Porcupine   VS fastify

I call it DaVinci. DaVinci uses Picovoice (https://picovoice.ai/) solutions for wake word and voice activity detection and for converting speech to text, Amazon Polly to convert its responses into a natural sounding voice, and OpenAI’s GPT 3.5 to do the heavy lifting. It’s all contained in about 300 lines of Python code.

Download pre-trained models: "Porcupine" from Porcupine Wake Word and Video Player Context from Rhino Speech-to-Intent repositories - You can also train a custom models on Picovoice Console.

Below are some useful resources: Open-source code Picovoice Platform SDK Picovoice website

Download the Porcupine model and turn the binary model into a base64 string.

Download Porcupine (i.e. Deep Neural Network). Run the following to turn the binary model into a base64 string, from the project folder.

Porcupine SDK Porcupine SDK is on GitHub. Find libraries for supported MCUs on the Porcupine GitHub repository. Arduino libraries are available via a specialized package manager offered by Arduino.

Looking for more? Explore other languages on the Picovoice Console and check out for fully-working demos with Porcupine on GitHub.

Don't forget to visit Porcupine's Wake Word's Github repository to see Python demos. If you want to do something similar to the video above, find the open-source codes here

I sometimes see the term "end-to-end", and it always passes right by my ears as marketing jargon. For example, there was a recent post today that linked to this page: https://picovoice.ai/, and you'll find the statement "... end-to-end platform for adding voice to anything on your terms". I did a quick Google search and it seems like the term is used in many different contexts (e.g., encryption, enterprise software for product development, etc.), but to be honest, I'm just not getting it. Maybe someone can explain here within the realm of embedded software? Could you provide some examples as well?

For example, Fastify removed the instanceof operatorfrom its codebase because it was causing problems for those developers that rely on jest as a testing framework.

Both are frameworks but NextJs is for Forntend (web app in browser that use ReactJs under the hood) and NestJs is for Backend (server app running on a server witch use Express or Fastify). The only thing similar between them is the typed language in which they were written.

Restify & Fastify Hapi

Let's create a sample Node API project we can work with throughout to build a new docker image. We will leverage Fastify to create an API that we configure via the fastify-cli.

Fastify

In setting out to build this service, we wanted to use gRPC for its APIs. We’ve been reaching for REST when building APIs so far, primarily out of necessity, i.e., our public APIs needed auto-generated client SDKs and docs for developers working with them. We built those APIs with Fastify and Typebox but felt burned by a code-first approach to generating an OpenAPI spec. I’ll spare you the details and save that experience/learning for another article. Suffice it to say we love gRPC’s schema-first approach. This blog post summarizes our feelings well

Fastify – Fast and Low overhead web framework

Custom Functions are powered by Fastify (a light-weight Node.js framework that claims to be faster than Express.js), so they’re extremely flexible.