Personal collective of ideas, thoughts and notes
Hellava good time!
| Day 1 | Colorado to Moab in a shady van organized by Scott |
| Day 2 | Slick Rock |
| Day 3 | Dead Horse Point Tour in the AM and Outer Bike and North 40 in the PM |
| Day 4 | Delicate Arch Hike for Sunrise and Dead Mans/Bar B |
| Day 5 | The Whole Enchilada |
| Day 6 | Hymasa and Captain Ahab |
GameShell is the a modular, portable game console with a GNU/Linux embedded operating system. It allows you to play thousands of classic games from Atari, GB, GBA, NES, SNES and many of history’s greatest consoles. GameShell supports programming languages like C, Python, Lua, and LISP.
Such an awesome little device …
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 [1].
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.
You must be logged in to post a comment.