Iced tea and related cold beverages, at home and cheap

By Christopher Allan Webber on Mon 01 May 2017

In spring through fall, we always have a couple of pitchers (one decaf and one caffeinated) of "iced tea" in the fridge (though it's not necessarily the tea plant, if you care about that term... personally I think people are a bit snobby about having only one plant be The Acceptable Drink Herb). It's easy enough... just get a pitcher, add some tea bags to it (or loose tea in some sort of reusable loose-tea-holder or something, but honestly I'm too lazy for that) and some sweetener if that's your bag (we've been using stevia + erythritol sweetener stuff, sold as Truvia or whatever) and fill it with water, stirring once.

Now put it in the fridge.

You're done! You now have a delicious cold beverage that you can drink whenever and which costs about as little as a flavored beverage can.

"Tea" I like to drink:

  • Celestial Seasonings makes a lot of "fruity" herbal tea things that are pretty good in combination with black tea. I put in half black tea bags and half peach/raspberry/mint/whatever.
  • Chai, caffeinated or decaf. If you make this with Stevia or whatever, then pour it into a glass with a tablespoon of creamer, that's 1/5 or less the calories of the "iced chai" sold at a common coffee shop.
  • Some combination of: cucumber slices, lime wedges, lemon wedges, mint leaves.

Takes up some space in the fridge, but IMO it's worth it. And hey, better than filling it up with soda probably...

Gush: A stack based language eventually for genetic programming

By Christopher Allan Webber on Thu 06 April 2017

(This blogpost was going to be just about a project I'm working on Gush, but instead it's turned into a whole lot of backstory, and then a short tutorial about Gush. If you're just interested in the tutorial, skip to the bottom I guess.)

I recently wrote about possible routes for anti-abuse systems. One of the goofier routes I wrote about on there discussed genetic programming. I get the sense that few people believe I could be serious... in some ways, I'm not sure if I myself am serious. But the idea is so alluring! (And, let's be honest, entertaining!) Imagine if you had anti-abuse programs on your computer, and they're growing and evolving based on user feedback (hand-waving aside exactly what that feedback is, which might be the hardest problem), adapting to new threats somewhat invisibly from the user benefiting from them. They have a set of friends who have similar needs and concerns, and so their programs propagate and reproduce with programs in their trust network (along with their datasets, which may be taught to child programs also via a genetic program). Compelling! Would it work? I dunno.

A different, fun use case I can't get to leave my mind is genetic programs as enemy AI in roguelikes. After all, cellular automata are a class of programs frequently used to study genetic programs. And roguelikes aren't tooooooo far away from cellular automata where you beat things up. What if the roguelike adapted to you? Heck, maybe you could even collect and pit your genetic program roguelike monsters against your friends'. Roguelike Pokémon! (Except unlike in Pokémon, "evolving" actually really means "evolving".)

Speculating on the future with Lee Spector

Anyway, how did I get on this crazy kick? On the way back from LibrePlanet (which went quite well, and deserves its own blogpost), I had the good fortune to be able to meet up with Lee Spector. I had heard of Lee because my friend Bassam Kurdali works in the same building as him in Hampshire College, and Bassam had told me a few years ago about Lee's work on genetic algorithms. The system Lee Spector works on is called PushGP (Push is the stack language, and PushGP is Push used for genetic programming). Well, of course once I found out that Push was a lisp-based language, I was intrigued (hosted on lisps traditionally, but not always, and Push itself is kind of like Lisp meets Forth), and so by the time we met up I was relatively familiar with Lee's work.

Lee and I met up at the Haymarket Cafe, which is a friendly coffee shop in Northampton. I mentioned that I had just come from LibrePlanet where I had given a talk on The Lisp Machine and GNU. I was entertained that almost immediately after these words left my mouth, Lee dove into his personal experiences with lisp machines, and his longing for the kind of development experiences lisp machines gave you, which he hasn't been able to find since. That's kind of an aside from this blogpost I suppose, but it was nice that we had something immediately to connect on, including on a topic I had recently been exploring and talking about myself. Anyway, the conversation was pretty wild and wide-ranging.

I had also had the good fortune to speak to Gerald Sussman again at LibrePlanet this year (he also showed up to my talk and answered some questions for the audience). One thing I observed in talking with both Sussman and Spector is they're both very interested in thinking about where to bring computing in terms of examining biological systems, but there was a big difference in terms of their ideas; Sussman is very interested in holding machines "accountable", which seems to frequently also mean being able to examine how they came to conclusions. (You can see more of Sussman's thinking about this in the writeup I did of the first time I ever got to speak with Sussman when I was at FSF's 30th anniversary party... maybe I should try to capture some information about the most recent chat too, before it gets lost to the sands of time...) Spector, on the other hand, seems convinced that to make it to the next level of computing (and maybe even the next level of humanity), we have to be willing to give up on demanding that we can truly understand a system, and that we have to allow processes to run wild and develop into their own things. There's a tinge of Vernor Vinge's original vision of the Singularity, which is that there's a more advanced level of intelligence than humans are able to currently comprehend, and to understand it there you have to have crossed that boundary yourself, like the impossibility of seeing past the event horizon of a black hole. (Note that this is a pretty different definition of singularity from some of the current definitions of Singularity that have come since, which are more about possible outcomes of such a change rather than about the concept of an intellectual/technological event horizon itself.) That's a possible vision for the future of humanity, but it's also a vision of what maybe the right direction is for our programming too.

Of course, this vision that code may be generative in a way where the source is mostly unintelligible to us feels possibly at odds to our current understanding of software freedom, a movement which spends a lot of time talking about inspecting source code. The implications of that is a topic of interest of mine (I wrote about it in the FSF bulletin not too long ago) and I did needle Spector about it... what does copyright mean in a world where humans aren't writing software? Spector seems to acknowledge that it's a concern (he agrees that examples of seed-DRM and genetic patents in the case of Monsanto are troubling) but I got the sense that it's not his biggest interest... Spector thinks that the future of that side of software freedom and copyright might be that humanity realizes how absurd software copyright and patents and other intellectual restriction regimes are once things generate far enough. But I get the sense that more than talking about the legal/licensing aspects of the auto-generative future, Spector would rather be building it. Fair enough!

Anyway, somewhere along these lines I mentioned my interest in distributed anti-abuse systems. We talked about how more basic approaches such as Bayesian filtering might not be good enough to combat modern abuse beyond just spam especially, because the attack patterns taken change so frequently. Suddenly it hit me: I wonder whether or not genetic programs would work pretty well in a distributed system... after all, you could use your web of trust to breed the appropriate filtering programs with your friends' programs... would it work?

Anyway, on the ride back I began playing with some of Push's ideas, and (with a lot of helpful feedback from Lee Spector... thank you, Lee!) I started to put together a toy design for a language inspired by PushGP but with some properties that I think might be more applicable to an anti-abuse system that needs to keep around "memories" between generations. (Whether it's better or not, I don't really know yet.) So...

A little Gush tutorial

At this point, Gush exists. It has the stack based language down, but none of the genetic programming. Nonetheless, it's fun to hack around in, looks an awful lot like Push but also is far enough along to demonstrate its differences, and if you've never played with a stack based language before, it might be a good place to start.

Let's do some fun things. First of all, what does a Push program look like?

(1 2 + dup *)

Ok, that looks an awful lot like a lisp program, and yet not at the same time! If you've installed Gush, you can run this example:

> (use-modules (gush))
> (run '(1 2 + dup *))
$1 = (9)

Whoo, our program ran! But what happened? Gush programs operate on two primary stacks... there's an "exec" stack, which contains the program being evaluated in progress, and a "values" stack, with all the values currently built up by the program. Evaluation happens like so:

exec> '(1 2 + dup *)  ; initial exec stack
vals> '()             ; initial empty values stack
exec> '(2 + dup *)    ; [=> 1] popped off from exec stack
vals> '(1)            ; push 1 (a literal) onto values stack
exec> '(+ dup *)      ; [=> 2] popped off from exec stack
vals> '(2 1)          ; push 2 (a literal) onto values stack
exec> '(dup *)        ; [=> +] popped off of exec stack
vals> '(3)            ; apply `+', which is bound to an operation which takes
                      ;   top two numbers on the values stack and adds them,
                      ;   pushing result onto values stack... 2 + 1 = 3,
                      ;   so 2 and 1 are removed and 3 is added
exec> '(*)            ; [=> dup] popped off of exec stack
vals> '(3 3)          ; `dup' takes top item on stack and duplicates it
exec> '()             ; [=> *] popped off of exec stack
vals> '(9)            ; apply `*', which is bound to an operation which takes
                      ;   top two numbers on values stack and multiplies them,
                      ;   pushing result onto values stack... 3 * 3 = 9,
                      ;   so 3 and 3 are removed and 9 is added
                      ; Nothing left to do on exec, so we're done!

Okay, great! What else can we run?

;; Complicated arithmetic runs
> (run '(3 2 / 4 +))
$2 = (14/3)

;; We can assign variables to values and then reference them
> (run '(88 'foo define foo 22 +))
$3 = (110)

;; However, base operations "know" what types to apply for, and search
;; the stack... the string will be "skipped over" in search for
;; a number here.  This means that we can randomly generate code
;; and we won't run into type errors.
> (run '(1 "two" 3 +))
$4 = (4 "two")

;; Nested parentheses will be "unnested" and applied inline
> (run '(98 ("balloons" "red") 1 +))
$5 = (99 "red" "balloons")
;; so that's the same as
> (run '(98 "balloons" "red" 1 +))
$6 = (99 "red" "balloons")

;; Variables are actually stacks!  Which means we can build up
;; complicated operations on them...
> (run '('+ 'foo define      ; set foo to '(+)
         88 'foo var-push    ; append 88, so foo is now '(88 +)
         2 foo))             ; apply variable stack foo
$7 = (90)

;; Conditionals, etc also work.
> (run '(1 1 + 'b define  ; assign b to the value of 1 + 1
         2 b = if         ; check if b is 2
           "two b"        ; if-then clause
           "not two b"))  ; if-else clause
$8 = ("two b")

There's more to it than that, but that should get you started.

How is Gush different from Push?

Gush takes almost all its good ideas from Push, but there are two big differences.

Both Gush and Push try to avoid type errors. You can do all sorts of code mutation, and whether or not things will actually do anything useful is up for grabs, but it shouldn't crash to a halt. The way Push does it is via different stacks for each value type. This is really clever: it means that each operation applies to very specific types, and if you always know your input types carefully, you can always be safe on a type level and shouldn't have programs that unexpectedly crash (if there aren't enough values on the appropriate type stack, Push just no-ops).

But what if you want to run operations that might apply to more than one type? For example, in Gush you might do:

> (run '(1 2 / 4.5 +))
$9 = (6.5)

In Push, you'd probably do something like this:

$9 = (6.5)

(And of course, if you wanted rational numbers rather than just floats, you'd have to add another type stack to that...)

I really wanted generic methods that were able to determine what types they were able to apply to. For one thing, imagine you have a program that's doing a lot of complicated algebra... it should be able to operate on a succession of numbers without having to do type coercion and hit/miss on whether it chose the right of several typed operators, when it could just pick one operator that can apply to several items.

I also wanted to be able to add new types without much difficulty. As it is, I don't have to rewire anything to throw hash-maps into Gush:

> (run '(42 "meaning of life" make-hash hash-set))
$10 = (<hash-table>)

This would just work, no need to wire anything new up!

The way Gush does it is it uses generic operators which know how to check the predicates for each type, and which "search the stack" for values it knows it can apply. (It also no-ops if it can't find anything.) If bells and alarms are going off, you're not wrong! In Gush's current implementation, this does have the consequence that any given operation might be worst case O(n) of the size of the values stack! Owch! However, I'm not too worried. Gush checks how many operations every program takes (and has the option to bail out if a program is taking too many steps) and searching the stack after failing to match initially counts against a program. I figured that if programs are auto-generated, one fitness check can be how many steps it takes for the program to finish its computation, and so programs would be incentivized to keep the appropriate types near where they would be useful. I'm happy to say that it turns out I'm not the only one to think this; unknown to me when I started down this path, there's another Push derivative named Push-Forth which has only one stack altogether (not even separate stacks for exec and values!) and it does some similar-ish (but not quite the same) searching (or converging on a fixed point) by currying operations until the appropriate types are available. (Pretty cool stuff, but to be honest I have a hard time following the Push-Forth examples I've seen.) It comes to the same conclusion that by checking the number of steps a program takes to execute as part of its fitness, programs will be encouraged to keep types in good places anyway. However! There's more reasons to not despair; I'm relatively sure that there are some clever things that can be done with Gush's value stack so that predicate information is cached and looking for the right type can be made O(1). That has yet to be proven though. :)

The other feature Gush differentiates itself from Push is that Gush variables are stacks rather than single values. This ties in nicely with the classic Push approach that lists are unwrapped and applied to the exec stack at the time they are to be evaluated anyway, so it makes no behavioral change in the case that you just use "define" (which will always clobber the state of the stack, whether or not it exists, to replace it with a stack with a single element of the new value). But it also allows you to build up collections of information over time... or even collections of code. An individual variable can be appended to and modified as the program runs, so you could write or even modify subroutines to variables. (Code that writes code! Very lispy, but also a bit crazier because it might happen at runtime.) Push also has this feature, but it has one specific, restricted stack for it, named the CODE stack appropriately. Why have one of these stacks, when you could have an unlimited number of them?

That wasn't the original intent for having variables as values though; I only realized that you could make each variable into a kind of CODE stack later. My original intent was driven by a concern/need to be able to carry information from parent process to child process. I added a structure to Gush programs named "memories", and I figured that parent programs could "teach" their memories to child programs. So this was really just a hash table of symbols to stacks that persisted after the program ended (which, since if you use run-application you get the whole state of the program as the same immutable structure that is folded over during execution, you have that information attached to the application anyway). The idea of "memories" was that parent programs could have another program that, after spawning a child program, could "teach" the things they knew to their children (possibly either by simply copying, or more likely through a separate genetic program applied to that same data). That way a database of accrued information could be passed around from generation to generation... a type of genetic programming educational system (or folklore). So that was there, but then when I began adding variables around the same time I realized that a variable that contained a single value and which was pushed onto to the exec stack was, due to the way Push "unwraps" lists, exactly the same as if there was just that variable alone pushed onto the stack. Plus, it seemed to open up more paths by having the cool effect of having any variable be able to take on the power of Push's CODE stack. (Not to mention, removing the need for a redundant CODE stack!)

Are these really improvements? I don't know, it's hard to say without actually testing with some genetic programming examples. That part doesn't exist yet in Gush... probably I'll follow the current lead of the Push community and do mutation on the linearized Plush representation of Push code.

Anyway, I also want to give a huge thank you to Lee Spector. Lee has been really patient in answering a lot of questions, and even in the case that Gush does have some improvements, they're minor tweaks compared to the years of work and experimentation that has gone into the Push/PushGP designs.

And hey, it was a lot of fun! Not to mention, a great way to procrastinate on the things I should be working on...

Possible routes for distributed anti-abuse systems

By Christopher Allan Webber on Tue 04 April 2017

I work on federated standards and systems, particularly ActivityPub. Of course, if you work on this stuff, every now and then the question of "how do you deal with abuse?" very rightly comes up. Most recently Mastodon has gotten some attention, which is great! But of course, people are raising the question, can federation systems really protect people from abuse? (It's not the first time to come up either; at LibrePlanet in 2015 a number of us held a "social justice for federated free software systems" dinner and were discussing things then.) It's an important question to ask, and I'm afraid the answer is, "not reliably yet". But in this blogpost I hope to show that there may be some hope for the future.

A few things I think you want out of such a system:

  • It should actually be decentralized. It's possible to run a mega-node that everyone screens their content against, but then what's the point?
  • The most important thing is for the system to prevent attackers from being able to deliver hateful content. An attack in a social system means getting your message across, so that's what we don't want to happen.
  • But who are we protecting, and against what? It's difficult to know, because even very progressive groups often don't anticipate who they need to protect; "social justice" groups of the past are often exclusionary against other groups until they find out they need to be otherwise (eg in each of these important social movements, some prominent members have had problems including other social justice groups: racist suffragists, civil rights activists exclusionary against gay and lesbian groups, gay and lesbian groups exclusionary against transgender individuals...). The point is: if we haven't gotten it all right in the past, we might not get it all right in the present, so the most important thing is to allow communities to protect themselves from hate.

Of course, keep in mind that no technology system is going to be perfect; these are all imperfect tools for mitigation. But what technical decisions you make do also affect who is empowered in a system, so it's also still important to work on these, though none of them are panaceas.

With those core bits down, what strategies are available? There are a few I've been paying close attention to (keep in mind that I am an expert in zero of these routes at present):

  • Federated Blocklists: The easiest "starter" route. And good news! If you're using the ActivityPub standard, there's already a Block activity, and you could build up group-moderated collections of people to block. A decent first step, but I don't think it gets you very far; for one thing, being the maintainer of a public blocklist is a risky activity; trolls might use that information to attack you. That and merging/squashing blocklists might be awkward in this system.
  • Federated reputation systems: You could also take it a step further by using something like the Stellar consensus protocol (more info in paper form or even a graphic novel). Stellar is a cryptographically signed ledger. Okay, yes, that makes it a kind of blockchain (which will make some peoples' eyes glaze over, but technically a signed git repository is also a blockchain), but it's not necessarily restricted to use of cryptocurrencies... you can track any kinds of transactions with it. Which means we could also track blocklists, or even less binary reputation systems! But what's most interesting about Stellar is that it's also federated... and in this case, federation means you can choose what groups you trust... but due to math'y concepts that I occasionally totally get upon being explained to me and then forget the moment someone asks me to explain to someone else, consensus is still enforced within the "slices" of groups you are following. You can imagine maybe the needs of an LGBT community and a Furry community might overlap, but they might not be the same, and maybe you'd be subscribed to just one or both, or neither. Or pick your other social groups, go wild. That said, I'm not sure how to make these "transactions" not public in this system, so it's very out there in the open, but since there's a voting system built-in maybe particular individuals won't be as liable for being attacked as individuals maintaining a blocklist are. Introducing a sliding-scale "social reputation system" may also introduce other dangerous problems, though I think Stellar's design is probably the least dangerous of all of these since it probably will still keep abusers out of a particular targeted group, but will allow marginalized-but-not-recognized-by-larger groups still avenues to set up their own slices as well.
  • "Charging" for distributing messages: Hoo boy, this one's going to be controversial! This was suggested to me by someone smart in the whole distributed technology space. It's not necessarily what we would normally consider real money that would be charged to distribute things... it could be a kind of "whuffie" cryptocurrency that you have to pay. Well the upside to this is it would keep low-funded abusers out of a system... the downside is that you've now basically powered your decentralized social network through pay-to-play capitalism. Unfortunately, even if the cryptocurrency is just some "social media fun money", imaginary currencies have a way of turning into real currencies; see paying for in-game currency in any massively multiplayer game ever. I don't think this gives us the power dynamics we want in our system, but it's worth noting that "it's one way to do it"... with serious side effects.
  • Web of trust / Friend of a Friend networks: Well researched in crypto systems, though nobody's built really good UIs for them. Still, a lot of potential if the system was somehow made friendly and didn't require showing up to a nerd-heavy "key-signing party"... if the system could have marking who you trust and who you don't (and not just as in terms of verifying keys) built as an elegant part of the UI, then yes I think this could be a good component for recognizing who you might allow to send you messages. There are also risks in having these associations be completely public, though I think web of trust systems don't necessarily have to be public... you can recurse outward from the individuals you do already know. (Edit: My friend ArneBab suggests that looking at how Freenet handles its web of trust would be a good starting point for someone wishing to research this. I have 0 experience with Freenet, but here are some resources.)
  • Distributed recommendation systems: Think of recommender systems in (sorry for the centralized system references) Amazon, Netflix, or any of the major social networks (Twitter, Facebook, etc). Is there a way to tell if someone or some message may be relevant to you, depending on who else you follow? Almost nobody seems to be doing research here, but not quite nobody; here's one paper: Collaborative Filtering with Privacy. Would it work? I have no idea, but the paper's title sure sounds compelling. (Edit: ArneBab also points out that credence-p2p might also be useful to look at. Relevant papers here.)
  • Good ol' Bayesian filtering: Unfortunately, I think that there's too many alternate routes of attacks for just processing a message's statistical contents to be good enough, though I think it's probably a good component of an anti-abuse system. In fact, maybe we should be talking about solutions that can use multiple components, and be very adaptive...
  • Distributed machine learning sets: Probably way too computationally expensive to run in a decentralized network, but maybe I'm wrong. Maybe this can be done in a the right way, but I get the impression that without the training dataset it's probably not useful? Prove me wrong! But I also just don't know enough about machine learning. Has the right property of being adaptive, though.
  • Genetic programs: Okay, I hear you saying, "what?? genetic programming?? as in programs that evolve?" It's a field of study that has quite a bit of research behind it, but very little application in the real world... but it might be a good basis for filtering systems in a federated network (I'm beginning to explore this but I have no idea if it will bear fruit). Programs might evolve on your machine and mine which adapt to the changing nature of social attacks. And best of all, in a distributed network, we might be able to send our genetic anti-abuse programs to each other... and they could breed and make new anti-abuse baby programs! However, for this to work the programs would have to carry part of the information of their "experiences" from parent to child. After all, a program isn't going to very likely randomly bump into finding out that a hateful group has started using "cuck" as a slur. But programs keep information around while they run, and it's possible that parent programs could teach wordlists and other information to their children, or to other programs. And if you already have a trust network, your programs could propagate their techniques and information with each other. (There's a risk of a side channel attack though: you might be able to find some of the content of information sent/received by checking the wordlists or etc being passed around by these programs.) (You'd definitely want your programs sandboxed if you took this route, and I think it would be good for filtering only... if you expose output methods, your programs might start talking on the network, and who knows what would happen!) One big upside to this is that if it worked, it should work in a distributed system... you're effectively occasionally bringing the anti-abuse hamster cages together now and then. However, you do get into an ontology problem... if these programs are making up wordlists and binding them to generated symbols, you're effectively generating a new language. That's not too far from human-generated language, and so at that point you're talking about a computer-generated natural language... but I think there may be evolutionary incentive to agree upon terms. Setting up the "fitness" of the program (same with the machine learning route) would also have to involve determining what filtering is useful / isn't useful to the user of the program, and that's a whole challenging problem domain of its own (though you could start with just manually marking correct/incorrect the way people train their spam filters with spam/ham). But... okay by now this sounds pretty far-fetched, I know, but I think it has some promise... I'm beginning to explore it with a derivative of some of the ideas from PushGP. I'm not sure if any of these ideas will work but I think this is both the most entertainingly exciting and crazy at the same time. (On another side, I also think there's an untapped potential for roguelike AI that's driven by genetic algorithms...) There's definitely one huge downside to this though, even if it was effective (the same problem machine learning groups have)... the programs would be nearly unreadable to humans! Would this really be the only source of information you'd want to trust?
  • Expert / constraint based systems: Everyone's super into "machine learning" based systems right now, but it's hard to tell what on earth those systems are doing, even when their results are impressive (not far off from genetic algorithms, as above! but genetic algorithms may not require the same crazy large centralized datasets that machine learning systems tend to). Luckily there's a whole other branch of AI involving "expert systems" and "symbolic reasoning" and etc. The most promising of these I think is the propagator model by Sussman / Radul / and many others (if you've seen the constraint system in SICP, this is a grandchild of that design). One interesting thing about the propagator model is that it can come to conclusions from exploring many different sources, and it can tell you how it came to those conclusions. These systems are incredible and under-explored, though there's a catch: usually they're hand-wired, or the rules are added manually (which is partly how you can tell where the conclusions came from, since the symbols for those sources may be labeled by a human... but who knows, maybe there's a way to map a machines concept of some term to a human's anyway). I think this won't probably be adaptive enough for the fast-changing world of different attack structures... but! but! we've explored a lot of other ideas above, and maybe you have some combination of a reputation system, and a genetic programming system, and etc, and this branch of study could be a great route to glue those very differing systems together and get a sense of what may be safe / unsafe from different sources... and at least understand how each source, on its macro level, contributed to a conclusion about whether or not to trust a message or individual.

Okay, well that's it I think! Those are all the routes I've been thinking about. None of these routes are proven, but I hope that gives some evidence that there are avenues worth exploring... and that there is likely hope for the federated web to protect people... and maybe we could even do it better for the silos. After all, if we could do filtering as well as the big orgs, even if it were just at or nearly at the same level (which isn't as good as I'd like), that's already a win: it would mean we could protect people, and also preserve the autonomy of marginalized groups... who aren't very likely to be well protected by centralized regimes if push really does come to shove.

I hope that inspires some people! If you have other routes that should be added to this list or you're exploring or would like to explore one of these directions, please contact me. Once the W3C Social Working Group wraps up, I'm to be co-chair of the following Social Community Group, and this is something we want to explore there.

Update: I'm happy to see that the Matrix folks also see this as "the single biggest existential threat" and "a problem that the whole decentralised web community has in common"... apparently they already have been looking at the Stellar approach. More from their FOSDEM talk slides. I agree that this is a problem facing the whole decentralized web, and I'm glad / hopeful that there's interest in working together. Now's a good time to be implementing and experimenting!

Wireworld in Emacs

By Christopher Allan Webber on Fri 10 March 2017

It is a truth universally acknowledged, that a hacker under the pressure of a deadline must be in want of a distraction. So it has been with me; I've a TODO list a mountain high, and I've been especially cracking under the stress of trying to get things moving along with ActivityPub. I have a test suite to write, and it's turned out to be very hard, and this after several other deadlines in a row. I've also meant to blog about several things; say the talks I gave at FOSDEM or at ChicagoLUG. I've got a leak in my inbox that's been running for so long that the basement of my email has developed an undertow. So today, instead of getting what I knew I should be doing done, I instead went off and did something much more interesting, which is to say, I implemented Wireworld in emacs.

Wireworld in emacs screenshot

What is Wireworld? It's a cellular automaton, not unlike Conway's Game of Life. Except with Wireworld, the "cells" in play are a bit more constrained... you have a set of wires, and electrons run along them, multiply, and die out, but the paths stay the same. The rules are very simple to implement (Wikipedia says all there is to know). But you can build incredible things with it... even a fully working computer!

Anyway, like many hacks, this one appeared out of boredom/distraction. I had long wanted to play with Wireworld, and I was reminded of it by seeing this cool hack with a digital clock implemented in Conway's Game of Life. It reminded me just how much I wanted to try implementing that computer, or even much simpler circuitry, but I had never been able to get started, because I couldn't find a working implementation that was easy for me to package. (I started packaging Golly for Guix but got stuck for reasons I can't remember.) I started thinking about how much I liked typing out ASCII art in Emacs, and how cool would it be to just "draw out" circuits in a buffer? I started experimenting... and within two hours, I had a working implementation! Two more hours later, I had a major mode with syntax highlighting and a handy C-c C-c keybinding for "advancing" the buffer. Live hacking in Emacs is amazing!

More could be done. It would be nice to have a shortcut, say C-c C-s, that starts up a simulation in a new buffer and runs through the simulation automatically without clobbering your main buffer. (It could work the way M-x life does.) Anyway, the code is here should you want to play around.

Happy (circuit) hacking!

Gems from really old lisp mailing lists

By Christopher Allan Webber on Thu 09 March 2017

... which are archived here. I'm especially finding the CADR lisp machine mailing list to be interesting.

The lispnews list is a bit hard to read, but unveils some key lisp ideas one after another in their earliest state; fascinating stuff. First reference to unwind-protect, and the details of backquote/quasiquote are being worked out here. (EDIT: more on backquote's history.)

Here's some interesting bits: David Moon (who worked on Common Lisp, helped develop Emacs, and was one of the original developers of the the lisp machine) mentioning Common Lisp and the CADR switching to it; rms (who was a maintainer of lisp software at the time) not being so pleased about it, or the way it was announced, and Guy L. Steele (who was editing the Common Lisp standard) replying. Later RMS seems to be investigating how to make it work together.

Sadly it seems that debate was discouraged on that list, and I don't see the BUG-LISPM list around anywhere.

You probably noticed that I was cherry-picking reading emails by RMS. It's no coincidence... I knew this was coming up, and here it is:

Here also is where Symbolics started to move out of the AI lab and where they announced that MIT may use their software, but may not distribute it outside the lab... which is, according to my understanding, one of the major factors frustrating rms and leading to the founding of GNU. A quote from that email:

This software is provided to MIT under the terms of the Lisp System License Agreement between Symbolics and MIT. MIT's license to use this software is non-transferable. This means that the world loads, microloads, sources, etc. provided by Symbolics may not be distributed outside of MIT without the written permission of Symbolics.

There it is, folks! And here's another user, Martin Connor, raising concerns about what the Symbolics agreement will mean. That person seems to be taking it well. But guess who isn't? Okay, you already guessed RMS, and were right. Presumably a lot of argument about this was happening on the BUG-LISPM list. I guess it's not important, but here is an amusing back and forth. I wonder if anyone has access to the BUG-LISPM or BUG-LISPM-MIT lists still?

Notably RMS wants to clarify that his work doesn't go to Lisp Machines Incorporated specifically, either, even though he was more okay with them.

I'm giving a talk at LibrePlanet 2017 on the Lisp Machine and GNU, which explains why I'm reading all this! Okay, well maybe I would have read it anyway.