Hashstack provides permission less, under-collateralised loans upto 300% borrower&#x27;s collateral.

Hashstack - Crunchbase Company Profile & Funding

Hashstack provides a permissionless zk-native money market protocol enabling secure under-collateralised loans to the crypto retail. Built on Starknet L2 [announcement], Hashstack leverages the capability of zero-knowledge proofs to provide a cost &amp; capital-efficient lending solution.

Hashstack | Under-collateralised loans | Defi

Hashstack is a decentralized financial network that provides multi-chain interoperable complex blockchain applications and protocols to offer under-collateralized loans. It provides a permissionless native money market protocol enabling secure under-collateralized loans to the crypto retail. The company ...

Hashstack - Products, Competitors, Financials, Employees, ...

Weekly Mainnet V1 Metrics: 1. Liquidity Locked: $683.32k 2. Utilisation rate: 48.29% (Capital efficiency: &gt;1.58 nearest competitor) 3. Average liquidity per account: $662.13 4. Top market: $ETH (supply), $USDC (borrow) 5. Hashstack v1 instant access: https://hstk.fi/hashstack

Hashstack | LinkedIn

We provide design, development and rapid application prototyping services in Chicago

Hashstack - Chicago web development and rapid prototyping

Information on valuation, funding, cap tables, investors, and executives for Hashstack. Use the PitchBook Platform to explore the full profile.

Hashstack Company Profile: Valuation & Investors | PitchBook

Hashstack Finance - DeFi under-collateralised lending protocol. Raised a total funding of $1M over 1 round from 6 investors.

Hashstack Finance - Company Profile - Tracxn

Permissionless under-collateralised loans. Built on #Starknet. - Hashstack

Hashstack · GitHub

HashStack Developers where innovation meets technology seamlessly. We are not just a software company, We are the architects of digital solutions that are redefining industries.

Hashstack Developers Software Company

1 billion members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

Hashstack Careers and Current Employee Profiles - LinkedIn

Read writing from Hashstack on Medium. A non-custodial money market protocol enabling secure under-collateralised loans on the blockchain. Website- https://hashstack.finance.

Hashstack – Medium

Partnered with the Monetary Authority of Singapore’s (MAS) Project Greenprint, STACS aims to enable Sustainable Finance, unlocking value in global markets.

STACS - The Nexus of ESG Finance

Collection of software profiles for HashDist. Contribute to hashdist/hashstack development by creating an account on GitHub.

GitHub - hashdist/hashstack: Collection of software profiles for ...

Apply to jobs at Hashstack Finance today. Learn about careers at Hashstack Finance in software, marketing, sales and operations. Let Hashstack Finance and other companies compete to hire you!

Hashstack Finance Careers | Jobs at Hashstack Finance - Instahyre

HASHSTACK | 132 followers on LinkedIn. Building solutions, powering innovation | We are a software development company dedicated to creating innovative and user-friendly digital experiences. Our team of experts specializes in designing and developing custom websites and mobile applications ...

HASHSTACK | LinkedIn

At Hashstack, we are disrupting the defi landscape through Open protocol

Hashstack

Bringing Undercollaterilzed Lending to the Masses HashStack has received $1 million in seed investment after releasing the Open Protocol testnet. Moonrock Capital, GHAF Capital Partners, MarketAcross and Chainridge Capital were among the investors in the round. Hashstack is the first DeFi lending platform to give loans with little collateral. As you may be aware, there is a fundamental issue in the DeFi financing landscape: over-collateralization. You must put up at least $142 in collateral if you wish to borrow $100. The protocol solves the problem by allowing loans with up to 3x collateral. To get a $100 loan, you need to put down $33.33 as security instead of $142. According to the company, Hashstack may be connected with other DeFi systems, such as Pancakeswap, to simplify in-app market swaps and boost loan usage. Borrowers may use this technique to exchange loaned tokens for other primary or secondary currencies without turning DApps. Assets from other chains, such as Ethereum and Avalanche C-Chain, are likewise bridged using the open protocol. DeFi Lending: What is it and How Does It Work? Like conventional peer–to–peer lending platforms, DeFi lending services allow users to loan their assets to others. They are compensated with interest payments. These platforms are exclusively concerned with cryptocurrencies. They only collect bitcoin interest payments. The monetary gains are paid directly to the users because DeFi structures operate without intermediates. Anyone who has gotten a loan from a traditional lender or a DeFi platform is acquainted with collateralized loans. As the name implies, collateral is anything of value that the borrower pledges as security in exchange for a loan. It is a type of loan secured by collateral that is wholly supported. In other words, the collateral is worth more than the loan’s principal. On the other hand, undercollateralized loans don’t have all of their assets collateralized. The collateral would not support the principle that the loan was defaulted on. Decentralized lending is as simple as putting your hand in your wallet and lending money to others. The smart contract and decentralized software, on the other hand, operate as your mediators and negotiators. For example, to make a $1,000,000 loan with DApp, all you have to do is press a few buttons, and you’re done. The process is swift and simple since you must pick any DApp application that provides a smart contract and borrowers. The user must choose the interest rate on loan, enter it into the app, and the loan must be approved. Once you’ve located a borrower, the smart contract will expedite the whole loan and borrowing process. Undercollateralized loans may raise questions and concerns, but their purpose is to protect both the borrower and the lender. The pinnacle of DeFi since 2017 has been overcollateralized lending. Platforms like Maker, Compound, and Aave demonstrate how DeFi may fulfil circular use cases in its current overcollateralized state. Only bitcoin traders are willing to maintain leverage of 1.5–3X. Decentralized credit markets may become more inexpensive for a broader range of use cases if they are undercollateralized, making DeFi more accessible to the general public. As a significant shift in the lending business, uncollateralized lending relies on only one of the “five Cs” of credit, collateral. Dynamic loan approval solutions are increasingly being leveraged by uncollateralized lending. With flash loans, which are short-term loans that are uncollateralized and often only out for a few seconds or minutes at a time, the spotlight is clearly on them. Sceptics who doubted that on-chain lending could maintain quality without collateral have been convinced by the ongoing rise of uncollateralized protocols in 2022. Catch all the breaking news, and Don’t forget to like the story! Image credits: and . Victor He Swapnil Bapat Disclaimer: The purpose of this article is to remove informational asymmetry existing today in our digital markets by performing due diligence, asking the right questions, and equipping readers with better opinions to make informed decisions. The writer holds Bitcoin, Ethereum, Cardano, Solana and Cosmos. The writer has a vested interest in the story.

HashStack Receives $1 million in Seed Investment from Moonrock Capital, GHAF Capital and others

Bringing Crypto Lending Back to the People with Under-Collateralized Loans As new lending protocols continue to attract money and NFT-backed loans become more popular, the DeFi lending and borrowing industry has risen dramatically in volume. The much waited under-collateralized and non-custodial loans are coming to DeFi. Hashstack Finance is testing Open Protocol, a first-ever DeFi lending protocol to offer non-custodial, secure under-collateralized loans. The open protocol is based on the Harmony blockchain, allowing borrowers to take out loans with a 1:3 collateral-to-loan ratio. This implies that an individual can obtain up to $300 with only $100 in collateral. Users can extract 70% of the collateral, or $70 in this scenario while trading with $230 in-platform capital. According to Hashstack, DeFi lending is often over-collateralized, with borrowers providing a minimum of 42 percent extra collateral against the loan they want to take out. Hashstack Finance founder, Vinay said: Today, if you want to borrow $100 on Compound, or Aave, or even MakerDAO, you are required to provide a collateral of at least $142. This breaks the primary intent behind loan procurement, and has restrictive use-cases for the borrower. The protocol uses a three-pronged strategy to minimize risks related to crypto lending by: Clear compartmentalization of APY and APR of deposits/loans with that of their minimum commitment period (MCP) Effective asset utilization through diversification of available assets via lending and providing trading capital Under-collateralized loans Borrowers can use different techniques to exchange borrowed tokens for other primary or secondary coins without switching DApps. The open protocol likewise bridged assets from other chains, such as Ethereum and Avalanche C-Chain. What is DeFi Lending and how does it work? DeFi lending services, like traditional peer–to–peer lending platforms, enable users to lend their assets to others. They receive interest payments in return. Since these platforms primarily engage in cryptocurrencies, they receive cryptocurrency interest payments exclusively. Because DeFi platforms work without intermediaries, the monetary benefits are paid directly to the users. Decentralized lending is as straightforward as lending money to others by placing your hand in your pocket. However, the smart contract and decentralized programme represent your mediators and negotiators. For instance: to give out a $10000 loan using DApp, all you would need to do is hit a few buttons, and you’re through. Because you must choose any DApp application that gives a smart contract and borrowers, the overall process is very quick and uncomplicated. Users must choose the loan’s interest rate, enter it into the app, and approve the loan. The smart contract will streamline the whole lending and borrowing arrangement once you’ve found the borrower. What is under-collateralized lending? Collateralized loans are familiar to everyone who has obtained a loan through a regular route or DeFi platforms. Any asset of value that the lender accepts as a security from the borrower is referred to as collateral. A collateralized loan is a loan approved with the security of fully supporting collateral. To put it another way, the collateral exceeds the loan’s principle. On the other hand, an undercollateralized loan is not wholly collateralized. If the loan were defaulted on, the collateral would not sustain the principal. Even though the concept of undercollateralized loans may create doubts and worries, it is designed to safeguard the borrower and the lender’s interests. Since 2017, undercollateralized lending has been the difficult-to-achieve holy grail in DeFi. DeFi in its existing overcollateralized form, as manifested by platforms like Maker, Compound, and Aave, fulfils circular use cases. The only parties ready to post 1.5–3X leverage are cryptocurrency traders. Undercollateralized lending might make decentralized credit markets more affordable to a broader range of use cases, bringing DeFi into the masses. Uncollateralized lending is a significant change for the lending industry, primarily relying on only one of the conventional “five Cs” of credit, namely collateral. Uncollateralized lending is progressively leveraging dynamic remedies to the loan approval dilemma. In contrast, flash loans - that is, ultra-short-term uncollateralized loans, frequently out for only seconds or minutes at a time - have undoubtedly grabbed the crypto-limelight. Although industry analysts were initially optimistic but apprehensive about how on-chain lending could retain quality without collateral, the continued growth of uncollateralized protocols in 2022 has persuaded sceptics. Catch all the breaking news, and Don’t forget to like the story!

Hashstack is Bringing Under Collateralized Loans to Crypto Lending

The Tech Behind Tornado Cash (Line by Line for Programmers)

( ) A Merkle tree, as present in a typical blockchain The aim of this post is to provide an overview of the basic Merkle Tree data structure and a jumping off point for more advanced topics related to Merkle Trees. While taking the class on Coursera I learned the basics of how hash based data structures could be used to verify the integrity of data in peer-to-peer systems. One of the core data structures mentioned in the class was the , which is used in the to verify the existence of a transaction in a way that conserves both space and time quite effectively (more on that later in this post!). It wasn’t until I dug a little deeper into the Merkle Tree that I realized how prolific this data structure really was, and decided to build an example . Bitcoin and Cryptocurrency Technologies Merkle Tree Bitcoin blockchain Merkle Tree of my own If you’re curious about whether you should take the Bitcoin and Cryptocurrency Technologies class, you should read my reflections on it . here Explanation After construction, a Merkle Tree looks something like this: (A basic Merkle Tree. I’ve abbreviated the middle nodes as H(cd) and H(ab) respectively, but without this shorthand the root hash could also be called H(H(H(a) + H(b)) + H(H(c) + H(d)))) and are some data elements (files, public/private keys, JSON, etc) and is a hash function. If you’re unfamiliar, a hash function acts as a “digital fingerprint” of some piece of data by mapping it to a simple string with a low probability that any other piece of data will map to the same string. Each node is created by hashing the concatenation of its “parents” in the tree. You’ll notice that the Merkle tree here is a binary tree, most Merkle Trees are binary, but there are employed in platforms like Ethereum. Here we will just cover the binary case as it is by far the most common. a, b, c, d H non-binary Merkle Trees The tree can be constructed by taking nodes at the same height, concatenating their values, and hashing the result until the root is reached. A special case needs handled when only one node remains before the tree is complete, but other than that the tree construction is somewhat straightforward (more on this in the implementation section). Once built, data can be audited using the root hash in logarithmic time to the number of leaves (this is also known as a Merkle-Proof). Auditing works by recreating the branch containing the piece of data from the root to the piece of data being audited. In the example above, if we wanted to audit (assuming we have the root hash) we would need to be given H(d) and H(H(a) + H(b)). We would hash to get H(c), then concatenate and hash H(c) with H(d), then concatenate and hash the result of that with H(H(a) + H(b)). If the result was the same string as the root hash, it would imply that is truly a part of the data in the Merkle Tree. only c , c c In a case such as torrenting, another peer would provide the piece of data, H(d), and H(H(a) + H(b)). If you’re concerned about the security of this approach, recall that in a hash function it is computationally infeasible find some such that H(e) = H(c). This means that so long as the root hash is correct, it would be difficult for adversaries to lie about the data they were providing. c, e Outputting the authentication path of some data is as simple as recreating the branch leading up until the root. Traversing the entire tree to produce the leaves and their respective authentication data becomes important when using the Merkle Tree in digital signature schemes, and this can actually be accomplished in under logarithmic time. There are some clever (but complex) algorithms to accomplish this described in . this paper Selected Implementation Methods You can find a complete version of the code . I’ll explain the bulk of the tree creation and auditing methods. Note that both the and methods are instance methods from a larger class. here build_tree _audit def build_tree(self): stack = []
    self._handle_solo_node_case() self.root_hash == None: len(stack) >= and stack[ ].height == stack[ ].height:
            mom = stack.pop()
            dad = stack.pop()
            child_hash = self._md5sum(mom.hash + dad.hash)
            child = self.Node(mom, dad, child_hash)
            self.node_table[child_hash] = child
            mom.child = child
            dad.child = child child.height == self.max_height:
                self.root_hash = child.hash

            stack.append(child)
        elif len(self.leaves) > :
            leaf = self.leaves.pop()
            self.node_table[leaf.hash] = leaf
            stack.append(leaf)
        # Handle where last nodes not match height by # last node :
            stack[ ].height += self.is_built = True "" " Builds a merkle tree by adding leaves one at a time to a stack,
        and combining leaves in the stack when they are of the same height.
        Expected items to be an array of type Node.
        Also constructs node_table, a dict containing hashes that map to 
        individual nodes for auditing purposes.
    " "" while if 2 -1 -2 if 0 case 2 do in "graduating" else -1 1 Building the tree works by adding leaves to a stack and checking to see if the top two nodes in the stack are the same height. When they are, the nodes have a “child” (the hash of the concatenation of their two hashes), and when they are not a new node is appended to the stack. A small edge case needs handled when the last two nodes are of different heights. The method above will fail in the single node case because no conditions will be met, so I added a small method to handle this for completeness. def _audit(self, questioned_hash, proof_hashes): proof_hash = proof_hashes.pop() not proof_hash self.node_table.keys(): False

    sibling = self.node_table[proof_hash]
    child = sibling.child

    # Because the order which the hashes are concatenated matters,
    # we must test to see questioned_hash is the or # its child (the hash is always build mother + father).
    if child.mom.hash == questioned_hash:
        actual_hash = self._md5sum(questioned_hash + sibling.hash)
    elif child.dad.hash == questioned_hash:
        actual_hash = self._md5sum(sibling.hash + questioned_hash) : False actual_hash != child.hash: False actual_hash == self.root_hash: True self._audit(actual_hash, proof_hashes) "" " Returns a boolean testing if questioned_hash
        is contained in the merkle tree.
        
        proof_hashes are the nodes to hash questioned_hash with
        in order from the bottom of the tree to the second-to-last
        level. len(proof_hashes) is expected to be the height of the
        tree, ceil(log2(n)), as one node is needed for proof per layer.
    " "" if in return in if "mother" "father" of as else return if return if return return This is the auditing process described in the explanation section of this post. Some preconditions are checked in the public audit method, which is why I put the bulk of the logic inside this private version. Applications Merkle Trees have gotten a lot of attention recently due to their use in blockchain applications. In many peer-to-peer (P2P) systems (not just blockchains!) individuals need to be able to request data from untrusted peers with some proof that what those peers sent them is part of the real content they requested. Torrents are an example of this problem: When you download a torrent, you receive files from others “seeding” that torrent online, but how can you be sure those files are really a part of what you’re trying to download, and not garbage or malware? The Merkle Tree can authenticate the data received from your peers to solve this trust problem. A similar problem applies to cryptocurrencies like Bitcoin and Ethereum: If someone claims that in some transaction another peer paid them, how can a node on the network verify that transaction really happened? One option is that the node could store the entire history of every transaction that has ever occurred, however, this is prohibitive in terms of both time and space costs to that node. Merkle Trees provide a solution that provide time and space savings for nodes on the network. By creating a Merkle Tree out of the transaction data in each block, transactions can be audited in logarithmic time instead of linear time. Additionally, it opens the door for some bitcoin clients can save space by only storing the root of the Merkle Tree: Not needing to store every transaction that has ever happened in the history of Bitcoin is a huge value! Beyond blockchains and torrents, Merkle Trees see usage in any system that needs to detect inconsistencies efficiently: Certificate authorities (CAs) use Merkle Trees as a means of . Here, public and private key pairs are treated as the leaves of the Merkle Tree. This is one mechanism CAs use to prevent situations where one CA might “go rogue” and try to certify a domain without the owner of the domain knowing of the certification. certificate transparency Highly scalable databases like and handling failures for replica databases on the network. This process is known as “Anti-Entropy” and is described in some depth on the and to a lesser extent in Apache Cassandra Dynamo DB Apache Cassandra Blog Amazon Dynamo DB paper. . In this case the root of the Merkle Tree acts as a public key and the individual nodes are used as one-time signatures. Recently some more work has been done to advance this techniques as it has been theorized to be resistant to quantum computing attacks (unlike RSA, which powers most public key cryptography today). Digital signature alternatives to RSA The applications of Merkle Trees are indeed numerous, and their utilization in any particular domain could be the subject of an entire blog post. I hope this served as a reasonable introduction. If you have any more questions or just want to chat you can contact me through my, evankozliner@gmail.com or message me on Twitter: @evankozliner . EDIT: Adds & fixes an image, fixes typos, bit of clarification on the basic example too

Merkle Tree Introduction

In this article, we’ll dive into bytecode, and examine the bytecode generation and constant pool in detail. 1. Bytecode Generation The GenerateUnoptimizedCodeForToplevel is responsible for transforming an AST tree into bytecodes. 1. MaybeHandle<SharedFunctionInfo> GenerateUnoptimizedCodeForToplevel(
2. Isolate* isolate, ParseInfo* parse_info, AccountingAllocator* allocator,
3. IsCompiledScope* is_compiled_scope) {
4. //omit............
5. while (!functions_to_compile.empty()) {
6. //omit............
7. if (job->ExecuteJob() == CompilationJob::FAILED ||
8. FinalizeUnoptimizedCompilationJob(job.get(), shared_info, isolate) ==
9. CompilationJob::FAILED) {
10. return MaybeHandle<SharedFunctionInfo>();
11. }
12. if (FLAG_stress_lazy_source_positions) {
13. SharedFunctionInfo::EnsureSourcePositionsAvailable(isolate, shared_info);
14. }
15. if (shared_info.is_identical_to(top_level)) {
16. *is_compiled_scope = shared_info->is_compiled_scope();
17. DCHECK(is_compiled_scope->is_compiled());
18. }
19. }
20. parse_info->ResetCharacterStream();
21. return top_level;
22. } Line 7 reads an AST tree from parse_info and generates bytecode. By debugging it, we step into the following function which analyses every AST node and generates a corresponding bytecode. job->ExecuteJob() 1. void BytecodeGenerator::GenerateBytecodeBody() {
2. VisitArgumentsObject(closure_scope()->arguments());
3. VisitNewTargetVariable(closure_scope()->new_target_var());
4. FunctionLiteral* literal = info()->literal();
5. if (IsResumableFunction(literal->kind())) {
6. BuildGeneratorObjectVariableInitialization();
7. }
8. if (FLAG_trace) builder()->CallRuntime(Runtime::kTraceEnter);
9. //省略.........................
10. BuildIncrementBlockCoverageCounterIfEnabled(literal, SourceRangeKind::kBody);
11. VisitDeclarations(closure_scope()->declarations());
12. VisitModuleNamespaceImports();
13. builder()->StackCheck(literal->start_position());
14. if (IsBaseConstructor(function_kind())) {
15. if (literal->requires_brand_initialization()) {
16. BuildPrivateBrandInitialization(builder()->Receiver());
17. }
18. if (literal->requires_instance_members_initializer()) {
19. BuildInstanceMemberInitialization(Register::function_closure(),
20. builder()->Receiver());
21. }
22. }
23. VisitStatements(literal->body());
24. if (!builder()->RemainderOfBlockIsDead()) {
25. builder()->LoadUndefined();
26. BuildReturn();
27. }
28. } In the above function, lines 11, 12, and 23 first analyze the declaration, import modules, and statements respectively, and then generate corresponding bytecodes. In our case, we will examine line 11 VisitDeclarations() in more depth. In VisitDeclarations(), the following function is called. 1. BytecodeArrayBuilder& BytecodeArrayBuilder::LoadConstantPoolEntry(
2. size_t entry) {
3. OutputLdaConstant(entry);
4. return *this;
5. }
6. //============================
7. #define DEFINE_BYTECODE_OUTPUT(name, ...) In line 3, OutputLdaConstant stores one global constant into the constant-pool and generates bytecode LdaGlobal for loading this constant, so that we can get out the constant through the LdaGlobal during execution. About the constant pool, I will talk later. By debugging OutputLdaConstant(), we step into the EmitBytecode() which generates one bytecode for a corresponding statement and also is the function that generates the LdaGlobal for our global constant. 1. void BytecodeArrayWriter::EmitBytecode(const BytecodeNode* const node) {
2. //omit..........
3. for (int i = 0; i < operand_count; ++i) {
4. switch (operand_sizes[i]) {
5. case OperandSize::kNone:
6. UNREACHABLE();
7. break;
8. case OperandSize::kByte:
9. bytecodes()->push_back(static_cast<uint8_t>(operands[i]));
10. break;
11. case OperandSize::kShort: {
12. uint16_t operand = static_cast<uint16_t>(operands[i]);
13. const uint8_t* raw_operand = reinterpret_cast<const uint8_t*>(&operand);
14. bytecodes()->push_back(raw_operand[0]);
15. bytecodes()->push_back(raw_operand[1]);
16. break;
17. }
18. case OperandSize::kQuad: {
19. const uint8_t* raw_operand =
20. reinterpret_cast<const uint8_t*>(&operands[i]);
21. bytecodes()->push_back(raw_operand[0]);
22. bytecodes()->push_back(raw_operand[1]);
23. bytecodes()->push_back(raw_operand[2]);
24. bytecodes()->push_back(raw_operand[3]);
25. break;
26. }
27. }
28. }
29. } From lines 4 to 25, calculate the operand size for our LdaGlobal, because in execution, a bytecode needs the operand size to operate the operand correctly. Back to GenerateBytecodeBody(), in line 23, the VisitStatements() reads one AST node that represents a corresponding JavaScript statement, and generate one bytecode. If one AST node represents a JavaScript statement block, the VisitStatements() will recursively call itself to generate many bytecodes. Note: void BytecodeGenerator::VisitStatements(
 const ZonePtrList<Statement>* statements) {
 for (int i = 0; i < statements->length(); i++) {
 // Allocate an outer register allocations scope for the statement.
 RegisterAllocationScope allocation_scope(this);
 Statement* stmt = statements->at(i);
 Visit(stmt);
 if (builder()->RemainderOfBlockIsDead()) break;
 }
} Figure 1 shows the VisitStatements’s call stack. 2. Constant Pool function test(x)
{
 if(x>0)
 add3(x);//x=x+3
 dosomething........
} When compiling the function test(), the compiler infers that the add3 will be called, and the add3 address should be pushed into the constant pool. But, there’s a question here. We have no address if the add3() is not compiled yet. In this case, the compiler reserves a slot for the add3 in the constant pool and fills it once the add3() is compiled, namely DeferredConstants. Let’s give a glance at FinalizeBytecode(). 1. Handle<BytecodeArray> BytecodeGenerator::FinalizeBytecode(
2. Isolate* isolate, Handle<Script> script) {
3. DCHECK_EQ(ThreadId::Current(), isolate->thread_id());
4. AllocateDeferredConstants(isolate, script);
5. if (block_coverage_builder_) {
6. info()->set_coverage_info(
7. isolate->factory()->NewCoverageInfo(block_coverage_builder_->slots()));
8. if (FLAG_trace_block_coverage) {
9. info()->coverage_info()->Print(info()->literal()->GetDebugName());
10. }
11. }
12. if (HasStackOverflow()) return Handle<BytecodeArray>();
13. Handle<BytecodeArray> bytecode_array = builder()->ToBytecodeArray(isolate);
14. if (incoming_new_target_or_generator_.is_valid()) {
15. bytecode_array->set_incoming_new_target_or_generator_register(
16. incoming_new_target_or_generator_);
17. }
18. return bytecode_array;
19. } In line 4, AllocateDeferredConstants creates the DeferredConstants. 1. void BytecodeGenerator::AllocateDeferredConstants(Isolate* isolate,
2. Handle<Script> script) {
3. // Build global declaration pair arrays.
4. for (GlobalDeclarationsBuilder* globals_builder : global_declarations_) {
5. Handle<FixedArray> declarations =
6. globals_builder->AllocateDeclarations(info(), script, isolate);
7. if (declarations.is_null()) return SetStackOverflow();
8. builder()->SetDeferredConstantPoolEntry(
9. globals_builder->constant_pool_entry(), declarations);
10. }
11. // Find or build shared function infos.
12. for (std::pair<FunctionLiteral*, size_t> literal : function_literals_) {
13. //omit..................
14. }
15. // Find or build shared function infos for the native function templates.
16. for (std::pair<NativeFunctionLiteral*, size_t> literal :
17. native_function_literals_) {
18. //omit..................
19. }
20. // Build object literal constant properties
21. for (std::pair<ObjectLiteral*, size_t> literal : object_literals_) {
22. ObjectLiteral* object_literal = literal.first;
23. //omit..................
24. }
25. }
26. // Build array literal constant elements
27. for (std::pair<ArrayLiteral*, size_t> literal : array_literals_) {
28. //omit..................
29. }
30. // Build class literal boilerplates.
31. for (std::pair<ClassLiteral*, size_t> literal : class_literals_) {
32. //omit..................
33. }
34. // Build template literals.
35. for (std::pair<GetTemplateObject*, size_t> literal : template_objects_) {
36. //omit..................
37. }
38. } From lines 4 to 10, the loop-for store global variables in the constant pool. From lines 11 to 35 store the sharedfunction, constant property, and other stuff in the constant pool. 3. FinalizeBytecode The FinalizeBytecode() is the last step for generating bytecode. Let’s give a glance at line 13 of the FinalizeBytecode, the reorganizes the bytecodes and returns a bytecode array that holds a bytecode array representing your JavaScript code. What is “reorganize”? It is that just put bytecode, constant pool, etc. together because all the stuff is already done well before ToBytecodeArray. builder()->ToBytecodeArray 1. Handle<BytecodeArray> BytecodeArrayBuilder::ToBytecodeArray(Isolate* isolate) {
2. DCHECK(RemainderOfBlockIsDead());
3. DCHECK(!bytecode_generated_);
4. bytecode_generated_ = true;
5. int register_count = total_register_count();
6. if (register_optimizer_) {
7. //omit
8. }
9. Handle<ByteArray> handler_table =
10. handler_table_builder()->ToHandlerTable(isolate);
11. return bytecode_array_writer_.ToBytecodeArray(
12. isolate, register_count, parameter_count(), handler_table);
13. } Line 9 gets out the handler_table, namely the dispatch_table that I have talked about. Line 11 stores the handler_table address into a bytecode array. Remember that each bytecode has only one handler that is the actual worker and is held by the dispatch_table. Let’s give a glance at line 11 bytecode_array_writer_.ToBytecodeArray. 1. Handle<BytecodeArray> BytecodeArrayWriter::ToBytecodeArray(
2. Isolate* isolate, int register_count, int parameter_count,
3. Handle<ByteArray> handler_table) {
4. DCHECK_EQ(0, unbound_jumps_);
5. int bytecode_size = static_cast<int>(bytecodes()->size());
6. int frame_size = register_count * kSystemPointerSize;
7. Handle<FixedArray> constant_pool =
8. constant_array_builder()->ToFixedArray(isolate);
9. Handle<BytecodeArray> bytecode_array = isolate->factory()->NewBytecodeArray(
10. bytecode_size, &bytecodes()->front(), frame_size, parameter_count,
11. constant_pool);
12. bytecode_array->set_handler_table(*handler_table);
13. return bytecode_array;
14. } Line 7 generates constant_pool, and line 9 generates a bytecode array that the source code is below: 1. Handle<BytecodeArray> Factory::NewBytecodeArray(
2. int length, const byte* raw_bytecodes, int frame_size, int parameter_count,
3. Handle<FixedArray> constant_pool) {
4. DCHECK_LE(0, length);
5. if (length > BytecodeArray::kMaxLength) {
6. isolate()->heap()->FatalProcessOutOfMemory("invalid array length");
7. }
8. // Bytecode array is AllocationType::kOld, so constant pool array should be
9. // too.
10. DCHECK(!Heap::InYoungGeneration(*constant_pool));
11. int size = BytecodeArray::SizeFor(length);
12. HeapObject result = AllocateRawWithImmortalMap(size, AllocationType::kOld,
13. *bytecode_array_map());
14. Handle<BytecodeArray> instance(BytecodeArray::cast(result), isolate());
15. instance->set_length(length);
16. instance->set_frame_size(frame_size);
17. instance->set_parameter_count(parameter_count);
18. instance->set_incoming_new_target_or_generator_register(
19. interpreter::Register::invalid_value());
20. instance->set_osr_loop_nesting_level(0);
21. instance->set_bytecode_age(BytecodeArray::kNoAgeBytecodeAge);
22. instance->set_constant_pool(*constant_pool);
23. instance->set_handler_table(*empty_byte_array());
24. instance->set_source_position_table(*undefined_value());
25. CopyBytes(reinterpret_cast<byte*>(instance->GetFirstBytecodeAddress()),
26. raw_bytecodes, length);
27. instance->clear_padding();
28. return instance;
29. } Line 12 allocates memory from the V8 heap, and then lines 15–25 copy the bytecode array into the memory, and line 28 returns the bytecode finally. Takeaways Constant pool holds constants, function addresses, etc. If a variable name is known, it as a constant is stored in the constant pool; Note: Constant pool always is at bytecode array[0]. In other words, given a bytecode array, the first element of the array is always the constant pool; A bytecode array is a heap object and its type is FixedArray. Okay, that wraps it up for this share. I’ll see you guys next time, take care! Please reach out to me if you have any issues. : qq9123013 : WeChat Email v8blink@outlook.com Also Published here

Understanding Chrome V8 - Chapter 22: Compiler Workflow Bytecode, Constant Pool

The Intergovernmental Panel on Climate Change (IPCC) recently concluded that, at the current rate of human activity relating to global greenhouse gas emission pathways, we are facing a threat of a . This may not sound like a large number, but the consequences are dire — think rising sea levels leading to flooding of lowland areas, degradation of tundras and rainforests, and increased CO₂ concentration in oceans leading to mass bleaching of coral reef ecosystems. That last one hits particularly close to home, being a scuba-diver myself. 1.5°C of global warming above pre-industrial levels To combat this issue, many organisations have looked to technology — blockchain in particular. One such organisation is the that serves to “put the Paris Agreement on the blockchain” and “catalyse climate action by connecting the national carbon accounts of the world.” Blockchain for Climate Foundation Source: “ ” Putting the Paris Agreement on the Blockchain Of course, when it comes to utilising blockchain technology to reduce the impact of climate change, the irony isn’t lost to me — it’s no secret that many iterations of distributed ledger technology at work today are inherently energy-intensive. In fact, the entire premise of digital asset mining revolves around the deliberate expenditure of processing power. The orange areas show where global Bitcoin mining consumes more electricity than the entire country But is there a silver lining? In , a publication by the World Economic Forum and PWC, blockchain technology is identified as a key driver in what has been coined the “Fourth Industrial Revolution.” Along with other emergent technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI), it is posited that blockchain technology will serve to “seismically affect economies, values, identities and possibilities for future generations” (4). Building Block(chain)s for a Better Planet The central premise of the PWC report revolves round the notion that, while currently in its infancy and thus still fundamentally inefficient, future implementations of blockchain technology could “dramatically improve operating efficiencies in some sectors while completely disrupting others” (13). As a firm believer in and user of blockchain technology, I concur with this sentiment, and have established some findings as to how exactly distributed ledger technology, in its current or future forms, can — pardon the melodramatic Grand Statement — save the planet: Decentralised Carbon Markets Carbon credits, or “ representing the right to emit one tonne of carbon dioxide,” were created with all the right intentions — to act as an incentive for businesses to implement carbon-friendly operational practices, with the ultimate goal of balancing the world’s carbon footprint. tradable certificates or permits Even as the multi-billion dollar market it is today, it is rife with limitations caused by intermediaries. The seeks to change this through their development of a “complementary protocol layer [that enables] blockchain networks and DApps to efficiently access carbon credits to offset their carbon footprint.” Carbon Grid Protocol “Bitcoin and Ethereum transactions emit . This is compared to just 0.3 grams of carbon dioxide being emitted for each VISA transaction” 60kg and 10kg of carbon dioxide respectively Blockchain platforms could be harnessed to use cryptographic tokens with a tradable value to optimise existing market platforms for carbon (or other substances) and create new opportunities for carbon credit transactions. (PWC 7) Incentivised Prosumption The term “prosumption” is derived from the notion of something being An hypothetical example of this would be the implementation of a solar-powered Bitcoin mining rig in which surplus rewards are used to purchase carbon credits. Within the context of blockchain technology, this can be linked to the t. produced by consumers . micro-generation energy marke In a blockchain-powered prosumption system, it is posited that “smart meters could be used to account and register the micro-generated energy on a distributed ledger” (76). We can already see iterations of this taking place today, with one example being MIT startup . SolarCoin Can blockchain give power back to the grid? Distributed ledgers, in combination with smart-metering systems and next-generation batteries (to accumulate energy locally), have the potential to open the energy-market to prosumer production. (Government Office for Science 76) Sustainable Finance Legacy issuance, trading, clearing, and settlement processes of the traditional financial industry are highly inefficient, with high latency T+3 cycles being the norm for the last two decades. Today, organisations like , with their , are primed to disrupt long-standing bastions of traditional finance. With their implementation of a hybrid ledger architecture consisting of a public/permissioned blockchain, STACS enables the complete tokenisation of capital markets for faster, cheaper, and more secure issuance, trading, clearing, and settlement of securities. Hashstacs Inc Securities Trading Asset and Classification (STACS) Protocol And when it comes to the tokenisation of said capital markets, there are infinite possibilities beyond traditional financial securities — real estate, art, and even , can all easily be digitised. solar assets Imagine a world where you can seamlessly issue securities for the crowdfunding of solar energy, forest conservation, and water purification projects Looking ahead, blockchain could be a real game changer for “blended finance” investment in projects seeking to deliver the UN’s Sustainable Development Goals. (PWC 25) Closing Thoughts While blockchain technology still has a ways to go before it can realistically deliver its promises of creating a cleaner, more sustainable, and more harmonious planet, I believe we’re on the right track. Every nascent technology necessarily undergoes three main phases before it achieves mass adoption and scalability: , where “ ” R&D incomes from inputs are negative and where the prospects of failure are high , where “out-of-pocket costs have been recovered and the technology begins to gather strength” Ascension , where “gain is high and stable” and the markets achieve equilibrium Maturity Based on the current state of the blockchain industry, we’re right in the thick of the ascension phase, with many of the organisations identified above gaining serious traction towards Series B funding. If you’re as much a tech and blockchain geek as I am, you’ll no doubt be excited (and even hyped!) over what this space will have to offer us in the coming months, years, and beyond when it comes to saving the planet. _Join LinkedIn My daily work revolves around building distinctive, compelling, and transformative brands for finance and…_www.linkedin.com Marcus Tan - Marketing and Business Development Director - Broctagon Fintech Group | LinkedIn

Going Green: Can Blockchain Save the Planet?

Stackonomics: 3 Ways Towards an Instant Blockchain Economy

Hello! We are developers from the Navigine company. We decided to test the Wi-Fi RTT technology and share the results with you. What is Wi-Fi RTT Wi-Fi RTT (Round-trip time) is a brand new technology that allows locating devices with unprecedented sub-meter accuracy. Basically, it measures the distance between a device and Wi-Fi routers which is way more accurate than using a GPS signal. The latter can’t provide an acceptable level of accuracy at all when it comes to indoor positioning. Wi-Fi RTT outperforms RSSI-based methods as well, thanks to reduced latency and better accuracy. The Use Today, this technology is the next step to improve services that use indoor positioning. We expect that its implementation will become a better solution for many industries. Wi-Fi RTT is applicable for tracking vehicles and cargo within warehouses and production plants. Comparing to BLE, this technology provides enhanced accuracy and reduced latency, making processes more rapid and reducing delays. It is effective for public hotspots as well, such as offices and malls, airports, exhibitions and not only. Indoor navigation is the solution users need for making their visits to public places more comfortable and enjoyable. Through this, they can plan their time effectively and find locations they need immediately. The Advantages Our solution goes further with unique algorithms that enhance the basic capabilities of Wi-Fi RTT. demonstrates a submeter accuracy in checkpoints 95% of the time and around 30 cm accuracy 50% of the time. The positioning engine is a particle filter combining Pedestrian Dead Reckoning (PDR), based on inertial sensors readings, and Wi-Fi RTT readings for correction. Meanwhile, the dynamic test shows that delay in position estimation does not exceed one second. The latter result is achieved by complementing Wi-Fi RTT measurements with PDR via particle filter. Semi-static test in our office The technology is not just an innovation in itself, but also a practical solution that improves business efficiency and customer experience. Wi-Fi RTT provides longer distance measurements and can be integrated into existing Wi-Fi network infrastructure. Current status Now, it is too early to refer to any cases, while we are currently working on raising experts interest from LinkedIn. We can see the real need for this technology, so the first implementations are coming soon in Q1 2020. Today, we have confirmed orders from a big carrier and one logistics company. Why Wi-Fi RTT? The industries using indoor positioning have to realize that often BLE is not the solution that meets their needs. Wi-Fi RTT is a significantly more relevant technology for many cases. Our solution based on Wi-Fi RTT delivers clear advantages for customers, including high accuracy, low latency, ease of use and integration. With just 2 weeks between order and integration, it is one of the most rapid and flexible solutions. The combination of high accuracy, availability, and low price, can make the technology an Indoor positioning market leader.

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