Cryptoasset Valuations

Background I have had some conversations about the maturity of the cryptocurrencies market related to the maturity of the companies and how I miss to have the fundamental indicators as you can have for a public company (Revenue, Costs, P/E, balance sheets…). The companies publishing cyptocurrencies are private companies and they do not have  obligation … Read more

OODA loop by John Boyd

Reading the chapter “Better for Less” of Wardley Maps I found a new concept to me: OODA loop. It’s a strategy cycle that stands for: Observe the environment, Orient around it, Decide your path and Act The creator of this concept was John Boyd , which Wikipedia page is really interesting to read, the basis are represented in the diagram … Read more

Cryptocurrencies scams

The cryptocurrencies have a side B, the scams. There are so many, and we cannot ignore them. Ponzi schemes, false roadmaps, … Shit happens, but with a proper due dilligence you can minimize the risks. Basic due dilligence (basic questions) The general consensus about due dilligence is: do a standard due diligence as if it … Read more


Corda is an open source blockchain project, designed for business from the start. Created in 2016 by the R3 consortium of financial institutions. Key features No unnecessary global sharing of data: only parties with a legitimate need to know can see the data within an agreement. It choreographs workflow between firms without a central controller. Corda … Read more

Keep track of space

how many thousand things you can see here. It’s amaizing Created by Theodore Kruczek to help visualize orbital calculations for application to Ground Based Radars and Optical Telescopes. Based on the original work of James Yoder. All information is open source publically available information. Orbits are derived from TLEs found on public websites. Payload … Read more

Support vector machine (SVM)

The basis Support vector machine (SVM) is a supervised learning method. It can be used for regression or classification purposes. SVM is useful for hyper-text categorization, classification of images, recognition of characters… The basic visual idea is the creation of planes (lines) to separate features. The position of these planes can be adjusted to maximized the … Read more

Machine learning, source of errors

Before to start What is an error? Observation prediction error = Target – Prediction = Bias + Variance + Noise The main sources of errors are Bias and Variability (variance). Underfitting or overfitting. Underclustering or overclustering. Improper validation (after the training). It could be that comes from the wrong validation set. It is important to divide … Read more

k-means clustering

The basis K-means clustering is an unsupervised learning method. The aim is to find clusters and the CentroIDs that can potentially identify the What is a cluster? a set of data points grouped in the same category. What is a CentroID? center or average of a given cluster. What is “k”? the number of CentroIDs … Read more

Naive Bayes classification

The basis It’s based on Bayes’ theorem (check the wikipedia link, and see how complex the decision trees could be). Assumes predictors contribute independently to the classification. Works well in supervised learning problems. Works with continuous and discrete data. Can work with discrete data sets. It is not sensitive to non-correlating “predictors”. Naives Bayes plot Example: … Read more