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release deployment build Commitizen friendly

This workspace is managed by pnpm and Lerna. It follows the semantic versioning using conventional commits - all orchestrated by lerna.

Preparing for release

Ensure you are on a feature branch. You'd then need to create a pull request for the feature branch to main. After all the review ceremonies are complete you then proceed with the following steps. ⛔️⛔️⛔️ NOTE: DO NOT MERGE THE PR!!!!!

Steps to prepare release

  1. In your local repo, checkout to your feature branch
  2. Ensure your working directory is clean (stash any untracked or uncommitted changes)
  3. Prepare your changes for release by running;
pnpm release:prepare

If the command above is run successfully, you'd be automatically checkout to the release branch (i.e releases/*).

  1. On the release branch, create a PR to main by running;
pnpm release:pr

The command above will automatically create a pull request targeting main from your release branch.

After all review ceremonies on the release pull request are complete, merge to master. ⛔️⛔️⛔️ NOTE: DO UPDATE THE RELEASE BRANCH DIRECTLY. You should update your feature branch instead then star from Step 1.

Steps to publish

  1. Merge your release pull request to main once ready. This will kickstart the release workflow in the CI - which publishes changed packages to npm and creates tagged releases with notes. As a side effect, your feature branch pull request will be automatically closed.
  2. After the release workflow is successful, confirm your release and published packages on npm.

Ayomide Bakare's Projects

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🎒 Backpack is a minimalistic build system for Node.js projects.

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Blynk library for embedded hardware. Works with Arduino, ESP8266, Raspberry Pi, Intel Edison/Galileo, LinkIt ONE, Particle Core/Photon, Energia, ARM mbed, etc.

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The Flix Programming Language

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The goal of this project is to enable users to create cool web demos using the newly released OpenAI GPT-3 API with just a few lines of Python.

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A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

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