I came across a tweet about the potential price of Ergo five years from now when the mining block rewards are gradually reduced from 66 to 3: I don't know who needs to hear this: Currently block reward for #Ergo miners is 66 $ERG per block mined, that is approx. \(154,440 a day<br>In about 5 years block reward will be 3 <a href="https://twitter.com/search?q=%24ERG&amp;src=ctag&amp;ref_src=twsrc%5Etfw">\)ERG per block mined, to maintain \(154,440 a day in rewards that brings the price of <a href="https://twitter.com/search?q=%24ERG&amp;src=ctag&amp;ref_src=twsrc%5Etfw">\)ERG to $71.5 🐂 ...

Last updated: 2022-01-31 This is a quick “copy & paste guide” for data scientists and developers who are interested in storing the Ergo blockchain to a standardized database format using a Raspberry Pi 4. We will be using the Ergo Blockchain Explorer (backend) but focus our efforts specifically on the Chain Grabber module. A few prerequisites before we get started… Clean install of Raspberry Pi 4+ Root file system mounted to SSD (60GB+) SSH enabled with stable internet connection ...

Once in awhile I find some great charts which make me stop and think. Below is one example of this: 1% better every day is all it takes to completely change your life. pic.twitter.com/kYbgGOzzZv — Santiago (@svpino) September 10, 2021 I really appreciate the inspirational message behind it: being deliberate about the change you wish to see in the world. With that in mind, I want to recreate that chart below using the R programming language (regardless if the trajectory path is realistic or not). ...

It may be tempting for new cryptocurrency investors to buy “cheap” coins because they assume a price of $0.03, for example, can turn them into future millionaires just by HODL-ing until it hits $100. Unfortunately, this is a common misconception as that future price may never materialize. This perspective only considers the demand side of the equation and completely omits data about the supply side to realistically estimate future crypto prices. ...

tl;dr Messaribot (code) is a very simple bot which plots cryptocurrency data to assigned Discord channels. This is written in Python, crytpo data is pulled from the Messari API, and relies on the discord.py API wrapper to publish the finished graphs. Overview Most of the interesting crypto discussions happen on Discord (or Telegram). However, looking up market prices can be a hassle when you need to switch back and forth between different desktop or mobile apps. ...

Context: I had a friend email me about cryptocurrency mining and thought my response was helpful enough to share over Instagram. This led to other friends asking me about the crypto space in general. To keep the spirit of it I have posted the same email below (with some edits). Cryptocurrencies rely on distributed public ledger technology where you need a network of computers (in this case miners) to validate all those transactions. Different cryptocurrencies use different types of consensus algorithms to carry out that validation work by solving complex mathematical puzzles. This ensures the integrity of all past, present, and future records on the blockchain. ...

I love assigning project code names. However, after a certain point the increase in projects decrease the amount of time I am able to commit to assigning a meaningful code name to each one. I needed something more scalable that I could quickly use and revisit later if the idea or prototype had any legs to stand on. Why spend all that time on something if it would never see the light of day? ...

My original exploratory analysis on the topic can be found at Firearm Sales: How are Americans coping with 2020? This post is a quick #rstats follow-up to visualize the final tally for 2020 data. Load libraries library(tidyverse) library(lubridate) library(scales) Download & parse data df_raw <- read_csv("https://raw.githubusercontent.com/BuzzFeedNews/nics-firearm-background-checks/master/data/nics-firearm-background-checks.csv") df <- df_raw df_clean <- df %>% filter(month >= "2016-01" & month < "2021-01") %>% select(month, state, handgun, long_gun) %>% arrange((month)) %>% mutate(month = as.Date(paste0(month, "-01"))) %>% group_by(month) %>% summarize(handgun = sum(handgun), long_gun = sum(long_gun)) %>% mutate(index_month = as.factor(month(month, label = TRUE)), index_year = as.factor(year(month))) %>% ungroup() Visualize data df_clean %>% group_by(index_year) %>% mutate(handgun = cumsum(handgun), long_gun = cumsum(long_gun)) %>% ungroup() %>% select(month, index_month, index_year, handgun, long_gun) %>% pivot_longer(handgun:long_gun, names_to = "type") %>% ggplot(aes(index_month, value, color = index_year, group = index_year)) + geom_line() + geom_point() + scale_y_continuous(labels = comma_format()) + scale_color_brewer(palette = 'Paired') + expand_limits(y = 0) + facet_grid(type ~ .) + labs(color = NULL, x = NULL, y = NULL, title = "NICS Firearm Background Checks: monthly cumulative per year by type", caption = "by: @eeysirhc\nsource: Federal Bureau of Investigation") + theme_bw() + theme(legend.position = 'top') ...