Bitcoin mining the hard way: the algorithms, protocols ...

Generation of Coinbase Transaction

Hello Again,
I'm researching solo mining, but I need help in finding information on how the Coinbase Transaction is Generated:
It should be explained within the 'getblocktemplate' page on the Bitcoin Wiki (, but this only explains how to build a coinbase transaction with the help of "coinbasetxn"; and it's my understanding that "coinbasetxn" is only supported within Mining Pools, not the Bitcoin Wallet - remember, I'm researching solo mining.
What's also making things complicated is "Segregated Witness" (Segwit): With Segwit, all transactions now have a 'txid' and a 'hash'; sometimes, the 'txid' and 'hash' will match, but most of the time (including in some Coinbase Transactions) they don't. And I can't find anything online on how to generate a Coinbase Transaction while accounting for Segwit.
Can anyone find an example algorithm that can build a Coinbase Transaction in a solo mining environment while also accounting for Segwit?
You can share it in any code you like, but I'd prefer Python.
submitted by sparky77734 to Bitcoin [link] [comments]

Blockchain in the Public Sector – Webcast Q&A

Blockchain in the Public Sector – Webcast Q&A
Link to our website: fourth webcast titled "Digital Transformation of the Public Sector & The Upcoming Legislation of Blockchain Technology in Cyprus” was an immense success. We gathered some of the best experts in the field, Deputy Minister Kyriacos Kokkinos, Jeff Bandman, Steve Tendon, and Christiana Aristidou to share their experience and discuss with us the latest updates regarding Blockchain in the Public Sector.
In its fourth series of webcasts, gathered 281 people watching the event from 41 different countries, for a two-hour webcast where guests answered participants’ questions. Following the impressive outcome and response we received from the audience,’s team has done its best to address all the questions for which public information is available.
Below is a list of the questions that were made and were not answered due to time constraints during the webcast. For the remaining questions from our audience, the team will reach out to our distinguished guests to receive their comments and feedback. Please note, that the below information is only for informational purposes!
Question 1:
How can asset tracing be accomplished with bitcoins and cryptocurrency? And how can this be regulated? Team Answer:
Digital Asset tracing may be accomplished with cryptocurrency intelligence solutions such as Cipher Trace and the ICE cryptocurrency intelligence program. FATF (Financial Action Task Force) embarked on a program of work from summer 2018 to June 2019 to strengthen and update the provisions dealing with virtual assets and virtual asset service providers. FATF updated Recommendations in October 2018 and Guidance in June 2019 include several new obligations that apply to VASPs. The so-called “Travel Rule” FATF announced in October 2019 agreed on the assessment criteria for how it will assess countries’ compliance with the new global standards. Under the Travel Rule, the transmitter’s financial institutions must include and send information in the transmittal order such as Information about the identity, name, address, and account number of the sender and its financial institution Information about the identity, name, address and account number of the recipient. The ”Travel Rule” is effectively being applied to cryptoasset transfers when there is a virtual asset service provider (VASP) involved. The scope of focus has broadened from “convertible” virtual assets to any virtual asset. Countries should make sure businesses can freeze crypto wallet or exchange accounts for sanctioned individuals.
Question 2:
Which kind of software or technical knowledge is required to develop cryptocurrency? Team Answer:
It depends on the type of cryptocurrency you wish to create, as well as the preferred functionality and features, and characteristics of the token or coin (i.e. will it be pre-mined, what type of hashing or cryptographic algorithm will be used (i.e. proof of work (POW) or proof of stake (POS) or a hybrid of both), etc. Likewise, it is useful to utilize a programming language that is broadly used and supported by a vast and active development community; more data could be found here: more information could be found here: top programming languages in 2015/2016, published by IEEE here, and TIOBE. Hypothetically, you can utilize any programming language to make cryptocurrency digital money, however, the most widely recognized are C, C++, Java, Python, Perl. The beauty of cryptocurrencies is that you can literally have access to the entire Bitcoin and Ethereum open-source programming scripts, and create your alternate coin (altcoin).
Question 3:
Hello all, I want to know about the current status of the European Union Blockchain initiative in currency or public identity. Team Answer:
Please refer to the European Services Blockchain Infrastructure (EBSI) website.
Question 4:
Mining is also the process of confirmation of transactions in the Bitcoin Blockchain. What is the process of confirmation of transactions in the Blockchain of an Organization? How do we call it? Team Answer:
That would depend on the specific consensus algorithm used for the confirmation of transactions. The consensus algorithm is part of the blockchain protocol that defines the rules on how consensus is reached on that blockchain. In order to participate, entities on the blockchain must obey and follow the same consensus algorithm. Make sure to check our glossary for more information.
Question 5:
How does a small business implement blockchain into its current non-blockchain software systems? Who do they hire to install it? Team Answer:
It is easy when there are APIs to connect the various software. For more information, you can check API.
Question 6:
What is your opinion on digitizing developing economies like India by using AI and blockchain? Team Answer:
Watch a very interesting webinar on the matter by Mr. Prasanna:
Question 7:
Blockchain technologies have been around since 2008. What would you say has been the biggest obstacle in widespread adoption? Team Answer:
In our opinion, the biggest obstacles are volatile cryptoasset prices, complicated UIs, undefined blockchain technology standards. Moreover, the legislation around the technologies is still now being developed and does not offer legal certainty for broader adoption.
Question 8:
Limitations to Blockchain Usability in the Public Sector? Team Answer:
Blockchain in the Public Sector, like any other innovative concept with big potential, cannot be a solution to every problem. Users and developers are still figuring out technological and managerial challenges. From a technological perspective, some aspects such as platform scalability, validation methods, data standardization, and systems integration must still be addressed. From a managerial point of view, the questions include business model transformation, incentive structure, and transaction scale, and maturity. Read more here.
Question 9:
How can these blockchain initiatives be practical for the African context Team Answer:
As long as the internet infrastructure is in place, these blockchain initiatives may have the same benefits for the African region.
Question 10:
What are some compelling use cases you’ve seen lately, and how do they serve to further legitimize blockchain as a solution? Team Answer:
You can see the global trends from all around the world when it comes to further legitimization as a solution, with China leading the way. Read more here.
Question 11:
How does digital currency manage the issue of money laundering? Team Answer:
Depends under which context you are looking at the term digital currency. A digital currency usually refers to a balance or a record stored in a distributed database, in an electronic computer database, within digital files or a stored-value card. Some examples of digital currencies are cryptocurrencies, virtual currencies, central bank digital currencies (CBDCs), and e-Cash. The Financial Action Task Force (FATF) is an intergovernmental body established in 1989 on the initiative of the G7 to develop policies to fight money laundering. Since 2001 FATF is also looking into terrorism financing. The objectives of FATF are to set standards and promote effective implementation of legal, regulatory and operational measures for combating money laundering, terrorist financing, and other related threats to the integrity of the international financial system. FATF is a “policy-making body” that works to generate the necessary political will to bring about national legislative and regulatory reforms in these areas. FATF monitors progress in implementing its Recommendations through “peer reviews” (“mutual evaluations”) of member countries. It is the global watchdog for anti-money laundering & counter-terrorist finance. In June 2019, it updated its guidance paper for Virtual Assets Service Providers (VASPs) regarding the transfer of digital assets. There was an insertion of a new interpretive note that sets out the application of the FATF Standards to virtual asset activities and service providers. To apply FATF Recommendations, countries should consider virtual assets as “property,” “proceeds,” “funds,” “funds or other assets,” or other “corresponding value.” Countries should apply the relevant measures under the FATF Recommendations to virtual assets and virtual asset service providers (VASPs). Read more about the FATF recommendations here).
Question 12:
To what extent can blockchain be used to improve the privacy of healthcare? team Answer:
Please refer to our previous webcast, blog, and articles for more information.
Question 13:
What is Blockchain technology in Shipping? team Answer:
The shipping sector has been in the hold of phony maritime institutes charging exorbitant fees via agents, issuing certificates to candidates who do not have the imperative attendance, or those candidates who just pay the fees for the course and ask for the certificate. In view of these fake accreditations, the possibility exists that someone could be harmed or killed, and we could face any number of potential ecological disasters. Having the option to easily verify the genuine origin of a certificate by an approved maritime center is foremost for shipping companies to fast-track their operation and streamline their labor.
Question 14:
Different uses of blockchain other than cryptocurrency? team Answer:
Please refer to our blog and glossary.
Question 15:
Upcoming trends in Blockchain concerning Advertising, Marketing, and Public Relations in the Public and Private sectors. Team Answer:
Regarding the application of blockchain technology to media copyrights, please see use case proposal during the Bloomen Ideathon.
Question 16:
How to create a decentralized blockchain? Team Answer:
An excessive number of individuals feel that blockchain is some supernatural innovation that makes up a decentralized system. In truth, this innovation only enables decentralization. Which means, it permits cryptocurrency to work in a decentralized way. Yet, it doesn’t give any guarantees that it will work that way. Along these lines, it’s really, some outer variables that decide genuine decentralization. Technology, itself never really guarantees it. That is the reason it’s a mistake to expect that if it’s a blockchain — it’s decentralized. From a technical perspective, both blockchains, centralized, and decentralized are comparative, as they take work on distributed peer to peer to network. This implies every node is individually responsible to verify and store the shared ledger. Both Blockchains utilize either a proof-of-work or proof-of-stake mechanisms to make a solitary record and they have to give upper and lower limits on the security and productivity of the system. For more information please refer to our infographic.
Question 17:
Dubai government Blockchain implementation progress? Team Answer:
You can see more information here.
Question 18:
How Blockchain and IoT can be integrated to secure data being transmitted through IoT devices. Team Answer:
You can read more about it here.
Question 19:
How can the Nigerian government use Blockchain to effectively implement its existing launched eGovernment master plan? Team Answer:
Perhaps it can draw its attention to the initiatives of Dubai, Estonia, and Malta to prepare an implementation framework.
Question 20:
What impact is blockchain going to have in today world of business especially in the financial sector Team Answer:
Please refer to our recent article titled Benefits of Blockchain Technology in the Banking Industry.
Question 21:
Is Blockchain Technology affect individuals? Team Answer:
The social effect of blockchain innovation has just started to be acknowledged and this may simply be a hint of something larger. Cryptocurrencies have raised questions over financial services through digital wallets, and while considering that there are in excess of 3,5 billion individuals on the planet today without access to banking, such a move is surely impactful. Maybe the move for cryptocurrencies will be simpler for developing nations than the process of fiat cash and credit cards. It is like the transformation that developing nations had with mobile phones. It was simpler to acquire mass amounts of mobile phones than to supply another infrastructure for landlines telephones. In addition to giving the underprivileged access to banking services, greater transparency could also raise the profile and effectiveness of charities working in developing countries that fall under corrupt or manipulative governments.
An expanded degree of trust in where the cash goes and whose advantages would without a doubt lead to expanded commitments and backing for the poor in parts of the world that are in urgent need of help. Blockchain technology is well placed to remove the possibility of vote-apparatus and the entirety of different negatives related to the current democratic procedure. Obviously, with new innovation, there are new obstacles and issues that will arise, yet the cycle goes on and those new issues will be comprehended with progressively modern arrangements. A decentralized record would give the entirety of the fundamental information to precisely record votes on an anonymous basis, and check the exactness and whether there had been any manipulation of the voting procedure.
Question 22:
As Andreas Antonopoulos often says in his MOOC: ”is a blockchain even needed?” Ie. Are there better methods? Team Answer:
In combination with nascent technologies, IoT, distributed computing, and distributed ledger technologies, governments can provide inventive services and answers for the citizens and local municipalities. Blockchain can provide the component to create a safe framework to deal with these functions. In particular, it can provide a safe interoperable infrastructure that permits all smart city services and capacities to work past presently imagined levels. On the off chance that there were better techniques, they would be researched.
Question 23:
Would any of this be also applicable to the educational sector (as part of the general public sector), and if so in which way? Team Answer:
Yes, please refer to our Webcast on Education and our blog post.
Question 24:
Will we be able to get a hold of this recording upon completion of the meeting? Team Answer:
Yes, here is a link to the recording of our webcast Blockchain in the Public Sector.
Question 25:
Was wondering if there are any existing universal framework in governing the blockchain technology? Team Answer:
The short answer is NO, as this framework is currently being prepared in collaboration with the various Member States.
We would like to thank everyone for attending our webcast and hoping to interact with you in future webinars. If you would like to watch the webinar again, then click here!
For more info, contact directly or email at [[email protected]](mailto:[email protected]).
Tel +357 70007828
Get the latest from, like and follow us on social media:
submitted by BlockDotCo to u/BlockDotCo [link] [comments]

Hashgraph Vs Blockchain- Top 7 Differences That You Must Know

You know that blockchain technology is continuously evolving at a rapid pace. Blockchains impact is powerful and is impacting business, finance, education, governance, healthcare even in sports, music. There are other similar distributed ledger technologies(DLT) to replace blockchain technology by providing a better solution. Hashgraph is one of them. It solves the distributed ledger differently and claims to be safer, fast, and fair. So here, we will compare two technologies, Hashgraph and Blockchain and which one is better. Before we proceed to the comparison, let us see each one in detail.

What Is Hashgraph?

Hashgraph is a form of distributed consensus which offers another approach to distributed ledger technology. It is a peer-to-peer platform that expels the requirement for any intermediary to complete transactions. It offers a secure, fair and fast network, and its is implemented using Java and Lisp programming languages. This means it supports solidity. One of the best advantages of Hashgraph is its speed. It can handle thousands of transactions per second and is able to verify more than one million signatures per second.

What Is Blockchain?

Blockchain is a popular form of distributed ledger technology. This technology is used by every cryptocurrency ie., bitcoin. Here, peers communicate between themselves to form a peer-to-peer network. Bitcoin uses the basic form of blockchain technology but is not so efficient. Hence we have seen an ascent in another type of blockchain technology. Ethereum is successful with a huge active community. It is a second generation blockchain which means it supports dApps and smart contracts.
In technical terms, blockchain is a series of blocks or records and supports append-only structure. Despite that, the database is immutable means that data that once written, cannot be deleted or altered by anyone else. Hence blockchain is a perfect solution for the issue where data immutability is necessary. Best use cases of blockchain are, supply chain management, voting and finance industry.

Hashgraph vs Blockchain-

1. Programming Language-

Blockchain makes use of languages- Java, C++, Solidity, Ruby and Python. For hashgraph, Lisp and Java languages are used.

2. Approach-

Hashgraph makes use of directed acyclic graph to store and access information. Blockchains are an open-source technology. It stores data in blocks in a linear way. The append approach works seamlessly but it is not always a way for blockchain solutions out there. In both DLTs, each node as a copy of the ledger that makes it decentralized.

3. Consensus Mechanism –

Hashgraph is based on asynchronous Byzantine-Fault Tolerance (aBFT), which provides an improved model of DLT by catering solutions to established cryptocurrency platforms. It uses Gossip about Gossip and virtual voting as a form of gaining network consensus.
Blockchain does not depend on a single approach to consensus. Contingent on cryptography and currency, Blockchain uses numerous consensus algorithms, like Proof of Work, Proof-of-Elapsed Time, Practical Byzantine Fault Tolerance, Proof of Stake, and so on.

4. Speed-

Speed of blockchain varies according to the solution(platform, cryptocurrency, etc.). But it is slower than Hashgraphs. Hashgraph can reach a speed of 5,00,000 transactions per second. Blockchain solutions like ethereum, bitcoin are slow and can do 100 to 10,000 transactions per second. And the Hashgraph Gossip method is a reason behind its speed. With this, less information needs to be propagated across a network.

5. Security-

Blockchain uses a different approach where they use cryptographic methods to ensure security. As we already know, blockchain is a series of blocks or records and that blocks are tamper-proof and no malicious actor can change the integrity of data.
To secure a network from malicious actor, Hashgraph uses an approach- Asynchronous Byzantine Fault Tolerance(aBFT).

7. Efficiency-

Blockchain’s block approach makes it hard for miners to work on a block. There are many instances where two blocks are mined at the same time. At this time, miner’s community needs to decide on one block, and discard the other. At last, miner’s effort is wasted which results in a less efficient network.
Hashgraph is 100% efficient. Hashgraph doesn’t rely on block creation, hence it doesn’t suffer problem.

6. Development Stage-

We have seen that Hashgraph is secured, efficient and offers speed, but it lacks in terms of adoption. Blockchain technology is adopted by various companies and organizations but the adoption rate of Hashgraph is slow.

7. Fairness-

Blockchain is less fair with regards to miners or users. The miner has more power to select orders, process and stop transactions. This is not fair to anyone who is directly or indirectly connected to the network.
Hashgraph manages fairness in a different way. It allocates nodes randomly and uses consensus time stamping, meaning others can not be affected due to the order of transactions. However, the concept of fairness is still vague and isn’t clarified in the Hashgraph whitepaper. It is one of the significant aspects of Hashgraph versus Blockchain comparison.

Use Cases-

Use cases for Hashgraph-
Use cases for Blockchain-

Final words-

Hashgraph is a latest technology as compared to blockchain. But it doesn’t mean that it replaces blockchain. There are a lot of projects that can use blockchain instead of Hashgraph.
submitted by SolaceInfotech to DLT [link] [comments]

Monthly Nano News: December 2019 + Year Recap Special

This is what NANO has been up to lately. I don't think I lie if I say it has been quite an amazing year!
See you soon and happy new year! Something nice is coming soon that I have been working on for a while, stay tuned..

December 2019

November 2019

October 2019

September 2019

August 2019

July 2019

June 2019

May 2019

Apr 2019

Mar 2019

Feb 2019

Jan 2019

More news here:
submitted by Joohansson to nanocurrency [link] [comments]

How to Create Your Own Cryptocurrency Using Python 2020

A blockchain is a public database that irreversibly documents and authenticates the possession and transmission of digital assets. Digital currencies, like Bitcoin and Ethereum, are based on this concept. Blockchain is an exciting technology that you can use to transform the capabilities of your applications.
Of late, we’ve been seeing governments, organizations, and individuals using the blockchain technology to create their own cryptocurrencies—and avoid being left behind. Notably, when Facebook proposed its own cryptocurrency, called Libra, the announcement stirred many waters across the world.

What if you could also follow suit and create your own version of a cryptocurrency?

I thought about this and decided to develop an algorithm that creates a crypto.
I decided to call the cryptocurrency fccCoin.
In this tutorial, I’m going to illustrate the step-by-step process I used to build the digital currency (I used the object-oriented concepts of the Python programming language).
Here is the basic blueprint of the blockchain algorithm for creating the fccCoin:
class Block: def __init__(): #first block class pass def calculate_hash(): #calculates the cryptographic hash of every block class BlockChain: def __init__(self): # constructor method pass def construct_genesis(self): # constructs the initial block pass def construct_block(self, proof_no, prev_hash): # constructs a new block and adds it to the chain pass u/staticmethod def check_validity(): # checks whether the blockchain is valid pass def new_data(self, sender, recipient, quantity): # adds a new transaction to the data of the transactions pass u/staticmethod def construct_proof_of_work(prev_proof): # protects the blockchain from attack pass u/property def last_block(self): # returns the last block in the chain return self.chain[-1]
Now, let me explain what is taking place…
1. Building the first Block class A blockchain comprises of several blocks that are joined to each other (that sounds familiar, right?).
The chaining of blocks takes place such that if one block is tampered with, the rest of the chain becomes invalid.
In applying the above concept, I created the following initial block class
import hashlib import time class Block: def __init__(self, index, proof_no, prev_hash, data, timestamp=None): self.index = index self.proof_no = proof_no self.prev_hash = prev_hash = data self.timestamp = timestamp or time.time() u/property def calculate_hash(self): block_of_string = “{}{}{}{}{}”.format(self.index, self.proof_no, self.prev_hash,, self.timestamp) return hashlib.sha256(block_of_string.encode()).hexdigest() def __repr__(self): return “{} – {} – {} – {} – {}”.format(self.index, self.proof_no, self.prev_hash,, self.timestamp)
As you can see from the code above, I defined the __init__() function, which will be executed when the Block class is being initiated, just like in any other Python class.
I provided the following parameters to the initiation function:
self—this refers to the instance of the Block class, making it possible to access the methods and attributes associated with the class; index—this keeps track of the position of the block within the blockchain; proof_no—this is the number produced during the creation of a new block (called mining); prev_hash—this refers to the hash of the previous block within the chain; data—this gives a record of all transactions completed, such as the quantity bought; timestamp—this places a timestamp for the transactions. The second method in the class, calculate_hash, will generate the hash of the blocks using the above values. The SHA-256 module is imported into the project to assist in obtaining the hashes of the blocks.
After the values have been inputted into the cryptographic hash algorithm, the function will return a 256-bit string representing the contents of the block.
This is how security is achieved in blockchains—every block will have a hash and that hash will rely on the hash of the previous block.
As such, if someone tries to compromise any block in the chain, the other blocks will have invalid hashes, leading to disruption of the entire blockchain network.
Ultimately, a block will look like this:
{ “index”: 2, “proof”: 21, “prev_hash”: “6e27587e8a27d6fe376d4fd9b4edc96c8890346579e5cbf558252b24a8257823”, “transactions”: [ {‘sender’: ‘0’, ‘recipient’: ‘Quincy Larson’, ‘quantity’: 1} ], “timestamp”: 1521646442.4096143 }
2. Building the Blockchain class The main idea of a blockchain, just as the name implies, involves “chaining” several blocks to one another.
Therefore, I’m going to construct a Blockchain class that will be useful in managing the workings of the whole chain. This is where most of the action is going to take place.
The Blockchain class will have various helper methods for completing various tasks in the blockchain.
Let me explain the role of each of the methods in the class.
a. Constructor method This method ensures the blockchain is instantiated.
class BlockChain: def __init__(self): self.chain = [] self.current_data = [] self.nodes = set() self.construct_genesis()
Here are the roles of its attributes:
b. Constructing the genesis block The blockchain requires a construct_genesis method to build the initial block in the chain. In the blockchain convention, this block is special because it symbolizes the start of the blockchain.
In this case, let’s construct it by simply passing some default values to the construct_block method.
I gave both proof_no and prev_hash a value of zero, although you can provide any value you want.
def construct_genesis(self): self.construct_block(proof_no=0, prev_hash=0) def construct_block(self, proof_no, prev_hash): block = Block( index=len(self.chain), proof_no=proof_no, prev_hash=prev_hash, data=self.current_data) self.current_data = [] self.chain.append(block) return block
c. Constructing new blocks
The construct_block method is used for creating new blocks in the blockchain.
Here is what is taking place with the various attributes of this method:
d. Checking validity
The check_validity method is important in assessing the integrity of the blockchain and ensuring anomalies are absent.
As mentioned earlier, hashes are essential for the security of the blockchain as even the slightest change in the object will lead to the generation of a completely new hash.
Therefore, this check_validity method uses if statements to check whether the hash of every block is correct.
It also verifies if every block points to the right previous block, through comparing the value of their hashes. If everything is correct, it returns true; otherwise, it returns false.
u/staticmethod def check_validity(block, prev_block): if prev_block.index + 1 != block.index: return False elif prev_block.calculate_hash != block.prev_hash: return False elif not BlockChain.verifying_proof(block.proof_no, prev_block.proof_no): return False elif block.timestamp <= prev_block.timestamp: return False return True
e. Adding data of transactions
The new_data method is used for adding the data of transactions to a block. It’s a very simple method: it accepts three parameters (sender’s details, receiver’s details, and quantity) and append the transaction data to self.current_data list.
Anytime a new block is created, this list is allocated to that block and reset once more as explained in the construct_block method.
Once the transaction data has been added to the list, the index of the next block to be created is returned.
This index is calculated by adding 1 to the index of the current block (which is the last in the blockchain). The data will assist a user in submitting the transaction in future.
def new_data(self, sender, recipient, quantity): self.current_data.append({ ‘sender’: sender, ‘recipient’: recipient, ‘quantity’: quantity }) return True
f. Adding proof of work
Proof of work is a concept that prevents the blockchain from abuse. Simply, its objective is to identify a number that solves a problem after a certain amount of computing work is done.
If the difficulty level of identifying the number is high, it discourages spamming and tampering with the blockchain.
In this case, we’ll use a simple algorithm that discourages people from mining blocks or creating blocks easily.
u/staticmethod def proof_of_work(last_proof): ”’this simple algorithm identifies a number f’ such that hash(ff’) contain 4 leading zeroes f is the previous f’ f’ is the new proof ”’ proof_no = 0 while BlockChain.verifying_proof(proof_no, last_proof) is False: proof_no += 1 return proof_no u/staticmethod def verifying_proof(last_proof, proof): #verifying the proof: does hash(last_proof, proof) contain 4 leading zeroes? guess = f'{last_proof}{proof}’.encode() guess_hash = hashlib.sha256(guess).hexdigest() return guess_hash[:4] == “0000”
g. Getting the last block
Lastly, the latest_block method is a helper method that assists in obtaining the last block in the blockchain. Remember that the last block is actually the current block in the chain.
u/property def latest_block(self): return self.chain[-1]
Let’s sum everything together
Here is the entire code for creating the fccCoin cryptocurrency.
You can also get the code on this GitHub repository.
import hashlib import time class Block: def __init__(self, index, proof_no, prev_hash, data, timestamp=None): self.index = index self.proof_no = proof_no self.prev_hash = prev_hash = data self.timestamp = timestamp or time.time() u/property def calculate_hash(self): block_of_string = “{}{}{}{}{}”.format(self.index, self.proof_no, self.prev_hash,, self.timestamp) return hashlib.sha256(block_of_string.encode()).hexdigest() def __repr__(self): return “{} – {} – {} – {} – {}”.format(self.index, self.proof_no, self.prev_hash,, self.timestamp) class BlockChain: def __init__(self): self.chain = [] self.current_data = [] self.nodes = set() self.construct_genesis() def construct_genesis(self): self.construct_block(proof_no=0, prev_hash=0) def construct_block(self, proof_no, prev_hash): block = Block( index=len(self.chain), proof_no=proof_no, prev_hash=prev_hash, data=self.current_data) self.current_data = [] self.chain.append(block) return block u/staticmethod def check_validity(block, prev_block): if prev_block.index + 1 != block.index: return False elif prev_block.calculate_hash != block.prev_hash: return False elif not BlockChain.verifying_proof(block.proof_no, prev_block.proof_no): return False elif block.timestamp <= prev_block.timestamp: return False return True def new_data(self, sender, recipient, quantity): self.current_data.append({ ‘sender’: sender, ‘recipient’: recipient, ‘quantity’: quantity }) return True u/staticmethod def proof_of_work(last_proof): ”’this simple algorithm identifies a number f’ such that hash(ff’) contain 4 leading zeroes f is the previous f’ f’ is the new proof ”’ proof_no = 0 while BlockChain.verifying_proof(proof_no, last_proof) is False: proof_no += 1 return proof_no u/staticmethod def verifying_proof(last_proof, proof): #verifying the proof: does hash(last_proof, proof) contain 4 leading zeroes? guess = f'{last_proof}{proof}’.encode() guess_hash = hashlib.sha256(guess).hexdigest() return guess_hash[:4] == “0000” u/property def latest_block(self): return self.chain[-1] def block_mining(self, details_miner): self.new_data( sender=”0″, #it implies that this node has created a new block receiver=details_miner, quantity= 1, #creating a new block (or identifying the proof number) is awarded with 1 ) last_block = self.latest_block last_proof_no = last_block.proof_no proof_no = self.proof_of_work(last_proof_no) last_hash = last_block.calculate_hash block = self.construct_block(proof_no, last_hash) return vars(block) def create_node(self, address): self.nodes.add(address) return True u/staticmethod def obtain_block_object(block_data): #obtains block object from the block data return Block( block_data[‘index’], block_data[‘proof_no’], block_data[‘prev_hash’], block_data[‘data’], timestamp=block_data[‘timestamp’])
Now, let’s test our code to see if it works.
blockchain = BlockChain() print(“***Mining fccCoin about to start***”) print(blockchain.chain) last_block = blockchain.latest_block last_proof_no = last_block.proof_no proof_no = blockchain.proof_of_work(last_proof_no) blockchain.new_data( sender=”0″, #it implies that this node has created a new block recipient=”Quincy Larson”, #let’s send Quincy some coins! quantity= 1, #creating a new block (or identifying the proof number) is awarded with 1 ) last_hash = last_block.calculate_hash block = blockchain.construct_block(proof_no, last_hash) print(“***Mining fccCoin has been successful***”) print(blockchain.chain)
It worked!
Here is the output of the mining process:
***Mining fccCoin about to start*** [0 – 0 – 0 – [] – 1566930640.2707076] ***Mining fccCoin has been successful*** [0 – 0 – 0 – [] – 1566930640.2707076, 1 – 88914 – a8d45cb77cddeac750a9439d629f394da442672e56edfe05827b5e41f4ba0138 – [{‘sender’: ‘0’, ‘recipient’: ‘Quincy Larson’, ‘quantity’: 1}] – 1566930640.5363243]
There you have it!
That’s how you could create your own blockchain using Python.
Let me say that this tutorial just demonstrates the basic concepts for getting your feet wet in the innovative blockchain technology.
If this coin were deployed as-is, it could not meet the present market demands for a stable, secure, and easy-to-use cryptocurrency.
Therefore, it can still be improved by adding additional features to enhance its capabilities for mining and sending financial transactions.
Nonetheless, it’s a good starting point if you decide to make your name known in the amazing world of cryptos.
If you have any comments or questions, please post them below.
Happy (crypto) coding!
Source: Cryptoors
submitted by djkloud to CryptoTechnology [link] [comments]

A better anti-reorg algorithm using first-seen times to punish secret/dishonest mining

Bitcoin currently allows a malicious miner with at least 51% of the network hashrate to arbitrarily rewrite blockchain history. This means that transactions are reversible if they belong to a miner with a hashrate majority, and such transactions are subject to double-spend attempts. Bitcoin SV's miners have repeatedly threatened to perform this attack against exchanges using BCH by mining a secret, hidden chain which they only publish after they have withdrawn funds in a different currency from the exchange. It would be nice if we could prevent these secret mining re-org attacks.
Yesterday, I came up with a new algorithm for making secret re-org attacks very expensive and difficult to pull off. This new algorithm is designed to avoid the permanent chainsplit vulnerabilities of ABC 0.18.5 while being more effective at punishing malicious behavior.
The key to the new algorithm is to punish exactly the behavior that indicates malice. First, publishing a block after another block at the same height has arrived on the network suggests malice or poor performance, and the likelihood of malice increases as the delay increases. A good algorithm would penalize blocks in proportion to how much later they were published after the competing block. Second, building upon a block that was intentionally delayed is also a sign of malice. Therefore, a good algorithm would discount the work done by blocks based not only on their own delays, but the delays that were seen earlier in that chain as well. Since the actions at the start of the fork are more culpable (as they generate the split), we want to weight those blocks more heavily than later blocks.
I wrote up an algorithm that implements these features. When comparing two chains, you look at the PoW done since the fork block, and divide that PoW by a penalty score. The penalty score for each chain is calculated as the sum of the penalty scores for each block. Each block's penalty score is equal to the apparent time delay of that block relative to its sibling or cousin[1], divided by 120 seconds[2], and further divided by the square[3] of that block's height[4] from the fork.[5]
This algorithm has some desirable properties:
  1. It provides smooth performance. There are no corners or sharp changes in its incentive structure or penalty curve.
  2. It converges over very long time scales. Eventually, if one chain has more hashrate than the other and that is sustained indefinitely, the chain with the most hashrate will win by causing the chain penalty score for the slower (less-PoW) chain to grow.
  3. The long-term convergence means that variation in observed times early in the fork will not cause permanent chainsplits.
  4. Long-term convergence means that nodes can follow the standard most-PoW rule during initial block download and get the same results unless an attack is underway, in which case the node will only temporarily disagree.
  5. Over intermediate time scales (e.g. hours to weeks), the penalty given to secret-mining deep-reorg chains is very large and difficult to overcome even with a significant hashrate advantage. The penalty increases the longer the attack chain is kept secret. This makes attack attempts ineffective unless they are published within about 20 minutes of the attack starting.
  6. Single-block orphan race behavior is identical to existing behavior unless one of the blocks has a delay of at least 120 seconds, in which case that chain would require a total of 3 blocks to win (or more) instead of just 2.
  7. As the algorithm strongly punishes hidden chains, finalization becomes much safer as long as you prevent finalization from happening while there are known competitive alternate chains. However, this algorithm is still effective without finalization.
I wrote up this algorithm into a Python sim yesterday and have been playing around with it since. It seems to perform quite well. For example, if the attacker has 1.5x as much hashrate as the defenders (who had 100% of the hashrate before the fork), mine in secret for 20 minutes before publishing, and if finalization is enabled after 10 blocks when there's at least a 2x score advantage, then the attacker gets an orphan rate of 49.3% on their blocks and is only able to cause a >= 10 block reorg in 5.2% of cases, and none of those happen blindly, as the opposing chain shows up when most transactions have about 2 confirmations. If the attacker waits 1 hour before publishing, the attack is even less effective: 94% of their blocks are orphaned, 95.6% of their attempts fail, 94.3% of the attacks end with defenders successfully finalizing, and only 0.6% of attack attempts result in a >= 10 block reorg.
The code for my algorithm and simulator can be found on my antiReorgSim Github repository. If you guys have time, I'd appreciate some review and feedback. To run it:
git clone cd antiReorgSim python # use pypy if you have it, as it's 30x faster 
Thanks! Special thanks to Jonald Fyookball and Mark Lundeberg for reviewing early versions of the code and the ideas. I believe Jonald is working on a Medium post based on some of these concepts. Keep an eye out for it.
Edit: I'm working on an interactive HTML visualization using Dash/Python! Here's a screenshot from a preliminary version in which convergence (or attacker victory, if you prefer) happens after 88.4 hours. In this scenario, the attacker wins because of the rule in Note 5.
Edit 2: An alpha website version of the simulator is up! The code is all server-side for the simulation, so it might get overloaded if too many people hit it at the same time, but it might be fine. Feel free to play around with it!
Note 1: This time delay is calculated by finding the best competing chain's last block with less work than this one and the first block with more work than this one and interpolating the time-first-seen between the two. The time at which the block was fully downloaded and verified is used as time-first-seen, not the time at which the header was received nor the block header's timestamp.
Note 2: An empirical constant, intended to be similar to worst-case block propagation times.
Note 3: A semi-empirical constant; this balances the effect of early blocks against late blocks. The motivation for squaring is that late blocks gain an advantage for two multiplicative reasons: First, there are more late blocks than early blocks. Second, the time deltas for late blocks are larger. Both of these factors are linear versus time, so canceling them out can be done by dividing by height squared. This way, the first block has about as much weight as the next 4 blocks; the first two blocks have as much weight as the next 9 blocks; and the first (n) blocks have about as much weight as the next (n+1)2 blocks. Any early advantage can be overcome eventually by a hashrate majority, so over very long time scales (e.g. hours to weeks), this rule is equivalent to the simple Satoshi most-PoW rule, as long as the hashrate on each chain is constant. However, over intermediate time scales, the advantage to the first seen blocks is large enough that the hashrate will likely not remain constant, and hashrate will likely switch over to whichever chain has the best score and looks the most honest.
Note 4: The calculation doesn't actually use height, as that would be vulnerable to DAA manipulation. Instead, the calculation uses pseudoheight, which uses the PoW done and the fork block's difficulty to calculate what the height would be if all blocks had the fork block's difficulty.
Note 5: If one chain has less PoW than the other, the shorter chain's penalty is calculated as if enough blocks had been mined at the last minute to make them equal in PoW, but these fictional blocks do not contribute to the actual PoW of that chain.
submitted by jtoomim to btc [link] [comments]

Vitalik's Tweet Storm - History and state of Ethereum's Casper research

His tweet here
Today I am going to make a tweet storm explaining the history and state of Ethereum's Casper research, including the FFG vs CBC wars, the hybrid => full switch, the role of randomness, mechanism design issues, and more.
Ethereum proof of stake research began in Jan 2014 with Slasher Though the algorithm is highly suboptimal, it introduced some important ideas, most particularly the use of penalties to solve the nothing at stake problem. That said, the penalties that I used were very small, only canceling out signing rewards. Vlad Zamfir joined in mid-2014, and he quickly moved toward requiring validators to put down deposits, much larger in size than rewards, that could be taken away for misbehavior. Here's Vlad's retelling
We spent much of late 2014 trying to deal with "long range attacks", where attackers withdraw their stake from deposits on the main chain, and use it to create an alternate "attack chain" with more signatures than the main chain, that they could fool clients into switching to. If the attack chain diverges from the main chain at a fairly recent point in time, this is not a problem, because if validators sign two conflicting messages for the two conflicting chains this can be used as evidence to penalize them and take away their deposits.
But if the divergence happened long ago (hence, long range attack), attackers could withdraw their deposits, preventing penalties on either chain. We eventually decided that long range attacks are unavoidable for pretty much the reasons PoW proponents say. However, we did not accept their conclusions. We realized that we could deal with long range attacks by introducing an additional security assumption: that clients log on at least once every four months (and deposits take four months to withdraw), and clients simply refuse to revert further than that. This was anathema to PoW proponents because it feels like a trust assumption: you need to get the blockchain from some trusted source when you sync for the first time. But to us dirty subjectivists, it did not seem like a big deal; you need some trusted source to tell you what the consensus rules of the blockchain are in any case (and don't forget software updates), so the additional trust required by this PoS assumption is not large. Here's Vlad's retelling
Now that we settled on deposits and penalties, we had to decide what those deposits and penalties are. We knew that we wanted an "economic finality" property, where validators would sign on blocks in such a way that once a block was "finalized", no conflicting block could be finalized without a large portion of validators having to sign messages that conflict with their earlier messages in a way that the blockchain could detect, and hence penalize. I went on a big long, and ultimately unproductive, tangent on a direction I called "consensus by bet
Consensus by bet was an interesting construction where validators would make bets on which block would be finalized, and the bets themselves determined which chain the consensus would favor. The theory was that PoW also has this property, as mining is a bet where if you bet on the right chain, you gain (reward - mining cost), and if you bet on the wrong chain, you lose the mining cost, except with PoS we could push the odds on the bets much higher. The odds on validators' bets would start off low, but as validators saw each other getting more and more confident about a block, everyone's odds would rise exponentially, in parallel, until eventually they would bet their entire deposits on a block. This would be "finality".
In the meantime, Vlad started heavily researching mechanism design, particularly with an eye to making Casper more robust against oligopolies, and we also started looking at consensus algorithms inspired by traditional byzantine fault tolerance theory, such as Tendermint. Vlad decided that traditional BFT was lame (he particularly disliked hard thresholds, like the 2/3 in PBFT and Tendermint), and he would try to effectively reinvent BFT theory from scratch, using an approach that he called "Correct by Construction" (CBC). In Vlad's own words
The correct-by-construction philosophy is very different from traditional BFT, in that "finality" is entirely subjective. In CBC philosophy, validators sign messages, and if they sign a message that conflicts with their earlier message they have to submit a "justification" proving that, in the relevant sense, the new thing they are voting for "has more support" than the old thing they were voting for, and so they have a right to switch to it. To detect finality, clients look for patterns of messages that prove that the majority of validators is reliably voting for some block B in such a way that there is no way they can switch away from B without a large fraction of validators "illegally" switching their votes.
For example, if everyone votes for B, then everyone votes on a block that contains everyone's votes for B, that proves that they support B and are aware that everyone else supports B, and so they would have no legitimate cause for switching to something other than B. I eventually gave up on consensus-by-bet because the approach seemed too fundamentally risky, and so I switched back to trying to understand how algorithms like PBFT work. It took a while, but after a few months I figured it out. I managed to simplify PBFT and translate it into the blockchain context, describing it as four "slashing conditions", rules that state what combinations of messages are self-contradictory and therefore illegal if a block is finalized, then there is no way for a conflicting block to get finalized without >= 1/3 violating a slashing condition (ii) if a block is finalized, 2/3 honest validators can always cooperate to finalize a new block. So the algorithm can neither "go back on its word" nor "get stuck" as long as > 2/3 are honest.
I eventually simplified the minimal slashing conditions down from four to two, and from there came Casper the Friendly Finality Gadget (FFG), which is designed to be usable as an overlay on top of any PoW or PoS or other blockchain to add finality guarantees. Finality is a very significant advancement: once a block is finalized, it is secure regardless of network latency (unlike confirmations in PoW), and reverting the block requires >= 1/3 of validators to cheat in a way that's detectable and can be used to destroy their deposits. Hence, the cost of reverting finality can run into the billions of dollars. The Casper CBC and Casper FFG approaches both achieve this, though in technically different ways. Note that Casper CBC and Casper FFG are both "overlays" that need to be applied on top of some existing fork choice rule, though the abstractions work in different ways.
In simplest terms, in Casper CBC the finality overlay adapts to the fork choice rule, whereas in Casper FFG the fork choice rule adapts to the finality overlay. Vlad's initial preference for the fork choice rule was "latest message-driven GHOST", an adaptation of GHOST to proof of stake, and my initial preference was to start off with hybrid PoS, using proof of work as the base fork choice rule. In the initial version of Casper FFG, proof of work would "run" the chain block-by-block, and the proof of stake would follow close behind to finalize blocks. Casper CBC was full proof of stake from the start. At the same time, Vlad and I were both coming up with our own respective schools of thought on the theory of consensus incentivization. Here, a very important distinction is between uniquely attributable faults, where you can tell who was responsible and so can penalize them, and non-uniquely attributable faults, where one of multiple parties could have caused the fault.
The classic case of a non-uniquely-attributable fault is going offline vs censorship, also called "speaker-listener fault equivalence". Penalizing uniquely attributable faults (eg. Casper FFG slashing conditions) is easy. Penalizing non-unquely-attributable faults is hard. What if you can't tell if blocks stopped finalizing because a minority went offline or because a majority is censoring the minority? There are basically 3 schools of thought on this issue:
In November 2017, the Casper FFG slashing conditions, plus my ideas for solving "the 1/3 go offline" problem through a "quadratic leak" mechanism, became a paper . Of course, I was well aware that appealing to the social layer to solve 51% attacks was not a very nice thing to do, so I started looking for ways to at least allow online clients to automatically detect which chain is "legitimate" and which is the "attack" in real time.
Here is one of my earlier ideas It was something, but still suboptimal; unless network latency was exactly zero, there was only a guarantee that clients' suspicion scores would differ by at most delta, not that clients would fully agree In the meantime, my main criticism of Vlad's model had to do with "discouragement attacks", where attackers could credibly threaten to make a 51% attack that causes everyone to lose money, thereby driving everyone else to drop out, thus dominating the chain at near-zero cost. Vlad (along with Georgios Piliouras) started doing economic modeling to estimate the actual cost of such an attack under his model.
It's worth noting here that all of these issues are not unique to proof of stake. In fact, in proof of work, people tend to simply give up and assume preventing 51% attacks is outright impossible, and a 51% attack is a doomsday that must be prevented at all costs. But, as is the Ethereum tradition, Vlad and I were both unaware that the word "ambitious" can be anything but a compliment, and kept on working on our separate approaches to disincentivizing, mitigating and recovering from 51% attacks.
In early 2018, Vlad's work on CBC started to move forward quickly, with great progess on safety proofs. For the state of progress in March 2018, see this epic two-hour presentation In the meantime, Casper FFG was making huge progress. A decision to implement it as a contract that would be published to the Ethereum blockchain made development easy. On Dec 31, 2017 at 23:40, we released a testnet written in python
Unfortunately, development of FFG then slowed down. The decision to implement FFG as a contract made some things easier, but it made other things harder, and it also meant that the eventual switch from EVM to EWASM, and single-chain Casper to sharded Casper, would be harder. In addition, the team's work was being split between "main chain Casper" and "shard chain Casper" and it was clear there was enormous unneeded duplication of effort going on between the Casper and sharding teams.
In June 2018, we made the fateful decision to scrap "hybrid Casper FFG as a contract", and instead pursue full Casper as an independent chain, designed in such a way that integrating sharding would be much easier. The switch to full proof of stake led me to start thinking much harder about proof of stake fork choice rules. Casper FFG (and CBC) both require the entire validator set to vote in every "epoch" to finalize blocks, meaning there would be tens to hundreds of signatures coming in every second. BLS signature aggregation makes this practical in terms of computational overhead but I wanted to try to take advantage of all of these extra signatures to make the chain much more "stable", getting "100 confirmations" worth of security within a few seconds.
Here were my initial attempts However, all of these approaches to the fork choice rule had a weakness: they split up validators into "attesters" and "proposers", and the proposers, being the key drivers of block production, had outsized power. This was undesirable, primarily because it required us to have a strong source of on-chain random number generation to fairly pick the proposers. And on-chain randomness is hard, with simple approaches like RANDAO looking more and more problematic
Justin Drake and I went off to solve this problem in two ways, Justin by using verifiable delay functions which have a deterministic and verifiable output, but take a large amount of unparallelizable sequential time to compute, making manipulation ahead of time impossible and myself by making a major concession to the Cult of Vlad™, using GHOST-based fork choice rules to greatly reduce the dependence on proposers, allowing the chain to grow uninterrupted even if >90% of proposers are malicious, as long as >50% of attesters are friendly.
Vlad was very happy, though not fully: he preferred a version of GHOST based on validators' latest messages, whereas I preferred a version based on immediate messages Around this time I also managed to come up with a way to "pipeline" Casper FFG, reducing time-to-finality from 2.5 epochs to the theoretically optimal 2 epochs: I was very happy that the RPJ fork choice rule (which I have since renamed "immediate message-driven GHOST") is nicely compatible with Casper FFG in a way that most others are not and that it has a very important "stability" property: that the fork choice is a good prediction of the future fork choice. This seems obvious, but is very easy to accidentally make fork choice rules that do not have this property.
The most recent development of all is a result that latest message driven GHOST may, due to a technicality, only give 25% fault tolerance within two rounds, but immediate driven message GHOST (with FFG or CBC) still gives the full 33% (no writeup yet). The main tradeoff between FFG and CBC is that CBC seems to have nicer theoretical properties, but FFG seems to be easier to implement. In the meantime, a lot of progress on verifiable delay functions has been made
Also, I recently decided to look into Leslie Lamport's old 1982 paper, where he had a consensus algorithm that has 99% fault tolerance if you add the assumption that all nodes, including observers, are online with low network latency The network latency assumptions arguably make this unsuitable as a primary consensus algorithm. However, there is one use case where it works really well: as a substitute for suspicion scores for 51% censorship detection.
Basically, if a 51% coalition starts censoring blocks, other validators and clients can detect that this is happening and use the 99% fault tolerant consensus to agree that this is happening and coordinate a minority fork. The long-run goal of this research is to reduce reliance on the social layer as much as possible, and maximizing the cost of destabilizing the chain enough so that reverting to the social layer is necessary.
What's left now? On the FFG side, formal proofs, refinements to the specification, and ongoing progress on implementation (already started by >=3 teams!), with an eye to safe and speedy deployment. On the CBC side, much of the same. Onward and upward!
submitted by OneSmallStepForLambo to ethtrader [link] [comments]

190 университетов бесплатно выложили сотни своих курсов. Если вы не слышали, университеты по всему миру предлагают свои курсы онлайн бесплатно (или, по крайней мере, частично бесплатно). Эти курсы называются БООК или Большие открытые онлайн–курсы. Часть 1


Введение в CS50 по разработке игр от Гарвардского университета; Разработка мобильных приложений для CS50 с React Native от Гарвардского университета; Веб–программирование CS50 с использованием Python и JavaScript от Гарвардского университета; Функции, методы и интерфейсы в Go от Калифорнийского университета, Ирвин; Совпадения в Go от Калифорнийского университета в Ирвине; Начало работы с Go от Калифорнийского университета, Ирвин; Вычислительные в Python I: основы и процедурное программирование от Технологического института Джорджии; Вычислительные в Python IV: объекты и алгоритмы от Технологического института Джорджии; Вычислительные в Python III: структуры данных от Технологического института Джорджии; Вычислительные в Python II: структуры управления от Технологического института Джорджии; Проект программирования (Java) от Политехнической Школы Лозанны; Пиксельное искусство для видеоигр от Мичиганского государственного университета; Веб–дизайн: стратегия и информационная архитектура от Калифорнийского института искусств; Веб–разработка с React от Гонконгского университета науки и технологии; Разработка мультиплатформенного мобильного приложения с React Native от Гонконгского университета науки и технологий; Автоматизированное тестирование программного обеспечения: практические навыки для разработчиков Java от Технологического университета Делфта; Автоматизированное тестирование программного обеспечения: передовые навыки для разработчиков Java от Делфтского технологического университета; Погружение в Python от Московского физико–технического института; Основы разработки на C ++: красный пояс от Московского физико–технического института; AR (дополненная реальность) и услуги потокового видео от Университета Йонсей; Интеллектуальные устройства и новые мобильные технологии от Университета Йонсей; Решение проблем, программирование и видеоигры от Университета Альберты; Введение в дополненную реальность и ARCore от Google Daydream Impact; Основы NetLogo от Института Санта–Фе; Ключевые навыки конкурентоспособного программиста от Санкт–Петербургского государственного университета; Проблемы бизнеса и программные решения от Университета Дикина; Введение в язык Котлин от Санкт–Петербургского государственного политехнического университета; Веб–доступ для разработчиков от Университета Райерсона; Разработка карманных приложений для AR с Unity от Unity; Системные информационные ресурсы Интернет с MySQL / PHP и Joomla от Университета Росарио; Котлин для разработчиков Java от JetBrains; Введение в основы XR: VR, AR и MR от Unity; 3D–арт и аудио конвейер от Unity; Программирование прикладных систем от Unity; 3D взаимодействия и навигация от Unity; Программирование ядра взаимодействия от Unity.


Анализ транспортных явлений I: математические методы от Массачусетского технологического института; Строение космического челнока от Массачусетского технологического института; Искусство структурной инженерии: хранилища от Принстонского университета; Совместная безопасность роботов: проектирование и развертывание от Университета в Буффало; Электроэнергетические системы от Университета в Буффало; Современная робототехника, курс 5: Манипуляции с роботами и мобильные роботы на колесах от Северо–Западного университета; Робот Thymio от Университета естественных наук от Национальной Политехнической Школы Лозанны; Принцип полупроводниковых приборов Часть I: Полупроводники, PN–переходы и биполярные переходные транзисторы от Гонконгского университета науки и техники; Привет (реальный) мир с ROS — роботизированной операционной системой от Делфтского технологического университета; Разум Вселенной — Роботы в обществе: благословение или проклятие? от Делфтского технологического университета; Hyperloop: изменение будущего транспорта от Делфтского технологического университета; Электромобили: технология от Делфтского технологического университета; Электромобили: политика от Делфтского технологического университета; Глазами инженеров — расширение видения: инженерная механика с помощью эксперимента, анализа и проектирования от Университета Нового Южного Уэльса; Взглядом инженеров — представление о концепции: инженерная механика с помощью эксперимента, анализа и проектирования от Университета Нового Южного Уэльса; Конструкция оптической системы первого порядка от Университета Колорадо в Боулдере; Проектирование высокопроизводительных оптических систем от Университета Колорадо в Боулдере; Оптическая эффективность и разрешение от Университета Колорадо Боулдер; Двигатели и схемы управления двигателем от Университета Колорадо в Боулдере; Физика полупроводников от Университета Колорадо в Боулдере; Датчики и сенсорная схема от Университета Колорадо в Боулдере; Транзистор — полевой транзистор и транзистор с биполярным переходом от Университета Колорадо в Боулдере; Диод — pn Junction и Metal Semiconductor от Университета Колорадо в Боулдере; Diseño de diques rompeolas con cubípodos от Политехнического университета Валенсии; Введение в энергетику солнечной фотовольтаики от Политехнического университета Валенсии; Ознакомление со строительством от Университета Политехники в Валенсии; BIM Основы для инженеров от Национального университета Тайваня; Приложение BIM для инженеров от Национального университета Тайваня; Распределение электроэнергии от Института технологий и высшего образования в Монтеррее; Интеллектуальная сетка: фундаментальные технологии от Института технологий и высшего образования в Монтеррее; Передача электроэнергии от Института технологий и высшего образования в Монтеррее; Электрическая мощность: концепции и принципы от Института технологий и высшего образования в Монтеррее; Интеллектуальная сетка: электрические сети будущего от Института технологий и высшего образования в Монтеррее; Основы МОП–транзисторов от Университета Пердью; Основы протекания тока от Университета Пердью; Учебник по основам полупроводников от Университета Пердью; Введение в цепи от Университета Федерико II в Неаполе; Стандартизация от EIT Digital; Введение в системы управления батареями от Колорадского университета; Имитация модели ячейки эквивалентной цепи от Университета Колорадо; Оценка состояния батареи (SOC) от системы Университета Колорадо; Введение в веб–картографию: часть 2 от Швейцарской высшей техническая школы Цюриха; Высокоэффективное моделирование методом конечных элементов — часть 2 от Королевского технологического института; Интеграция энергетических систем: тенденция или революция? от Университета им. К.Ю. Ленвена Распространение радио от Института Mines–Télécom; «Monotsukuri» делает вещи в Японии: машиностроение от Токийского технологического института; Транспортная инженерия от Католического университета Чили; Введение в контрольную цифру Computadora от Университета Тенарис; Agile для управления проектами от Университеа штата Мэриленд.


Машинное обучение с Python: от линейных моделей до глубокого обучения от Массачусетского технологического института; Технология блокчейн от Калифорнийского университета, Беркли; Биткойн и криптовалюты от Университета Калифорнии, Беркли; Введение в машинное обучение от Университета Дьюка; Введение в компьютерное программирование от Лондонского университета международных программ; Как работают компьютеры от Лондонского университета международных программ; ИТ–инфраструктура и новые тенденции от Университета Миннесоты; Корпоративные системы от Университета Миннесоты; Управление ИС / ИТ от Университета Миннесоты; Встроенное программное и аппаратное обеспечение от Университета Колорадо в Боулдере; Промышленные рынки Интернета вещей и безопасность от Университета Колорадо в Боулдере; Введение в технологию блокчейн от Московского физико–технического института; Искусственный интеллект — обучение и теория от Национального университета Тайваня; Введение в аппаратное обеспечение Verilog от Университета Галилео; Интернет вещей: Новые технологии беспроводных и облачных вычислений от Университета Йонсей; Информационные технологии от Университета Федерико II в Неаполе; Естественный язык, от человека к машине от Университета Федерико II в Неаполе; Новые цифровые технологии от Университета Федерико II в Неаполе; Программирование на C: языковые фонды от Института Mines–Télécom; Программирование на C: модульное программирование и управление памятью от Дартмута; Программирование на C: расширенные типы данных от Дартмута; Основы Linux: интерфейс командной строки от Дартмута; Программирование на C: использование инструментов и библиотек Linux от Дартмута; Программирование на C: Начало работы от Дартмута; Программирование на C: указатели и управление памятью от Дартмута; Введение в теорию вычислений от Института Санта–Фе; Основы машинного обучения от Института Санта–Фе; Введение в FinTech от Университета Гонконга; FinTech: Этика и риски от Университета Гонконга; Блокчейн и FinTech: основы, приложения и ограничения от Университета Гонконга; DDoS–атаки и защита от Университета Колорадо; Безопасность облачных вычислений от системы Университета Колорадо; Введение в программирование на языке C: Инструкции по контролю над текстами от Университета Мадрида; Введение в программирование на языке C: Типы и структура от Университета Мадрида; Введение в программирование на языке C: Функции и указатели от Автономного университета Мадрида; Кибер–физические сети от Королевского технологического института KTH; Понимание сути Интернета: сети операторов от Института Института Mines–Télécom; Усовершенствованная алгоритмика и теория графов на Python от Института Mines–Télécom; Программирование Arduino, от новичка до ниндзя от Института Mines–Télécom; Кибербезопасность: атакует противников от Университета Рей Хуана Карлоса; Цифровая трансформация и команда ИТ от Университета Витватерсранда; Стратегические и трансформационные информационные технологии от Университета штата Мэриленд; Основы сетевой безопасности от Университета Ковентри; Введение в криптографию от Университета Ковентри; Информационная система по глобальной сети Интернет Microsoft Access от Университета Росарио; Основы кибербезопасности I сетей Пало–Альто от сетей Пало–Альто; Фонд кибербезопасности Академии сетей Пало–Альто от сетей Пало–Альто; Шлюз I кибербезопасности сетей Пало–Альто от сетей Пало–Альто; Шлюз ІІ кибербезопасности сетей Пало–Альто от сетей Пало–Альто; Основы ІІ кибербезопасности сетей Пало–Альто от сетей Пало–Альто; Блокчейн: основы и варианты использования от Академии ConsenSys; Архитектура сети и безопасности с VMware NSX от VMware.
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Nebulas Technical White Paper Review January 20, 2018

Nebulas Technical White Paper Review January 20, 2018

Whitepaper version: 1.0 September, 2017.

Built on ground-breaking innovation, Nebulas brings blockchain technology into the 3rd generation.
Nebulas offers two different white papers; while the first is a basic overview, the second is technical.
The technical white paper describes the specifics of the project, and with each part broken down into details, it is not only quite long, it is also considered one of the most technical white paper of any blockchain technology to date. Although detailed information provides transparency and answers questions, many people are finding it difficult to comprehend.
No doubt, most investors are looking for the next hot coin that will provide a good pay day! While I believe that Nebulas can provide just that, I also feel that it is always important to understand what you are investing in. If you take the time to read everything carefully, Nebulas’ technical white paper shows the entire system in its final glory!
Therefore, the comments below compile my analysis of the technical white paper (in combination with other reliable sources). I will also do my best to include the page where you can find these facts in the technical white paper. Therefore, I suggest that rather than taking my word for it, read it for yourself.
Based on pros and cons, let’s break down the primary elements of Nebulas:

Nebulas Rank (NR)

Nebulas Rank (NR) will be the first to integrate search engine capability into blockchain. In other words, Nebulas Rank is the protocol responsible for making search engine a viable element in the blockchain. Right off the bat, let’s address an important question, "What good is a ranking system inside a blockchain?"
Currently, there is no way to search the blockchain for meaningful data (other than simple transactions), and, therefore, it’s impossible to find dApps or locate smart contracts. If this doesn’t sound like a big deal, imagine trying to search the internet without google or some other search engine – it would be impossible!
Just as the first internet search engine evolved the internet into what it is today, the first blockchain search engine will inevitably evolve blockchain. Not only a stepping stone for the future of blockchain, we’re talking about a new foundation for blockchain technology.
By providing a blockchain search engine, the Nebulas Ranking system will allow users to locate quality dApps (decentralized apps) and smart contracts. For example, let’s say that you are looking for a dApp like CryptoKitties. No doubt, there could be dozens of similar apps. So, based on multiple data resources, such as blockchain activity, github activity, and even google search history, the ranking algorithm (NR) orders similar apps, and then lists them in a manner that the user can evaluate and select.

Now, can you see why Nebulas is being compared to google?

But, this is only the beginning…. Nebulas Rank is also interwoven into the Developer Incentive Protocol (DIP) and the Proof of Devotion (PoD) Consensus Algorithm. Without Nebulas Rank, these other two elements could not operate as the white paper states.
Based on the current white paper, let’s spotlight some potential negatives about the Nebulas Rank(NR) protocol. However, also keep in mind that these potential issues could be completely eliminated as the project develops (thanks to Nebulas Force – more on this later).

Now the potential negative:

However, while the white paper describes the search engine being centralized, it also says "In current stage..." Thereby indicating that Nebulas developers have a better solution in the long run. Perhaps a sidechain just for searching? The white paper also states that "the complete code for searching backend is available to the community and third-party developers can create their own searching services on this basis." Hopefully, this will keep the ranking honest.
Since the Nebulas blockchain is based on the Nebulas Rank (NR) system, now that we have highlighted the most important aspects of Nebulas Rank (NR), we can dive deeper into specific functions.

Proof of Devotion (PoD) Consensus Algorithm

In the cryptocurrency world, Proof of work (PoW) means mining. While damaging to the environment, few can argue that this is a terrible waste of natural resources. As an alternative, the cryptocurrency world also has Proof of Stake (PoS). Proof of Stake allows token/coin holders to stake (aka hold un-spendable tokens), and to be rewarded with more tokens when they create a new block. For example, if there are 100 people staking and there are 100 new blocks per hour, every stake will, on average, receive one block reward per hour.
While better for the environment, Proof of Stake creates an imbalance where major coin holders (aka whales) are rewarded with even more coins, and this allows "whales" to stake even more coins (this means that there could be a potential to monopolize the system).
Now, Nebulas brings us Proof of Devotion (PoD)[iii]. As far as I know, there is currently nothing like this in blockchain technology (nor ready to be released). Proof of Devotion essentially awards developers who make awesome things (such as dApps) on the Nebulas blockchain.
If you develop an dApp that’s performing well on the Nebulas network, you will have the option to be a validator (aka validate submitted transactions), and, in return, receive token rewards from the blockchain. To be a validator, you will need to stake (deposit) X amount of tokens. Then, multiple validators (per transaction) will have to agree on the result[iv], and, each will be rewarded 1.5x the amount staked.
The generation of new blocks[v] will be carried out by "highly important" accounts that Nebulas Rank (NR) calculates. As stated in the whitepaper, "PoD empowers the selected accounts to have the bookkeeping right with equal probability to participate in new block generation in order to prevent tilted probability that may bring about monopoly".
The bottom line... when it comes to Proof of Devotion, why use Ethereum to create a dApp when you can create the same dApp on Nebulas and make a profit? Needless to say, this is a huge incentive for developers to make dApps on the Nebulas network, and, consequently, it will increase the value of the network. Furthermore, since Nebulas will provide developer tools, it will be easier to create dApps.

Now the potential negative:

Because it inspires developers to create awesome dApps, and, at the same time, profit directly from blockchain, I personally love this idea! No longer will dApp creators require insane ICO’s nor will they need some other stream of revenue. However, participating in PoD does not stop developers from benefiting from other income streams. Truly groundbreaking!

Developer Incentive Protocol (DIP)

Not only can Proof of Devotion give incentive to developers, quality developers will also receive extra coins/tokens for their hard work. Based on Nebulas Rank(NR), Nebulas will use an algorithm for reward distribution[vii]. The rewards will be automatically distributed to the smart contract cash-out address every 7 days.
There is really nothing negative to add to this. It’s truly a powerful incentive!

Nebulas Force (NF)

Who needs hard forks? Nebulas Force will allow developers to introduce new features/protocols into the Nebulas blockchain without a fork. The Nebulas white paper calls it "Self-evolving blockchain technology" but I don’t believe this is quite correct. Rather than being self-evolving, it is actually community driven! Because this will build the blockchain community, in my opinion, this is even better!
With other blockchains for example, if a developer has an awesome idea for a dApp but it needs a new protocol that does not exist on any blockchain, the developer would have to centralize the dApp or chuck it altogether.
With Nebulas, new ideas can be developed, and if they provide positive contribution, the Nebulas community (Nebulas token holders) can vote on and approve changes to the network protocol. Once approved, Nebulas developers can add the new protocol into the Nebulas blockchain. Perhaps, further in the development, sub-chains will also support new protocols for full implementation.

Upgradable Smart Contracts

Revolutionary for blockchain, Nebulas Force will include upgradable smart contracts[viii]. Why is this important? Well, due to bugs in smart contracts, investors can lose funds in any blockchain network that uses smart contracts. Once submitted to the blockchain, nothing can be done to fix the bugs, and, as a result, tens of millions of dollars have already been lost.
Nebulas plans to overcome this problem through the implementation of upgradable smart contracts. In a nutshell, token holders will vote on proposed changes (to fix specific bugs), and when the overall vote is affirmative, bugs can be eliminated at any time. By saving investors millions, it will restore lost confidence!

Now the potential negative:

  • The Nebulas protocol is only modifiable by the Nebulas core developers. Although this is not really a negative, I would not call it "self-evolving". If you look at Bitcoin, there is a handful of developers responsible for source code, and, subsequently, the source code for all alt coins that use Bitcoin core in some capacity (such as LTC, BCC, BTG, DOGE, etc…)
  • The protocol updates will be applied via a hard coded signature into the genesis block[ix] and this means that there is a potential for network compromise.
  • Although there are some ethical issues with modifying smart contracts, overall, it is a great idea! Since token holders will have to vote on any changes, there could be an issue with whales (monopoly owners) controlling contracts.
Even with the negatives, this is a powerful feature.

The above includes Nebulas’ most innovative features, and although these features stand out, there is even more to Nebulas:

Anti-cheating algorithms[x]

To ensure fairness, the above protocols contain anti-cheating algorithms that are manipulation resistant, and, if someone is found trying to cheat, there are penalties.

Smart contracts almost anyone can write![xi]

Nebulas will support smart contracts written in Javascript, Python, Java and more! And this means that any coder can create a logical contract!

Full voting protocol[xii]

Since Nebulas includes a full voting protocol in the blockchain, you and I, as token holders, can help decide the direction of Nebulas. As an example, the coin "Decred[xiii]" also has a voting system; giving end-users a voice keeps them engaged.

Domain Name Service[xiv]

Although blockchain users are accustomed to "please send funds to: 0x488B2630CEdB5Bfd5e02c33A3653227170743357", it’s simply not logical. If you miss a letter, change a number, or simply enter an address incompletely, funds are sent into the abyss - forever. To correct this inherent problem, Nebulas will implement the use of "meaningful names." For instance, using a meaningful name, your Nebulas address could be "" Users will have the opportunity to bid for requested names, and renew yearly - just like a web based domain name.

Lightning Network[xv]

As many of you probably already know, bitcoin can now use a Lightning Network. This will allow multiple small transactions to be signed without clogging up the blockchain and memory pool. It keeps an open ledger between two entities and can be closed at any time by either party, resulting in one transaction on the network instead of potentially dozens or hundreds.If the Bitcoin network started with the Lightning Network, it would currently be able to handle all transactions per second without any problems. Without the Lightening Network, Bitcoin can only handle 7~ transactions per second (and usually less). With the Lightening Network initially in place, the Nebulas network will be able to handle the required transactions and close the lightning ledgers when requested by users. It would also not cost $20.00++ to send $5.00 nor would it take an hour. I won’t get into the ludicrous prices of Bitcoin transactions fees and how we got here, but if you don’t know much about it, you should learn more. As an important feature of Nebulas, the Lightning Network will provide quick and cheap transactions.

High Strength Encryption

Nebulas uses SHA3-256 encryption. Although you won’t find this in the white paper, SHA3-256 is Highly Quantum Resistant[xvi] - research it yourself. Why is this so important? Well, as an inevitable evolution of quantum computing, previous generations of encryption will be rendered inadequate, and, consequently, susceptible to decryption of private keys. Basically, this means that once quantum computers are developed, you can lose your money in a non-quantum resistant blockchain. Since Quantum Resistance is a very important feature, many new coins (such as the QRL coin[xvii]) are being intentionally created for this purpose.

So, what role does the NAS token play in the network?

Directly from the white paper[xviii]; "The Nebulas network has its own built-in token, NAS. NAS plays two roles in the network. First, as the original money in the network, NAS provides asset liquidity among users, and functions as the incentive token for PoD bookkeepers and DIP. Second, NAS will be charged as the calculation fee for running smart contracts. The minimum unit of NAS is 10−18 NAS." If interested, the white paper goes into detail. If you question the purpose of NAS, simply ask yourself, "What role does ETHER play in the Ethereum network?" As of this writing, ETHER’s current price is $1098.00USD – and that’s not even it’s high. I believe that common sense indicates the potential value of the NAS coin!

Nebulas will have a maximum of 100,000,000 tokens

Many of the top 10 cryptocurrencies will distribute coins/tokens in the tens of billions, and, in fact, Ethereum will have an indefinite amount (albeit, they will taper off in time). However, when there are significantly less coins/tokens, the value of each increases. Treasure each NAS token!

A web-based playground for developer tools[xix]

To help developers create smart contracts easier and faster, Nebulas will offer developer tools. Nebulas will also support multiple IDE’s.
Although the list of features and functions goes on, this should give you an overview of what the Nebulas network can do, how it can evolve blockchain technology, and why it will be a very attractive option for future dApps. Having said all this, please be clear, it is not financial advice.
Also, keep in mind that the above statements are based on my analysis of the white paper (version: 1.0 September, 2017), but this is not to say that the developers don’t have a different perspective. With that being said, Nebulas staff and co-founder, Robin Zhong, actively responds to questions in their Slack channel. This leads us to a review of the Nebulas team.

The Nebulas Team

When looking at a new, and yet to be released, project, it’s not only important to understand the innovation, it’s also important to understand the team behind the innovation. Although not the largest team, the developers are highly educated with real blockchain experience. In fact, many have worked at Google, IBM, Alibaba, Alibaba financial, Airbnb, etc… Additionally, two Nebulas founders previously co-founded the NEO coin (formerly Antshares) which on January 20, 2018 trades at $140.00 (not even its high) per coin/token.
No doubt, the team is influential in past, current and future blockchain innovation. In fact, playing a huge part in bringing blockchain to China, Hitters Xu created Bitsclub, and many other team members started blockchain communities. If you have not yet learned about the team, I strongly suggest you do. Check out their LinkedIn pages and also look at the developers Githubs.

Full disclosure:

As a fellow investor and fan of blockchain technology, I got into the crypto world in 2012. Since then, I have mined, traded, and even created an arbitrary trading system. My portfolio includes dozens of different types of tokens/coins. My focus is on innovation rather than "rinse and repeat."
I first learned about Nebulas in the beginning of January 2018. After reading the technical white paper multiple times and fully understanding Nebulas (what it is and what it’s not), I confidentially purchased NAS (ERC-20) tokens.
As with any great blockchain, Nebulas will not be the last, but it is a crucial step to the next generation of blockchain innovation! Without doubt, I see the true potential of blockchain technology, and, if you ask me, Nebulas is an amazing short, medium and long term project, and I’m excited about the future!
To quote a Nebulas founder, "Ask not what blockchain can do for you, ask what you can do for blockchain..." - Hitters Xu

Quick Update (January 31, 2018)

For full transparency, I wanted to add that I have been asked by the Nebulas Team Reddit manager if I would be willing to be a moderator of the Nebulas subreddit. I told them that I would happy to continue helping the community and accepted. There is no extra benefit to me and does not change my opinion about Nebulas. I look forward to continuing helping the community!


i: Pg 41 – 6.2
ii: Pg 24 – Last bullet point
iii: Pg 34 - 5.3.1
iv: Pg 35 – 3.3.3
v: Pg 34 – 5.3.1
vi: Visit for more info – Watch the video for an example of what Nebulas will do.
vii: Pg30 – 4.2
viii: Pg 27 – 3.3.2
ix: Pg 26 – Paragraph2
x: Many locations – There are many parts of the white paper that talk about anti-cheating in different capacities.
xi: Pg 26 – 3.3.1
xii: Many locations – There are many parts of the white paper that talk about voting in different capacities.
xiii: Visit for more information. For full disclosure, I do own DCR and stake them.
xiv: Pg 45 – 7.1
xv: Pg 45 – 7.2
xvi: Visit for more information.
xvii: Visit for more information. And yes, for full disclosure, I like this project as well, and have invested post ICO.
xviii: Pg 47 - 8
xix: Pg 46 – 7.3
submitted by satoshibytes to nebulas [link] [comments]

Here's a list of 520+ free online programming/CS courses (MOOCs) with feedback(i.e. exams/homeworks/assignments) that you can start this month (October 2016)

Unfortunately I couldn't fit all the courses here because of Reddit's 40,000 character limit. So I removed older self-paced courses from the list. These courses are always open for registration.
They can be found here:
~300 Self Paced Programming and Computer Science courses
I have also started categorizing the courses listed here by the programming language they are taught in. You can find the list here:
~250 MOOCs categorized by Programming Language
This is not the complete list of MOOCs starting in October 2016, just the ones relevant to this community. The complete list of courses starting in October 2016 can be found over at Class Central (1800+ courses). I maintain a much bigger list of these courses over at Class Central
Get this list every month via email : Subscribe
NOTE: Unfortunately Coursera has converted many of its courses to 'Premium Grading'. Which basically means that you need to pay if you want to access graded assignments :(. You can also apply for Financial Aid -
Course Name Start Date Length (in weeks) Rating
AP® Computer Science Principles via edX Self paced NA NA
Introduction to CSS3 via Coursera 3rd Oct 4 4.6★ (7)
[NEW] Introduction to Web Development via Coursera 3rd Oct NA NA
Internet History, Technology, and Security via Coursera 3rd Oct 10 4.6★ (28)
Data to Insight: an Introduction to Data Analysis via FutureLearn 3rd Oct 8 4★ (2)
Programming Foundations with JavaScript, HTML and CSS via Coursera 3rd Oct 4 3.8★ (9)
Introduction to Cyber Security via FutureLearn 3rd Oct 8 4.2★ (18)
Introduction to Programming with Java, Part 1: Starting to Code with Java via edX 4th Oct NA 3★ (2)
How To Create a Website in a Weekend! (Project-Centered Course) via Coursera 10th Oct 3 5★ (1)
Ruby on Rails: An Introduction via Coursera 10th Oct 3 3.1★ (48)
Learn to Code for Data Analysis via FutureLearn 10th Oct 4 3★ (1)
Build a Modern Computer from First Principles: From Nand to Tetris (Project-Centered Course) via Coursera 10th Oct 6 4.8★ (12)
Code Yourself! An Introduction to Programming via Coursera 10th Oct 5 4.3★ (6)
CODAPPS: Coding mobile apps for entrepreneurs via Coursera 10th Oct 8 5★ (1)
[NEW] Python Programming: A Concise Introduction via Coursera 10th Oct NA NA
HTML, CSS, and Javascript for Web Developers via Coursera 10th Oct 5 5★ (4)
HTML, CSS and JavaScript via Coursera 10th Oct 3 4.1★ (13)
Introduction to HTML5 via Coursera 10th Oct 3 4.1★ (30)
Introduction to the Internet of Things and Embedded Systems via Coursera 10th Oct 4 3.7★ (6)
An Introduction to Interactive Programming in Python (Part 2) via Coursera 17th Oct 4 4.8★ (40)
Usable Security via Coursera 17th Oct 7 2.9★ (8)
Introduction to Programming with MATLAB via Coursera 17th Oct 9 4.9★ (141)
An Introduction to Interactive Programming in Python (Part 1) via Coursera 17th Oct 5 4.9★ (2816)
Creative Programming for Digital Media & Mobile Apps via Coursera 24th Oct NA 4★ (10)
[NEW] AP Computer Science A: Java Programming Data Structures and Loops via edX 24th Oct NA NA
Learn to Program: The Fundamentals via Coursera 24th Oct 10 4.8★ (81)
Introduction à la programmation orientée objet (en Java) via Coursera 24th Oct 7 5★ (1)
Begin Programming: Build Your First Mobile Game via FutureLearn 31st Oct 7 3.9★ (7)
Course Name Start Date Length (in weeks) Rating
M233: Getting Started with Spark and MongoDB via MongoDB University Self paced NA NA
Android Basics: Data Storage via Udacity Self paced NA NA
[NEW] Essential Design Principles for Tableau via Coursera 1st Oct NA NA
Software Defined Networking via Coursera 1st Oct NA 4★ (5)
Client Needs and Software Requirements via Coursera 1st Oct 4 4.3★ (6)
Client Needs and Software Requirements via Coursera 1st Oct 4 4.3★ (6)
Agile Planning for Software Products via Coursera 1st Oct 4 3★ (2)
VLSI CAD Part I: Logic via Coursera 1st Oct 10 5★ (3)
Software Processes and Agile Practices via Coursera 1st Oct 4 4.3★ (9)
Introduction to Software Product Management via Coursera 1st Oct 2 4.2★ (10)
Reviews & Metrics for Software Improvements via Coursera 1st Oct 4 NA
Discrete Optimization via Coursera 2nd Oct 9 4.3★ (11)
Internet Emerging Technologies via Coursera 3rd Oct 3 3★ (2)
Java Programming: Arrays, Lists, and Structured Data via Coursera 3rd Oct 4 4.3★ (3)
Multiplatform Mobile App Development with Web Technologies via Coursera 3rd Oct 4 5★ (1)
Responsive Web Design via Coursera 3rd Oct 4 3.3★ (10)
Big Data Integration and Processing via Coursera 3rd Oct NA NA
Robotic Vision via EdCast 3rd Oct 9 4.8★ (4)
Algorithms on Strings via Coursera 3rd Oct NA 3★ (1)
Introduction To Swift Programming via Coursera 3rd Oct 5 1.2★ (5)
Fundamentals of Visualization with Tableau via Coursera 3rd Oct NA NA
Mastering the Software Engineering Interview via Coursera 3rd Oct 4 5★ (1)
Cloud Computing Applications, Part 1: Cloud Systems and Infrastructure via Coursera 3rd Oct 5 3.4★ (7)
Data Management and Visualization via Coursera 3rd Oct 4 2.4★ (5)
Cryptography via Coursera 3rd Oct 7 4.2★ (6)
Managing Data Analysis via Coursera 3rd Oct 1 1.8★ (6)
iOS App Development Basics via Coursera 3rd Oct 5 4★ (2)
Data Analysis Tools via Coursera 3rd Oct 4 3★ (3)
Principles of Machine Learning via edX 3rd Oct NA NA
Testing with Agile via Coursera 3rd Oct NA NA
Internet of Things: How did we get here? via Coursera 3rd Oct 2 2★ (5)
Cloud Computing Concepts: Part 2 via Coursera 3rd Oct 5 4.8★ (4)
Cybersecurity and Mobility via Coursera 3rd Oct NA NA
Data Science in Real Life via Coursera 3rd Oct 1 3★ (8)
Introduction to Meteor.js Development via Coursera 3rd Oct 4 5★ (3)
[NEW] The R Programming Environment via Coursera 3rd Oct NA NA
Big Data: Mathematical Modelling via FutureLearn 3rd Oct 2 NA
Process Mining: Data science in Action via Coursera 3rd Oct 6 4.3★ (12)
[NEW] Programming Languages, Part C via Coursera 3rd Oct NA NA
Big Data, Cloud Computing, & CDN Emerging Technologies via Coursera 3rd Oct 3 3.3★ (4)
Wireless Communication Emerging Technologies via Coursera 3rd Oct 5 3.7★ (3)
Algorithms, Part I via Coursera 3rd Oct 6 4.4★ (37)
Running Product Design Sprints via Coursera 3rd Oct 5 NA
Internet of Things & Augmented Reality Emerging Technologies via Coursera 3rd Oct 2 2.5★ (2)
R Programming via Coursera 3rd Oct 4 2.7★ (210)
The Data Scientist’s Toolbox via Coursera 3rd Oct 4 3.2★ (141)
Getting and Cleaning Data via Coursera 3rd Oct 4 3.4★ (47)
Practical Machine Learning via Coursera 3rd Oct 4 3.4★ (20)
Exploratory Data Analysis via Coursera 3rd Oct 4 3.8★ (32)
Cybersecurity and the X-Factor via Coursera 3rd Oct NA NA
Regression Models via Coursera 3rd Oct 4 2.6★ (27)
Statistical Inference via Coursera 3rd Oct 4 2.7★ (26)
Dealing With Missing Data via Coursera 3rd Oct NA NA
Reproducible Research via Coursera 3rd Oct 4 3.7★ (22)
Machine Learning via Coursera 3rd Oct 11 4.8★ (204)
Internet of Things: Setting Up Your DragonBoard™ Development Platform via Coursera 3rd Oct 10 3★ (3)
Introduction to Big Data via Coursera 3rd Oct 3 2.6★ (27)
Algorithms: Design and Analysis, Part 1 via Coursera 3rd Oct 6 4.7★ (52)
Algorithmic Toolbox via Coursera 3rd Oct 5 4.7★ (6)
Cryptography I via Coursera 3rd Oct 7 4.7★ (38)
A Crash Course in Data Science via Coursera 3rd Oct 1 3.3★ (14)
Data Visualization and Communication with Tableau via Coursera 3rd Oct 5 4★ (7)
Java Programming: Solving Problems with Software via Coursera 3rd Oct 4 3.3★ (8)
Database Management Essentials via Coursera 3rd Oct 7 3.8★ (4)
Hadoop Platform and Application Framework via Coursera 3rd Oct 5 1.9★ (19)
Front-End Web UI Frameworks and Tools via Coursera 3rd Oct 4 4.3★ (6)
Algorithms: Design and Analysis, Part 2 via Coursera 3rd Oct 6 4.8★ (16)
A developer's guide to the Internet of Things (IoT) via Coursera 3rd Oct NA 4★ (1)
Java for Android via Coursera 4th Oct 4 NA
Data Visualization via Coursera 10th Oct 4 3.2★ (15)
Framework for Data Collection and Analysis via Coursera 10th Oct NA 3.5★ (2)
Interactivity with JavaScript via Coursera 10th Oct 4 4.3★ (6)
Responsive Website Basics: Code with HTML, CSS, and JavaScript via Coursera 10th Oct 4 3.9★ (20)
Introduction to Spreadsheets and Models via Coursera 10th Oct 4 4.7★ (3)
Functional Program Design in Scala via Coursera 10th Oct NA NA
The Raspberry Pi Platform and Python Programming for the Raspberry Pi via Coursera 10th Oct 4 3.5★ (2)
Biology Meets Programming: Bioinformatics for Beginners via Coursera 10th Oct 4 5★ (5)
Best Practices for iOS User Interface Design via Coursera 10th Oct 4 5★ (1)
Algorithms on Graphs via Coursera 10th Oct NA 4★ (1)
Big Data Modeling and Management Systems via Coursera 10th Oct NA NA
Advanced Algorithms and Complexity via Coursera 10th Oct NA NA
Java Programming: Principles of Software Design via Coursera 10th Oct 4 4.7★ (3)
Programming Mobile Applications for Android Handheld Systems: Part 2 via Coursera 10th Oct 5 4.5★ (12)
Interfacing with the Arduino via Coursera 10th Oct 4 4★ (4)
Toward the Future of iOS Development with Swift via Coursera 10th Oct 4 NA
Advanced Data Structures in Java via Coursera 10th Oct 5 NA
Interfacing with the Raspberry Pi via Coursera 10th Oct 4 1★ (1)
Web Application Development with JavaScript and MongoDB via Coursera 10th Oct 4 4.2★ (5)
Data Manipulation at Scale: Systems and Algorithms via Coursera 10th Oct 4 2.5★ (4)
Data Structures and Performance via Coursera 10th Oct 5 5★ (3)
Approximation Algorithms Part I via Coursera 10th Oct 5 5★ (2)
Getting Started: Agile Meets Design Thinking via Coursera 10th Oct 5 5★ (1)
Text Retrieval and Search Engines via Coursera 10th Oct 4 3.2★ (5)
Games, Sensors and Media via Coursera 10th Oct 4 NA
Advanced Styling with Responsive Design via Coursera 10th Oct 4 4.7★ (3)
Beginning Game Programming with C# via Coursera 10th Oct 12 3.4★ (14)
Programming Mobile Applications for Android Handheld Systems: Part 1 via Coursera 10th Oct 5 4.1★ (35)
Managing an Agile Team via Coursera 10th Oct NA 2★ (1)
Cybersecurity and the Internet of Things via Coursera 10th Oct NA NA
Data Warehouse Concepts, Design, and Data Integration via Coursera 10th Oct 5 5★ (1)
Responsive Website Tutorial and Examples via Coursera 10th Oct 4 5★ (1)
App Design and Development for iOS via Coursera 10th Oct 5 3★ (2)
Foundations of Objective-C App Development via Coursera 10th Oct 4 3★ (2)
Functional Programming Principles in Scala via Coursera 10th Oct 7 4.8★ (45)
Ruby on Rails Web Services and Integration with MongoDB via Coursera 10th Oct 4 4.8★ (4)
Object Oriented Programming in Java via Coursera 10th Oct 6 4.8★ (10)
Build Your First Android App (Project-Centered Course) via Coursera 10th Oct 5 3★ (1)
Managing Big Data with MySQL via Coursera 10th Oct 5 3.8★ (5)
Rails with Active Record and Action Pack via Coursera 10th Oct 4 4★ (1)
Data Structures via Coursera 10th Oct 4 2★ (2)
Single Page Web Applications with AngularJS via Coursera 10th Oct NA NA
Software Architecture for the Internet of Things via Coursera 10th Oct NA NA
The Arduino Platform and C Programming via Coursera 10th Oct 4 3.3★ (7)
Cloud Computing Concepts, Part 1 via Coursera 10th Oct 5 2.6★ (17)
Server-side Development with NodeJS via Coursera 10th Oct 4 5★ (1)
Mining Massive Datasets via Stanford OpenEdx 11th Oct 7 4.6★ (17)
M101J: MongoDB for Java Developers via MongoDB University 11th Oct 7 4.5★ (15)
M101JS: MongoDB for Node.js Developers via MongoDB University 11th Oct 7 4.4★ (8)
M101N: MongoDB for .NET Developers via MongoDB University 11th Oct NA 4★ (3)
M101P: MongoDB for Developers via MongoDB University 11th Oct 7 4.8★ (8)
M102: MongoDB for DBAs via MongoDB University 11th Oct 7 4.5★ (8)
M202: MongoDB Advanced Deployment and Operations via MongoDB University 11th Oct 7 5★ (4)
Software Security via Coursera 17th Oct 6 4.7★ (20)
Global Warming II: Create Your Own Models in Python via Coursera 17th Oct 5 2★ (1)
Programming Languages, Part A via Coursera 17th Oct NA 4.9★ (16)
Algorithmic Thinking (Part 1) via Coursera 17th Oct 4 4.1★ (13)
C++ For C Programmers, Part B via Coursera 17th Oct NA NA
Interactive Computer Graphics via Coursera 17th Oct 8 3.5★ (2)
[NEW] Advanced R Programming via Coursera 17th Oct NA NA
Principles of Computing (Part 1) via Coursera 17th Oct 5 4.6★ (25)
[NEW] How to Win Coding Competitions: Secrets of Champions via edX 17th Oct NA NA
Front-End JavaScript Frameworks: AngularJS via Coursera 17th Oct 4 3.8★ (4)
Internet of Things: Communication Technologies via Coursera 17th Oct 4 3★ (2)
Algorithmic Thinking (Part 2) via Coursera 17th Oct NA 4.4★ (8)
Introduction to Neurohacking In R via Coursera 17th Oct NA NA
Cloud Networking via Coursera 17th Oct 5 4.3★ (3)
Introduction to Architecting Smart IoT Devices via Coursera 17th Oct NA NA
Principles of Computing (Part 2) via Coursera 17th Oct NA 4.3★ (14)
Programming Languages, Part B via Coursera 17th Oct NA NA
[NEW] Internet of Things for Active Aging via FutureLearn 17th Oct NA NA
[NEW] Cyber Security Economics via edX 19th Oct NA NA
Introduction to Computational Thinking and Data Science via edX 19th Oct 9 4.4★ (20)
Genomic Data Science with Galaxy via Coursera 24th Oct 4 1.8★ (11)
Bioinformatics: Introduction and Methods 生物信息学: 导论与方法 via Coursera 24th Oct 14 NA
Web Application Development: Basic Concepts via Coursera 24th Oct NA NA
Python for Genomic Data Science via Coursera 24th Oct 4 2.4★ (11)
Introduction to Genomic Technologies via Coursera 24th Oct 4 2.7★ (10)
[NEW] Julia Scientific Programming via Coursera 24th Oct NA NA
Introduction to Data Science in Python via Coursera 24th Oct NA NA
Computer Architecture via Coursera 24th Oct 11 4.5★ (4)
Statistics for Genomic Data Science via Coursera 24th Oct 4 2★ (2)
C++ For C Programmers, Part A via Coursera 24th Oct NA 3.2★ (9)
Документы и презентации в LaTeX (Introduction to LaTeX) via Coursera 31st Oct 5 NA
Big Data: Data Visualisation via FutureLearn 31st Oct 2 NA
Course Name Start Date Length (in weeks) Rating
Bitcoin and Cryptocurrency Technologies via Coursera 1st Oct 7 4.6★ (9)
[NEW] Nature, in Code: Biology in JavaScript via edX 1st Oct NA NA
[NEW] Recommender Systems: Evaluation and Metrics via Coursera 1st Oct NA NA
Nearest Neighbor Collaborative Filtering via Coursera 1st Oct NA NA
Machine Learning: Clustering & Retrieval via Coursera 3rd Oct NA 4.5★ (2)
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center via Coursera 3rd Oct 7 4★ (1)
Text Mining and Analytics via Coursera 3rd Oct 4 3.7★ (6)
Embedded Hardware and Operating Systems via Coursera 3rd Oct NA NA
System Validation: Automata and behavioural equivalences via Coursera 3rd Oct NA NA
Machine Learning for Data Analysis via Coursera 3rd Oct 4 3★ (3)
Neural Networks for Machine Learning via Coursera 3rd Oct 8 4.5★ (11)
Quantitative Formal Modeling and Worst-Case Performance Analysis via Coursera 3rd Oct 4 4★ (2)
Advanced Linear Models for Data Science 1: Least Squares via Coursera 3rd Oct NA NA
Developing Data Products via Coursera 3rd Oct 4 3.9★ (16)
Cluster Analysis in Data Mining via Coursera 3rd Oct 4 2.6★ (5)
Machine Learning: Regression via Coursera 3rd Oct 6 4.7★ (13)
Introduction to Natural Language Processing via Coursera 3rd Oct NA 3.8★ (6)
Regression Modeling in Practice via Coursera 7th Oct 4 5★ (2)
Parallel programming via Coursera 10th Oct NA 5★ (1)
Pattern Discovery in Data Mining via Coursera 10th Oct 4 2.2★ (19)
Finding Hidden Messages in DNA (Bioinformatics I) via Coursera 10th Oct 4 4.5★ (16)
Graph Analytics for Big Data via Coursera 10th Oct 4 2.4★ (5)
Introduction to Recommender Systems: Non-Personalized and Content-Based via Coursera 10th Oct NA NA
Nearest Neighbor Collaborative Filtering via Coursera 10th Oct NA NA
Practical Predictive Analytics: Models and Methods via Coursera 10th Oct 4 2.5★ (2)
Hardware Security via Coursera 10th Oct 6 3★ (9)
Approximation Algorithms Part II via Coursera 10th Oct 4 NA
Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud via Coursera 10th Oct NA NA
Genomic Data Science and Clustering (Bioinformatics V) via Coursera 10th Oct 2 3.5★ (2)
[NEW] Big Data, Genes, and Medicine via Coursera 10th Oct NA NA
Genome Sequencing (Bioinformatics II) via Coursera 10th Oct 4 5★ (3)
Machine Learning Foundations: A Case Study Approach via Coursera 10th Oct 6 4.2★ (30)
Relational Database Support for Data Warehouses via Coursera 10th Oct 5 2★ (1)
[NEW] Quantum Cryptography via edX 10th Oct NA NA
[NEW] Introduction to OpenStack via edX 12th Oct NA NA
Machine Learning: Classification via Coursera 17th Oct 7 4.8★ (6)
Bioconductor for Genomic Data Science via Coursera 24th Oct 4 3.3★ (3)
Advanced Java Concurrency via Coursera 24th Oct NA NA
Computational Neuroscience via Coursera 24th Oct 8 3.8★ (6)
Algorithms for DNA Sequencing via Coursera 24th Oct 4 4.5★ (16)
Probabilistic Graphical Models 1: Representation via Coursera 31st Oct 11 4.4★ (10)
submitted by dhawal to learnprogramming [link] [comments]

Bitcoin Mining Algorithm Example Bitcoin Mining Algorithm CHANGING? Bitcoin Tutorial #36 - Der Mining Algorithmus in Python Simple Blockchain in Python WITH MINING! - YouTube How To Start Mining Bitcoin In 5 Minutes In 2020 ...

A Practical Introduction to Blockchain with Python ... In the case of Bitcoin, ECDSA algorithm is used to generate Bitcoin wallets. Bitcoin uses a variety of keys and addresses, but for the sake of simplicity, we will assume in this blog post that each Bitcoin wallet has one pair of private/public keys and that a Bitcoin address is the wallet's public key. I recommend this article, if you're ... Python with bitcoin is extensively used for Bitcoin mining algorithm Python tools, which solve very complex engineering problems. According to the Python Software Foundation information, there are more than 177,515 Python third-party software tools available in the market in the first quarter of 2019. More than 2,000 software tools are adding to this number on a monthly basis. Bitcoin Mining in Python. A Python implementation of the Bitcoin mining algorithm. This small script is a pseudo-simulation of the Bitcoin Genesis block mining process. Given the Genesis block's data, this script double-hashes it using SHA-256 and attempts to find a hash less than the Genesis target. Sample Output In this post, we will learn to build a very simple miner in Python. Of course this miner will be comparatively slow and limited and only be useful in our test network, but it will hopefully help to explain the principles behind mining. When we want to mine a block, we first need some information… Bitcoin mining uses cryptography, with a hash function called double SHA-256. A hash takes a chunk of data as input and shrinks it down into a smaller hash value (in this case 256 bits). With a cryptographic hash, there's no way to get a hash value you want without trying a whole lot of inputs. But once you find an input that gives the value you want, it's easy for anyone to verify the hash ...

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Bitcoin Mining Algorithm Example

Bitcoin Mining Algorithm Example - Purchase hashrate contract, getet mining payouts hourly, withdraw income to your wallet I W... In this video we'll be creating our own blockchain in Python! We'll also be using SHA256 for our proof-of-work to mine our blockchain. Go to https://howcode.... Today I am showing you how anyone can start mining bitcoins using their current or old computer!! Nice Hash - Check out the official B... This video illustrates the concepts of Hashing, Encryption, Blockchain and Bitcoin Mining by the use of straightforward Python code. It is from a free Webina... Now a days bitcoin is more popular cryptocurrency. Everyone wants to mine bitcoin. NiceHash is the world’s largest crypto-mining marketplace. Sellers are pro...