Welcome! My name is Alexandre Villares and since January, 2018 I have been coding sketches everyday, publishing the source code in the same repository that stores this page. The results here are mostly tentative, exploratory, and I don’t feel like they need to be relevant or meaningful on any particular day. The everyday practice leads to the emergence of ideas that I consider interesting, worthy of further exploration. Some of those have been added to selected work, this collection itself became valuable for me, and it is my pleasure to share it with anyone willing to explore coding as a creative and expressive medium.
Has a full list of tools used for all of the attempts. The most commonly used tool is py5 (Processing 4 + Python 3).
Web-based spectrum analyzer and GPS signal analysis platform. Does frequency analysis in realtime. Can process GPS and GNSS signals. Can also decode ADSB and ISM data.
Network Vector is a powerful, Python-based network scanning tool that performs comprehensive TCP port discovery without relying on external tools like nmap or masscan. It creates beautiful, interactive D3.js visualizations to map network topology and security posture.
Scans 750 unique ports without external dependencies. Up to 1000 concurrent threads for fast scanning. Randomizes IP and port scan order to evade detection patterns. Optional random delays between hosts for IDS evasion. Automatic CIDR-based network hierarchy visualization. SMB share enumeration. Automatic reverse DNS lookup for discovered hosts. Advanced fingerprinting using 100+ port signatures. Visual host coloring based on detected operating systems.
Pretty straightforward to set up and use, works just fine with a venv. Outputs HTML pages with all of the data and javascript baked into them, so you don't need to keep the application running to look at the data.
Spectre is a free and open source receiver-agnostic program for recording and visualising radio spectrograms. It's geared for hobbyists, citizen scientists, and academics who want to achieve scientifically interesting results at low cost.
Runs natively on Linux, including the Raspberry Pi. Intuitive CLI tool. Live record spectrograms and I/Q data. Offers fixed and sweeping center frequency modes. Backend web server with a discoverable RESTful API.
Applications include solar and Jovian radio astronomy, educational outreach, citizen science, amateur radio experimentation, lightning and atmospheric event detection, and RFI monitoring.
Simple yet flexible JavaScript charting library for the modern web. Tries to be as configuration-light as possible, just load and go. If size and loading times are a factor you can register and use only the components you need.
This repo contains all files related to the flip-card project, which is a business card that runs a fluid-implicit-particle (FLIP) simulation. The PCB design files are in the "kicad-pcb" folder. The flip-card project is inspired by mitxela's fluid simulation pendant project.
The RF Anomaly Detection System is a cutting-edge tool designed to monitor radio frequency (RF) spectrums and identify unusual signal patterns. Using powerful machine learning algorithms (especially unsupervised learning techniques), it can detect potential security threats, interference sources, and unauthorized transmissions—without prior knowledge of the specific threats.
Continuous monitoring of the RF spectrum for rapid anomaly detection. Advanced machine learning algorithms detect unusual signal patterns without the need for pre-existing threat data. Automatically categorizes detected anomalies based on signal characteristics. Compatible with RTL-SDR hardware for capturing real-world RF signals. Rich visualization tools like waterfall displays and heatmaps to simplify signal analysis. Configurable notifications to keep you updated on detected anomalies.
This is a modern re-implementation of the old Veles visualization. Unlike the original implementation proposed by codisec, this implementation pre-processes the data into a 3D voxel space which is then rendered via 3D textures. As a result, rendering performance is completely independent of the data size and even large input files can be visualized without problems.
Veles combines advanced hex explorer and data visualization features with an extensible framework for binary data analysis. Reverse engineering binaries? Exploring file system images? Steganography? Veles supports your work in all these fields.
With the number of different architectures, file formats and encodings you can't always expect to find the tool you need online. Veles uses client-server architecture, where each analyzer can run in a separate process. All you need to do is get the data from the server, implement a Python function to parse it and return the results.
It is a very difficult task for a human to notice subtle patterns in large amounts of binary data, however, us humans are very good at finding patterns in images. Statistical visualizations let you find the important bits in a sea of binary data - all at a glance.
Veles combines advanced hex explorer and data visualization features with an extensible framework for binary data analysis. Reverse engineering binaries? Exploring file system images? Steganography? Veles supports your work in all these fields.
With the number of different architectures, file formats and encodings you can't always expect to find the tool you need online. Veles uses client-server architecture, where each analyser can run in a separate process. All you need to do is get the data from the server, implement a Python function to parse it and return the results.
Available for Windows, OSX, and Linux.
Github: https://github.com/codilime/veles
In the AUR.
Syd is a system for creating a data viewing GUI that you can view in a jupyter notebook or in a web browser. And guess what? Since it can open in a web browser, you can even open it on any other computer on your local network! For example, your PI's computer. Gone are the days of single random examples that they make infinitely stubborn conclusions about. Now, you can look at all the examples, quickly and easily, on their computer. And that's why Syd stands for share your data!
Okay, so what is it? Syd is an automated system to convert some basic python plotting code into an interactive GUI. This means you only have to think about what you want to plot and which parameters you want to be interactive. Syd handles all the behind-the-scenes action required to make an interface. And guess what? That means you get to spend your time thinking about your data, rather than writing code to look at it. And that's why Syd stands for Science, Yes! Dayummmm!
If you run it in Jupyter, it should just work: viewer.show()
You can also have it open your default browser: viewer.share()
Plots are interactive, too - there are controls on the page.
A real-time MQTT message visualizer inspired by the Matrix digital rain effect. Displays incoming MQTT messages with customizable colors, sound effects, and dynamic visual effects.
skies-adsb transforms your browser into a real-time 3D air traffic display. Using ADS-B data from an RTL-SDR receiver, you can explore local air traffic, surrounding airspace, and geography with customizable 3D maps. Runs on all major modern browsers. skies-adsb requires a build process prior to deployment and cannot be run directly from source code.
A powerful and user-friendly software tool for scanning radio frequencies using RTL-SDR devices. This scanner covers a wide frequency range with customizable settings, real-time signal analysis, and an intuitive interface.
Scan from 24 MHz to 1766 MHz. Set start/stop frequencies and step sizes with optional default values. Analyze and display signal strengths using a dynamic power spectrum plot. Welcomes users with a stylish interface including author credit and GitHub link.
A guide to using ffmpeg for artists. ffmpeg is a free tool that allows you to create, convert and manipulate video, really quickly and without opening a heavy GUI-based program. You can script it to make full use of it's power and convert folders of files for example. The following is a cookbook of handy examples I myself use to easily create videos for social media, websites, etcetera – mostly from a source material of audio files and/or photos.
Use py2mappr to render fully interactive networks from simple csv files of node and links. Use tag2network to build a similarity network from a list of nodes with associated tags (e.g. documents with keywords). Nodes can host images, video, Twitter and Instagram feeds, and music streams.
Easily style the appearance of nodes, links, and node labels. Reveal patterns in network structure with scatterplot and geospatial layouts. Save custom views and layouts with seamless animated transitions between them. Share a fully interactive network by publishing openmappr files to any static website.
Code-free data exploration with dynamic filters and search tools to query nodes by any combination of node characteristics. Effortlessly zoom in to node details and zoom out to see them in the broader network context. See summaries of node characteristics - both for the entire network and selected groups of nodes.
You'll have to explore a bit to find the tools' links to Github. It could be better organized.
astroterm is a terminal-based star map written in C. It displays the real-time positions of stars, planets, constellations, and more, all within your terminal—no telescope required! Configure sky views by date, time, and location with precise ASCII-rendered visuals.
Choose any date, time, and location to explore past, present, or future celestial events. View the moon, stars, and planets with as much precision as terminal graphics allow. Precise lunar phases in real-time. Detailed constellation shapes. Lightweight and fast ASCII rendering.
If you compile it yourself, be sure to download the astronomical data as well. Requires Meson to compile.
An open source threat intel and sharing platform. Lots of ad-hoc visualization methods are available to make sense of data. Includes lots of taxonomies to organize data and do some of the work for you.
You can store your IOCs in a structured manner, and thus enjoy the correlation, automated exports for IDS, or SIEM, in STIX or OpenIOC and synchronize to other MISPs. You can now leverage the value of your data without effort and in an automated manner. The primary goal of MISP is to be used. This is why simplicity is the driving force behind the project. Storing and especially using information about threats and malware should not be difficult. MISP is there to help you get the maximum out of your data without unmanageable complexity. MISP will make it easier for you to share with, but also to receive from trusted partners and trust-groups. Sharing also enabled collaborative analysis and prevents you from doing the work someone else already did before.
Threat Intelligence is much more than Indicators of Compromise. This is why MISP provides metadata tagging, feeds, visualization and even allows you to integrate with other tools for further analysis thanks to its open protocols and data formats. Having access to a large amount of Threat information through MISP Threat Sharing communities gives you outstanding opportunities to aggregate this information and take the process of trying to understand how all this data fits together telling a broader story to the next level. We are transforming technical data or indicators of compromise (IOCs) into cyber threat intelligence. MISP comes with many visualization options helping analysts find the answers they are looking for.
Github: https://github.com/MISP/
Of interest:
- MISP/MISP is the core platform (the analytics and dashboard part)
- MISP/misp-galaxy looks like the correlation engine
- MISP/PyMISP is the supported Python module for interacting with the API
- MISP/misp-taxonomies are used for organizing information
- MISP/misp-galaxy does the work of clustering data points to find patterns
- MISP/misp-modules are additional functional units (because of course there are)
- MISP/misp-warninglists looks like sets of known false positives for cutting things down a bit.
There are more repos but I haven't gone through them yet.
Radio Receiver is an HTML5 webpage that uses an USB digital TV receiver plugged into your system to capture radio signals, demodulates them in the browser, and plays the demodulated audio through your computer's speakers or headphones. This is called SDR (Software-Defined Radio), because all the radio signal processing is done by software running in the computer instead of purpose-built hardware.
Radio Receiver was written to work with an RTL-2832U-based DVB-T (European digital TV) USB receiver, with a R820T tuner chip. This hardware configuration is a little dated, but support for newer tuner chips is planned.
npm install esbuildnpm run buildnpm run dist
Output in dist/. dist/apps/radioreceiver/ is where the web front-end stuff is, dist/tools/ is where the utility stuff lives.
Picking apart the demo site, it looks like you only need to serve dist/apps/radioreceiver/ because the only three files that get pulled down from it are index.html, main.js, and favicon.png (which implies that everything in there needs to be uploaded).
Requires a browser that supports the HTML5 USB API, which is pretty much everything but Firefox.
PyBonsai is inspired by the amazing cbonsai repository. Whereas cbonsai grows bonsai trees, PyBonsai trees look more like trees you would find in a forest (oak, ash and so on). The trees are configurable via CLI options to make them different sizes, more or less complex, grow at different rates, or use a different set of characters. See useage for more information. Currently, PyBonsai supports 4 different types of tree.
Has no external dependencies.
A feature-rich Software Defined Radio (SDR) spectrum analyzer with real-time visualization, demodulation, and signal analysis capabilities. Real-time spectrum analysis and waterfall display. Multiple visualization modes (spectrum, waterfall, persistence, surface, gradient). Supports FM, AM, SSB demodulation with audio output. Frequency scanning and signal classification. Bookmark management for frequencies of interest. Automatic Gain Control (AGC). Recording capabilities for both RF and audio. Band presets for common frequency ranges. Configurable display and processing parameters.