People usually compare the computer to the head of the human being. I would say that hardware is the bone of the head, the skull. The semiconductor is the brain within the head. The software is the wisdom. And data is the knowledge.
This is a basic and simplistic implementation of RSA in JS which used to understand the implementation/math required for encryption/decryption and opportunities for hacking RSA using Quantum Computing.
If you are looking for a nice article on RSA and a small practical example, this might be helpful https://simple.wikipedia.org/wiki/RSA_algorithm
Hacking RSA using Prime Number Factorization
Hacking RSA uses the numeric public exponent from the public key and tries to calculate its largest common multiple factors (p and q) – from those two numbers you can calculate the Private Key. Using traditional computing to hack “small” RSA public keys can be done with a few modern algorithms, including the currently fastest General Number Field Sieve.
A nice library for General Number Field Sieves is http://cado-nfs.gforge.inria.fr/
You can use this site to factor a prime without having to install anything https://asecuritysite.com/encryption/factors. Enter the Public Key which gets generated by the code (should be < 100 bits for the site to be able to factor)
Edit the index.js file if you would like to edit the size or message being encrypted:
// Message const message = 'Hello'; // Generate RSA keys (bits), max is 232 digits (768 bits) const keys = RSA.generate(80);
Run the code
npm run start
Public Key Exponent (e):65537
Random Prime (p): 798000088811
Random Prime (q): 563631878177
Totient (lcm of (p-1)(q-1)): 224889144420297550405280
Public Key (n = p * q): 449778288841956732777547
Public Key Length: 24 digits (79 bits)
Private Key (d = e multiplicative inverse (totient)): 210473481577786144493313
Private Key Length: 24 digits (78 bits)
Encrypted (c = encoded message (m) ^ e modulo n): 426078873740860671226694
Decrypted (m = encrypted message (c) ^ d modulo n): 72101108108111
Plaid is a company in the financial technology, or more commonly known as the “fintech” space, which was founded in 2013, pivoted sometime in 2014 and was purchased by Visa in January 2020 for $5.3 billion dollars.
Plaid was founded by two entrepreneurs who set out to develop a consumer app in the budgeting and account reconciliation field. A mobile or app-based version of Quicken or Intuits Quick Books where users could provide their credit cards and bank account and the interface would allow them to get some insights into their spending habits and create budgets and reports to better manage their personal finances. The startup entered and won a prestigious grand prize award during a TechCrunch Disrupt hackathon in New York in 2013 with their application which at that time was called “Rambler”. During the development process the founders recognized that one of the biggest challenges to building “Rambler” was the bank connectivity component – it was time consuming and resource intensive to develop a solution which connected to each financial institution. The duo wanted their application to connect to the majority of US banks and this required writing code which would need to securely connect and consume the banks exposed API’s for retrieving account information, transaction reports and transaction details. This development exercise was required for each new financial institution the company wanted to include in their app.
Source Code: https://github.com/paschmann/blockchain_origin
Like a lot of my side projects, golfedout.com was built out of a personal need. Golfedout is intended to track the variety of golf courses I have played over the last 6 or 7 years since I took up the game a little more seriously. When I started the project it was a simple list of courses, and then evolved into a more elaborate application which allowed you to follow other golfers, partake in a leader board, view course details such as architects or addresses and I also added a gamification aspect. Another aspect I was interested in, is how many of the top 100 golf courses have I played? How many PGA tour courses have I played? In the end I believe I may have made golfedout.com considerably more complex than others might have needed. If you are a avid golfed or interested, here is a link to my golfedout.com profile: https://golfedout.com/profile/rangerat
Rasa is an open source machine learning framework to automate text-and voice-based conversations.
Rasa UI is a web application built on top of, and for Rasa. Rasa UI provides a web application to quickly and easily be able to create and manage bots, NLU components (Regex, Examples, Entities, Intents, etc.) and Core components (Stories, Actions, Responses, etc.) through a web interface. It also provides some convenience features for Rasa, like training and loading your models, monitoring usage or viewing logs.
I developed Rasa UI to help me manage my bots as well as creating and managing the training data. The app is developed on NodeJS, and uses a simple SQLite DB for persistence (previously PostgresDB).
LendingClub is a US peer-to-peer lending company headquartered in San Francisco, California, and has helped over 2.5 million customers simplify their finances in the last 10 years. LendingClub improves the loan process for borrows by offering a fast and easy online application. For investors, the company offers historical returns of 3 – 8% and anyone can invest with as little as $1000 .
Because LendingClub relies heavily on technology to evaluate their borrowers, getting an accurate risk analysis for each applicant requires systems which can quickly assess the applications, and upon approval, offer these loans to interested investors at a given interest rate. Of the $7.9 billion dollars loaned in 2018, $233 million was written off as defaulted loans. While this may seem insignificant at 2.9%, this does represent risks and losses which investors and the company would prefer to avoid. In order to mitigate risk, lending companies traditionally apply a fitting interest rate to each loan. For example, loans for a home or a car may have lower interest rates because the risk is reduced due to directly related collateral. In another example, someone with a poor credit history or having declared bankruptcy may have a higher interest rate due to the inherent risk of history repeating.
If there has been one email/newsletter I am excited to receive monthly, its from a company called Springwise. They share innovative and creative ideas from around the world. It’s a great resource to find inspiration or follow emerging trends which you are interested in. A prime example: Snapchat 🙂