CFD Bitcoin (2020) » Die Kryptowährung BTC beim Broker ...

IG cdf shorted yipee /r/Bitcoin

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Bitcoin mentioned around Reddit: [Maks Cardenas from CDF] Alexis: "I've already decided my future. But I can't tell you" /r/Gunners

Bitcoin mentioned around Reddit: [Maks Cardenas from CDF] Alexis: submitted by BitcoinAllBot to BitcoinAll [link] [comments]

A new whitepaper analysing the performance and scalability of the Streamr pub/sub messaging Network is now available. Take a look at some of the fascinating key results in this introductory blog

A new whitepaper analysing the performance and scalability of the Streamr pub/sub messaging Network is now available. Take a look at some of the fascinating key results in this introductory blog

Streamr Network: Performance and Scalability Whitepaper
The Corea milestone of the Streamr Network went live in late 2019. Since then a few people in the team have been working on an academic whitepaper to describe its design principles, position it with respect to prior art, and prove certain properties it has. The paper is now ready, and it has been submitted to the IEEE Access journal for peer review. It is also now published on the new Papers section on the project website. In this blog, I’ll introduce the paper and explain its key results. All the figures presented in this post are from the paper.
The reasons for doing this research and writing this paper were simple: many prospective users of the Network, especially more serious ones such as enterprises, ask questions like ‘how does it scale?’, ‘why does it scale?’, ‘what is the latency in the network?’, and ‘how much bandwidth is consumed?’. While some answers could be provided before, the Network in its currently deployed form is still small-scale and can’t really show a track record of scalability for example, so there was clearly a need to produce some in-depth material about the structure of the Network and its performance at large, global scale. The paper answers these questions.
Another reason is that decentralized peer-to-peer networks have experienced a new renaissance due to the rise in blockchain networks. Peer-to-peer pub/sub networks were a hot research topic in the early 2000s, but not many real-world implementations were ever created. Today, most blockchain networks use methods from that era under the hood to disseminate block headers, transactions, and other events important for them to function. Other megatrends like IoT and social media are also creating demand for new kinds of scalable message transport layers.

The latency vs. bandwidth tradeoff

The current Streamr Network uses regular random graphs as stream topologies. ‘Regular’ here means that nodes connect to a fixed number of other nodes that publish or subscribe to the same stream, and ‘random’ means that those nodes are selected randomly.
Random connections can of course mean that absurd routes get formed occasionally, for example a data point might travel from Germany to France via the US. But random graphs have been studied extensively in the academic literature, and their properties are not nearly as bad as the above example sounds — such graphs are actually quite good! Data always takes multiple routes in the network, and only the fastest route counts. The less-than-optimal routes are there for redundancy, and redundancy is good, because it improves security and churn tolerance.
There is an important parameter called node degree, which is the fixed number of nodes to which each node in a topology connects. A higher node degree means more duplication and thus more bandwidth consumption for each node, but it also means that fast routes are more likely to form. It’s a tradeoff; better latency can be traded for worse bandwidth consumption. In the following section, we’ll go deeper into analyzing this relationship.

Network diameter scales logarithmically

One useful metric to estimate the behavior of latency is the network diameter, which is the number of hops on the shortest path between the most distant pair of nodes in the network (i.e. the “longest shortest path”. The below plot shows how the network diameter behaves depending on node degree and number of nodes.

Network diameter
We can see that the network diameter increases logarithmically (very slowly), and a higher node degree ‘flattens the curve’. This is a property of random regular graphs, and this is very good — growing from 10,000 nodes to 100,000 nodes only increases the diameter by a few hops! To analyse the effect of the node degree further, we can plot the maximum network diameter using various node degrees:
Network diameter in network of 100 000 nodes
We can see that there are diminishing returns for increasing the node degree. On the other hand, the penalty (number of duplicates, i.e. bandwidth consumption), increases linearly with node degree:

Number of duplicates received by the non-publisher nodes
In the Streamr Network, each stream forms its own separate overlay network and can even have a custom node degree. This allows the owner of the stream to configure their preferred latency/bandwidth balance (imagine such a slider control in the Streamr Core UI). However, finding a good default value is important. From this analysis, we can conclude that:
  • The logarithmic behavior of network diameter leads us to hope that latency might behave logarithmically too, but since the number of hops is not the same as latency (in milliseconds), the scalability needs to be confirmed in the real world (see next section).
  • A node degree of 4 yields good latency/bandwidth balance, and we have selected this as the default value in the Streamr Network. This value is also used in all the real-world experiments described in the next section.
It’s worth noting that in such a network, the bandwidth requirement for publishers is determined by the node degree and not the number of subscribers. With a node degree 4 and a million subscribers, the publisher only uploads 4 copies of a data point, and the million subscribing nodes share the work of distributing the message among themselves. In contrast, a centralized data broker would need to push out a million copies.

Latency scales logarithmically

To see if actual latency scales logarithmically in real-world conditions, we ran large numbers of nodes in 16 different Amazon AWS data centers around the world. We ran experiments with network sizes between 32 to 2048 nodes. Each node published messages to the network, and we measured how long it took for the other nodes to get the message. The experiment was repeated 10 times for each network size.
The below image displays one of the key results of the paper. It shows a CDF (cumulative distribution function) of the measured latencies across all experiments. The y-axis runs from 0 to 1, i.e. 0% to 100%.
CDF of message propagation delay
From this graph we can easily read things like: in a 32 nodes network (blue line), 50% of message deliveries happened within 150 ms globally, and all messages were delivered in around 250 ms. In the largest network of 2048 nodes (pink line), 99% of deliveries happened within 362 ms globally.
To put these results in context, PubNub, a centralized message brokering service, promises to deliver messages within 250 ms — and that’s a centralized service! Decentralization comes with unquestionable benefits (no vendor lock-in, no trust required, network effects, etc.), but if such protocols are inferior in terms of performance or cost, they won’t get adopted. It’s pretty safe to say that the Streamr Network is on par with centralized services even when it comes to latency, which is usually the Achilles’ heel of P2P networks (think of how slow blockchains are!). And the Network will only get better with time.
Then we tackled the big question: does the latency behave logarithmically?
Mean message propagation delay in Amazon experiments
Above, the thick line is the average latency for each network size. From the graph, we can see that the latency grows logarithmically as the network size increases, which means excellent scalability.
The shaded area shows the difference between the best and worst average latencies in each repeat. Here we can see the element of chance at play; due to the randomness in which nodes become neighbours, some topologies are faster than others. Given enough repeats, some near-optimal topologies can be found. The difference between average topologies and the best topologies gives us a glimpse of how much room for optimisation there is, i.e. with a smarter-than-random topology construction, how much improvement is possible (while still staying in the realm of regular graphs)? Out of the observed topologies, the difference between the average and the best observed topology is between 5–13%, so not that much. Other subclasses of graphs, such as irregular graphs, trees, and so on, can of course unlock more room for improvement, but they are different beasts and come with their own disadvantages too.
It’s also worth asking: how much worse is the measured latency compared to the fastest possible latency, i.e. that of a direct connection? While having direct connections between a publisher and subscribers is definitely not scalable, secure, or often even feasible due to firewalls, NATs and such, it’s still worth asking what the latency penalty of peer-to-peer is.

Relative delay penalty in Amazon experiments
As you can see, this plot has the same shape as the previous one, but the y-axis is different. Here, we are showing the relative delay penalty (RDP). It’s the latency in the peer-to-peer network (shown in the previous plot), divided by the latency of a direct connection measured with the ping tool. So a direct connection equals an RDP value of 1, and the measured RDP in the peer-to-peer network is roughly between 2 and 3 in the observed topologies. It increases logarithmically with network size, just like absolute latency.
Again, given that latency is the Achilles’ heel of decentralized systems, that’s not bad at all. It shows that such a network delivers acceptable performance for the vast majority of use cases, only excluding the most latency-sensitive ones, such as online gaming or arbitrage trading. For most other use cases, it doesn’t matter whether it takes 25 or 75 milliseconds to deliver a data point.

Latency is predictable

It’s useful for a messaging system to have consistent and predictable latency. Imagine for example a smart traffic system, where cars can alert each other about dangers on the road. It would be pretty bad if, even minutes after publishing it, some cars still haven’t received the warning. However, such delays easily occur in peer-to-peer networks. Everyone in the crypto space has seen first-hand how plenty of Bitcoin or Ethereum nodes lag even minutes behind the latest chain state.
So we wanted to see whether it would be possible to estimate the latencies in the peer-to-peer network if the topology and the latencies between connected pairs of nodes are known. We applied Dijkstra’s algorithm to compute estimates for average latencies from the input topology data, and compared the estimates to the actual measured average latencies:
Mean message propagation delay in Amazon experiments
We can see that, at least in these experiments, the estimates seemed to provide a lower bound for the actual values, and the average estimation error was 3.5%. The measured value is higher than the estimated one because the estimation only considers network delays, while in reality there is also a little bit of a processing delay at each node.


The research has shown that the Streamr Network can be expected to deliver messages in roughly 150–350 milliseconds worldwide, even at a large scale with thousands of nodes subscribing to a stream. This is on par with centralized message brokers today, showing that the decentralized and peer-to-peer approach is a viable alternative for all but the most latency-sensitive applications.
It’s thrilling to think that by accepting a latency only 2–3 times longer than the latency of an unscalable and insecure direct connecion, applications can interconnect over an open fabric with global scalability, no single point of failure, no vendor lock-in, and no need to trust anyone — all that becomes available out of the box.
In the real-time data space, there are plenty of other aspects to explore, which we didn’t cover in this paper. For example, we did not measure throughput characteristics of network topologies. Different streams are independent, so clearly there’s scalability in the number of streams, and heavy streams can be partitioned, allowing each stream to scale too. Throughput is mainly limited, therefore, by the hardware and network connection used by the network nodes involved in a topology. Measuring the maximum throughput would basically be measuring the hardware as well as the performance of our implemented code. While interesting, this is not a high priority research target at this point in time. And thanks to the redundancy in the network, individual slow nodes do not slow down the whole topology; the data will arrive via faster nodes instead.
Also out of scope for this paper is analysing the costs of running such a network, including the OPEX for publishers and node operators. This is a topic of ongoing research, which we’re currently doing as part of designing the token incentive mechanisms of the Streamr Network, due to be implemented in a later milestone.
I hope that this blog has provided some insight into the fascinating results the team uncovered during this research. For a more in-depth look at the context of this work, and more detail about the research, we invite you to read the full paper.
If you have an interest in network performance and scalability from a developer or enterprise perspective, we will be hosting a talk about this research in the coming weeks, so keep an eye out for more details on the Streamr social media channels. In the meantime, feedback and comments are welcome. Please add a comment to this Reddit thread or email [[email protected]](mailto:[email protected]).
Originally published by. Henri at on August 24, 2020.
submitted by thamilton5 to streamr [link] [comments]

Finding SHA256 partial collisions via the Bitcoin blockchain

This is not a cryptocurrency post, per se. I used Bitcoin's blockchain as a vehicle by which to study SHA256.
The phrase "partial collision" is sometimes used to describe a pair of hashes that are "close" to one another. One notion of closeness is that the two hashes should agree on a large number of total bits. Another is that they should agree on a large number of specific (perhaps contiguous) bits.
The goal in Bitcoin mining is essentially (slight simplification here) to find a block header which, when hashed twice with SHA256, has a large number of trailing zeros. (If you have some familiarity with Bitcoin, you may be wondering: doesn't the protocol demand a large number of leading zeros? It does, kind of, but the Bitcoin protocol reverses the normal byte order of SHA256. Perhaps Satoshi interpreted SHA256 output as a byte stream in little endian order. If so, then this is a slightly unfortunate choice, given that SHA256 explicitly uses big endian byte order in its padding scheme.)
Because Bitcoin block header hashes must all have a large number of trailing zeros, they must all agree on a large number of trailing bits. Agreement or disagreement on earlier bits should, heuristically, appear independent and uniform at random. Thus, I figured it should be possible to get some nice SHA256 partial collisions by comparing block header hashes.
First, I looked for hashes that agree on a large number of trailing bits. At present, block header hashes must have about 75 trailing zeros. There are a little over 2^19 blocks in total right now, so we expect to get a further ~38 bits of agreement via a birthday attack. Although this suggests we may find a hash pair agreeing on 75 + 38 = 113 trailing bits, this should be interpreted as a generous upper bound, since early Bitcoin hashes had fewer trailing zeros (as few as 32 at the outset). Still, this gave me a good enough guess to find some partial collisions without being overwhelmed by them. The best result was a hash pair agreeing on their final 108 bits. Hex encodings of the corresponding SHA256 inputs are as follows:
(I will emphasize that these are hex encodings of the inputs, and are not the inputs themselves.) There were a further 11 hash pairs agreeing on at least 104 trailing bits.
Next, I searched for hashes that agree on a large number of total bits. (In other words, hash pairs with low Hamming distance.) With a little over 2^19 blocks, we have around (2^19 choose 2) ~= 2^37 block pairs. Using binomial distribution statistics, I estimated that it should be possible to find hash pairs that agree on more than 205 bits, but probably not more than 210. Lo and behold, the best result here was a hash pair agreeing on 208 total bits. Hex encodings of the corresponding SHA256 inputs are as follows:
There were 8 other hash pairs agreeing on at least 206 total bits.
So how interesting are these results, really? One way to assess this is to estimate how difficult it would be to get equivalent results by conventional means. I'm not aware of any clever tricks that find SHA256 collisions (partial or full) faster than brute force. As far as I know, birthday attacks are the best known approach.
To find a hash pair agreeing on their final 108 bits, a birthday attack would require 2^54 time and memory heuristically. Each SHA256 hash consists of 2^5 bytes, so 2^59 is probably a more realistic figure. This is "feasible", but would probably require you to rent outside resources at great expense. Writing code to perform this attack on your PC would be inadvisable. Your computer probably doesn't have the requisite ~600 petabytes of memory, anyway.
The hash pair agreeing on 208 of 256 bits is somewhat more remarkable. By reference to binomial distribution CDFs, a random SHA256 hash pair should agree on at least 208 bits with probability about 2^-81. A birthday attack will cut down on the memory requirement by the normal square root factor - among ~2^41 hashes, you expect that there will be such a pair. But in this case, it is probably necessary to actually compare all hash pairs. The problem of finding the minimum Hamming distance among a set doesn't have obvious shortcuts in general. Thus, a birthday attack performed from scratch would heuristically require about 2^81 hash comparisons, and this is likely not feasible for any entity on Earth right now.
I don't think these results carry any practical implications for SHA256. These partial collisions are in line with what one would expect without exploiting any "weaknesses" of SHA256. If anything, these results are a testament to just how much total work has been put into the Bitcoin blockchain. Realistically, the Bitcoin blockchain will never actually exhibit a SHA256 full collision. Still, I thought these were fun curiosities that were worth sharing.
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First DeFiChain Vote

Quick DeFiChain update: Anchoring the DeFiChain on Bitcoin costs money, and this is taken from the 200 DFI blockreward - namely 5 DFI / block. That goes into a pool and whenever a stakeholder wants to, as the pool has grown big enough, he/she anchors the DeFiChain on Bitcoin and gets the accumulated reward. In one of the last PRs (Pull Requests) there was a faulty entry, where these 5 DFI actually should come from (from the 180 or the 20) - which led to the strange rewards for the past blocks, which the community noticed. The short term solution now is to put the accumulated pool of almost 12,000 DFI, which actually belongs to Cake for the anchoring, into the Community Development Fund. The CDF should not be punished for this. The long term solution is, that until the end of the month all DFI Stakers will vote whether the 5 DFI will be taken from the 180 (then there will be less staking returns) or from the 20 (then there will be less Community Development Funds). My suggestion would be here to take it from the 20 from the CDP, as it is now actually, as for now the staking returns seem to be more important, but at the end DFI Stakers must vote on this. DeFiChain Foundation will refrain from voting - so it is in the hands of all DFI hodlers out there. We will post details on this later this week - it will actually be our first DeFiChain vote.
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On the topic of Bitcoin and currencies name confusion (Bitcoin Cash, Bitcoin SV, Bitcoin BTC, Ethereum ETH, Ethereum ETC..)

The topic of cryptocurrencies name confusion come back regularly.
It is often presented as a proof that Bitcoin Cash is trying to mislead newbie and steal the “Bitcoin” brand.
While I agree currencies sharing similar name can be confusing, the situation is not new and in reality extremely common.
It is the norm, not the exception.
It is actually relatively rare to have a currency with unique, non-shared name.
Any peoples with only a bit of knowledge should know this situation exist and be prepared for it when trading currencies whatever it is crypto or regular FIAT.
If anything crypto have shown so far a remarkable consistency. (Somewhat surprising as crypto are open source project while FIAT currencies are state enforced)
Here I collect some examples, the list is pretty exhaustive fell free to let me know if you have other examples I will add them to the list.
US Dollars USD
Australian Dollar AUD
New Zealand NZD
Barbadian dollars BBD
Bermudian dollars BMD
Brunian dollars BND
Bahamian dollars BSD
Belizean dollars BZD
Canadian dollars CAD
Finjian dollars FJD
Taiwan new dollars TWD
Britsh pounds GBP
Egyptian pounds EGP
Falkland island pounds FKP
Guerney pounds GGP
Gibraltar pounds GIP
Isle of man pounds IMP
Jersey pounds JE Lebanese pounds LBP
Sudanese pounds SDG
Saint Helenian pounds SHP
Syrian pounds SYP
Argentinian Pesos ARS
Chilan Pesos CLP
Combian Pesos COP
Cuban Pesos CUC
Dominican Pesos DOP
Mexican Pesos MXP
Philipine Pesos PHP
Uruguayan Pesos UYU
Belaruzian rubles BYB
Russian rubles RUB
Nowegian Krona NOK
Swedish Krona SEK
Danish Krona DKK
Icelandic Krona ISK
Croatian Krona HRK
Czeck Krona CZK
French francs (dead)
Swiss francs CHF
Francs CFA XOF
Burudian francs BIF
Congolese francs CDF
Djibutian francs DJF
Guinean francs GNF
Comorian francs KMF
Rwanda francs RWF
Bahraini Dinars BHD
Algerian Dinars DZD
Iraqi Dinars IQD
Jordanian Dinars JOD
Kuwaiti Dinars KWD
Lybian Dinars LYD
Serbian Dinars RSD
Tunisian Dinars TND
Kenyan shillings KES
Somali shillings SOS
Tanzanian shillings TZD
Ugandan shillings UGX
Indian rupees INR
Sri lankan rupees LKR
Mauritian rupees MUR
Nepalese rupees NPR
Pakistan rupees PKR
Seychellois rupees SCR
Indonesian rupiahs IPR
I recommend to link this post next time you encounter this argument again.
I think there is no need wasting energy debating such points.
It is a normal characteristics of currencies and while possibly annoying it has to be accepted (and is simply unavoidable).
Edit: format hate me
submitted by Ant-n to btc [link] [comments]

College Education Resources

Not a complete list, but somewhere to start
United States
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04-09 11:42 - 'A Reliable Blockchain Platform for Renewable Energy Sector' (self.Bitcoin) by /u/TetianaVoit removed from /r/Bitcoin within 128-138min

What do you think?
[A Reliable Blockchain Platform for Renewable Energy Sector]1
A Reliable Blockchain Platform for Renewable Energy Sector
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Author: TetianaVoit
1: h*ckernoo*.com**-reliabl*-block*h**n-pl**form-f*r-*en**a*le*en*r*y*sector*b25a7e*cdf*d
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BIP Proposal: Compact Client Side Filtering for Light Clients | Olaoluwa Osuntokun | Jun 01 2017

Olaoluwa Osuntokun on Jun 01 2017:
Hi y'all,
Alex Akselrod and I would like to propose a new light client BIP for
This BIP proposal describes a concrete specification (along with a
reference implementations13) for the much discussed client-side
filtering reversal of BIP-37. The precise details are described in the
BIP, but as a summary: we've implemented a new light-client mode that uses
client-side filtering based off of Golomb-Rice coded sets. Full-nodes
maintain an additional index of the chain, and serve this compact filter
(the index) to light clients which request them. Light clients then fetch
these filters, query the locally and maybe fetch the block if a relevant
item matches. The cool part is that blocks can be fetched from any
source, once the light client deems it necessary. Our primary motivation
for this work was enabling a light client mode for lnd4 in order to
support a more light-weight back end paving the way for the usage of
Lightning on mobile phones and other devices. We've integrated neutrino
as a back end for lnd, and will be making the updated code public very
One specific area we'd like feedback on is the parameter selection. Unlike
BIP-37 which allows clients to dynamically tune their false positive rate,
our proposal uses a fixed false-positive. Within the document, it's
currently specified as P = 1/220. We've done a bit of analysis and
optimization attempting to optimize the following sum:
filter_download_bandwidth + expected_block_false_positive_bandwidth. Alex
has made a JS calculator that allows y'all to explore the affect of
tweaking the false positive rate in addition to the following variables:
the number of items the wallet is scanning for, the size of the blocks,
number of blocks fetched, and the size of the filters themselves. The
calculator calculates the expected bandwidth utilization using the CDF of
the Geometric Distribution. The calculator can be found here: Alex also has an empirical
script he's been running on actual data, and the results seem to match up
rather nicely.
We we're excited to see that Karl Johan Alm (kallewoof) has done some
(rather extensive!) analysis of his own, focusing on a distinct encoding
type [5]. I haven't had the time yet to dig into his report yet, but I
think I've read enough to extract the key difference in our encodings: his
filters use a binomial encoding directly on the filter contents, will we
instead create a Golomb-Coded set with the contents being hashes (we use
siphash) of the filter items.
Using a fixed fp=20, I have some stats detailing the total index size, as
well as averages for both mainnet and testnet. For mainnet, using the
filter contents as currently described in the BIP (basic + extended), the
total size of the index comes out to 6.9GB. The break down is as follows:
* total size: 6976047156 * total avg: 14997.220622758816 * total median: 3801 * total max: 79155 * regular size: 3117183743 * regular avg: 6701.372750217131 * regular median: 1734 * regular max: 67533 * extended size: 3858863413 * extended avg: 8295.847872541684 * extended median: 2041 * extended max: 52508 
In order to consider the average+median filter sizes in a world worth
larger blocks, I also ran the index for testnet:
* total size: 2753238530 * total avg: 5918.95736054141 * total median: 60202 * total max: 74983 * regular size: 1165148878 * regular avg: 2504.856172982827 * regular median: 24812 * regular max: 64554 * extended size: 1588089652 * extended avg: 3414.1011875585823 * extended median: 35260 * extended max: 41731 
Finally, here are the testnet stats which take into account the increase
in the maximum filter size due to segwit's block-size increase. The max
filter sizes are a bit larger due to some of the habitual blocks I
created last year when testing segwit (transactions with 30k inputs, 30k
outputs, etc).
 * total size: 585087597 * total avg: 520.8839608674402 * total median: 20 * total max: 164598 * regular size: 299325029 * regular avg: 266.4790836307566 * regular median: 13 * regular max: 164583 * extended size: 285762568 * extended avg: 254.4048772366836 * extended median: 7 * extended max: 127631 
For those that are interested in the raw data, I've uploaded a CSV file
of raw data for each block (mainnet + testnet), which can be found here:
 * mainnet: (14MB):
 * testnet: (25MB):
We look forward to getting feedback from all of y'all!
-- Laolu
-- Laolu
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RT @MEKhoko: Marc Andreessen on Bitcoin, Money, and God - Crypto Insider Info - Whales's

Posted at: November 12, 2018 at 10:22PM
RT @MEKhoko: Marc Andreessen on Bitcoin, Money, and God
Automate your Trading via Crypto Bot :
Join Telegram Channel for FREE Crypto Bot: Crypto Signal
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Latin America List

Welcome Guest.
We are constantly adding to this list. If you want more information please visit: We support Creditcard & Bitcoin (Alt-coins also). You can find tutorials on our website to help you get setup quickly. Once payment is made check junk/spam/all-mail if you did not get your account information. If you need more help please open a support ticket.
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[USA-FL][H] Chromebox with Kodi, Apple Laptop, Windows netbook, HP Microservers, Android tablet, Cell Phones, 3d Printers, Xbox 360, Other misc items. [W] Paypal, Google Wallet

I have a bunch of different items for sale. All items do not include shipping. Please PM me your zip and I will calculate shipping. I will consider best offer on anything. I will also consider trades however I do not need anything specific so would prefer cash. I have prices listed for Paypal. I prefer google wallet and will deduct paypal fees. All items come in original box. If you would like any additional pictures please let me know.
I also have available a $50 Google Play Credit that I am looking to sell for $40 Google Wallet Only. Must be activated by tomorrow (31st). Will send code via PM right after payment.
Zotac Zbox
3d Printer
Pebble Time
Android Tablet
Cell Phones
Sony Ereader
Port multiplier
Android TV Box
Litecoin Miner
Xbox 360
Exploding Kittens
Misc Items
submitted by mastercpt to hardwareswap [link] [comments]

01-04 00:12 - 'GDAX Has kept over $25,000 worth of my LTC for hostage Pending for 23 days and counting!!!!!!!!!!!!!' (self.Bitcoin) by /u/Cryptomic7777 removed from /r/Bitcoin within 5-15min

The LTC I deposited into Gdax on 12/12/17 has not credited me. Coinbase says it received it on 12/12/2017 9:12 , and transferred to Gdax on 12/12/2014 9:44AM. When I look in GDAX it just says pending. I've sent several emails, Called on the phone, and am even thinking about getting a lawyer. When I transferred the LTC it was worth 42k now it's about 25k. I'm depress and distraught. I was planing on selling for Christmas to buy a new car for my family and have lost so much money. GDax is a nice platform but I think they can't handle everyone's money. I heard Gemini is better. I don't know what to do. I need help. Block ID [link]1
GDAX Has kept over $25,000 worth of my LTC for hostage Pending for 23 days and counting!!!!!!!!!!!!!
Go1dfish undelete link
unreddit undelete link
Author: Cryptomic7777
1: l*ve.blockcy**e*.**m**tc/**/2d6*fe23e5a175*1ff5ab84**b2497**9292af*c6f*16a5eb4b88f*cdf*57***/
Unknown links are censored to prevent spreading illicit content.
submitted by removalbot to removalbot [link] [comments]

Don’t know what to do with your old Bitcoin©, Litecoin©, Ether© or Bitcoin Cash©?

Don’t know what to do with your old Bitcoin©, Litecoin©, Ether© or Bitcoin Cash©? Then come on down to bitcoinpanhandler001 and throw your coins at this person. Hell, he will even take Dodgecoin©. This person got in late to the game of crytocurrency and only know has barely figured out what a blockchain is. (For a while, they thought it was something from Minecraft©) Bitcoinpanhandler001 doesn’t believe in getting something for nothing. Hell no dawg, they have some information for you. In 2005, did not have a comments section. Upvotes used to be called boosts.. You could see the title and the source, the person who posted it and how long ago it was posted. You could also save the post. For example: WWII d00d ( By bugbear 1 day ago with 9 boosts save One of the founders Alexis Ohanian had the user name kn0thing
Did you like the show “Scrubs”? Remember Elliot who had a relationship with John? Her name is Sarah Chalke. She is from Canada and voices Beth (Rick’s daughter) on “Rick and Morty”. I knew you knew that voice from somewhere! As early as 2005, Congress has had bills about net neutrality in an effort to reform the Telecommunications Act of 1996.
Bernie Sanders once spoke on the Senate floor for 8 hours straight in attempt to stop tax breaks for the rich.
Facts about Hawaii. It has the only royal palace in the United States and Jurassic Park the movie, was filmed in Hawaii.
If you liked any of these facts then throw some coins or etherium bitcoinpanhandler001’s way as a donation (No COD’s, rainchecks or IOU’s) See below.
Bitcoin and Ether wallet Bitcoin 12UJEWjua3CA89uPHn3XVo3F5oPUJSdZGo Ether 0x60b9B3BaA30a67B697F698E5481bC0fea6C39DA6 Bitcoin cash 1BFtPSp7uZjzVQu9B6Dzx5BA56bTrcfPYo
Here is another wallet Bitcoin 37npjCdfGmU6kDTB3mrDjdSTg2qZz8VXqa Dodgecoin 9z8u6gYS3tbJjmSKx1mPa3M8fW3YySeFpP Litecoin 3HA7gLC78BcJaXBu7tyZ4KdxHPCUUbor4F
submitted by bitcoinpanhandler001 to crypto_currency [link] [comments]

Of Wolves and Weasels - Day 703 - Weekly Wrapup #94

Hey all, GoodShibe here!
This was your week in Dogecoin:
This Week’s oWaWs
Top Images/Memes of the Week
Other Dogecoin Communities
Dogecoin Attractions – Neat or interesting things to check out/ Take part in this Week
Other Interesting Stuff
Did I miss anything? Do you have a Dogecoin community you want featured? Let me know!
All of these places are seeds. Their potential is infinite. You do not have to ‘Leave’ Reddit in order to help build up these other communities.
But take part in them. Take part in one of them. Make it your own.
Each of these different communities offer Shibes different options, different speeds, different conversations.
What will you do there? What will you build there?
It’s 8:16AM EST and Sunday is FunDay, right? Right? Our Global Hashrate is holding at ~1070 Gigahashes per second and our Difficulty is down from ~21645 to ~20614.
As always, I appreciate your support!
submitted by GoodShibe to dogecoin [link] [comments]

What's going on with Slush's Pool?

Is it normal to take 8 hours on a block?
Current round started at: 2013-02-26 07:46:52 UTC
Current round duration: 8:34:32
Current shares CDF: 99.84 %
Current Bitcoin block, difficulty: 223227, 3651011
Pool luck (1 day, 7 days, 30 days): 81%, 91%, 105%
Current server load (60 sec average): 279 getwork/s
Connected workers, Tor workers, Stratum: 0, 0, 11196
Total score of current round: 98772340.9291
Shares contributed in current round: 23466624
Hashrate on Stratum interface (30 min average): 2732.931 Ghash/s (81%)
Approx. cluster performance (30 min average): 3345.032 Ghash/s
submitted by FapFlop to Bitcoin [link] [comments]

Comment vendre le Bitcoin CFD ? How to Mine Bitcoin Using Your Windows PC - YouTube Hohe Gewinne mit Bitcoin-CFDs? Finger weg? - YouTube Invertir En Bitcoin CFD Ya Dieter Bohlen, Günther Jauch & ARD – Bitcoin Trader Betrug ...

Ich finde bitcoin zu gefährlich xD aber jedem das seine, allerdings sehe ich hier das Problem, das ein „echter“ handel für diese Platform nicht zu bewerkstelligen ist, soweit ich weis, dauert es bis zu 3 Tage, bis eine Transaktion abgeschlossen ist, zudem sehe ich ein problem, da die Nachschusspflicht verboten wurde, und z.b. Etherum binnen weniger Minuten mal um 90% gefallen ist ... Bitcoin CFD Trading ist bislang nur bei sehr wenigen Brokern möglich, obgleich die Bitcoins selbst der letzte Schrei unter Anlegern sind. Das liegt auch an ihrer rasanten Kursentwicklung, die wiederum auf der hohen Nachfrage versus Limitierung basiert. Dieses Spannungsfeld bescherte den Bitcoins einen unerhörten Kursverlauf: Wer Bitcoins für 10 Dollar im Jahr 2009 erwarb, kann sie heute ... Mit einem Bitcoin CFD von Admiral Markets können Sie jedoch von steigenden und fallenden Kursen profitieren. Wie viel Risiko steckt im Bitcoin Trading? Bitcoin ist ein volatiles Instrument in dem auch tägliche Preisschwankungen von 10% und mehr recht häufig vorkommen, wodurch es ein sehr riskantes Medium für Investitionen und Trading darstellt. Natürlich ist ohne ein gewisses Risiko auch ... Welche Bitcoin CFD Broker sind die besten? Es wurden viele Tests 2017 von Experten durchgeführt, um den besten Broker zu finden. Ebenso gibt es immer wieder Bewertungen von Kunden aus 2016 und 2017, um sich ein Bild der ganzen Anbieter zu machen. Für das Handeln mit den Bitcoin CFD sind vor allem die folgenden fünf Anbieter zu benennen: plus500 Beim Bitcoin CFD Handel spekuliert der Händler mittels CFDs auf die Kursentwicklung des Bitcoins. Er kann mit einem CFD Bitcoin auf steigende oder fallende Kurse wetten. Das Angebot an CFDs auf Kryptowährungen ist überschaubar und umfasst die wichtigsten Coins. Neben Bitcoin gehören dazu meist Bitcoin Cash, Ethereum, Ripple, Litecoin, IOTA ...

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Comment vendre le Bitcoin CFD ?

Unser CMC Espresso ist Koffein für Trader - wir fassen aktuelle Börsen-Nachrichten zusammen, erläutern Zusammenhänge und beleuchten die möglichen Auswirkunge... Doch ich spreche auch andere Punkte wie Bitcoin CFD Trading im Video an. Natürlich ist dieses Video zu Bitcoin deutsch. Jede Bitcoin Alternative wird ausführlich und verständlich vorgestellt ... CFD trading is ideal for investors who want the opportunity to try and make a better return for their money. Contracts for difference (CFDs) are one of the world's fastest-growing trading instruments. Découvrez maintenant comment vendre le Bitcoin CFD avec Admiral Markets ! Trader le CFD Bitcoin n'est pas très différent par rapport au trading de toute autre paire de devises ou de CFDs ... A very simple video tutorial showing you how to get started mining Bitcoin using your regular Windows desktop or Laptop computer. In this guide I'll take you...