You are correct, but I'd say this one is fundamentally ambiguous (I'm Portuguese myself, where this also applies), as it is a one-to-two mapping here. Without further context you can't really choose one or the other, so we just left it as is :).
I sell cheap but high-quality Anki decks for language learning: https://deckmill.com
Created using a mix of automation (TTS, machine translation, etc.) and human reviews.
Built it with a friend, making around $500 a month, very stable over the last couple of years. Spend 1 or 2 hours a month on it, mostly customer support.
Yes. At Deckmill (https://deckmill com) we hated subscriptions for language products, so we built a language product that you buy once and get the content forever (much like a book).
It's possible (likely) that Alameda has invested that money in iliquid assets or assets that have since lost value. Also, since the loans of FTX to Alameda were backed by FTT, which has since collapsed, even if FTX makes a margin call, the collateral has a fraction of the original value.
I'd like to point out that this seems extremely similar to Nimbo (https://nimbo.sh), to the extent that even some of the terminal messages are exactly the same, and even parts of the docs are copy pasted. E.g:
Nimbo docs: "In order to run this job on Nimbo, all you need is one tiny config file and a Conda environment file (to set the remote environment), and Nimbo does the following for you:"
SpotML docs: "In order to run this job on SpotML, all you need is one tiny config file and a Docker file (to set the remote environment), and SpotML does the following for you:"
My friend and I developed Nimbo (https://nimbo.sh), a dead-simple command line tool that allows you to run code on AWS as if you were running it locally.
We decided to build this because we were frustrated with how cumbersome using AWS was, and we just wanted to be able to run jobs on AWS as easily as we run them locally (and make use of our credits). All in all, we didn't like the current AWS user experience, and we thought we could drastically simplify it for the machine learning/scientific computing niche.
For this reason, we also provide many useful commands to make it faster and easier to work with AWS, such as one-command Jupyter notebooks on EC2, easily checking prices, logging onto an instance, syncing data to/from S3, etc.
Unlike other similar services, we are solely client-side, meaning that the code runs on your EC2 instances and data is stored in your S3 buckets (we don't have a server; all the infrastructure orchestration happens in the Nimbo package).
We have tons of ideas for Nimbo, such as docker support and one-command neural network deployments.
We will be hosting a live Nimbo setup session, so if you are interested register here http://eepurl.com/hx-jyv.
I (Miguel) am an ML PhD from the University of Edinburgh and Juozas is a Software Engineer also from Edinburgh.
Together we developed Nimbo, a dead-simple CLI that wraps the AWS CLI, allowing you to run code on AWS as if you were running it locally. You can find the source code here (https://github.com/nimbo-sh/nimbo) and the docs here (https://docs.nimbo.sh).
We decided to build this because we were frustrated with how cumbersome using AWS was, and we just wanted to be able to run jobs on AWS as easily as we run them locally. At the same time, we wanted to make use of the cheap spot instances (on Nimbo, this is a single parameter). All in all, we didn't like the current user experience of working with AWS, and we believed it was possible to vastly improve it.
For this reason, we also provide many useful commands to make it faster and easier to work with AWS, such as launching notebooks on EC2, easily checking prices, logging onto an instance, or syncing data to/from S3 (you can see some useful commands at https://docs.nimbo.sh/useful-commands).
Unlike other similar services, we are solely client-side, meaning that the code runs on your EC2 instances and data is stored in your S3 buckets (we don't have a server; all the infrastructure orchestration happens in the Nimbo package). We are also open contribution, meaning that all the source code is publicly available on our GitHub, and we welcome community contribution.
We have tons of ideas for Nimbo, like adding docker support, and providing instances with preloaded datasets like ImageNet, so that you don't have to download and store it yourself - you simply spin the instance, and the dataset is available at /datasets. We are currently working on adding GCP support, so that you can use AWS or GCP with the same config file.
We are happy to receive any feedback and suggestions you have.