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|__/ \_______/ \___/ |_______/ \_______/ \_______/
Posts
2025
2024
2023
2022
2021
2020
Projects
Reconic
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All-in-one reconnaissance tool for cybersecurity professionals and bug
hunters.
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Helps map, analyze, and secure digital infrastructures with multiple
integrated scanning features.
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Features include:
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WHOIS Lookup: Retrieves domain registration
details, including ownership and administrative contacts.
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DNS Resolution: Resolves DNS records to uncover
domains, subdomains, and configurations.
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SSL/TLS Certificate Inspection: Inspects
certificates for validity, expiration, issuer, subject, and
configuration details.
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HTTP Header Analysis: Captures and analyzes HTTP
headers to identify security issues and misconfigurations.
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Port Scanning: Detects open ports and exposed
services to understand the target’s attack surface.
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Subdomain Discovery: Identifies active subdomains
using DNS zone methods, expanding visibility of a domain.
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Directory Traversal: Searches for accessible
directories, revealing sensitive information or hidden admin panels.
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JavaScript File Enumeration: Lists JavaScript files
for deeper analysis of potential vulnerabilities or data leaks.
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Technology Scanner: Detects frameworks, libraries,
and platforms used by the target system.
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CVE Integration: Provides direct links to known
vulnerabilities for identified technologies.
Nerve
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Basic implementation of a neural network library for C and C++
applications.
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Designed for projects that need a simple neural network without the
overhead of large and complex libraries.
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Implements a multilayer perceptron neural network with backpropagation
training.
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Supports configurable momentum, learning rate, and trainable bias.
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Lightweight and fast, with easy integration into existing applications.
- Portable and easy to extend for different use cases.
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Includes an example application for training a network to recognize
handwritten digits.
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Licensed under MIT, ensuring flexibility for both personal and
commercial use.
- Tested on Arch, Debian and Fedora environments.
GenCrack
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Demonstrates the use of a genetic algorithm to evolve a population of
random strings towards a predefined target string.
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Applies the principles of natural selection, where the fittest
individuals are chosen to reproduce and form the next generation.
- Population initialization starts with randomly generated strings.
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Selection uses a simple tournament approach, choosing two random
individuals and advancing the fitter one.
- Crossover combines two parents to produce a child string.
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Mutation introduces randomness, with each character having a chance to
change based on a predefined mutation rate.
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Elitism ensures that a percentage of the fittest individuals move
directly to the next generation without alteration.
MathParser
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Mathematical Expression Evaluator: Parses and evaluates mathematical
expressions with precision and efficiency.
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Written in C++: Uses only standard libraries with zero external
dependencies for easy compilation and hassle-free debugging.
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Lightweight and Fast: Designed to handle a wide range of expressions
without performance overhead.
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Expression Tree Architecture: Transforms infix notation into a
structured tree of nodes, enabling accurate and efficient computation.
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Extensible Design: Supports basic arithmetic, advanced trigonometric,
and hyperbolic functions, with the flexibility to add more in the
future.
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Linear Algebra Support: Includes functionality such as 2x2 matrix
inversion, opening the door for advanced mathematical use cases.
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Robust and Tested: Comprehensive testing ensures correctness across
simple and complex mathematical operations.
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Practical Use Cases: Suitable for educational purposes, software
development, and advanced mathematical research.
iNeural
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Open-source library for artificial neural networks with minimal
dependencies.
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Requires only a few external libraries and tools — most functionality is
fully coded within the project.
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Not optimized for GPUs, but designed to run efficiently on systems with
low hardware requirements.
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Capable of high-performance execution on platforms such as robotics once
the project is fully completed.
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Suitable for:
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Developers seeking an open-source neural network for
problem-solving.
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Those who want to integrate neural network technology into their
projects.
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Students learning the fundamentals and tricks of neural networks.
- Researchers, machine learning, and deep learning enthusiasts.
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Inspired by libraries such as FANN, pylearn2, EBLearn, and Torch7.
- Primarily written in C++ with additional support from Python.
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Developed with a focus on minimal dependencies, making it ideal for
systems with limited resources.
Barcode Generator
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Convert any data, number, or text into a valid barcode that can be
scanned universally.
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Generate barcodes instantly with a fast and practical solution for
product organization or data management.
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Use barcodes to securely encode sensitive data such as passwords,
without relying on any external storage.
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All barcodes produced can be scanned and saved with standard barcode
readers, making them suitable for shop or inventory systems.
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Built entirely with Vanilla JavaScript — no database, no backend, and no
registration required.
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Safe to use, as all processes run locally in the browser without any
data being stored or transmitted.
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