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Vibe Coding, Kotlin, Finance, and Data Visualization

Recently, I came across a paper discussing an experiment and tried to reproduce it. Here’s a brief summary: - Portfolio A: In a bull market, grows by 20%; in a bear market, drops by 20%. - Portfolio B: In a bull market, grows by 25%; in a bear market, drops by 35%. - Bull market probability: 75%. According to the paper, both portfolios should have a one-year expected return of 10%.

Treat life like a marathon, not like a sprint

Like most of us, I am daily flooded with thoughts about life, my objective position in it, whether I am missing anything, or whether I need to do better. Am I providing enough for my family? Is my career on track? Am I being healthy enough? Am I just passing through life instead of aiming to strive? Those thoughts have been slowly mitigated, but they never got away. Over time, I have been slowly accepting this reality, and I came to realise that all the marathon training and long-distance running have helped me come to terms with these facts.

Uploading SARIF Reports to GitHub

Recently I wanted to add Lint reports to a repository on GitHub. The goal is to report potential Lint violations when new code is committed, to make sure that all the committed code is lint-warning-free and pretty. My first idea was to look for a GitHub action that could run ./gradlew lint and report it as a PR comment. After asking about ideas in the Android Study Group, Carter Jernigan and Justin Brooks suggested me to upload directly the SARIF files into GitHub.

KotlinConf 2024 announcements

The first day of the KotlinConf 2024 is over, and there has been a significant amount. After 5 years the conference happened again at The Bella Center in Copenhagen, a fantastic venue close to the historical center of the Danish capital. The last two weeks have been intense, with the Google I/O announcing another set of relevant features for Android and Kotlin developers. Most notably, Google is now supporting KMP for Android development.

HTTP chunk requests with Android and ktor

In this very short article, I will explain briefly what is a chunk or streamed HTTP request, what are the benefits of using it, and how it works in Android. Android apps use HTTP requests to download data from a backend. This information is stored and processed on the app to make it functional. HTTP requests are executed using different frameworks on Android. The most common ones are Retrofit or OkHttp.

My Investing Summary of 2022

Another solar rotation passed, and the world experienced a plethora of unexpected events. In the aftermath of the Corona epidemic that altered the course of the last couple of years, we had the unfortunate invasion of Ukraine by Russian forces, the tightening of Corona measures in China (and toward the end of the year, their withdrawal and gradual reopening of the economy), an ongoing economic recession, the rate hike by the FED and the general uncertainty of the most immediate future.

KMP, iOS Developers and Production

Kotlin Multiplatform (or KMP, KMM Mobile, etc) has been widely used for a number of years in applications that are currently in production. JetBrains compiled a website listing some of the companies that are currently using KMP. Since the advent of the mobile platforms we enjoy today, there has always been a certain market interest to push multiplatform technologies, such as Cordova, Xamarin, and others. With more or less success, those technologies aimed to provide a unified framework to develop multiple codebases, mostly focusing on the aspect of pricing (create code once, deploy multiple times).

A recapitulation of investing in pandemic times

It has been around 14 months since the pandemic started. We have all been affected by it to a greater or lesser degree, and the investing world has not been an exception (although surprisingly, the stock market is one of the winners of the pandemic). In this post I will share how the pandemic changed my investment thesis, the things I learned, and the mistakes I did. 14 months into the crisis of our generation (and with a few months to recover whatever the new normal will be), we now know that things will never be the way they used to be.

A short story of randomness (I)

I have always been fascinated by the above comic strip. A discussion on randomness and determinism becomes as much a philosophical issue as it is a practical one. They are used in a variety of applications: from the obvious cryptography, gaming or gambling to the less evident politics or arts. How can we be sure that a number is random? Will observing the process mine our efforts on generating the random number, similar to the observation of a cat inside a box with a decaying radioactive atom?

From Java to Kotlin and back (III): Calling Java from Kotlin

This article is part of a series. You can find the remaining article of the series here: From Java to Kotlin and back (I) — Calling Kotlin from Java From Java to Kotlin and back (II): Calling Kotlin from Java In this last chapter of the series, we will evaluate consideration when calling Java code from Kotlin. One could argue that even in this situation happens often, keeping considerations in mind for some code that might legacy is not that practical.

A short story of randomness (I)

Planted April 4, 2021

I have always been fascinated by the above comic strip. A discussion on randomness and determinism becomes as much a philosophical issue as it is a practical one. They are used in a variety of applications: from the obvious cryptography, gaming or gambling to the less evident politics or arts.

How can we be sure that a number is random? Will observing the process mine our efforts on generating the random number, similar to the observation of a cat inside a box with a decaying radioactive atom? Despite the potential complexity of generating random numbers, we can provide an initially simple method to generate them.

When I participate in meetups or conferences as an organizer, there is one simple game I like to practice at the end of the session, and it is developing a script together with the audience that selects a random participant eligible for a gift (a cup, a t-shirt, etc… any swag generally provided by the hosting organization). The list of participants usually comes in a .CSV file, and coming from the Java world live-coding this can be a daunting task: opening streams, closing them… Damn, I am myself scared of even trying this on stage. However, in Kotlin the solution is rather simple:

View Gist on Medium

(except the part of guessing how to specify the path, which is impossible to get right at the first try).

While playing this little game, very often the name of a participant comes up twice, so we jokingly express our doubts on whether the process is being truly random, or the randomness algorithm has been tweaked to favor one individual and let him/her go back home with all the swag. Developing on stage an algorithm that is able to provide a random number might require more time than using java.util.Random. However, we can dive into it before the stage event, and understand how randomness is implemented in Java.

OpenJDK provides the open-source code of java.util.Random, and it is quite interesting to see how it works. It uses a 48-bit LCG generator, based on the chapter of The Art of Computer Programming by Donald Knuth. An LCG generator uses a formula similar to the following one:

LCG generation formula

Let’s take a look at each component in this formula:

  • *Xn *is our starting seed, which should be unpredictable enough. For instance, it could be the amount of time that has passed since our system started, or the amount of RAM currently being used, or a XOR of the current Date.
  • a is a multiplier — randomly selected
  • c is an increment- randomly selected
  • m is the number we use to execute the modulo operation- fixed and randomly selected

The selection of the previous values may affect the quality or randomness of the output, so we can go down the rabbit hole and increase the entropy for those selected values (by either making them random as well or using a more convoluted process to generate them). In fact, Java generates the above parameters from rand48, so they get as random as it is acceptable for most usages.

The following code excerpt is an implementation in Kotlin of a linear congruential generator:

View Gist on Medium

LCG is fast and apt for most computers to be used. Require minimal memory, and it can execute fast. However, its usage is not intended in every field (for instance, cryptographic applications should use secure pseudorandom number generators for salt and key generation).

Summary

Most of the randomness generators created since the early computing days use some sort of variation of the abovementioned mechanism. However, its usage is not apt for all systems. In the upcoming article, we will see which systems should use a different random numbers generator, and which algorithms are currently available for that.

I write my thoughts about Software Engineering and life in general on my Twitter account. If you have liked this article or if it did help you, feel free to share, 👏 it and/or leave a comment. This is the currency that fuels amateur writers.