Most open-source quadcopter projects are designed for seasoned developers or rely on complex, proprietary hardware. This creates a barrier for educators, students, and hobbyists who want to build and customize a quadcopter from scratch without deep embedded systems expertise.
What This Does
Flix is an open-source ESP32-based quadcopter designed for education and research. It uses general-purpose components and provides a simple, clean firmware (<2k lines in Arduino) for stability and control. The project includes comprehensive documentation, simulation with Gazebo, and tools for scripting and analyzing flights.
Key components:
Firmware: Minimal Arduino code (flix/firmware.ino) for flight control and MAVLink protocol communication.
Simulation: Gazebo-based simulator (gazebo/) that runs the original firmware for testing.
Python Tools: CLI (tools/cli.py) and library (tools/pyflix/) for scripting and analyzing flight data.
The control flow starts with the firmware (flix/firmware.ino), which:
Reads sensor data from the IMU (IMU.h).
Processes input from the gamepad or remote control (flix/controls.h).
Applies PID control to stabilize the quadcopter (flix/pid.h).
Sends commands to the motors via PWM (flix/motors.h).
Communicates with the ground station using MAVLink over Wi-Fi or ESP-NOW (flix/mavlink.h).
The Python tools (tools/) provide a CLI (cli.py) for command-line interaction and a library (pyflix/) for scripting and data analysis. The Gazebo simulator (gazebo/) uses the same firmware for accurate simulation.
How To Use It
Setup: - Clone the repository: git clone https://github.com/moses-y/flix - Install dependencies: pip install -r tools/requirements.txt - Build and flash the firmware to your ESP32 board using the Arduino IDE or esptool.py.
Configuration: - Configure the IMU and motor connections in flix/config.h. - Set up MAVLink communication in flix/mavlink.h.
Running it: - Start the Gazebo simulator: cd gazebo && make - Use the CLI to control the quadcopter: python tools/cli.py
Real-World Use
Flix is ideal for educational institutions and hobbyists who want to learn about quadcopter design and control. For example, a university robotics lab could use Flix to teach students about PID control, sensor fusion, and wireless communication. The Gazebo simulator allows students to test their code before flying a physical quadcopter.
Code Health & Issues
Medium/SDLC: No LICENSE file - unclear usage/redistribution rights - root
Low/Risk: Dependencies declared without a lockfile - non-reproducible builds - tools/pyproject.toml
The Bottom Line
Flix is a well-documented, educational quadcopter project that uses general-purpose components and provides a simple firmware for stability and control. It is ideal for educators, students, and hobbyists who want to build and customize a quadcopter from scratch. However, the lack of a LICENSE file and dependency lockfile could be improved for better reproducibility and legal clarity.
What the analyser found
Deployment readiness
4/7
✗Container image
✓CI pipeline
✗Lockfile committed
✓Test suite
✓README
✗License
✓No committed secrets
Composition
279 files
Markdown18
C/C++ Header17
Python13
JSON8
YAML3
CSS3
Module dependencies
The 10 most depended-upon modules of 16, from static import analysis. Red outline marks a module in an import cycle.
Ranked by severity × confidence × production reach. Reach is the honest discriminator across a collection that is mostly other people's code: the same finding matters more in something that ships.
high4
medium6
low1
Pin third-party GitHub Actions to a commit SHAhigh
.github/workflows
matiasalbarello/wait-on-check-action-ts@v1.2.0
A tag can be moved, so the action running with your token and secrets is whatever its owner last pushed; this is how tj-actions/changed-files leaked secrets from thousands of repos.
Fix: Replace each @vN with the 40-character commit SHA, keep # vN as a comment, and let Dependabot bump the SHAs.
Add a LICENSE; redistribution rights are undefinedhigh
no licence file at the repository root
With no licence the default is all rights reserved, so this code cannot legally be reused, which matters most for the owner's own work carried into client engagements.
Fix: Add MIT or Apache-2.0 at the repository root.
Add a test suite; this repository has nonehigh14 occurrences
14 source files, no test files
Any change ships with no signal that existing behaviour still holds, so a regression reaches production undetected.
Fix: Add one test per public entry point, then a CI step that runs them.
Commit a lockfile beside the manifesthigh
tools/pyproject.toml
manifest with no lockfile in scope
An unlocked range means the artifact you tested and the artifact you ship can contain different transitive code, so a malicious patch release reaches production with no diff.
Fix: Run the package manager once and commit the generated lockfile.
Declare least-privilege permissions for GITHUB_TOKENmedium2 occurrences
.github/workflows/build.yml
2 workflow(s) declare no permissions
With no declaration the token inherits the repository default, so any injected step can push commits or mint releases from inside your own CI.
Fix: Add permissions: contents: read at the top of the workflow and widen per job only where needed.
Enable Dependabot or Renovatemedium
2 manifest(s), no update bot configured
Without a bot a published advisory sits unpatched until someone audits by hand, which across 1,322 repositories means never.
Fix: Commit .github/dependabot.yml covering the repo ecosystems plus github-actions.
Gate pull requests on a dependency vulnerability scanmedium
.github/workflows
no dependency scan in CI
This is the one gate that would catch a known-vulnerable package before it reaches a build, and no repository in the sample had it.
Fix: Add dependency-review-action on pull_request, or osv-scanner on push and a schedule.
Move large binaries to Git LFS or out of the repositorymedium
gazebo/models/floor/materials/textures/floor.jpg
1 blobs over 5MB: floor.jpg 11.3MB
One repository here carries twenty blobs over 5MB including a 10.9MB spreadsheet, so every clone and every CI checkout pays for data nobody diffs.
Fix: Track those extensions with LFS, or move datasets to object storage and fetch them in a setup step.
Give the outbound request a timeoutmedium2 occurrences
tools/metadata.py
2 outbound call(s) with no timeout
requests has no default timeout, so a peer that accepts the connection and then says nothing holds the worker forever, and with a small worker pool a single slow dependency takes the whole service down without anything appearing to fail.
Fix: Pass timeout= on every call, or use a session with one configured.
Set persist-credentials: false on checkoutmedium
.github/workflows/docs.yml
checkout keeps the token, then dependencies are installed
The token stays in .git/config for every later step, so a malicious postinstall script reads a pushable credential without one ever being passed to it.
Fix: Add with: persist-credentials: false, and pass an explicit token only to the step that pushes.
Set timeout-minutes on the workflow jobslow3 occurrences
.github/workflows/build.yml
3 workflow(s) declare no job timeout
A wedged step runs to the six-hour platform default, which on a two-hourly schedule means three runs overlap behind it.
Fix: Add timeout-minutes with a realistic bound to each job.
Checked deterministically against the repository tree and a bounded set of its files: committed credentials, unpinned actions and base images, missing lockfiles and update bots, workflows that discard failures, published advisories against the declared dependencies, runtime configuration, licensing and notebook reproducibility. No language model is involved in this section.