WEBVTT 00:00:00.660 --> 00:00:13.020 Welcome to the Friday wrap up for May 15th, 2026. This short episode is where I talk about three things. What's on my mind, recommended reading, and recommended media. 00:00:13.540 --> 00:00:29.960 Streamline Solopreneur is the show to help you build more reliable solopreneur systems so you can take time off worry-free. I hope this roundup and reflection will help you think more about your own systems. I'm Joe Casabona and here's what's on my mind. 00:00:31.160 --> 00:00:40.260 Okay, so earlier this week, I found myself fighting with Claude about something I felt was pretty basic. 00:00:41.000 --> 00:00:46.000 A problem that I've actually used Claude to solve before. 00:00:47.480 --> 00:00:55.900 I kept going back and forth with the LLM and I would ask it questions, and then it would do things that I didn't even remotely ask it to do. 00:00:57.180 --> 00:01:05.440 And so I started to form this weird theory that in Opus 4.7, it's designed to waste tokens, 00:01:06.140 --> 00:01:09.380 which I know is like a weird conspiracy, but maybe it's not that weird. I don't know. 00:01:09.820 --> 00:01:10.940 Sound off in the comments. 00:01:11.860 --> 00:01:16.300 But I'm actually worried that it's worse than that. 00:01:17.260 --> 00:01:22.040 And I, first of all, this is like wild unsubstantiated speculation. 00:01:23.880 --> 00:01:25.080 But we've seen it before. 00:01:25.700 --> 00:01:36.260 So in his book, Hooked, Near Aal talks about how social media sites and addictive products employ variable rewards. 00:01:37.120 --> 00:01:42.880 The general idea is that our craving for the reward is stronger than the reward itself. 00:01:43.840 --> 00:01:50.360 So we invest time and money into pursuit of the award. 00:01:50.520 --> 00:01:52.500 And that's what actually satiates the craving. 00:01:52.740 --> 00:02:01.560 So if you are scrolling on a social media site like TikTok, maybe 75% of the videos you don't care about. 00:02:02.060 --> 00:02:07.480 But 25% of the time, you're going to get a video that you really like. 00:02:07.580 --> 00:02:08.820 And it gives you that endorphin hit. 00:02:08.920 --> 00:02:12.560 And so you keep scrolling on TikTok. 00:02:14.220 --> 00:02:21.959 This is why social media sites, gambling sites, prediction markets, and so many other things are so 00:02:22.380 --> 00:02:33.280 addicting. As I had my argument with Claude convinced that I could get a computer program to act 00:02:33.420 --> 00:02:41.900 logically, which I don't think is a wild request, I started wondering if large language models 00:02:42.620 --> 00:02:51.520 offer a sort of variable reward system. After all, it actually does perform certain tasks really, 00:02:51.780 --> 00:02:52.460 really well. 00:02:54.580 --> 00:03:01.580 And what if that variable reward is enough to convince most people, as well as momentarily 00:03:01.860 --> 00:03:08.140 trick others like me, that the LLMs are actually good at a lot more than they are good at? 00:03:09.120 --> 00:03:14.860 We are pleasantly surprised by the results of a single task or a category of task like 00:03:15.680 --> 00:03:21.600 vibe coding something or going out to our calendar and, and, 00:03:21.660 --> 00:03:26.100 grabbing stuff or going to our email and sending stuff to our to do list, all very like 00:03:26.280 --> 00:03:32.640 computery things. So we start to crave that feeling. The I can't believe a robot actually did 00:03:32.760 --> 00:03:38.700 this so I don't have to do this anymore feeling. And we pursue that craving for efficiency. 00:03:39.700 --> 00:03:48.760 We pursue it with time and tokens. And again, this is totally unsubstantiated. It's a probably a weird 00:03:48.860 --> 00:03:56.220 theory. I don't think it's whereas with social media sites and gambling sites and prediction 00:03:57.290 --> 00:04:05.840 markets, it's in the best interest of those websites to employ variable rewards because it's in 00:04:05.840 --> 00:04:12.360 their best interest for people to stay on those sites. I actually don't think it is in the best 00:04:12.600 --> 00:04:18.739 interest of large language models to employ variable rewards. They want to want to. They want to 00:04:18.760 --> 00:04:28.580 want a higher hit rate because the productivity or efficiency angle is the thing that keeps 00:04:28.660 --> 00:04:29.300 people coming back. 00:04:29.400 --> 00:04:30.940 But it's just something. 00:04:31.240 --> 00:04:37.340 It was so weird in the beginning of the week where I had successfully vibe coded something 00:04:38.100 --> 00:04:38.320 running. 00:04:38.780 --> 00:04:41.700 And then I tried to do it again with a different project. 00:04:41.880 --> 00:04:43.520 And it just like went off the rails. 00:04:44.860 --> 00:04:52.240 And so as I am wasting time trying to bend the LLM to my will, I thought, is this a variable 00:04:52.520 --> 00:04:56.460 rewards thing where like it pleases me a certain amount of the time? 00:04:56.620 --> 00:04:58.040 And so I keep coming back to it. 00:04:58.800 --> 00:05:00.700 Again, I don't know. 00:05:00.860 --> 00:05:05.880 I don't think that large language models would actually benefit from that. 00:05:05.960 --> 00:05:07.080 But it's something I was thinking about. 00:05:07.720 --> 00:05:08.300 What do you think? 00:05:08.360 --> 00:05:09.540 Is it plausible, likely? 00:05:10.080 --> 00:05:11.040 Way off base? 00:05:12.040 --> 00:05:14.820 Let me know either in the comments below or over. 00:05:14.840 --> 00:05:23.200 at streamlinedfeedback.com. I'd love to hear your thoughts on variable rewards in large language 00:05:24.610 --> 00:05:30.840 models. Now, moving on to recommended reading. Usually I like to make recommended reading like 00:05:30.940 --> 00:05:36.660 a heavier, interesting think piece and the recommended media to be something fun and light to bring you 00:05:36.670 --> 00:05:44.800 into your weekend. But it's reversed this week. So the recommended reading is from the 00:05:44.880 --> 00:05:50.180 it's called the colorful impact of Spike Lee's Red Yankee Hat request 30 years ago. 00:05:51.340 --> 00:05:54.520 I am a chronic Yankees hat collector. 00:05:55.780 --> 00:06:04.160 And I suspect that my collection of about a dozen or so Yankee hats pales in comparison to some. 00:06:04.280 --> 00:06:11.300 But still, I have those dozen or so Yankee hats emblazoned with the classic interlocking 00:06:11.340 --> 00:06:15.560 NY that has persisted for over a hundred years. 00:06:17.110 --> 00:06:18.820 In other words, I love a dope hat. 00:06:20.420 --> 00:06:29.840 And arguably, we would not have the vibrant dope hat market that we have today without Spike Lee. 00:06:31.020 --> 00:06:39.560 30 years ago, 1996, Spike Lee, a diehard Yankees fan, wanted a red Yankees hat to match his 00:06:39.480 --> 00:06:40.020 red jacket. 00:06:41.700 --> 00:06:47.200 And when he tried to get it made, he was unable to do to licensing. 00:06:48.180 --> 00:06:53.720 New Era, the company that makes, I believe it's New Era, the company that makes the on-field 00:06:53.920 --> 00:07:00.900 hats only had license to make hats the teams actually wore on-field. 00:07:02.520 --> 00:07:08.320 So Spike Lee doing something that only Spike Lee and a handful of other people could do, 00:07:09.100 --> 00:07:12.500 Went to the boss, Yankees owner George Steinbrenner. 00:07:13.340 --> 00:07:18.380 And he got George to approve a red Yankees hat for him to wear. 00:07:19.560 --> 00:07:25.960 And now, of course, we have all sorts of hats, all sorts of on-field hats, different colors. 00:07:26.180 --> 00:07:32.280 I have that red Yankee hat, and I wore it on red hat, red Yankee hat day, 00:07:32.340 --> 00:07:34.200 whatever that was called a couple of weeks ago. 00:07:35.160 --> 00:07:41.220 And I have blue hats and yellow, a yellow Yankees hat to match my brand. 00:07:41.920 --> 00:07:47.100 And it's just, it's so, it's a, it's a fun thing to have. 00:07:47.930 --> 00:07:53.280 And I love this article because it provides such an interesting bit of history and context 00:07:54.320 --> 00:07:59.660 to something that wouldn't necessarily seem to have an interesting backstory, right? 00:08:00.060 --> 00:08:07.120 You would think, oh, MLB realized they could make a lot more money if they printed different color hats. 00:08:09.360 --> 00:08:15.640 But Spike Lee kind of pioneered this because he also wanted to wear a dope hat. 00:08:16.100 --> 00:08:17.000 So love that story. 00:08:17.440 --> 00:08:19.080 I'll link it in the description and the show notes. 00:08:19.720 --> 00:08:20.440 I think it's a fun read. 00:08:22.340 --> 00:08:29.680 And now, because I'm not following the news cycle of like hit you with the bad story and then 00:08:29.780 --> 00:08:35.860 end with a nice story. It's called the recency effect or recency bias where you remember the last 00:08:35.860 --> 00:08:47.620 thing they talked about. But this video, I think, is too important to not mention. It is by, oh, 00:08:47.620 --> 00:08:55.060 I linked something else recently to them. Right. More Perfect Union. It's called, I tracked down 00:08:55.160 --> 00:08:58.560 the hidden workers secretly powering chat GPT. 00:09:00.140 --> 00:09:01.760 this is something totally different, right? 00:09:01.920 --> 00:09:07.820 It's the video talks about companies that recruit people who train large language models. 00:09:08.880 --> 00:09:14.420 And so a lot of large language models are trained with, I forget what it's called, 00:09:14.580 --> 00:09:18.660 it's like human reinforced learning, something like that. 00:09:19.260 --> 00:09:25.520 This used to happen in, like, countries that were struggling economically. 00:09:27.000 --> 00:09:41.040 Which is why, for example, chat GPT would use delve so much because the humans that were reinforcing the training, I think in South Africa, used the word delve a lot. 00:09:41.720 --> 00:09:47.580 So there's a little tidbit as to why delve is used so much in large language models. 00:09:48.300 --> 00:09:54.840 But now, as the companies are trying to be more Ph.D. level, right? 00:09:54.920 --> 00:09:56.720 you've heard Sam Altman say this. 00:09:56.840 --> 00:10:03.160 You've heard Amodi say this, Dario Amadhi from Anthropic, right? 00:10:03.780 --> 00:10:08.540 That older models could do these things, but now they have PhD level knowledge. 00:10:08.740 --> 00:10:09.660 And that is not true. 00:10:10.360 --> 00:10:12.860 They are hype men trying to make billions of dollars. 00:10:13.360 --> 00:10:15.860 And so they need to paint their products in the best way possible. 00:10:17.020 --> 00:10:24.820 But they are hiring PhDs to do human reinforced training on these. 00:10:24.900 --> 00:10:33.460 large language models. And so the problems this video highlights is twofold. It is the predatory 00:10:33.800 --> 00:10:41.200 nature of recruiting experts in a way that is dehumanizing, like selling to the lowest bidder, 00:10:42.160 --> 00:10:47.760 making them or like offering like higher contracts, but like you've got to be available at all 00:10:47.940 --> 00:10:54.820 hours to do it. No price negotiation. You get what we give you. And, and it's, 00:10:54.840 --> 00:11:01.480 It's terrible work, and the people who are doing it are possibly in dire economic straits. 00:11:02.740 --> 00:11:03.840 So that's one side of it. 00:11:04.440 --> 00:11:12.020 But the other side of it is this chilling mindset behind AI companies who want to own knowledge 00:11:12.380 --> 00:11:13.940 and sell it back to us. 00:11:15.560 --> 00:11:22.820 So, you know, I think Sam Altman is quoted in this video as saying, like, yeah, we want to make knowledge available 00:11:23.100 --> 00:11:25.400 and you purchase tokens to get a little bit of that knowledge. 00:11:26.580 --> 00:11:27.340 And that's awful. 00:11:28.900 --> 00:11:32.320 It cuts against what got us here, right? 00:11:32.440 --> 00:11:36.140 The reason the internet was created 00:11:36.900 --> 00:11:40.780 was so that researchers could quickly share knowledge with each other. 00:11:42.060 --> 00:11:47.060 And large language models appear to be trying to paywall that knowledge. 00:11:48.880 --> 00:11:50.580 So this video is really interesting. 00:11:50.860 --> 00:11:52.380 I think like more perfect union. 00:11:53.380 --> 00:11:58.640 I've been watching a few of their videos and I want to dig a little bit deeper into them and like where they came from and this and that. 00:12:00.520 --> 00:12:06.160 But it's at the very least seems like they do this like deeply researched work and it's well produced. 00:12:09.200 --> 00:12:16.320 And it's just an it's an interesting thing that one might not think about when it comes to large language models. 00:12:16.540 --> 00:12:20.300 Now there is a hopeful call to action at the end. 00:12:21.380 --> 00:12:27.520 but ultimately I'm sharing this because I think it's an important message for anybody who uses large language models to hear. 00:12:29.900 --> 00:12:35.080 All right, that is it for the Friday wrap on May 15th, 2026. 00:12:35.500 --> 00:12:43.440 If you want to get a written version of this delivered directly to your inbox, as well as an exclusive automation of the week, 00:12:44.460 --> 00:12:48.900 join my newsletter over at streamlined.fm slash wrap. 00:12:49.960 --> 00:12:52.700 This week, maybe somewhat hypocritically, 00:12:52.880 --> 00:12:53.880 given what I just talked about, 00:12:54.440 --> 00:12:57.420 I am sharing an automation I've set up in Claude. 00:12:58.280 --> 00:13:02.180 But that is it for this episode of the streamlined solopreneur 00:13:02.180 --> 00:13:03.100 and the Friday wrap-up. 00:13:03.100 --> 00:13:04.000 I hope you enjoyed it. 00:13:04.480 --> 00:13:08.880 If you did, again, sign up for my newsletter. 00:13:09.480 --> 00:13:10.480 Thanks so much for listening. 00:13:11.260 --> 00:13:13.940 And until next time, I hope you find some space. 00:13:14.540 --> 00:13:15.160 in your weekend.