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In this show we talk with Treehouse Teacher, Andrew Chalkley, about how he works on 'blank slate problems'.
Example Starter Projects
- boilrplate is a curated list of Boiler Plate Projects
- IBM Watson Starter Kits
- Alexa Skills Sample Projects on GitHub pairs well with Build an Alexa Skill
- .NET MVC Starter Projects
- Django Girls Starter Project
- Spring Boot Samples
- React Starter Code
- Express.js Examples
- Android Samples
- iOS Sample Code
- Rails - Getting Started Guide
- Laravel Starter Project
Hi, I'm Craig.
0:00
Welcome to The Treehouse Show.
0:01
The Treehouse Show is our weekly
conversation with the Treehouse community.
0:02
Have you ever had that feeling where
you're ready to start using a language or
0:09
some new technology that
you've just learned about, but
0:13
then you realize I don't
know where to start.
0:15
Now this is a common enough problem that
I thought I'd reach out to our resident
0:18
expert teacher Chalkers
to get some advice.
0:22
Thanks for being on the show Chalkers.
0:24
>> It's good to be here.
0:26
>> I was thinking, that we could talk
about a common problem that students have,
0:27
and that is the blank slate problem.
0:31
And by that I mean when you
get to some technology.
0:34
You've gone through it a little bit.
0:38
You've gotten your hands
a little bit wet with it.
0:39
You've done some exploring, but you go
to start your own project and there is.
0:41
Now what do I do?
0:47
Now how do I start?
0:48
What do I even think about?
0:49
How do I use these tools
that are in front of me?
0:51
What's your take on
the blank slate problem?
0:54
>> My take on the problem is that
you've just gotta start somewhere.
0:55
>> Okay.
0:59
>> And getting something working,
1:00
in a fashion,
is probably the best solution.
1:04
So can you install this
stuff on your machine, and
1:10
get it just running with a demo project?
1:12
>> Okay.
1:14
What do you mean by demo project?
1:17
>> So say for example a lot of
these services out there, or
1:19
even frameworks normally have
a hello world example project.
1:22
>> Okay.
>> That you can just clone.
1:27
Some boiler plate code that you
can just run on your own computer.
1:29
And it's like, that works.
1:34
I'm getting somewhere.
1:35
Because I think it's useful
to have a working model in
1:37
your mind of what the system's doing.
1:41
>> Okay.
1:44
>> And then you can maybe translate
that to the documentation a bit better
1:45
later on.
1:48
But just start tweaking
example code first.
1:49
>> Okay, so kind of just grab,
and most projects have that.
1:52
>> Yeah yeah, yeah, yeah.
1:56
I think it's almost needed if you're going
to interact with developers in any way,
1:58
is to have some example projects, and
2:03
sometimes there're these things
called kitchen sink projects.
2:06
So I've worked in a lot of different
mobile environments where they say, hey,
2:09
here's a kitchen sink application where
it shows you all the different menus,
2:13
the different styles of animations,
things like that.
2:16
So you can see the thing in action, and
then you can maybe find that in the code,
2:19
and then import that into your project.
2:24
>> And then kinda take it and
make it your own.
2:26
>> Yeah.
>> Okay.
2:28
>> Exactly.
>> Neat, so
2:28
you pointed earlier this is
IBM Watson,this is what you're
2:29
working on here?
2:32
>> Yes, so Watson is a set of
cognitive services from IBM.
2:32
It basically allows you to take
input from say human speech or text.
2:37
>> Okay.
2:43
>> And then get the intent and then assign
programming functionality to that intent.
2:44
So say for example, in this example
I've got the treehouse assistant.
2:52
And it says what would you like
to know about, so I could say.
2:56
What is the definition of HTML?
3:00
And Watson responds with
the definition of HTML.
3:08
Now I didn't program necessarily the
specific wording of what is the definition
3:12
of HTML, but Watson can extract
the intent to get a definition.
3:16
And then the coding that I provide can
3:23
decorate the conversation
with these meanings.
3:28
>> So it's breaking down
the English of that sentence,
3:29
and finding out what it
was you actually meant.
3:32
>> Yes.
3:34
>> You could ask that something else-
>> Exactly, so-
3:35
>> In a different way.
3:36
>> So you can see here that the intent
that Watson found was getDefinition.
3:37
>> Okay.
>> And with 94% confidence.
3:44
>> Okay, and
getDefinition is something you wrote?
3:48
That's code that you wrote okay?
3:50
>> Exactly.
3:51
>> Can I see that code?
3:52
>> Sure, so.
3:54
The way that Watson works is that you
can give it definitions yourself or
3:56
you can let your code
give the definitions.
4:02
Or you can give it an HTML document or
4:05
a PDF or
a JSON file with other content in, and
4:09
that's called a discovery service.
4:13
So what I first did was getDefinition.
4:17
I wrote all this code
myself called handleIntent.
4:25
So I don't know if you're familiar
with middleware in web frameworks.
4:28
>> Okay.
>> I basically wrote my middleware for
4:33
the conversation.
4:35
So if it can find the definition with me,
then that's what you'll respond here.
4:36
If it can't find the definition
with the definitions that I have,
4:42
it'll go after the discovery service and
discover the meaning.
4:47
So let's look at what my
code looks like first.
4:50
I've created this definition resolver and
all this does is it
4:53
looks in this definitions file,
and then it just extracts itself.
4:58
Let me show you what the JSON looks like.
5:03
It just looks like this.
5:06
So it basically finds HTML,
find CSS or JavaScript and
5:07
then gets the definition and
spits that out into the check.
5:10
>> Okay.
5:14
>> So that's basically what
that definition resolver did.
5:15
And that's a lot occurred.
5:19
>> So
this was based on an example project?
5:21
>> Yeah.
5:25
>> Okay, so
how did you do that with Watson?
5:25
What's the example project
that you looked at?
5:28
>> So the example project was Watson
5:29
simple chats, conversation.
5:34
So here it is.
5:39
>> Okay, cool.
5:42
It's a GitHub.
>> It's a GitHub thing, and
5:44
you pull it down and
you can check the demo here.
5:46
So this example was to augment a car.
5:49
So turn on the windscreen wipers or
something like that.
5:53
Turn on wipers,
let's see if this will do it.
5:57
I'll turn on the wipers for you.
6:00
So, this is the initial
project that I fought.
6:01
I fought this in and
6:05
then I started augmenting this
with the treehouse assistant.
6:06
>> Cool.
>> So previously I've worked on an Alexis
6:10
scale that did something very similar.
6:13
>> Right,
I thought I was getting deja vu there.
6:15
Okay so the Alexa Skill does
something similar to that.
6:17
>> Very similar,
it translates the English into an intent,
6:19
which is getDefinition, and
then we can grab the definition out.
6:23
>> And these are actually kind of similar,
aren't they?
6:27
I think they actually
are both called intents.
6:28
>> Yes, exactly, so once you've
worked with Alexa, it's easy to
6:31
translate some of those skills over into
Watson and these other services as well.
6:36
All of these cognitive services,
because if you think about when you speak,
6:42
when you say a sentence,
there's always an intent behind it.
6:47
Or when you ask a question
there's an intent derived, so
6:51
these cognitive services
help derive intent.
6:55
So it can,
6:59
Also get the sentiment behind things and-
>> Like
7:01
if you're angry or-
>> Yeah.
7:07
>> Wow.
>> Yes, say for
7:08
example-
>> That's really neat.
7:09
>> You could, [LAUGH], pass all
the information through from a chat from
7:11
a custom service representative and
a customer, and see their relationship,
7:15
and see if anything can be improved-
>> Right, wow.
7:23
>> There and things like that.
7:26
>> This conversation may be recorded and
sent through Watson.
7:28
[LAUGH]
>> Exactly,
7:32
so this is the project
that I branched up off.
7:34
>> And so,
let me show you what it looks like.
7:37
So if you look here
there's an app.js file.
7:43
And in here there is just this
conversation message with a payload.
7:46
And it updates the message and it just
sends the response back to the client.
7:54
So that's all that does.
8:00
It just sends a response back to
the client, which just happens to be JSON.
8:01
So what I thought was instead of
8:06
making my code all spaghettified inside
this message, is to pass the payload
8:12
forward, or the response port forward,
and then augment that with my data.
8:18
>> Okay.
8:23
>> So that, that was the HTML,
JavaScript, and CSS.
8:23
>> And did you get there because
you started following this, and
8:26
were like, this is gonna be.
8:29
This is not good.
8:31
This doesn't feel good to [CROSSTALK]
>> Yeah, yes.
8:33
Some of the example code is very like,
hey, where should my code live?
8:36
There's no best practices for this stuff.
8:42
It's just, here's an example for you.
8:44
And it works.
8:47
>> Right, it's kinda to spark idea, right?
8:49
>> Yeah.
8:50
>> So now,
you've worked on two voice-based apps.
8:51
Do you have any ideas that came from that?
8:54
>> Yeah, definitely.
8:58
Yeah, I want to augment
the house with Watson, so that.
8:59
And whether if it's through voice, through
Alexa, you can tie that into Watson or
9:02
whether if it's a chat bot that I can just
send text messages to or things like that.
9:08
I can basically switch off my
the lights around my house because all
9:14
the intents are switch off my lights or
turn them on.
9:18
>> Right.
>> You can derive the intent from that
9:22
speech.
9:24
>> Like a butler of sorts.
9:25
>> Yeah, exactly.
9:26
>> Okay, cool.
9:27
>> Yeah.
9:28
>> So that butler idea, that came
about because you played with this.
9:29
You didn't know how these things worked
before, and what you could do with it.
9:34
>> No, and
I think that's the whole point is that,
9:38
as soon as you get a model in
your mind then you can apply that
9:41
to the world around you, or problems
that you're having, or you can see.
9:46
I'm very into JavaScript and
the Internet of things, so
9:52
that was a very natural place for
me to go with this.
9:55
But I can see that many people who start
playing around with this sort of stuff
9:59
if they're working QA, or
if they're working support,
10:03
or all these different areas, they can
say, well I can augment the experience,
10:06
whether if it's a full
pledged AI that can help.
10:11
Like hey, can you tell me my latest order?
10:14
And what's your order number?
10:18
And then it can grab the order number and
set from an API and
10:19
send that back in a chat.
10:22
Rather than clicking through
the menus of a webpage,
10:24
you can more have
a conversation about the thing.
10:27
>> So you kind of follow this
methodology all the time.
10:31
You always just dive into the tech, and I
am always amazed, what are you doing now?
10:33
You must have started this a long time
ago, this practice of just diving in.
10:37
If you could tell yourself, give yourself
advice right when you were first
10:43
learning how to code, what would that be?
10:47
>> Persevere, the pain that you
go through, it always pays off,
10:50
because eventually something will click
in your mind, that that's how it works.
10:54
And literally, you can do anything
as long as you persist with it,
11:01
and stay at it.
11:07
But when I say stay at it I'm not
saying you should have a break because
11:10
generally a lot of the ha moments is when
you go walk away from the computer and
11:13
you go, it's that.
11:17
>> Yeah.
11:18
>> But yeah, definitely that feeling of
being uncomfortable that this is hard,
11:19
this is difficult,
you'll never understand this.
11:24
Those type of feelings are normal and
it does pay off eventually.
11:27
>> All right.
11:31
Thanks, excellent advice.
11:33
>> Awesome.
11:34
>> Thanks for
being on the show again, Chalkers.
11:35
>> No worries.
11:36
Thank you.
[SOUND]
11:37
>> Check the notes attached to this video
11:42
for links to some great
example starter projects.
11:43
Blank slate be gone.
11:46
Thanks for watching the Treehouse Show.
11:47
To get in touch with the show,
reach out to me on Twitter, or
11:49
hit us up in the Treehouse community.
11:51
See you next time.
11:52
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