Even though it might appear to be pretend Internet money, by design, there are a finite number of Bitcoins available. In the same way that the limited amount of gold on the planet and the effort required to extract it from the ground keeps prices high, the scarcity of Bitcoin is intended to make sure it remains valuable. As of right now, over 80% of all the Bitcoins that will ever exist have already been put into circulation. That sounds like a lot, but it’s expected to take another 100+ years to free up the remaining ones, so we’ve still got a way to go.
Even though his device will probably no longer exist when the final Bitcoin hits the pool, [Jonty] has built a ticker that will count down as the final coins get mined from the digital ground. The countdown function is of course a bit tongue-in-cheek, but the gadget also shows slightly more pertinent information such as the current Bitcoin value, so you can always remember what a huge mistake it was not to invest while they were still worth pennies.
On the hardware side, this is a pretty simple project. The enclosure is laser cut 5 mm MDF, and it holds a Raspberry Pi 3, a MAX7219 32×8 LED dot matrix display, and a 10 mm white LED with accompanying resistor. The white LED is placed behind an acrylic diffuser to give the Bitcoin logo on the side of the display a soft pleasing glow when the device is powered up. There are no buttons or other controls on the ticker, once the software has been configured it just gets plugged in and away it goes.
As for the software, it takes the form of a Python script [Jonty] has created which uses Requests and Beautiful Soup to scrape the relevant data from bitcoinblockhalf.com. The script supports pulling any of the 19 variables listed on the site and displaying it on the LED matrix, which range from the truly nerdy stats like daily block generation to legitimately useful data points that anyone with some Bitcoin in their digital wallets might like to have ticking away on their desks.
The first decade of Bitcoin has been a pretty wild ride, not only monetarily, but in the wide array of hardware now involved in cryptocurrency mining and trading. From Bitcoin traffic lights to custom-made mining rigs that are today more useful as space heaters, it takes a lot of hardware to support these virtual coins.
Continue reading “Raspberry Pi Counts Down to the Last Bitcoin”
Page rankings are the secret sauce of websites that automatically aggregate user submissions. The basic formula used by Hacker News was published a few years back. But there are several pieces of the puzzle that are missing from that specification. [Ken Shirriff] recently published an analysis that digs deeper to expose the article penalization system used by Hacker News’ ranking engine.
One might assume that the user up and down votes are what determine a page’s lifespan on the front page. But it turns out that a complex penalization system makes a huge difference. It takes into account keywords, and domain names but also weighs controversy. It’s a bit amusing to note that this article on the topic was itself penalized, knocking it off of the front page.
You can get the full details of the system from his post, but we found his investigation methods to be equally interesting. He scraped two pages of the news feed every minute using Python and the Beautiful Soup package (a pretty common scraping practice). This data set allowed him to compare the known algorithm with actual results. What was left were a set of anomalies that contained enough sense for him to reverse engineer the unpublished formulas being used.
[Rajesh] put web scraping to good use in order to gather the information important to him. He’s published two posts about it. One scrapes Amazon daily to see if the books he wants to read have reached a certain price threshold. The other scrapes Rotten Tomatoes in order to display the audience score next to the critics score for the top renting movies.
Web scraping uses scripts to gather information programmatically from HTML rather than using an API to access data. We recently featured a conceptual tutorial on the topic, and even came across a hack that scraped all of our own posts. [Rajesh’s] technique is pretty much the same.
He’s using Python scripts with the Beautiful Soup module to parse the DOM tree for the information he’s after. In the case of the Amazon script he sets a target price for a specific book he’s after and will get an email automatically when it gets there. With Rotten Tomatoes he sometimes likes to see the audience score when considering a movie, but you can’t get it on the list at the website; you have to click through to each movie. His script keeps a database so that it doesn’t continually scrape the same information. The collected numbers are displayed alongside the critics scores as seen above.