Leaderboard

https://pwnthecode.com/ Enjoy! PS: Momentan nu te poti loga cu ajutorul social media, dar cred ca te descurci si cu cont normal. Nu prea e timp si fiecare face ce poate.

Replicate the AlphaZero methodology to teach a machine to learn Connect4 strategy through self-play and deep learning In this article I’ll attempt to cover three things: Two reasons why AlphaZero is a massive step forward for Artificial Intelligence How you can build a replica of the AlphaZero methodology to play the game Connect4 How you can adapt the code to plug in other games AlphaGo → AlphaGo Zero → AlphaZero In March 2016, Deepmind’s AlphaGo beat 18 times world champion Go player Lee Sedol 4–1 in a series watched by over 200 million people. A machine had learnt a super-human strategy for playing Go, a feat previously thought impossible, or at the very least, at least a decade away from being accomplished. Match 3 of AlphaGo vs Lee Sedol This in itself, was a remarkable achievement. However, on 18th October 2017, DeepMind took a giant leap further. The paper ‘Mastering the Game of Go without Human Knowledge’ unveiled a new variant of the algorithm, AlphaGo Zero, that had defeated AlphaGo 100–0. Incredibly, it had done so by learning solely through self-play, starting ‘tabula rasa’ (blank state) and gradually finding strategies that would beat previous incarnations of itself. No longer was a database of human expert games required to build a super-human AI . A graph from ‘Mastering the Game of Go without Human Knowledge’ A mere 48 days later, on 5th December 2017, DeepMind released another paper ‘Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm’ showing how AlphaGo Zero could be adapted to beat the world-champion programs StockFish and Elmo at chess and shogi. The entire learning process, from being shown the games for the first time, to becoming the best computer program in the world, had taken under 24 hours. With this, AlphaZero was born — the general algorithm for getting good at something, quickly, without any prior knowledge of human expert strategy. There are two amazing things about this achievement: 1. AlphaZero requires zero human expertise as input It cannot be understated how important this is. This means that the underlying methodology of AlphaGo Zero can be applied to ANY game with perfect information (the game state is fully known to both players at all times) because no prior expertise is required beyond the rules of the game. This is how it was possible for DeepMind to publish the chess and shogi papers only 48 days after the original AlphaGo Zero paper. Quite literally, all that needed to change was the input file that describes the mechanics of the game and to tweak the hyper-parameters relating to the neural network and Monte Carlo tree search. 2. The algorithm is ridiculously elegant If AlphaZero used super-complex algorithms that only a handful of people in the world understood, it would still be an incredible achievement. What makes it extraordinary is that a lot of the ideas in the paper are actually far less complex than previous versions. At its heart, lies the following beautifully simple mantra for learning: Mentally play through possible future scenarios, giving priority to promising paths, whilst also considering how others are most likely to react to your actions and continuing to explore the unknown. After reaching a state that is unfamiliar, evaluate how favourable you believe the position to be and cascade the score back through previous positions in the mental pathway that led to this point. After you’ve finished thinking about future possibilities, take the action that you’ve explored the most. At the end of the game, go back and evaluate where you misjudged the value of the future positions and update your understanding accordingly. Doesn’t that sound a lot like how you learn to play games? When you play a bad move, it’s either because you misjudged the future value of resulting positions, or you misjudged the likelihood that that your opponent would play a certain move, so didn’t think to explore that possibility. These are exactly the two aspect of gameplay that AlphaZero is trained to learn. How to build your own AlphaZero Firstly, check out the AlphaGo Zero cheat sheet for a high level understanding of how AlphaGo Zero works. It’s worth having that to refer to as we walk through each part of AlphaZero. There’s also a great article here that explains how it works in more detail. The code Clone this Git repository, which contains the code I’ll be referencing. To start the learning process, run the top two panels in the run.ipynb Jupyter notebook. Once it’s built up enough game positions to fill its memory the neural network will begin training. Through additional self-play and training, it will gradually get better at predicting the game value and next moves from any position, resulting in better decision making and smarter overall play. We’ll now have a look at the code in more detail, and show some results that demonstrate the AI getting stronger over time. N.B — This is my own understanding of how AlphaZero works based on the information available in the papers referenced above. If any of the below is incorrect, apologies and I’ll endeavour to correct it! Connect4 The game that our algorithm will learn to play is Connect4 (or Four In A Row). Not quite as complex as Go, but there are still 4,531,985,219,092 game positions in total, so not trivial for a laptop to learn how to play well with zero human input. Connect4 The game rules are straightforward. Players take it in turns to enter a piece of their colour in the top of any available column. The first player to get four of their colour in a row — each vertically, horizontally or diagonally, wins. If the entire grid is filled without a four-in-a-row being created, the game is drawn. Here’s a summary of the key files that make up the codebase: game.py This file contains the game rules for Connect4. Each squares is allocated a number from 0 to 41, as follows: Action squares for Connect4 The game.py file gives the logic behind moving from one game state to another, given a chosen action. For example, given the empty board and action 38, the takeAction method return a new game state, with the starting player’s piece at the bottom of the centre column. You can replace the game.py file with any game file that conforms to the same API and the algorithm will in principal, learn strategy through self play, based on the rules you have given it. run.ipynb This contains the code that starts the learning process. It loads the game rules and then iterates through the main loop of the algorithm, which consist of three stages: Self-play Retraining the Neural Network Evaluating the Neural Network There are two agents involved in this loop, the best_player and the current_player. The best_player contains the best performing neural network and is used to generate the self play memories. The current_player then retrains its neural network on these memories and is then pitched against the best_player. If it wins, the neural network inside the best_player is switched for the neural network inside the current_player, and the loop starts again. agent.py This contains the Agent class (a player in the game). Each player is initialised with its own neural network and Monte Carlo Search Tree. The simulate method runs the Monte Carlo Tree Search process. Specifically, the agent moves to a leaf node of the tree, evaluates the node with its neural network and then backfills the value of the node up through the tree. The act method repeats the simulation multiple times to understand which move from the current position is most favourable. It then returns the chosen action to the game, to enact the move. The replay method retrains the neural network, using memories from previous games. model.py A sample of the residual convolutional network build using Keras This file contains the Residual_CNN class, which defines how to build an instance of the neural network. It uses a condensed version of the neural network architecture in the AlphaGoZero paper — i.e. a convolutional layer, followed by many residual layers, then splitting into a value and policy head. The depth and number of convolutional filters can be specified in the config file. The Keras library is used to build the network, with a backend of Tensorflow. To view individual convolutional filters and densely connected layers in the neural network, run the following inside the the run.ipynb notebook: current_player.model.viewLayers() Convolutional filters from the neural network MCTS.py This contains the Node, Edge and MCTS classes, that constitute a Monte Carlo Search Tree. The MCTS class contains the moveToLeaf and backFill methods previously mentioned, and instances of the Edge class store the statistics about each potential move. config.py This is where you set the key parameters that influence the algorithm. Adjusting these variables will affect that running time, neural network accuracy and overall success of the algorithm. The above parameters produce a high quality Connect4 player, but take a long time to do so. To speed the algorithm up, try the following parameters instead. funcs.py Contains the playMatches and playMatchesBetweenVersions functions that play matches between two agents. To play against your creation, run the following code (it’s also in the run.ipynb notebook) from game import Game from funcs import playMatchesBetweenVersions import loggers as lg env = Game() playMatchesBetweenVersions( env , 1 # the run version number where the computer player is located , -1 # the version number of the first player (-1 for human) , 12 # the version number of the second player (-1 for human) , 10 # how many games to play , lg.logger_tourney # where to log the game to , 0 # which player to go first - 0 for random ) initialise.py When you run the algorithm, all model and memory files are saved in the run folder, in the root directory. To restart the algorithm from this checkpoint later, transfer the run folder to the run_archive folder, attaching a run number to the folder name. Then, enter the run number, model version number and memory version number into the initialise.py file, corresponding to the location of the relevant files in the run_archive folder. Running the algorithm as usual will then start from this checkpoint. memory.py An instance of the Memory class stores the memories of previous games, that the algorithm uses to retrain the neural network of the current_player. loss.py This file contains a custom loss function, that masks predictions from illegal moves before passing to the cross entropy loss function. settings.py The locations of the run and run_archive folders. loggers.py Log files are saved to the log folder inside the run folder. To turn on logging, set the values of the logger_disabled variables to False inside this file. Viewing the log files will help you to understand how the algorithm works and see inside its ‘mind’. For example, here is a sample from the logger.mcts file. Output from the logger.mcts file Equally from the logger.tourney file, you can see the probabilities attached to each move, during the evaluation phase: Output from the logger.tourney file Results Training over a couple of days produces the following chart of loss against mini-batch iteration number: Loss against mini-batch iteration number The top line is the error in the policy head (the cross entropy of the MCTS move probabilities, against the output from the neural network). The bottom line is the error in the value head (the mean squared error between the actual game value and the neural network predict of the value). The middle line is an average of the two. Clearly, the neural network is getting better at predicting the value of each game state and the likely next moves. To show how this results in stronger and stronger play, I ran a league between 17 players, ranging from the 1st iteration of the neural network, up to the 49th. Each pairing played twice, with both players having a chance to play first. Here are the final standings: Clearly, the later versions of the neural network are superior to the earlier versions, winning most of their games. It also appears that the learning hasn’t yet saturated — with further training time, the players would contain to get stronger, learning more and more intricate strategies. As an example, one clear strategy that the neural network has favoured over time is grabbing the centre column early. Observe the difference between the first version of the algorithm and say, the 30th version: 1st neural network version 30th neural network version This is a good strategy as many lines require the centre column — claiming this early ensures your opponent cannot take advantage of this. This has been learnt by the neural network, without any human input. Learning a different game There is a game.py file for a game called ‘Metasquares’ in the games folder. This involves placing X and O markers in a grid to try to form squares of different sizes. Larger squares score more points than smaller squares and the player with the most points when the grid is full wins. If you switch the Connect4 game.py file for the Metasquares game.py file, the same algorithm will learn how to play Metasquares instead. Summary Hopefully you find this article useful — let me know in the comments below if you find any typos or have questions about anything in the codebase or article and I’ll get back to you as soon as possible. Sursa: https://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188

AS FLYING, CAMERA-WIELDING machines get ever cheaper and more ubiquitous, inventors of anti-drone technologies are marketing every possible idea for protection from hovering eyes in the sky: Drone-spotting radar. Drone-snaggingshotgun shells. Anti-drone lasers, falcons, even drone-downing drones. Now one group of Israeli researchers has developed a new technique for that drone-control arsenal—one that can not only detect that a drone is nearby, but determine with surprising precision if it's spying on you, your home, or your high-security facility. Researchers at Ben Gurion University in Beer Sheva, Israel have built a proof-of-concept system for counter-surveillance against spy drones that demonstrates a clever, if not exactly simple, way to determine whether a certain person or object is under aerial surveillance. They first generate a recognizable pattern on whatever subject—a window, say—someone might want to guard from potential surveillance. Then they remotely intercept a drone's radio signals to look for that pattern in the streaming video the drone sends back to its operator. If they spot it, they can determine that the drone is looking at their subject. In other words, they can see what the drone sees, pulling out their recognizable pattern from the radio signal, even without breaking the drone's encrypted video. "This is the first method to tell what is being captured in a drone's [first-person-view] channel" despite that encryption, says Ben Nassi, one of the Ben Gurion researchers who wrote a paper on the technique, along with a group that includes legendary cryptographer and co-inventor of the RSA encryption algorithm Adi Shamir. "You can observe without any doubt that someone is watching. If you can control the stimulus and intercept the traffic as well, you can fully understand whether a specific object is being streamed." The researchers' technique takes advantage of an efficiency feature streaming video has used for years, known as "delta frames." Instead of encoding video as a series of raw images, it's compressed into a series of changes from the previous image in the video. That means when a streaming video shows a still object, it transmits fewer bytes of data than when it shows one that moves or changes color. That compression feature can reveal key information about the content of the video to someone who's intercepting the streaming data, security researchers have shown in recent research, even when the data is encrypted. Researchers at West Point, Cornell Tech, and Tel Aviv University, for instance, used that feature as part of a technique to figure out what movie someone was watching on Netflix, despite Netflix's use of HTTPS encryption. The encrypted video streamed by a drone back to its operator is vulnerable to the same kind of analysis, the Ben Gurion researchers say. In their tests, they used a "smart film" to toggle the opacity of several panes of a house's windows while a DJI Mavic quadcopter watched it from the sky, changing the panes from opaque to transparent and back again in an on-off pattern. Then they showed that with just a parabolic antenna and a laptop, they could intercept the drone's radio signals to its operator and find that same pattern in the drone's encrypted data stream to show that the drone must have been looking at the house. In another test, they put blinking LED lights on a test subject's shirt, and then were able to pull out the binary code for "SOS" from an encrypted video focused on the person, showing that they could even potentially "watermark" a drone's video feed to prove that it spied on a specific person or building. All of that may seem like an elaborate setup to catch a spy drone in the act, when it could far more easily be spotted with a decent pair of binoculars. But Nassi argues that the technique works at ranges where it's difficult to spot a drone in the sky at all, not to mention determine precisely where its camera is pointed. They tested their method from a range of about 150 feet, but he says with a more expensive antenna, a range of more than a mile is possible. And while radar or other radio techniques can identify a drone's presence at that range, he says only the Ben Gurion researchers' trick actually know where it's looking. "To really understand what’s being captured, you have to use our method," Nassi says. Rigging your house—or body—with blinking LEDs or smart film panels would ask a lot of the average drone-wary civilian, notes Peter Singer, an author and fellow at the New America Foundation who focuses on military and security technology. But Singer suggests the technique could benefit high-security facilities trying to hide themselves from flying snoops. "It might have less implications for personal privacy than for corporate or government security," Singer says. DJI didn't respond to WIRED's request for comment. Nor did Parrot, whose drones Nassi says would also be susceptible to their technique. If the Ben Gurion researchers' technique were widely adopted, determined drone spies would no doubt find ways to circumvent the trick. The researchers note themselves that drone-piloting spies could potentially defeat their technique by, for instance, using two cameras: one for navigation with first-person streaming, and one for surveillance that stores its video locally. But Nassi argues that countermeasure, or others that "pad" video stream data to better disguise it, would come at a cost of real-time visibility or resolution for the drone operator. The spy-versus spy game of aerial drone surveillance is no doubt just getting started. But for the moment, at least, the Israeli researchers' work could give spying targets an unexpected new way to watch the watchers—through their own airborne eyes - WIRED.

This post requires you to click the Likes button to read this content. http://a.pomf.se/pjmwvx.png """ OLX.ro scraper Gets name, phone no., Yahoo! & Skype addresses, where applicable http://a.pomf.se/pjmwvx.png """ import re import json import requests from bs4 import BeautifulSoup as b pages = 1 # How many pages should be scraped # Category URL, a.k.a. where to get the ads from catURL = "http://olx.ro/electronice-si-electrocasnice/laptop-calculator/" # Links to the Ajax requests ajaxNum = "http://olx.ro/ajax/misc/contact/phone/" ajaxYah = "http://olx.ro/ajax/misc/contact/communicator/" ajaxSky = "http://olx.ro/ajax/misc/contact/skype/" def getName(link): # Get the name from the ad page = requests.get(link) soup = b(page.text) match = soup.find(attrs={"class": "block color-5 brkword xx-large"}) name = re.search(">(.+)<", str(match)).group(1) return name def getPhoneNum(aID): # Get the phone number resp = requests.get("%s%s/" % (ajaxNum, aID)).text try: resp = json.loads(resp).get("value") except ValueError: return # No phone number if "span" in resp: # Multiple phone numbers nums = b(resp).find_all(text=True) for num in nums: if num != " ": return num else: return resp def getYahoo(aID): # Get the Yahoo! ID resp = requests.get("%s%s/" % (ajaxYah, aID)).text try: resp = json.loads(resp).get("value") except ValueError: return # No Yahoo! ID else: return resp def getSkype(aID): # Get the Skype ID resp = requests.get("%s%s/" % (ajaxSky, aID)).text try: resp = json.loads(resp).get("value") except ValueError: return # No Skype ID else: return resp def main(): for pageNum in range(1, pages+1): print("Page %d." % pageNum) page = requests.get(catURL + "?page=" + str(pageNum)) soup = b(page.text) links = soup.findAll(attrs={"class": "marginright5 link linkWithHash \ detailsLink"}) for a in links: aID = re.search('ID(.+)\.', a['href']).group(1) print("ID: %s" % aID) print("\tName: %s" % getName(a['href'])) if getPhoneNum(aID) != None: print("\tPhone: %s" % getPhoneNum(aID)) if getYahoo(aID) != None: print("\tYahoo: %s" % getYahoo(aID)) if getSkype(aID) != None: print("\tSkype: %s" % getSkype(aID)) if __name__ == "__main__": main() Tocmai scraper: https://rstforums.com/forum/98245-tocmai-ro-scraper-nume-oras-numar-telefon.rst

Comertul de date cu caracter personal este interzis. Mucles.

4.5.2.3.Banca isi rezerva dreptul sa nu initieze efectuarea de transferuri pentru tranzactiile legate de servicii incadrate in categoria jocurilor de noroc, de tranzactii legate de achizitia de produse si/sau servicii pornografice (inclusiv videochat sau alte servicii similare), achizitia de arme/munitii fara indeplinirea conditiilor prevazute de lege, tranzactii cu monede virtuale, in cazul identificarii unor potentiale riscuri sau a unor cerinte impuse de institutiile de credit implicate in circuitele bancare dedecontare. https://www.bancatransilvania.ro/files/documente-utile/conditiile_generale_de_afaceri_ale_bt_aplicabile_persoanelor_fizice.pdf @mov_0ah_01 ai si semnat hartia aia?

va zic din experienta personala ca pe formularul de extragere din banca poti scrie la provenienta sumei: bani proveniti din: prostitutie cu minori, spalare de bani, droguri. edit1 (si ei tot iti dau banii, atata timp cat nu exista plangere pe ei ca-s proveniti din activitati ilicite, ei tii dau)

Si in alte tari in EU le inchid. Dar atat timp cat se evita mentiunea de bitcoin in tranzactii (fie la reference, fie la nume, etc) pare sa fie ok, mai ales daca se fac plati cu bun simt, sa nu ridice suspiciuni. Ca sa nu fie probleme de volum, multi isi incropesc o firma rapida si apoi folosesc contul bancar “castraveti limited” si ii doare la basca, au volum de cateva milioane eur anual in transferuri bancare fara probleme. Asta-i deocamdata, cred ca vor strange surubul mai mult peste tot.

https://theoutline.com/post/2944/what-science-is-like-in-north-korea?zd=2

Multumesc, om serios, banii la timp. Up, am timp liber.

Am rezolvat , a fost o mica neintelegere dar totul a fost ok dupa . Recomand , face proiectele in timp si e serios !

Ma, BAC-u in pula mea inseamna minimu de cultura generala pe care trebuie sa il ai . Programarea ca programarea da macar ia-ti BAC-u sa stii si tu cine a scris Corola de minuni a lumii si alte cele... puii mei de inculti.

Salut, Acum un an, scriam niste cateva randuri pe acest forum cu privire la dropshipping si afilieri, cred ca unele topicuri au disparut insa...pentru ca primesc intrebari pe privat, skype, mail referitoare la domeniul dropshippingului si vin pe forum sa caut cele ce le-am scris deja sa dau lumii sa citeasca si nu mai gasesc...probabil odata cu upgradeul forumului...s-a mai pierdut date..n-am idee... O sa mai scriu odata bazele acestui domeniu asa cum le stiu eu, ce fac, cum fac, de unde iau si ce mai stiu... ------------------ Am pornit la drum acum un an, cu 16 dolari care ii aveam pe paypal, auzisem de dropshipping, citisem cateva tutoriale si mi se parea super ideea, se potrivea cu vorba unui om care avea o influenta destul de mare asupra mea : Nepoate, eu nustiu cum functioneaza treburile astea cu internetul, am 69 de ani, dar tot vad la televizor. comanda online, cumpara pe internet...asta inseamna ca cineva are ceva de vandut, si face bani....face reclama si la televizor, a dracului...fix in timpul filmelor mai bune...sa intri si sa cumperi. Am vazut, treburi dealea de masaj...saltele...tratamente Daca tu nu ai ce sa vinzi, vinde si tu pentru ei, daca ei vind o saltea cu 2 lei tu vindea altuia cu 2lei 50, asa se fac bani... S-a stins intre timp... In fine, plecand de la ideea aceasta, am facut o corelatie cu dropshippingul, care este cam acelasi lucru...lumea vinde produsele altora. In Romania, daca iti deschizi o firma pe aceasta ramura se numeste "Intermediere de servicii", asta am invatat mai tarziu. Am luat hosting, domeniu, am pus wordpress pe el, am cautat o tema tip "ecommerce" pe care am incarcat-o, cateva pluginuri + pluginul principal Woocommerce si asta a fost tot - ramasesem si fara bani. Am cautat o nisa cu produse pe aliexpress / alibaba / dinodirect etc si am inceput sa incarc produse, MANUAL. Luam numele produsului, poze, descriere, pret, tot si incarcam in site-ul meu. A fost o munca care mi se parea zadarnica uneori...pentru ca trebuia sa scot de multe ori watermarkuri la poze...si pierdeam ore in photoshop...trebuiau incarcate preturi in woocommerce, facute clase, (cei care a-ti mai utilizat stiti despre ce vorbesc ) apoi incarcate pozele, descriere, aranjare in pagina, calculare shipping etc. Am adaugat multe produse asa...am muncit saptamani intregi. Nu mai stiu cate produse am adaugat ca nu aveam un numar in cap, trebuia sa mi se para mie plin, populat, diversificat. Am terminat site-ul entuziasmat si mi-am dat seama ca nu am cu-i sa vand. Pe nisa care o alesesem nu aveam nici o sansa in urmatorul an sa ajung macar in primele 10 pagini de google... Avem cateva pagini de facebook si am tot da shareuri acolo....apoi am observat ca lumea tot intreba de produse, pret, shipping etc, atunci am stiu unde o sa fac promovare => Social Media. Am inceput sa fac vanzari destul de repede spre surprinderea mea (cei care mi-au citit celelalte topicuri pe tema, au vazut si printurile cu veniturile ), Faceam si affiliere pe vremea aceea. Nu mai am poze...am gasit un singur print de la affilieri: Primele luni, 80% din venituri le-am reinvestit in oameni, adica am cumparat conturi de social media si altele (proxyuri, massplanner etc). In acest moment detin 20 de conturi de instagram, 20 de twitter, 31 de pagini de fb si vreo 15 grupuri, vreo 8 pagini de pinterest. Saptamana trecuta facusem un calcul si ma adresam la aproximativ 6 milioane de oameni, toti targetati pe nise, majoritatea din USA / UK. ------------------------- In momentul de fata, colaborez direct cu cinezii...pe care i-am abordat pe alibaba si aliexpress...dar doar cu cei care au si depozite in EU si / sau in USA - credeti-ma sunt destui. Platesc ceva in plus ca sa imi lipeasca niste stickere cu logoul meu si sa nu puna chinezariile lor pe colete. Detin 41 de site-uri, nise diferite...la un moment dat am avut mai multe dar am renuntat... Ma adresez la aprox 6 mil oameni. Procentul meu este de 20%, reprezentand pretul cu care il cumpar Am 2 VA care ma ajuta Am firma mea Inca nu mi-am dat demisia de la job - e destul de permisiv..si stau foare mult in fata pc-ului (sysadmin) asa ca am timp sa lucrez si la ale mele... Tot ce am facut a fost disponibil pe internet pentru toata lumea. Nu am avut acces la vreo metoda secreta sau la buget mai mare, nu, am plecat cu 16 dolari - si no sa uit niciodata intrucat rad inca cu familia mea pe aceasta tema si am ajuns sa fac destuii bani incat sa nu am nevoie de job - dar la care inca merg. Cu timpul a aparut si primul plugin de dropshipping, care este destul de scump dar merita, Yaross a muncit extrem de mult la el, practic automatizeaza TOT, se vinde cu tot cu tema wordpress. Nustiu cum o sa se numeasca, nu e liber pentru toata lumea, insa eu am avut onoarea sa fiu unul din beta testari si am ramas uimit de ce poate acest plugin, practic iti pune siteul pe picioare cu tot cu tema in 5 minute + produse + clase+ clacule, shipping tot ...ai tot, complet. Un exemplu de site facut cu acest plugin si tema : https://kittenrules.com/ Sa nu mi cereti inca acest plugin ca nu e gata , inca mai lucreaza la el. Va propun sa va strangeti profile de social media si sa le cresteti, sa va creati o audienta, caci ei sunt clientii vostri ! Unii m-au intrebat cati bani fac in momentul acesta, ei bine nu pot raspunde la aceasta intrebare pentru ca fluctueaza, azi noapte, cat am dormit, am facut aproape 400 de dolari - profit, ieri noapte a fost mai putin. Acum sa nu va ganditi ca din acestia 400 nu cheltuii....VPS-uri...hosting, domenii...trackers...numere de skype...proxyuri..VA, taxe, contabili ..toate astea costa...insa raman si eu cu 170-200. Daca va apucati nu luati in ras clientii...stati de vorba cu ei, cumparati-va un nr de telefon de la skype, sunatii, intrebatii daca e in ordine etc, ei pun mare pret pe asa ceva, imi scriu review-uri, dat share la articole, siteuri, si revin sa faca cumparaturi. Am vrut sa scriu multe aici, nustiu cate am apucat ca scriu de vreo 4 ore timp in care tot fug sa fac acte altceva...revin...sper sa nu fi ametit tot pe aici... Ideea e ca in acest moment conduc o afacere din spatele pc-ului, care merge destul de binisor, mi-am luat deja un apartament din banii pe care i-am facut pe net (m-au mai ajutat si ai mei) si ma gandesc acum sa ma extind, adica sa imi inchiriez un spatiu cu 3-4 oameni care sa se ocupe de toata treaba. Am un de pozit in UK unde tineam produse....era cineva care avea grija de ele si pe care il plateam lunar ( transfer bancar) - m-am dus lunile trecute acolo...am reusit sa ajung...si mai avea putin si ma batea...nu ma cunostea...credea ca's la furat. Nu fac nimic special, si voi puteti face la fel. Spre exemplu m-am afiliat cu un tip Roman, care are un site despre accesorii de catei...ei bine, eu am un magazin online cu mancare de catei...pet food...asa ca el vinde..si da vouchere oamenilor de 10% pe site-ul meu..si invers, ne promovam unul pe altul. Se pot face multe....doar sa va apucati de treaba. Numai Bine sper sa raspunda intrebarilor unora. PS: Scuze de greselile gramaticale, promit sa editez.

O poveste foarte interesanta. Lacheii care cauta metode de facut bani ar trebui sa citeascas asta si sa puna osu la treaba. Nu iti trebuie mai mult de-atat.

Si eu minez bitcoin in cloud. Parerea mea sunt doar 3 companii legitime pentru asa ceva. Genesis Mining, Hashflare si Hashzone. Ultima in prezent ofera un cont de minat bitcoin gratis cu noi inregistrari. E bazata in Olanda. Mai multe informatii aici: Hashzone.io

Fratilor daca doriti un cont mic gratis de bitcoin cloud-mining, Hashzone ofera unul gratis pentru timp limitat. Compania e bazata in olanda, si poti reinvesti suma pentru a mina Litecoin, Ethereum sau ZCash. Eu sunt cu ei de o luna deja Mai multe informatii aici: //removed