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Well, I had the chance to be in London last week or the week before.

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I don't really remember for DevOps UK.

3
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I haven't been to a DevOps conference in a long time.

4
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I guess it was two weeks ago because they just posted the recording of the talk that I gave

5
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and they noted that it was given two weeks ago.

6
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So I'm going to assume I'm going to go with what they said there instead of researching myself.

7
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And well, first of all, you know, the DevOps people are nice.

8
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There's at least two types of conferences that I go to speak at.

9
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I don't know about you, Ben, but the first type is what you could call there's some real professionals

10
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and they get very excited about, you know, checking on you, making sure you show up.

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They always want to have lots of pre-arrangement stuff.

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They might want you to send your slides ahead of time.

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They seem very professional and that can be annoying, I guess.

14
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And then there's the people more like DevOps where they're just cool.

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They're just like, hey, that's great.

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Come talk here.

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Here's some emails to remind you that it happened.

18
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And then you kind of just walk in there and there's the AV person and, you know, you got to get your microphone.

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And then maybe someone from the conference might come up and be like, hey, how's it going?

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Everything good?

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And then you're like, yeah, it's great.

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And they're like, cool.

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Enjoy your talk.

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And they're super nice.

25
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They're chilled out.

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They know who the talent is.

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They know you've done it before.

28
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That's what it is.

29
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Like, oh, Coté, relax.

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He's got this covered.

31
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I was going to say they even had bottles of water up there, right?

32
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So they know what's going on.

33
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That's the sign of a good.

34
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And you don't want that that that sparkling water with gas water, because I don't know about you, but that is not refreshing when you want to clear your throat.

35
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You want you want some you want some liquid watery water, not not this manmade stuff.

36
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And what kind of sadist leaves that underneath the podium on the stand?

37
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Yeah, exactly.

38
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Just waiting to blow up.

39
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But I had a different question before we get on to conference food, which I'm sure you also partake partook in.

40
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How did you find the cycling experience difference between Amsterdam and London?

41
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Because I think London is more like Mad Max and the Thunderdome.

42
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Yeah.

43
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Whereas like Amsterdam is so chilled out and so relaxed.

44
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It's it's a this is this is this is a interesting, astute question here, Ben.

45
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Now, I'm going to give you a trick answer, which is I don't really when I go to London, I don't really cycle in London because I haven't won.

46
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I've done it once or twice, but I haven't really taken the time to research how those whole city bikes things work.

47
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I think you have to, like, return them to it's San San Santadar.

48
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How do you say that?

49
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Santander.

50
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Santander sponsors it now.

51
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So they're all over the place.

52
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I forget what it was back when I first moved over here.

53
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But I think you've got to go from one Santander docking station to the next.

54
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Just totally fine.

55
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Right.

56
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But but yeah, like when I look around, it looks like I mean, Mad Max is a pretty good description of it.

57
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Like I look around and I'm like, I think if I biked here, I'd get killed.

58
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Right.

59
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Yeah.

60
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Or even even just being a pedestrian.

61
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Oh, yes.

62
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Not not to mention the fact.

63
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Not to mention the fact that, like, you know, I am of the part of the world who drives on different sides of the road.

64
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So, like, I just I would get very confused about what was happening and probably, you know, I'd get hit by someone just because thankfully the parts of London that I'm always in, you know, they put on the street.

65
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Look left or look right as as is appropriate, which I find very handy.

66
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But I always feel bad because I have this habit that I learned at a long long age at a young age.

67
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And it's reinforced when you're cycling.

68
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Right.

69
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Because when you're cycling, even in a nice place like Amsterdam, I always have in my head.

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My goal is to not get killed and not kill anyone else.

71
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So, like, you just got to have complete situational awareness.

72
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And so I'm always looking all ways when I'm crossing the street.

73
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And I just know there's some localers who are like, stupid foreigner.

74
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You don't need to look that way.

75
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But, you know.

76
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Yeah, I think that's true.

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It's very noble of you not to want to damage anyone.

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I think.

79
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Thank you.

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That's very good.

81
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And I feel like the cycle lanes in London, it's a bit like the wildebeest scene in The Lion King.

82
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Trying to cross one of those things.

83
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Right.

84
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There's a 50-50 chance you won't come out of it alive.

85
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Yeah.

86
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I mean, I think.

87
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Do you remember the, like, Manhattan messenger, like, sort of, like, idea that existed in the 90s?

88
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Where, like, you know, in Manhattan there would be these, like, bike people on messengers with messenger bags, which since became very popular.

89
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And, you know, they would just be sort of, like, grungy, like, cyclers who would go around delivering pieces of paper between the firms.

90
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And, you know, I'm sure there was at least one movie made where they were some adventuring swashbucklers, you know, and some romance involved.

91
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But I feel like that might be what it would be like to bike in London.

92
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You would just be constantly, like, going between cars, hang on to some of them, like that guy in Back to the Future, skateboarding around.

93
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You know, it would just, every now and then you'd just, you'd, like, skid over the hood or, as you would say, bonnet of some car and be like, hey, I'm trying to drive here.

94
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But, I don't know.

95
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Yeah, I think you're right.

96
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I think there's a scene a little bit like that in Hackers as well.

97
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And that's from the 90s, isn't it?

98
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I reckon it might be in there.

99
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But, yeah, you know those folks, they have the fixies, they call them, don't they?

100
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Yes.

101
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No brakes.

102
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It's just, like, everything through the pedals.

103
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Yeah.

104
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You're just living the dream.

105
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Yeah.

106
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But I think what I need to do is learn how those rent-a-bikes work.

107
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And I just need to bite the bullet or bite the car and start biking around there.

108
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You'll probably bite the pavement.

109
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That's right.

110
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Yeah.

111
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But it would be nice.

112
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Because there are, like, you know, to be fair, right?

113
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Like, I walk around a lot when I'm in London.

114
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And it's actually more or less a lot calmer than certain intersections and areas are.

115
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So biking would be totally fine.

116
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It wouldn't be a huge deal.

117
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But, you know, also on that note, I picked up two bags of weird crisps, as you would say.

118
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How do you say crisps or crisps?

119
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How do you say that?

120
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What's the plural?

121
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Crisps.

122
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Crisps.

123
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You got to really just, like, crisps.

124
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See, I can't do it.

125
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I'm going to have to practice.

126
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A bag of crisps.

127
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Oh, okay.

128
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So there are kind of two S's there at the end.

129
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Anyways, I got one that was the – I'll put some links to the reviews that my daughters and I did.

130
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But we did one that was the – basically, it tastes like charcoal grilled steak.

131
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It was pretty amazing.

132
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Like, it was very genuine.

133
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And then we got another one that was the Herefordshire sausage and mustard flavored chips.

134
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For everyone else in the UK, that's Herefordshire.

135
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There you go.

136
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Yeah.

137
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Thank you.

138
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I only know how to speak English from the Lord of the Rings.

139
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Oh, you murdered that one.

140
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Murdered it.

141
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Tell me what's up with that part of the country.

142
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What do they do?

143
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A lot of sausage and mustard?

144
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Yeah.

145
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I mean, there's lots and lots of areas that are, you know, very big agricultural zones in the UK.

146
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Herefordshire would be one of them, I suppose.

147
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Herefordshire.

148
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There's lots of them, yeah.

149
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Okay.

150
00:07:59,460 --> 00:07:59,940
Huh.

151
00:08:00,600 --> 00:08:06,540
Well, now, if I were to tell you you were going to have Herefordshire sausage and mustard chips,

152
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would you be like, oh, I know that flavor.

153
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I know what that's going to taste like.

154
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Okay.

155
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Yeah, absolutely.

156
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Yeah.

157
00:08:11,660 --> 00:08:12,440
Yeah, definitely.

158
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For sure.

159
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I mean, I don't know if we do – do US, UK sort of do sausage in the same way?

160
00:08:24,020 --> 00:08:26,260
In the US, I think of a hot dog, right?

161
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Yeah.

162
00:08:26,620 --> 00:08:29,860
And in the UK, it's kind of artisanal almost.

163
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You know, there's all sorts you can get in sausages.

164
00:08:32,340 --> 00:08:32,540
Yeah, yeah.

165
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You can get all kinds of weird and wonderful things.

166
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Yeah.

167
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Well, you know, for our frequent sausage corner section of the podcast, I think – I would

168
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say in the States, there's only three types of sausages, one of which we don't consider

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a sausage.

170
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You've pointed it out already, the hot dog.

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I think if you were to do a survey, a little person on the street thing, no one would say

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a hot dog was a sausage, its own thing, but as we know, it is a sausage.

173
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And then you have – what would you call it?

174
00:09:01,080 --> 00:09:03,880
You have like – what is the equivalent over here?

175
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They have – I forget.

176
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I don't know what they call it here in the Netherlands, but it's this sausage that you

177
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can either buy off the shelf or in the cool section that's basically just like ground

178
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up stuff in a sausage that's maybe about an inch thick.

179
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And it usually comes in like a deep red tube and like, you know, you slice it up and it's

180
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like not that great, but it's kind of like a typical sausage you would have.

181
00:09:33,040 --> 00:09:36,820
And then you have like barbecue sausages and those are just their own things.

182
00:09:36,860 --> 00:09:40,460
And I guess, you know, there's Italian sausages as well.

183
00:09:40,660 --> 00:09:41,820
So you've got that.

184
00:09:41,980 --> 00:09:47,380
And I'm sure the nation of Italy is like, yes, which of the 50 different types is it?

185
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Can you be more specific?

186
00:09:51,380 --> 00:10:02,120
Yeah, but an Italian sausage basically just means usually like a spicy sausage made with

187
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pork is what you've got going on there.

188
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So I think if we've got any listeners left, if they can tell us, first of all, are there

189
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any sausages that are kind of protected, right?

190
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You get European protections, don't you, for different things, right?

191
00:10:14,960 --> 00:10:15,300
That's right.

192
00:10:15,740 --> 00:10:19,380
The Cornish pasty would be an example of that from the UK, right?

193
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It's protected.

194
00:10:20,440 --> 00:10:21,960
Are there any protected sausages?

195
00:10:22,120 --> 00:10:22,700
I'd love to know.

196
00:10:23,000 --> 00:10:29,820
And also, as a sort of plant-based diet person, if you've got any top tips on, you know, plant-based

197
00:10:29,820 --> 00:10:32,600
sausages, things I might not have tried yet, let me know.

198
00:10:32,920 --> 00:10:33,960
I'd love to hear them.

199
00:10:34,080 --> 00:10:36,160
So yeah, for our one listener who's left.

200
00:10:36,920 --> 00:10:39,520
Well, and that's sausage corner.

201
00:10:40,500 --> 00:10:41,200
Well, that's it.

202
00:10:42,220 --> 00:10:43,900
That's it for this sausage episode.

203
00:10:44,000 --> 00:10:44,840
Until next time.

204
00:10:46,700 --> 00:10:50,760
Well, I thought it was one of our main topics.

205
00:10:51,760 --> 00:10:54,040
We'll see if we have time for a second one.

206
00:10:54,160 --> 00:10:56,560
But I came across a survey this morning.

207
00:10:56,560 --> 00:11:03,260
It was from, you know, the US that said it was something like, you know, 50 or 60% of people

208
00:11:03,260 --> 00:11:10,140
have some interests or thoughts, know about chat GPT and AI and that kind of stuff.

209
00:11:10,380 --> 00:11:13,620
But only 14% of them had actually used it.

210
00:11:13,940 --> 00:11:18,620
And that kind of, like, added some numbers to this intuition I've been built.

211
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Intuition.

212
00:11:19,560 --> 00:11:25,060
This gut, this thought I've been building up over the past maybe 90 days or so.

213
00:11:26,060 --> 00:11:30,520
But that is, like, there's not actually that many people who have used this thing.

214
00:11:30,980 --> 00:11:36,800
And so, sort of, like, that's why it's easy for there to be all this, like, both hype

215
00:11:36,800 --> 00:11:42,440
and paranoia around it about, you know, it's going to take over the world or, like, threaten

216
00:11:42,440 --> 00:11:47,340
things or that it just, like, you know, equally that it does all these magical things.

217
00:11:47,340 --> 00:11:54,180
Like, I was at a dinner with a bunch of some executive types last week.

218
00:11:54,300 --> 00:11:55,560
That's why I was in London last week.

219
00:11:56,160 --> 00:11:59,260
And, of course, we had a conversation about AI stuff.

220
00:11:59,420 --> 00:12:05,760
And I think, you know, there was actually out of six of us, like, half of us had actually

221
00:12:05,760 --> 00:12:07,820
used it directly and used it frequently.

222
00:12:08,140 --> 00:12:11,440
But then, you know, the conversation we had with the other three people is they were just,

223
00:12:11,540 --> 00:12:14,880
it was just sort of like, oh, this thing is, like, potentially scary.

224
00:12:14,880 --> 00:12:18,600
And it could do all sorts of weird things and what's going to go on with it.

225
00:12:18,660 --> 00:12:23,700
And so, when I saw that 14% of people had actually used it, like, it kind of clarified

226
00:12:23,700 --> 00:12:24,280
in my head.

227
00:12:24,340 --> 00:12:26,480
I use it all the time, almost every day.

228
00:12:26,680 --> 00:12:32,180
And I think once you get around, at least you wouldn't using ChatGPT and even things

229
00:12:32,180 --> 00:12:37,560
like MidJourney, like, you use it after a while and you realize, like, oh, this thing

230
00:12:37,560 --> 00:12:44,540
is not a big deal or not as big of a deal and it's not magic.

231
00:12:44,880 --> 00:12:45,040
Right.

232
00:12:45,060 --> 00:12:53,120
Like, it's really just a tool that used properly is magical, but it's not like some gigantic

233
00:12:53,120 --> 00:12:55,940
big, I don't know, thing.

234
00:12:56,100 --> 00:12:58,940
Now, the thing with computers is every year they get better.

235
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So, maybe it would get better.

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But so far, and I think I'm copying this from some commentator, but my reflection on it has

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been that, like, there is, and I know there's people who very strictly like to say, instead

238
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of saying AI, they'll say LLMs or something generation, which I guess is technically accurate.

239
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But in ruining the joke that I'm about to make here is, like, whenever you use an AI, you

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realize that there's pretty much no intelligence to it.

241
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Like, there's no, like, like, I try to get it to write lots of text for me and it just,

242
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like, it kind of does it, but you can see that it's not really doing a great job.

243
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So, you have to go in and, like, help it out a lot.

244
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Right?

245
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It's great.

246
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It's great as, like, an entry-level person who would do a lot of the drudge work and the

247
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toil for you, but you really got to go in there and human it up, so to speak.

248
00:13:51,700 --> 00:13:57,140
Now, that said, I've used it to generate, like, Python scripts and stuff, and that, for

249
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my use, is pretty awesome.

250
00:13:59,820 --> 00:14:01,820
Like, it just, like, works and does stuff.

251
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However, when I was discussing that at that dinner I was at, someone did raise up the point

252
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that, like, yeah, but, like, can you have it, like, go in and, like, refactor things for

253
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you and evolve it and add new features?

254
00:14:15,540 --> 00:14:20,400
And, like, where are we as far as, like, you know, you could generate those initial scripts,

255
00:14:20,400 --> 00:14:23,580
but then, like, two years later, what's the deal with that?

256
00:14:23,720 --> 00:14:27,020
And, you know, I guess I haven't really tested out.

257
00:14:27,100 --> 00:14:31,520
Go into the script and modify it so that now you can work with Apple Pay.

258
00:14:31,980 --> 00:14:32,880
That would be fun.

259
00:14:33,060 --> 00:14:38,480
I should have the scripts and say, like, make this compatible with key passes or whatever.

260
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Maybe it would spit out, like, five pages of things I need to install, which would be

261
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cool.

262
00:14:43,880 --> 00:14:49,000
But I thought I would, with that opening monologue there, I know you've been looking

263
00:14:49,000 --> 00:14:51,240
at it from stuff that you putter around with.

264
00:14:51,340 --> 00:14:53,960
What have you been doing with the AI stuff?

265
00:14:54,200 --> 00:14:57,680
I've got loads of responses to what you've said, really.

266
00:14:58,000 --> 00:14:59,040
So, yeah.

267
00:14:59,080 --> 00:15:01,040
Let's make this interactive, as they say.

268
00:15:02,740 --> 00:15:04,180
I have been playing with it.

269
00:15:04,260 --> 00:15:04,880
Yeah, definitely.

270
00:15:05,320 --> 00:15:08,180
And, first of all, you're absolutely right.

271
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You know, the more that you play with it, you do start to notice the rough edges and the

272
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things that it can and can't do.

273
00:15:17,440 --> 00:15:22,920
And one way that you can definitely see that is what I've been doing recently is learning

274
00:15:22,920 --> 00:15:24,000
how to run it locally.

275
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So, just running it on regular desktop hardware, no sort of fancy graphics cards, all that sort

276
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of stuff, just laptop sort of hardware.

277
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And when you run it on that sort of hardware and you start to then be constrained by things

278
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like processing speed, memory, that sort of stuff, you have to use smaller, large language

279
00:15:43,700 --> 00:15:44,360
models, right?

280
00:15:44,440 --> 00:15:48,960
So, the models shrink in size so that you can fit them in the environment that you've

281
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got.

282
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So, a regular laptop, maybe a 16-gig laptop or whatever, it might be able to take maybe a

283
00:15:56,220 --> 00:15:58,000
7-gig model, something like that.

284
00:15:58,000 --> 00:16:01,100
So, it's going to load it all into memory and then use that for its parsing.

285
00:16:01,220 --> 00:16:07,300
And then when you then chat with that model, its responses are much more dulled than they

286
00:16:07,300 --> 00:16:10,420
are when you're using something like ChatGPT online.

287
00:16:10,600 --> 00:16:17,000
So, you really, really notice then that actually, yeah, I can see that this is a system that

288
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is mimicking behavior.

289
00:16:18,520 --> 00:16:19,940
It doesn't actually have behavior.

290
00:16:20,160 --> 00:16:22,000
It just sort of mimics it, right?

291
00:16:23,600 --> 00:16:29,580
And the other thing I've sort of learned recently, I suppose, from reading around and listening

292
00:16:29,580 --> 00:16:35,780
to others in the space and what they have to say about it is that, you know, there are

293
00:16:35,780 --> 00:16:42,000
various sort of levels of dumbness that you can experience and there are ways that you

294
00:16:42,000 --> 00:16:48,980
can tune these models just through the setup of the model to sort of make it behave in radically

295
00:16:48,980 --> 00:16:49,860
different ways.

296
00:16:51,060 --> 00:16:56,940
A good example is setting the prompt to be, okay, whenever you respond to me, respond as

297
00:16:56,940 --> 00:17:00,480
a pirate and it will change all of its responses.

298
00:17:00,720 --> 00:17:04,660
So, it's like, argh lad, I think you should try this.

299
00:17:05,040 --> 00:17:08,200
It sounds very much like it's from the West Country, which is where I sort of live.

300
00:17:08,420 --> 00:17:14,640
So, that's kind of fascinating that you can, you know, you can sort of change these behaviors

301
00:17:14,640 --> 00:17:20,620
and the behavior itself is kind of dependent on the back-end model.

302
00:17:21,160 --> 00:17:27,020
And the other thing you said there was about coding, you know, wouldn't it be nice if you

303
00:17:27,020 --> 00:17:29,980
could get it to elaborate on your code for you?

304
00:17:30,380 --> 00:17:34,960
And there are several models that have sprung up that are tuned to do just that.

305
00:17:35,240 --> 00:17:38,720
So, an example would be the star coder model, I think.

306
00:17:39,060 --> 00:17:40,680
I hope I'm remembering that correctly.

307
00:17:41,200 --> 00:17:44,800
Folks, get in touch if I'm making a mess of this, you know, set us straight.

308
00:17:45,560 --> 00:17:52,520
But a star coder model, one of the things that that can do is take a piece of code that you've

309
00:17:52,520 --> 00:17:58,060
already written in the prompt and in that piece of code, you can sort of add a comment to say,

310
00:17:58,240 --> 00:18:01,880
like, fill out this section because it's not, you know, I want more.

311
00:18:02,040 --> 00:18:06,440
Like you said, maybe make it work with Apple Pay or make it do something that it's not currently

312
00:18:06,440 --> 00:18:06,740
doing.

313
00:18:07,160 --> 00:18:13,040
And it will go away and have a look at that and come back with code that fills out the

314
00:18:13,040 --> 00:18:16,820
middle part of that particular piece of code that you've got.

315
00:18:16,820 --> 00:18:25,260
So, the learning from that is that there's not just ChatGPT's GPT-4 model.

316
00:18:25,620 --> 00:18:29,980
There are lots of models, hundreds of models, thousands of models.

317
00:18:30,160 --> 00:18:36,640
And they're all fine-tuned to work against perhaps subtly different use cases or perhaps

318
00:18:36,640 --> 00:18:43,720
created in the first place with different data, right?

319
00:18:43,780 --> 00:18:48,760
So, you can get ones that are open source that are based on completely open source data,

320
00:18:48,900 --> 00:18:55,000
ones that are closed source that are based on data sets and data that wasn't, you know,

321
00:18:55,000 --> 00:18:59,620
made available publicly and you can't test, for example, you know, what's in there.

322
00:19:00,100 --> 00:19:06,300
Some that are clean, some that are specific to coding problems or customer service problems

323
00:19:06,300 --> 00:19:07,220
or whatever it might be.

324
00:19:07,320 --> 00:19:09,120
There are literally hundreds of them.

325
00:19:09,280 --> 00:19:16,640
So, it's a really fascinating space to get into because you then start to really realize,

326
00:19:16,780 --> 00:19:23,020
yeah, actually, this is a tool and it's not necessarily something that you should be afraid of.

327
00:19:23,660 --> 00:19:28,120
It's just, in some cases, a time-saving measure.

328
00:19:28,380 --> 00:19:28,480
Right.

329
00:19:28,540 --> 00:19:32,720
And in other cases, it needs a lot of work before it be even remotely useful.

330
00:19:32,720 --> 00:19:36,700
So, you know, I'm not worried about it.

331
00:19:36,760 --> 00:19:40,120
I think it's just really exciting, the sort of things that it can do.

332
00:19:40,960 --> 00:19:44,480
Well, I think, you know, I haven't done stuff locally or changed different models,

333
00:19:44,580 --> 00:19:48,800
but I think that would be the next level, at least for me, of interestingness, right?

334
00:19:48,920 --> 00:19:55,100
And I mean, well, so first of all, like, how do you, like, is it difficult to make a model

335
00:19:55,100 --> 00:19:58,200
or do you just give it a bunch of stuff and it just figures it out?

336
00:19:58,920 --> 00:20:01,780
I haven't tried making my own model, right?

337
00:20:01,840 --> 00:20:07,120
But there are, the first thing that you need is, I think, tons and tons of data, right?

338
00:20:07,120 --> 00:20:09,880
So, you do need a large data set from somewhere.

339
00:20:10,660 --> 00:20:19,060
And usually that is, an example would be, there's a data set called the stack or the big stack, I think.

340
00:20:19,380 --> 00:20:23,780
And it's basically based on stack overflow sort of data.

341
00:20:24,040 --> 00:20:28,300
So, you imagine all the information that's going into stack overflow over years.

342
00:20:28,800 --> 00:20:36,060
You know, pulling that out and cleaning that so that it's removed all the sort of information

343
00:20:36,060 --> 00:20:40,820
that shouldn't be there, like personally identifiable information for GDPR or, you know,

344
00:20:40,860 --> 00:20:45,140
just removing all the sort of stuff that you don't need.

345
00:20:45,220 --> 00:20:47,060
That's the first sort of exercise.

346
00:20:47,600 --> 00:20:50,340
And I can imagine that's quite time consuming, I think.

347
00:20:51,100 --> 00:20:51,320
Yeah.

348
00:20:51,560 --> 00:20:59,420
But then you take all that stuff and run it through these chat model makers, you know, things.

349
00:20:59,740 --> 00:21:02,380
I think an example might be something like TensorFlow.

350
00:21:02,640 --> 00:21:04,680
Like I said, I've not really looked into it very much.

351
00:21:05,140 --> 00:21:08,360
The creation of the model is something that I've not dabbled with.

352
00:21:08,360 --> 00:21:19,020
But, you know, it can go through those steps to create you finally a sort of binary file that can then be parsed by these model parsers.

353
00:21:19,500 --> 00:21:20,680
But then it's not over.

354
00:21:20,780 --> 00:21:31,900
You can then also fine tune that again using things like language chains, where you could maybe take a generative model that's good at talking, right?

355
00:21:31,900 --> 00:21:40,820
It can actually create responses that look realistic and maybe add to that some of your own proprietary information,

356
00:21:41,040 --> 00:21:43,920
maybe technical documentation for a product, for example.

357
00:21:44,080 --> 00:21:45,580
It's one that comes up quite regularly.

358
00:21:46,280 --> 00:21:50,520
And then the model will then be able to answer questions about that product.

359
00:21:51,020 --> 00:21:52,280
Another example is Kubernetes.

360
00:21:52,620 --> 00:21:58,360
There's one called K8GPT, which you can run.

361
00:21:59,360 --> 00:22:01,960
It relies on a general model.

362
00:22:02,200 --> 00:22:09,000
So something like a GPT-4 relies on that general model, but then understands Kubernetes.

363
00:22:09,260 --> 00:22:12,940
And it acts like a bit like a Kubernetes operator would act.

364
00:22:13,300 --> 00:22:15,400
So, for example, you can give it your cluster.

365
00:22:15,920 --> 00:22:25,660
And if it spots that one of your apps is misbehaving in that cluster, it can start to recommend remedies and troubleshoot that problem so that you can make that problem go away.

366
00:22:25,660 --> 00:22:34,100
So there's all sorts of specialisms and ways that you can modify these general purpose models afterwards to make them more fine-tuned and useful.

367
00:22:34,960 --> 00:22:35,020
Yeah.

368
00:22:35,220 --> 00:22:46,600
Like, it seems like, I mean, I'm sure, well, I would hope people are already doing this, but it seems like, you know, in the spring community, they would, like, make their own, like, model for doing whatever, right?

369
00:22:46,600 --> 00:23:01,520
Like, you know, if anything, you know, you could have a first line of, like, reviewing contributions or, you know, just asking questions about how to use things or looking into the code base or just generating code.

370
00:23:02,140 --> 00:23:10,940
And because there's a lot of content about, there's documentation, there's all the different versions, there's all the source code for Spring.

371
00:23:11,180 --> 00:23:15,400
And then there's also stuff that uses it and conversation around it.

372
00:23:15,460 --> 00:23:22,460
So you've got, like, a huge body of knowledge you could start using to have a little Spring programmer at your side.

373
00:23:22,600 --> 00:23:23,780
I think so, yeah.

374
00:23:23,900 --> 00:23:37,280
I think, I think, I don't think I would say any of the models I've seen so far, any of this stuff I've experimented with so far has given me any confidence that a model will be able to make a decision about something.

375
00:23:37,280 --> 00:23:39,560
Like, which is the best way to go?

376
00:23:39,940 --> 00:23:42,600
I don't, I haven't seen much that does that very well.

377
00:23:42,600 --> 00:23:58,120
But I know there are lots of startups and lots of folks working on things like flow processing, you know, where, okay, I've got this administrative function that I need to do, like, I don't know, booking a vacation or whatever it might be.

378
00:23:59,020 --> 00:24:04,320
You know, within these parameters, here's sort of what I want, go away and sort it out.

379
00:24:04,320 --> 00:24:17,280
And it will then split that into multiple tasks that can then be broken down and then tackled by the AI and then sort of, you know, start to make suggestions or whatever.

380
00:24:17,420 --> 00:24:24,920
And I guess it's all based on weighting, you know, there's a probability weighting that this is probably what you want, so I'll do this.

381
00:24:25,140 --> 00:24:28,520
But they're not great at decision making, I wouldn't have thought.

382
00:24:28,620 --> 00:24:30,460
I haven't seen much of that anyway.

383
00:24:31,020 --> 00:24:43,160
Yeah, yeah, I went one day I need to make some, some, I don't know, local way of running it and just like feed it everything I've ever written, because I always want to be like, just write it like I wrote it.

384
00:24:44,020 --> 00:24:52,480
Like, like, like, I've even tried some experiments to like give it my writing and say like, how would you describe this writing, because then you can like feed it back to it at any point.

385
00:24:52,580 --> 00:24:57,860
And I would assume also, if you run things locally, you can like, give it a lot more information.

386
00:24:57,860 --> 00:25:08,880
Like, that's the problem I always have with wanting to use it for writing nowadays is you can, you reach a maximum character or token or whatever, like you reach a maximum amount of text that you can give it.

387
00:25:09,000 --> 00:25:16,960
And then you can also see if you're like working iteratively on a chunk of text, you can almost detect when it starts to forget stuff from the beginning.

388
00:25:16,960 --> 00:25:23,720
Like, it's just kind of throwing off text that it can no longer fit into whatever it's, it's buffer.

389
00:25:24,200 --> 00:25:25,060
I don't know what people call it.

390
00:25:25,060 --> 00:25:25,420
Yeah, context.

391
00:25:25,720 --> 00:25:26,860
Yeah, they call it a context.

392
00:25:26,960 --> 00:25:31,360
The context, you know, kind of limited in size right at the present time.

393
00:25:31,360 --> 00:25:41,940
But again, I saw something just last week that was about a company that's figured out a much more efficient way to do context so that they can grow to larger in size.

394
00:25:41,940 --> 00:25:51,480
So I think the average context at the moment is somewhere between sort of one and 4,000 characters or something like that.

395
00:25:51,640 --> 00:25:53,600
So the context is kind of limited.

396
00:25:54,100 --> 00:25:55,640
You know, there's only so much you can do.

397
00:25:56,440 --> 00:26:00,580
This new one could easily handle 70,000 characters.

398
00:26:00,840 --> 00:26:04,160
So characters, was it chunks?

399
00:26:04,280 --> 00:26:05,680
I can't remember how they described it.

400
00:26:05,720 --> 00:26:19,140
But basically, it was enough to be able to take in an entire text of a book and then give you a pricey back about what was in it, you know, and tell you, answer questions about the story and do all that sort of stuff.

401
00:26:19,140 --> 00:26:29,560
Because the context is both the sort of the context that you're working in and the memory available to be able to go back and keep asking questions about the same thing.

402
00:26:30,320 --> 00:26:40,220
I don't know if you've ever noticed, but if you're using something like chat and GPT, the further you go, it kind of sometimes stops being useful anymore.

403
00:26:40,480 --> 00:26:42,920
It's like, oh, you're just repeating yourself or, you know.

404
00:26:43,080 --> 00:26:44,000
Yeah, it gets tired.

405
00:26:44,580 --> 00:26:48,940
Yeah, it gets sort of tired and kind of goes off the rails a little bit.

406
00:26:49,060 --> 00:26:55,020
And that's because the context has started to lose the initial part of the conversation and it's gone away.

407
00:26:55,300 --> 00:27:00,620
And it's focusing on just maybe the last three or four sort of questions and responses.

408
00:27:01,060 --> 00:27:06,780
So, yeah, it's then lost the context of what you were working on and it can kind of go off the rails a little bit.

409
00:27:07,400 --> 00:27:07,620
Yeah.

410
00:27:07,740 --> 00:27:07,900
Yeah.

411
00:27:07,900 --> 00:27:12,760
I always wish I could ask it about itself to be like, do you still remember what we were talking about?

412
00:27:12,880 --> 00:27:15,700
Like, you know, things like that, but maybe, yeah.

413
00:27:16,500 --> 00:27:21,840
Well, the answer would be yes, because the model is designed to be convincing in its answers.

414
00:27:22,160 --> 00:27:31,220
That's one of the things that you notice about these is that they are purposefully designed to create convincing text, even when it's completely wrong.

415
00:27:33,020 --> 00:27:35,660
Or, you know, yeah, no, I'm absolutely fine.

416
00:27:35,740 --> 00:27:38,500
I think you'll find that you've got the problem.

417
00:27:41,300 --> 00:27:44,680
That's why it'll be perfect at being a programmer.

418
00:27:45,140 --> 00:27:46,660
It'll work out very well.

419
00:27:48,900 --> 00:27:49,300
Absolutely.

420
00:27:50,020 --> 00:27:50,380
Yeah.

421
00:27:50,760 --> 00:27:52,340
But it's a brilliant, brilliant thing.

422
00:27:52,460 --> 00:27:53,720
It's a great area.

423
00:27:53,720 --> 00:27:59,280
And the stuff every week I see something new that I think, oh, that's clever.

424
00:27:59,440 --> 00:28:02,040
That's a really good, you know, use case.

425
00:28:02,120 --> 00:28:05,120
I could see that being influential in some way.

426
00:28:05,460 --> 00:28:05,640
So, yeah.

427
00:28:05,760 --> 00:28:06,340
Yeah, yeah.

428
00:28:06,900 --> 00:28:11,540
Yeah, it'll be interesting to see what if the more options you get if you run things locally.

429
00:28:11,740 --> 00:28:15,120
Maybe one day I can just download something and run it instead of having to know what to do.

430
00:28:15,160 --> 00:28:16,420
And I'll check that out.

431
00:28:16,600 --> 00:28:18,240
I can show you after the show.

432
00:28:18,340 --> 00:28:20,520
It's like it's ridiculously easy.

433
00:28:21,240 --> 00:28:23,620
Oh, maybe maybe today is tomorrow.

434
00:28:23,620 --> 00:28:25,240
No, tomorrow is today.

435
00:28:29,540 --> 00:28:41,120
Well, the other thing I wanted to talk about just briefly, like I think since last time we talked, I, you know, we had our we have our state of Kubernetes survey, which which I've made a lot of stuff out of.

436
00:28:41,160 --> 00:28:52,420
And there's two more posts that I wrote about, I think if I remember the one we talked about last time was about like the multi cloud, like management, like people using multiple things.

437
00:28:52,420 --> 00:29:01,420
And I've since published one about like the benefits to developers and the business side and also the tools that people use.

438
00:29:01,420 --> 00:29:11,480
And I think, you know, the fun thing about the tools thing is being a vendor, of course, we're interested in tools people would pay for, which is which is a fun set of responses to get.

439
00:29:11,480 --> 00:29:28,720
And if if, you know, when you look at the tools people are willing to pay for, it's more or less what you would expect, like essentially doing, you know, people like to say monitoring nowadays, but like keeping up with the the runtime status if things are going well with with your old Kubernetes stuff.

440
00:29:28,720 --> 00:29:36,700
And people are always interested in paying for security tools and things like that, of course, which which is fine.

441
00:29:37,160 --> 00:29:46,060
But, you know, the other thing I wanted to check in because I also like have encountered some some sentiment from people recently.

442
00:29:46,060 --> 00:29:50,480
In fact, there was a thread just yesterday in a Slack channel that I'm in.

443
00:29:50,540 --> 00:29:57,820
I was looking for some some recommendations of people who would talk about like, you know, what what developers should know about Kubernetes and then true.

444
00:29:58,720 --> 00:30:05,520
Technical, technical expert fashion, this this conversation quickly devolved into questioning the premise of the question.

445
00:30:06,160 --> 00:30:10,020
And at that point, I was like, oh, looks like it's time for me to go make dinner.

446
00:30:10,420 --> 00:30:12,180
Yeah, y'all have fun with this conversation.

447
00:30:13,040 --> 00:30:23,460
But, you know, this is this is like the perennial question of of Kubernetes things is how how directly are you expecting developers to access this thing?

448
00:30:23,460 --> 00:30:26,860
And, you know, I think I think we contemplate this often.

449
00:30:26,980 --> 00:30:29,080
We don't really know for for a little while.

450
00:30:29,920 --> 00:30:44,400
But, you know, the one thing that at least I think we'll see that I I've I've been thinking I'm pretty confident in the following statement that unless you have some, you know, I guess, pun intended edge case.

451
00:30:45,560 --> 00:30:51,560
Probably you're just going to use like whatever managed Kubernetes stuff that the big cloud vendors have out there.

452
00:30:51,560 --> 00:30:56,940
And maybe you'll you'll have a reason to like have to run it on your own.

453
00:30:56,940 --> 00:31:09,680
But I think a lot of this consternating and discussion and and and stuff that it's complex, like a lot of it is like removed if you don't actually have to like run and manage the Kubernetes distro.

454
00:31:10,080 --> 00:31:12,120
And or let me phrase that another way.

455
00:31:12,260 --> 00:31:17,160
I would hope it was because if that's not included in people saying Kubernetes is complex and.

456
00:31:18,780 --> 00:31:23,440
It's a lot worse if people don't take that into account.

457
00:31:24,020 --> 00:31:29,460
I think there's a chance that there are two schools of thought on this as a as a as a developer.

458
00:31:29,700 --> 00:31:31,580
You know, I've been around developers a lot.

459
00:31:31,660 --> 00:31:33,220
I've been in development teams a lot.

460
00:31:33,300 --> 00:31:38,100
And I think it sort of boils down to to developers too broad a title.

461
00:31:38,280 --> 00:31:38,400
Right.

462
00:31:38,460 --> 00:31:40,240
It's just really, really broad.

463
00:31:40,860 --> 00:32:01,380
And I think if you're the sort of developer who really likes fiddling about with Kubernetes and getting involved in the weeds of how that's all operating and making, you know, making all that stuff work, then you should go and join the platform engineering team because you'll be happy like every day.

464
00:32:01,440 --> 00:32:01,580
That's right.

465
00:32:01,580 --> 00:32:05,600
You'll be skipping into work because you'll have that stuff to do in abundance.

466
00:32:06,280 --> 00:32:24,120
But if you're the sort of developer who perhaps prefers to be working with end users and gathering requirements and making, you know, end users dreams come true, if you know what I mean, that's probably not going to happen by by moving the levers on Kubernetes.

467
00:32:24,120 --> 00:32:30,980
So you're probably more in the sort of user satisfaction space, if you like.

468
00:32:31,140 --> 00:32:34,300
And I think there's a sort of a rough sort of separation there.

469
00:32:34,420 --> 00:32:38,320
I think there's definitely developers who enjoy doing both.

470
00:32:38,840 --> 00:32:49,760
But if your primary sort of, you know, enjoyment comes from digging down into the technology and being in the weeds, you're probably a platform engineer and you'd probably really enjoy that space.

471
00:32:49,760 --> 00:32:56,060
So you should probably, you know, try and spend some, try and get more time in that space if you're not in it already.

472
00:32:56,800 --> 00:32:57,200
Yeah.

473
00:32:57,380 --> 00:32:59,680
No, I think that distinction is key.

474
00:32:59,840 --> 00:33:03,740
And I try to be disciplined about saying application developers versus whatever.

475
00:33:03,740 --> 00:33:17,820
But it is, it can be very difficult to tell in surveys and commentary if it's sort of like infrastructure or platform developers or engineers, I guess, or more application developers.

476
00:33:17,820 --> 00:33:34,720
I think that's a fun distinction is if you were to say engineer, that perhaps means lower down the stack than when you say developer, which always feels to me a little more like, you know, that's your, comes to work in like flip flops and, you know, Ironman t-shirts and things like that.

477
00:33:34,720 --> 00:33:45,000
I think your application developers, whereas, you know, the engineers, I think they might wear pants is, you know, and longer, more formal dress, probably what they show up in.

478
00:33:45,520 --> 00:33:49,420
Maybe it also comes down to whether or not you're willing to be on call.

479
00:33:49,420 --> 00:34:09,540
Because if you're dipping about in the sort of Kubernetes space, you know, and people are relying on you for those reliability numbers, you know, the five nines and all that, then it's more likely than not, sooner or later, you're going to get a call in two in the morning, you know, to try and start something out.

480
00:34:09,540 --> 00:34:23,340
But that doesn't tend to happen in the kind of user satisfaction space, you know, there's no user suddenly has a bright idea at two in the morning and really must, you know, have a brainstorm around a whiteboard.

481
00:34:23,800 --> 00:34:28,020
Yeah, yeah, maybe, maybe the application developers creed is no nines here.

482
00:34:28,300 --> 00:34:28,700
Just.

483
00:34:33,440 --> 00:34:38,300
They have a t-shirt with one of those big red signs that's crossed out that has a nine.

484
00:34:39,540 --> 00:34:41,140
Oh, everyone's welcome.

485
00:34:41,360 --> 00:34:45,080
But, you know, you, you have your, you know what your own preferences are.

486
00:34:45,700 --> 00:34:52,160
If you're okay with being woken up at two in the morning, then, you know, again, probably platform engineers a good space.

487
00:34:53,020 --> 00:35:03,020
Now, now speaking, speaking of, you know, the, the ongoing trend of like, you know, the bumpy waters of, of the hype cycle, which, which seems to accelerate more.

488
00:35:03,120 --> 00:35:04,840
It seems to repeat itself.

489
00:35:04,840 --> 00:35:09,180
We, we go, you know, kind of like a bumpy waves, you kind of slap your boat along.

490
00:35:09,540 --> 00:35:14,580
Like what's, what's your take on the recent, uh, microservices versus monolith commentary been?

491
00:35:15,000 --> 00:35:25,320
I think, you know, just to summarize some, some, uh, Amazon prime video streaming people, they wrote this blog post that they moved from a serverless back to a monolith application.

492
00:35:25,320 --> 00:35:33,560
And this created a whole big hoopla on both sides of like, Oh, you know, serverless and microservices were always ridiculous.

493
00:35:33,560 --> 00:35:51,940
And then as often happens, the sort of, uh, champion is the wrong word, but the, the, the rat, the, the level-headed semi boosters of microservices came in and they're like, well, we've always said it depends that, you know, it's not like, it's not a hundred percent thing that you use that.

494
00:35:52,020 --> 00:35:56,740
And, you know, here's, here's links and footnotes and citations to us saying it's always depends.

495
00:35:57,300 --> 00:36:09,980
And, uh, you know, I always feel like there's a third part left out, which is, let's call them the, uh, the just like consumer developer who, who's like, yeah, I thought that was what we were supposed to all be doing at this point.

496
00:36:10,120 --> 00:36:18,840
So that between these three forces, it's always like a, uh, a little, a little, uh, tri-cornered spider diagram of, of weirdness that's happening.

497
00:36:18,840 --> 00:36:27,340
But what's, what's your, uh, what's, what's your summary is that, what have we learned from the most recent brouhaha of, uh, monoliths versus microservices and serverless?

498
00:36:28,020 --> 00:36:36,200
Um, I think, you know, personally, I was over it years ago, like the whole argument between these two, you know, types.

499
00:36:36,340 --> 00:37:02,700
If you look into stuff that, um, Ollie does, for example, on the spring team, sorry, Ollie, your surnames escaped me for a second, but, um, Ollie's doing micro lifts, for example, where you might, you might design something in an, imagine it in a, in a monolithic fashion for deployment, but it's constructed in such a way that you can then break those pieces apart.

500
00:37:02,920 --> 00:37:18,540
And start to use them as microservices if you want to, but there's flexibility in the design to allow you to either coalesce them together or separate them apart at will, as you wish using things like event-driven architecture, for example.

501
00:37:18,760 --> 00:37:23,660
So, you know, it's interesting to see all that stuff going on.

502
00:37:24,060 --> 00:37:39,980
Um, I was never totally a fan of microservices for everything because it tend, I mean, particularly with rest, for example, it tended to introduce a lot of architectural issues that you then had to solve elsewhere, um, you know, with other patterns.

503
00:37:39,980 --> 00:37:56,700
So, um, I'll think of a good example of that and put it in the show notes, but, um, but there are, there are, there are several sort of issues with, with that, um, which you then start to compensate for by having lots of other things all around you.

504
00:37:56,700 --> 00:38:11,420
Like, um, uh, you know, where you've got lots of, um, tools inside the gateway to compensate for other issues that you might have, like services disappearing and coming back because the network's not very reliable.

505
00:38:11,920 --> 00:38:26,640
So all sorts of issues around that, particularly with rest and I, I spent a lot of time doing event-driven architecture instead where you're, you're, you're messaging rather than using HTTP and then some of those problems go away.

506
00:38:26,640 --> 00:38:26,900
Right.

507
00:38:26,960 --> 00:38:29,020
So I'm over it.

508
00:38:29,020 --> 00:38:31,240
It's like, yeah, do, do what makes sense.

509
00:38:31,240 --> 00:38:34,660
Don't do what is popular.

510
00:38:35,300 --> 00:38:35,740
Yeah.

511
00:38:35,920 --> 00:38:36,120
Yeah.

512
00:38:36,240 --> 00:38:36,500
Yeah.

513
00:38:36,560 --> 00:38:40,740
I mean, it, it, it seems like, like, like, uh, my, my, my feel of it.

514
00:38:40,820 --> 00:38:44,480
Again, I haven't programmed since 2005, so what do I know?

515
00:38:44,620 --> 00:38:53,940
But like, you know, my, my program was always like, yeah, it's a good idea to think, make things like modular and, uh, have kind of clear boundaries between them.

516
00:38:54,400 --> 00:38:56,440
And, uh, you should do that.

517
00:38:56,440 --> 00:39:04,420
And it, and if you need for it to be like a network, network components that talk with each other, then do that as well.

518
00:39:04,420 --> 00:39:10,460
And if you need to like deal with unreliable things and shifting stuff around, then you should add that in as well.

519
00:39:10,460 --> 00:39:16,300
But like, beyond that, I, I don't know if there's that much new going on.

520
00:39:16,300 --> 00:39:18,400
I mean, other than just redoing it.

521
00:39:18,400 --> 00:39:26,000
And then you get to the point where, uh, uh, you know, I, I wrote this up recently in my newsletter that it's, it's bad to like bad.

522
00:39:26,120 --> 00:39:42,960
It's not preferred to, uh, kind of talk about the benefits of something, to market something based on the, uh, the sharp bag of knives theory, which is like, you know, and I think all of us have been guilty of this kind of like talking about stuff to sort of be like, oh, microservices are awesome.

523
00:39:42,960 --> 00:39:44,420
But they're really complicated.

524
00:39:44,420 --> 00:39:53,000
So you need to like, like, you know, the classic thing from, I think, uh, even like a Martin Fowler blog was like the sign of like, you know, you need to be this tall to ride the ride.

525
00:39:53,000 --> 00:39:57,880
To get all these great benefits, you need to deal with the complexity because it's very dangerous and weird.

526
00:39:58,680 --> 00:40:06,340
And, you know, that, that kind of conversation about me always strikes me as like, well, sounds like this isn't good for me to use.

527
00:40:06,340 --> 00:40:09,120
Then I'll just go back to what I was doing.

528
00:40:09,120 --> 00:40:12,060
Like, yeah, I don't know.

529
00:40:12,140 --> 00:40:22,540
I don't know what you think, but I feel like I've noticed a lot less tub thumping about serverless in the last sort of year or so as well.

530
00:40:22,600 --> 00:40:25,400
And this was also included in this blog from Amazon, wasn't it?

531
00:40:25,440 --> 00:40:33,100
They were moving away from their own serverless technology to sort of get better performance by not using it anymore.

532
00:40:33,100 --> 00:40:34,720
So I don't know.

533
00:40:35,140 --> 00:40:44,540
Serverless is perhaps another one of those things that I don't know if I've noticed as many people caring as deeply about it recently as perhaps a year ago or two years ago.

534
00:40:45,280 --> 00:40:45,420
Yeah.

535
00:40:45,640 --> 00:40:45,940
Yeah.

536
00:40:46,140 --> 00:40:47,740
Well, we'll have to, we'll have to check in.

537
00:40:48,320 --> 00:40:57,700
And after, after, you know, in a couple of months after another sausage corner, we'll check in on if, if Kubernetes should be used directly by, by developers.

538
00:40:58,440 --> 00:41:07,320
And if serverless is finally going to like bust out of the, the kind of market share concern that it has at the moment.

539
00:41:07,320 --> 00:41:08,860
And by concern, I don't mean worrying.

540
00:41:09,060 --> 00:41:10,640
I just mean activity.

541
00:41:11,020 --> 00:41:12,700
Isn't that a meaning of the word concern?

542
00:41:12,880 --> 00:41:16,320
Like a going concern, like a going business that you have?

543
00:41:17,060 --> 00:41:18,540
Probably definition number four.

544
00:41:18,880 --> 00:41:19,660
Not really sure.

545
00:41:19,760 --> 00:41:27,640
Well, speaking of definition number four, I think the fourth thing we need to go over is that you've been listening to Tanzu Talk or watching it.

546
00:41:27,920 --> 00:41:35,100
And if you want to get the show notes for this episode, you can go to TanzuTalk.com and find them for, for this episode.

547
00:41:35,500 --> 00:41:44,200
And, or if you're watching the video, you can just look below in the description and I'll put show notes there once I publish it after the streaming here.

548
00:41:44,200 --> 00:41:45,940
And with that, we'll see everyone next time.

549
00:41:46,080 --> 00:41:46,560
Bye-bye.