The 10x Engineer

At Innolitics, we strive to be 10x engineers. We certainly don't claim that we are truly 10x better than the average software engineer, and it seems unreasonable to even believe you could know that if you were. Still, everyone on our team is passionate about software development and is striving to be the best we can be.

This section of our website contains various content highlighting how we strive to be 10x engineers. Every week we read a technical article or work on a mini-project, and discuss it as a team. You can view a list of articles we have discussed here. We write articles to solidify our know-how. Finally, we have an ever-evolving and growing discussion of our software development philosophy and best practices.

If you are a software engineer, we hope that you find this content useful and interesting. Perhaps it can help you strive to also be a 10x engineer.

Lessons 🔗

Overview

We provide a number of training lessons that we use to train Innolitics team members. Here are a few examples of historical training lessons.

Innolitics Culture

These lessons are meant especially for new members of the Innolitics team. We are excited to have you on board!

• Our Mission and Values

• Improving at all Levels

Learning to Learn

These "meta" lessons focus on how to effectively learn new technologies.

• Reading Protocols and Standards (WIP)

Process

These lessons focus on the design and development processes we follow at Innolitics.

• Figma, White-Boarding, and UI Design

• User Centered Design: Writing Personas, Journey Mapping, and Conducting User Interviews (WIP)

• Information Security

• Requirements Gathering (WIP)

Core Technologies

These lessons focus on the fundamentals of technologies we use frequently at Innolitics.

• The Linux Command-Line (WIP)

• Git and GitHub (WIP)

• HTML, the DOM, and Browsers (WIP)

Programming

• Intermediate Python: Dunder-Methods, Decorators, and Context Managers

• CSS (WIP)

• Relational Databases (WIP)

• JavaScript (WIP)

• React (WIP)

• MATLAB (WIP)

• Qt (WIP)

Medical

• DICOM Basics (WIP)

• Introduction to HL7

• Medical Device Software Regulations (WIP)

10x Time 🔗

Every Wednesday afternoon we read technical articles, watch videos, work on mini-projects, and then we discuss it on a joint video call. Here is the list of historical 10x topics.

• Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions
• Hacker Laws
• Cython
• Structural Pattern Matching
• Cross-Platform Mobile Development with React Native
• The Socratic Method: A Practioner's Handbook by Ward Farnsworth
• Learning Resource Sharing
• Small Group Session Planning
• An Introduction to GitHub Actions by Gabriel Tanner
• Parameter-Based Testing Using Hypothesis by Hypothesis Works
• The C4 model for visualising software architecture by Simon Brown
• Docker Documentation Review by Docker
• Risk-First Software Development - On Estimating by Rob Moffat
• Risk-First Software Development by Rob Moffat
• Complete Guide to Bringing a Medical Device to Market by Tom Rish
• How to Write a Git Commit Message by Chris Beams
• What is Elasticsearch?
• Web Content Accessibility Guidelines (WCAG) 2.1 by Ben Caldwell, Michael Cooper, Loretta Guarino Reid, Gregg Vanderheiden, et. al.
• GraphQL
• Julia
• Write code that is easy to delete, not easy to extend. by tef
• Video Series - Learn how the Internet works by Ben Eater
• Will handheld ultrasound bring imaging into the home?
• D. Richard Hipp - SQLite [The Databaseology Lectures - CMU Fall 2015] by D. Richard Hipp
• Top 10 Application-Design Mistakes by Jakob Nielsen and Page Laubheimer
• Files are fraught with peril by Dan Luu
• Digital Image Processing by Wilhelm Burger and Mark J. Burge
• Medical Imaging Signals and Systems (2nd Edition) by Jerry L. Prince and Jonathan Links
• Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) by FDA
• Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) by FDA
• Coursera: Cryptography
• Stanford: Implementing an OS in Rust on a Raspberry Pi
• MIT OCW: Applied Category Theory
• PEP 557 -- Data Classes by Eric V. Smith
• Round Table Discussion of 2018 FDA Safety Communications by USA Food and Drug Administration
• Round Table Discussion of Attack's in OWASP's Cyber Security Catalog
• Overview of Fluorescence Glucose Sensing: A Technology with a Bright Future by David C. Klonoff
• Write tests. Not too many. Mostly integration. by Kent C. Dodds
• The Practical Test Pyramid by Ham Vocke
• Just Say No to More End-to-End Tests by Mike Wacker
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art by Maciej A. Mazurowski et. al.
• Why Doctors Hate Their Computers by Atul Gawande
• Linux Kernel Development, 3e: Chapter 14 on The Block I/O Layer by Robert Love
• Linux Kernel Development, 3e: Chapter 13 on The Virtual File System by Robert Love
• How Browsers Work by Tali Garsiel
• Networking Tutorial by Ben Eater
• HIPAA Security Rule Regulation
• PEP484 - Type Hints
• Bash Scripting Best Practices by Yaroslav Tkachenko
• Defensive BASH programming by Kfir Lavi
• Beyond PEP 8 -- Best practices for beautiful intelligible code by Raymond Hettinger
• Automated Medical Image Segmentation Techniques
• Localization Project by J. David Giese and Yujan Shrestha, MD
• Regulatory Documentation Manager by J. David Giese
• HIPAA Compliance for Developers: Getting Started by Aptible
• What Every Computer Scientist Should Know About Floating Point Arithmetic by David Goldberg
• Distributing Python Modules - Writing the Setup Script
• PEP 427 -- The Wheel Binary Package Format 1.0 by Daniel Holth
• Miniproject: Interpolation and Registration by J. David Giese
• Medical Image Registration by Derek L G Hill, Philipp G Batchelor, Mark Holden and David J Hawkes
• Class activation maps in Keras for visualizing where deep learning networks pay attention by Jacob Gildenblat
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps by Karen Simonyan, et al.
• Alice in Python Projectland by Vicki Boykis
• The Law of Leaky Abstractions by Joel Spolsky
• The Iceberg Secret, Revealed by Joel Spolsky
• Things You Should Never Do, Part I by Joel Spolsky
• Recursive Neural Tensor Nets
• Learning From Data, Section 2.3 by Yaser S. Abu-Mostafa, et al.
• Learning From Data, Section 2.2 by Yaser S. Abu-Mostafa, et al.
• Nuts and Bolts of Applying Deep Learning by Andrew Ng
• Learning From Data, Section 2.1 by Yaser S. Abu-Mostafa, et al.
• Concurrent computing with Python
• A stable and fast implementation of natural neighbor interpolation by Luming Liang & Dave Hale
• Education of a Programmer by Terry Crowley
• Learning From Data, Section 1.4 by Yaser S. Abu-Mostafa, et al.
• Learning From Data, Section 1.3 by Yaser S. Abu-Mostafa, et al.
• Learning From Data, Section 1.2 by Yaser S. Abu-Mostafa, et al.
• Learning From Data, Section 1.1 by Yaser S. Abu-Mostafa, et al.
• What is the expectation maximization algorithm? by Chuong B Do & Serafim Batzoglou
• What is self-documenting code and can it replace well documented code? [SO]
• How to Write the Perfect Pull Request by Keavy McMinn
• CT Image Reconstruction by Terry Peters
• The Python 3 Data Model by Jeff Donahue et al.
• Getting started with three.js and WebGL by James Sanchez Elias
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition by Jeff Donahue et al.
• Fully Convolutional Networks for Semantic Segmentation by Evan Shelhamer, Jonathan Long, and Trevor Darrell
• Pixel Recurrent Neural Networks by Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu
• Visualizing and Understanding Convolutional Networks by Matthew D. Zeiler and Rob Fergus
• Deep Learning, Self-Taught Learning and Unsupervised Feature Learning by Andrew Ng
• Quality Work Doesn't Mean Quality Service by David H. Maister
• Dermatologist-level classification of skin cancer with deep neural networks by Andre Esteva, et al.
• Why Django Sucks by Emil Stenström
• PyOpenCL Examples - First Six Mini Projects by Tiago Ramalho
• On Writing Well - Chapter 10, Bits and Pieces by William Zinsser
• The Rust Programming Language: Chapter 4 on Syntax and Semantics by The Rust Development Team
• React Quick Start by The React Development Team
• Understanding the Basis of the Kalman Filter
• Digital Image Processing: Sampling, Discrete Fourier Transform, Sample Rate by Aggelos K. Katsaggelos
• Digital Image Processing: 2D Convolution, Filtering in Spatial Domain, 2D Fourier Transform by Aggelos K. Katsaggelos
• Redux: UI Framework by Dan Abramov
• RFC 3986: Uniform Resource Identifier (URI): Generic Syntax by Berners-Lee, et al.
• The Humble Programmer by Edsger W. Dijkstra
• Clean Code - Chapter 3 on Functions by Robert C. Martin
• Clean Code - Chapter 2 on Meaningful Names by Robert C. Martin
• Medical Image registration by Derek L G Hill, et al.
• RFC 793: Transmission Control Protocol by Information Sciences Institute
• Code Complete, 2ed - Chapter 20 on The Software Quality Landscape by Steve McConnell
• Code Complete, 2ed - Chapter 7 on High-Quality Routines by Steve McConnell
• Django by Django Software Foundation
• Logical fallacies in software development by Artur Martsinkovskyi
• Git Deep Dive by GitHub.com
• Disassembly with Ghidra by The National Security Agency
• Writing Professionaly by Writing Commons
• Automation
• Tools Discovery
• Generative Adversarial Networks (GANs) by Jason Brownlee
• Haskell and Functional Programming Lessions by Miran Lipovaca
• FIPS-140-2 and -3
• The Usability of Advanced Type Systems
• Intended Use/Indications for Use and the FDA Browser by Jim Luker
• Debugging Tools
• ChatGPT Prompt Engineering for Developers by Isa Fulford and Andrew Ng
• (125%)**10 == 10x
• Introduction to Biostatistics by Caitlin Ward and Collin Nolte