Aztec Network
Feb 7th, 2019
## min read

From zero to nowhere: smart contract programming in Huff (1/4)

In this series, learn smart contract programming in Huff directly from Zac.

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Written by
Zac Williamson
Edited by

Hello there!

I want to write about a piece of runoff that has oozed out of the primordial slop on the AZTEC factory floor: …Huff.

Huff is an Ethereum smart contract programming ‘language’ that was developed while writing weierstrudel, an elliptic curve arithmetic library for validating zero-knowledge proofs.

Elliptic curve arithmetic is computationally expensive, so developing an efficient implementation was paramount, and not something that could be done in native Solidity.

It wasn’t even something that could be done in Solidity inline assembly, so we made Huff.

To call Huff a language is being generous — it’s about as close as one can get to EVM assembly code, with a few bits of syntactic sugar bolted on.

Huff programs are composed of macros, where each macro in turn is composed of a combination of more macros and EVM assembly opcodes. When a macro is invoked, template parameters can be supplied to the macro, which themselves are macros.

Unlike a LISP-like language or something with sensible semantics, Huff doesn’t really have expressions either. That would require things like knowing how many variables a Huff macro adds to the stack at compile time, or expecting a Huff macro to not occasionally jump into the middle of another macro. Or assuming a Huff macro won’t completely mangle the program counter by mapping variables to jump destinations in a lookup table. You know, completely unreasonable expectations.Huff doesn’t have functions. Huff doesn’t even have variables, only macros.

Huff is good for one thing, though, which is writing extremely gas-optimised code.The kind of code where the overhead of the jump instruction in a function call is too expensive.

The kind of code where an extra swap instruction for a variable assignment is an outrageous luxury.At the very least, it does this quite well. The weierstrudel library performs elliptic curve multiple-scalar multiplication for less gas than the Ethereum’s “precompile” smart contract. An analogous Solidity smart contract is ~30–100 times more expensive.

It also enables complicated algorithms to be broken down into constituent macros that can be rigorously tested, which is useful.

Huff is also a game, played on a chess-board. One player has chess pieces, the other draughts pieces. The rules don’t make any sense, the game is deliberately confusing and it is an almost mathematical certainty that the draughts player will lose. You won’t find references to this game online because it was “invented” in a pub by some colleagues of mine in a past career and promptly forgotten about for being a terrible game.

I found that writing Huff macros invoked similar emotions to playing Huff, hence the name.

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Programming in Huff

Given the absence of any documentation, I figured it might be illuminating to write a short series in how to write a smart contract in Huff. You know, if you’re looking for time to kill and you’ve run out of more interesting things to do like watch paint dry or rub salt in your eyes.

If you want to investigate further, you’ll find Huff on GitHub. For some demonstration Huff code, the weierstrudel smart contract is written entirely in Huff.

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“Hello World” — an ERC20 implementation in Huff

Picture the scene — the year is 2020 and the world is reeling from a new global financial crisis. With the collapse of the monetary base, capital flees to the only store of stable value that can be found — first-generation Crypto-Kitties. Amidst this global carnage, Ethereum has failed to achieve its scaling milestones and soaring gas fees cripple the network.It is a world on the brink, where one single edict is etched into the minds citizens from San Francisco to Shanghai — The tokens must flow…or else.

This is truly the darkest timeline, and in the darkest timeline, we code in Huff.

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Finding our feet

We’re going to write an ERC20 token contract. But not just any ERC20 contract— we’re going to write an ERC20 contract where every opcode must justify its place, or be scourged from existence…

Let’s start by looking at the Solidity interface for a ‘mintable’ token — there’s not much point in an ERC20 contract if it doesn’t have any tokens, after all.

function totalSupply() public view returns (uint);

function balanceOf(address tokenOwner) public view returns (uint);

function allowance(address tokenOwner, address spender) public view returns (uint);

function transfer(address to, uint tokens) public returns (bool);

function approve(address spender, uint tokens) public returns (bool);

function transferFrom(address from, address to, uint tokens) public returns (bool);

function mint(address to, uint tokens) public returns (bool);

event Transfer(address indexed from, address indexed to, uint tokens);

event Approval(address indexed tokenOwner, address indexed spender, uint tokens);

That doesn’t look so bad, how hard can this be?

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Bootstrapping

Before we start writing the main subroutines, remember that Huff doesn’t do variables. But there’s a macro for that! Specifically, we need to be able to identify storage locations with something that resembles a variable.

Let’s create some macros that refer to storage locations that we’re going to be storing the smart contract’s state in. For Solidity smart contracts, the compiler will (under the hood) assign every storage variable to a storage pointer and we’re doing the same here.

First up, the storage pointer that maps to token balances:

#define macro BALANCE_LOCATION = takes(0) returns(1) {
   0x00
}

The takes field refers to how many EVM stack items this macro consumes. returns refers to how many EVM stack items this macro will add onto the stack.

Finally, the macro code is just 0x00 . This will push 0 onto the EVM stack; we’re associating balances with the first storage ‘slot’ in our smart contract.

We also need a storage location for the contract’s owner:

#define macro OWNER_LOCATION = takes(0) returns(0) {
   0x01
}

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Implementing SafeMath in Huff

SafeMath is a Solidity library that performs arithmetic operations whilst guarding against integer overflow and underflow.

We need the same functionality in Huff. After all, we wouldn’t want to write unsafe Huff code. That would be irrational.ERC20 is a simple contract, so we will only need addition and subtraction capabilites.

Let’s consider our first macro, MATH__ADD . Normally, this would be a function with two variables as input arguments. But Huff doesn’t have functions.

Huff doesn’t have variables either.

...

Let’s take a step back then. What would this function look like if we were to rip out Solidity’s syntactic sugar? This is the function interface:

function add(uint256 a, uint256 b) internal view returns (uint256 c);

Under the hood, when the add function is called, variables a and b will be pushed to the front of the EVM’s stack.

Behind them on the stack will be a jump label that corresponds to the return destination of this function. But we’re going to ignore that — It’s cheaper to directly graft the function bytecode in-line when its needed, instead of spending gas by messing around with jumps.

So for our first macro, MATH__ADD , we expect first two variables to be at the front of the EVM stack; the variables that we want to add. This macro will consume these two variables, and return the result on the stack. If an integer overflow is triggered, the macro will throw an error.

Starting with the basics, if our stack state is: a, b , we need a+b . Once we have a+b , we need to compare it with either a or b . If either are greater than a+b, we have an integer overflow.

So step1: clone b , creating stack state: b, a, b . We do this with thedup2 opcode. We then call add , which eats the first two stack variables and spits out a+b , leaving us with (a+b), b on the stack.

Next up, we need to validate that a+b >= b. One slight problem here — the Ethereum Virtual Machine doesn’t have an opcode that maps to the >= operator! We only have gt and lt opcodes to work with.

We also have the eq opcode, so we could check whether a+b > band perform a logical OR operation with a+b = b . i.e.:

// stack state: (a+b) b
dup2 dup2 gt // stack state: ((a+b) > b) (a+b)
bdup3 dup3 eq // stack state: ((a+b) = b) ((a+b) > b) (a+b) b
or           // stack state: ((a+b) >= b) (a+b) b

But that’s expensive, we’ve more than doubled the work we’re doing! Each opcode in the above section is 3 gas so we’re chewing through 21 gas to compare two variables. This isn’t Solidity — this is Huff, and it’s time to haggle.

A cheaper alternative is to, instead, validate that (b > (a+b)) == 0 . i.e:

// stack state: (a+b) b
dup1 dup3 gt // stack state: (a > (a+b)) (a+b)
biszero       // stack state: (a > (a+b) == 0) (a+b) b

Much better, only 12 gas. We can almost live with that, but we’re not done bargaining.

We can optimize this further, because once we’ve performed this step, we don’t need bon the stack anymore — we can consume it. We still need (a+b)on the stack however, so we need a swap opcode to get b in front of (a+b) on the program stack. This won’t save us any gas up-front, but we’ll save ourselves an opcode later on in this macro.

dup1 swap2 gt // stack state: (b > (a+b)) (a+b)
iszero        // stack state: ((a+b) >= b) (a+b)

Finally, if a > (a+b) we need to throw an error. When implementing “if <x> throw an error”, we have two options to take, because of how thejumpi instruction works.

jumpi is how the EVM performs conditional branching. jumpi will consume the top two variables on the stack. It will treat the second variable as a position in the program’s program counter, and will jump to it only if the first variable is not zero.

When throwing errors, we can test for the error condition, and if true jump to a point in the program that will throw an error.

OR we can test for the opposite of the error condition, and if true, jump to a point in the program that skips over some code that throws an error.

For example, this is how we would program option 2 for our safe add macro:

// stack state: ((a+b) >= b) (a+b)
no_overflow jumpi
   0x00 0x00 revert // throw an error
no_overflow:
// continue with algorithm

Option one, on the other hand, looks like this:

// stack state: ((a+b) >= b) (a+b)
iszero // stack state: (b > (a+b)) (a+b)
throw_error jumpi
// continue with algorithm

For our use case, option 2 is more efficient, because if we chain option 2 with our condition test, we end up with:

dup2 add dup1 swap2 gt
iszero
iszero
throw_error jumpi

We can remove the two iszero opcodes because they cancel each other out! Leaving us with the following macro

#define macro MATH__ADD = takes(2) returns(1) {
   // stack state: a b
   dup2 add
   // stack state: (a+b) b
   dup1 swap2 gt
   // stack state: (a > (a+b)) (a+b)
   throw_error jumpi}

However, we have a problem! We haven’t defined our jump label throw_error , or what happens when we hit it. We can’t add it to the end of macro MATH__ADD , because then we would have to jump over the error-throwing code if the error condition was not met.

We would prefer not to have macros that use jump labels that are not declared inside the macro itself. We can solve this by passing the jump label throw_error as a template parameter. It is then the responsibility of the macro that invokes MATH__ADD to supply the correct jump label — which ideally should be a local jump label and not a global one.

Our final macro looks like this:

template <throw_error_jump_label>
#define macro MATH__ADD = takes(2) returns(1) {
   // stack state: a b
   dup2 add
   // stack state: (a+b) a
   dup1 swap2 gt
   // stack state: (a > (a+b)) (a+b)
<throw_error_jump_label> jumpi
}

The jumpi opcode is 10 gas, and the others cost 3 gas (assuming <throw_error_jump_label> eventually will map to a PUSH opcode) — in total 28 gas.As an aside — let’s consider the overhead created by Solidity when calling SafeMath.add(a, b)First, values a and b are duplicated on the stack; functions don’t consume existing stack variables. Next, the return destination, that must be jumped to when the function finishes, is pushed onto the stack. Finally, the jump destination of SafeMath.add is pushed onto the stack and the jump instruction is called.

Once the function has finished its work, the jump instruction is called to jump back to the return destination. The values a , b are then assigned to local variables by identifying the location on the stack that these variables occupy, calling a swap opcode to manoeuvre the return value into the allocated stack location, followed by a pop opcode to remove the old value. This is performed twice for each variable.

In total that’s…

  • 4 dup opcodes (3 gas each)
  • 2 jump opcodes (8 gas each)
  • 2 swap opcodes (3 gas each)
  • 2 pop opcodes (2 gas each)
  • 2 jumpdest opcodes (1 gas each)

To summarise, the act of calling SafeMath.add as a Solidity function would cost 40 gas before the algorithm actually does any work.

To summarise the summary, our MATH__ADD macro does its job in almost half the gas it would cost to process a Solidity function overhead.

To summarise the summary of the summary, this is acceptable Huff code.

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Subtraction

Finally, we need an equivalent macro for subtraction:

template <throw_error_jump_label>
#define macro MATH__SUB = takes(2) returns(1) {
   // stack state: a b
   // calling sub will create (a-b)
   // if (b>a) we have integer underflow - throw an error    dup1 dup3 gt
   // stack state: (b>a) a b<throw_error_jump_label> jumpi
   // stack state: a b
   sub
   // stack state: (a-b)
}

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Utility macros

Next up, we need to define some utility macros we’ll be using . We need macros that validate that the transaction sender has not sent any ether to the smart contract, UTILS__NOT_PAYABLE. For our mint method, we’ll need a macro that validates that the message sender is the contract’s owner, UTILS__ONLY_OWNER:

template<error_location>
#define macro UTILS__NOT_PAYABLE = takes(0) returns(0) {
   callvalue <error_location> jumpi
}

#define macro UTILS__ONLY_OWNER = takes(0) returns(0) {
   OWNER_LOCATION() sload caller eq is_owner jumpi
       0x00 0x00 revert
   is_owner:
}

N.B. revert consumes two stack items. p x revert will take memory starting at x, and return the next p bytes as an error code. We’re not going to worry about error codes here, just throwing an error is good enough.

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Creating the constructor

Now that we’ve set up our helper macros, we’re close to actually being able to write our smart contract methods. Congratulations on nearly reaching step 1!

To start with , we need a constructor. This is just another macro in Huff. Our constructor is very simple — we just need to record who the owner of the contract is. In Solidity it looks like this:

constructor() public {
   owner = msg.sender;
}

And in Huff it looks like this:

#define macro ERC20 = takes(0) returns(0) {
   caller OWNER_LOCATION() sstore
}

The EVM opcode caller will push the message sender’s address onto the stack.

We then push the storage slot we’ve reserved for the owner onto the stack.

Finally we call sstore, which will consume the first two stack items and store the 2nd stack item, using the value of the 1st stack item as the storage pointer.

For more information about storage pointers and how smart contracts manage state — Anreas Olofsson’s Solidity workshop on storage is a great read.

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Parsing the function signature

Are we ready to start writing our smart contract methods yet? Of course not, this is Huff. Huff is efficient, but slow.

I like think of Huff like a trusty tortoise, if the tortoise is actually a hundred rats stitched into a tortoise suit, and each rat is a hundred maggots stitched into a rat suit.

…anyhow, we still need our function selector. But Huff doesn’t do functions; we’re going to have to create them from more basic building blocks.

One of the first pieces of code generated by the Solidity compiler is code to unpick the function signature. A function signature is a unique marker that maps to a function name.

For example, consider the solidity functionfunction balanceOf(address tokenOwner) public view returns (uint balance);The function signature will take the core identifying information of the function:

  • the function name
  • the input argument types

This is represented as a string, i.e. "balanceOf(address)". A keccak256 hash of this string is taken, and the most significant 4 bytes of the hash are then used as the function signature.

This online tool makes it easier to find the signature of a function.

It’s a bit of a mouthful, but it creates a (mostly) unique identifier for any given function — this allows contracts to conform to a defined interface that other smart contracts can call.

For example, if the function signature for a given function varied from contract to contract, it would be impossible to have an ‘ERC20’ token, because other smart contracts wouldn’t know how to construct a given contract’s function signature.

With that out of the way, we will find the function signature in the first 4 bytes of calldata. We need to extract this signature and then figure out what to do with it.

Solidity will create function signature hashes under the hood so you don’t have to, but Huff is a bit too primitive for that. We have to supply them directly. We can identify the ERC20 function signatures by pulling them out of remix:

We can parse a function signature by extracting the first 4 bytes of calldata and then perform a series of if-else statements over every function hash.

We can use the bit-shift instructions in Constantinople to save a bit of gas here. 0x00 calldataload will extract the first 32 bytes of calldata and push it onto the stack in a single EVM word. i.e. the 4 bytes we want are in the most significant byte positions and we need them in the least significant positions.

We can do this with 0x00 calldataload 224 shr

We can execute ‘functions’ by comparing the calldata with a function signature, and jumping to the relevant macro if there is a match. i.e:

0x00 calldataload 224 shr // function signature
dup1 0xa9059cbb eq transfer jumpi
dup1 0x23b872dd eq transfer_from jumpi
dup1 0x70a08231 eq balance_of jumpi
dup1 0xdd62ed3e eq allowance jumpi
dup1 0x095ea7b3 eq approve jumpi
dup1 0x18160ddd eq total_supply jumpi
dup1 0x40c10f19 eq mint jumpi
// If we reach this point, we've reached the fallback function.
// However we don't have anything inside our fallback function!
// We can just exit instead, after checking that callvalue is zero:
UTILS__NOT_PAYABLE<error_location>()
0x00 0x00 return

We want the scope of this macro to be constrained to identifying where to jump — the location of these jump labels is elsewhere in the code. Again, we use template parameters to ensure that jump labels are only explicitly called inside the macros that they are defined in.

Our final macro looks like this:

template <transfer, transfer_from, balance_of, allowance, approve, total_supply, mint, error_location>
#define macro ERC20__FUNCTION_SIGNATURE = takes(0) returns(0) {
   0x00 calldataload 224 shr // function signature
   dup1 0xa9059cbb eq <transfer> jumpi
   dup1 0x23b872dd eq <transfer_from> jumpi
   dup1 0x70a08231 eq <balance_of> jumpi     dup1 0xdd62ed3e eq <allowance> jumpi
   dup1 0x095ea7b3 eq <approve> jumpi    dup1 0x18160ddd eq <total_supply> jumpi
   dup1 0x40c10f19 eq <mint> jumpi
   UTILS__NOT_PAYABLE<error_location>()
   0x00 0x00 return
}

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Setting up boilerplate contract code

Finally, we have enough to write the skeletal structure of our main function — the entry-point when our smart contract is called. We represent each method with a macro, which we will need to implement.

#define macro ERC20__MAIN = takes(0) returns(0) {


   ERC20__FUNCTION_SIGNATURE<
       transfer,
       transfer_from,
       balance_of,
       allowance,
       approve,
       total_supply,
       mint,
       throw_error
>()

   transfer:
       ERC20__TRANSFER<throw_error>()
   transfer_from:
       ERC20__TRANFSER_FROM<throw_error>()
   balance_of:
       ERC20__BALANCE_OF<throw_error>()
   allowance:
       ERC2O__ALLOWANCE<throw_error>()
   approve:
       ERC20__APPROVE<throw_error>()
   total_supply:
       ERC20__TOTAL_SUPPLY<throw_error>()
   mint:
       ERC20__MINT<throw_error>()
   throw_error:
       0x00 0x00 revert
}

…Tadaa.

Finally we’ve set up our pre-flight macros and boilerplate code and we’re ready to start implementing methods!But that’s enough for today.

In part 2 we’ll implement the ERC20 methods as glistening Huff macros, run some benchmarks against a Solidity implementation and question whether any of this was worth the effort.

Cheers,

Zac.

Click here for part 2

Read more
Aztec Network
Aztec Network
22 Jul
xx min read

How Gas Works on Aztec

Gas on Aztec

Gas on Aztec is known as Fee Juice and is used to pay for transaction costs. This is the same as $ETH on Ethereum. Some apps will handle transaction costs for you under the hood, but if you are using a browser extension wallet, you will not be able to send transactions without it. Fee Juice can be obtained by bridging the $AZTEC token on Ethereum to the Aztec Network L2. This means that under the hood, all activity that happens on Aztec is underpinned by the $AZTEC token bridged into the network. Some bridges like Shield (by human.tech) handle this for you, allowing you to allocate a portion of your bridged transaction to convert into Fee Juice and land in your wallet automatically.

Public vs Private Assets

Assets and transactions on the Aztec Network can be either public or private. If you bridge publicly, your tokens will arrive as public, traceable tokens visible to all. Privately bridging, on the other hand, will give you private assets that are visible only to you. These assets can then be sent privately to another user or wallet without ever revealing who you are, what tokens were sent, how many, or who the recipient is.

Public vs Private Gas

Like tokens on the Aztec Network, Fee Juice (gas) can also be public or private. The reason for this is that even if what you are sending is private, the gas you spend to execute that transaction could still be visible if you are using public Fee Juice, potentially revealing transaction patterns and activity. Private Fee Juice keeps your entire transaction footprint hidden. When you send a private transaction, you can use private Fee Juice, and when you send a public transaction, you can use public Fee Juice, which means your transaction costs are always aligned with the type of transaction you're making.

Fee Juice in Apps

Aztec has native fee abstraction, which means apps could let you pay for transactions in any token you want, or cover your fees entirely. Apps like Nyx may choose to cover part or all of a user's transaction costs, or allow you to pay in tokens that are convenient for you. This means you will most likely never see Fee Juice in an app; instead, you'll pay in whatever makes sense for what you're doing, on your terms. Similarly, you might never even see an Aztec wallet at all, because the app itself becomes your interface that you connect to using your MetaMask wallet.

Fee Juice in Browser Wallets

If you're using a browser extension like Azguard, you'll manage Fee Juice directly in your wallet alongside your private and public balances, converting between tokens as needed to cover transaction costs.

When you bridge tokens in, you'll need enough Fee Juice to cover the cost of your first transaction, then you'll need to monitor how much Fee Juice you have available to make transactions. Browser wallets will allow you to send either publicly or privately to other users and will default to using either public or private Fee Juice depending on the type of transaction. Both private Fee Juice and public Fee Juice will appear by default in your token list.

Wrapping up

How you handle Fee Juice depends on where you're transacting: apps can abstract it away entirely and let you pay in any token, while a browser wallet like Azguard puts it in your hands to manage across public and private balances. Match your gas to your transaction, keep private activity private down to the fee, and you move on your terms.

Aztec Network
Aztec Network
21 Jul
xx min read

Introducing Alpha V5

The Aztec Network today activated Alpha V5, a major protocol upgrade passed by token-holder governance and executed onchain. Alpha V5 reduces private-transaction proving times by more than 2x compared to the previous version, lowers the cost of a fully private transaction by roughly 50%, resolves the critical issues found in V4, and sees the first wave of apps go live. Users can now send private transactions and earn yield on Aave simply by connecting their Ethereum wallets on Nyx, bridge from Ethereum to Aztec using Shield or TRAIN, privately collect NFTs on RavenHouse, or play Dark Forest Aztec, a hidden-information strategy game in a universe that lives entirely onchain. 

"Alpha V5 continues Aztec's work at the frontier of client-side proving, with cryptographic breakthroughs that cut proving times by more than half this release," said Zac Williamson, Co-founder, Aztec Foundation. "We believe Aztec is now the fastest system in the world for proving a fully private transaction entirely on a user's own device, and every release moves the industry closer to private transactions at public transaction speeds."

As the only decentralized privacy L2, Aztec is the credibly neutral privacy layer for Ethereum. Aztec allows anyone to write smart contracts that include both private and public aspects – every private transaction is proven on the user's own device, so no operator, sequencer, or intermediary can see the data. The Alpha V5 proving improvements come from cryptographic advances that make this client-side proving faster than any prior release. The network remains in alpha, but with V5 it is ready for teams to begin building and deploying applications.

Performance - 2.5 second fully private transactions 

Making private transactions practical comes down to how quickly a proof can be generated on a user's own device, without offloading that work to a server that would learn what the user is doing. On Alpha V5, proving a private token transfer natively now takes approximately 2.5 seconds on a consumer laptop, down from 5.2 seconds on V4, and about 6.8 seconds in a browser, down from 12.5 seconds. Across every measured transaction flow, client-side proving times improved by approximately 2x compared with V4.

Bench machine: an M2 MacBook (12 cores, throttled to 8). "Native" runs Aztec's C++ proving binary; "WASM" runs the same prover in a browser engine (Node on V8).

Alpha V5 lowers ECDSA signature-verification cost by approximately 2x, speeds up Poseidon2 hashing by approximately 3x, and reduces the protocol circuit gate count by approximately 50% (gate count is the number of individual operations a proving circuit must perform, and it is the main driver of how long a proof takes to generate). Each of these lowers the amount of work a device performs to prove a transaction, and the reduction in gate count in particular compounds across every proof the network generates.

Apps - send, receive, and earn privately on Ethereum

Alpha V5 launches the first wave of apps on a network where privacy is built into the protocol rather than managed by an operator. On other networks that claim privacy, transactions still pass through an operator or node that reads them in plaintext, or depend on a viewing key that a third party holds, so users rely on someone else to protect their data and to decide when it gets disclosed. On Aztec, every private transaction is proven on the user's own device, so the app, the sequencer, and any operator never need to see the underlying data. Nyx is one of these apps, allowing users to privately send transactions and privately earn yield on Aave. 

"On Ethereum, everything you do is public. That's why we built Nyx: a private account governed by your Ethereum wallet", said Nikhil, Co-founder of Nyx. "Now you can send, receive and earn in private. Nyx was the first app live on the Aztec Alpha, and we're excited to expand participation to more users with the added stability of Alpha V5."

Other apps on Alpha V5 include Azguard and Nethermind (wallets), Shield, TRAIN, and RavenHouse (bridges), and the Aztecscan block explorers. Also launching is Dark Forest Aztec, a game where users explore a universe, control planets, manage planetary energy, expand territory, and launch attacks through strategic play with private state and hidden actions.

Dark Forest Aztec private universe-building gameplay

Lower costs, higher security 

Transaction fees on Aztec come from two main sources: the cost of proving a transaction and the cost of verifying the rollup proof on Ethereum. Alpha V5 reduces both. It lowers the network's proving-cost parameter by 50%, and it reduces the L1 gas required to verify a rollup proof by approximately 40%. Because rollup proofs are verified on Ethereum and that cost is shared across all transactions in a batch, the L1 reduction lowers fees for every user, while the lower proving-cost parameter reduces the per-transaction proving fee directly. Together, these bring the average cost of a fully private token transfer to under a $0.05 transaction cost.

Alpha V5 also hardens the network on several fronts. It resolves critical vulnerabilities found in Alpha V4 along with additional bugs discovered since launch. Aztec's bug bounty program on Cantina also drew more than 234 security researchers to participate. The network remains in alpha, and further bugs may surface as usage grows, but each release has closed the issues found in the last and strengthened the protocol against new ones. With the critical V4 issues resolved and these safeguards in place, Alpha V5 is stable enough for teams to begin building and deploying applications.

Availability

Alpha V5 is live now, view the Alpha V5 landing page for a full list of features, performance updates, and live apps to explore. 

About Aztec

Aztec is the only decentralized, privacy-first Layer 2 on Ethereum. Developers write private and public logic in the same smart contract, and private functions are executed and proven on the user's own device, so no operator sees the underlying data. The protocol is upgraded through onchain governance, and the network settles to Ethereum. For more information, visit aztec.network.

Aztec Network
Aztec Network
30 Jun
xx min read

Inside an Aztec Transaction

On Ethereum today, each transaction reveals everything publicly. The token you moved, the size, the timing, the wallet it came from, every action you take. Given the limitations of this type of transparent network, the industry is now focusing on bringing privacy onchain as a top priority. The response to this has mostly been to enable private transactions that shield transfers in various ways. But when we look at how privacy works on Web2, it’s clear that users and developers need granular privacy controls: the ability to decide what is public or private and who is able to see different types of data.

Aztec was built so that one transaction can carry two halves. A private half that runs on your own device and never leaves it, and a public half that the network runs in the open. Apps can choose which aspects are private or public, and users can choose what they want to reveal and when.

This article will follow an example transaction on Aztec: a vote in an onchain election built on Aztec, where who you are and which candidate you chose stay private, while the running tally for each candidate stays public for anyone to verify.

Public and private in one move

Picture the vote you cast in our example as two aspects that seamlessly weave together. In the first step, you act in private: an app records your vote on your device and hands the network a proof that the vote is valid without revealing it. In the second, the network acts in public: it checks that proof, then adds one to the chosen candidate's public tally. It is one transaction: one part stays with you, one part goes to the network. Both parts end up recorded onchain, in two separate state trees, one private and one public. The walkthrough below follows how these two aspects work together and what this means for how your transaction lands onchain. 

It starts on your device

You open the voting app and connect an Aztec wallet. That first step looks like any onchain app. The difference is inside the wallet. An Aztec wallet carries a private execution environment, the PXE, pronounced "pixie", which runs on your phone or in your browser. The PXE is where the private half of your transaction executes, and where the proof of that work gets made, on your hardware, under your exclusive control.

Every account on Aztec is a smart contract rather than a bare key. That design, account abstraction, allows a wallet to authorize a transaction however its owner chooses without writing an identity onto the network for everyone to read. The wallet is the front door, and on Aztec you can decide if the door is open or closed, who you share your information with. 

The private half runs on your device

The voting app is a smart contract with two kinds of functions. The private functions run first, and they run inside your PXE. Your identity and the candidate you picked are the private inputs, and they stay on your device.

The only thing to leave your device is a proof confirming the legitimacy of your vote. Aztec's client-side proving system, Chonk, takes the private execution and produces a zero-knowledge proof: a compact cryptographic receipt that your vote followed the rules, that you are eligible, and have not voted before, while revealing nothing about who you are or who you voted for. Think of it as a sealed ballot the network can confirm is valid without opening it. The network learns only that a legitimate vote happened. It does not learn how you voted, or even which account voted. 

This is the part that used to be too slow to be practical. Generating a proof on a phone was the bottleneck every privacy app hit. Aztec’s Chonk is purpose-built for fast proving on low-memory devices, both natively and in the browser, so the private half runs on the device in your hand instead of on someone else's server.

The public half runs in the open

Some elements of a vote should be public. The tally is shared infrastructure, the number everyone relies on to trust the result. Thanks to programmable privacy on Aztec, the app marks that part public. Public functions live on the network and run in the open, the way functions do on Ethereum.

On Aztec, private and public logic live in the same contract, and the developer decides which is which, function by function and variable by variable. Programmable privacy is a dimmer, not a switch. The voting app turns it up on the individual ballot and turns it down on the running tally. That boundary is a design decision written into the contract, and it is the thing no transparent chain and no fixed-privacy chain can offer.

The network checks the proof and runs the public part

Your vote leaves your device as a bundle: the zero-knowledge proof of the private half, plus the call to the public function that updates the count. It goes to Aztec's sequencers, a decentralized set of thousands of independent operators, with more than 3,500 of them running the network today.

The sequencers do two jobs at once. They verify the proof of your private vote, confirming it is valid and eligible without seeing the choice behind it, and they run the public function that adds one to the chosen candidate and updates the public tally. Your ballot stays sealed. The count goes up by one for everyone to see. The same proof guarantees you cannot vote twice, even though no one learns which ballot is yours.

Two state trees, both onchain

Aztec has two main state trees, and both live onchain. One holds private state, the other holds public state, so the full record of what happened sits on the network rather than on any one person's laptop. The two trees store each record in two different ways depending on if it needs to be private or public. 

The private tree uses a UTXO model, the same note-based design used by Zcash. In this model, state is written as commitments: each entry is a sealed record that a valid vote was cast, with the voter and the choice kept private. Just like with Zcash or Bitcoin, you do not edit a private entry in place. You write a new one, and the design stops the same vote from being cast twice (old state is nullified). The vote stays private, and the record of a legitimate vote happening is onchain for the network to check.

The public tree uses an account-based model, the same shape Ethereum uses: values that update in place, readable by anyone. This is where each candidate's tally lives.

One transaction wrote information to both trees. The private tree recorded that you voted, sealed. The public tree recorded the new totals, in the open. Everything is onchain. The difference between the two trees is how much each one reveals.

Every private app on Aztec writes into that same private tree. A vote, a payment, and a payroll run all land in one shared record of activity, so each user's privacy grows stronger as the network grows, instead of splitting into a separate pool for every app.

A block is proposed, and Ethereum records it

Aztec is an L2 on Ethereum, so everything settles to Ethereum L1. A sequencer on Aztec gathers transactions into a proposed block. Other sequencers validate it before it goes to Ethereum's pending chain. At that point the block sits on Ethereum, ordered and recorded, waiting for its proof. The network has agreed on what happened and the proposed block is just waiting a final proof. 

Anyone can prove it

Proving a block is its own job, and on Aztec, it belongs to no one in particular. A decentralized, permissionless set of provers competes to take a full epoch, a 32-block stretch of the chain, and compresses it into a single zero-knowledge proof of the entire epoch. Anyone with the hardware can run a prover and bid for the work. There is no privileged operator, no committee you have to trust, no outside network holding a key.

That openness is the whole point of a privacy layer. A system that protects your data but routes it through one trusted server has only moved the exposure rather than removed it. Aztec keeps proving permissionless and your private inputs on your device, thereby avoiding any exposure.

The economics land in the voter's favor too. As an L2 network, Aztec spreads the cost of that one L1 proof across thousands of transactions in the rollup, so a vote costs pennies, not the millions of gas a private proof would cost verified alone on Ethereum.

Settled on Ethereum, verifiable by anyone

A prover then posts the epoch proof to Ethereum's proven chain, and the Aztec state is final. Ethereum verifies one proof and inherits the correctness of everything inside it. Aztec extends Ethereum and settles to Ethereum, so your hybrid transaction carries Ethereum's security without carrying Ethereum's enforced transparency.

Anyone can now verify that the result is valid and that every counted vote was legitimate. No one can see how any individual voted. The tally is on the shared ledger where it belongs, and your ballot stayed yours the whole way through.

What this unlocks

For the voter, their ballot was never a broadcast. The candidate you chose stayed yours, with no record tying your wallet to a name for anyone to read later, and you can still check that your vote was counted and the result is honest. You took part without your choice becoming data for systems built to act on it.

For a founder, the election app in this walkthrough is easy to implement without needing to build extensive custom code. Secret ballots with a public, verifiable count, in one contract, is a product category that opens up only because the boundary is programmable. You can build governance, elections, and polls where people vote without fear and the result still proves itself. And of course you can build anything that requires both public and private state to work seamlessly together. 

For an infrastructure provider, the same machinery serves clients who need a result they can stand behind without exposing the people who produced it. Selective disclosure lets a client prove exactly what a counterparty needs to see, the count and the integrity of the process, and protect everything else, on their own terms. That is a guarantee a transparent chain cannot make.

A real vote needs two things at once: a secret ballot and a count anyone can check. A transparent chain makes you give up the first to get the second. On Aztec, you get both. The tally settled on Ethereum for anyone to verify, and how you voted stayed yours. The infrastructure is in place, what will you create with it? 

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Aztec Network
Aztec Network
23 Jun
xx min read

The Devil's Bargain - Privacy Without Credible Neutrality

Crypto is in a long night. It is no secret that the industry is facing challenging circumstances and there has been a clear consolidation of the industry. Right now we are seeing a focus on real traction, demonstrable value projects shipping practical solutions that will meaningfully reach users. 

Some of that discipline is overdue. However, in times like these the properties that made crypto structurally different begin to look expendable. Decentralization slows you down. It makes upgrades harder. It makes institutional sales harder. It removes the control surfaces that the existing financial world knows how to buy.

We used to accept those costs as the price of building something durable. But, in a famine, they look like unaffordable affectations. Discarding them wholesale, however, is like selling the land out from under our feet.

Permissionless, uncensorable transaction networks with rich composability - this is the clay from which our industry was grown. The long term commercial health of our industry depends on preserving these properties in an age of privacy and institutional adoption.

These trade-offs become more challenging and pernicious when privacy is involved. Privacy is the narrative for crypto in 2026, and for good reason. It’s the missing piece that will deliver the traction and real use-cases that the industry so desperately needs. 

The challenges of decentralization multiply under the constraints of privacy and what we are seeing in the industry is not a pivot, but a complete capitulation of all of the differentiable value that made crypto valuable.

I have spent nearly a decade building a network that marries programmable privacy with decentralization. A network where users keep their data, where applications are composable with one another, where transactions can settle without a privileged party learning everyone’s business or deciding which products are allowed to exist. That required new cryptography, new programming models, new state architecture, new wallets, and a fairly insane number of tradeoffs that are invisible until you try to build the thing yourself. There are easier products to ship. 

A centralized privacy service can give institutions something legible quickly, replicating how the existing financial sector works: a responsible operator, a viewing key, a way to block transactions, a way to explain the whole thing to a risk committee. Some of these products will be useful. Some will be good businesses. But they are not the thing we came here to build.

The Devil’s Bargain

Institutional and enterprise adoption is one of the core growth areas in this crypto-winter and the playbook is simple: use the language of crypto as a skin-suit to sell products and services that pattern match onto existing financial rails, with their need for complete visibility, censorship, centralized network operators and all of the liabilities this incurs.

This is a tempting bargain because it shortens the path to adoption. It gives buyers and regulators a shape they understand. A company. A contract. A switch. But the moment you accept that bargain, the system changes character. It may still be encrypted. It may still contain proofs. It may still call itself private. But, it now behaves like and is an operated service. 

There is a party with privileged knowledge and privileged control. Builders must shape themselves around it. Institutions negotiate with it. Regulators may pressure it. Attackers target it. Users ultimately depend on it. By a backdoor I mean something specific: a network or protocol-level viewing key where the product developer does not control who can see their users’ data, especially when paired with network-level controls that can block transactions or ban smart contracts entirely. I do not mean application-level controls. I do not mean user-authorised disclosure. I do not mean a dapp deciding that users must prove something before using it. Regulated applications will need rules. The issue is that the disclosure boundary of your application belongs to somebody else, and the same layer that sees can also decide whether your users are allowed to transact. In short, users lack a platform that has credible neutrality.

The Platform Risk

Privacy on top of centralized rails is fatal. If one party can see everything and stop anything, that party may be treated as responsible for seeing and stopping.

This compounds into substantial platform risk. If an entity builds on top of such a system they must surrender visibility and control to the network operator to satisfy their liabilities without consideration for yours. Decentralization and ultimately credible neutrality is the difference between whether you own durable infrastructure or are renting a service whose rules can change on a whim. Worse, you cannot “just build things”. For novel transaction flows approval must be sought and granted. Tell me, would Ethereum have grown if every smart contract deployment required approval from the Ethereum Foundation?

Privacy needs the same freedom. A private credit market, for example, touches identity, collateral, repayment history, payment flows, liquidation logic, lender disclosures, auditor access and borrower privacy. If every component lives inside a different permissioned service, each with its own operator and viewing assumptions, that is a bureaucratic friction that negates blockchain’s core value proposition; composability.

A decentralized and credibly neutral privacy network prevents the settlement layer from becoming the single place where all surveillance and censorship obligations naturally accumulate. It allows product developers to scope their code to satisfy their own narrow requirements without consideration for the obligations of a centralized operator.

Building for credible neutrality

A lot of today’s privacy narrative treats architecture as if it were a detail. It is not. You cannot take a transparent ledger, staple confidentiality onto the edge, add a viewing key for comfort, and expect to get programmable private infrastructure.

If the state model is not private from the ground up you get wrappers, third party tools, data custodians, ad hoc disclosure paths and a pile of assumptions that every application drags into the next. Developers do not get a normal programming model where private contracts can call private contracts and users keep state on their own devices. They do not get composability.

The difference matters. In a real private execution environment, users generate transactions locally. They do not outsource their intent to a third party who learns what they are doing. Private contracts interact through a state model designed for privacy. The network settles proofs without becoming the party that knows everyone’s business. Privacy is part of the architecture.

This is why Aztec has taken so long. We built something that makes programmable private state and decentralised settlement live inside the same system. That means proving systems that run on consumer hardware, a transaction architecture built around local private execution, and a programming model where privacy is idiomatic and just works out of the box.

A centralized service can skip much of this. It can hold the key, run the prover, approve the flow and call the result privacy. It gets to market faster because it is not trying to arrive at the same place.

The edge

Adding decentralization does not make obligations disappear. Applications, issuers, frontends, custodians and regulated businesses will continue to exist in a web of obligations and responsibilities. Anyone pretending otherwise is unserious.

The question is where those obligations live. If they are pushed into the settlement layer, the settlement layer is no longer credibly neutral. It needs visibility into everyone and controls over everyone. 

The better answer is selective disclosure. Users and applications should prove specific facts to specific parties for specific purposes. A regulated application may need to know that a user passed a check, that a transaction satisfies a policy, or that an auditor can inspect a particular flow. None of that requires the base network to hold a permanent key into everyone’s activity.

This will be harder to explain to the existing world. New infrastructure always fails to fit the categories built for the old infrastructure. Bitcoin did not arrive as a neatly regulated bank product. Ethereum did not wait for every lawyer to understand smart contracts. Stablecoins and DeFi forced institutions, regulators and users to develop new language around rails that kept existing.

If the standard for privacy infrastructure is to plug into the old world without changing anything, the answer will always be a service with a backdoor. And the result will be to catch crumbs falling from the tables of the old world.

The market worth building

The market we should be building is, well, a market. A private financial system that compounds: assets, liquidity, identity, credentials, credit and applications interacting through a shared settlement layer without forcing users to surrender their data to whoever sits in the middle. 

Traditional finance is built out of vertically integrated information silos. Those silos are its moat. Banks, exchanges, custodians, payment processors and data brokers all benefit from controlling the information that flows through them. A global private settlement layer attacks that advantage directly. It lets liquidity and credentials move while outsourcing information custody to neutral cryptographic infrastructure. 

A company wants a moat. A settlement layer wants surface area. A permissioned privacy provider can ration access, raise fees, exclude applications, shape disclosure rules and define acceptable use around its own risk tolerance. These are products pretending to be networks, and not durable financial infrastructure. What bothers me is this compounding category confusion. Networks adding protocol-level viewing keys and transaction controls are using the same language as decentralised programmable privacy, and commentators are treating them as variations of the same thing. They are not.

We have spent nine years walking the hard road. Now, just as we are close, the market has lost faith. Everyone is reaching for whatever lifeline looks immediate. Some of those lifelines will be real. Some will make money. But if crypto responds to its long night by rebuilding financial privacy as permissioned services, then we will have survived by surrendering the property that made the industry worth building.

Markets can grow when the platform is removed from the position where it can dictate the rules. It would be perverse to forget that lesson while building privacy, the domain where control over information matters most.

The land we till

Crypto is in a famine. The land is struggling. We could sell our land for a pittance and survive the season. But the famine will pass, and when it does the land will blossom again. Without the land we are nothing.

We have struggled immensely to create a permissionless network that can marry privacy with decentralisation: an indestructible network whose users cannot be surveilled and whose transactions cannot be censored. This is the soil we have to grow our crops. To surrender a backdoor or a centralized operator for temporary relief is to sell our land for the price of a stablecoin. And we cannot sell the land.


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