1
00:00:00,520 --> 00:00:03,200
Speaker 1: Welcome to Cannabis Talk one oh one featuring Blue with

2
00:00:03,360 --> 00:00:07,159
Speaker 1: Joe Bronde, the world's number one source for everything cannabis.

3
00:00:07,200 --> 00:00:09,440
Speaker 2: Hello, welcome to Cannabis Talk one oh one, the world's

4
00:00:09,520 --> 00:00:12,120
Speaker 2: number one source for everything cannabis. I want to thank

5
00:00:12,200 --> 00:00:14,960
Speaker 2: you guys for listening to our podcasts all around the world.

6
00:00:15,320 --> 00:00:17,920
Speaker 2: Blue is not here today, but Joe Brande is holding

7
00:00:17,960 --> 00:00:20,000
Speaker 2: it down. Make sure you check out our website, Cannabis

8
00:00:20,040 --> 00:00:21,959
Speaker 2: Talk one on one dot com, as we have so

9
00:00:22,000 --> 00:00:24,360
Speaker 2: many great articles and blogs on our site for you

10
00:00:24,440 --> 00:00:27,160
Speaker 2: to enjoy. Feel free to call us anytime at one

11
00:00:27,160 --> 00:00:30,520
Speaker 2: eight hundred or twenty nineteen eighty and you could always

12
00:00:30,600 --> 00:00:33,640
Speaker 2: check us out on YouTube by g all social media

13
00:00:33,800 --> 00:00:36,919
Speaker 2: at Cannabis Talk one oh one. Well, my brother from

14
00:00:36,920 --> 00:00:40,080
Speaker 2: another mother, Blue is at the number one Christopher Wright,

15
00:00:40,479 --> 00:00:43,080
Speaker 2: and you could always catch me on social media at

16
00:00:43,159 --> 00:00:46,000
Speaker 2: Joe Brande fifty two and you could always see some

17
00:00:46,040 --> 00:00:48,080
Speaker 2: of the crazy things that I love posting.

18
00:00:48,640 --> 00:00:49,559
Speaker 3: I love that they have that.

19
00:00:49,840 --> 00:00:51,440
Speaker 2: By the way, there's a picture of me there too

20
00:00:51,479 --> 00:00:52,960
Speaker 2: with the Leo blow and a candle out of my ass,

21
00:00:53,000 --> 00:00:55,520
Speaker 2: so you get to see at least a little bit there.

22
00:00:55,800 --> 00:00:59,160
Speaker 2: Kelly effects shout out to Kelly affects you guys. Today,

23
00:00:59,160 --> 00:01:02,160
Speaker 2: I'll up on the podcas. We have a man behind

24
00:01:02,160 --> 00:01:05,840
Speaker 2: one of the most exciting and innovative entities that is

25
00:01:06,080 --> 00:01:11,319
Speaker 2: really revolutionizing the way we cultivate cannabis and other crops alike.

26
00:01:12,480 --> 00:01:16,000
Speaker 2: I'm really excited to talk with you, Elmer, because not

27
00:01:16,040 --> 00:01:19,560
Speaker 2: only is Elmer Mayor the founder and CEO of neat Leaf,

28
00:01:19,800 --> 00:01:22,920
Speaker 2: but he's obviously a very smart, intelligent weirdo that I'm

29
00:01:22,920 --> 00:01:24,679
Speaker 2: gonna pick into his brain to feel like, how the

30
00:01:24,760 --> 00:01:28,080
Speaker 2: hell did you come up with some weird shit like this?

31
00:01:28,280 --> 00:01:33,240
Speaker 2: Because a cultivation management platform that uses AI and robotics

32
00:01:33,240 --> 00:01:36,399
Speaker 2: to save labor and improve yield. I mean basically, they're

33
00:01:36,400 --> 00:01:40,160
Speaker 2: experts from the robotics, the automation, and the AI industries

34
00:01:40,440 --> 00:01:44,679
Speaker 2: that have all come together and really revolutionizing the agriculture

35
00:01:44,720 --> 00:01:47,559
Speaker 2: industry by designing technologies that are going to improve the yields,

36
00:01:47,560 --> 00:01:49,720
Speaker 2: which is gonna be great to hear how he even

37
00:01:49,760 --> 00:01:52,080
Speaker 2: thought about doing this and using this and this is what,

38
00:01:52,240 --> 00:01:54,840
Speaker 2: of course every kind of field needs. But I mean,

39
00:01:54,840 --> 00:01:56,240
Speaker 2: this is going to be able to help I would

40
00:01:56,240 --> 00:01:59,360
Speaker 2: think even strawberries and everything else, right, So that's what's

41
00:01:59,400 --> 00:02:02,320
Speaker 2: even crazy about what we're about to talk with because

42
00:02:02,800 --> 00:02:06,200
Speaker 2: it's not just cannabis. And I'm smelling as I'm reading

43
00:02:06,240 --> 00:02:10,280
Speaker 2: and studying about your company earlier. The neat Leaf Spider,

44
00:02:10,320 --> 00:02:14,000
Speaker 2: you guys, is the first of its kind. Now, I

45
00:02:14,000 --> 00:02:16,680
Speaker 2: hope you heard me say it's the first of its kind.

46
00:02:16,919 --> 00:02:22,360
Speaker 2: The neat Leaf Spider autonomous robotic platform currently being deployed

47
00:02:22,760 --> 00:02:26,440
Speaker 2: in greenhouses and indoor cultivation facilities all around the globe

48
00:02:27,000 --> 00:02:30,640
Speaker 2: at more than just cannabis. If you're out there, neatleaf

49
00:02:30,760 --> 00:02:33,760
Speaker 2: dot com is the website n e A T l

50
00:02:33,880 --> 00:02:36,919
Speaker 2: e a f to schedule a demo for your cultivation.

51
00:02:37,520 --> 00:02:39,920
Speaker 2: You definitely want to do that because you want to see, hey,

52
00:02:40,520 --> 00:02:42,400
Speaker 2: can this work for me? And there of course you're

53
00:02:42,400 --> 00:02:45,120
Speaker 2: gonna say yes, right, It's not like oh no, but

54
00:02:45,200 --> 00:02:48,160
Speaker 2: it's like how, And then the how is going to

55
00:02:48,200 --> 00:02:50,120
Speaker 2: be the incredible part. And that's what we're gonna here

56
00:02:50,160 --> 00:02:53,960
Speaker 2: now to schedule your demo without further ado giving up

57
00:02:53,960 --> 00:02:58,880
Speaker 2: for Elmer in the building, folks, Elmer, no fud. Where

58
00:02:58,919 --> 00:02:59,600
Speaker 2: are you from? Elmer?

59
00:02:59,600 --> 00:03:01,840
Speaker 3: What's the much for having me? What's the dialect where

60
00:03:01,919 --> 00:03:02,200
Speaker 3: you from?

61
00:03:02,280 --> 00:03:02,840
Speaker 4: I'm from.

62
00:03:04,400 --> 00:03:08,960
Speaker 5: I'm from Italy, the Northern Italian Italian Alps the Dolomites.

63
00:03:08,960 --> 00:03:11,880
Speaker 3: Wow, how old are you when you moved to the States.

64
00:03:12,400 --> 00:03:14,480
Speaker 4: What's thirty and move the States ten years ago?

65
00:03:14,560 --> 00:03:15,120
Speaker 3: Thirty?

66
00:03:15,919 --> 00:03:16,919
Speaker 2: Wow?

67
00:03:17,400 --> 00:03:19,440
Speaker 3: Did you grow up speaking English or when did you

68
00:03:19,480 --> 00:03:20,240
Speaker 3: learn English? Oh?

69
00:03:20,360 --> 00:03:20,960
Speaker 4: Super late.

70
00:03:21,200 --> 00:03:24,760
Speaker 5: I grew up speaking like, speaking of weird dialect in

71
00:03:24,800 --> 00:03:27,480
Speaker 5: the mountains, you know, like to speak of the weird languages,

72
00:03:28,120 --> 00:03:31,320
Speaker 5: German dialect. And then I obviously learned Italian and living

73
00:03:31,320 --> 00:03:33,640
Speaker 5: in Italy and so standard German.

74
00:03:33,760 --> 00:03:37,160
Speaker 4: And then at some point in high school English.

75
00:03:37,040 --> 00:03:40,440
Speaker 3: So you speak German, Italian and English.

76
00:03:40,600 --> 00:03:44,680
Speaker 5: Right, and a few sens of Spanish and Italian. You

77
00:03:44,800 --> 00:03:48,040
Speaker 5: learned a little bit of Japanese. I was curious to

78
00:03:48,120 --> 00:03:51,080
Speaker 5: learn about it. It's such a different language, which is

79
00:03:51,560 --> 00:03:53,839
Speaker 5: I just wanted to understand how that language might work.

80
00:03:53,880 --> 00:03:55,720
Speaker 5: But there's no chance I would be able.

81
00:03:55,520 --> 00:03:58,040
Speaker 3: To speak What brought you to the States?

82
00:03:59,360 --> 00:03:59,880
Speaker 4: I wanted to.

83
00:03:59,800 --> 00:04:02,760
Speaker 5: Live nearby the ocean, honestly, that was kind of the interest,

84
00:04:02,840 --> 00:04:05,560
Speaker 5: and in the English speaking country, And so I ended

85
00:04:05,640 --> 00:04:10,040
Speaker 5: up getting a job in the Bay Area, And no

86
00:04:10,120 --> 00:04:12,400
Speaker 5: one told me that the water is freaking cold up there.

87
00:04:12,520 --> 00:04:15,920
Speaker 2: The horrible doors. The Bay Area beach is doors. Yeah,

88
00:04:15,960 --> 00:04:18,080
Speaker 2: it's not like, oh, this is so nice. It's fucking go.

89
00:04:18,760 --> 00:04:20,320
Speaker 4: There is some nice surf in Santa Cruz.

90
00:04:20,400 --> 00:04:22,840
Speaker 5: I must have met, but yeah, you need a bad

91
00:04:22,839 --> 00:04:23,520
Speaker 5: suit obviously.

92
00:04:24,560 --> 00:04:25,159
Speaker 4: But yeah, so I.

93
00:04:25,160 --> 00:04:29,120
Speaker 5: Ended up there and I really enjoyed the opportunities there

94
00:04:29,160 --> 00:04:33,080
Speaker 5: to work, to learn, to meet amazing people, and to

95
00:04:33,120 --> 00:04:36,000
Speaker 5: be creative an open minded about trying out new technologies

96
00:04:36,040 --> 00:04:36,799
Speaker 5: developed things.

97
00:04:36,880 --> 00:04:39,240
Speaker 2: You were in San Francisco for this, I was in

98
00:04:39,400 --> 00:04:42,360
Speaker 2: Mountain View. Mountain View, okay? Oh, which was and then

99
00:04:42,360 --> 00:04:43,000
Speaker 2: what year was this?

100
00:04:44,040 --> 00:04:45,120
Speaker 4: Twenty fourteen?

101
00:04:45,680 --> 00:04:48,280
Speaker 2: Okay, So it's already been busted. The whole industry has

102
00:04:48,320 --> 00:04:52,000
Speaker 2: been cracking. There wasn't like Adobe just came out our Facebook,

103
00:04:52,040 --> 00:04:53,880
Speaker 2: But I mean, that's Silicon Valley right there.

104
00:04:54,080 --> 00:04:56,800
Speaker 3: I'm originally from San Jose. Oh nice, So I know

105
00:04:56,839 --> 00:04:57,440
Speaker 3: the whole area.

106
00:04:57,480 --> 00:05:00,680
Speaker 2: Well, so as you say these areas, I know Mountain View, Saratoga,

107
00:05:01,160 --> 00:05:04,480
Speaker 2: you know, Palo Alto or whatever. And it's just funny

108
00:05:04,520 --> 00:05:09,000
Speaker 2: how that's where a lot of the world's technology comes from,

109
00:05:09,040 --> 00:05:11,520
Speaker 2: which a lot of people may not even understand that,

110
00:05:12,200 --> 00:05:15,080
Speaker 2: but a lot of the technology that allows the world

111
00:05:15,120 --> 00:05:19,120
Speaker 2: to talk to each other comes from that area, which

112
00:05:19,160 --> 00:05:20,000
Speaker 2: is crazy, right.

113
00:05:20,040 --> 00:05:20,360
Speaker 4: It is.

114
00:05:20,400 --> 00:05:23,400
Speaker 5: It's a melting pot of talent basically, like all these

115
00:05:23,400 --> 00:05:26,360
Speaker 5: big companies that acquired or hired all this amazing talent,

116
00:05:26,400 --> 00:05:28,560
Speaker 5: and now you have these amazing people there. It's a

117
00:05:28,680 --> 00:05:31,400
Speaker 5: very open mindset. There's a lot of you know, people

118
00:05:31,440 --> 00:05:34,799
Speaker 5: who are there to go to try something new, explore.

119
00:05:35,279 --> 00:05:38,400
Speaker 5: Might also be the influence from Berkeley and like San Francisco,

120
00:05:38,560 --> 00:05:40,200
Speaker 5: the open mindset, hippin.

121
00:05:40,120 --> 00:05:42,880
Speaker 2: That's been around forever there too. Yeah, good old Telegraph

122
00:05:42,960 --> 00:05:45,080
Speaker 2: and all that stuff. I've loved it. So you come

123
00:05:45,080 --> 00:05:48,480
Speaker 2: out here, where'd you go to college at in Germany?

124
00:05:48,800 --> 00:05:49,880
Speaker 4: So in Austria and Germany.

125
00:05:50,200 --> 00:05:53,040
Speaker 5: My bachelors in Austria and then the master's and my

126
00:05:53,120 --> 00:05:55,520
Speaker 5: PhD in Germany. So it came a little bit around

127
00:05:55,520 --> 00:05:59,040
Speaker 5: in Europe, but yeah, ended up now last ten years here.

128
00:05:59,440 --> 00:06:02,240
Speaker 2: Would you say the schooling in Europe and let alone

129
00:06:02,320 --> 00:06:06,240
Speaker 2: Germany is like their cars just a little better than America.

130
00:06:07,640 --> 00:06:11,320
Speaker 4: I think it's different, I little stronger, tougher.

131
00:06:11,440 --> 00:06:13,680
Speaker 2: I mean, you know, I love their cars. But I

132
00:06:14,000 --> 00:06:17,920
Speaker 2: would also you know, I would imagine it's a fair

133
00:06:18,080 --> 00:06:21,440
Speaker 2: argument to say their educational is pretty high up there too.

134
00:06:21,520 --> 00:06:22,039
Speaker 2: In Germany.

135
00:06:22,080 --> 00:06:25,599
Speaker 5: Well, No, definitely, like they have some very strong universities

136
00:06:25,720 --> 00:06:28,080
Speaker 5: and education is free.

137
00:06:28,880 --> 00:06:31,120
Speaker 4: Imagine that it is.

138
00:06:31,880 --> 00:06:33,160
Speaker 3: Yeah, yeah, but hold on a second.

139
00:06:33,160 --> 00:06:35,160
Speaker 2: I didn't know that if you grew up in Germany

140
00:06:35,640 --> 00:06:37,160
Speaker 2: you can go to college for free.

141
00:06:37,279 --> 00:06:40,080
Speaker 4: Yeah yeah, so it's basically basically for free.

142
00:06:40,240 --> 00:06:43,920
Speaker 5: And but just what it means is also that there's

143
00:06:44,040 --> 00:06:47,960
Speaker 5: less of incline of the professors to like teach it

144
00:06:48,040 --> 00:06:50,520
Speaker 5: because you can't really demand anything because you don't pay

145
00:06:50,560 --> 00:06:52,880
Speaker 5: for it, and so you're more on your own. I

146
00:06:52,920 --> 00:06:55,520
Speaker 5: would say, there's great education, but there's less support as

147
00:06:55,520 --> 00:06:59,159
Speaker 5: in US universities in my experience.

148
00:06:59,160 --> 00:07:01,360
Speaker 2: And that's a nice anecdotal look at it.

149
00:07:01,720 --> 00:07:02,240
Speaker 3: I like that.

150
00:07:02,400 --> 00:07:04,279
Speaker 5: And so when you come out of that, you're more

151
00:07:04,320 --> 00:07:07,080
Speaker 5: like the ones who survive in general, more like they didn't.

152
00:07:07,200 --> 00:07:09,760
Speaker 5: They can acquire knowledge on their own a bit like

153
00:07:10,040 --> 00:07:12,760
Speaker 5: better because they didn't have that support. But there's it's

154
00:07:12,800 --> 00:07:15,120
Speaker 5: it's harder to get fruit probably in some way. But

155
00:07:15,480 --> 00:07:17,640
Speaker 5: I mean the universities in the US are as well.

156
00:07:18,320 --> 00:07:20,640
Speaker 2: Well that's good. So you come then from Germany, you're

157
00:07:20,680 --> 00:07:23,560
Speaker 2: an educated young man, and what did you start doing

158
00:07:23,560 --> 00:07:25,320
Speaker 2: first in the Bay Area with technology?

159
00:07:26,320 --> 00:07:27,240
Speaker 4: I got offered.

160
00:07:27,440 --> 00:07:31,120
Speaker 5: My background is robotics, automation, self engineering, so applied at

161
00:07:31,120 --> 00:07:35,160
Speaker 5: a robotics group at Robert Bosch actually in baba Alto,

162
00:07:35,600 --> 00:07:37,320
Speaker 5: and they said like, oh, yeah, sure you can have

163
00:07:37,360 --> 00:07:39,600
Speaker 5: the job, but we also have this self driving car

164
00:07:39,600 --> 00:07:43,960
Speaker 5: thing going on, and I was like, I'm not the cars, guys,

165
00:07:44,920 --> 00:07:47,920
Speaker 5: but I really was intrigued by the idea to work

166
00:07:47,960 --> 00:07:51,640
Speaker 5: on self like on a project robotics project with a

167
00:07:51,720 --> 00:07:54,440
Speaker 5: larger group of people, because it was still very researchy

168
00:07:54,480 --> 00:07:57,000
Speaker 5: and you always had like just a few people. And

169
00:07:57,040 --> 00:07:59,280
Speaker 5: so I really was intrigued by that idea, and I

170
00:07:59,400 --> 00:08:02,880
Speaker 5: joined the self having car game at Bosch and driving

171
00:08:02,960 --> 00:08:06,480
Speaker 5: up and down to eighty right its off seventy an hour.

172
00:08:07,000 --> 00:08:08,960
Speaker 3: Really we're scared.

173
00:08:09,160 --> 00:08:11,280
Speaker 5: There's nothing more scary than when you run your own

174
00:08:11,320 --> 00:08:13,920
Speaker 5: coat in that car and like it us all safe.

175
00:08:14,080 --> 00:08:15,239
Speaker 4: That sounds like, but it's.

176
00:08:15,040 --> 00:08:17,040
Speaker 2: Still you're like you're saying it's brand new, your b

177
00:08:17,760 --> 00:08:20,120
Speaker 2: it's just something that you don't know you created it.

178
00:08:20,600 --> 00:08:22,520
Speaker 2: You don't know it's gonna fucking work or crack. You

179
00:08:22,520 --> 00:08:24,880
Speaker 2: don't really know. You guys are the weirdos, Like I

180
00:08:24,920 --> 00:08:26,440
Speaker 2: said at the beginning of the show, you guys are

181
00:08:26,440 --> 00:08:29,080
Speaker 2: the smarty, weirdo dors that are figuring it out, that

182
00:08:29,200 --> 00:08:32,120
Speaker 2: have to work, that have to test drive it right right, yeah,

183
00:08:32,120 --> 00:08:34,800
Speaker 2: and then the fucking good it's crazy, yeah, And.

184
00:08:34,720 --> 00:08:37,240
Speaker 5: I mean the car just decides what it desires. It's

185
00:08:37,440 --> 00:08:39,880
Speaker 5: changed to all lanes at once, and it's like, okay,

186
00:08:40,040 --> 00:08:42,000
Speaker 5: why not it was free? But like you're sitting there like,

187
00:08:42,000 --> 00:08:42,640
Speaker 5: what's going on?

188
00:08:43,320 --> 00:08:46,480
Speaker 2: I don't think you put the right initials there? Do

189
00:08:46,600 --> 00:08:48,280
Speaker 2: the coding? Like that? Is it like x y Z.

190
00:08:48,480 --> 00:08:51,440
Speaker 2: That's like what is this coding that you guys do

191
00:08:51,480 --> 00:08:53,920
Speaker 2: to or something like that in a layman's term.

192
00:08:54,200 --> 00:08:57,240
Speaker 4: Oh yeah, it's it's I mean, that's the challenge. That's

193
00:08:57,240 --> 00:08:57,680
Speaker 4: the challenge.

194
00:08:57,720 --> 00:09:00,640
Speaker 5: That's why we don't see more robots out there in

195
00:09:00,679 --> 00:09:03,320
Speaker 5: this real world is because the real world is messy

196
00:09:03,360 --> 00:09:05,760
Speaker 5: and it's challenging, and it's a you know, like there's

197
00:09:05,840 --> 00:09:09,439
Speaker 5: humans which impact the robots and whatever.

198
00:09:09,559 --> 00:09:11,839
Speaker 4: So it's it's incredibly challenging.

199
00:09:11,440 --> 00:09:14,400
Speaker 5: To come up with rules which really work in these

200
00:09:15,160 --> 00:09:18,200
Speaker 5: in like in general situations. And so it was a

201
00:09:18,240 --> 00:09:20,480
Speaker 5: lot of simulation, a lot of testing. We had a

202
00:09:20,480 --> 00:09:22,600
Speaker 5: lot of data, but we could drive things. Before we

203
00:09:22,640 --> 00:09:24,800
Speaker 5: could we really deployed it on the actual car, but

204
00:09:25,000 --> 00:09:27,760
Speaker 5: tested there, and then we all had to do safety

205
00:09:28,400 --> 00:09:32,640
Speaker 5: safety tests and safety licenses to actually be able to

206
00:09:32,760 --> 00:09:35,319
Speaker 5: supervise the car while it's driving there. We are limited

207
00:09:35,600 --> 00:09:39,400
Speaker 5: to thirty minutes only because it's incredibly exhausting. If you're

208
00:09:39,440 --> 00:09:43,800
Speaker 5: sitting there and you can't, you have no influence. For Viole,

209
00:09:43,880 --> 00:09:46,720
Speaker 5: the car is driving itself, but you have to foresee

210
00:09:46,880 --> 00:09:49,280
Speaker 5: what the car might be doing. Because when you drive

211
00:09:49,280 --> 00:09:51,680
Speaker 5: yourself and you want to do a lane change, you think, okay,

212
00:09:51,679 --> 00:09:53,600
Speaker 5: I want to do a lane change, You check, and

213
00:09:53,679 --> 00:09:56,320
Speaker 5: you do the lane change. But if you're supervising it,

214
00:09:56,800 --> 00:09:59,160
Speaker 5: you at all times have to know what's going on

215
00:09:59,240 --> 00:10:01,720
Speaker 5: around everything because the car at some point is gonna

216
00:10:01,840 --> 00:10:03,840
Speaker 5: make the blinker and you have to know whether you

217
00:10:03,880 --> 00:10:07,120
Speaker 5: have to intervene or not. And so it's really really exhausting.

218
00:10:07,360 --> 00:10:08,560
Speaker 5: That's what people don't realize.

219
00:10:08,800 --> 00:10:10,480
Speaker 2: When you guys are on a test drive, it's more

220
00:10:10,520 --> 00:10:12,520
Speaker 2: than just a like, you guys are looking at every

221
00:10:12,600 --> 00:10:17,760
Speaker 2: simple little detail. Definitely in more than just a simple way.

222
00:10:17,960 --> 00:10:20,160
Speaker 2: Thank you for doing what you've done, you and your team. No,

223
00:10:20,200 --> 00:10:22,000
Speaker 2: I'm serious though, Like there's a lot of work that

224
00:10:22,040 --> 00:10:25,000
Speaker 2: you guys have done and you personally that that's you know,

225
00:10:25,800 --> 00:10:27,199
Speaker 2: Like I said at the beginning, I was excited to

226
00:10:27,240 --> 00:10:28,960
Speaker 2: talk with you and I'm not kidding, you know. I mean,

227
00:10:28,960 --> 00:10:31,400
Speaker 2: because guys like you are fucking what's amazing to me.

228
00:10:31,960 --> 00:10:34,280
Speaker 2: My brothers like that, I call them. I call you

229
00:10:34,320 --> 00:10:37,520
Speaker 2: guys like the Beverly Hills of the Bay Area.

230
00:10:37,640 --> 00:10:41,520
Speaker 3: You guys are the superstars. That's what the Bay Area is.

231
00:10:41,559 --> 00:10:44,400
Speaker 2: It's a bunch of geek, dorky rich people that are

232
00:10:44,440 --> 00:10:47,200
Speaker 2: superstars in my book. And they were in the high

233
00:10:47,240 --> 00:10:49,760
Speaker 2: school the geek and now I'm like, they're the guys

234
00:10:49,760 --> 00:10:52,439
Speaker 2: that are the superstars in the Bay Area, at least

235
00:10:52,520 --> 00:10:54,760
Speaker 2: in the tech world, right, Like, like that's what it

236
00:10:54,880 --> 00:10:55,520
Speaker 2: ends up being.

237
00:10:55,640 --> 00:10:58,120
Speaker 5: It definitely changed. It was kind of interesting. I'm being

238
00:10:58,120 --> 00:11:01,200
Speaker 5: a nerd growing up, very nerdy and like, yeah that stuff.

239
00:11:01,360 --> 00:11:03,600
Speaker 5: It was just like I grew up, it was like, well, oh,

240
00:11:03,640 --> 00:11:07,079
Speaker 5: you're like into computers and believing and robots. It was like, okay,

241
00:11:07,120 --> 00:11:09,080
Speaker 5: this is like the uninteresting nerd. And then you come

242
00:11:09,120 --> 00:11:10,920
Speaker 5: to the Bay Area and all of a sudden, I'm like, oh, wow,

243
00:11:11,080 --> 00:11:13,840
Speaker 5: you're doing this cool thing and it's like, oh yeah.

244
00:11:13,480 --> 00:11:16,120
Speaker 2: You guys spot each other everywhere at the cafe and

245
00:11:16,120 --> 00:11:21,439
Speaker 2: everybody You're like, hey, I see you. We're all together here, right,

246
00:11:21,520 --> 00:11:22,079
Speaker 2: you know what I mean?

247
00:11:22,240 --> 00:11:23,480
Speaker 3: Work and mindy styles.

248
00:11:23,640 --> 00:11:26,000
Speaker 4: I see you know the area, Yeah, but it.

249
00:11:26,120 --> 00:11:27,920
Speaker 2: Just it's just I get it because I've seen my

250
00:11:27,920 --> 00:11:30,800
Speaker 2: brother and I can get the culture. And I'm from

251
00:11:30,840 --> 00:11:34,840
Speaker 2: the Bay and I watched it happen and develop and

252
00:11:34,880 --> 00:11:37,080
Speaker 2: I love it and I love what these guys have

253
00:11:37,160 --> 00:11:39,880
Speaker 2: done and do because I have no clue, right, like,

254
00:11:39,960 --> 00:11:42,520
Speaker 2: I can't imagine figuring out things. And what did you

255
00:11:42,559 --> 00:11:43,960
Speaker 2: do after that? And I want to just lead up

256
00:11:43,960 --> 00:11:46,560
Speaker 2: to your company because I knew your background was interesting

257
00:11:46,880 --> 00:11:49,480
Speaker 2: and I wanted the people to hear who you are

258
00:11:49,960 --> 00:11:53,480
Speaker 2: as a man and what your experiences is before you

259
00:11:53,679 --> 00:11:57,760
Speaker 2: enter the cannabis industry, so to speak, every industry to

260
00:11:57,880 --> 00:12:02,520
Speaker 2: help out because I knew you come from a serious

261
00:12:02,559 --> 00:12:08,960
Speaker 2: background with real intelligence and real data that you use

262
00:12:09,120 --> 00:12:12,240
Speaker 2: to create net Leaf. So what did you do after

263
00:12:12,320 --> 00:12:14,119
Speaker 2: you did that with the cars?

264
00:12:14,400 --> 00:12:16,840
Speaker 5: Yeah, so I thot I was broking on the Southern

265
00:12:16,880 --> 00:12:18,960
Speaker 5: cars of Bosh and the Lucid motors. And then I

266
00:12:19,040 --> 00:12:21,319
Speaker 5: went on to Google X where I broke that the

267
00:12:21,600 --> 00:12:24,439
Speaker 5: Everyday Robot project and I was.

268
00:12:24,440 --> 00:12:26,680
Speaker 3: Oh, so you're a high engineer, your Google X guy.

269
00:12:26,720 --> 00:12:27,000
Speaker 2: Wow.

270
00:12:27,880 --> 00:12:30,120
Speaker 4: Yeah, I worked there for like four and a half years.

271
00:12:30,200 --> 00:12:32,200
Speaker 2: I mean, the car alone and then go to Google X.

272
00:12:32,200 --> 00:12:32,959
Speaker 3: That's your big time.

273
00:12:33,160 --> 00:12:34,040
Speaker 4: It was kind of fun.

274
00:12:34,440 --> 00:12:36,040
Speaker 5: It was like they were like you had like a

275
00:12:36,080 --> 00:12:39,800
Speaker 5: lot of fleet of robots driving around the facility, interacting

276
00:12:39,840 --> 00:12:42,640
Speaker 5: with the environment, grabbing stuff while you were like broking there.

277
00:12:42,760 --> 00:12:43,480
Speaker 4: It was surreal.

278
00:12:43,600 --> 00:12:47,319
Speaker 2: And Google's the best place right, free everything, snacks, food, layer,

279
00:12:47,480 --> 00:12:49,760
Speaker 2: layout area. Like my brother does stuff with Google all

280
00:12:49,760 --> 00:12:51,480
Speaker 2: the time, and when he goes there, he's like, dude,

281
00:12:51,480 --> 00:12:52,760
Speaker 2: this fucking place is incredible.

282
00:12:52,960 --> 00:12:53,680
Speaker 3: It's the nicest place.

283
00:12:54,360 --> 00:12:55,840
Speaker 4: Like they did treat you well.

284
00:12:55,960 --> 00:12:57,599
Speaker 5: And so you were there for how long four and

285
00:12:57,640 --> 00:13:00,640
Speaker 5: a half years, okay, And all these projects, it's always

286
00:13:00,679 --> 00:13:03,559
Speaker 5: we're very far fetched or like that long road my

287
00:13:03,679 --> 00:13:05,760
Speaker 5: self driving cars, Like yeah, generally like.

288
00:13:05,720 --> 00:13:08,440
Speaker 2: If there's no it's done in two weeks, two years,

289
00:13:08,720 --> 00:13:11,920
Speaker 2: we don't know. We're fucking working on it. Every day.

290
00:13:11,920 --> 00:13:14,360
Speaker 2: You're just working on some ship, Like we don't know

291
00:13:14,360 --> 00:13:16,760
Speaker 2: how far we're gonna get today, but let's figure it

292
00:13:16,800 --> 00:13:17,920
Speaker 2: out exactly.

293
00:13:18,320 --> 00:13:20,839
Speaker 5: And so I was just like I really enjoyed working

294
00:13:20,840 --> 00:13:23,200
Speaker 5: at Lucid Motors when it was too small and small team,

295
00:13:23,360 --> 00:13:29,000
Speaker 5: really amazing engineers all together creating something valuable and meaningful.

296
00:13:29,440 --> 00:13:30,040
Speaker 4: And so that.

297
00:13:30,120 --> 00:13:32,640
Speaker 5: Definitely life changing, right right. It was just I want

298
00:13:32,679 --> 00:13:34,840
Speaker 5: to work in the small environment again. And at the

299
00:13:34,880 --> 00:13:36,760
Speaker 5: same time, I live in Santa Cruz, you know, and

300
00:13:36,800 --> 00:13:40,760
Speaker 5: as you know, the Monterey Bay is packed with agriculture.

301
00:13:40,840 --> 00:13:42,199
Speaker 4: There's so many cultivators.

302
00:13:42,200 --> 00:13:45,400
Speaker 5: There's riskles there and like a lot of farms, and

303
00:13:45,600 --> 00:13:49,480
Speaker 5: so I learned about, you know, cultivations and their challenges,

304
00:13:49,520 --> 00:13:53,320
Speaker 5: and everyone talked about the Fourth agricultural Revolution, you know,

305
00:13:53,400 --> 00:13:58,720
Speaker 5: like data, data driven agriculture and automation and controlled environment

306
00:13:58,760 --> 00:14:02,120
Speaker 5: agriculture SEA. And they showed me like their greenhouses and

307
00:14:02,160 --> 00:14:04,679
Speaker 5: they pointed at this sensor in the middle, like we

308
00:14:04,880 --> 00:14:07,000
Speaker 5: was dangling there, and they're like, well, this is what

309
00:14:07,120 --> 00:14:10,560
Speaker 5: drives our controllers and everything is the data we collect there.

310
00:14:10,960 --> 00:14:14,000
Speaker 5: And I was like, I know what you can do

311
00:14:14,080 --> 00:14:15,679
Speaker 5: with it. I know that you can do a lot

312
00:14:15,960 --> 00:14:19,520
Speaker 5: with data. That's my whole career, but this is not

313
00:14:19,520 --> 00:14:22,840
Speaker 5: going to cut it. And then I looked at how

314
00:14:22,880 --> 00:14:25,520
Speaker 5: they operate these facilities, you know, like there were still

315
00:14:25,520 --> 00:14:27,480
Speaker 5: a lot of facilities which had pen and paper, you know,

316
00:14:27,520 --> 00:14:30,280
Speaker 5: like at the little displays and they're writing down the

317
00:14:30,360 --> 00:14:34,720
Speaker 5: notes and they read there. And I saw that the

318
00:14:34,760 --> 00:14:39,240
Speaker 5: cultivators go into these environments and they really heavily rely

319
00:14:39,360 --> 00:14:43,160
Speaker 5: on their visual feet. They look at the plants, they

320
00:14:43,200 --> 00:14:46,400
Speaker 5: absorb what they see there, they understand how like the

321
00:14:46,480 --> 00:14:49,760
Speaker 5: language of the plants, so to say, and that's how

322
00:14:49,760 --> 00:14:50,640
Speaker 5: they make their decisions.

323
00:14:51,320 --> 00:14:52,800
Speaker 4: And as I.

324
00:14:52,840 --> 00:14:55,240
Speaker 5: Learn more and more about it, I got incredibly intrigued

325
00:14:55,280 --> 00:14:59,720
Speaker 5: because this is an incredibly complex problem these people are solving.

326
00:15:00,160 --> 00:15:03,960
Speaker 5: There is so many factors impacting the plants, like the

327
00:15:04,080 --> 00:15:08,200
Speaker 5: air is flowing, the temperature, humidity, nutrients, irrigation, you know

328
00:15:08,400 --> 00:15:11,720
Speaker 5: COO two light, and all that is correlated, you know,

329
00:15:11,720 --> 00:15:13,360
Speaker 5: like if you increase one thing, you have to change

330
00:15:13,360 --> 00:15:15,520
Speaker 5: the other thing, etcetera. And they are in there and

331
00:15:15,560 --> 00:15:19,040
Speaker 5: they have to guarantee that the plants are happy every day,

332
00:15:19,360 --> 00:15:22,760
Speaker 5: every minute. If something gets screwed up, it's like done,

333
00:15:22,800 --> 00:15:24,720
Speaker 5: you know, like that impacts the yield and the damage

334
00:15:24,760 --> 00:15:25,200
Speaker 5: is done.

335
00:15:25,560 --> 00:15:27,360
Speaker 4: And so you use a lot of money and you

336
00:15:27,480 --> 00:15:28,320
Speaker 4: lose a lot of money.

337
00:15:28,680 --> 00:15:32,360
Speaker 5: And then I also like encountered like cannabis cultivators and

338
00:15:33,040 --> 00:15:37,480
Speaker 5: realize that cannabis is an incredibly fascinating plant, not just

339
00:15:37,640 --> 00:15:39,000
Speaker 5: because of when.

340
00:15:38,800 --> 00:15:40,960
Speaker 4: You consume it, but also like when you grow it.

341
00:15:40,960 --> 00:15:43,880
Speaker 5: It's one of the fastest growing plants on earth really

342
00:15:44,000 --> 00:15:47,240
Speaker 5: yeah it's cannabis. Is like, yeah, it's one at least

343
00:15:47,240 --> 00:15:51,600
Speaker 5: some land of interest bamboo and then cannabis. But it's

344
00:15:51,640 --> 00:15:54,960
Speaker 5: really also like a challenging plant, not just because it

345
00:15:55,000 --> 00:15:57,240
Speaker 5: grows so fast, but also because it has odor like

346
00:15:57,240 --> 00:16:01,600
Speaker 5: attacks pasts, and it's not easy and no one has

347
00:16:01,640 --> 00:16:03,800
Speaker 5: really started it because it was illegal.

348
00:16:03,960 --> 00:16:05,280
Speaker 4: And so now you have this.

349
00:16:05,360 --> 00:16:08,880
Speaker 5: New plan, you know, like this once in a lifetime opportunity.

350
00:16:08,920 --> 00:16:11,080
Speaker 5: We have a new plan which no one really understands

351
00:16:11,080 --> 00:16:12,760
Speaker 5: how to grow because no one really has done it

352
00:16:12,760 --> 00:16:16,960
Speaker 5: at large scale and with high margins and a lot

353
00:16:17,000 --> 00:16:18,840
Speaker 5: of demand, and yeah, how do you do it? And

354
00:16:18,880 --> 00:16:21,840
Speaker 5: so there was this crazy gap of like being able

355
00:16:21,880 --> 00:16:26,280
Speaker 5: to quickly learn and adjust and improve all these processes

356
00:16:26,360 --> 00:16:30,200
Speaker 5: for cannabis cultivators. And that's why it was not even

357
00:16:30,240 --> 00:16:31,800
Speaker 5: like by our choice that we said like, okay, we're

358
00:16:31,840 --> 00:16:34,280
Speaker 5: gonna target cannabis right away. It was like cannabis just

359
00:16:34,280 --> 00:16:36,040
Speaker 5: pulled us in. It was like we need that, we

360
00:16:36,120 --> 00:16:39,040
Speaker 5: want that, we want to be the first. And that's

361
00:16:39,040 --> 00:16:40,360
Speaker 5: where we said, like, you know, this makes a lot

362
00:16:40,400 --> 00:16:43,160
Speaker 5: of sense, even Tesla, if you think about it, if

363
00:16:43,200 --> 00:16:46,320
Speaker 5: you develop a new technology that's just started with the

364
00:16:46,560 --> 00:16:49,200
Speaker 5: roadster with the highest margin market and then the bend

365
00:16:49,240 --> 00:16:50,000
Speaker 5: to the model.

366
00:16:49,720 --> 00:16:51,520
Speaker 4: Free and it's a model ass model free.

367
00:16:51,560 --> 00:16:54,040
Speaker 5: So that's how you also, like we develop a new

368
00:16:54,040 --> 00:16:56,920
Speaker 5: technology is starting with the highest margin market cannabis and

369
00:16:56,960 --> 00:16:58,280
Speaker 5: then go to other crops.

370
00:16:59,240 --> 00:17:00,280
Speaker 4: Very sleeping.

371
00:17:01,920 --> 00:17:04,320
Speaker 2: So anyone can use this. We're gonna take a break

372
00:17:04,359 --> 00:17:06,480
Speaker 2: real quick. We're gonna come back talk to elmre on

373
00:17:06,520 --> 00:17:08,879
Speaker 2: how it works. And I want to know more and

374
00:17:08,920 --> 00:17:12,280
Speaker 2: geek out on Like, I'm sure you couldn't tell me

375
00:17:12,440 --> 00:17:14,600
Speaker 2: all of the things that you use for the data

376
00:17:14,640 --> 00:17:17,520
Speaker 2: for it. I'm just sure there's so many. But what

377
00:17:17,640 --> 00:17:20,240
Speaker 2: kind of things on top of what you just mentioned?

378
00:17:20,280 --> 00:17:22,840
Speaker 2: How do you gather that data? How does this thing work?

379
00:17:23,200 --> 00:17:26,480
Speaker 2: I watched the demo online. It reminds me of if

380
00:17:26,480 --> 00:17:30,520
Speaker 2: you're listening the camera on Monday night football that's following

381
00:17:30,520 --> 00:17:33,400
Speaker 2: everybody in the middle of the football field. It kind

382
00:17:33,400 --> 00:17:35,960
Speaker 2: of reminds me of that in a just broad let

383
00:17:35,960 --> 00:17:38,000
Speaker 2: me explain it to you in a way weird way

384
00:17:38,280 --> 00:17:40,480
Speaker 2: of how can this thing work? Well, it's like that

385
00:17:40,520 --> 00:17:42,440
Speaker 2: camera in the middle of the football field, but that's

386
00:17:42,480 --> 00:17:45,359
Speaker 2: your crop. That's a good way of describing it.

387
00:17:45,359 --> 00:17:48,080
Speaker 4: It is it's like a football stadium camera in your

388
00:17:48,080 --> 00:17:49,680
Speaker 4: greenhouse contactingo facility.

389
00:17:49,760 --> 00:17:52,840
Speaker 5: Yeah, keeping an eye on your glast tinety or seven

390
00:17:53,200 --> 00:17:56,600
Speaker 5: and mess enough doesn't capture images, but all it's like temperature.

391
00:17:56,640 --> 00:17:58,160
Speaker 2: Well, we're gonna talk about that when we come back.

392
00:17:58,160 --> 00:18:00,879
Speaker 2: It's kind of just top one oh one neatly dot com.

393
00:18:01,000 --> 00:18:02,320
Speaker 3: We'll be right back after this place.

394
00:18:02,800 --> 00:18:06,960
Speaker 1: Follow d at one Christopher Wright, Follow Joe Grunde at

395
00:18:07,080 --> 00:18:10,760
Speaker 1: Joe Grunde fifty two. Subscribe to our weekly newsletter on

396
00:18:10,800 --> 00:18:22,400
Speaker 1: our website, Cannabis Talk one on one dot com. Welcome

397
00:18:22,440 --> 00:18:24,080
Speaker 1: back to Cannabi's Talk one on.

398
00:18:24,160 --> 00:18:25,760
Speaker 4: One number one.

399
00:18:27,359 --> 00:18:29,240
Speaker 2: An I want you guys to turn your typical into

400
00:18:29,240 --> 00:18:31,439
Speaker 2: something special. Folks, when it comes down to use products

401
00:18:31,440 --> 00:18:32,600
Speaker 2: to play viutation.

402
00:18:32,320 --> 00:18:34,320
Speaker 3: Be just as enjoyable as the feeling your experience.

403
00:18:34,560 --> 00:18:38,440
Speaker 2: Make sure you chere about the website lorn Oils dot com. Elmermare,

404
00:18:38,560 --> 00:18:39,680
Speaker 2: the founder and.

405
00:18:39,720 --> 00:18:41,600
Speaker 3: The CEO of neat Leaf.

406
00:18:42,400 --> 00:18:46,159
Speaker 2: I love how you found it, how cannabis attracted you

407
00:18:46,280 --> 00:18:52,679
Speaker 2: to make this. Tell me how this works and what

408
00:18:52,760 --> 00:18:54,560
Speaker 2: did you do to put into this to make it

409
00:18:54,600 --> 00:18:57,360
Speaker 2: do what it's doing right. We'll get closer to that night.

410
00:18:57,880 --> 00:19:01,960
Speaker 5: Uh well, so so as I said earlier like, if

411
00:19:01,960 --> 00:19:04,159
Speaker 5: you think about what cultivator's view is, you have to

412
00:19:04,280 --> 00:19:07,360
Speaker 5: understand the environment. They have to see their plans and

413
00:19:07,400 --> 00:19:09,719
Speaker 5: like from different angles. It's not like they go in

414
00:19:09,760 --> 00:19:11,760
Speaker 5: there and to start staring at the plant from one

415
00:19:11,760 --> 00:19:14,000
Speaker 5: point of view, but they go around they check out

416
00:19:14,040 --> 00:19:15,600
Speaker 5: the bands to really understand what's going on.

417
00:19:15,880 --> 00:19:17,959
Speaker 4: So we s like, well, we have to come up

418
00:19:17,960 --> 00:19:20,920
Speaker 4: with a system which is a low cost because it's

419
00:19:20,920 --> 00:19:23,520
Speaker 4: in cultivation from they always applied to their expenses.

420
00:19:24,480 --> 00:19:27,240
Speaker 5: It has to operate twenty four seven out of the

421
00:19:27,280 --> 00:19:30,199
Speaker 5: way of cultivators, so it's not a burn, and it

422
00:19:30,200 --> 00:19:32,720
Speaker 5: has to measure all these factors to give a comprehensive

423
00:19:32,760 --> 00:19:36,000
Speaker 5: picture of what's going on in that space. And we

424
00:19:36,080 --> 00:19:38,160
Speaker 5: looked at the space and we realized, well, the one

425
00:19:38,160 --> 00:19:41,439
Speaker 5: space which is in general available and free and accessible.

426
00:19:40,920 --> 00:19:42,520
Speaker 4: Because their air has to go through and the light

427
00:19:42,560 --> 00:19:44,280
Speaker 4: has to come through, is above the canopy.

428
00:19:44,720 --> 00:19:47,359
Speaker 5: And so let's put some system there which moves around

429
00:19:47,440 --> 00:19:50,040
Speaker 5: and takes all these measurements and captures all this data.

430
00:19:50,440 --> 00:19:53,680
Speaker 5: And yeah, that's what the need of Spider is. It's

431
00:19:52,760 --> 00:19:56,960
Speaker 5: a cable based robot so to say, which is like

432
00:19:57,000 --> 00:19:59,600
Speaker 5: a football stadium camera and in the middle it has

433
00:19:59,600 --> 00:20:02,120
Speaker 5: this little box which is in my hand here, and

434
00:20:02,240 --> 00:20:06,960
Speaker 5: it has sensors like temperature, humidity CO two. It measures

435
00:20:06,960 --> 00:20:10,359
Speaker 5: the plant height for biomass estimation. It measures the leaf

436
00:20:10,400 --> 00:20:13,199
Speaker 5: temperature for so that you can compute the leaf VPD,

437
00:20:13,320 --> 00:20:15,600
Speaker 5: not just the airpd, the leaf EPD, which is one

438
00:20:15,600 --> 00:20:18,880
Speaker 5: of the most crucial factors which you put like which

439
00:20:18,920 --> 00:20:23,560
Speaker 5: you can use in cultivation. Different images, regular RGB images,

440
00:20:23,640 --> 00:20:26,320
Speaker 5: multispector images. And on the other side, we have a

441
00:20:26,400 --> 00:20:31,440
Speaker 5: light sensor for actually measuring your light intensity PPFD and

442
00:20:31,800 --> 00:20:35,160
Speaker 5: that moves around you canopy now twenty four seven. It's

443
00:20:35,160 --> 00:20:37,600
Speaker 5: on cable, so it can also lower itself down to

444
00:20:37,720 --> 00:20:41,240
Speaker 5: the canopy, so measure where it really matters and do

445
00:20:41,400 --> 00:20:43,920
Speaker 5: that out of your way. So at the end of

446
00:20:43,960 --> 00:20:46,399
Speaker 5: the day you get multiple times a day a full

447
00:20:46,480 --> 00:20:50,680
Speaker 5: overview of your cannopy images like Google maps basically, and

448
00:20:50,760 --> 00:20:54,640
Speaker 5: we have AI picking up on plant stress, you pick

449
00:20:54,760 --> 00:20:59,720
Speaker 5: up on yellowing, the crosis, folding, beast, spowdery, mildew out

450
00:20:59,720 --> 00:21:03,080
Speaker 5: of things, and we report that as a heat map,

451
00:21:03,119 --> 00:21:05,680
Speaker 5: so you see where in your space are issues.

452
00:21:06,119 --> 00:21:07,920
Speaker 2: Is that how it shows up on your computer or

453
00:21:08,240 --> 00:21:09,960
Speaker 2: will it show you each plant.

454
00:21:09,640 --> 00:21:11,919
Speaker 4: Too, Yes, so you basically can.

455
00:21:12,080 --> 00:21:13,919
Speaker 2: So it's giving you, like you said, the Google maps

456
00:21:14,240 --> 00:21:18,959
Speaker 2: of Google Map, a live read of the room, every

457
00:21:19,119 --> 00:21:22,840
Speaker 2: single street corner, like that's the street corner whatever, whatever, right.

458
00:21:22,920 --> 00:21:28,080
Speaker 5: And even better, it counts all these occurrences. It counts

459
00:21:28,119 --> 00:21:31,480
Speaker 5: each yellow leaf, each necrotic leaf, so that you can

460
00:21:31,520 --> 00:21:34,600
Speaker 5: compare it. You can go back in time, understand when

461
00:21:34,600 --> 00:21:36,639
Speaker 5: did it start, how did it evolve? How much did

462
00:21:36,640 --> 00:21:39,439
Speaker 5: I have yesterday. There's no way that anyone can go

463
00:21:39,520 --> 00:21:42,240
Speaker 5: into these facilities and like say, oh there's two percent

464
00:21:42,280 --> 00:21:44,840
Speaker 5: more yellowing today, there's five percent less folding.

465
00:21:44,920 --> 00:21:46,760
Speaker 4: No one can count these things. It's all.

466
00:21:46,920 --> 00:21:48,640
Speaker 2: But now your system count you can.

467
00:21:49,160 --> 00:21:51,960
Speaker 5: Now you have an immediate feedback loop which tells you

468
00:21:52,000 --> 00:21:54,399
Speaker 5: when you make a change, you change the nutrients, you

469
00:21:54,480 --> 00:21:56,919
Speaker 5: make an an sop, you know, like spraying or whatever,

470
00:21:57,200 --> 00:21:59,560
Speaker 5: you get immediate feedback whether this was a good decision

471
00:21:59,600 --> 00:22:01,560
Speaker 5: or not. Us your plans are gonna tell you you're

472
00:22:01,600 --> 00:22:04,200
Speaker 5: gonna see two percent more of something like yellowing on

473
00:22:04,240 --> 00:22:05,000
Speaker 5: the chlorosis.

474
00:22:05,560 --> 00:22:06,720
Speaker 4: And yeah, and.

475
00:22:06,640 --> 00:22:09,960
Speaker 5: So that's new and there's no such system out there.

476
00:22:10,040 --> 00:22:10,800
Speaker 2: Yeah, I've never heard this.

477
00:22:11,119 --> 00:22:13,600
Speaker 4: And you can change the way how you cultivate.

478
00:22:14,040 --> 00:22:17,159
Speaker 2: Really, I mean, some people are losing their jobs. But

479
00:22:17,240 --> 00:22:19,240
Speaker 2: it happens. I mean, somebody's got to monitor that at least.

480
00:22:19,280 --> 00:22:20,480
Speaker 2: I mean, there's a few cats that are going to

481
00:22:20,560 --> 00:22:22,800
Speaker 2: lose your job. But you know, I'm gonna save you money.

482
00:22:23,040 --> 00:22:25,040
Speaker 2: So if it's saves you money on a body or two,

483
00:22:25,640 --> 00:22:27,320
Speaker 2: how much does this cost me to put up?

484
00:22:28,920 --> 00:22:29,960
Speaker 4: We have two options.

485
00:22:30,000 --> 00:22:32,720
Speaker 5: You can either rent it, so we basically just got

486
00:22:32,720 --> 00:22:35,240
Speaker 5: a rental. The first system be installed for free. Just

487
00:22:35,280 --> 00:22:37,320
Speaker 5: come to your place, install it for free, and you

488
00:22:37,320 --> 00:22:39,879
Speaker 5: can rent it. So there's no commitment necessary. We believe

489
00:22:39,920 --> 00:22:41,800
Speaker 5: in the system and so far we haven't lost a

490
00:22:41,880 --> 00:22:42,520
Speaker 5: single customer.

491
00:22:42,640 --> 00:22:43,840
Speaker 4: So that's why we go.

492
00:22:43,840 --> 00:22:45,879
Speaker 2: Ahead and rent it. Start off with the rental, just

493
00:22:46,000 --> 00:22:48,080
Speaker 2: we don't want to buy a call, rent it, let

494
00:22:48,080 --> 00:22:49,600
Speaker 2: it tell us, tell us if it doesn't.

495
00:22:49,359 --> 00:22:50,720
Speaker 4: Want to go and improve it, and like you can

496
00:22:50,920 --> 00:22:52,040
Speaker 4: give it back no problem.

497
00:22:52,320 --> 00:22:53,800
Speaker 3: And so that's fucking brilliant.

498
00:22:53,800 --> 00:22:56,879
Speaker 2: Dog. That'sy I'm so confident with. Just try it, rent it.

499
00:22:56,880 --> 00:22:58,200
Speaker 2: If you don't like it, turned back, turned back and

500
00:22:58,359 --> 00:23:00,520
Speaker 2: way whatever, right, just rent it out for now. If

501
00:23:00,560 --> 00:23:02,239
Speaker 2: it doesn't work for you, then I'll take it right back.

502
00:23:02,480 --> 00:23:07,080
Speaker 2: Right you're reading it, And that's balls, that's ballsy.

503
00:23:07,200 --> 00:23:09,120
Speaker 5: We don't want to sell a contract. We want to

504
00:23:09,160 --> 00:23:09,840
Speaker 5: provide value.

505
00:23:09,920 --> 00:23:12,000
Speaker 2: No Elmer, You're putting your nuts on the table and said,

506
00:23:12,000 --> 00:23:15,320
Speaker 2: I can guarantee my shit, it's gonna work. And I

507
00:23:15,440 --> 00:23:17,480
Speaker 2: know it. Why because I've worked for the fucking Eye

508
00:23:17,520 --> 00:23:21,000
Speaker 2: topp this car, Google everybody else. Yeah, and I've seen

509
00:23:21,080 --> 00:23:22,960
Speaker 2: Joe's ass burn mark, so you know what I mean.

510
00:23:23,200 --> 00:23:24,639
Speaker 2: I've been around for a little while.

511
00:23:25,320 --> 00:23:27,399
Speaker 4: I could tell, I could tell yeah.

512
00:23:27,480 --> 00:23:29,399
Speaker 5: Yeah. But it's really like, you have to believe in

513
00:23:29,400 --> 00:23:31,480
Speaker 5: your product, and we do, and so far, I think

514
00:23:31,560 --> 00:23:34,280
Speaker 5: we got really good success with that. So we we

515
00:23:34,320 --> 00:23:35,600
Speaker 5: have it like, yes, what's.

516
00:23:35,440 --> 00:23:37,760
Speaker 2: The rental cost? How much a monthly? What is it?

517
00:23:37,800 --> 00:23:40,680
Speaker 5: So it's three hundred dollars a month for the hardware,

518
00:23:40,920 --> 00:23:43,440
Speaker 5: So if you think about it depends on your size.

519
00:23:43,480 --> 00:23:45,240
Speaker 5: Now we can go up to five thousand square foot.

520
00:23:45,840 --> 00:23:48,560
Speaker 2: That's nothing. Three hundred bucks a month. Perfect, that's great.

521
00:23:48,600 --> 00:23:51,040
Speaker 4: And then there's a service that's fucking great. Yeah.

522
00:23:51,320 --> 00:23:53,439
Speaker 5: Well there's no service fee for like the AI and

523
00:23:53,480 --> 00:23:55,360
Speaker 5: all this stuffis comes on top, which is twenty cents

524
00:23:55,359 --> 00:23:57,479
Speaker 5: of square foot. So it depends then on that scales

525
00:23:57,520 --> 00:23:58,280
Speaker 5: with the size.

526
00:23:58,720 --> 00:24:02,520
Speaker 2: But okay, so it's three hundred plus your size twenty cents.

527
00:24:02,680 --> 00:24:06,480
Speaker 2: Let's work. But yeah, still not bad, no, okay, And

528
00:24:06,520 --> 00:24:09,399
Speaker 2: the AI is grabbing every well, what is some of

529
00:24:09,400 --> 00:24:11,400
Speaker 2: the information that's grabbing as it's going up and down

530
00:24:11,440 --> 00:24:12,240
Speaker 2: the plants and everything.

531
00:24:12,280 --> 00:24:17,320
Speaker 5: Temperature, humidity CO two leaf VPD, leaf temperature on each plant,

532
00:24:17,840 --> 00:24:21,000
Speaker 5: on everywhere. It's like going around twenty four to seven

533
00:24:21,160 --> 00:24:21,879
Speaker 5: measuring distance.

534
00:24:22,000 --> 00:24:24,640
Speaker 2: And then you can pull up the program on your

535
00:24:24,640 --> 00:24:30,040
Speaker 2: computer and see my six hundred plants. I'm gonna see

536
00:24:30,080 --> 00:24:33,400
Speaker 2: row one, row two, row three, row four, and I'm

537
00:24:33,400 --> 00:24:35,680
Speaker 2: gonna be able to see in row four this plant

538
00:24:35,720 --> 00:24:38,880
Speaker 2: in the top middles got a little mark on it.

539
00:24:40,000 --> 00:24:42,320
Speaker 5: But if you're gonna get told that, like AI is

540
00:24:42,359 --> 00:24:44,600
Speaker 5: gonna pick up on that, and that's like, hey, you

541
00:24:44,640 --> 00:24:47,480
Speaker 5: want to check out this, there's something going on. And

542
00:24:47,960 --> 00:24:51,119
Speaker 5: so you have the summary, so be constantly evolving this

543
00:24:51,160 --> 00:24:51,560
Speaker 5: whole thing.

544
00:24:51,560 --> 00:24:54,760
Speaker 4: But it's really amazing what you can do with this data.

545
00:24:54,960 --> 00:24:57,800
Speaker 5: You don't just like measure the plants precip we also

546
00:24:58,119 --> 00:25:02,960
Speaker 5: detect each butt and so you basically can say I

547
00:25:03,000 --> 00:25:05,080
Speaker 5: have five percent more abouts in this corner, I have

548
00:25:05,280 --> 00:25:09,520
Speaker 5: more larger butt size in this area. You get micro climates,

549
00:25:09,640 --> 00:25:11,840
Speaker 5: you have you understand, Oh this is my beat corner here,

550
00:25:11,920 --> 00:25:14,280
Speaker 5: have a systematic issue with my environment wage I need

551
00:25:14,320 --> 00:25:18,000
Speaker 5: to fix. And yeah, so you but you can really

552
00:25:18,160 --> 00:25:20,960
Speaker 5: tie this loop now where you know you can forecast

553
00:25:21,000 --> 00:25:23,080
Speaker 5: your yield you cannot get a better expectation of what

554
00:25:23,200 --> 00:25:25,359
Speaker 5: labor you need in terms of like trimming, et cetera.

555
00:25:26,160 --> 00:25:29,200
Speaker 5: But also you really understand how the different parameters impact

556
00:25:29,240 --> 00:25:30,920
Speaker 5: that your yield so that you can do a way

557
00:25:30,960 --> 00:25:32,840
Speaker 5: better job at your prop steering.

558
00:25:33,200 --> 00:25:36,119
Speaker 2: Wow, and as I look at your website, pull it

559
00:25:36,200 --> 00:25:39,320
Speaker 2: up on the YouTube, you guys, it's just so dope

560
00:25:39,400 --> 00:25:41,800
Speaker 2: how it just pulls it everywhere like that. I mean,

561
00:25:43,440 --> 00:25:46,040
Speaker 2: I just feel like it's just beyond cool. And the

562
00:25:46,080 --> 00:25:48,479
Speaker 2: extra cord that's there and able to go up and

563
00:25:48,560 --> 00:25:52,120
Speaker 2: down and around. You guys figured it out. I mean,

564
00:25:52,160 --> 00:25:55,000
Speaker 2: it's it's a genie. How big is the other component?

565
00:25:55,320 --> 00:25:57,920
Speaker 2: Are all the corners have the big component? Like a

566
00:25:59,320 --> 00:26:03,520
Speaker 2: probably like how would you call that not a garage

567
00:26:03,640 --> 00:26:07,640
Speaker 2: but a garage for the cable, you know, how it houses.

568
00:26:07,200 --> 00:26:09,200
Speaker 4: The cable or like a corner piece?

569
00:26:09,640 --> 00:26:11,040
Speaker 3: How big is that those pieces?

570
00:26:11,200 --> 00:26:14,000
Speaker 4: It's about like I've been this size, Like I think

571
00:26:14,119 --> 00:26:15,840
Speaker 4: two feet is right right size?

572
00:26:16,400 --> 00:26:16,600
Speaker 3: Uh?

573
00:26:16,640 --> 00:26:19,160
Speaker 2: And on each corner, just one on each corner corner

574
00:26:19,320 --> 00:26:21,040
Speaker 2: taking a boom boom, boom boom, and.

575
00:26:21,000 --> 00:26:23,920
Speaker 4: It just moves around. But it's super easy to install.

576
00:26:24,160 --> 00:26:26,600
Speaker 5: It takes like a couple of hours and it's done

577
00:26:26,640 --> 00:26:28,840
Speaker 5: so like some unistrats at your wall or at your

578
00:26:28,880 --> 00:26:32,679
Speaker 5: greenhouse infrastructure and you have a mountain plate force crews

579
00:26:32,880 --> 00:26:35,399
Speaker 5: it on, hang in the corner pieces, you pull the

580
00:26:35,440 --> 00:26:39,320
Speaker 5: cable connected to the platform. It play, It calibrates itself,

581
00:26:39,480 --> 00:26:41,920
Speaker 5: boom down. So we show up in general in the

582
00:26:41,960 --> 00:26:43,560
Speaker 5: morning and were out of your hair in the afternoon

583
00:26:43,560 --> 00:26:44,360
Speaker 5: at the systems.

584
00:26:44,040 --> 00:26:47,040
Speaker 2: Isn't And then what that just connects to a website,

585
00:26:47,280 --> 00:26:47,919
Speaker 2: a app?

586
00:26:48,000 --> 00:26:49,800
Speaker 3: How do I are both? Or what is it?

587
00:26:49,720 --> 00:26:52,640
Speaker 5: It uploads the data to the cloud where the AI

588
00:26:52,800 --> 00:26:55,000
Speaker 5: does the processing, and then you can look at the

589
00:26:55,080 --> 00:26:58,160
Speaker 5: data from anywhere, from your phone, from your tablet, from

590
00:26:58,200 --> 00:27:00,320
Speaker 5: anywhere in the world, from your beat, from the each

591
00:27:00,400 --> 00:27:03,520
Speaker 5: and yeah, get these reports and these analysis.

592
00:27:04,240 --> 00:27:05,320
Speaker 3: I really like the product.

593
00:27:05,840 --> 00:27:10,439
Speaker 2: I think what you're doing, Elmer, in my opinion for

594
00:27:10,480 --> 00:27:16,000
Speaker 2: the cannabis industry is bringing that expertise that we all

595
00:27:16,080 --> 00:27:18,639
Speaker 2: need and love in any industry, right like the doctor

596
00:27:18,680 --> 00:27:24,840
Speaker 2: of the medical whatever. You're bringing your engineering expertise that

597
00:27:24,920 --> 00:27:29,560
Speaker 2: you've created from the worlds from Germany to the States

598
00:27:30,200 --> 00:27:34,440
Speaker 2: to experience on working with technology and advancements where have

599
00:27:34,560 --> 00:27:36,760
Speaker 2: no idea how you're going to get there, but you

600
00:27:36,840 --> 00:27:38,800
Speaker 2: can get there, and you've done it and you've figured

601
00:27:38,840 --> 00:27:43,280
Speaker 2: things out, and now you're using that brain and bringing

602
00:27:43,320 --> 00:27:47,560
Speaker 2: it to the cannabis space to help grow medicine for

603
00:27:47,640 --> 00:27:52,280
Speaker 2: everyone around the world. Because your product can be used

604
00:27:52,280 --> 00:27:55,760
Speaker 2: around the world, doesn't matter where you're at, even in

605
00:27:55,960 --> 00:28:00,719
Speaker 2: illegal countries or using crops whatever, you have a valid

606
00:28:00,800 --> 00:28:04,119
Speaker 2: company that it's an ancillary product, has nothing to do

607
00:28:04,200 --> 00:28:06,760
Speaker 2: with cannabis, right, I mean nothing at all.

608
00:28:07,320 --> 00:28:08,280
Speaker 3: How'd you come up with the name?

609
00:28:08,920 --> 00:28:09,040
Speaker 5: Uh?

610
00:28:09,320 --> 00:28:11,280
Speaker 4: Well, it was more a reverse search.

611
00:28:11,359 --> 00:28:14,840
Speaker 5: We tried to have like a dot com to Maine,

612
00:28:14,880 --> 00:28:18,440
Speaker 5: which is incredibly heart these days. Uh and so we yeah,

613
00:28:18,480 --> 00:28:21,840
Speaker 5: we just brainstormed names and came up with Neatly dot

614
00:28:21,880 --> 00:28:25,600
Speaker 5: com and it was available and got it.

615
00:28:25,600 --> 00:28:27,720
Speaker 2: It sounds like a company that's been around for a while.

616
00:28:28,080 --> 00:28:30,119
Speaker 5: We have been around for like four years now, so

617
00:28:30,160 --> 00:28:31,560
Speaker 5: it's we have been around for a while. We have

618
00:28:31,600 --> 00:28:34,240
Speaker 5: been running these systems for two years in the field.

619
00:28:34,560 --> 00:28:37,080
Speaker 5: We haven't really made a lot of fuss yet. Because

620
00:28:37,119 --> 00:28:39,800
Speaker 5: we wanted to ensure that everything works reliable before we

621
00:28:39,840 --> 00:28:42,240
Speaker 5: go out there. Uh. And since last summer, we have

622
00:28:42,280 --> 00:28:44,720
Speaker 5: remped up production and we are installing more and more

623
00:28:44,760 --> 00:28:45,760
Speaker 5: systems in the field.

624
00:28:46,200 --> 00:28:48,200
Speaker 3: And now people are talking about you guys that I'm

625
00:28:48,240 --> 00:28:48,920
Speaker 3: making some noise.

626
00:28:49,080 --> 00:28:51,400
Speaker 5: It's definitely the word is spreading and it's exciting. We

627
00:28:51,480 --> 00:28:54,120
Speaker 5: have a backlog of orders, we have booked with installations

628
00:28:54,160 --> 00:28:57,440
Speaker 5: until mid of April, and it's exciting. So yeah, it's

629
00:28:57,480 --> 00:29:00,000
Speaker 5: a and we haven't really done a lot of marketing yet.

630
00:29:00,000 --> 00:29:01,040
Speaker 4: That's just starting.

631
00:29:01,160 --> 00:29:03,160
Speaker 2: Well, we've got a network, you guys with a bunch

632
00:29:03,160 --> 00:29:05,080
Speaker 2: of people almor because that's what we do here. We

633
00:29:05,120 --> 00:29:07,360
Speaker 2: could have events, so we could invite all the people

634
00:29:07,760 --> 00:29:09,920
Speaker 2: set one up maybe on the outside, you.

635
00:29:09,920 --> 00:29:10,240
Speaker 3: Know what I mean.

636
00:29:10,320 --> 00:29:12,440
Speaker 2: We should just set one up and show people this

637
00:29:12,640 --> 00:29:14,240
Speaker 2: is what you guys could have. We can invite a

638
00:29:14,280 --> 00:29:15,920
Speaker 2: bunch of growers here and we throw a big event

639
00:29:15,960 --> 00:29:16,240
Speaker 2: here or.

640
00:29:16,240 --> 00:29:19,240
Speaker 4: Something totally MJ business. We had the system running life

641
00:29:19,360 --> 00:29:23,440
Speaker 4: the last years. Oh really, yeah, I'll do running like life.

642
00:29:23,480 --> 00:29:26,160
Speaker 2: And I'm just saying that's what we do here on

643
00:29:26,200 --> 00:29:29,720
Speaker 2: the campus, where we curate relationships like that and establish

644
00:29:29,840 --> 00:29:31,960
Speaker 2: events around companies.

645
00:29:31,560 --> 00:29:32,560
Speaker 3: Like, like what you got?

646
00:29:32,680 --> 00:29:34,320
Speaker 2: You know what I mean? And then you invite the

647
00:29:34,360 --> 00:29:37,720
Speaker 2: key players that you're looking to meet and go some

648
00:29:37,840 --> 00:29:39,480
Speaker 2: drinks and food at them, hang out and look at

649
00:29:39,520 --> 00:29:39,920
Speaker 2: my product.

650
00:29:39,920 --> 00:29:42,480
Speaker 4: This is how it works, right, No, definitely.

651
00:29:42,200 --> 00:29:44,160
Speaker 2: Let's do something like that. It's great, We're gonna come back.

652
00:29:44,440 --> 00:29:46,200
Speaker 2: We're to do the high five with them and hear what

653
00:29:46,280 --> 00:29:48,400
Speaker 2: else this company is doing. Because I'm telling you right now,

654
00:29:48,480 --> 00:29:53,240
Speaker 2: neat leaf it's neat and it's counting all the leafs

655
00:29:53,920 --> 00:29:56,280
Speaker 2: and it's just amazing to me. I kind of geek

656
00:29:56,360 --> 00:30:00,200
Speaker 2: out off it, like how much it does? Can we

657
00:30:00,280 --> 00:30:04,320
Speaker 2: come back? How long did it take to make that unit?

658
00:30:04,560 --> 00:30:07,280
Speaker 2: Right there? Cannabis Talk one oh one will be right

659
00:30:07,320 --> 00:30:09,200
Speaker 2: back after this breaking. You want to hear your name

660
00:30:09,320 --> 00:30:12,120
Speaker 2: counted out live on the hill, Come at any time.

661
00:30:13,720 --> 00:30:15,880
Speaker 5: Nineteen eighty can leave on the point mail.

662
00:30:16,120 --> 00:30:18,840
Speaker 1: Make sure you like, follow and subscribe to Cannabis Talk

663
00:30:18,880 --> 00:30:27,800
Speaker 1: one on one. Now now back to the number one

664
00:30:27,800 --> 00:30:29,200
Speaker 1: cannabis show on the planet.

665
00:30:29,880 --> 00:30:32,440
Speaker 2: You know what get Now.

666
00:30:32,360 --> 00:30:35,000
Speaker 1: Back to the number one cannabis show in the universe,

667
00:30:35,240 --> 00:30:36,880
Speaker 1: Cannabis Talk one oh one.

668
00:30:37,000 --> 00:30:39,120
Speaker 2: I don't know if you're ready to make your own edibles, folks,

669
00:30:39,160 --> 00:30:43,120
Speaker 2: but troughly Made offers the highest quality, most affordable tabletop

670
00:30:43,160 --> 00:30:47,160
Speaker 2: candy depositors and silicon molds in the market. They're made

671
00:30:47,320 --> 00:30:50,640
Speaker 2: in Germany, the same place Elmer went to school at,

672
00:30:51,360 --> 00:30:53,760
Speaker 2: so you know they're good and they're developed by candy

673
00:30:53,800 --> 00:30:57,239
Speaker 2: makers four candy makers. Troughly Made can help you with

674
00:30:57,280 --> 00:31:00,840
Speaker 2: your recipe services such as developing and jus right piece customs,

675
00:31:00,880 --> 00:31:04,080
Speaker 2: silicon molds, and they can come to your facility with

676
00:31:04,200 --> 00:31:07,320
Speaker 2: on site consulting and trend. The whole team up with

677
00:31:07,400 --> 00:31:11,320
Speaker 2: those candy depositors. It's just for your home our commercial use.

678
00:31:11,400 --> 00:31:14,640
Speaker 2: You can manufacture over ten thousand pieces of candy per hour.

679
00:31:15,320 --> 00:31:19,760
Speaker 2: Order yours today, Visit the website Troughlymade dot com or

680
00:31:19,880 --> 00:31:23,520
Speaker 2: call them direct at six one nine five hundred three

681
00:31:23,640 --> 00:31:26,200
Speaker 2: one zero two. I want to thank everybody around here

682
00:31:26,240 --> 00:31:28,680
Speaker 2: for making the show happen, from Adrian to Amy to Amir,

683
00:31:28,840 --> 00:31:33,680
Speaker 2: Mary Magazine, Mondo, Mikayla Nix is holding down the fort

684
00:31:33,760 --> 00:31:36,000
Speaker 2: over there. It's funny when you meet a new intern

685
00:31:36,520 --> 00:31:38,600
Speaker 2: that you just kind of like and love. Put the

686
00:31:38,640 --> 00:31:41,840
Speaker 2: camera on you, Nicks, please so everybody can see what's

687
00:31:41,880 --> 00:31:45,600
Speaker 2: going on. This young lady right here, not only is

688
00:31:45,680 --> 00:31:48,160
Speaker 2: she a thug and was in eight mile? Uh?

689
00:31:48,240 --> 00:31:49,560
Speaker 3: She's rocking the look today, right.

690
00:31:52,000 --> 00:31:54,600
Speaker 2: She's just special and learning so much and it's great

691
00:31:54,600 --> 00:31:57,520
Speaker 2: to have her on the team. And I love you, Elizabeth,

692
00:31:57,560 --> 00:32:01,000
Speaker 2: Teddy the Show Dog, Ice Dog, Zeus, Daniel Diego, Joe Lupita,

693
00:32:01,520 --> 00:32:06,840
Speaker 2: Lindsay Logan, Lorenzo, Gary Francisco, Carly Connor, She's gonna take

694
00:32:06,880 --> 00:32:12,200
Speaker 2: a job cam Beach, Barcelaar Brandon, t Ellie Muffins, Sunday Og,

695
00:32:12,320 --> 00:32:16,400
Speaker 2: Skinny Ruvy, Goldie Brother Pitts, Mark Carnes, Chris Francino, Jennifer Erica,

696
00:32:16,400 --> 00:32:18,760
Speaker 2: and Elvis. Thank you guys all for doing what you're

697
00:32:18,800 --> 00:32:23,080
Speaker 2: doing around here. But Elmer, I love what neat Leaf

698
00:32:23,120 --> 00:32:23,520
Speaker 2: is doing.

699
00:32:23,800 --> 00:32:26,000
Speaker 3: I loved your history. I love learning about you.

700
00:32:27,240 --> 00:32:30,360
Speaker 2: I love men like you and women like you because

701
00:32:30,360 --> 00:32:33,680
Speaker 2: it's guys like and people like you, guys that really

702
00:32:33,720 --> 00:32:38,400
Speaker 2: have changed this world, right, And it's just amazing that

703
00:32:38,520 --> 00:32:42,280
Speaker 2: you guys figure out shit like this to do things

704
00:32:42,320 --> 00:32:47,200
Speaker 2: like this, and as you created this, what made you

705
00:32:47,360 --> 00:32:52,080
Speaker 2: leave a solid gig doing whatever it is right before

706
00:32:52,880 --> 00:32:57,480
Speaker 2: thinking I'm gonna invest my time, my money, my this

707
00:32:58,480 --> 00:33:05,600
Speaker 2: to become the found and CEO of Wordplay game neat leaf.

708
00:33:08,240 --> 00:33:10,040
Speaker 2: How long does it take to make it up like that?

709
00:33:10,760 --> 00:33:12,400
Speaker 2: Because you didn't have it before you made the company,

710
00:33:12,480 --> 00:33:12,959
Speaker 2: right right?

711
00:33:13,040 --> 00:33:13,280
Speaker 5: No?

712
00:33:13,280 --> 00:33:15,000
Speaker 2: No, no, I mean so you decide to make the

713
00:33:15,000 --> 00:33:15,880
Speaker 2: company and then you make this.

714
00:33:16,160 --> 00:33:18,680
Speaker 5: Well yeah, and this was not like you didn't start

715
00:33:18,680 --> 00:33:22,400
Speaker 5: with this, like we just understood the challenges and this

716
00:33:22,840 --> 00:33:27,600
Speaker 5: opportunity and agriculture to really help cultivators, and so we iterated.

717
00:33:27,680 --> 00:33:31,600
Speaker 5: So we embraced what we learned that Google X and

718
00:33:32,440 --> 00:33:37,920
Speaker 5: basically develop your product with the customer. So within a

719
00:33:38,000 --> 00:33:41,280
Speaker 5: month we had our first prototype in the field. It

720
00:33:41,360 --> 00:33:43,800
Speaker 5: was like an IoT system like a stationary camera with

721
00:33:43,920 --> 00:33:47,160
Speaker 5: data collection, and we deployed multiple in a facility and

722
00:33:47,200 --> 00:33:49,960
Speaker 5: we learn and we deployed more. We learned what works,

723
00:33:49,960 --> 00:33:52,160
Speaker 5: what doesn't work, and we understood that we need something

724
00:33:52,160 --> 00:33:54,880
Speaker 5: which really moves around. If you want to do what

725
00:33:55,080 --> 00:33:57,800
Speaker 5: needs to happen like all the AI the models that

726
00:33:57,920 --> 00:33:59,800
Speaker 5: you need to learn, then you have to be able

727
00:33:59,840 --> 00:34:01,960
Speaker 5: to see plans from different viewpoints. You have to be

728
00:34:02,000 --> 00:34:04,880
Speaker 5: able to move the camera around. That's how humans do

729
00:34:04,960 --> 00:34:07,600
Speaker 5: it and that's in general how this works. And so

730
00:34:07,640 --> 00:34:09,360
Speaker 5: we had to come up with this system. So we

731
00:34:09,440 --> 00:34:13,320
Speaker 5: quickly iterrated through some ideas, and then about six months

732
00:34:13,320 --> 00:34:17,439
Speaker 5: in we realized, Okay, the Spider system is the thing.

733
00:34:17,560 --> 00:34:20,000
Speaker 5: It's like it checks all the boxes which we are

734
00:34:20,000 --> 00:34:23,799
Speaker 5: looking for. And then we started developing it and nine

735
00:34:23,840 --> 00:34:26,760
Speaker 5: months later we had the first system running with a customer.

736
00:34:26,840 --> 00:34:28,120
Speaker 4: So it's the whole thing.

737
00:34:28,320 --> 00:34:30,359
Speaker 2: Nine months nine months for the whole thing.

738
00:34:30,360 --> 00:34:32,839
Speaker 4: A very small team. You're six people, and it.

739
00:34:32,920 --> 00:34:35,239
Speaker 2: Was so you and five other people. Were they all

740
00:34:35,239 --> 00:34:38,080
Speaker 2: working at Google extra with you? No, no random other

741
00:34:38,120 --> 00:34:39,680
Speaker 2: engineers from around, different.

742
00:34:39,640 --> 00:34:43,279
Speaker 5: From different from from the network. So basically people I

743
00:34:43,400 --> 00:34:45,200
Speaker 5: worked with before, or some of them at least, and

744
00:34:45,239 --> 00:34:48,200
Speaker 5: then they brought in some people they knew and yeah,

745
00:34:48,239 --> 00:34:50,920
Speaker 5: so we have a company alls in Germany because we

746
00:34:50,920 --> 00:34:53,200
Speaker 5: were able to recruit some talent from one of my

747
00:34:53,280 --> 00:34:55,680
Speaker 5: network there and amazing talent.

748
00:34:56,000 --> 00:34:58,280
Speaker 4: We didn't have to compete with the Googles and facebooks

749
00:34:58,280 --> 00:35:02,360
Speaker 4: and apples there, right, Yeah, yeah, so.

750
00:35:02,239 --> 00:35:06,120
Speaker 2: That's amazing because this is groundbreaking in my opinion. I mean,

751
00:35:06,160 --> 00:35:08,320
Speaker 2: I'm looking at the thing and seeing how it works,

752
00:35:08,760 --> 00:35:11,919
Speaker 2: and I'm sitting there in awe because Elmer, I think

753
00:35:11,960 --> 00:35:12,920
Speaker 2: this is game changer.

754
00:35:13,880 --> 00:35:16,160
Speaker 4: It's gonna happen, that's no question.

755
00:35:16,000 --> 00:35:18,560
Speaker 2: Right, Like some like you have development. Somebody else is

756
00:35:18,560 --> 00:35:21,080
Speaker 2: gonna be working on something, but like guys like you

757
00:35:21,200 --> 00:35:23,279
Speaker 2: that come from that world are going, fuck, I gotta

758
00:35:23,280 --> 00:35:24,520
Speaker 2: help this cannabis industry out.

759
00:35:24,560 --> 00:35:26,200
Speaker 3: Let me figure out some things.

760
00:35:26,000 --> 00:35:28,239
Speaker 2: That we can do. And then someone's watching you do

761
00:35:28,400 --> 00:35:30,560
Speaker 2: this to go, oh, I know how he did that.

762
00:35:30,680 --> 00:35:32,319
Speaker 2: Let me add this to that, and let me add

763
00:35:32,360 --> 00:35:33,040
Speaker 2: that to that.

764
00:35:33,400 --> 00:35:36,080
Speaker 5: Right, And I mean, this is definitely what the missing

765
00:35:36,080 --> 00:35:38,680
Speaker 5: puzzle piece, the missing pussile piece was to collect. Is

766
00:35:38,719 --> 00:35:42,279
Speaker 5: to capture this data to really do what the cultivator

767
00:35:42,440 --> 00:35:44,440
Speaker 5: is doing or like what's going on, or like the

768
00:35:44,480 --> 00:35:46,080
Speaker 5: assessment which cultivators can.

769
00:35:45,960 --> 00:35:48,680
Speaker 2: Do, and then doing it better because let's just face it,

770
00:35:48,719 --> 00:35:51,920
Speaker 2: if you can do it this way, you're able to

771
00:35:52,040 --> 00:35:56,240
Speaker 2: see and catch things before the naked eye can see.

772
00:35:56,280 --> 00:36:00,200
Speaker 2: By you using the percentages talk, by you using this

773
00:36:00,480 --> 00:36:04,799
Speaker 2: much is unwatered, unhealthy, you're able to tell more what

774
00:36:04,880 --> 00:36:07,760
Speaker 2: this plan is ever done in the history of the world.

775
00:36:08,400 --> 00:36:10,200
Speaker 4: Yeah, and we can. It's a box.

776
00:36:10,239 --> 00:36:12,560
Speaker 5: We can put in any sensor, so we have multi

777
00:36:12,560 --> 00:36:16,120
Speaker 5: spectral cameras. We have temperature sensors for leaf temperatures, so

778
00:36:16,120 --> 00:36:18,760
Speaker 5: we can obviously measure things which a human cannot measure,

779
00:36:19,239 --> 00:36:21,719
Speaker 5: and that's what obviously gives you the lag up to

780
00:36:21,760 --> 00:36:26,400
Speaker 5: do a better job, like a better job. It's probably

781
00:36:26,440 --> 00:36:29,560
Speaker 5: exaggerated at this point because cultivators are still amazing and

782
00:36:29,680 --> 00:36:31,960
Speaker 5: have so much experience, but over time the system is

783
00:36:31,960 --> 00:36:35,640
Speaker 5: definitely gonna provide be able to see more plans than

784
00:36:35,640 --> 00:36:38,239
Speaker 5: a human will ever be able to see. That's why

785
00:36:38,360 --> 00:36:41,200
Speaker 5: also if you think about doctors and X ray images,

786
00:36:41,400 --> 00:36:46,320
Speaker 5: like the AI is now better than what the human

787
00:36:46,360 --> 00:36:49,440
Speaker 5: can do because the aid can see way more images

788
00:36:49,680 --> 00:36:52,520
Speaker 5: and a human will in a lifetime be able to see.

789
00:36:53,040 --> 00:36:56,880
Speaker 4: And so the same will happen also in that space.

790
00:36:57,000 --> 00:36:59,440
Speaker 5: As we capture more and more data, we're gonna get

791
00:36:59,440 --> 00:37:03,640
Speaker 5: better and in identifying and the issues, which right now

792
00:37:03,680 --> 00:37:04,640
Speaker 5: is super hard.

793
00:37:05,000 --> 00:37:10,120
Speaker 2: So Elmer, does that update daily weekly? Are you guys

794
00:37:10,200 --> 00:37:12,880
Speaker 2: just constantly doing updates and sending them to the units

795
00:37:12,960 --> 00:37:13,239
Speaker 2: or how.

796
00:37:13,120 --> 00:37:14,560
Speaker 4: Does that work constantly?

797
00:37:14,600 --> 00:37:17,240
Speaker 2: So like like the Tesla or something right right over.

798
00:37:17,080 --> 00:37:21,000
Speaker 4: The air updates and improving it overnight and the next Yeah.

799
00:37:20,840 --> 00:37:23,760
Speaker 2: And just because some growers are gonna be like Elmer,

800
00:37:23,880 --> 00:37:26,040
Speaker 2: let's talk about you know, I wanted to do this

801
00:37:26,280 --> 00:37:28,879
Speaker 2: or I noticed we should do this or that, right,

802
00:37:28,960 --> 00:37:30,960
Speaker 2: I mean, you guys are open for discussions.

803
00:37:31,760 --> 00:37:34,360
Speaker 5: We are a little bit overwhelmed with future requests because

804
00:37:34,400 --> 00:37:37,279
Speaker 5: there's so much we can do and could do, and

805
00:37:37,360 --> 00:37:40,360
Speaker 5: so we constantly keep cranking out the most important features

806
00:37:40,360 --> 00:37:41,320
Speaker 5: and improving things.

807
00:37:41,360 --> 00:37:43,600
Speaker 4: And it's exciting. It's really exciting.

808
00:37:43,640 --> 00:37:47,080
Speaker 5: And I was really skeptical at the beginning the customers

809
00:37:47,160 --> 00:37:50,560
Speaker 5: or cultivators would be hesitant because it's you know, like

810
00:37:50,640 --> 00:37:53,799
Speaker 5: in general, they have a lot of experience. They're navigating

811
00:37:53,800 --> 00:37:56,759
Speaker 5: this complex space and there's a lot of opinions and

812
00:37:56,800 --> 00:38:00,439
Speaker 5: you know, like the sensitive to auto opinions as that space,

813
00:38:00,560 --> 00:38:04,760
Speaker 5: understandably because it's so challenging. But this is not an opinion.

814
00:38:05,160 --> 00:38:09,040
Speaker 5: This is data, and cultivators love data because it allows

815
00:38:09,080 --> 00:38:09,440
Speaker 5: them to.

816
00:38:09,440 --> 00:38:12,200
Speaker 4: Learn, to improve, to get better. And that was.

817
00:38:13,320 --> 00:38:16,800
Speaker 2: Exactly because that is not an opinion. No, that doesn't

818
00:38:16,800 --> 00:38:21,160
Speaker 2: give a shit about the seed. About your feelings about

819
00:38:21,200 --> 00:38:23,280
Speaker 2: how bad you want this one to work.

820
00:38:24,160 --> 00:38:26,480
Speaker 5: Does matters, Like, this is the data, this is how

821
00:38:26,520 --> 00:38:28,160
Speaker 5: this is the number of butts right now, you know

822
00:38:28,239 --> 00:38:29,479
Speaker 5: that you wanted these kind of butts?

823
00:38:29,560 --> 00:38:30,040
Speaker 4: What happened?

824
00:38:30,080 --> 00:38:31,799
Speaker 5: Go back in dime, check it out, look at this

825
00:38:31,800 --> 00:38:34,120
Speaker 5: stuff and you can assess it. You can learn, you

826
00:38:34,160 --> 00:38:38,839
Speaker 5: can understand finally in this crazy black box, what's going on?

827
00:38:39,160 --> 00:38:43,399
Speaker 5: Think about like other industries, in every other industry, when

828
00:38:43,440 --> 00:38:46,560
Speaker 5: you try to optimize a process, the first thing you

829
00:38:46,640 --> 00:38:48,360
Speaker 5: do is to collect the data.

830
00:38:48,840 --> 00:38:50,960
Speaker 2: When can you get this to collect more data on

831
00:38:51,000 --> 00:38:55,000
Speaker 2: how to solve more like medical conditional problems of like,

832
00:38:55,480 --> 00:38:57,759
Speaker 2: because we still haven't gotten to the bottom of how

833
00:38:57,840 --> 00:39:04,000
Speaker 2: much this plant can do with THC A, thcv, cbdn C.

834
00:39:04,719 --> 00:39:07,000
Speaker 2: It's creating more and more as we break it down

835
00:39:07,000 --> 00:39:10,839
Speaker 2: with molecules, And is that something that you can dive into,

836
00:39:11,080 --> 00:39:13,960
Speaker 2: because that's like a never indiing thing. You're a guy

837
00:39:13,960 --> 00:39:17,640
Speaker 2: that handles never ending outcomes, right, you know what I mean?

838
00:39:18,680 --> 00:39:21,920
Speaker 5: And you're hitting a really good point there because especially

839
00:39:22,360 --> 00:39:25,600
Speaker 5: when you're now first the one step is to understand

840
00:39:25,960 --> 00:39:31,200
Speaker 5: what the different cannabinized therapies and whatever mean for like people,

841
00:39:31,360 --> 00:39:33,600
Speaker 5: And there I would also differentiate because it's such a

842
00:39:33,640 --> 00:39:36,640
Speaker 5: subjective experience to some extent where you know everyone reacts

843
00:39:36,680 --> 00:39:38,879
Speaker 5: differently to something, so you need to figure out what's

844
00:39:38,920 --> 00:39:41,480
Speaker 5: really your your set which works for you and all that.

845
00:39:41,960 --> 00:39:45,560
Speaker 5: But as you know that, then you want to repeatedly

846
00:39:45,600 --> 00:39:49,120
Speaker 5: be able to produce the same results. And in order

847
00:39:49,160 --> 00:39:51,920
Speaker 5: to do that you need to be highly precise in

848
00:39:51,960 --> 00:39:53,800
Speaker 5: how you grow these plans, and that's where a system

849
00:39:53,840 --> 00:39:56,360
Speaker 5: like this becomes very, very powerful and critical.

850
00:39:57,239 --> 00:39:59,080
Speaker 2: I think this is going to be a game changer.

851
00:39:59,239 --> 00:40:02,319
Speaker 2: I really love what you're doing and thank you for

852
00:40:02,360 --> 00:40:05,680
Speaker 2: bringing your skills in your brain to the cannabis industry.

853
00:40:06,400 --> 00:40:07,960
Speaker 2: I like to do the high five with all the

854
00:40:07,960 --> 00:40:11,080
Speaker 2: guests that come in here. So question number one, Elmer,

855
00:40:11,160 --> 00:40:13,439
Speaker 2: as you are the founder and CEO of neat Leaf

856
00:40:13,600 --> 00:40:16,360
Speaker 2: neatleaf dot com, n e A T L e a

857
00:40:16,640 --> 00:40:18,879
Speaker 2: f dot com, how old were you, Elmer the first

858
00:40:18,920 --> 00:40:20,839
Speaker 2: time you smoked cannabis and where'd you get it from?

859
00:40:21,640 --> 00:40:23,680
Speaker 4: Around fourteen years probably.

860
00:40:23,560 --> 00:40:26,000
Speaker 3: And in Germany in Italy Italy.

861
00:40:26,719 --> 00:40:28,640
Speaker 5: Italy is a bit more like depending on where you

862
00:40:28,640 --> 00:40:32,360
Speaker 5: are in Italy, but it's more open like minded to cannabis,

863
00:40:32,440 --> 00:40:35,400
Speaker 5: especially the south more than the north. Dok oh really

864
00:40:35,480 --> 00:40:38,560
Speaker 5: and yeah they are big. It's close by Africa and.

865
00:40:38,680 --> 00:40:43,600
Speaker 2: Like getting the hasheesh exactly all the good stuff from there.

866
00:40:46,080 --> 00:40:48,520
Speaker 4: And yeah, so from a from a friend.

867
00:40:50,000 --> 00:40:51,160
Speaker 3: Back then and good to Italy.

868
00:40:51,239 --> 00:40:53,520
Speaker 2: Question number two the high five, what is your favorite

869
00:40:53,520 --> 00:40:54,840
Speaker 2: way to use cannabis?

870
00:40:55,600 --> 00:40:56,920
Speaker 4: Probably edibles.

871
00:40:57,680 --> 00:41:01,200
Speaker 5: I'm not so big in the smoke aspect, but also

872
00:41:01,239 --> 00:41:04,120
Speaker 5: I really like the smooth hit as it builds up.

873
00:41:04,200 --> 00:41:06,560
Speaker 4: So that's something which I prefer in general.

874
00:41:08,360 --> 00:41:12,879
Speaker 5: Yeah, I'm I'm quite a lightweight in that sense where

875
00:41:12,960 --> 00:41:15,479
Speaker 5: I don't need much to get into a very pleasant state.

876
00:41:16,680 --> 00:41:17,960
Speaker 4: And then I also.

877
00:41:17,719 --> 00:41:21,080
Speaker 5: Turn more into like a listener, where like I just

878
00:41:21,200 --> 00:41:24,800
Speaker 5: I love my brain go wild and flow and.

879
00:41:25,080 --> 00:41:29,640
Speaker 2: Just want to zone out. And my brother's that way.

880
00:41:29,680 --> 00:41:31,680
Speaker 2: I told you, like my brothers. My brother is one

881
00:41:31,680 --> 00:41:34,040
Speaker 2: of the first it guys, and he just he's an

882
00:41:34,080 --> 00:41:37,000
Speaker 2: engineer and he creates and working for a big company

883
00:41:37,000 --> 00:41:38,960
Speaker 2: that deals with Google and does all kinds of stuff.

884
00:41:38,960 --> 00:41:40,000
Speaker 3: But he's like that too. He smokes.

885
00:41:40,040 --> 00:41:42,439
Speaker 2: He just thinks and he creates, and.

886
00:41:42,600 --> 00:41:47,320
Speaker 5: Just everyone reacts differently, and I mean understanding, you know,

887
00:41:47,360 --> 00:41:49,919
Speaker 5: like I depending on what I take to smoke. It's

888
00:41:50,000 --> 00:41:53,680
Speaker 5: like it's just such a different reaction. And but I

889
00:41:53,800 --> 00:41:56,400
Speaker 5: stopped talking more like it's just totally.

890
00:41:56,480 --> 00:42:01,400
Speaker 2: Right, creative, find your different things. Okay, question number three

891
00:42:02,040 --> 00:42:05,400
Speaker 2: of the high five craziest place you ever used our

892
00:42:05,480 --> 00:42:08,120
Speaker 2: smoked cannabis.

893
00:42:08,280 --> 00:42:12,600
Speaker 4: That's a good one. Probably a church.

894
00:42:15,239 --> 00:42:17,440
Speaker 2: During the church or like on a random Tuesday.

895
00:42:18,440 --> 00:42:24,480
Speaker 5: It was during mass, actually praise god, yeah in Germany?

896
00:42:24,560 --> 00:42:26,400
Speaker 3: Or where were you in Italy?

897
00:42:26,440 --> 00:42:26,680
Speaker 1: Again?

898
00:42:27,760 --> 00:42:29,120
Speaker 4: I was an altar boy?

899
00:42:29,480 --> 00:42:32,320
Speaker 3: Were you well Italian?

900
00:42:32,440 --> 00:42:34,600
Speaker 4: Like you are a Catholic Italian?

901
00:42:34,600 --> 00:42:36,720
Speaker 3: As I grew up part of the culture.

902
00:42:36,760 --> 00:42:37,360
Speaker 2: What you had to do?

903
00:42:37,920 --> 00:42:40,600
Speaker 4: Yeah and yeah, but you have to put proud?

904
00:42:41,239 --> 00:42:44,000
Speaker 2: Was it fun? Was there like a bunch of ultra boys? Hey,

905
00:42:44,000 --> 00:42:44,760
Speaker 2: what's up Larry?

906
00:42:44,800 --> 00:42:48,439
Speaker 4: Hey, what's up hat? And doing crazy things and being

907
00:42:48,520 --> 00:42:50,239
Speaker 4: like just you know, crazy guys.

908
00:42:50,560 --> 00:42:53,440
Speaker 2: Right, Just don't let this priest touch us. As long

909
00:42:53,480 --> 00:42:55,680
Speaker 2: as we're good here at this church. Fuck them other

910
00:42:55,719 --> 00:42:58,480
Speaker 2: bad church. But we us boys are sticking together here.

911
00:42:59,719 --> 00:43:01,840
Speaker 2: And no what you around or just don't don't kiss,

912
00:43:01,840 --> 00:43:05,600
Speaker 2: don't tell Question number four the high five? What did

913
00:43:05,600 --> 00:43:07,399
Speaker 2: your go too munchies after you get high?

914
00:43:07,719 --> 00:43:13,279
Speaker 5: Oh gosh, I'm a goldfish. I just I really, My

915
00:43:13,320 --> 00:43:15,600
Speaker 5: wife has to save me whatever, it's like, I just

916
00:43:15,800 --> 00:43:16,359
Speaker 5: save you.

917
00:43:16,360 --> 00:43:18,120
Speaker 2: You have the bigger you gottle bag.

918
00:43:18,200 --> 00:43:21,360
Speaker 5: And I just a friend of mine once took me

919
00:43:21,480 --> 00:43:26,800
Speaker 5: to All you Can Eat in them after like consuming

920
00:43:26,840 --> 00:43:29,719
Speaker 5: and it was just I The only reason why I

921
00:43:29,760 --> 00:43:32,080
Speaker 5: realized that everyone was cracking up in the whole space

922
00:43:32,120 --> 00:43:34,640
Speaker 5: because I just never I was just the chicken.

923
00:43:34,800 --> 00:43:37,440
Speaker 4: Was just like amazing and I couldn't stop.

924
00:43:37,760 --> 00:43:39,960
Speaker 5: And the only way I realized something is wrong is

925
00:43:40,000 --> 00:43:42,040
Speaker 5: because my belt got so tight.

926
00:43:42,200 --> 00:43:44,040
Speaker 4: But I did not get the feeling.

927
00:43:43,800 --> 00:43:48,000
Speaker 3: Of being like full, Oh that's hilarious. Afterwards it like,

928
00:43:48,920 --> 00:43:52,600
Speaker 3: so that's good food. I like that goldfish. The goldfish

929
00:43:52,680 --> 00:43:53,280
Speaker 3: is what happens?

930
00:43:53,360 --> 00:43:56,480
Speaker 2: Question number five with the high five Elmer the founder

931
00:43:56,520 --> 00:43:57,839
Speaker 2: and see oh neat leaf.

932
00:43:58,360 --> 00:44:00,760
Speaker 3: It's very special what you're doing, brother, very special.

933
00:44:01,080 --> 00:44:04,280
Speaker 2: But if you can smoke cannabis with anyone dead or alive,

934
00:44:04,760 --> 00:44:05,759
Speaker 2: who would it be and why?

935
00:44:07,200 --> 00:44:13,640
Speaker 5: I I think my wife is just so ridiculously hilarious

936
00:44:13,880 --> 00:44:16,640
Speaker 5: when she gets high. So I just love to like

937
00:44:16,680 --> 00:44:18,160
Speaker 5: when we get high, like to get hired of her

938
00:44:18,160 --> 00:44:20,160
Speaker 5: because I turn more into like the listener and she

939
00:44:20,400 --> 00:44:24,120
Speaker 5: just becomes like storyteller and like and she's cracks me up,

940
00:44:24,200 --> 00:44:28,800
Speaker 5: like we should bring it into the show and like her, she's.

941
00:44:28,680 --> 00:44:30,799
Speaker 4: Gotta keep going. It's really hilarious.

942
00:44:30,840 --> 00:44:33,319
Speaker 5: But general, like, yeah, I think anyone who would be

943
00:44:33,440 --> 00:44:36,279
Speaker 5: very exciting to listen to, and I mean Dave Chappelle.

944
00:44:35,960 --> 00:44:36,719
Speaker 4: Would that will be fun.

945
00:44:38,520 --> 00:44:42,080
Speaker 2: Dave Chappelle's an interesting character, fun guy. Anything else you

946
00:44:42,120 --> 00:44:44,520
Speaker 2: want to talk about? Almer, about the company, about you

947
00:44:44,520 --> 00:44:46,640
Speaker 2: guys got going on what's going to be next. I

948
00:44:46,680 --> 00:44:48,920
Speaker 2: know you guys are probably just constantly looking to improve

949
00:44:49,000 --> 00:44:53,920
Speaker 2: with more data from growers and cultivroving.

950
00:44:53,000 --> 00:44:57,680
Speaker 5: The system, making it better, easier to install other features

951
00:44:57,719 --> 00:45:00,879
Speaker 5: like improving what you can do with the data. If

952
00:45:00,920 --> 00:45:02,839
Speaker 5: you're curious and want to check it out, please come

953
00:45:02,920 --> 00:45:05,960
Speaker 5: and contact us and like bring us. You can go

954
00:45:06,000 --> 00:45:08,640
Speaker 5: to our website and apply there for a demo or

955
00:45:08,719 --> 00:45:11,480
Speaker 5: send us an email to info at a neatlyaf dot

956
00:45:11,520 --> 00:45:15,759
Speaker 5: com and yeah, just get in touch and try it out.

957
00:45:15,800 --> 00:45:17,200
Speaker 4: That's all. And you're not gonna recrete it.

958
00:45:17,239 --> 00:45:19,680
Speaker 2: Because it's like the dope man, just try it. Just

959
00:45:19,680 --> 00:45:22,920
Speaker 2: take one here, Just take one here, So how you

960
00:45:22,960 --> 00:45:24,000
Speaker 2: gotta do? Take one?

961
00:45:24,120 --> 00:45:27,800
Speaker 5: It is a little bit like the smartphone experience nowhere,

962
00:45:28,200 --> 00:45:30,320
Speaker 5: I didn't feel like I need a smartphone.

963
00:45:30,360 --> 00:45:32,680
Speaker 4: He checks their email on their phone back then and

964
00:45:32,760 --> 00:45:34,839
Speaker 4: it came out. But nowadays you know.

965
00:45:34,920 --> 00:45:38,160
Speaker 5: Like it's just exactly and that's the same thing, because

966
00:45:38,160 --> 00:45:40,160
Speaker 5: it's a new concept and new way how you can

967
00:45:40,239 --> 00:45:41,800
Speaker 5: run your population, new data.

968
00:45:42,160 --> 00:45:44,680
Speaker 4: What does it really need it and whatever? Try it

969
00:45:44,680 --> 00:45:46,680
Speaker 4: out and you know you don't like it and send

970
00:45:46,680 --> 00:45:47,040
Speaker 4: it back.

971
00:45:47,080 --> 00:45:50,120
Speaker 5: But it is you're gonna see what the value provides,

972
00:45:50,160 --> 00:45:52,960
Speaker 5: and we've got some impressive success.

973
00:45:53,480 --> 00:45:57,200
Speaker 2: Well, folks, it looks amazing to me. I don't have one.

974
00:45:57,280 --> 00:46:00,200
Speaker 2: I've never seen one. I have seen these guys and

975
00:46:00,200 --> 00:46:02,920
Speaker 2: they're very reputable. And you heard what the man has

976
00:46:02,960 --> 00:46:06,400
Speaker 2: done before in the past. So neat Leaf is a

977
00:46:06,480 --> 00:46:09,880
Speaker 2: nice company and looks great. Appreciate how you being on

978
00:46:09,880 --> 00:46:11,520
Speaker 2: the show with us. You guys, It's Cannabis Talk one

979
00:46:11,560 --> 00:46:14,000
Speaker 2: on one. If nobody else loves you, we do.

980
00:46:14,320 --> 00:46:17,120
Speaker 1: Follow Cannabis Talk one on one on all social media

981
00:46:17,320 --> 00:46:20,040
Speaker 1: and Cannabis Talk one oh one. Thank you for listening

982
00:46:20,040 --> 00:46:22,560
Speaker 1: to Cannabis Talk one on one with Blue with Joe Brande,

983
00:46:22,960 --> 00:46:26,520
Speaker 1: the world's number one source for everything cannabis, and make

984
00:46:26,560 --> 00:46:29,319
Speaker 1: sure you like, follow, and subscribe to Cannabis Talk one

985
00:46:29,320 --> 00:46:30,040
Speaker 1: o one now