Finding The Random Seed Of Minecraft’s Title Screen

July 20, 2020 by Lewin Day 12 Comments

Minecraft is a game about exploring procedure-generated worlds. Each world is generated from a particular “seed” value, and sharing this seed value allows others to generate the same world in their own game. Recently, the distributed computing project Minecraft@Home set about trying to find the seed value of the world shown in the Minecraft title screen, and have succeeded in their goal.

The amount of work required to complete this task should not be underestimated. 137 users contributed 181 hosts with 231 GPUs to the effort, finding a solution in under 24 hours. The list of contributors to the project is a long one. It appears the method to find the seed involved comparing screenshots from various seed worlds to the original image. This took a lot of reverse engineering in order to calculate the camera FOV and other settings of the original capture, such that the results could be compared accurately. Interestingly, the group found two seeds that can generate the requisite world, suggesting the world generator code has some collisions between seed values.

We’re not sure what’s more astounding, the amount of work that went into the project, or that there’s a distributed computing project tackling advanced Minecraft research. Either way, we’re no strangers to Minecraft hacks around these parts. Video after the break. Continue reading “Finding The Random Seed Of Minecraft’s Title Screen” →

A 4-bit Random Number Generator

May 23, 2020 by Jenny List 43 Comments

Randomness is a pursuit in a similar vein to metrology or time and frequency, in that inordinate quantities of effort can be expended in pursuit of its purest form. The Holy Grail is a source of completely unpredictable randomness, and the search for entropy so pure has taken experimenters into the sampling of lava lamps, noise sources, unpredictable timings of user actions in computer systems, and even into sampling radioactive decay. It’s a field that need not be expensive or difficult to work in, as [Henk Mulder] shows us with his 4-bit analogue random number generator.

One of the simplest circuits for generating random analogue noise involves a reverse biased diode in either Zener or avalanche breakdown, and it is a variation on this that he’s using. A reverse biased emitter junction of a transistor produces noise which is amplified by another transistor and then converted to a digital on-off stream of ones and zeroes by a third. Instead of a shift register to create his four bits he’s using four identical circuits, with no clock their outputs randomly change state at will.

A large part of his post is an examination of randomness and what makes a random source. He finds this source to be flawed because it has a bias towards logic one in its output, but we wonder whether the culprit might be the two-transistor circuit and its biasing rather than the noise itself. It also produces a sampling frequency of about 100 kbps, which is a little slow when sampling with he Teensy he’s using.

An understanding of random number generation is both a fascinating and important skill to have. We’ve featured so many RNGs over the years, here’s one powered by memes, and another by a fish tank.

’75 Nixie Multimeter As Digital Dice

January 20, 2020 by Bryan Cockfield 10 Comments

For the casual Monopoly or Risk player, using plain six-sided dice is probably fine. For other games you may need dice with much more than six sides, and if you really want to go overboard you can do what [John] did and build electronic dice with a random number generator if you really need to remove the pesky practice of rolling physical dice during your games of chance.

The “digital dice” he built are based on a multimeter from 1975 which has some hardware in it that was worth preserving, including a high quality set of nixie tubes. Nixies can be a little hard to come by these days, but are interesting pieces of hardware in their own right. [John] added some modern hardware to it as well, including an AVR microcontroller that handles the (pseudo) random number generation. A hardware switch tells the microcontroller how many sides the “die” to be emulated will need, and then a button generates the result of the roll.

This is a pretty great use for an old piece of hardware which would otherwise be obsolete by now. [John] considers this a “Resto-Mod” and the finish and quality of the build almost makes it look all original. It’s certainly a conversation piece at the D&D sessions he frequents.

Roll The Bones Chernobyl Style

January 2, 2020 by Dan Maloney 18 Comments

We’re suckers for the Fallout aesthetic, so anything with a post-apocalyptic vibe is sure to get our attention. With a mid-century look, Nixie tubes, a brushed metal faceplate, and just a touch of radioactivity, this quantum random number generator pushes a lot of design buttons, and it pushes them hard.

Charmingly named “Chernobyl Dice”, this little gadget comes to us from [Nathan Griffith], and appears to be one of those “Why not?” builds we love so much. The heart of any random number generator is a source of entropy, for which [Nathan] chose to use six slightly radioactive uranium glass marbles. Those feature prominently in the front panel of the device, occasionally made to fluoresce with a few UV LEDs just because it looks cool. A Geiger tube inside the case is used to look for decay events from the marbles every millisecond. After some adjustment for the bias toward zeroes due to the relative rarity of decay events, the accumulated bits are displayed on eight Nixies. The box can be set to generate a stream of random numbers up to 31 bits long and send it over a USB port, or make random throws of a die with a settable number of sides. And when it’s not doing random stuff, it can just be a cool Nixie clock.

There are lots of ways to generate the entropy needed for truly random number generation, from a wall of lava lamps to bubbles in a fish tank. They’ve all got style, but something about this one just works.

Continue reading “Roll The Bones Chernobyl Style” →

Twitter RNG Is Powered By Memes

January 25, 2018 by Tom Nardi 8 Comments

Twitter is kind of a crazy place. World leaders doing verbal battle, hashtags that rise and fall along with the social climate, and a never ending barrage of cat pictures all make for a tumultuous stream of consciousness that runs 24/7. What exactly we’re supposed to do with this information is still up to debate, as Twitter has yet to turn it into a profitable service after over a decade of operation. Still, it’s a grand experiment that offers a rare glimpse into the human hive-mind for anyone brave enough to dive in.

One such explorer is a security researcher who goes by the handle [x0rz]. He’s recently unveiled an experimental new piece of software that grabs Tweets and uses them as a “noise” to mix in with the Linux urandom entropy pool. The end result is a relatively unpredictable and difficult to influence source of random data. While he cautions his software is merely a proof of concept and not meant for high security applications, it’s certainly an interesting approach to introducing humanity-derived chaos into the normally orderly world of your computer’s operating system.

Noise sampling before and after being merged with urandom

This hack is made possible by the fact that Twitter offers a “sample” function in their API, which effectively throws a randomized collection of Tweets at anyone who requests it. There are some caveats here, such as the fact that if multiple clients request a sample at the same time they will both receive the same Tweets. It’s also worth mentioning that some characters are unusually likely to make an appearance due to the nature of Twitter (emoticons, ~~octothorps~~ pound signs, etc), but generally speaking it’s not a terrible way to get some chaotic data on demand.

On its own, [x0rz] found this data to be a good but not great source of entropy. After pulling a 500KB sample, he found it had an entropy of 6.5519 bits per byte (random would be 8). While the Tweets weren’t great on their own, combining the data with the kernel’s entropy pool at /dev/urandom provided something that looked a lot less predictable.

The greatest weakness of using Twitter as a source of entropy is, of course, the nature of Twitter itself. A sufficiently popular hashtag on the rise might be just enough to sink your entropy. It’s even possible (though admittedly unlikely) that enough Twitter spam bots could ruin the sample. But if you’re at the point where you think hinging your entropy pool on a digital fire hose of memes and cat pictures is sufficient, you’re probably not securing any national secrets anyway.

(Editor’s note: The way the Linux entropy pool mixes it together, additional sources can only help, assuming they can’t see the current state of your entropy pool, which Twitter cats most certainly can’t. See article below. Also, this is hilarious.)

We’ve covered some fantastic examples of true random number generators here at Hackaday, and if you’re looking for a good primer for the Kingdom of the Chaotic, check out the piece by our own [Elliot Williams].

What Is Entropy And How Do I Get More Of It?

November 2, 2017 by Elliot Williams 55 Comments

Let’s start off with one of my favorite quotes from John von Neumann: “Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin. For, as has been pointed out several times, there is no such thing as a random number — there are only methods to produce random numbers, and a strict arithmetic procedure of course is not such a method.”

What von Neumann is getting at is that the “pseudo” in pseudorandom number generator (PRNG) is really a synonym for “not at all”. Granted, if you come in the middle of a good PRNG sequence, guessing the next number is nearly impossible. But if you know, or can guess, the seed that started the PRNG off, you know all past and future values nearly instantly; it’s a purely deterministic mathematical function. This shouldn’t be taken as a rant against PRNGs, but merely as a reminder that when you use one, the un-guessability of the numbers that it spits out is only as un-guessable as the seed. And while “un-guessability” isn’t a well-defined mathematical concept, or even a real word, entropy is.

That’s why entropy matters to you. Almost anything that your computer wants to keep secret will require the generation of a secret random number at some point, and any series of “random” numbers that a computer generates will have only as much entropy, and thus un-guessability, as the seed used. So how does a computer, a deterministic machine, harvest entropy for that seed in the first place? And how can you make sure you’ve got enough? And did you know that your Raspberry Pi can be turned into a heavy-duty source of entropy? Read on!

Continue reading “What Is Entropy And How Do I Get More Of It?” →

Dual-mode Avalanche And RF Random Number Generator

October 31, 2014 by Ethan Zonca 28 Comments

[Paul] designed a new open-hardware RNG (random number generator) that includes two sources of entropy in a small package. The first source of entropy is a typical avalanche diode circuit, which is formed by a pair of transistors. This circuit creates high-speed random pulses which are sampled by the onboard microcontroller.

What makes this design unique is a second entropy source: a CC2531 RF receiver. The RF receiver continuously skips around channels in the 2.5Ghz band and measures the RF signal level. The least-significant bit of the signal level is captured and used as a source of entropy. The firmware can be configured to use either source of entropy individually, or to combine both. The firmware also supports optionally whitening the entropy byte stream, which evens out the number of 1’s and 0’s without reducing entropy.

The OneRNG uses the USB-CDC profile, so it shows up as a virtual serial port in most modern operating systems. With the rngd daemon and a bit of configuration, the OneRNG can feed the system entropy source in Linux. [Paul] also has a good writeup about the theory behind the entropy generator which includes images of his schematic. Firmware, drivers, and hardware design files are open-source and are available for download.