It was great to work and make this project, Tuti is now deprecated https://divyendusingh.com/farewell-tuti
Note: this was a private project, this is made public. Some CI/CD scripts might be outdated. This is only for reference and might not be deployable as is.
docker build . -t lingoparrot docker tag lingoparrot:latest <docker remote> docker run -p 3000:3000 --env-file ./.env.local.docker lingoparrot:latest docker push <docker remote>
Setup for Content Website
cd carrd-language-learners-20220623 npx serve
We use a specific dev/production setup. For all the relevant commands, yarn scripts have two variants like:
yarn run set-webhook
yarn run set-webhook-production
The 1st one uses
.env and the 2nd one uses
.env.production. With sensible defaults, I believe that this setup is most convenient for bot development and yields least mistakes. Open to feedback.
.env_sample file to
.env.production for a production setup - details later) and fill the required values.
Run the following commands to start receiving
@<bot-name> requests on your local machine.
- runs the project locally (via
sls offline start, supports hot realoading).
- creates a tunnel (languagelearnersclub.localtunnel.me) from local to the internet.
- points the bot to local development version (uses
.envand localtunnel url).
yarn run watch- to watch and compile TS to JS.
Generally, I have two versions of the bot i.e. development and production. I point my dev bot to my local setup using the above steps.
yarn run deploy - deploys to Lambda using parameters from
yarn run deploy-production - deploys to Lambda using parameters from
- https://ibb.co/rx3tsGD (Pricing for a conversational app)