S1: Welcome to Unsupervised Learning, a security, AI and meaning focused
S1: podcast that looks at how best to thrive as humans
S1: in a post AI world. It combines original ideas, analysis,
S1: and mental models to bring not just the news, but
S1: why it matters and how to respond. All right. Welcome
S1: to unsupervised Learning. This is Daniel Meisler. I'm going to
S1: jump into this episode this week. So someone asked me
S1: last week about the best way to run AI models
S1: I think they were specifically talking about based on the question.
S1: They were talking about local models. So here's a breakdown.
S1: So and this is for AI in general. So I
S1: prefer using APIs for any sort of thing that I'm
S1: doing over and over. I don't like to use front
S1: ends like ChatGPT or Claude's version of ChatGPT. I like
S1: to go directly to the API using my own code
S1: for day to day use. I prefer using fabric, which
S1: is my own project, and that is because it has
S1: specific use cases for dozens, almost 100 different regular human
S1: problems that we do every day. So I would recommend
S1: using fabric for that. And again, the project is here.
S1: And if you look at patterns, these are all the
S1: different things that you could do with it. Right. So
S1: it's like analyzing text, doing job analysis, creating presentations, finding
S1: flaws in arguments. You know, taking exquisite manual like notes
S1: on a four hour video and doing it in 30s,
S1: extracting the ideas from a book, pulling out predictions that
S1: a person made, like all sorts of stuff you could do.
S1: So that is like my number one way of interacting
S1: with AI. If you just want to show off what
S1: you could do with local models, I would recommend Olama,
S1: which is really cool. I'll show you that real quick.
S1: So you just go to models and if you click
S1: on Lama three, you can just run this model right
S1: here and you just literally copy this. And once you
S1: have Al-ummah installed and running, which takes a few seconds,
S1: you basically run this and it will download the model
S1: and put you into a shell that allows you to
S1: talk directly to it. So it's really cool. So let
S1: me just show you what that looks like if I
S1: am over here. Okay. This one is almost done. I
S1: was doing it like the pie is in the oven
S1: type thing. We still got a little bit more time 99%.
S1: This one is the 70 B version. So this is
S1: a giant model. This is like 40 something gigs and
S1: have it running here. You see the command I put
S1: in Olama run Al-umma three colon 70 B. All right.
S1: So that's all you basically do. And then you get
S1: a shell and you are talking to the model completely
S1: locally just on your own machine. So that's really cool. Okay.
S1: So if you go to back to here. So that
S1: is a way to like demo models and show them
S1: off and show what you could potentially do with them.
S1: To me it's a little bit gimmicky because I would
S1: rather use fabric or the next one I'm going to
S1: talk about, which is this one. This is a new tool.
S1: It's not new. It's been out for a while, but
S1: it's growing in popularity. It's called LM studio. This is
S1: a full Mac application. It also runs on Windows and
S1: Linux and essentially what you do is you install this
S1: thing and you just type into this, the search bar here,
S1: the name of the model you want to get, and
S1: it pulls down models from like hugging face and like
S1: all sorts of different places, it downloads it for you,
S1: loads it for you, which could be really difficult if
S1: you try to do it manually. And then when you
S1: click on this icon here, you're actually in a chat
S1: interface very similar to ChatGPT, except for it's a local
S1: thing and it's working completely local for local models, and
S1: it's just really powerful. And there's actually even a comparison,
S1: one where you can actually compare different models. But the
S1: most important thing about this is that you could try
S1: new things. You could try things that just came out
S1: like yesterday or the day before or today. Whereas with
S1: Olama this one, if you go to models, it's really
S1: just kind of like the major ones. There's only a
S1: few here. I mean, I say a few, but it's
S1: like a couple of dozen. But if you look at
S1: like hugging face, let's look at that number. Yeah 629,000.
S1: So it's pretty different versus a couple dozen. So that's
S1: the type of thing you could do. If you're using
S1: LM studio, you just have access to way more models
S1: to mess around and play with and even compare the models.
S1: So I would say this is like what I would
S1: recommend for someone who is gooey oriented and wants to
S1: do lots of tinkering. And if you want to just
S1: be a workhorse and get lots of things done, I
S1: would recommend Fabrique, which is a command line interface and
S1: also a GUI. Now, like I said before, using other models,
S1: lots of cutting edge models. That's kind of the advantage
S1: of LM studio. And you could do that with fabric
S1: as well. We're adding the ability to do that. But
S1: my current favorite one is this llama 370 B instruct.
S1: This one's really cool from Quant Factory. It's really powerful.
S1: One I've been messing with some different ones. Lots of
S1: different groups put out their modified. Versions of Lama essentially,
S1: and this is a version of 70. Be fairly fast,
S1: really high quality. And yeah, just really cool way to
S1: interact with stuff. And again, the name of the tool
S1: is LM studio. We had a really great book club
S1: this week. We talked for like an hour and ten minutes, ten,
S1: 15 minutes. And then we pick the next book. And yeah,
S1: really thankful to be part of the UL community and
S1: doing these book clubs. We do book clubs once a month.
S1: We do a weekly or a monthly meetup halfway through
S1: the month where we talk about we share tips. We
S1: share like automation, hacking tips like security tools, productivity tools.
S1: We talk about life. It's a very sort of intimate
S1: and sharing and uplifting sort of group. And you should
S1: come join us if that appeals to you. It's just
S1: becoming a premium member of unsupervised learning. And I just
S1: signed up for my first full body MRI, which is
S1: going to be super fun. Not looking forward to being
S1: inside of a tube. I guess I'll just meditate to
S1: get through that, but I'm not overly claustrophobic. It just
S1: doesn't sound like a good time, especially since the full
S1: body one is 60 minutes and RSA is next week,
S1: so that's going to be exciting to see everybody. So
S1: I found a good reason for college and specifically good colleges.
S1: And this is thanks to Paul Graham who I read
S1: a bunch of his essays where he talked about college.
S1: And I think there was one where he talked about
S1: this particular concept. So I just wrote it up in
S1: a separate post because he didn't do that. He basically
S1: just mentioned it kind of as an aside. So this
S1: is basically the reason you want to go to elite
S1: colleges or send your kids to elite colleges is because
S1: there's very few things that are more valuable than your
S1: kid being surrounded by highly resourced, highly motivated, highly intelligent
S1: and highly disciplined people. And that is what you find
S1: at the higher and higher tiers of college. All the
S1: way up to like Stanford and Harvard. It's just like
S1: their parents were rich, their parents were probably smart, they're
S1: probably smart, and they had to be disciplined to get
S1: decent grades. So it's like the combination of things just
S1: stacks up. And if you look at all these different
S1: companies that are being founded, they're usually being founded by companies,
S1: by people who went to these schools, even if they
S1: dropped out. So it's like, don't think of it as
S1: the education because the education is very similar at like
S1: a decent state school compared to like an Ivy. But
S1: it's not about the education, it's about the connections. So
S1: just kind of an interesting little tip there way to
S1: frame education. Oh, I will say that the thing that
S1: this makes me think of is how can we get
S1: that somewhere else? So let's disambiguate or let's break apart
S1: these two things education and the networking. Let's find other
S1: ways to do the networking. So we don't have to
S1: have this weird college experience. Now, maybe that's not possible
S1: because the college experience is actually what gives you the
S1: connection in the networking with all the drinking and all
S1: the parties and everything, and then all the just forced
S1: time together in these dorms. Maybe that's the magic sauce,
S1: but I bet you there is a better way to
S1: do that. You could have organized off sites more like
S1: hacking competitions or hackathons or summer camp or something like that,
S1: where you're actually forging these connections separately from doing the
S1: education piece, which arguably could be done better with YouTube
S1: and online courses combined with like in-person workshops. So let's
S1: take the best of education and let's take the best
S1: of networking and maybe do them separately, or even mix them,
S1: but not be confused about thinking that they're the same.
S1: Because that's the problem currently with college. And the result
S1: for most people is worse networking and worse education. Okay,
S1: so security MI5 is starting to vet key researchers going
S1: to top universities in Britain to make sure that they're
S1: not essentially from a country like China, where they're just
S1: coming in to steal all the stuff. And I really
S1: think the US needs to start doing something like this
S1: as well. And speaking of that, Germany just arrested three
S1: people who are suspected Chinese spies for stealing secrets. And
S1: this is right after their chancellor was giving crap to
S1: the to China publicly basically talking about IP theft. And
S1: then they made those arrests. I don't know if that
S1: was coordinated or not. After moving forward with a possible
S1: ban on TikTok, the US is now looking at DJI drones,
S1: basically saying, look, they have so much of the market
S1: and they're flying all over the country. Who knows what
S1: they're doing with those pictures. And it's a state, partially
S1: state owned Chinese company, which means that China runs it.
S1: I mean, they have full control if they want it.
S1: Of any company. So yeah, this makes sense. This one
S1: is insane. Former athletic director arrested for using AI to
S1: mimic a principal's voice, and basically had him saying a
S1: bunch of racist and anti-Semitic things in the voice of
S1: that person he was attacking. And then that started a
S1: whole investigation, and it turned out it was a deep fake.
S1: But yeah, yet another abuse case for deepfakes, character assassination.
S1: And I think it's going to be super cool to
S1: watch all these different attacks. But I think it's also
S1: going to make it harder. This is kind of a
S1: sad thing. It's going to make it harder to believe
S1: people when they bring forth evidence. It's like, hey, my
S1: boyfriend did this, my boss did this, and you show
S1: him a video of it, of them doing it, or
S1: you show them a recording, have them listen to a
S1: recording and they're like, yeah, I mean, I hear them
S1: saying it, but like, it's easy to make anybody say
S1: anything and you go ask the actual person. They're like, yeah,
S1: I never said that. That was a deepfake. So it's
S1: going to take some power out of the hands of,
S1: I think, victims. And so we're going to have to,
S1: as a society, sort of figure that out. Nation state
S1: attackers have been using two zero days in Cisco firewalls.
S1: And this is not the first time that's happened to Cisco.
S1: And it's also happening to other firewall providers as well.
S1: And US lawmakers gave the okay to extend warrantless surveillance
S1: under FISA, section 702, these laws that allow the government
S1: to do these things, they kind of get perpetually extended.
S1: I am mostly okay with it. I feel like as
S1: long as there's transparency and oversight, then it's probably necessary.
S1: But I don't like when they sort of become secret
S1: programs and don't have oversight. Then you start worrying about
S1: what they can actually do to Americans and change healthcare.
S1: Paid 22 million in Bitcoin to ransomware attackers, but the
S1: files were already compromised, so they're not sure exactly what
S1: that fallout is going to be. And the Air Force
S1: is moving forward with Anduril. I'm guessing that's how you
S1: pronounce it. And General Atomics to develop unmanned fighter jets,
S1: sidelining giants like Boeing and Lockheed Martin. So Anduril and
S1: General Atomics, interesting unmanned fighter jets, just like we've always
S1: predicted in sci fi and Detective Fi's external attack surface
S1: management solution is now in AWS and that is un.
S1: Oh yeah, defend defy versus Detectify. I was going to
S1: say I think that was an accident. Technology. Amazon's robot
S1: workforce has expanded to over 750,000 robots. And it says
S1: overtaking 100,000 jobs. It's not super clear how they're getting
S1: to that number, but whatever. I mean, we all know
S1: where this is going, right? And like I say, don't
S1: be surprised about this. Expect it. Companies will do what
S1: makes sense for margins, not for people. The responsibility is
S1: to shareholders. And human employees are like, I hate to
S1: say it, they're the worst, right? It they're to be avoided.
S1: You don't want to have to hire somebody if you
S1: have a hot dog stand and it works perfectly and
S1: it's just you're the sole proprietor of this establishment and
S1: you have to hire people, that is not good. Now,
S1: of course, if you have to hire because you want
S1: to sell more hot dogs, that's fine. But it's much
S1: better if you have some robots that you can buy,
S1: and they can make more hot dogs and make more
S1: people happy, and you get to share all the stuff,
S1: and the robots don't get sick and they don't complain.
S1: So that is the way companies see this, and we
S1: just have to be ready for that. Voyager one is back.
S1: Sharing information like it kind of went silent for a while.
S1: We were worried about it. This thing is an interstellar space.
S1: That means it's outside of our solar system. Like iPhones
S1: don't perform this well, like nothing performs this well that
S1: we have ever made. This thing was launched. What? When
S1: I was a kid, I mean, decades ago. And it's
S1: still working. I just can't believe this thing. And. Okay,
S1: someone at Google senior executive is pushing for speed, basically
S1: saying the 25,000 person strong knowledge and information team has
S1: to adapt to a new operating reality with tighter project timelines. Yeah,
S1: I wouldn't characterize it as tighter timelines. I would say
S1: have vision, right. And like I said, this is progress.
S1: But they need a whole new way of thinking about
S1: making products at Google. And I think that requires a
S1: new leader. Police are now using GPT four powered body
S1: cams to turn audio into reports. That's great for transparency,
S1: I think. How about this for every stop, you basically
S1: publish the transcripts and that goes on an open website.
S1: That would be fantastic. And they could clean it up.
S1: For privacy reasons, but you can see the whole interaction.
S1: And then you also publish the video and the transcript.
S1: That would be community policing, especially in AI, because you
S1: can have AI monitoring the city, looking at every transcript
S1: of every stop. And you could characterize and actually have,
S1: you know, a way to profile like, how is this
S1: police department doing, are they doing more community management or
S1: more like predictive enforcement, or are they rude? Are they racist? Whatever. Okay.
S1: Apple eight open source LMS pushing the envelope for on
S1: device text generation with models up to 3 billion parameters.
S1: Apple doing crazy stuff in open source. Cannot wait for
S1: iOS 18 and llama three. Llama three is huge. Yeah
S1: 800 llama three variations on hugging face insane. It is
S1: really good. It is really good. And I think it's
S1: getting so good that the people like OpenAI and anthropic,
S1: they need to be afraid. And Google need to be
S1: very afraid of this. Because what I like about the
S1: open source is it empowers the whole world to compete
S1: with OpenAI and Google, right? Because if they're like, look,
S1: this is how good it was for us. I mean,
S1: the barriers are essentially new techniques and the size of
S1: your infrastructure and the quality of the data that you have. Right.
S1: So new techniques, the crowd, the group of public researchers,
S1: they can jump ahead really quickly there. It's harder for
S1: them to compete with one of these big companies, of course,
S1: in the size of the cluster and how long you
S1: could train and stuff like that, because that's super expensive.
S1: And then the other one is data. It's also hard
S1: to compete there, but I think the technique part might
S1: be such a big lever that it allows some companies
S1: to really catch up and maybe even jump ahead in
S1: some cases. And then of course they'll get lapped the
S1: next time the big company moves. But I feel like
S1: community AI with open source models will move so often
S1: and iterate so often that speed it might. Get very
S1: close or even equal with or in some cases surpass
S1: the big models. Now, I do think the big models
S1: will have bigger leaps and they'll go way ahead, but
S1: it could be that open source catches up in the
S1: meantime just because of the speed of iteration. And I'm
S1: guessing here, but it's kind of just an intuition. Open
S1: voice is crushing voice cloning with the ability to mimic tone, emotion,
S1: and even cross-lingual speech. Snowflake just shipped another LM. Simone
S1: turns YouTube videos into blog posts US is finally getting
S1: its first high speed rail. Elon wants to turn Tesla
S1: cars into a distributed AI compute network, kind of like
S1: AWS for AI, but on wheels, and Tesla's Autopilot and
S1: self-driving are under scrutiny because there's been some deaths, and
S1: I think this is probably going to be the case.
S1: But I think over time, autonomous driving is going to
S1: be better than human driving because humans get tired, humans
S1: get distracted, humans are texting while driving or watching TikTok
S1: while driving, and that's probably worse. Again, this is an
S1: empirical question, so we'll have to see the actual data
S1: and how it plays out. But that that again, is
S1: my intuition. And yeah, example of this. We had someone
S1: arrested for vehicular homicide after distraction with his iPhone or yeah,
S1: I assume iPhone, but distraction with his phone laid to
S1: a led to a fatal collision with a motorcyclist while
S1: he was on autopilot, and Volvo brought in a Chinese
S1: EV to the US market. Okay, Stanford found out you
S1: could predict political leanings by looking at someone's face and
S1: specifically the size of your face. The bigger your face,
S1: the more likely you are to be a conservative. Evidently,
S1: according to this paper, and the smaller your face, the
S1: more likely you are to be a liberal. And I'm
S1: trying to figure out like what exactly that means. How
S1: do you measure the size of your face? I don't
S1: get it, but whatever. I'm sure it's in the paper.
S1: Article claims that 30% of kids 5 to 7 are
S1: on TikTok. 5 to 7. No bueno. I am very
S1: much in Jonathan Heights camp and you need to go
S1: read his book. I think he is touching on some
S1: really good stuff. FTC has banned nearly all non-compete agreements.
S1: This is great news. I think it's going to spawn
S1: even more innovation in AI and tech, and some are
S1: worried about IP theft, but if you move, you'll just
S1: steal the content. It's already illegal to steal from companies,
S1: so you don't need a law to keep them there
S1: so they don't steal. Like again, should be two separate laws,
S1: one for leaving and moving and one for stealing. Stealing
S1: is already illegal, so let's just stick with that. Supreme
S1: court is set to decide if cities can penalize the homeless.
S1: And I've got a vibe here. So I've been thinking
S1: about this for a while. I think if you're mentally ill,
S1: let's get you help or get you housed or whatever.
S1: If you're on drugs, let's get you treated and then
S1: see if you're mentally ill after that. If you're not
S1: one of those, let's figure out if you actually want
S1: to work. And if you do want to work and
S1: you're not one of these two, then let's get you help.
S1: Let's get you job support, training, all these different things
S1: like interview help, right. Try to get you on your feet.
S1: And if you're not any one of those, so you're
S1: not on drugs, you're not mentally ill, but you don't
S1: want to work and you don't want to participate in society. Well,
S1: let's get you somewhere where you're not bothering other people
S1: who are trying to be useful. And I think most
S1: homeless are actually dealing with one and two, so let's
S1: help them. That's our responsibility. And if it's not that
S1: situation and a person just really doesn't want to participate
S1: in society, they're like a Ted Kaczynski but not violent. Cool.
S1: Let's help them go do that somewhere else, you know,
S1: in a peaceful way. All right. Using AI for actual science,
S1: innovation and research. So AI is now helping physicists explore
S1: the vast possibilities of string theory. So this is great.
S1: I mean, think about what Einstein did. Einstein went from
S1: there not being a curved space time to just thinking
S1: of curved space time. I think about this all the
S1: time because I'm reading a lot of experimental physics, theoretical
S1: physics lately, and I really think that with my prompting skills,
S1: if I were better at the science, which I'm not,
S1: but if I were actually, I could probably just do
S1: this with AI without even knowing the science. If I
S1: had a big enough sort of resources to work with
S1: in the AI was a little bit further along. What
S1: you do is you essentially say, hey, look, here's all
S1: the different ways that people have crossed over and had
S1: these creative breakthroughs. You describe the science before Einstein, you
S1: describe what Einstein did, and you do that for multiple
S1: different places inside of science. Right. And what this does
S1: is it teaches it what it looks like to be creative.
S1: And you say, look, you're looking for more things like this.
S1: Then you feed it the current state of thinking with
S1: quantum physics and multiple universes and all these different things.
S1: You feed it all the math and everything, and you say,
S1: now that you have all the current do the same
S1: thing Einstein did, but do it for the current state now.
S1: And the idea is it should come up with really
S1: weird stuff. It's like, what if we're inside of a fishbowl,
S1: inside of a mormon, you know, simulation cluster running on
S1: Linux or whatever? It just starts making up things and
S1: then the humans can be like, oh, well, actually, I
S1: never thought of that. Let's go mess with that. And
S1: then even better, and this is something that Joseph Thacker
S1: and I talked about multiple times. It was his idea
S1: initially was like, how do you automate not just the
S1: having of the idea but the testing. And so we've
S1: been working on like an infrastructure that you could potentially
S1: do that with. And this is like the most incredible
S1: thing I think about AI because think about this, okay.
S1: Global warming, cancer, aging okay. People have to die at
S1: 90 at 180, 90, 100. We describe all the reasons
S1: that we're dying at 89 to 100. And we say, look,
S1: we want to live longer. We want to live to 150.
S1: How can we live to 150? And we tell it
S1: to go and crunch and do what Einstein did, but
S1: for aging, and you give it all the genomes, you
S1: give it all the illnesses, you give it all the
S1: doctor reports, you give it all the autopsies, and it's like, okay, cool. Well,
S1: have you thought of this? If you do this to
S1: the mitochondria, it'll slow this down and you'll live to
S1: 180 years old. And it could just churn out these
S1: things and even better, churn out testing for those things.
S1: So it could be like, look, I need a lab
S1: with the following, you know, beakers and petri dishes and
S1: we need to combine these different things. And I think
S1: I could make a drug that'll do this. You have
S1: to be careful, though, when you start building things that
S1: the AI asks you to build. But that's a separate
S1: talk show. But just think of that automated thinking combined
S1: with experimentation to to yield a result. I think it's
S1: super exciting. Okay, meticulous planning and luck led to capturing
S1: a breathtaking eclipse photo. This is the advantage of doing video.
S1: I'm going to open it up. Look at this thing.
S1: Look at this thing. Is that ridiculous? Like the wing
S1: is just like perfectly there and you've got the flare
S1: around it. I mean, that is that's gorgeous. Margaret MacDonald
S1: argues philosophical theories are more like artful stories than scientific facts.
S1: Quite a good essay. Here you are, what you read,
S1: even if you don't always remember it. I call this
S1: osmotic learning, which is basically just it absorbs into you
S1: in a way that's not attributable to the book or
S1: the video that you got it from. It just it
S1: soaks into you. Right. And I think I've read close
S1: to like 800 books and I feel myself getting smarter,
S1: but I can't point to why my models are being updated,
S1: which I call algorithmic learning. If you actually say Huberman
S1: said this, therefore I'm putting that into an algorithm to
S1: update what I do in the morning for my routine.
S1: That's algorithmic. But if you just consume the video, you
S1: don't write anything down, but you find yourself changing over time.
S1: I call that osmotic for like osmosis seeps in. And
S1: turns out the reason you're not landing that job in
S1: 2024 might be because of ghost jobs, which are like
S1: basically fake jobs that nobody's applying for, and they're not
S1: real jobs. And you can actually get it. And got
S1: an argument here that your personality and creative application strategies
S1: can actually land you a job. So they're basically saying
S1: it's not about skills, it's about personality and creativity. And
S1: I think I've been talking about this quite a bit.
S1: It's about how you broadcast yourself. It's about how you
S1: present yourself. This is why I'm doing the whole human
S1: 3.0 thing. So I definitely agree with this. I would
S1: characterize it a little differently, but yeah, pretty much right.
S1: All right. Continuous recording within ideas and analysis. So I
S1: think we're heading towards a place where everything is recorded,
S1: or at least where you can expect someone in a
S1: public space is probably recording you. And I like what
S1: that does for getting value out of conversations. But oftentimes
S1: the value is just having the talk in the first
S1: place and having it with somebody who you actually trust.
S1: So I worry a lot about the fact that people
S1: might be more guarded now when having conversations, just assuming
S1: that it is being recorded. So I expect there to
S1: be some sort of conversation indicator, like a red light
S1: or a blue light or something, or a green light,
S1: or a visual indicator on somebody's person that displays one that.
S1: They are recording or two, that they're okay with being recorded,
S1: or that they are not okay with being recorded. And
S1: I think we're going to have some sort of visual
S1: protocol like this indicates someone's comfort level. And I think
S1: venues will also have that, like you'll walk in and
S1: it'll be like a red light, like a stop sign
S1: or something, and it'll be like, not cool to record
S1: inside this building or Starbucks maybe does that for all internal,
S1: you know, Starbucks or whatever. Now question of enforcement that's separate.
S1: But the point is, the social contract I think is
S1: going to be there. It's going to need to be.
S1: So I think it's going to be interesting to see
S1: how that all affects how we interact with each other.
S1: One option is we stop talking and being like fresh
S1: and open with each other. Another option is we stop
S1: caring because there's no other ways to be embarrassed or canceled,
S1: because it's already happened to everyone already. And another option
S1: is that we break into pockets. So some places, like
S1: nobody's embarrassed about anything. Everyone's recording, everything is being transcribed.
S1: It's just radical transparency. And in other places it'll be
S1: like maybe Germany or like the Amish or something. It's like,
S1: no tech, no recording, complete privacy. And, you know, just
S1: like the polar opposite. And you'll have lots of different
S1: societies in between. Recommendation of the week. I recommend the
S1: book of the week or actually book of the month,
S1: actually The Courage to Be Disliked. This was the book
S1: that we just did the book club on. It was
S1: the book for April and it was wonderful. Absolutely loved it.
S1: Courage to be disliked. And it's about the teachings of Adler.
S1: It's called Adlerian philosophy or Adlerian psychology. Highly recommend the
S1: book and the aphorism of the week be who you
S1: are and say what you feel, because those who mind
S1: don't matter and those who matter don't mind. Be who
S1: you are and say what you feel, because those who
S1: mind don't matter and those who matter don't mind. Unsupervised
S1: learning is produced and edited by Daniel Meisler on a
S1: Neumann U87 AI microphone using Hindenburg. Intro and outro music
S1: is by zombie with the why and to get the
S1: text and links from this episode, sign up for the
S1: newsletter version of the show at Daniel meisler.com/newsletter. We'll see
S1: you next time.