The response was overwhelming:

• ~22,000 visitors to the site.
• ~7,000 uses of the Slincode app.
• ~2,400 YouTube views of the intro.
• Made the front page of Hacker News.
• 100+ reactions on dev.to.
• 57 retweets of the launch.

This has been a humbling experience and I'm grateful to every person who checked it out and those who gave me feedback. Thank you!

Since then I've been recording screencasts for getting started with technologies like React, Vue.js, and SVG live-coding in Slingcode. You can find the screencasts at slingcode.net/screencasts.html or on the YouTube playlist embedded here:

I hope this tool and the screencasts are useful to you.

Enjoy!

I thought back to when I was learning to code with my mother on our Apple IIe. The computer was ours. The code was ours. The data was ours.

I thought back to shareware. First diskettes and then FTP. I thought about my first website and those first Multi User Dungeons. Nethack and newsgroups and bulletin board systems. Trading ware with friends. I remembered running Linux for the first time on a Pentium. The freedom and power of getting to do what you want with your computer and a network connection. How can we take our computers back?

I started thinking about what a personal computing platform would look like today. A platform that a kid could jump right into and start coding. A platform where a kid could build cool stuff without asking for permission. Systematic convex tinkering with computers. Where they could own their software, and their data, and their device.

A way to make, run, and share web applications without needing site hosting, SSL certificates etc. An app repository and a text editor in a single web app. A way to share apps peer-to-peer, directly between trusted friends, family, and associates.

Then I re-read this:

• Personal computers – in the original visions of many personal computing pioneers (e.g. many members of the Homebrew Computer Club), the PC was intended as personal property – the owner would have total control (and understanding) of the software running on the PC, including the ability to copy bits on the PC at will. Software complexity, Internet connectivity, and unresolved incentive mismatches between software publishers and users (PC owners) have substantially eroded the reality of the personal computer as personal property.

This desire is instinctive and remains today. It manifests in consumer resistance when they discover unexpected dependence on and vulnerability to third parties in the devices they use.

Nick Szabo in Trusted Third Parties Are Security Holes.

This is the important thing. It has to be self sufficient. It has to work properly whilst depending as little as possible on third parties.

Last Monday I started building this. It's called Slingcode. With it, you can write, run, and share your own web applications directly in the browser. You don't need any build system, hosting provider, SSL certificate, or any thing else. You don't even need an internet connection. Just a web browser and the single HTML file containing the Slingcode web app.

Stay tuned.

Recorded audio is even easier to fake.

In recent years we're discovering that even video, with its high information bandwidth, is easy to fake via carefully trained neural networks.

Seth Godin notes that anybody now has the ability to generate photos of completely fake people:

it's worth confirming the source before you believe what you see

-- Seth Godin

Where does this leave us? How do we reliably confirm the source? Physical reality prevents us from receiving most information first-hand. If most information that is not first-hand can be faked how can we ensure authenticity?

The answer is good old cryptography.

One way to authenticate media with a high degree of certainty is to have it cryptographically signed. This provides a level of reputational consistency. If a president signs every speech they make with a certain cryptographic key then you have a way to check that the president who gave the first speech very likely is the same president gave the latest one.

Much more so than relying on indications of fakeness from the item of media itself. Additionally, the slightest alteration of the media will render the signature invalid and everybody will be able to see that it has been tampered with.

How far away are we from public figures cryptographically signing their statements? From people's phones signing the photographs they take? From organisations routinely signing blog posts, tweets, and everything they output into the world?

Last week I was in Singapore with my friends PVI Collective, consulting with local artists Ekamatra and Drama Box. We were hacking on eachother's artworks in their studio loft above Chinatown. Locative code, smart watches and dark LARP-arts - it was like I'd stepped into a post-cyberpunk William Gibson novel somehow.

I also gave a talk at the Singapore JS meetup on Bugout, the library I wrote to help build decentralized web applications. You can watch the talk here:

https://www.youtube.com/watch?v=zfPX-dR6LeM

This trip gave me the headspace to think about priorities and progress on my side projects. At the moment I'm focused on two main things apart from paid work.

Firstly, I'm working towards the launch of the paid version of SVG Flipbook, an SVG animation utility for people using Inkscape and Illustrator. Hopefully this will be ready for launch in the next couple of weeks. This is a project I started a while ago and I'm coming back to now.

Secondly, I had some time to think about and work on WebRTC Signaling Mesh, which is coming along nicely. It will be a way to do WebRTC signaling without resorting to centralized services. I've had the design floating around my head for a long time and I've finally begun implementation. Will hopefully have an update with progress on that soon.

Thanks for tuning in!

The Bugout Box is a decentralized web appliance. Its a Raspberry Pi that any browser can connect to from anywhere in the world over WebRTC - a censorship-resistant way to serve data, APIs and apps.

Examples of things you can do with the box:

• Seed WebTorrents.
• Run decentralized Bugout server apps.

Head to bugout.network/box if you want to find out more and sign up to the pre-release list.

Security BSides

This Sunday, September 22nd, Perth, Western Australia.

I'm presenting "Bugout: practical decentralization on the modern web." It's a talk about the library I built on top of WebTorrent for building web based decentralized systems.

Clojure eXchange

December 2nd-3rd, London, UK.

I'm giving a keynote: a show and tell of the multitude of strange things I've been building with the Clojure[Script] family of programming languages, and how Clojure enables the bad habit of starting way too many projects. I'll also give an update on Thumbelina, the tiny MIDI controller I've been working on with my friend Dimity.

Linux.conf.au

January 13th-17th, Gold Coast, Australia.

I'm talking about Piku, and how it helps you do git push deployments to your own servers. I've made a bunch of contributions to this open source project in recent months. I've personally found a huge productivity gain from being able to deploy internet services without having to think too much, and I'm excited to show others this too.

Here are some notes I took on his talk:

• Philosophical inspiration to Cypherpunks who invented Cryptocurrency:

  • Ayn Rand: Galt's Gulch - independence from corrupt institutions.
  • Tim May: "protect yourself with cryptography" (cyber Galt's Gulch.)
  • Friederich Hayek: Institutions of property, contracts, money are actually important to human freedom.

• Use computer science to minimize vulnerability to strangers.

• Non-violently enforce the good services of institutions.

• "Try to secure as much as possible" not just communication.

• Cryptography: only secures communications from 3rd parties.

• David Chaum: let's apply this to money too.

• Centralization problem remained in digital cash startups.

• Bad assumptions in computer security: trusted third parties like certificate authorities are secure.

• Trusted third parties are security holes.

• Centralization is insecure.

• E.g. Communists were able to get stranglehold with just control of railroads, newspapers, radio.

• Gold is insecure

  • Spanish looted Aztec gold, pirates looted Spanish gold.
  • Part of end of gold standard was German U-boat threat to British gold transportation.
  • Franklin Roosevelt's government confiscated gold.
  • In modern times xray machines detect gold easily.

• Decentralization per computer science is much more automated & secure than traditional security.

• CS decentralization can only replace small fraction of traditional security but with very high cost savings.

• Traditional security isn't the protocol itself, requires strong external law enforcement.

• Computer security can be secure across national borders instead of siloed inside jurisdictions.

• Cryptocurrency helps solve this through decentralization.

• Separation of duties: several independent people to perform a task to get it done.

• Each node as independent as possible.

• E.g. crude measure of independence: geographic diversity of nodes.

• Number of nodes is only a proxy measure of decentralization.

• Smart contract:

  • Long lived process or "distributed app".
  • Acts like a contract.
  • Performance, verification etc.
  • Generally 2 parties + blockchain (replacing TPP).

• Wet code = traditional law. Dry code = smart contract.

• Law is subjective, enforced with coercion, flexible, highly evolved.

• Smart contracts are mathematically rigorous, cryptographically enforced, rigid, very new.

• Law is jurisdicionally siloed, and expensive to execute.

• Smart contracts are super-national & independent and low cost.

• Seals in clay/wax were important when writing was invented: signature + tamper evident.

• Modern seals at e.g. crime scenes: sealing door, evidence bag with numeric identifier.

• Blockchain can keep secure log with both semantics (serial number) and proof of evidence (photo hash).

• Put proof of evidence on blockchain as well as semantic reference for contract code to interface with.

• Can secure physical spaces with same mechanism.

• Proplets: blockchain can tell them which keys have which capabilities.

  • For almost any valuable property that can be controlled digitally
  • Example: Auto-repo collateral upon contract breach.
  • Example: creditors without access to offshore oil rig used as collateral.

• Recent project:

  • Trust minimized token: secure property titles, colored coins. Securing transfer of ownership.
  • Trust minimized cash flows (dividends, coupons, etc).

• Idea: social networks for blockchains. Execute payment swaps & smart contracts after linking social accounts together.

• Let's try to think about security more broadly instead of only encryption.

• Let's try to protect everything that's important to us, without centralization.

In 2014 Arvid Norberg and Steven Siloti came up with a BitTorrent extension called BEP44. The basic purpose of BEP44 is to allow people to store small pieces of information in a part of the BitTorrent network called the DHT. The DHT ("distributed hash table") is a key/value lookup table that is highly decentralized. Prior to BEP44 it was used to look up the IP addresses of peers from the hashes of the torrent they were sharing.

BEP44 introduces two new ways of storing key-value data in the DHT. The first is the ability to look up small bits of information keyed directly on their hash. The second allows for the storage of cryptographically authenticated data, keyed on the public key. What this means is if you have some public key K then you can look up authenticated blobs of data stored in the DHT by the owner of that key. This opens up a variety of useful abilities to people building decentralized applications.

For instance when you distribute a file on BitTorrent it is immutable - you can't change it - but BEP44 provides a way to tell people "hey, there is a new version of this file that I shared before which you can find over here" in a secure and authenticated way. It turns out that this basic mechanism can be used to build a wide variety of decentralized, authenticated functionality and people have built cool experiements like a decentralized microblog using it.

The purpose of this post is to show you how the datastructure works and how to apply it more widely in your own software.

If you just want a working implementation you can use, check out the decentral-utils JavaScript library which has a single-function implementation of the datastructure and algorithm which you can use in the browser. Web browsers unfortunately do not have direct access to the BitTorrent DHT, but the library is still useful for authenticating up-to-date data blobs that are passed around between browsers, for example over WebRTC.

What's good about BEP44

Most of the advantage of BEP44 comes from the cryptographic signing. What this offers is a way for somebody (let's call her Alice) to verify that a piece of data from somebody else (let's call him Bob) is authentic and that it has not been tampered with. You can do cryptographic signing without BEP44 of course.

However, the BEP44 specification brings some other features for building decentralized systems:

• Namespacing against a public key.
• Replay attack prevention.
• A compare-and-swap primitive.

The namespacing feature means that a single public key can have a bunch of different data blobs that others can authenticate. Because the datastructure is keyed on both the public key and a "salt" field, a single public key can store multiple blobs with different "salt" values as a sub-key.

Replay attack prevention is accomplished with the sequence field, which in BEP44 is a monotonically increasing integer. The way a replay attack works is some adversary keeps an old copy of something you have signed and then when you are offline they send it again and pretend its a new message. Because the message is signed with your key people are fooled by the adversary into thinking the old data is current. In BEP44 the sequence field prevents this because the adversary will have a data blob with a lower sequence number than the last one you shared and they can't generate a new blob with a higher sequence number because they do not have your private key. So the replayed data blob will be rejected.

Finally, the compare-and-swap primitive works by specifying a previous sequence number that the current data blob should replace. Peers will reject data blobs which don't replace the current sequence number they hold. In this way it's possible to acheive basic synchronisation and atomicity of data blobs. Using compare-and-swap guarantees that the new value you want to insert into the DHT can be calculated based on up-to-date information.

Example usage

Imagine you take upon yourself the small task of building a decentralized social network. Users have a timeline of posts which they have written. When they write a new post it should be appended to their timeline and people should see the updated timeline with the new post.

In the centralized social network case this is easy. The social network provider TwitFace has an internal copy of the poster's timeline to which they add the new post. Followers are notified of the update and the updated timeline is sent to them by the provider. The way the authentication works in this case is the original poster has logged in with their password and so the provider knows who they are, but the receivers of the update must trust the provider.

How do readers know that the post is authentic and comes from the original poster? The reader must trust the provider completely. They must trust that the provider is showing them the authentic timeline of messages, that the messages have not been modified, that fake messages have not been injected into the timeline, that new messages have been added to the feed in a timely fashion, and that no message has been censored and removed from the feed by the provider.

The problem with this model is that trusted third parties are security holes. Centralized social networks do modify things people have said. They do inject posts into people's timelines (ads and worse). They do change the order and timing of posts to suit their own goals. Perhaps worst of all, they do censor posts completely.

I won't get into the politics of censorship. Suffice it to say that censorship is fine and good right up until the point where your values differ from the entity doing the censoring. Values change, mysterious outside influences are myriad, and few people have complete alignment of values even with our noble corporate overlords in Silicon Valley.

The basic goal here is that if you've chosen to read somebody's timeline you can trust that the feed of posts you are reading from them is authentic and complete and as they intended.

Happily, we can use the BEP44 technique to route around the trusted-third-party centralized-social-network-provider security hole. BEP44 provides a way to receive an update and verify it cryptographically. It provides a way to know that this is the latest and complete version. It allows the verifier to do this using only the received data structure without requiring any kind of secret server side logic or password based authentiction like you would find in a centralized social network.

How it works

At the heart of the algorithm is a small datastructure which can be shared, updated by the sharer, and then shared again. Receivers can verify the updates are authentic. The idea is that you can share a small piece of authenticated data which points to a larger piece of immutable data. For instance, you can share an authenticated datastructure with the hash of a torrent containing all of your posts (an RSS feed for instance). Receivers then know that torrent is the current representation of your timeline of posts.

The datastructure has the following fields:

• value
• seq
• cas [optiona]
• salt [optional]
• pubkey
• signature

When a verifier receives a copy of this data structure they perform the following checks:

• Is the size of the value field below the maximum size?
• Does the value conform to the expected format / encoding?
• Is the seq number higher than the last seq number I saw (if any)?
• If cas is present does it match the previous seq number I have?
• Is the attached signature made with the private key corresponding to the attached pubkey over the datastructure's value, seq, and salt fields?

In this final point they are checking that the structure has been digitally signed with the author's private key. This is accomplished by concatenating salt + seq + value and then checking that the signature is valid for that data and the given public key.

In this way verifiers can know that an update from a known keypair/identity is authentic and current.

Read more posts on the subject of cryptography & decentralized systems.

In this 15 minute tutorial we're going to build a simple decentralized chat application which runs entirely in a web browser.

All you will need is a text editor, a web browser, and a basic knowledge of how to save HTML files and open them in the browser.

We're going to use Bugout, a JavaScript library that takes care of the peer-to-peer networking and cryptography.

• If you just want the files, download index.html in this repo.

Get the full tutorial on GitHub

Read more posts on the subject of cryptography & decentralized systems.

Hashcash is a mechanism for defending against spam and denial-of-service attacks in email and other decentralized systems. Implementors of systems using hashcash face the issue of how to set the proof-of-work difficulty. A general solution to this problem is given which can be used to predictably allocate resources in decentralized systems where individual nodes each contribute finite resources. Some emergent effects of this solution are explored.

Hashcash

Nodes in decentralized systems face two closely related issues around the use of their resources. The first problem is that of how to fairly allocate resourcess between connecting clients, and the second problem is that of how to protect themselves against resource depletion attacks. Some systems, like Bittorrent, largely sidestep these issues because the goals of participants (download the data quickly) are aligned. In other systems such as email, nodes are vulnerable to resource depletion attacks like spam and denial-of-service (DoS) attacks.

In 1997 Adam Back proposed an ingenious & practical scheme for countering DoS attacks and spam emails called hashcash. This scheme was famously referenced by, and used with some tweaks, in the software which launched the cryptocurrency revolution: Bitcoin.

As noted in the 2002 Hashcash paper the idea of using a proof-of-work in this way predates Hashcash in the work of Cynthia Dwork & Moni Naor, 1993 and later in the client-puzzles work of Ari Juels & John Brainard, 1999.

Adam Back's implementation is notable because it was based on a modern widely implemented cryptographic hash function, used a cleverly simple leading-zeroes hash collision mechanism which is verifiable with the naked eye, and most importantly of all it came with concise source code which anybody could compile and run ("cypherpunks write code").

The common idea behind these proof-of-work schemes is that you do not allocate resources to the processing or storage of incoming messages unless the sender has proven that they have done a certain amount of computational work. A spammer or DoS attacker is forced to multiply the work required by the number of attacking nodes they are utilising making it more expensive for them to attack. The key to the proof-of-work mechanism is that it is easier for the receiver to verify the proof than it is for the sender to construct the proof, and secondly, that the receiver can specify how much work the sender needs to perform in order to be considered. In Hashcash the construction of proofs-of-work and their more efficient verification is accomplished with a basic building block of cryptographic systems, a computational trapdoor: the hash function. The second key to proof-of-work, that of the receiver specifying the difficulty to require of clients, is not clearly addressed in this body of work. Here we'll describe a general solution to the problem of setting PoW difficulty and explore some emergent effects of the solution on decentralized systems.

The difficulty of calibrating hashcash difficulty

In Dwork & Naor (1993) they say:

There is a pricing authority who controls the selection of the pricing function and the setting of usage fees.

And later, in the section on the Fiat-Shamir signature scheme:

A reasonable choice is k = 10.

Where k here is equivalent to what we now call the difficulty in cryptocurrency proof-of-work contexts. Basically "here's a reasonable value to use on today's computers". This strategy is not tenable in many decentralized systems because a pricing authority, or "trusted third party" is an inherent security risk (Szabo, 2001).

In Adam Back's 2002 paper there is a section called "Dynamic Throttling":

With interactive hashcash it becomes possible to dynamically adjust the work factor required for the client based on server CPU load.

But it's not clear exactly how much difficulty should be assigned in proportion to the server CPU load.

Futher, on the "Bitcoin" page of hashcash.org:

Hashcash difficulty is static and eroded by Moore's law currently 20 bits.

Again we see a "reasonable value to use on today's computers".

In "How bitcoin uses hashcash":

Hashcash was expected to adjust difficulty every 6 months or year, manually based on observed moore's law estimates. It was proposed that this be measured against a benchmark machine by some vague/undefined human consensus process and broadcast via signed difficulty update headers, and/or long TTL DNS TXT records.

He further points out that Bitcoin's "automatic inflation control" innovation has solved this problem for the cryptocurrency case.

In the 2002 paper again we see the following in the section on mitigation of TCP connection-slot depletion, which concludes with:

Connections will be handed out to users collectively in rough proportion to their CPU resources, and so fairness is CPU resource based (presuming each user is trying to open as many connections as he can) so the result will be biased in favor of clients with fast processors as they can compute more interactive-hashcash challenge-response tokens per second.

The implementation detail of exactly how much difficulty to require per connection slot is again left as an exercise to the reader. Adam Back's original hashcash announcement on the Cypherpunks mailing list makes reference to a fixed difficulty of the first 20 bits of zeroes and as noted in the Wikipedia page on hashcash this becomes a sort of de-facto standard reference value.

Existing solutions

How high to set proof-of-work difficulty is of course not completely vague and unsolved. Bitcoin famously pits miners against each other using a hashcash difficulty that is adjusted every two weeks to a value computed from previously achieved hash difficulties. Referring back to the TCP connection slot depletion example in the 2002 paper, we can think of the Bitcoin miner hashcash use-case to be a slot of size 1 that is only available to be filled every 10 minutes. Hash rates are forced upwards by competition for that single slot between miners who will be rewarded under the incentive model of the Bitcoin system.

There is another mechanism in Bitcoin that we can also borrow from for decentralized systems (absent a fully functional and carefully engineered global store-of-value system called a Blockchain); the fixed block size. In Bitcoin the size of each block storing transaction data, and therefore ultimately the number of transactions, is fixed. This was a point of contention for some time amongst Bitcoin enthusiasts and stakeholders but in the end those supporting a fixed block size had their day and Bitcoin today retains a fixed block size into which transactions from the mempool must be squeezed.

Every Bitcoin transaction is accompanied by a fee paid to the miner who mines the block in which it is included. To decide which transactions should be accepted into the block they are minting miners naturally refer to these fees out of financial self-interest, to ensure they capture the highest aggregate fee possible. When a new block is minted a miner will sort the transactions from those with the highest fee paid to those with the lowest fee paid. Then they will insert into the block the top-N-by-fee transactions that will fit into the fixed block size.

So we have three models to build our solution on: the TCP connection slot example, Bitcoin's automatic inflation control, and Bitcoin's fixed block size.

Consider that in all three of these examples the fundamental resource being allocated is subject to fixed constraint, whether it is TCP slots, Bitcoin issued per 10 minutes, or the transaction-processing capacity of Bitcoin nodes. It is in fact always the case that there are resource limits placed on networked services even if that limit is sometimes so high as to give the illusion of being unlimited. Take for example the original use-case of hashcash, the email inbox: here the limited resource is your own time and attention. There is some practical upper bound on that resource such that you would be fundamentally unable to check your email if you were receiving e.g. 10,000 emails per hour. This is also true of the resources of nodes in any decentralized system.

We can use the fixed block size model in combination with hashcash to balance load and distribute resources efficiently in decentralised systems with automatic difficulty adjustment and without requiring a full cryptocurrency blockchain implementation or trusted central authority.

Fixed resource hashcash calibration

The "fixed block size" mechanism can be used more generally in decentralized systems to protect nodes from resource depletion and deterministically allocate resources by conducting a "hashcash auction" between clients over the available resources.

The hashcash auction works as follows:

• Each node specifies the number of clients they can comfortably serve as R, given their resource limits on disk, bandwidth, CPU, etc. This can often be computed based on simple measurement of the resource(s) in question.
• Nodes issue HMAC'ed tokens ("symmetric key certificates" in the hashcash paper) to clients who request access which include a timestamp so that they can be expired.
• To use a node's resources, clients must bid for a slot by performing a PoW over these tokens.
• Nodes publish the upper, lower, and median PoW currently acheived by clients occupying the available resource slots R.
• Connecting clients are added to the client pool, which is sorted by PoW difficulty-achieved, and only the top R by difficulty are serviced.
• (Optionally, the lower-bound minimum PoW can be capped, to emulate the same basic DoS protection that hashcash provides.)
• (Optionally, dependent on use-case, clients are disconnected after some time and required to re-bid for a slot).

In short, you decide how many clients you can service at a time (R), then sort connecting clients by their PoW height, and then deny access to, or disconnect clients who fall outside the top R.

Note that the server is not required to store any information relating to the the tokens it gives out to prospective clients because it can use the HMAC to verify the tokens it has issued and the time at which they were issued. In this way the issuance of tokens and their expiry becomes very cheap for a service node.

The natural consequence of a system like this is that clients must compete for available slots to use the node's resources, and a node does not need to specify exactly how much PoW each client must perform. Instead clients arrive at this value through an emergent bottom-up bidding process. The node no longer has to guess at "20 bits" and can instead honestly say "look, here is how hard other clients are working, if you want to access my resources you'll have to do better".

The clients themselves determine the proof-of-work difficulty setting in an ongoing auction where they must make a higher bid than other clients in order to be allowed access to consume the fixed set of resources, or go elsewhere.

To cope with large numbers of clients and provide a higher level of granularity in the PoW ranking, a simple less-than operator can be used so that clients can bid for intermediate values. For example 0x01fe < 0x01ff so a client bidding with a hash of 0x01fe would beat a client bidding with a hash of 0x01ff.

Properties of this system

What this restricted-resources scheme gives us is a way to measure the sacrifice (Nick Szabo, 2002,2005) that a client is willing to make for the privilege of using a node's resources. As with the human measure of time sacrificed in Szabo's article, it is not an ideal way to measure the importance of incoming client connections, but it is better than existing systems which are essentially a lottery, or in some cases "let the bad guy win". It is not perfect but rather a "proxy measure" which gives a service a deterministic ranking system of roughly those clients who most badly want or need access.

At first this system seems unfair - those clients with the most CPU are able to dominate access to resources - but notice that it is much fairer in the worst case where a motivated legitimate client can still get access under DoS or spam attack by working hard to out-compete just a single instance of the attacker. Given enough motivated legitimate users there now also exists a way for them to collectively out-compete a spam or DoS attack. In other words, utilising this scheme has a better failure mode than the free-for-all which normally prevails in networked systems.

Another way of looking at this system is that we have inverted the security aims from "keep out the bad guys" to "keep in the good guys". By giving motivated users a way to express the strength of their need we are able to keep those clients connected who signal the most need to access to the resources. We are able to preference those users who will actually utilise the resource with a higher probability than those users who will waste it, because wasting it is now costly.

A further benefit is that the service utilising this scheme is protected from hard failure. In the worst case failure mode an attacker is only able to deplete resources up to the comfortable hard-limit R specified by the server, meaning the server is never placed under such high load that it entirely crashes and burns, so long as R is specified within serviceable bounds.

In the real world there is always a constraint upon resources, and it is often the case that those wanting to consume a resource must hustle harder to get access. This scheme has an honesty that mirrors the reality of constrained resources rather than pretending that resources are infinite, which is what many historically optimistic decentralized systems do to their detriment. A computer cannot consume more disk space than 100% of disk, and it cannot utilise more CPU time than 100% of CPU time. Modern network services are often deployed without acknowledging these real constraints and with vague hand-waving about "scaling" by adding more machines to accommodate load. That's fine for Silicon Valley backed ventures with money to burn but decentralized systems are often run by volunteers contributing their precious personal resources. Silicon Valley assumptions on volunteer run networks are likely to lead to ruin.

Finally, this scheme has additional emergent benefits in the case where clients have a choice of decentralized service providers. This case is common in decentralized systems where some subset of participants run their own "full nodes" which other clients may then utilize.

Bottom-up load balancing

The further interesting consequence of the hashcash auction scheme arises when we have many service nodes providing the same decentralized service. In the auction-determined proof-of-work difficulty setting, clients now have a clear signal about which nodes are under most heavy use and can freely choose those nodes where the PoW, and hence the resource usage, is lower. Given a choice between node X which requires a very high PoW and node Y which requires a lower PoW, a client will naturally try to connect to node Y first.

The result of clients seeking lower-difficulty service nodes is that they spread themselves more evenly across the available servers. This less clustered network graph can lead to a healthier more decentralized system without requiring any kind of central coordination to acheive it. This emergent effect is a sort of bottom-up load balancing system.

Expiry and time preference

Decentralized systems differ in the way in which resources are allocated over time. Sometimes the resource provided by nodes is small and fast, such as UDP packets in Bittorrent's DHT. Other times the resource may be longer lived and more expensive on a per-unit basis, for example disk space storage or a regularly run cron job.

In the situation where resources are long lived and expensive to maintain it makes sense to require clients to periodically re-bid for their access to the resources in question. For example, rather than storing a connecting client's data on disk indefinitely the service node could require that a client re-bids for the disk space once a month or once a year.

A variant on this would be to use a time-based weighting factor on the PoW submitted by connecting clients such that the PoW of more recent connections are weighted higher during bid sorting than older connections. For example if you wanted to preference more recent connections instead of sorting on PoW you could sort on some variant of PoW / (t + 1) where t is the time since connection.

In the parlance of economics this introduces a time preference into the system.

Example

Let's close with an example.

Imagine a decentralized social network where users are able to make posts and collect and publish replies to those posts. This can be accomplished in a decentralized way by representing users as key pairs and having every post and every reply signed by its owner's key pair to demonstrate authenticity rather than relying on a centralized trusted third party for authentication. While a user is online they can host their own set of cryptographically signed posts for others to download via a peer-to-peer network, and collect replies to those posts in the same way. However, what happens when the user goes offline, turns off their devices, goes to sleep etc.?

One solution is to have "followers" of the user automatically mirror their posts for other users to download while they are offline. This solution is better than nothing but falls short when a user has few followers or only has followers in the same timezone such that they will all be offline around the same time. Segments of the social network would go dark for periods of time leading to a lack of reliability of access and a form of accidental time-based self-censorship. Some audiences may be satisfied with such a constraint as a tradeoff for a higher degree of privacy and overall censorship resistance, but a mainstream audience is unlikely to accept this lack of reliability.

A more robust solution would be to back this up with a set of volunteer run "full nodes" which users could enlist to mirror data and collect replies in their absence. This architecture immediately begs a number of questions around allocation of the "full node" contributed resources of disk space and bandwidth. How do nodes protect themselves against greedy clients taking all bandwidth and disk space? How do clients protect against spammy replies? How do nodes protect against censorship via resource exhaustion and DoS?

Using the "hashcash auction" we can get a reasonable solution to these problems, bringing higher availability to the system. Those nodes which provide the service of replicating user posts would measure how many clients they are willing and able to serve based on system resources. They can each do this by measuring their disk space for example and allocating a fixed amount of disk per user to a fixed number of users R keeping this constraint within the disk space each has available. Each full node then requires connecting users to bid for a storage slot for the hosting of their posts while offline. Only those clients who are able to demonstrate their need the most strongly will be able to use the offline-replication service of a node. When new nodes are deployed clients will naturally tend towards them as they choose the lower PoW requirement relative to nodes which are serving many existing connections and therefore have higher PoW requirements.

Those users who might run a full node, and others interested in the health of the network, also have a clear signal as to the current load on the whole system and the relative need for more nodes by looking at the PoW they require in aggregate. People who might run a voluneer node are also provided an assurance that there is a fixed cost on the resource they will be donating, making it more likely that people will be willing to run full nodes to support the system. Finally, there is an incentive to running a full node as the user could white-list the cryptographic keys of their own devices and those of their friends and family, meaning that those accounts are always given preference over the accounts of strangers, bringing benefit to both sets of users.

Addenda

1. In the spirit of "cypherpunks code" I recently published scrypt-hashcash which provides a basis upon which to validate some of these ideas on the web. For example browser based clients could bid for resources by performing a hashcash PoW using this library and submit it via HTTP, WebSocket, or WebRTC connection to the service conducting the resource auction.

2. It is interesting that Hal Finney touched on a related idea in RPOW enabled BitTorrent and in true cypherpunk tradition offered code implementing his ideas:

Imagine a version of BitTorrent where nodes that have completed their downloading (called "seeders") could earn RPOWs by continuing to upload. And what could they do with those RPOWs? Imagine further that this version of BitTorrent allowed nodes to send RPOWs in order to get higher priority from other nodes which are downloading. Just such an experimental, RPOW-enhanced version of BitTorrent is now available for download.

Read more posts on the subject of cryptography & decentralized systems.

A problem faced by decentralized systems is that of naming things. The problem is best expressed by Zooko's Triangle which conjectured that no single kind of naming system can provide names satisfying all three of the following properties:

• Human-meaningful: Meaningful and memorable (low-entropy) names are provided to the users.
• Secure: The amount of damage a malicious entity can inflict on the system should be as low as possible.
• Decentralized: Names correctly resolve to their respective entities without the use of a central authority or service.

Something as simple as a user handle or nickname becomes a complex issue in a decentralized system. What you ultimately want is something like a decentralized Twitter handle. The handle is a single human-memorable string which points to one and only one user in the system. The first user to register a particular name gets to claim that name. Obviously the case of Twitter handles is not decentralized as it requires the Twitter database as a single point of authority on who owns which name.

As the Wikipedia article points out, and Zooko has acknowledged, the invention of the Blockchain allows for solutions under certain conditions. However the effort required to design, build, and deploy a Blockchain along side your distributed application are quite steep. If you want to use an existing Blockchain implementation then either your users must be invested in it or your app must invest in it and pay for name registrations.

The former situation requires your users to not just install your app but also to acquire some particular Blockchain currency which steepens the barrier to entry for your app. The latter alternative requires your app to fork out for registrations which means the naming system of your decentralized app will fail if you or whichever entity is paying for the names shuts down.

The Keybase Method

In a decentralized system "one user" often means "one or more cryptographic key pairs". The site Keybase offers a good alternative naming system in the situation where "user" means "keypair". The solution is to use existing online identities held at different providers and link them cryptographically to the user's key(s). This means that third parties can verify an identity as follows:

@zooko on Twitter has public key B123h9EAgVdnXWjxUa3N9Amg1nmMeG2u7.

Or to put it more rigorously:

A person who has control of the private key corresponding to public key X also has write access to online service at Y.

A lot of the time when we want a name for something, the latter is what we actually want. We want to know that the existing relationship and trust I have for @zooko on Twitter can be carried over into this new decentralized system I have started using. If I have multiple existing relationships on different services with @zooko on Twitter then I can also verify those and in the decentralized app I can give @zooko the simple name "zooko" (which can often be inferred from usernames on services) and refer to the person with that handle from that point forward.

The way that Keybase does this is to help the user to sign a statement with their signing key saying "I am @name on service X" and then post the text and resulting signature on the service itself and keep a record of where somebody can find the post. If you do this it means you can prove to people that your signing key is associated with some existing internet identity such as your Twitter account. People who want to remain anonymous or pseudonymous with respect to their existing internet identities can simply skip this linking step.

This technique can actually be used to link any internet location that the user has write-access to back to a public key that they have the private key for. You simply sign a statement saying "I have write access to location X." and post it at "location X". Some examples:

• I have write access to https://mccormick.cx/ -> post signed message as textfile there.
• I have write access to https://twitter.com/mccrmx -> post signed message as tweet there.
• I have write access to https://github.com/chr15m -> post signed message as gist there.

Links to the signed post are kept and shared with other users who wish to verify the existing relationship.

The decentralized "Keybase method"

Keybase is a centralized website and system which means that it has convenient integrations and is easy to use, but it relies on their infrastructure and centralized, proprietary software. The good news is it's possible to use the exact same technique that they use in a decentralized system. Doing this gives a user the power to make statements like:

@zooko on Twitter is me (B123h9EAgVdnXWjxUa3N9Amg1nmMeG2u7) on this decentralized service.

And then other people can confidently start using the name "zooko:twitter" in the place of B123h9EAgVdnXWjxUa3N9Amg1nmMeG2u7 in the decentralized system. In fact if the person has signed proof-of-write-access on multiple services and they have the same username on those services we might as well just start calling them "zooko" instead of "zooko:twitter+github". If two zooko's come along then we can just refer to them by their specific service-postfixed names, or some other qualifier, so that we can differentiate when interacting with them - see below for more on solving this "two Johns" problem.

What this means is that "zooko" in the decentralized system becomes a short-hand way of referring to an entity with whom we interact on a constellation of sites. In some ways this is more robust than naming systems on centralized systems (where anybody can "steal" a username by registering it before somebody else) because the alias actually represents the relationship rather than an entry in one specific proprietary database.

So how can we do the same thing in a decentralized system? Quite easily:

For each service the user is on:

• They sign a statement saying they have write-access to their account at that proprietary service with the signing key that they use on the decentralized service.
• They then post the signed statement to the proprietary service.
• When users are connected for the first time each user shares their links to "proof of linked identities" (if any) so that they can verify any existing Internet identities.

No-namers & the contacts list model

What about in the case where somebody does not sign anything to say that they own some existing online aliases? What if your Mum starts using decentralized service X and she does not have a GitHub or Twitter account? What if somebody chooses not to link their existing identities and instead decides to start afresh?

In the real world we don't really get to name ourselves. We can suggest a name to people, but they get to call us whatever they want. This is how nicknames happen and it's a bit of an aberration that in the online world we get to choose one canonical name by which people call us on proprietary services.

What if naming in online systems worked more like the decentralized real world? What if we suggested a name by which somebody could call us, but it was up to them to use it or to choose a different name by which to call us? What if, in this new decentralized service that my Mum has started using, I simply refer to her as "mum"?

If somebody has no existing online identities they can simply suggest a name when two users first connect and trade keypairs. The receiving party can then choose to adopt the suggested handle for that user, or if there is some conflict in their existing set of collected aliases, add a qualifier, or choose an entirely different name that makes sense to them.

This is the "contacts list" model of naming. You can tell me your name but I don't have to use it for your entry in my contacts list. I can adopt the name you've suggested but if I know two "Johns" then I'm likely to add some other qualifier like "Tennis John" and "Football John".

Aside: it would be funny if even DNS worked like this. The first time you loaded Google it would suggest to you "please call me google.com" but you could choose whatever name you wanted to type into the URL bar from that point forward. Norms would form between humans using the internet for what different sites would be called, and sometimes they would be called something different than what the person owning the domain wanted.

I don't think I would use "google.com" for example. I can think of some better names for that site.

Summary

The system proposed here for naming users in decentralized way uses an amalgam of two models: Keybase-style service signing, and the contacts list model.

To summarize the process of managing identities in this system:

• Each user in the decentralized app is identified by one or more cryptographic key pairs.
• A user may set a suggested handle they'd like other people to use.
• Optionally a user is able to add existing identities (Twitter, GitHub).
• Optionally the app helps the user create cryptographically signed back-links from their existing internet identities to the keypair.
• The app stores the user's links to their signed posts if any.
• When user A sees some user B for the first time the client receives the following information about user B:
• Their public key or fingerprint.
• Their suggested handle.
• URLs of their cryptographically signed identity-linking posts.
• The app then fetches user B's identity-link posts and verifies the signatures found there.
• Optionally user A is able to modify or supply their own local name (handle) for user B.
• The app creates a contact entry for user B with this information.
• When displaying user B the application uses the handle (chosen by user A) stored in the contact card.
• Under the hood the application uses user B's public key or cryptographic fingerprint/address to refer to that user.
• In the user interface the contact card is visible appropriately: for example on hover of user B's handle.
• The displayed card shows user B's handle, identity-links with verification status, and public key or fingerprint.

The raw public key or fingerprint(s) can additionally be used to verify real world identity out-of-band such as at key signing parties, personal meeting, or over a known secure channel.

Read more posts on the subject of cryptography.

Web applications often follow a client-server model meaning that there is a piece of software which runs in your web browser (the client) and a piece of software which runs on a server somewhere. I'm interested in this model, where it came from, and where it's going, and I discuss this below. I'll also discuss a new model for self-hosting web applications that I've been exploring.

tl;dr

• To "self-host" a web application means to run the client and server on computers you own.
• Self-hosting is a basic foundation of decentralization.
• The "installable app" model on phones & PCs is quite decentralized.
• Decentralization is robust and anti-fragile.
• How can we bring self-hosting of web apps to everyone?

Brief history of software applications

In the old days most software people ran was in the form of applications that you would download onto your PC and run. This was quite a decentralised model in that you could find whatever software you liked and run it on your machine without asking permission from anybody.

The modern mobile-device app stores and the web have deviated from this model. In the case of app stores you can only download software that is sanctioned by the company who runs the app store. On Android it is quite easy to change the settings on your device to install software applications from any source but this is not the default, and on Apple devices it's very difficult to do.

The web has a more complicated approach. Running software in your web browser is generally as easy as visiting a URL and people are free to run whatever websites and "web apps" they like on their devices. As mentioned above however, the server-side portion of the web application you are using is often running on somebody else's server computer and not in your control.

Over time more and more functionality has moved from server side code into the web browser. Applications like Gmail pioneered this transition. Recent browser technologies like WebRTC and service workers have given a stronger base to build client-only applications on. The "add to homescreen" paradigm, whilst relatively obscure, presents a vector for running web applications on your computers and devices in a similar way to installed applications.

The buzz-word "serverless" captures the zeitgeist: moving more and more of the software off the server and into the web browser client. The reason software developers are generally in favour of this move is because it is easier to deploy code to web browsers than it is to deploy code to servers; it's easier to get users to run that software (just send them a link); and there is less of a maintenance burden if you don't have to keep a server running.

There's another strong reason why client side code is more favourable than server side code and that is because most server code is running on servers you don't own. For example when you run Gmail the server code and all of your emails are completely controlled by a third party and trusted third parties are security holes.

Even "serverless" in the context of web apps just means that the server component requires less infrastructure and is easier for developers to deploy. The code is still running on a machine owned by some third party who is neither the user or the developer.

Whilst this move towards client-side web applications is good in terms of user control, a problem even with "serverless" web applications that run in your browser is that the software can be modified without your consent. When you load up a web application tomorrow the developer might have made a new version with features you don't like and your browser will load the new version automatically even if you wanted the old version. Even worse, because you load a fresh copy of the software each time you use it, a man-in-the-middle attacker might sneak a malicious bit of code into the version you load tomorrow that is not there today.

There are also many use-cases where only a piece of server-side software will do. Generally these use-cases fall under the umbrella of asynchronous communication, such as storing a file for later retrieval, sending a message to one or more people which might only be received when you are offline, syncing state between devices which might be connected at different times etc.

Examples of things the server code will typically take care of:

• Connecting client apps together in real-time.
• Data manipulation functions.
• Proxying network requests.
• Persistent data storage.
• Running periodic jobs.
• Access control.

So the world is trending towards a more fragile situation of centralized control of the software we all rely on. It would be better for humanity if we were instead moving towards decentralized models of software where an individual can run and control the software they want without fear of a fragile centralized system failing or being used against them by powerful interests and hackers.

Self-hosting

So these latter problems are some of the reasons why self-hosting web applications is still a good move in terms of security and control. When self-hosting, all of the software and all of the data is running on your own computers and you don't have to trust any third party to not be evil, corrupt, make mistakes etc. You can choose which software to install and run and when without obstruction. You get the benefits of cloud connected software - shared state between devices and other users - without having to trust a third party.

The main problem with the self-hosting approach is that it is very technical and therefore simply not an option for most users. Some of the barriers to people self-hosting their web software stacks:

• Renting or building a server.
• Installation of server side code.
• SSL certificate management.
• Server maintenance.
• Access control management.
• Securing the server.

These are the types of things that systems administrators are paid a lot of money to take care of because they are difficult and skillful work. These are the things that big companies are doing for you when you use their online services. The unwritten contract we engage in today is that we give big companies control over our digital life and our valuable personal data in exchange for their building, installing, and maintaining the infrastructure code on their servers.

An additional problem is that even in the "host your own server" model there is a lot of centralization and passing around of valuable and private personal information. For example obtaining a domain name and SSL certificate requires central authorities who almost always require a lot of personal information. Not to mention forking over cash. Additionally, even if you host your code on a VPS - as is the case with almost all server side code on the internet - then some trusted 3rd party has unlimited access to the machine you are renting.

Some existing proposed solutions

So the interesting question is whether there is some way to make self-hosting possible for everybody in the same way that PCs and phones made "run the software I want" easy for everybody.

There was a time when a lot of web applications were deployed as simple PHP scripts with HTML, CSS & Javascript for the front-end. This was probably the simplest format for self-hosted applications as it was easy to find hosting providers who would let you run PHP, and installation was as simple as copying files up to a server. I think this is a big reason why PHP has remained popular for so long despite the fact that it's such an awful language with frequent security issues. It simply let you do what you want. These days though, a lot of modern software on the internet is not written in PHP and developers often prefer other langauge ecosystems.

Another idea for self-hosting was the Freedom Box pioneered by Free Software enthusiasts in the 2000s. The idea is pretty good but does not seem to have acheived widespread adoption. My guess is the use cases are still too obscure for most users. what does "run your own XMPP server" mean? Most people have no idea.

A lot of people today are of the mind that "blockchain" is the singular solution to the issue of decentralizing the software we run. In this model you pay in small increments for the server side code which runs on a distributed system shared amongst many participants. You maintain control of your piece of the distributed service and your data stored in it using cryptography. In this model the server component and app state is shared across many computers all at once and you basically pay for a small slice of it.

This is a fascinating idea but I am not completely convinced that "blockchain" will be the future of decentralized software applications because:

• So far it is unproven in all except one application, that of decentralized digital gold.
• There are a number of very successful decentralized systems that function well without any kind of token/monetary incentive (email, irc, http, bittorrent, git etc.) and that original model of decentralization by cooperating parties is still a sound, well proven one. People still use email.
• The blockchain design encapsulates a trade-off of low efficiency for immense security and trust minimization. This means that blockchains are very good at storing and securing transaction data but they are not very efficient at storing lots of other types of data. Imagine having your own dropbox folders also filled with encrypted copies of those of everybody else in the world. It does not scale.

In addition to the above ideas there exist new ecosystems like ownCloud and Sandstorm which enable people to deploy their own "stacks" hosted on their own servers. These are noble efforts at decentralization and self-hosting into which many people have poured a lot of work. The problem with these "host-it-yourself cloud stacks" is that they often still require trust in some 3rd party hosting service, and quite deep technical knowledge. Sure, to a nerd it is easy to upload a PHP script and configure a MySQL server, purchase VPS hosting, install a Let's Encrypt SSL certificate, but it is not always so easy to a nerd's mother, father or friend.

A simple platform for self-hosting

I've been thinking about all of this in recent years. I've been asking myself these questions:

What if running persistent server side code was available to anybody, even non-technical users? What if installing server side software was as click-tap easy as it is on a phone or PC? What if you could run cloud software and servers on the desktop PC in your office, or tablet, or Raspberry Pi on your home network? What does it look like when anybody in the world can self-host their "cloud" software?

Criteria for such a system would be:

• Easy to set up and administer.
• Easy to install and uninstall new services.
• Interfaces easily with web browser client software.
• Secure.
• Completely user-controlled and decentralized.

As I've thought about these ideas I've also been tinkering with WebTorrent. It's a cool piece of technology which is bringing a chunk of the BitTorrent architecture to the web.

Working with this technology made me realise something the other day: it's now possible to host back-end services, or "servers" inside browser tabs.

Instead of a VPS server running in some data center, you have a browser tab running on a spare computer. Instead of clients talking to servers over HTTP they can talk over WebRTC. You build your "backend" service code with the same tech as your client side web app, with HTML and Javascript.

When I realised this it blew my mind. I couldn't stop thinking about it. Maybe the persistent browser tab on the home PC can be the new server for self-hosting users? What if grandma could build out a server as easily as opening a browser tab and leaving it running?

So anyway, I've made this weird thing to enable developers to build "backend" services which run in browser tabs. It's called Bugout and it has been fun to hack together.

Check it out and let me know what you think.

Zooko Wilcox's blog post Lessons From The History Of Attacks On Secure Hash Functions gives us a nice overview of these and I've quoted his concise explanations below. Check out his great post for more detail and history on this topic.

A cryptographic hash function is an important building block in the cryptographic systems that keep us safe in our communications on the internet.

A hash function takes some input data and generates a hopefully unique string of bits for each different input. The same input always generates the same result.

The input to a secure hash function is called the pre-image and the output is called the image.

I use the following key below:

Red for inputs which can be varied by an attacker.

Green for inputs which can't be varied under the attack model.

To attack a hash function the variable inputs are generally iterated on in a random or semi-random brute-force manner.

Collision attack

A hash function collision is two different inputs (pre-images) which result in the same output. A hash function is collision-resistant if an adversary can't find any collision.

Pre-image attack

A hash function is pre-image resistant if, given an output (image), an adversary can't find any input (pre-image) which results in that output.

Second-pre-image attack

A hash function is second-pre-image resistant if, given one pre-image, an adversary can't find any other pre-image which results in the same image.

Hopefully these diagrams help to clarify how these attacks work!

Read more of my posts on the subject of cryptography.

You can simulate a network of peers by opening multiple browser tabs. Each peer can mine blocks and make transactions independently and the resulting blockchain will resolve conflicts correctly across all tabs.

A blockchain works by laying down a chain of blocks of transaction data.

Each block in the chain contains a cryptographic hash with two important properties:

• It proves a link to the previous block.
• It proves that difficult computational work has been done.

The proof-of-work is accomplished by iteratively updating a nonce until a low-probability hash is discovered.

These two properties mean a blockchain is digital amber.

If somebody wants to modify a transaction deep inside the amber it would be very difficult because they would have to re-create every layer of the blockchain by doing as much work as the original process required.

In my browser blockchain the hashing is implemented like this:

(hash-object [timestamp transactions previous-hash nonce])

As you can see the previous block's hash is included in the current block.

The hashing is performed iteratively in a loop until a hash with at least one byte of leading zeroes is found:

(loop [c 0]
  (let [candidate-block (make-block (now) transactions previous-hash new-index (make-nonce))]
    (if (not= (aget (candidate-block :hash) 0) 0)
      (recur (inc c))
      candidate-block)))

This is a Hexadecimal die that you can 3d print. Download the STL file or get the source code on GitHub. Hopefully useful when generating private keys and the like.