After Google killed XMPP support for Google Voice, I no longer had a house phone. I was using my Google Voice number as a home phone and for the gate info our building. This lead to the whole system being ignored and neglected. By the time the SD card gave up the ghost, even the backups were in poor shape. I rebuilt the whole system fresh from the latest RasPBX dist and it’s working better then ever. Bought a real DID so i could continue to use Google Voice on our handsets at home. It’s great to have my Asterisk back.
I visited this idea months ago, but for anyone who implemented it, it has been a nightmare. Each subsequent Unifi controller update broke the https in new and exciting ways. After remaining a very squeaky wheel with Ubiquity support, they’ve pushed out a version that should permanently resolve the problems. They even made promises of native Let’s Encrypt support. All this will prove true of false with time, but for now i wanted to share my working procedure for Unifi controller version 5.9.32.
This solution required me to become more familiar with Java’s keytool then i would have otherwise. Unifi has a hardcoded keytool path and password, don’t change that (thanks Corey F @ubnt). i don’t think alias matter, but they must be consistent. I used mykey. We start by generating a key and a code signing request for our domain. For permissions reasons, we will want to do this as root. . .
keytool -genkeypair -alias mykey -keyalg RSA -keysize 2048 -keystore keystore -dname "CN=custom.domain.name" -storepass aircontrolenterprise
Now we export the csr file we will give to Let’s Encrypt.
keytool -certreq -alias mykey -keystore keystore -file custom.domain.name.csr -ext san=dns:custom.domain.name -storepass aircontrolenterprise
Now we run the interactive certbot script to prove the domain is actually yours before they hand out a cert. Follow the instructions you can use DNS or hosting a file to verify.
certbot certonly --manual --csr custom.domain.name.csr
Continue reading “Hosted Unifi controller with Let’s Encrypt SSL take 2!”
When a colleague offered me a great price to buy his barely used Monoprice Maker Select V2 , I was instantly transported into the future. No more waiting for amazon deliveries, now i can download things straight into reality from the inerwebz! Well, that’s almost what happened.
The learning curve was not as steep as i expected. my 3rd print was actually perfect; or would have been if i hadn’t run out of filament. Funny story… i inherited with the printer a 1Kg spool of PLA. Unfortunately, the Monoprice Maker Select’s included spool holder doesn’t support large spools like this one. So I had to maintain the slack on the spool or break off pieces for prints. That is no way to live. The very next pint was of course was Maker Select V2 Spool Holder Mod by toastedsilicon
I met a pile of incredible people. Bought some amazing toys (for science), some i’ve even got working. Saw some talks and demos. Talked to some of my heroes and listened to even more. I saw Ladar Levison talk about epoxying your ports and adding thermite to your hard drives. I played with the ECU of a fake car! now i just have finish building the DarkNet Badge! enjoy my pictures. The hat data is still being analyzed. I’ll try to build something out of it eventually.
This is a silly project that i have spent probably too much time on. First I thought, why not time-lapse my upcoming defcon trip. Then i thought, why not live stream it. So… I started with a Pi Zero and a Pi Camera v1. Wired some pins to the GPIO for power. Made a custom wire and hot glued it into the hat. Done!
Defcon Short story contest entry by be3n
Chapter 1 : Knowledge Distribution and Collection
In a large lecture hall only moderately filled, Yohan stood before a class of first year computer science students. The dark rings under his brown eyes and matted light brown hair highlighted his rumpled and unkempt appearance. He was of average height with broad shoulders and a bit of a barrel chest. Barely older than his students, he hardly had their attention as he began to speak.
“Infinite monkeys pounding on keyboards will eventually produce Shakespeare! We’ve all heard this. Is that machine learning? Are these nearly infinite transistors your army of monkeys?”, he asks. A few students timidly raised hands before he answered himself, “No, we are not leveraging the power of infinite monkeys here. Here we teach learning systems not just how to learn, but how to teach themselves to learn even better,” he continued. The students started to raise eyes from their glowing screens to follow Yohan as he slowly drew their attention.
Speaking enthusiastically, his eyes began to brighten, “Your model is derived by the learning systems analysis of the data you feed it. Bad data creates bad learning. One famous example from the early days of machine learning was an Army effort to train a system to detect tanks from aerial reconnaissance. The scientists working on the project did not notice at the time that all the photographs taken containing tanks in the sample were taken on overcast days. Most of the photos that did not contain tanks, had been taken on sunny days. In the end, they did not train the model to detect tanks, but instead to detect cloudy weather. If you are not careful this can happen to you.
“False assumptions lead to false predictions and the model degrades. It is increasingly important to properly select the data points for your matrix as well as allowing for the training of weights and biases assigned to these data points.
“Just like our tank example, we can find other false assumptions. The racist biases that lead to the idea that immigrants bring crime can also be attributed to a bad learning data set. In our immigrant example, that learning data set could be the content produced by Fox News. Bias is not always bad, we need them to judge the significance of our data. For another example, say you wanted to go out to eat. In order to select a location, you might ask a friend, Paul for his favorite restaurant. He suggests a Bowling alley diner. Does this choice reflect bias? Might it help to know going in that Paul’s favorite food is hot dogs and bowls every weekend as part of a league? Should it affect the significance you mentally apply to his suggestion? These are the weights and biases that we apply in our everyday decision making. These same sort of weights and biases must be trained into the learning model. And they are, every time. It is usually not possible to eliminate input bias entirely..” Many in the class seemed a bit perplexed by all this new information.
“Rule of acquisition #74 states that knowledge equals profit. In our case knowledge equals model. The more training you can supply, the more points in your matrix, the greater and more accurate the predictability. How many of you have worked with confusion matrices?” Someone called out, “They’re all confusion matrices to me,” but Yohan didn’t reply, he just waited patiently. . In time nearly all the students raised their hands. . That was a good sign. He continued his lecture going over some examples of using bias of various data to increase the flexibility of the model. . . By the time he was talking about practical uses for stochastic gradiants, most of his students’ attention had wandered. Continue reading “My Defcon 26 Short Story Contest Entry Part 1! Finally!”