why wasn't NATS[1] used ?

Written in Go, single-binary deployment... there's a lot to love about NATS !

[1]https://nats.io/

require nats.io

About cost, see [1]. Also, S3 prices have been increasing and there's been a bunch of alternative offers for object store from other companies. I think people in here (HN) comment often about increasing costs of AWS offerings.

Distributed systems and consensus are inherently hard problem, but there are a lot of implementations that you can study (like Etcd that you mention, or NATS [2], which I've been playing with and looks super cool so far :-p) if you want to understand the internals, on top of many books and papers released.

Again, I never said it was "easy" to build distributed systems, I just don't think there's any esoteric knowledge to what S3 provides.

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1: https://en.wikipedia.org/wiki/Economies_of_scale

2: https://nats.io/

I am in a project were nats.io is used. Someone thought, it would be a great idea to link data in an event with data in a response using the response channel name.