Cover Tuner Uses NLP to Help Improve Your Cover Letters

May 4, 2020

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In this episode of Running in Production, Saad Malik talks about building a free cover letter analysis tool with Flask and Python. It uses NLP (Natural language processing) and has been up and running on Google App Engine since April 2020.

Saad goes over various Python NLP libraries, processing 400+ cover letters in his first month after shipping an MVP, using MongoDB as a primary database, keeping his front-end simple with a bit of jQuery, what it’s like to deploy a Python app using Google App Engine and more.

Topics Include

2:54 – You can upload your cover letter and get back an analysis without an account
3:50 – Motivation for using Flask and Python
4:48 – Writing the “business logic” in a standalone script before adding a web layer
5:48 – What is NLP (Natural language processing) and what Python libraries exist for it
7:01 – Using an NLP library vs using a full text search database
8:14 – About 1,000 users a month go to the site and 50% of them upload a cover letter
9:25 – Lots of users re-upload new copies of their cover letter after making changes to it
10:06 – Server side rendered templates with Jinja plus a touch of jQuery here and there
10:53 – After submitting a cover letter, an ajax response fills in the info after ~5 seconds
11:31 – Gunicorn is used as the app server for Flask
11:46 – Why Saad chose to use Google App Engine over using Google Compute Engine
12:43 – Motivation for using Google App Engine over Heroku and other PaaS alternatives
14:09 – It’s mostly a monolithic application but with a separate script that runs locally
14:59 – The local script helps validate cover letters
16:47 – MongoDB Atlas is used to host MongoDB along with Google Cloud Storage
17:51 – Why did you choose MongoDB over PostgreSQL or another SQL database?
18:50 – MongoDB Compass is a way for you to visually explore your data
19:29 – Docker isn’t being used in development but app engine uses it in production
20:20 – nginx isn’t needed because app engine handles all of that for you
20:57 – App engine is nice but it does come at a price (it’s quite a bit more expensive)
22:13 – App engine costs won’t necessarily scale linearly with your traffic
23:46 – A run down on all of the Google Cloud services Saad is using and how they connect
25:14 – Are MongoDB databases really schemaless?
26:05 – PyMongo is used to connect the Python app to MongoDB
27:13 – It only took 4-5 days to turn the standalone script into an MVP Flask app
29:13 – Only the Python NLP libraries are note worthy libs to make this app work
29:44 – There’s no user authentication needed because no user accounts are necessary
30:14 – WuFoo is used to accept form submissions using their free tier
30:35 – WTForms is also used to process the cover letter form submissions
31:17 – Google Search Console helped make the site more mobile friendly
32:09 – The site isn’t using Bootstrap, it’s just plain old hand rolled CSS and JavaScript
32:53 – Both app engine and MongoDB Atlas provide notifications for various events
33:26 – Walking through deploying code from development to production on app engine
34:38 – Saad has tests set up with Pytest
35:09 – What exactly is that YAML file with app engine?
35:48 – Dealing with secret keys
37:05 – Both MongoDB Atlas and Google App Engine have tools for disaster recovery
38:17 – Alerts can be set up to measure resource consumption, including cost limits
39:22 – App engine’s price is high, Saad would probably use Google Compute Engine instead
40:47 – Best tips? Be mindful of the SAAS tools you use and how they interact with your app
42:11 – If you crank out code ASAP to ship an MVP, don’t forget to go back and refactor
43:10 – Using the Spyder IDE to help develop certain features faster and easier
44:28 – If you want to contact Saad you can find him on LinkedIn

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Cover Tuner Uses NLP to Help Improve Your Cover Letters

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