Building advanced Pythonic interviews with docassemble

00:00 On this episode, we dive into Python for Lawyers and a special tool for conducting

00:04 legal interviews. Imagine you have to collect details for 20,000 participants in a class

00:09 action lawsuit. DocAssemble, a sweet Python web app, can do it for you with ease. Now,

00:14 you may be thinking, I'm not a lawyer, so this isn't for me. Hang on for a sec.

00:17 DocAssemble is actually a general purpose tool. If you ever have done anything like run a survey

00:24 on somewhere like SurveyMonkey or created a Google Forms to gather a bunch of information,

00:29 you could do something way more advanced with DocAssemble and control the workflow with Python

00:34 in a really creative and unique way. Join me as I talk with Jonathan Pyle, creator and maintainer

00:39 of DocAssemble. This is Talk Python to Me, episode 229, recorded August 27, 2019.

00:58 Welcome to Talk Python to Me, a weekly podcast on Python, the language, the libraries, the

01:03 ecosystem and the personalities. This is your host, Michael Kennedy. Follow me on Twitter

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01:23 Hey folks, before we get to the interview, I have some exciting news. We've teamed up with

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01:51 That's HB2019. And be sure to check it out before time runs out. Now let's get to that interview.

01:57 Jonathan, welcome to Talk Python to Me.

02:00 Thanks for having me.

02:01 Yeah, it's great to have you here. We're going to cover a topic that we have, I believe, yet to cover

02:06 really at all. And that is Python for lawyers or writing code that helps practicing lawyers do their

02:14 work. That should be a lot of fun to talk about.

02:15 Yeah, the intersection of law and coding is not very big, but there actually are a fair number of

02:20 people doing it.

02:21 Yeah, there are definitely some people doing it. And it's like I was saying before we hit record,

02:25 that a good friend of mine is a lawyer who does a lot of Python. And so I know that there's some

02:31 areas where there's really interesting stuff happening, but it's maybe not as easy to apply

02:37 programming to the problems as say, like chemistry or something like that, right? But it's, I think

02:43 there's still a lot of really interesting things going on there. And I'm looking forward to diving

02:45 into them with you. But before we get to those, let's start with your story. How'd you get into

02:50 programming in Python?

02:51 I got into programming longer than I can even remember. My dad had an IBM PC with a monochrome

02:58 monitor green screen and I was just magnetically attracted to the thing. It had like basic in

03:04 ROM BIOS. And so I would just go over and type programs into it, which I found by reading like

03:11 compute magazine and byte magazine, which in the eighties you could buy in the supermarket and they

03:15 had source code listings and you could type them in.

03:17 Do you remember when you would go to like bookstores and stuff and there was a whole

03:21 computer section of these magazines and these books and like computer shopper for building

03:26 computers out of parts and yeah, it was a different time, right?

03:29 Yeah. I think the books were just kind of more interesting back then.

03:32 And a lot of real do-it-yourself stuff.

03:35 Yeah, for sure. Okay. So you started typing these in like a fair number of folks have to kind of

03:41 get started programming. Like, okay, well, it says if I type this, like I get a little game or a little

03:46 something on my program, on my computer, because there was no internet, right? Like

03:49 this human transcription was how programs got transferred.

03:53 And it was great in basic because you could do interactive stuff very easily. There's this

03:57 command called input, which got you a line of input. And that's kind of hard to do in a lot

04:02 of languages these days because the interfaces are more complicated. So I started with basic,

04:06 but then as I got older, I taught myself C and assembly language and tried to do some real low-level

04:13 stuff on the, on the IBM PC. But then I went off to college, majored in physics, and then went to law

04:18 school and became a lawyer. But I never really got away from computer programming because wherever I

04:23 went, I just found applications for it because people were doing stupid things. And I thought,

04:28 hey, just write some code that gets around this problem. So I was like using OCR text and converting

04:35 it into spreadsheets using regular expressions in Perl and saved a client like $500,000 by doing that.

04:42 It's kind of weird. So, so I just sort of randomly found myself coding, you know, a good percentage

04:48 of my time as a lawyer just because I could do it. And so I've been using Linux and Perl and like

04:54 just random scripts for about 20 years, but I didn't start learning Python until I decided to start with

04:59 Doc Assemble, which was about four years ago.

05:02 Yeah. Doc Assemble is a really cool project, but before we get into any of that, you know, you talked

05:07 about converting OCR text into spreadsheets using regular expressions. You know, that's not

05:12 super easy to do, but it feels like you can kind of piece together some of these libraries and these

05:16 tools to make it happen. And it's one of these examples of, you know, this rant that I'm always

05:21 on is that we don't necessarily need 10 times more programmers, but all the people out there with

05:27 their specialty, they could have a little bit of programming skill. They could solve like major

05:32 problems in their area like law or biology or whatever. If you just say, you know what, this problem

05:38 actually is really easily solved or pretty easily solved with a little programming if you just knew

05:43 what to do, right? It sounds like that's a great example of it.

05:45 Yeah. And I think just because of my background, I was the only person in the entire firm who knew what

05:51 a regular expression was and even knew that there was a solution to this problem because everybody else

05:56 would just be like, oh, we need to hire some low cost labor in Vietnam to just type everything in

06:02 manually. And I was like, seriously, there's got to be a cheaper way.

06:06 Well, and more reliable and faster, right? Like, so suppose like you're like, you know,

06:11 we actually have this firm in Vietnam and these folks are really good. We can get this done. If it's that

06:16 much text that you're talking about, does it really make sense to wait the month for them to type it in?

06:21 And then you're always a little worried maybe about like a mistake slipping in, right?

06:25 Yeah. But I think the way that the legal field has adapted is that they kind of are not so

06:31 worried about mistakes. They kind of understand that they are to be expected. But yeah, there are a lot

06:38 of people who want to hold you accountable for every single OCR being absolutely 100% correct. And that

06:43 results in a lot of waste of money.

06:45 Yeah, I can imagine. All right. So that's the background and whatnot. How about day to day? What

06:51 do you do? Are you still working as a lawyer now?

06:53 I'm still working as a lawyer. I don't work at law firms anymore. I've been in the nonprofit legal

06:59 services world for about 10 years. So I work at this nonprofit called Philadelphia Legal Assistance,

07:05 which provides free legal services to low income people in civil matters. So nothing criminal,

07:10 but things like child custody, how to get welfare benefits, how to get out of mortgage foreclosure.

07:15 And I have a job there in management and administration. I'm responsible for compliance with

07:20 government regulations. But because of my computer background, I actually have sort of changed the

07:27 way that we do business here. And so I automate a lot of the stuff that would otherwise be done manually.

07:31 And there are a lot of grants available for tech work in the legal aid space to help low income people.

07:38 And so I've worked on a number of grants trying to bring computer programming and data analysis into

07:44 the legal aid world so that we can better advocate for low income people as a whole.

07:49 That sounds like a really good use of programming and legal skills. Now, I don't know very much about

07:56 nonprofit legal services. I certainly understand how for-profit legal agencies work, right?

08:05 But just give me a real quick sense of, you know, like how do businesses like that even run for folks who are not in the legal field and know about it?

08:12 The biggest thing is government funding. And in the United States, the Legal Services Corporation is a

08:18 federal government agency that hands out money to every county in the country. So the low income people have some

08:26 law firm that they can go to and get help for free.

08:29 And they've been a big supporter of technology. In fact, they, I think the legal aid nonprofit world is more

08:35 technologically forward thinking than the big law firms that have all the money in the world,

08:40 because we understand that it has benefits to helping low income people get answers to their legal questions.

08:47 Yeah.

08:47 So we've been pretty innovative in the, in the legal aid nonprofit world.

08:51 That's cool. Of course, you know, thinking about it, right? The, the large legal firms,

08:56 their incentives are not necessarily aligned with automating and massively speeding up some of these

09:05 actions, right? Like if I can push a button and get an answer in a couple of milliseconds,

09:10 they can't bill for that. Right.

09:12 I mean, I think they're very slowly. There are some price pressures on those, on the big firms.

09:18 And clients say, I'm not willing to pay for legal research because I know that this can be done with

09:24 computers and AI. And so I think they're eventually going to come along, but yeah,

09:28 they don't have any big incentive to save the clients much money.

09:31 Once they get the job, right? Like I can definitely see that in the competition. Like,

09:35 Hey, we could do this for half price of those guys because we're not going to charge you for this

09:38 thing. We automated, but yeah, it's, I guess it's a mixed bag. Interesting. So let's talk about

09:44 this project that you created called doc assemble, which it's a really nice project. What is it? And

09:53 what is it used for? Let's start there.

09:54 So it's a free and open source platform for developing guided interviews. And if you don't

10:00 know what a guided interview is, think about turbo tax. It's something that asks you one question at a

10:05 time. It can get very detailed. It can be very long, but at the end of it, you get say a tax return,

10:12 or in other contexts, you might get a legal document that you can file in court,

10:16 or you could even get just like legal advice at the end, like legal information or be directed to

10:21 a resource or do an application. There are a variety of guided interview software packages out there,

10:27 but none of them were free and open source or did what I want. So, so I created doc assemble because

10:32 it would serve my purposes and it'd be something that we could develop and crowdsource through the free

10:39 open source software movement. Yeah. It's, it's really nice. And it's a beautiful looking site.

10:43 I don't know too much about the legal websites, but I know like some of these gather your signature

10:51 sites and stuff, you go to them and they just look so bad and so old school and sketchy. You're like,

10:58 I really don't know if I want to put personal information into this thing. So it's nice that

11:02 this is a good looking web app that, you know, inspires confidence, I guess.

11:06 Yeah. I don't quite understand why people pay $30 a month for some service that gathers the

11:12 signature. Cause I figured out how to do that with a canvas element in JavaScript. It was really easy.

11:19 I don't really, I don't really know what the big deal is.

11:21 Well, see that's part of that programmer skill, right? Like, yeah.

11:24 Yeah. So if people want to experience this and get a sense for what it's about, I guess the first

11:30 thing is you can drop over at docassemble.org, right? And then there's a, at the very bottom,

11:35 you can run a demo and then it just goes through and ask you questions. Like, what is your name?

11:39 And then you hit it in you, what is your location? And then possibly based on what state you're in,

11:45 it might ask you different questions, right? It can kind of flow different questions together

11:49 based on your answers, right? Yeah. And while you can do some of that stuff in like SurveyMonkey

11:55 or Google Forms, this is kind of like the very advanced version of that, where you might have

12:01 incredibly complicated logic that would be so very difficult to manage if you were trying to

12:06 like hand code endpoints in a Flask app, for example, you'd just have so many endpoints and be

12:13 hard to keep track of them. But it's sort of a system for abstracting away that the logic and making

12:19 it easy and maintainable to go from what the law is or what the domain knowledge is to an interview

12:26 that gathers information. Yeah. And I guess it's worth pointing out and you do on your website or

12:31 the GitHub repo, I can't remember about, I read somewhere from your documentation that this is

12:36 developed in the context of a practicing lawyer, but it is not specific to law, right?

12:42 Yeah. I think one of my first users who found it on GitHub was in France and they were using it to

12:50 diagnose problems and mechanical equipment. So, you know, anything that's amenable to asking one

12:56 question at a time where you don't want to have to like hand code all of those screens, you could use

13:02 this system for. Yeah. I can see this almost like tech support even, right? Like in that context,

13:07 does the machine turn on? Yes or no? Yeah. Does it, does smoke come from it? Yes or no?

13:12 First turn it off. Okay. Now what's the next question? Yeah. That's, that's pretty interesting

13:16 actually. And it definitely seems more flexible than SurveyMonkey and you know, all those things are

13:21 commercial services that are SaaS and you can just take it the way it is or leave, right? And this is

13:27 obviously written in Python, something you can download from GitHub and customize.

13:32 And people do like to customize. So a lot of people don't like my standard bootstrap front end. And so

13:38 they, they write their own CSS. So there's really no limit to your ability to customize if you know

13:45 how to write JavaScript or know how to write Python. That's one of the nice things about open source

13:50 software is that like, I have no problem with ultimate extensibility. Yeah. That's super cool.

13:55 And it's, you know, a Python web app based on bootstrap. So that probably means that all the

14:01 fancy, nice bootstrap themes that you can find over at like wrap bootstrap or start bootstrap, or,

14:06 you know, probably a bunch of others that you don't know about. You can go find all these either

14:10 super cheap, like 10, $20 themes, or even free and open source ones. You can probably plug those in

14:16 and get the look and feel you want, right? Yeah. And that's kind of why I picked bootstrap

14:20 because it's widely used and it's themable. And there are a lot of different options if you don't

14:26 like the standard look and feel. Cool. So I definitely want to dig into the technical side

14:30 of things, but maybe just another quick question or two of the high level to set the stages. So

14:35 you're working, helping out these folks at the legal nonprofit. How do you use this in your day-to-day

14:42 job? Well, I'm so busy maintaining the platform and working my day job that I don't really have that

14:48 much time to deploy stuff, but I have been working on a very complicated interview that asks all the

14:54 questions necessary to help somebody file for bankruptcy. And that's primarily being done by

14:59 a nonprofit called Upsolve, but they're one of our sub grantees. So if you check out Upsolve.org and see

15:06 what they've done, they've kind of democratized chapter seven bankruptcy for the nation. Whereas before you

15:13 would have to hire a lawyer for $2,000 or try to find a pro bono lawyer to do it for free,

15:18 which is very difficult.

15:19 Which sounds horrible when you're literally filing for bankruptcy. You're in a financially bad place and

15:24 then you got to go pay to dig out from the hole, right? Which is rough.

15:29 Yeah. It costs a lot of money to be poor. You can go on Upsolve site and you can go through a very long

15:35 guided interview that's using Doc Assemble and it gathers all the information necessary for an 80 page

15:41 bankruptcy petition. And then they have a lot of other custom code after that, but the questionnaire

15:46 that they do is based on Doc Assemble. So I do get to work on some of that during the day,

15:51 but I also use the system in legal aid to do things like gather retainer agreements from clients. I can

15:57 send them a link. They can click on it with their smartphone, sign their name with their finger and

16:02 the signature goes into the document. So stuff like that is pretty useful.

16:06 That's really cool. And I'm, it seems like it really should be used a little bit more even

16:11 outside of the legal space. Cause it seems, seems quite interesting. So maybe one of the things that

16:17 we could talk about is just why Python is not like you were afraid of other languages, right? You

16:23 obviously did a bunch of C in assembly language, right? Like that's a pretty hardcore language.

16:29 So why do you choose Python for it?

16:31 Well, when I started the system, I was sort of a Perl hacker and I loved Perl, but my idea for the

16:38 system was I want to make this into this high level language so that you can basically code the law and

16:46 that a lawyer could sit down with minimal knowledge of computer programming, like just to do if else

16:52 statements that set true false variables, for example. I mean, that is not rocket science. And I wanted them

16:57 to be able to understand code and read it and work with it. Maybe they would get help from somebody to

17:03 clean up the syntax. But, so I was looking around for something that was very clean and readable.

17:08 Perl is great, but it has so many punctuation marks, whereas Python is like so neat and clean.

17:15 And also, I saw these books in the bookstore where it was like teach Python to your kid and

17:21 like integrate Python with Minecraft if you're, you know, 12 years old.

17:25 Yeah.

17:25 So I thought, well, if this language is good enough for six year olds, then, you know,

17:30 it's good enough for a lawyer who has an advanced degree. so I thought that would be a good

17:35 general purpose programming language to base this on. And I definitely wanted a general purpose

17:40 programming language. A lot of people argue with me and they say, oh, if you're encoding legal rules,

17:45 you really should have a declarative programming language. But the problem is that all those

17:50 declarative programming languages were developed in academia. They don't really get used much

17:55 in the real world and they don't have loads and loads of packages that you can just install if you

18:00 want to integrate it with Slack, for example.

18:02 Give us some examples of the declarative languages you're considering or people were suggesting.

18:07 Oh, I don't even remember them because I didn't give them too much of a thought, but everyone

18:12 that I found it, like, first of all, it was not that easy to read because it would use like weird

18:18 Greek notation or whatever.

18:20 Yeah.

18:20 I also read a article where they had tried to do some of the stuff in the eighties using one

18:25 of these declarative languages. And they found that the attorneys just like first wrote out procedural

18:31 code and then converted their procedural ideas into this declarative syntax. So I thought,

18:37 well, maybe this whole declarative stuff is really just something that's academically

18:42 interesting, but the way that people think is more aligned with the way the general purpose

18:46 programming languages that are procedural actually work.

18:49 Yeah.

18:49 Yeah.

18:49 And Python has a really...

18:51 But there's a big debate.

18:51 Yeah.

18:52 I can imagine. So Python has that joke, I guess. I don't know if you've seen it. It says it has

18:59 like a little paper or file or something with some, a pseudocode. It says, how do you convert

19:04 the pseudocode into Python? Like they put dot PY on the end of the file.

19:08 Yeah.

19:09 Python is one of the languages that's closer to the pseudocode in the way that people might

19:14 like sketch it out in words, not flow charts, but like little words, right? Or statements.

19:19 So it's pretty nice as opposed to, I don't know, Java or C# where you've like, well,

19:24 now we go create a class and you put the public static main void in here to get started. Like,

19:28 whoa, what is all this?

19:29 Yeah. And I remember when I was a teenager, I was reading a lot of Donald Knuth who did

19:33 LaTeX and stuff. And he had a whole book on literate programming. And I was really inspired

19:38 by that. Like, can we make programming as much like English as possible? And I think Python

19:43 really does that to a big extent.

19:44 Yeah, for sure.

19:45 Some people want me to use JavaScript and JavaScript is very nice, but like iterating

19:51 through an object or a dictionary, it takes like all this nonsense in JavaScript.

19:57 Definitely takes nonsense.

19:58 Whereas in Python, it's so simple.

19:59 I think JavaScript is nice in that it is super executable in lots of places, right? Like

20:06 we all have browsers, you can run it, but that doesn't necessarily mean it's a nice language.

20:10 If you take away its execution story, right?

20:14 Yeah. It's not horrible, but...

20:16 No, no, no.

20:17 There's certainly things that are worse.

20:18 But Python just has so many advantages.

20:19 Yeah. Especially when your goal is to write, create a way for folks who are not programmers

20:25 to write simple logic into it, right?

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21:20 One of the things that I thought was interesting was your use of YAML and Markdown. Now, Markdown,

21:28 I kind of expected, like that's not super surprising. But if you want to create one of these interviews,

21:33 you might want to ask the question like, what is your name and what is your age? Oh, and the age has

21:38 to be an integer and things like that. It has to be a number. So maybe talk a little bit about how you're

21:43 using YAML to let people create these interview flows. Well, I picked YAML in part for the same

21:49 reasons I picked Python, because it was machine-readable and human-readable at the same time.

21:55 Like rather than use JSON as a way to structure things like lists and dictionaries, I thought it

22:01 made sense to use YAML just because it had the minimum of punctuation. And also attorneys are used

22:07 to doing outlines because when they go to law school, that's how you study is by creating an outline

22:13 of the subject. And I just thought YAML looks so friendly because it's just like bullet points

22:18 made of hyphens. So that's why I settled upon that. I needed something that wasn't just code. It was more

22:23 of a data structure. Yeah, it looks really nice and clean. And I can certainly see if you give like a

22:29 little template example to somebody, they're like, oh yeah. Yeah, I try to teach by example.

22:33 Yeah, that's definitely a good way to do it. Yeah. So you can have like a dropdown with the list and

22:38 really, because that's pretty easy, right? And it's actually not that different than markdown,

22:42 like the dash, dash, dash item. And YAML is also would work in markdown as well. That's pretty

22:47 interesting that they're kind of similar. Yeah. And I chose markdown just because I didn't want a lot

22:52 of HTML characters. And also it can convert into so many different forms. So markdown is used sometimes

22:59 for documents that get turned into PDF, but it's also used for stuff that appears on the screen.

23:04 So it's a very flexible way to format stuff. Yeah. And the thing that I like about markdown,

23:09 like the reason I use it a lot is you can use other formats that are maybe richer, like

23:13 HTML fragments and stuff. But if the slightest thing goes wrong with it, everything is wrecked,

23:20 right? Yeah. It's so bad. And you've also got the potential problem of user input that could be

23:27 malicious, right? If you're accepting this like definition from someone else, right? But if it's

23:32 markdown, it's pretty safe. Yeah. Now, another thing that you do that I think is pretty interesting is

23:37 you can define these questions like, what is your favorite number? And it defines a variable like

23:44 best underscore number potentially. And it has a data type and so on. But then you can write Python code

23:50 that has conditionals, right? We talked about, so if I said I was in Oregon versus a different state,

23:56 it might ask me a different question because the rules in Oregon are different than they are in

24:00 Pennsylvania, for example, right? Right. And so you've got this little example here that says

24:06 something like, if user.iscitizen or user.islegalpermanentresident, user.islegible equals

24:12 true.else, user.islegible equals false. Now, that alone will actually sort of trigger some of the

24:20 questions that get shown or a flow in which the questions are asked. And so that sounds like magic.

24:27 How is this simple Python conditional and little tests like that that I'm writing actually controlling

24:33 the flow and the questions? Yeah. So the core logic engine of Doc Assemble is that it tries to evaluate

24:40 some Python code and then it traps any name errors. So if you go along and you write some Python code and

24:48 you refer to a variable that hasn't been defined yet in the namespace of the interview answers,

24:55 then it triggers a name error, which is just like core Python stuff.

25:00 Right. If I say user.iscitizen and that the user doesn't have a .iscitizen, obviously,

25:05 like you might get an error or something like that, right?

25:07 Yeah. That would be an attribute error. But if you refer to just a name that's not defined,

25:11 like I trap that. If user itself is not defined, for example.

25:15 Yeah. If user is not defined. Then I wrote code that then takes that variable name and then goes

25:21 and looks for a question in that YAML file that offers to define that variable. So it's sort of like,

25:28 and then it goes in, once it sets that variable, which might take a question to the user,

25:33 then it evaluates it again from the start.

25:36 That's funny. So it like runs it and it goes, oh, we stopped, we crashed on users. Do we,

25:40 we bet we have to ask the user question. Then you have the data value set for the user and you ask it,

25:45 you like rerun the Python code again, you get a little farther, you're like, oh,

25:49 yeah, is eligible as a attribute error. We got to figure out if we got an, is eligible or whatever,

25:53 right?

25:54 Yeah. So it's like every time the screen loads, it reevaluates everything from the top,

25:58 you know, which is moderately inefficient, but with computers, you know, it's very fast.

26:03 It's yeah. Yeah. Yeah. And so at the end of it, like it, once, if it gets all the way through,

26:07 then you're done with the interview. You have all the information you need. You've,

26:11 you've gone through the logical paths that you need to go through. And the nice thing about Python is

26:16 that if a user is citizen or user is an eligible alien, you know, it'll stop at a user is citizen

26:27 and it won't even try to evaluate the second part. So it won't trigger any name errors or any other

26:35 errors on the part after the or. So therefore the interview can be parsimonious about what it asks,

26:41 what questions it asks of the user. So the user is only asked for information that is logically

26:46 necessary. And that's all done sort of by tapping into the way that Python is parsed and evaluated.

26:52 Yeah. Interesting. Because Python short circuits the or, you might not have to ask them both,

26:58 are you a citizen and are you a legal resident? And it's two separate questions. Like only if they say,

27:04 no, I'm not a citizen, do you ask them about the residency?

27:06 Yeah, exactly.

27:07 Huh. All right. So this is pretty interesting. Like this is not how normal Python works,

27:12 but this is a pretty creative. And I would say in this context, really a positive way to build like

27:19 an extensible way to script out this flow. Like that's definitely better than, you know, flowchart,

27:24 draggy droppy backend or something.

27:26 I still have a lot of people who would prefer that I created a flowchart GUI interface for them. But

27:32 I just find with any service that offers a no code solution, what that means is the easy stuff is easy

27:40 and the moderately difficult stuff is nearly impossible. Whereas with code, the easy stuff is

27:46 kind of hard and the moderately difficult stuff is a little bit harder and really, really complex stuff

27:52 is doable. And so I'd rather have the latter.

27:55 Yeah, of course. And that makes a lot of sense because if you just want to ask like three questions

27:59 and whatever, it's like SurveyMonkey or something like that, right?

28:02 Or a Google form or, you know, you name it, right?

28:05 I like the idea of attorneys just being able to concentrate on specifying the law in if-then-else

28:13 statements and not having to worry about interview flow. Like they don't need to really think about

28:20 like what question is asked when. They can just concentrate on what they're good at, which is

28:25 envisioning the law and just writing it out and let the computer do the work of figuring out what

28:30 questions to ask in the order.

28:31 Yeah, it's really creative. You just rerun it over and over until it stops crashing.

28:36 Yeah.

28:36 You just like work your way through. I think that's pretty creative and I've not seen anything like

28:40 that before.

28:41 And I was giving the example of a name error where you refer to a name that doesn't exist,

28:45 but then in order to do attributes and indexes, indices, I had to create a new object that would

28:53 raise special exceptions because Python variables, and this is one of the limitations of Python,

28:58 like Python objects are not self-aware. Like they don't know their own name. They're just kind of

29:03 like a value that has pointers to them somewhere in the core. So I had to sort of give each object

29:11 an inherent identity. And so that confuses things, but it all works.

29:16 Yeah. Yeah, I know. It sounds like it works pretty well.

29:19 So you define these interviews in YAML files and then you define the flow by like stating the law or

29:25 your desired sort of flow of the interview in Python. And then it just doc assemble just pieces it all

29:31 together and makes it real.

29:32 Yeah. And at the end, you just write some logic that where the endpoint is presenting some final

29:39 screen to the user. And doc assemble just kind of uses dependency satisfaction to ask all the

29:45 appropriate questions in order to get there.

29:47 Yeah. And of course this wouldn't work with some kind of compiled language, right? Like a compiled

29:52 language would have to try to compile and then run it. And it would have to have all the elements

29:56 available at compilation time, not just the ones that it's trying to crash into as it makes its way

30:02 on the branches.

30:02 Well, there are people smarter than me who've built things like the, I don't know how to pronounce

30:07 it, the Jinja 2 templating system. I think what they do is they actually like parse out all of the

30:13 variables that are used and then creates these like stand-in objects for them. I think there are some

30:20 ways to sort of compile everything and then do the logic on it later. But I've just used the sort of

30:25 exception trapping system.

30:27 Yeah. Yeah. It's interesting. So on the website, which I said, it really presents things pretty

30:33 nicely for this open source project. You have a bunch of features and I think it might be worth going

30:38 through those features and then like digging into the technology behind it as a way to see more

30:42 of the various libraries and technologies that are at work here.

30:47 Sure.

30:48 Sound good?

30:48 Yeah. So let's just like start at the top one. It says you have a whizzy wig, what you see,

30:53 what you get at her and you can compose your templates as a Word document using a Word add-in

30:59 to get started. So how does that work? Like what do you, we talked about YAML being the definition

31:05 of these things, but then what's happening here?

31:07 Yeah. A lot of lawyers like to create Microsoft Word documents and very helpfully, there is a Python

31:14 package called Python doc X template that somebody developed. It uses two other packages. One is doc X,

31:21 which is kind of a utility for writing Microsoft Word files. And the other is Jinja 2, which I was

31:25 just talking about and just kind of mash them together by using Jinja 2 on XML because Microsoft

31:33 Word files are actually XML inside of a zip file. And so it created a system where you could do Jinja 2

31:39 on a Microsoft Word file. And so I also figured out that Microsoft had a pretty neat tool for putting an

31:47 add-in into Microsoft Word, both the online version and the desktop version that ran in a little sidebar.

31:54 So I was able to create a sidebar for Microsoft Word that had the variables in your interview and you

32:00 could just click them to insert them into your Word document. So that's how I was able to do some nice

32:06 Microsoft Word templating and to make it as WYSIWYG as possible. I'm actually not a fan of WYSIWYG. I put

32:12 that on the website because other people are. I would prefer that everybody used Markdown. I have another

32:17 document assembly tool that doesn't use Microsoft Word files that just converts Markdown to PDF.

32:22 And that's what I like better because it uses Pandoc on the backend, which in turn uses LaTeX.

32:28 I'm a big LaTeX fan. So yeah. Okay. Yeah. Sounds, sounds very interesting. We talked already about

32:34 gathering signatures. That's one of the things that it does. And it sounds like you're gathering those

32:38 with an HTML5 canvas in JavaScript. And then what you convert those to images or something like that?

32:44 Yeah. The canvas, I think it's transmitted as a PNG file encoded in a URL. It's like a base64

32:54 conversion and it turns, it's like a data URL. And so you just transmit that in a post request

33:00 up to the server. It's a, it's really pretty simple.

33:02 Yeah. I've never tried that, but that sounds totally simple. You also have live chat. So if you're hosting

33:09 an interview, like you're person receiving all the answers, right? You can assist users in real time

33:16 with even screen sharing and remote screen control. What's up with this one?

33:20 That was a lot of work. I figured out how to use WebSockets technology. There's a great package

33:27 called FlaskSocketIO that enables you to use this sort of WebSocket event-driven communications

33:35 protocol within sort of the Flask paradigm. And so I created this very responsive, quick chat system

33:46 where you can chat back and forth with somebody who's like works for your company, who helps out

33:51 users. And so you can get chat messages from users in real time facilitated by like Redis and this

33:58 socket IO system. And one of the cool things I figured out I was able to do with that is to have this

34:03 sort of pseudo screen share where I would store the HTML in Redis and then pull it down and let the

34:13 operator look at it. And it would be refreshed like on triggers in JavaScript. And so,

34:19 so you can just sit there as the operator and you can watch your users using the system

34:23 and it's not transmitting pixels. It's just transmitting HTML.

34:27 I see. So you're like, this is literally the DOM that they are looking at, right?

34:32 Yeah, basically it's inside of a little iframe. Yeah. I also figured out it was fairly easy for then

34:37 the, the operator to seize control of the user's browser just by sending over some events over the

34:45 WebSockets. So as the operator, you can click a button and control the other user's interview and

34:50 type stuff into their text boxes or click on their buttons. They can see you doing this in real time.

34:56 Yeah. It sounds pretty advanced, but it was actually pretty simple to implement.

34:59 No, it sounds really cool. And I haven't heard of too many things that are like at this level,

35:04 you know, I've definitely seen those little chat programs and stuff.

35:07 Yeah.

35:07 With the operators and whatnot, have some experience with that, but that sounds like a really cool

35:12 feature. I honestly didn't expect that to be in here.

35:14 Yeah. I thought it was a cool feature too, but like nobody is using it. I have no idea why.

35:18 Sometimes you create something and you think you're, you're going to get massive use and then nobody actually cares.

35:23 Well, yeah, that is always the challenges. And I suspect, you know, if I had to guess,

35:27 right, like this is super cool that you can help people this way, but you know, it's also

35:32 challenging to have a person sitting there that can always help. And I don't know, it's,

35:36 it's not a lot of fun to be a chat operator, to be honest. I've been one temporarily.

35:41 But I do think we need to get out of the paradigm of like, either it's a hundred percent,

35:46 a robotic service, or it's a hundred percent human service with the human touch. Like there has to be

35:52 some middle ground where a human gets involved when necessary, but otherwise they're using a web app.

35:59 So I think it's, it's good to have close contact with your users, at least for part of the time of

36:05 your app development, because then you, you really see what their pain points are in the real world.

36:09 Yeah, that's for sure. And you know, some of the stuff that you're doing is,

36:12 it's super important what the right answer is, right? It's not like, well, I clicked this thing

36:17 in this like spreadsheet app that you built and it didn't quite do what I wanted, right? This is,

36:21 you know, are you eligible for bankruptcy or something, right?

36:25 Yeah. And you know, you don't want your users to commit perjury when they're

36:28 talking to a court about what their property is, for example. So yeah, it's important to get the stuff right.

36:35 Yeah, exactly. And so I think the stakes are pretty high here. So that's pretty awesome.

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37:13 Kind of along those lines, you also have SMS and email, which I guess that's pretty much to be

37:20 expected, but maybe tell us about that real quick.

37:22 Yeah. Isn't there some adage that every piece of software bloats until it can send and receive email?

37:28 Pretty much.

37:29 Pretty much.

37:29 So yeah, so my software does, you know, sending email is not that difficult, although I

37:34 found a way to do it with Mailgun, which uses HTTP because SMTP is just so slow. And then the text

37:43 messaging was also super easy. You know, you just get a Twilio API key and it's sending messages is very

37:49 easy. And then I have another feature using email that nobody uses where you can actually run a mail

37:56 server on your server and you can sort of email into your interview. So if you have an interview

38:02 session, you can, if you want, you can mail documents to it. And so I programmed the mail server to intercept

38:08 those messages and then sort of make changes in the appropriate interview session.

38:13 Yeah, that's cool. I've done that before as well. Like you can either go to the site and log in and

38:18 answer this or type it in or whatever, or you just reply to this email, right? And then it just folds

38:22 it back into the database as if you had done that. Yeah, that's a different level than just sending an

38:27 email, but it is cool and it works.

38:29 Yep.

38:29 Now you talked earlier about the web sockets and the live share and Flask and all that. Let's talk a

38:35 little bit about the hosting. We have Redis, we have Flask, we have some kind of database,

38:39 I suspect. This sounds kind of out of the realm of standard lawyer, technical Linux capabilities.

38:47 Yeah. And unfortunately it's, it's like not a pure Python package. The way I distribute it is

38:53 through Docker because it has so many, it has so many non-Python dependencies and services that need to

38:59 be running. And it's just, you can script the orchestration of that with Docker instead of giving

39:05 people complicated instructions to run and you can get it nicely containerized. So there isn't much

39:11 you can do with it as a pure Python package. Although I tried to abstract it away so that you could sort

39:16 of use the core logic engine and Python by itself. But yeah, that's Docker has been extremely useful

39:22 because I don't think I could have gotten anybody to use it unless installation was, you know, one line

39:28 of bash command or something.

39:30 Yeah. That's cool. You know, Docker is really interesting in this regard. Like a lot of times it solves these

39:35 problems, but it also, it has its kind of its own complexity. Like Docker never feels super simple to me.

39:42 Like, okay, well I can start this, but then how do I make sure it's running or how do I like update a new

39:47 version as a beginner? You know, there's those things always feel pretty challenging.

39:51 Yeah. That's one of the big challenges of distributing software that gets updated is,

39:56 as you have to take into account all the existing users. And so like with something like the SQL

40:01 backend, it was really helpful that I used SQLAlchemy. I don't know how to pronounce it.

40:07 Yeah, that's right.

40:07 And it also has this sort of add-on feature called Alembic, which gives you this method for upgrading

40:14 your SQL. If you wanted to add a column, for example.

40:18 Right. Yeah. SQLAlchemy is awesome in that it lets you write simple Python classes that map to your

40:22 database. It'll even create the tables and the indexes and the relationships. But boy,

40:26 if you change anything, it hates it. Right.

40:30 Well, I think with Alembic, it's kind of adjusts for that in a pretty elegant way. So I haven't had

40:35 problems.

40:36 Right. But if you don't have Alembic, right, your app crashes as soon as you make the changes. So you

40:40 need to go and create the migrations and then automatically apply them like the next time you

40:44 start the app and all that kind of stuff. Right.

40:46 Yeah. So I actually spent the whole last week taking a vacation and working on upgrading from

40:52 Debian stretch to Debian buster and Python 3.5 to 3.6 and migrating the web server from Apache to

41:01 Nginx or however you pronounce it. Yeah.

41:03 And all that Docker stuff does take a lot of time because you have to get it just right because you

41:08 don't want some user to be like, oh, my system crashed. What do I do?

41:13 Yeah. Well, what's nice, though, about Docker is you build the base images like you figure out how

41:16 to create a Debian server with Nginx set up correctly. It's good. Right. You don't have to

41:21 think about it. It's now it's good. It's all set up. Right. And so you just kind of build it later

41:25 at a time. But yeah, it's the real challenge I see is migrating that over over time. So what do

41:32 you tell folks if you say, well, we're going to give them a new version? What do you say that they

41:37 should do? There's two ways that you can upgrade. One is by just clicking a button.

41:42 that does a Python upgrade that just runs pip and gives you Python packages and installs in a virtual

41:50 environment and restarts the services that use Python. So that is pretty painless and doesn't

41:56 have a lot of errors associated with it. But sometimes you need to upgrade all of the

42:00 backend stuff. And the way that you would do that is by basically stopping and removing your Docker

42:09 container entirely and then running a new one with the new Docker image. But then the problem is, well,

42:15 what about all your users data? And so you have to have systems for using Docker volumes or the

42:21 recommended thing is using cloud services like S3 or Azure blob storage. And so I have these complicated

42:27 systems where every time you shut down the server, it backs up all the information to the cloud. And then

42:33 when you start it up again, it restores from the cloud.

42:35 Oh, that's nice.

42:36 And so that automatic backup, which is probably something that's also challenging for folks.

42:40 Oh, yeah. Yeah. And so I've got like cron job running on this Docker machine that does backups.

42:46 So people have run into problems. They tend to do crazy things that you would never anticipate.

42:51 But yeah, it does work pretty reliably to back up to the cloud that way. And the nice thing about

42:57 having everything sort of cloud-based is that it was very simple to make it scalable. Like it's

43:03 not a big deal to add another web application to your cluster.

43:08 And, you know, you can bombard your DocAssemble system with loads and loads of requests. And you're really

43:15 only limited by the speed of your SQL server or your Redis server.

43:19 Yeah. So do you have like one Docker container for Redis, one for SQL, and then like separate web front

43:24 ends, you can fire up more of them or something like this?

43:27 Yeah, you can do it that way. Or you can use like a hosted solution from your cloud provider for your

43:34 Redis or your SQL, which I actually recommend because they have nice backup systems built in.

43:40 Yeah, that's good. Just point into that thing. And they already know how to make sure it's safe and

43:44 fails over and whatnot.

43:46 Yeah. But the problem I have is that people who really have no experience with system

43:50 administration are trying to teach themselves Amazon web services and like multi-server systems.

43:56 They just get confused. The problem with making things easy for people is that you get all these

44:02 curious people who don't have enough experience to sort out problems. So like they run into an error

44:08 and I say, well, get yourself a command line. And they're like, how do I do that?

44:11 Yeah.

44:12 SSH to your machine. How do I do that?

44:14 I typed SSH. It doesn't work.

44:16 Yeah. Then you go down a very long and windy path.

44:25 I'm like, can you teach me how to do this? And I'm like, I learned how to do this over a period of 20 years.

44:32 How am I going to teach you?

44:33 Yeah. Well, I mean, that's part of the trick of like being a programmer or in technologies.

44:37 People look at you and they're like, you know, all these things. I think that means you're

44:41 either super smart or you have some super amazing way to learn them. Like, no, you went through the

44:45 same painful steps, but you just like layered on these skills one at a time. You're like,

44:49 yeah, SSH used to be hard, but I figured out how to get the keys registered and like everything.

44:54 That's not a problem. It's gone on to autopilot. Now I'm on to the next problem,

44:57 like database migrations or whatever. Exactly. Yeah. Yeah. It's tough to communicate all that.

45:02 I don't even really know how to do that all at once.

45:05 Yeah. And they expect it to work so perfectly. And I'm just like, don't you understand what

45:09 benefit you're getting from Docker? It's amazing. We didn't have this 20 years ago.

45:15 We used to do it the hard way. Yeah. So let's go, let's keep going down the thing. I think that's

45:19 interesting here. So you have multiple language support. So like I could conduct the interview

45:25 so the person could say, I prefer Spanish or I prefer English. And then they may get their

45:29 questions in different languages. Right. Yeah. I had a lot of features to help with translations and

45:36 multiple languages. You can have like multiple YAML files, one for Spanish questions and one for

45:42 English questions and use them all in the same sort of logical interview. You can also write

45:47 everything in English and then generate an Excel spreadsheet that has all the text in it and then

45:53 send that spreadsheet to a translator and they translate the English into some other language.

45:58 And then you load that spreadsheet into the system and it will substitute the English with the other

46:04 language. Yeah. That's cool.

46:06 So people are using it for some, for like five language interviews. And I think using the Excel

46:11 spreadsheet is a nice medium because that's what translators are comfortable with.

46:16 Yeah, sure. So another one that's interesting is extensibility. Obviously this is where it gets to

46:21 more of the developer side of things, right? You can use the power of Python to extend the capabilities.

46:25 Maybe give us some examples so we know what's going on here. Cause you also have APIs and

46:31 integrating with third party apps. Maybe talk about those two at the same time. Cause they kind of seem

46:36 the same, but not exactly. Yeah. I tout the extensibility just because everybody wants to do their own

46:42 idea and I can't anticipate all the features that they're going to need. So I just give them the power of

46:49 Python and then they can install a package to do whatever they want. So some people wanted to do

46:54 integrations with Google sheets. So I looked up and found that there is, you know, a package for that

47:00 on PyPy and I can show them how to do that and how to set it up. And so, so people have integrated all

47:07 sorts of things just by importing the package into their system. Yeah. Really nice. And then APIs for

47:13 integrate with third party stuff. There are a number of things that I have like a GitHub integration.

47:16 There's this authoring system where you can write your own YAML right in the web browser.

47:22 And I have a GitHub button that then runs get on the backend and does pushes and commits and stuff like that.

47:30 Yeah. Cool. And for the login system, I'm using the built-in Flask username and password system,

47:36 but a lot of people want to have social logins or Auth0. So I have some APIs that integrate with that.

47:44 People also want to, they like writing the stuff in the web browser, but they also want it

47:49 saved to their desktop. And so I have an integration with a Google drive so that you can press a button

47:55 and sync to your Google drive and then run your interview files that you just synced.

48:00 So there are a lot of different ways that DocAssemble talks to other applications that people like to use.

48:06 Yeah. Cool. Another one kind of related to that is you say you can package your interviews and use

48:11 GitHub and PyPI to share your work with the DocAssemble user community.

48:15 So can you create like extension packages or something like that? And then people can add

48:21 them as a dependency of the app?

48:22 Yeah. So the way that I have structured the system is you write your YAML, but you can also write

48:30 modules like .py files. And the way you package and distribute things is using just the plain old

48:37 Python packaging system. And it will create the Python package for you. And there's a button to upload it

48:43 to PyPI as well as one for GitHub. I'm just sort of using the exact same software distribution system

48:51 that already exists. But the YAML files and other files are just under a data folder in your Python

48:58 package folder system.

49:00 Yeah. Okay.

49:00 So it's great. I didn't have to invent my own package distribution system. I'm just like, do what

49:06 everybody else does on GitHub and PyPI.

49:08 Yeah. Awesome. It also has support for background tasks. Like even when people are not interacting with

49:13 the website, it could be running stuff in the background. Is that using Redis or how's that

49:19 happening?

49:19 That mostly refers to Celery. So Celery is a distributed task queue system in Python, which is

49:28 amazing because the problem with web applications is you have to do everything quickly or else the

49:33 browser is going to time out. The user is going to be sick of looking at a spinner and stop the

49:38 connection or something. But if you use Celery, you can have long running code execute in a separate

49:45 process and then save its results to the place where you have your interview answer stored.

49:51 And the other great thing is they queue up in there. So I have some cool Celery stuff like where if you

49:57 upload a PDF file, it makes a PNG image out of every single page and then sort of in parallel,

50:03 OCRs them for you using Celery and all of its queuing magic.

50:09 Yeah. That's cool. Just grab a PNG of the page and throw it up there and say,

50:13 I mean, you get a chance OCR this and store it here or something like that, right?

50:15 It's really useful because sometimes people have really long documents that they need to

50:20 assemble. And if it takes 30 seconds to do that, you want to be entertaining the user while that

50:27 happens. And so you put it into a background task and maybe have the user answer some other questions.

50:32 And then you just check, oh, is the task ready? And then if it's ready, then you get the document.

50:37 Yeah.

50:38 So yeah, that's been a real lifesaver.

50:40 Nice. I guess maybe the last one to touch on here is the secure bit with server-side

50:45 encryption and document redaction and things like that. Maybe you're using Let's Encrypt or

50:51 something else along those lines. I want to talk about those things.

50:54 Yeah. A big concern of lawyers is that they're getting client information. They're getting personally

51:00 unidentifiable information and they want some reassurance that whatever software they're using

51:06 is not going to reveal those personal details to the world. So there are a number of features that I

51:13 use to try to increase security. One of which is I have Let's Encrypt built into the deployment system.

51:19 So all you need to do is give it your email address and set up your DNS properly and it'll do Let's Encrypt

51:24 for you and it'll renew your Let's Encrypt certificates. It also does server-side encryption.

51:30 And the way that interview answers are stored, I'm letting the interview answers just be a Python

51:36 namespace, which is just a dictionary. And I pickle that using the pickle package. And then I encrypt it

51:43 with the user's password. Every time they contact the server, they send this sort of secret password and

51:49 I use that to encrypt and decrypt. So that password is never stored on the server. And so I can have

51:55 encryption in the, of the pickled, a serialized data structure or right there in the SQL database.

52:02 And then other features too, like a multi-factor authentication for login. That was very easy to

52:08 implement with the various apps and SMS messaging. And a redaction, like I figured out a way to put,

52:16 replace text with like blocks of black ink or whatever. So there are a lot of different ways

52:22 that people can feel secure. I haven't figured out the whole encryption of files on the file system

52:27 problem yet. So if you upload a file, that's not encrypted server side, but everything else is.

52:34 You know, the project is open source and you probably would accept a pull request that would

52:39 add that feature or something like that, right?

52:41 Oh, absolutely.

52:42 Speaking of which, are people, are you looking for contributors to this project? Are other people

52:46 already working on it with you? What's the story there?

52:49 Well, what I've found is that there's no magic to open source and crowdsourcing. Maybe there is in

52:55 other areas, but there are a lot of people who are using the system, some of which are programmers

53:01 themselves. But it's pretty rare that people contribute something substantive to the code.

53:08 So I've still found that even though like I never took any CS classes and I just do this on the nights

53:14 and weekends, I'm still doing 99.9% of the coding. It just hasn't magically happened that other people

53:21 contribute. So if there is somebody who wants to really dig into this and contribute, I would totally

53:26 welcome that. Although I think because I've had 99% control, I kind of have a strong feeling of

53:32 authorship. And so I might have strong opinions about what gets brought into the system.

53:37 Yeah, sure. I mean, one of the things that people do sometimes that can be frustrating is they see

53:40 something, they're like, oh, I should add this feature or fix this thing. And they'll create a

53:44 pull request and do all the work and then submit it. And the maintainer will say, well, that doesn't fit

53:48 with my view of this or whatever. And you're like, but I did all this work.

53:52 All right. So, you know, maybe people could open like an issue in GitHub, say, hey, I'm considering

53:57 this feature. Here's what I'm thinking about doing to add it for you. Would you be interested or would

54:03 you hate this idea of having it in this project?

54:06 I love that. Like if people talk about it, we also have a very active Slack channel where we discuss

54:12 these things.

54:12 Yeah, that might be also a decent place. But, you know, GitHub is nice because Slack is super transient,

54:16 right? Like there might be five people that all have this idea that would say, yeah, that's great.

54:20 Or actually I see it this way or right. But if it was in Slack, it's gone if you weren't there.

54:25 Yeah. And so I use both Slack and GitHub for that sort of stuff.

54:29 Yeah.

54:29 But I would be grateful if there were more Python programmers out there who wanted to

54:34 try their hand at adding something to the system or just using it and contributing bug reports is also

54:40 really helpful.

54:41 Right. For sure. So one of the things that I noticed that jumped out to me when I was going through

54:46 the demo example, right? I was going through and answering the various questions and there's a

54:52 button up at the top. I suspect this is not in the real one, but in the demo one it is where you can

54:56 press and say, show me the source. And it'll actually pull up and like show you the YAML file

55:02 and the various other pieces and like some of the performance analysis stuff, which is pretty cool.

55:09 People can go check that out if they are looking for help.

55:11 But another thing that I think is nice is you have this readability score, I guess.

55:18 Not readability, the app, but like how easy is this to read? Like the Flesh Reading Ease or the Flesh

55:25 Kinkade Grade Level, things like that. That sounds pretty helpful if you're trying to have

55:30 questions that people want to answer correctly. You want to keep that probably as simple as possible,

55:35 right?

55:35 Yeah. I think that's what a lot of people don't understand is that really the hard part about

55:39 developing guided interviews is getting the language right and being able to be precise,

55:45 but also use plain English. And lawyers have a tendency to just go on for too long and use big

55:52 words. But if you, so I added that tool to sort of tell you what the grade level of the language you're

55:59 using on that question was in the hopes that people would go for a sixth grade reading level

56:03 and keep working on their language until it got to that level.

56:07 Yeah. That's cool.

56:09 That's thanks to this great package called TextStat. So it was one of the things that because of the

56:14 magic of the community, I was able to integrate it very quickly.

56:18 Yeah. That's cool. And that's what the main reason I wanted to prompt you to ask about it is like

56:22 how you're generating that in Python.

56:23 Yeah. It's that TextStat package.

56:25 Okay. That's really cool. I can see lots of uses. Maybe you don't show that to the user,

56:29 but in your app, you're like, actually, you know, the CMS or whatever you're building,

56:34 maybe it wants to have that kind of analysis in it. That's cool. Yeah. Awesome. Well,

56:39 Jonathan, I think that pretty much covers it. I think this is a nice project. And if you're out

56:45 there listening and you need to conduct surveys or interviews that are slightly better and more

56:49 controllable than like the standard SAS products, this seems like a pretty good option.

56:53 Well, thanks for having me.

56:54 Yeah. You bet. Now, before we get out of here, though, I've got the two questions at the end

56:58 of the show that I always ask you. So let me ask those to you now.

57:01 Oh, good. If you work on Doc Assemble, you can write some Python code. What editor do you use?

57:05 Emacs. I've always used Emacs. Yeah. And I don't think I'm ever going to do anything else. I

57:09 basically live in Emacs. I also use org mode to manage my life and track my time.

57:13 Okay. So yeah. So you run the Emacs operating system, basically.

57:17 Yeah, somewhat. Yeah. Cool. And then you've talked a lot about cool and interesting and unique

57:23 Python packages here. But maybe if you want to give a shout out to any additional packages that

57:29 you think are great for people to know about.

57:31 Well, one that I really encourage people to use is software called Lettuce, which is a Python

57:38 version of another package called Cucumber. So the idea of Lettuce is it's a testing platform

57:44 that uses behavior driven design, I think it's called, where you can express your tests in plain

57:50 English. And then it uses the Selenium package to do web browser automation, or some other type of

57:57 automation to then carry out those tests. So when I read a guided interview, I also in tandem write a

58:03 test script, which is human readable using this Lettuce package. And the Selenium package for web

58:10 browser automation is the best thing ever. I've done web browser automation with lots of other tools,

58:15 but Selenium is amazing.

58:16 Yeah. Oh, those are both really interesting. I love them.

58:18 Yeah. That's all the packages I can think of. Yeah.

58:20 Yeah. Great. So final call to action. People want to get involved with conducting these interviews

58:25 using DocAssemble. What do you tell them?

58:27 I think they can check out the website and join our Slack channel and get involved in the DocAssemble

58:33 community. We also have annual conferences now called Docacon, which take place in the summer every year.

58:39 Yeah.

58:40 And the other thing I just think people, what I would like Python developers to do is get jobs at law firms

58:47 and then sort of infiltrate and then find ways to automate what they're doing. Because there aren't

58:52 enough people with programming skills in the legal field as a whole. And so I think as a result,

58:58 we're kind of behind the times.

58:59 That's good advice. And it sounds like DocAssemble is coming along strong. It's pretty wild that you

59:04 already have a conference about it. So very cool. Well, congrats on the project and thanks for being on

59:09 the show.

59:09 Thank you.

59:10 You bet. Bye.

59:10 This has been another episode of Talk Python to Me. Our guest on this episode was Jonathan Pyle,

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