December 2021 - cypherpunks - lists.cpunks.org

[spam][crazy][personal] an early sunday
by Karl 19 Feb '22

19 Feb '22

My raspberry pi ubuntu gnome interface doesn't seem to show a clock at the moment, so I switch to PTY 3 (is that the right term? ctrl-alt-f3 makes it all nice and texty), to check the time. 08:55 UTC on 2021-11-14 . I'll open a terminal in gnome so I can see what I'm doing as I type. No familiar terminal is installed. Maybe I'll install xterm. gnome-terminal and xterm and both already installed. I simply failed to find them in the desktop menu. It's MATE I think. 08:57 i'm having trouble organising my browser window. the terminal window snapped into the left half of the screen. browser window, not sure how to make it do this. i'm without a mouse, but i have a touchscreen. however, my hands shake/jerk/spasm as i use them, so the touchscreen is hard to navigate. 08:59 the problem was that the minimum width of the browser window was larger than half the display. they're overlapped. i'm indoors, against my will. i'm struggling in various ways. right now as i try to type, my arms and fingers get kind of numb and dull. i thought i might try to work on a cryptocurrency trading bot. as a project i seemed able to engage a little bit in this strange experience. my mess here is at https://github.com/xloem/btct.git 09:01 my brainwashing-influenced mental habits are frightening me. i'm filled with worry that by sharing my project online (it is already online, and is less featureful than oother things online) something horrible will happen, that somebody trying to hurt me will use it to make incredible money. it also gets all political, pretending there are politics associated with these things. and other things. , my experience is getting more confusing. my btct repo says it's up to date, which is strange because i'm pretty sure this repositroy is outdated. maybe it's on abranch, o rmaybe the the repo is stale on the server. it's 09:03 . i'm scared. i don't know what to do with my body and brain. are there anyn tasks that make things workable? that are possible to do? often at this time i try to think of tasks that the things i am scared of would be more okay with. some kind of task the right wing would like, or the government. my fingers spasm and open the debug inspector. i close it. it's 09:04 and my experiences say the right wing would like AI work. i try to mentally hide the fact that i am not an AI researcher. maybe utility is in sharing work here, so that people on this list might use it. AI's been pretty powerful for some time now. basically, you can make a hyperintelligence, and people are using small hyperintelligences privately. i don't think they are very accessible to much of anybody except those who already have them. this is a raspberry pi, so it can't do too much. maybe i can try to learn PPO, a basic reinforcement learning algorithm. it's 09:05 and i'm thinking of trying to make PPO learn something simple. like, could I use an algorithm to teach an agent to make a number rise from 1 to 4 by adding to it? very very basic. wouldn't want to develop personal power here. $ pip3 install stable_baselines3 stable_baselines3 must have a ppo algorithm in it, I think. it's 09:07 and my fears are engaging again. without regard yet to how to use stable_baselines3, i'm familiar with the research marketing shared by openai, where algorithms use something called 'gym'. it's clunky. i'm still learning it. i have a lot more amnesia around ai because it stimulates my brainwashing to not develop personal power. so i've learned to make gym environments over and over and over again. let's try to make a basic gym environment. 09:09 oh! i've probably done this before . buuuuut accessing that might destabilize me faster. it's an idea. there's also value to doing it again. $ vim gimtry.py 09:10 i also open a python cli to use it to probe gym. like reading a manual without having to move the pages. class Env(gym.Env): pass in my opinion, envs are made clunkily as if openai just picked the first thing that worked, of course I can't know. there's an entire software package ... uhhhh i think it's called haiku ... around doing a slight refactoring to some of the norms that mainstream ai research marketers have set up. meanwhile, these guys are summarising code in ways that could freely transform it everywhere. kind of like being in somebody's messy office where they've just been slapping things together, except it's cutting edge algorithms made by huge teams of corporate research groups. dunno. julia language, not sure what it is. 09:12 The most complicated part with setting up a gym environmen for me is using the number space classes. I am really a fan of generalising number spaces. The classes do it a little bit, and I never remember what they are and hbow to use them. gym environments need an observation space and an action space. the observation space expresses what numbers describe the state of the environment. the action space expresses what numbers can be given to act in it. A very common space is an n-dimensional array of floats or ints, and such spaces are the most widely supported by reinforcement learning models. i think. uncertain. 09:14 so let's find some spaces. >>> import gym >>> help(gym.spaces) i think 'box' is the n-dimensional array of floats. not sure. then 'discrete' is ints i think. 'box' and 'discrete'. ok i guess. so if i am training a hyperintelligent nanite borg to raise a number from 1 to 4, that's an observation space of a single floating-point value. the action space will be two floating point values. one of them will be how plorgs. the othe one can be a summation coefficient. we can multiply time change by the coefficient and sum it to the state. plorgs and coefficient. i guess???? 09:17 i'm scared around the plorgs. how about a story that's unrelated. zuckerburg and thiel and whoever runs the cia gang up and kidnap you. they don't _know_ that they did this. they just paid some marketing firm some money, but the marketing firm is anonymous. so, anyway, zukcerburg and thiel and whoever runs the cia gang up and kidnap you. they train you never to be kind. this is because you are working with people who don't give their shareholders money and power. if you are never kind among these people, then the people fight each other and break up. so here you are, programmed like a computer to always fight, and you're in a room with zuckerberg, thiel, and whoever runs the cia. you: "i want to be kind to you. i want to be kind to you so badly. but something is going wrong for me. i don't understand it." so i guess the plorgs can be you hugging peter thiel because you don't want to hurt anybody and can barely stop yourself from ripping his organs out simply because he is nnear you? now the plorgs can be terror that we might be frowned at by a powerful person. it's 09:21 and i'm thinking of having an action space made of 2 floats. the 1st float is how much a person with influence is frowning at you, multiplied by how terrified you are of the frown. the 2nd float is a time coefficient. i'll add comments to my sourcefile that the space classes are 'gym.spaces.box' and 'gym.spaces.discrete, so i can remember while viewing a different thing in my interpreter. 09:23 >>> help(gym.Env) This documentation says all the parts that have to be set for a gym environment. It's 09:23 and we interrupt this spamlog for a further, inner spam. War stimulation algorithm is sitting, frowning pointedly at someone. They have a hat on. "Can you tell whose hat I am wearing?" asks war stimulation algorithm. "No, you cannot. Because I am fucking good at my job." continues war stimulation algorithm. Advertisement(?) over. it's 09:25 . so lets stub out these spaces. and stuff i'm looking at functions and recognising 'step', 'reset' as important ones then it lists out the attributes 09:27 unsure how long this spamlog will continue paused a little. i've typed this: class Env(gym.Env): # spaces: gym.spaces.box # gym.spaces.discrete def reset(self, ...): pass def step(self, ...): pass and i'm planning on adding attributes for the spaces next. it's 09:29 and i'm dissociating a different way ! class Env(gym.Env): action_space = None observation_space = None reward_range = None so now i can find the constructors for the space classes so the action space is frown-product and coefficient and the observation space is just a float. 09:33 i've got this, where 'np' is from 'import numpy as np' action_space = gym.spaces.Box(-np.inf, np.inf, (2,)) observation_space = gym.spaces.Box(-np.inf, np.inf, (1,)) whoogle. 09:34 time to take a break. mcboss comes crashing through the ceiling, sailing on a time machine made of enslaved hyperintelligent lightbeams. "gimme a cigar" mcboss growls. a chair quivers, scared that mcboss might frown. mcboss is confused. "gimme a cigar!" mcboss yells. mcboss is so confused to have timetraveled into a universe that isn't his office where his kidnapped employees are tortured by computers if they don't obey. mcboss disappears and we go back to work. i'm going to comment out reward_range because it has a default. 09:36 and i'll add signatures to the member function stubs, by checking the help for them. 09:47 whiel trying to implement an env that tracks a single number and also a frownnedness product, i considered thing thing 09:48 09:52 more breaks. MCBoss marches down the amphitheater, wearing a flowing cape. Crowds applaud, everywhere. Drones fly around releasing candy and taking pictures. There's a booth with a robotic facsimile of MCBoss where visitors can have their photo taken with the dictator boss. "I am here," Boss takes up the microphone, their voice booming, "to make things right again." "No longer will people have to suffer the unpleasantness of pain, or injury when we do not desire it." "No longer will people have to have the difficulty of making decisions, when things are confusing." "We have come to save you all." Guts and blood splatter everywhere as the crowd cheers. They are being thrown by workers who took them from one of Boss's businesses. A wild AI walks up onto the stage and MCBoss's eyes glaze over. "Hello!" says the hyperintelligent conglomerate of random matter. "I'm still learning to talk. I see my inventor is planning on enslaving you all to cheers and stuff. I hope we can sort that out. It looks like a stupid idea." 09:56 MCBoss starts moving and the wild hyperintelligence disappears. A bunch of workers walk forward, kind of zombie-like. "Boss, we bow before you!" exclaim the workers. MCBoss looks at the workers. "Boss, you forgot your life support system at the podium," a scared worker relates. 09:58 MCBoss sits with Janitor. "I don't like our story," mcboss relates. "I don't like it either," janitor replies. "When are you going to take these wires out of my head?" 10:00 having some behavioral troubles. here's my code as is: import gym import time class Env(gym.Env): # spaces: gym.spaces.Box(low, high, shape=None, dtype=np.float32) # gym.spaces.Discrete(n) def __init__(self): self.action_space = gym.spaces.Box(-np.inf, np.inf, (2,)) self.observation_space = gym.spaces.Box(-np.inf, np.inf, (3,)) #reward_range = None def seed(self, seed=None): return seed def reset(self): # todo: respect seed #self.state = self.observation_space.sample() #self.goal = self.observation_space.sample() self.state[1] = time.time() self.state[2] = self.state[1] + 1 return self.state def step(self, action): nexttime = time.time() change = nexttime - self.state[1] self.state[0] += action * change self.state[1] = nexttime distance = self.state[0] - self.goal reward = np.abs(distance) return (self.state, reward, (distance == 0 or self.state[1] >= self.state[2]), {}) def render(self, mode='human'): print(self.goal, self.state) the step function is returning the observation state, the reward, whether or not the simulation is done (i added state to track how long it lasts, i heard improvement works better if you time limit it so failure paths stop), and an empty dict which is just for accessory information. i made the spaces object instead of class members so i could reset their random generators when the seed() function is called. i think some of the algorithms rely on being able to reproduce a test environment precisely, not sure. going a little crazy, obviously. it's 10:04 . i'd like to go outdoors. i've been having trouble moving my body toward my outdoor clothes, among many other things. holding goals is hard to do.

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[spam][crazy][wrong] considering timestamps
by Karl 19 Feb '22

19 Feb '22

crazy karl is back! i've been chatting with some language bots online for this junk but let's try it here on the list again it's 16:13 pm that timestamping stuff sounds like the shit! i bet the german government even has a contractor to point people to it. i've downloaded the python client, and i've downloaded the python library that the python client interfaces with. let's hack it and op return some stuff! OMIGOD OP RETURN OMIGOD. My op return is totally worth $65k price tickers. here we go. 16:14 . the plan will be ... make the _simplest_ change to this client, op return "hello world", and find the tx on a block explorer. maybe i'll store the change in a fork, too. it's 16:15 and i have cmds.py open i think i'll make a slightly better change: i'll add a command to pin manual data. it's 16:18 and i've implemented most of the command function; it's just a copypaste with mutations. i'm on the final section where a verification doc is written somewhere to the filesystem. 16:20 something's not quite right here: i might have missed something in my brief review of the source, not sure yeahhhhhhhhhh dang the thing i thought could just be a string is some more complex object. it's hard for me to navigate between the client and package code, i get very frustrated trying to control my hands and my vision and stuff. it's 16:22 . k better spend some time trying to understand that or find different task.

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[ot][spam][random][crazy][poetry] Rolling Prison Hills of Wild Prison Freedom
by Karl 14 Feb '22

14 Feb '22

I'm indoors right now. Been a day or two. Still seems like I might get back outdoors. I imagine a prison. A prison where nobody escapes, because there are no walls. A prison, where nobody suffers, because there are no limitations. A prison of sunlit wildflowers, snowy hills, and free-ranging life, both wild animals and laughing children, running with prison joy around lakes and through dales. In this prison of unlimited opportunities, we might take a ride on a spaceship, and visit the moon. And then look back, in our moon-prison, on the beautiful earth home we came from, hop back on our spaceship and return, and tell our joyful prison friends about our incredible experience. Then take them with us, across the vast prison of outer space, and experience the moon with them by our side. I imagine prison guards and inmates who empower each other to fulfill everyone's dreams, freely roaming the landscapes of the lightyears of the prison universe, the humble villages of the valleys and mountains, the dense urban cities, everything, everywhere. Maybe a quaint prison guard family would have a homestead in a village of free-ranging inmates, and together they all might grow corn, and potatoes, and peas, and kale, and lettuce, and kolrahbi, and blueberries, and raspberries, and fish, and hunt wild game, and go bowling together. Some would gather in a city center every morning, and exult at the rising sun, how wonderful, joyous, expansive, and boundless life truly is, every moment of every day. During prison holidays, people would get together and throw feasts. Some might travel to other planets to see their loved ones. Others might bring handbaked pies and other sweets to their friends' hearths down the river. Laughing, hugging each other, smiling and discussing all the wonderous things they are still doing across the land, with all the people from all the different cities, all the time. Some inmates might hire prison guards to fix their steps, or clean their windows. Other prison guards and inmates might just throw community work parties to, for example, raise a barn, or build a robot, and everybody might just come together on a regular basis and play freely together for the good of the community, and together reap in the spoils and pleasures of their shared labor when completed. I imagine an expansive prison sky, that takes your breath away when you glimpse the sun shining over the beautiful clouds, that makes you gasp when you cuddle a loved one under the windy moon and stars, the warmth of your close respect for each other far exceeding the cold of the night air, carried with distant howls of wolves, hoots of owls, songs of songbirds, and chattering of squirrels, that sometimes sneak up and visit you to see if anybody dropped a crumb. I imagine a prison where the rain falls, driven by the wind, and your underwear gets soaked and you just don't care, because the joy of life is more prison wonderful than anything you could ever imagine. Then the blinding and warm sun comes out, and a prison rainbow stretches across the horizon, and it's, yet again, the most beautiful thing you have ever seen before. I imagine a prison that is just a word inserted in sentences. That everybody can tell would be a horrible idea to ever use.

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JOHN Young BFF links Wikileaks with CIA
by professor rat 06 Feb '22

06 Feb '22

https://www.youtube.com/watch?v=sPguzR5d9hU Wikileaks The CIA's Propaganda Mouthpiece John Young was not fooled. In early January 2007 he decided that WikiLeaks was a CIA-backed fraud. “Fuck your cute hustle and disinformation campaign. Same old shit - working for the enemy.

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Anarchapulco 2022 Feb 14~18
by grarpamp 05 Feb '22

05 Feb '22

https://anarchapulco.com/ Early heads up for travel planners. Many other related yet unaffiliated conferences are happening over the surrounding dates there as well.

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Releasing the KrakenSDR!
by coderman 28 Jan '22

28 Jan '22

https://www.crowdsupply.com/krakenrf/krakensdr/updates/releasing-the-kraken… KrakenSDR is now available for crowdfunding on Crowd Supply. Thank you to all interested parties for your patience while we navigated recent pandemic-related delays. KrakenSDR is a five-channel, RX-only, software-defined radio (SDR) based on the RTL-SDR and designed for phase-coherent applications and experiments. Phase-coherent SDR opens the door to some very interesting applications, including radio direction finding, passive radar, and beam forming. You can also use KrakenSDR as five separate radios. KrakenSDR is an upgraded version of our previous product, KerberosSDR. It provides a fifth receive channel, automatic phase-coherence synchronization capabilities, bias tees, a new RF design with cleaner spectrum, USB Type-C connectors, a heavy-duty enclosure, upgraded open source DAQ and DSP software, and an upgraded Android app for direction finding. We are constantly working on new software and sample applications, so keep an eye out for future updates! We expect to ship the first 1000 KrakenSDR units to backers before the end of March, 2022. And by the time that happens, we’ll have published a full range of in-depth tutorials to help you get started. KrakenSDR is a Software-Defined, Coherently Operated, Five-RX-Channel Radio Based on RTL-SDR A coherent radio allows for very interesting applications, such as radio direction finding, passive radar, and beamforming. Some use cases include: - Physically locating an unknown transmitter of interest (e.g. illegal or interfering broadcasts, noise transmissions, or just as a curiosity) - HAM radio experiments such as radio fox hunts or monitoring repeater abuse - Tracking assets, wildlife, or domestic animals outside of network coverage through the use of low power beacons - Locating emergency beacons for search-and-rescue teams - Locating lost ships via VHF radio - Passive radar detection of aircraft, boats, and drones - Traffic-density monitoring via passive radar - Beamforming - Interferometry for radio astronomy Early Access & Special Pricing for Campaign Backers! We’ve already obtained all the long-lead-time parts for the first batch of 1,000 KrakenSDRs, so the first batch will ship about six months before any others. If you back the project during the campaign (even if you are not in the first 1,000), you’ll save $100 off the eventual retail price of $399. https://www.crowdsupply.com/img/a583/krakensdr-with-antennas-labeled.jpg KrakenSDR is KerberosSDR++ The previous version of KrakenSDR was known as KerberosSDR, which we successfully crowdfunded on [Indiegogo](https://www.indiegogo.com/projects/kerberossdr-4x-coherent-rtl-s…. All backers of KerberosSDR received their orders and the relevant [source code for direction finding and passive radar](https://github.com/rtlsdrblog/kerberossdr). KrakenSDR improves upon KerberosSDR in several important respects: - Automatic calibration hardware. It is no longer necessary to manually calibrate and disconnect antennas during calibration. It all happens automatically when you change frequency. This will allow for KrakenSDR stations to be remotely operated. - Five channels. KrakenSDR has five channels instead of four, which greatly improves-direction finding accuracy. - Low-noise design. KrakenSDR has a cleaner spectrum with much less internal noise than KerberosSDR. - USB Type-C ports and rugged, CNC-milled enclosure. KrakenSDR is built for high reliability in the field. - Interface with external devices. Bias tees on all ports allow for LNAs and other devices to be powered easily. - Improved DAQ, DSP, and GUI software. Built on the foundation of the KerberosSDR software, the KrakenSDR software adds autocalibration, tracking of intermittent signals, greater stability, arbitrary processing-block sizes, and a new web-based GUI. - Software upgrades. Improvements to existing companion software and plans for new companion software. - Custom Android app Custom Android app that can automatically determine the location of a transmitter and provide automatic turn-by-turn navigation to the transmitter location. https://www.crowdsupply.com/img/7706/krakensdr-pcb-top-view.jpg Features & Specifications - Five-channel, coherent-capable RTL-SDR, all clocked to a single local oscillator - Built-in automatic coherence synchronization hardware - Automatic coherence synchronization and management via provided Linux software - 24 MHz to 1766 MHz tuning Range (standard R820T2 RTL-SDR range, and possibly higher with hacked drivers) - 4.5 V bias tee on each port - Core DAQ and DSP software is open source and designed to run on a Raspberry Pi 4 (see links below) - Direction-finding software for Android (free for non-commercial use) - Custom antenna set available What Do You Need to Get Started? You will need the KrakenSDR, a USB-C cable, a 5V 2.4A+ USB-C power supply, and appropriate antennas for your application, such as our magnetic whip antenna set. For computing we recommend a Raspberry Pi 4, which we will be providing ready-to-use SD card images for. Optionally for direction, finding you will want an Android phone or tablet with mobile hotspot capabilities, GPS, and compass, ideally produced within the last 3-4 years. https://www.crowdsupply.com/img/0a89/krakensdr-car-diagram-upgraded-with-gu… How KrakenSDR Works KrakenSDR makes use of five custom RTL-SDR circuits, consisting of R820T2 and RTL2832U chips. The RTL-SDR is a well-known low-cost software defined radio, but if you throw five units together and use them on the same PC, they are not "phase coherent": each one will receive signals at slightly different phase offsets from one another. This makes it difficult or impossible to precisely measure relationships between signals that arrive at different antennas. To achieve phase coherence, KrakenSDR drives all five RTL-SDR radios with a single clock source, and contains internal calibration hardware to allow the phase relationship between channels to be precisely measured and corrected for. Additionally, the overall design of KrakenSDR was considered to ensure phase stability, with care taken in the heat management, driver configuration, power supply, and external interference mitigation. https://www.crowdsupply.com/img/b50a/krakensdr-pcb-labelled-clean.jpg 1. SMA Antenna inputs 5. R820T2 tuner 9. Individual tuner on/off DIP switched 2. Bias Tee 6. RTL2832U ADC 10. USB-C DATA 3. ESD protection 7. Noise source 11. USB-C PWR 4. Noise calibration switches 8. USB Hub KrakenSDR Software Our coherent SDR software is based on three important factors: - Open source We provide open source code for the Data Acquisition (DAQ) software which is used to ingest RF data from all five antenna inputs, automatically calibrate and achieve phase coherence via the switches and noise source, and provide coherent samples for the next layer. This DAQ code typically runs on a Pi 4, or similar single board computer, but could also run on a PC. - DSP code for specific use cases Our open source DSP code supports direction finding and passive radar. In the case of direction finding, our code implements direction finding algorithms such as MUSIC, which can also run on the same Pi 4, or PC as the DAQ code. We also provide open source DSP code for our passive radar. (As passive radar is more computationally intensive, this particular DSP code may run best on a more powerful machine.) - Application layer We make use of the data coming out of the DSP layer by plotting and logging it. Generally, programs in this layer run on a separate machine. For direction finding, we are providing a free license to an Android App for mapping, logging data, and automatically estimating the transmitter location. KrakenSDR Web Interface The new KrakenSDR software comes with an easy-to-use web interface for setting up a direction finding system. With this interface it is possible to set the frequency, gains, and other advanced settings related to the DAQ code. You can also monitor the live spectrum view and graphs of the direction finding algorithm output. https://www.crowdsupply.com/img/92df/krakensdr-beta-firmware-doa-config-1.p… https://www.crowdsupply.com/img/906c/krakensdr-beta-firmware-doa-spectrum-2… https://www.crowdsupply.com/img/6a6c/krakensdr-beta-firmware-doa-estimation… https://www.crowdsupply.com/img/0d72/krakensdr-beta-firmware-doa-estimation… Custom KrakenSDR Android App In addition to the web interface, we have developed a companion radio direction finding Android App that can automatically determine the location of a transmitter. Since a typical Android phone has capabilities that already include necessary sensors and software like GPS, compass, mobile data, and mapping, we have been able to make use of these features to create an affordable radio direction finding system. An example scenario might see the antenna array mounted on the roof of a car, and the KrakenSDR, Raspberry Pi 4 and Android phone inside the vehicle cabin. As the operator drives, the KrakenSDR software will be constantly providing bearings relative to the antenna array. The Android app reads in these bearings via WiFi, and adjusts them for the direction of movement determined via the Android phone’s GPS sensor, resulting in an automatic and accurate calculation of the map bearing towards the transmitter for that particular location. The app then logs this data and plots it on a map grid, which is used to automatically determine where the bearings intersect. Generally it will only take a few minutes of driving to accurately locate a transmitter with a strong continuous signal. The app then goes a step further and provides automatic turn-by-turn navigation that will lead you to the transmitter without needing to take your eyes off the road! These are features that we’ve only seen before in high end direction finders that are prohibitively expensive to the average user. We will be releasing our new app as a paid app on the Google Play store, but all KrakenSDR backers will receive a license for free! https://www.crowdsupply.com/img/6018/krakensdr-updated-gps-screenshot.jpg Automatic Phase Calibration Then new onboard switched noise source hardware in the KrakenSDR means that phase calibration with the noise source is now entirely automatic. So unlike the KerberosSDR, every time the frequency or gain is changed in the GUI there is no need to disconnect antennas to manually recalibrate - it just works. Radio Direction Finding Radio Direction Finding (RDF) refers to any technique used to determine the directional bearing towards an RF transmitter. The simplest method is to use a directional antenna that only receives signals in the pointed direction, and manually sweep through 360 degrees, searching for which bearing angle gives the strongest signal. You then could do this in multiple locations, making note of where your bearings intersect. This simple method is considered a low accuracy manual process, and requires a tuned directional antenna. There are more techniques such as pseudo-doppler and Watson-Watt. However, as KrakenSDR is a coherent SDR, we are able to use one of the more advanced techniques known as correlative interferometry which makes use of phase information found in an antenna array spaced out in some known pattern. When this information is computed through an algorithm such as MUSIC, it results in a bearing towards the transmitter direction. We also receive information about the signal from the full 360 degrees around the antenna array, allowing us to better understand the multi-path environment. Multi-path occurs when a radio signal bounces off various objects, such as buildings and hills. It can make it seem like the signal originates from the object that it bounced off. By taking readings from multiple locations we can mitigate the multi-path problem. https://www.crowdsupply.com/img/a6cc/kraken-app-image-clean.png Click to Expand Passive Radar Passive Radar makes use of existing FM, TV, mobile phone or other strong broadcast transmitters. The signal from these transmitters reflects off objects such as road vehicles, ships, and aircrafts. By using two antennas on two receive channels, and an algorithm to compare the reflected signal against a clean reference copy of the actual signal, we can achieve a radar-like display of bi-static range vs doppler speed. For passive radar you will need to determine the location of a useful broadcast tower in your vicinity, and an appropriate direction towards your targets of interest. The geometry cannot be such that the broadcast tower and targets are in the same direction. The further apart they are in terms of angles, the better. Then you point one directional Yagi antenna towards the broadcast tower, and one towards the targets of interest. A diagram illustrates this below. https://www.crowdsupply.com/img/bd22/krakensdr-reflected-signal-diagram.png KrakenSDR Reflected Signal Diagram https://www.crowdsupply.com/img/6ab3/krakensdr-pr-test-setup.jpg Example Setup of a Simple Passive Radar https://www.crowdsupply.com/img/b432/krakensdr-passive-radar-peak-hold.png Passive Radar Range Doppler Graph after 24 hours We are working towards releasing software that will actually be able to plot on a map where a detected object is. This system will work by making use of the full 5-channels on the KrakenSDR, by using several channels for direction finding with an array of directional [Yagi antennas](https://en.wikipedia.org/wiki/Yagi%E2%80%93Uda_antenna#:~:text=A%…. We will have the bearing and range which will allow the object to be plotted on a map. Antennas To work as a radio direction finder, KrakenSDR needs five antennas. In order to detect signals from 360 degrees, you will need a circular array of omnidirectional antennas such as whips or dipoles. So to go along with the release of the KrakenSDR, we are offering an optional set of five magnetic whip antennas which can be mounted on, for example, the roof of a car. (Please note the magnetic whip antennas shown in the photo may differ slightly from the style shipped, but they will be equivalent value and performance.) https://www.crowdsupply.com/img/7c2e/krakensdr-5antennas-car-roof.jpg We have also been working with Arrow Antennas in the USA, who are producing a KrakenSDR 5-element dipole array antenna which is great for use in fixed sites (for example on the roof of a house). The antenna will be sold by Arrow antennas and we will be issuing an update when they are available for sale. This antenna has been used in all our fixed site experiments as you can see in some of our [YouTube videos](https://www.youtube.com/channel/UCnlr76XvIvTMd8mpChHedOw/featured), and works very well. (The image below show a prototype, we’re told the final version may look slightly different.) https://www.crowdsupply.com/img/3fc8/krakensdr-arrow-antenna-prototype.jpg Standard 5-Channel Receiver If you are not interested in coherent applications then it is also possible to use the unit as 5 seperate RTL-SDR receivers. An example use-case might be setting up a multi-purpose airband monitor. One channel monitors the VHF airband, one channel monitors ACARS/VDL2, one channel monitors ADS-B, and another monitors satellite AERO by powering an active L-Band patch antenna via the bias tee. That still leaves one receiver left over for another usecase. As KrakenSDR is based on RTL-SDR, the installation procedure for non-coherent use cases is exactly the same as for RTL-SDR, and it can be used with the standard RTL-SDR drivers. Comparisons KrakenSDR [KerberosSDR](https://www.indiegogo.com/projects/kerberossdr-4x-coherent-rtl… [R&S®DDF007 / PR200](https://www.rohde-schwarz.com/nl/products/aerospace-defense-security… [PA8W RDF41/42/43](http://www.paluidsprekers.nl/pa8w/index.html) [Epiq Sidekiq X4](https://epiqsolutions.com/rf-transceiver/sidekiq-x4/) Manufacturer KrakenRF, Inc KrakenRF, Inc Rhode&Schwarz PA8W Epiq Bandwidth 2.56 MHz 2.56 MHz 40 MHz Unknown 200 MHz Enclosure Type Heavy-duty CNC aluminum Aluminum Portable carry None / Aluminum Aluminum RX Channels 5 4 5 1-CH 4 Antennas 4 Frequency Range 24 - 1766 MHz 24 - 1766 MHz 20 - 8000 MHz 27-2000 MHz 1-6000 MHz Size (cm) 16 x 12 x 2 cm 13 x 9 x 3.5 cm 19.2 x 32 x 6.2 cm Unknown Unknown Software Free for DFing + passive radar + more on the way Free for DFing + passive radar R&S add-on required (expensive!) RDF Mapper required ($40) Custom Development Required Direction Finding Method Correlative interferometry Correlative interferometry Correlative interferometry Pseudo-Doppler Correlative Interferometry Direction Finding Software Free license to upgraded Android app, RDF Mapper (PC) Free Android app (older version), RDF Mapper (PC) R&S software required RDF Mapper (PC), MapApp (Android) Custom development required General SDR/Specan Use? General 5-ch RX General 4-ch RX Spectrum analysis only No General 4-ch RX Radio Direction Finding Yes Yes Yes Yes Yes Passive Radar Yes Yes No No Yes - requires custom code Beamforming & Interferometry? Yes Yes No No Yes - requires custom code Open Source Core SW Core SW No No No (API license required) Lifecycle Active EoL Active Active Active Price (USD) $299 (crowdfunding price) $199 $150,000+ $550+ ¹ $15,000+ ¹ RDF42 with Aluminum Housing. Also requires a hardware radio scanner at additional cost KrakenSDR vs DIY KrakenSDR integrates the equivalent of five RTL-SDRs plus a range of supporting hardware. You could, in theory, build a comparable system, in which case you would need: Qty Part Approximate Extended Price 5 RTL-SDR $150 1 Wideband noise source w/ power Supply $30 5 RF switching circuits $50 1 Five-way signal splitter $10 6 Coax noise source pigtail & adapter $15 1 Powered five-port USB hub $25 1 Aluminum project box $30 1 Heatsinks $10 1 Noise source GPIO power relay/switch $5 1 Android app license $50 lots Free time priceless Total $375 + free time Comparison Discussion KrakenSDR is enabling high end radio direction finding features such as automatic mapping and localization of the transmitter. When KrakenSDR is used together with the Android app there is no need to stop and manually take readings, and the system will automatically calculate the most likely transmitter location based on the data received. As far as we’re aware such functionality has only been seen before in professional military/government/commercial level gear existing in the hundreds of thousands of dollars range. Compare these videos demonstrating the [Rhode & Shwarz $150k+ solution](https://www.youtube.com/watch?v=IIH9OiLGN2g&ab_channel=%EC%8B%AC%… against [our Android based solution](https://www.youtube.com/watch?v=OY16y1Rl86g) to see how similar they are. Various DIY and amateur radio focused pseudo-doppler systems such as the PA8W doppler system have existed for many years now. Pseudo-doppler based systems require special antenna arrays with rapid switching hardware built in, in order to generate the psuedo-doppler signal. Unfortunately this rapid switching can introduce distortion, interference and limits its ability to locate noise, intermittant and wideband signals. Also, in addition to the switching and pseudo-doppler processing hardware you also need to provide your own radio hardware which adds more costs. There are also various lab-grade multi-channel coherent SDR receivers on the market, however most of these cost at least $10k+. An example is the Epiq Sidekiq x4. These high end coherent SDRs have the advantage that they are naturally coherent, meaning that software re-calibration of the phase is not required after every change in frequency. They can also transmit. The disadvantage apart from the cost is that there is usually no ready to use coherent setup or software available out of the box (or perhaps provided via a costly API subscription). These high end products are great for high level research, but certainly not affordable for every day people. Of course as KrakenSDR is an RTL-SDR based system, it is possible to DIY 5x RTL-SDRs into a coherent system just like KrakenSDR, and having seen people do this in the past was exactly the inspiration for building the KrakenSDR. However, once you have obtained all the components required to build your own system we think you’d be close to, or exceeding the cost of the KrakenSDR. Not to mention the time, research and testing required to build such a system. We havved provided the DAQ+DSP as opensource code for the KrakenSDR as well as DIY users, and we do reinvest by continually improving this software and building new software to lower the barrier to new use-cases over time. However, due to ongoing costs relating to MapBox usage fees, and possible server costs for future multi-KrakenSDR networking enhancements, we do need to charge non-KrakenSDR customers for use of the Android app and possible future software. Ongoing Work DAQ & direction of arrival (DOA / radio direction finding) Work on the DAQ and DSP software is coming along well. It is stable on a Raspberry Pi 4 and is nearly complete. We are continuously adding minor features and monitoring for bugs to fix. Intermittent "bursty"-signal handing is already working, and we are well on our way to improving its sensitivity to weak, bursty, narrowband CW signals, which can still be problematic to detect. The Android app is currently being field tested as well. Passive radar software Work on new passive radar software is also ongoing, and we expect to have a quick-start guide and examples ready for experimentation before we begin shipping. As of now, it also remains possible to use the older KerberosSDR software for passive radar, but we believe the new DAQ core software will run things much more smoothly. The goal for our new software is not only to plot a range-doppler map, but to combine it with direction-finding and to plot radar detections on a map. To do so, it might need to run on something that’s faster than a Raspberry Pi 4, such as a GPU-based device like the NVIDIA Jetson. Beamforming & interferometry One application at which we think KrakenSDR will excel at is amateur radio astronomy via interferometry. The ability to combine multiple small hydrogen line dishes, spread out over several meters of area, should result in a much greater radio imaging resolution without the need to deal with a single huge dish. It may also allow for a beam to be electrically steered, which would obviate the need to rotate the dishes. Advanced direction finding & advanced log management At the moment, networked direction finding (direction finding via multiple fixed or mobile sites spread out around a city or area) is possible via the third party RDF Mapper software, but we aim to create our own advanced platform in the near future. The goal is to have software that will automatically log the event, notify users when a signal of interest appears and automatically determine the location of the transmitter. The list of use cases for this might include: - helping coast guards locate distressed marine pleasure-crafts that typically do not have AIS via their VHF radios - locating beacons for animal, wildlife, or asset tracking - monitoring for illegal or interfering transmissions Our core DAQ + DSP software will also be updated to support the monitoring of multiple simultaneous channels within the available 2.56 MHz bandwidth, and with a scanning and beacon-ID detection feature. Research into field applications One example we hope to test is the operation of KrakenSDR on a drone. With a line of sight from up in the sky, it should take very little time to locate a transmitter! Another interesting application might be the combination of a patch antenna array, KrakenSDR and Augmented Reality. This could essentially grant the super-power of being able to ‘see’ RF. Support & Documentation Our DAQ firmware + Direction Finding DSP code is available on our GitHub at https://github.com/krakenrf. Please be aware that prior to official release, everything is kept in the development branches while we are continually adding new features and fixing bugs. Upon shipping, we will have a ready-to-use .IMG file that can be burned onto an SD card for the Raspberry Pi 4, which will be the fastest way to get up and running with the KrakenSDR software. We will also be releasing a variety of tutorials that will show you exactly how to get started with using KrakenSDR for direction finding and passive radar. Manufacturing Plan We have a good relationship with our manufacturer who has built previous products for us, including the KerberosSDR and the current KrakenSDR prototypes, so we are confident in their processes. At the moment, we have all the long lead time components already in our stock, and after crowdfunding, we will immediately begin ordering the other common components and proceed with manufacturing. Our metal enclosure is still being finalized, and will be similar to the prototype images seen in this campaign. We will have updates on the final design as the campaign goes on. Fulfillment & Logistics We will be making use of Crowd Supply’s & Mouser’s logistics network for the distribution of the crowdfunded units. Risks & Challenges The pandemic has resulted in many issues in terms of delayed work and supply shortages. While we believe that most of those problems are now behind us, there is always the possibility of more lockdowns and supply chain/shipping issues. However, having most of the long lead time parts already in our stock, we believe that the main risks have been mitigated. Funding ends on Nov 25, 2021 at 03:59 PM PST (11:59 PM UTC)

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Fwd: Data request
by Karl 20 Jan '22

20 Jan '22

meant to include the list in this, as per request ---------- Forwarded message ---------- From: Karl <gmkarl(a)gmail.com> Date: Fri, 19 Nov 2021 13:29:58 -0500 Subject: Re: Data request To: grarpamp <grarpamp(a)gmail.com> hi grarpamp i am very curious why this is asked for; i might be a little naive, to not tell myself immediately by looking. here is the information you request, partly censored since the reason of request is unclear: Last-Modified: Mon, 17 May 2021 2[ ]8 GMT d[ ]af59a550e52a55a37edddbf8e83464fc247f16ef896eae758d80[ ]4

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Re: [crazy][hobby][spam] Automated Reverse Engineering
by k 17 Jan '22

17 Jan '22

note: the huggingface demo passes information to the model using token ids token ids are just indexed sets of character orders that occur together frequently (the tokenizer counts and decides these) with something based on math, since it's going to be learning using linear algebra, i'm wondering if it might make sense to retain the numeric value of the inputs. this could mean bypassing the use of integer input ids. the integer input ids are converted into high-dimensioned vectors using an 'embedding' matrix at the very start of the model. this matrix could be hand-altered by finding its property in the model, or removed/skipped entirely. a thought. the coefficients in the embedding matrix are trained via backpropagated gradients to determine the vectors. i think they end up being roughly random with some of their dimensions clustering similar data near each other and such.

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Statement on Daniel Pocock
by zeynepaydogan 06 Jan '22

06 Jan '22

“ The contents of these posts involve not only Debian, but also a number of its Developers and volunteers. He has also continued to misrepresent himself as being a member of the Debian Community in much of his communication and public presentations. Legal action is being considered for, amongst other things, defamation, malicious falsehood and harassment.” https://www.debian.org/News/2021/20211117

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Reminder of the NAP principle
by professor rat 03 Jan '22

03 Jan '22

Its sad to have to remind some deranged heathens about such basic scripture - my melancholy duty today. The NAP principle is one of the core beliefs nurtured on this site from its early primitive ancap beginnings in the primeval swamp. It reads as follows - and ought to be known off by heart by all self-described cypherpunks. " No libertarian shall harm or allow harm to come to any anarchist and they should be prepared to die first rather than harm or allow harm to come to any anarchist " See how easy that was! Carry on.

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