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 Miessler. All right. What
S1: have we got here? Lots of content this week. Super
S1: excited for this episode. Uh, did an all text episode
S1: this time, which is kind of a callback to how
S1: I used to do it. Got upcoming speaking at the
S1: Snyk conference in October, Cyberstorm in Switzerland and Blackhat in Riyadh.
S1: One tool in AI that you should be trying out,
S1: that everyone is talking about, including Karpathy and a bunch
S1: of other people, is called cursor AI. So it's cursor.com
S1: is the domain. I thought it was cursor AI, but
S1: it is not. But the big feature appears to be
S1: that it basically it looks and feels like VSCode, but
S1: what you do is you upload your entire repository into it.
S1: I guess I guess technically VSCode could also see your
S1: all your code if it's all in there as well.
S1: But I think what cursor is supposedly doing well is
S1: it's taking all of that content and kind of using
S1: it in context to understand it better, not just the
S1: current file that you're in. So I believe that's the
S1: big feature. If I'm wrong about that, somebody correct me. Okay.
S1: My work, a couple of massive episodes or essays that
S1: I put out this week, and I actually sent them
S1: out directly, which I only do for things that I
S1: think are really decent and also evergreen. So one of
S1: them is called We Were Lied To About Work or
S1: the Real Problem with the job market. I had two
S1: different titles actually, but it's basically why layoffs, hiring the
S1: job market, and work in general just really sucks right now.
S1: And I would say it's probably one of my top
S1: 20 essays ever. So highly, highly recommend that one. And
S1: I've got a new way to explain AI and specifically
S1: Llms to people, and I think this one is short enough.
S1: In fact, I'm just going to give the highlights of it.
S1: So let me take you into this one. So here
S1: is the basic concept five levels of LLM understanding. So
S1: the first level at the bottom is it's just predicting
S1: text like the next text token in a in a
S1: text sequence. That's all it's doing. It's not magical. Right.
S1: And this is kind of the most common argument for
S1: why llms and AI is not all that special or
S1: specifically AI based on Llms. People are just like, look,
S1: it's just next token prediction. No big deal. If you
S1: pull away from that one level, it's like, look, it's
S1: just predicting the next item in a sequence, okay? So
S1: it's just next token prediction okay. One abstraction away from there.
S1: And this all comes from this thing from Eliezer Yudkowsky
S1: who had this, this great kind of little statement on
S1: X that made me think about this, but essentially what
S1: it is, is for this level. Level three, it's predicting
S1: the next token in the description of an answer. Okay.
S1: So what Yudkowsky said was any predicting the next token
S1: is isomorphic with predicting the next token of an answer. Okay.
S1: And that is really, really powerful. And it wasn't the
S1: exact quote, but that's essentially it. If you go one
S1: level above that okay, it's predicting answers to insanely difficult questions.
S1: So so here are the four levels so far. It's
S1: predicting the next token in a piece of text. It's
S1: predicting the next token third level. It's predicting the next
S1: token of an answer. Second level it's predicting answers. And
S1: the top level it seems to know everything okay. And
S1: this all comes from this. Here it is right here
S1: it just predicts the next token literally any well posed
S1: problem is isomorphic to predict the next token of the answer.
S1: And literally anyone with a grasp of undergraduate comp sci
S1: is supposed to see that without being told. I don't
S1: agree with that last part. This part here, I don't
S1: agree with that. I don't think that's accurate. In fact,
S1: I think the vast majority of people that's very much
S1: not the case. Very much not the case. I would
S1: say 95%. So I would say I disagree on multiple
S1: levels for that second piece. The part that I like
S1: is this here, which I highlighted and separated on purpose,
S1: literally any well posed problem is isomorphic to predict the
S1: next token of the answer. That is extraordinary. That is brilliant.
S1: That is the answer to the it's just next token
S1: prediction argument. That is, the immediate counter of that well
S1: posed problem equals prediction of next token of answer. So
S1: that's that one. And basically this essay is a full
S1: breakdown of that. So it's a standalone evergreen essay about
S1: why this is a powerful concept and why you shouldn't
S1: trust that next token argument. Okay. Security. CrowdStrike did their
S1: 2024 report talking about how North Koreans have infiltrated over
S1: 100 US based companies in all sorts of important places
S1: like aerospace, defense, retail and tech. They didn't mention much
S1: about Blue Friday. Not sure why that was. State linked
S1: Chinese entities are using cloud services from Amazon and other
S1: competitors of Amazon to access advanced US chips and AI capabilities,
S1: so basically they can't get the chips themselves. But you
S1: can go to AWS. Anybody can, or most people can.
S1: You go to AWS, you sign up and you just
S1: attach Nvidia chips to that instance and you start doing workloads.
S1: And so this is what China is doing. They're like, well,
S1: we can't get the chips, but we can use a
S1: service that leverages the chips. So I'm sure Amazon and
S1: the government are in conversations about that trying to get
S1: that fixed. Cisco has patched multiple vulnerabilities, including a high
S1: severity bug in its Unified Communications Manager product. Thanks to
S1: Threatlocker for sponsoring. Two US lawmakers are urging the Commerce
S1: Department to investigate cybersecurity risks associated with TP-Link routers, citing
S1: vulnerabilities and potential data sharing with the Chinese government. So
S1: kind of a Huawei situation. A mini Huawei Quarks lab
S1: found a major back door in RFID cards made by
S1: Shanghai Fudan Microelectronics, one of China's top chip manufacturers. I
S1: don't think this is a China influence story. I think
S1: this is just vulnerabilities and chips, and the impact here
S1: is the fact that it's smart cards for like office
S1: stores and hotel rooms and whatever. And it's a big
S1: company that does this. And lots of RFID chips and
S1: cards come from China. So just an impact due to
S1: the size of the market type situation. What is this
S1: bouncing now? Now you're seeing my Diablo four chat messages.
S1: It's really important stuff I'm working on in Diablo four.
S1: All right. What are we talking about here? Yeah. Thanks
S1: to defender five for sponsoring. Next one. Researchers found a
S1: way to exfiltrate data from Slack's AI by using indirect
S1: prompt injection. US Navy is rolling out Starlink on its
S1: warships to provide high speed, reliable internet connections, improving operations
S1: and crew morale. AI and Tech Anthropic has published the
S1: system prompts for its latest AI models, including opus, Sonnet
S1: and Haiku. AGI bot is a Chinese company and they
S1: just unveiled unveiled a fleet of advanced humanoid robots to
S1: compete directly with Optimus, which is the one from Tesla,
S1: and they're designed for tasks ranging from household chores to
S1: industrial operations. And they're going to start shipping supposedly by
S1: the end of this year. So like immediately. And Optimus
S1: is nowhere near ready for that kind of timeline. So
S1: I'm basically anti-Chinese imports for both robotaxis and humanoid robots,
S1: because China is too far ahead and they're too cheap
S1: and I would say just too good. So I don't
S1: want to give them a head start. And I don't
S1: like being anti-competitive against any sort of country. I don't
S1: like slowing pressure from the outside, but if this were
S1: India or Ireland, I would actually be okay with them
S1: applying pressure to the US but not China because they're
S1: too obviously a malicious actor who actually just like, wants
S1: to crush the United States in like all aspects, including
S1: the United States going away as an economic power. So
S1: it's not like friendly competition. So I think we should
S1: actually just either tax the hell out of them or
S1: not allow them to function until until we have proper
S1: footing and like, can compete properly. And speaking of that,
S1: Tesla is hiring people to train its Optimus humanoid robot
S1: by wearing a motion capture suit and mimicking the different actions.
S1: You get like $48 an hour, but you got to
S1: walk over seven hours a day carrying £30 while wearing
S1: a VR headset. That's a tough job. Waymo is looking
S1: to launch a subscription service called Waymo Teen, so this
S1: is basically to help parents not have to shuffle kids around.
S1: Although I'm not sure, depending on the age of the teen,
S1: should they have their own car, I'm not sure. But anyway,
S1: cool idea. I scientists developed by the University of British Columbia,
S1: Oxford and Sakana AI is creating its own machine learning experiments. Okay,
S1: let's back up. An AI scientist is creating its own
S1: machine learning experiments and running them autonomously. I think this
S1: is where most innovation will come from. AI not just
S1: implementing tasks, but in doing new research. And I talked
S1: about it in a post there. Victor Miller, a mayoral
S1: candidate in Wyoming's capital city, has vowed to let its
S1: customized ChatGPT named Vic Virtual Integrated Citizen help run the
S1: local government. I'm actually working on how to articulate a
S1: political platform for any level of office using substrate. Basically,
S1: define exactly what you want to do, how it branches
S1: out with problem strategies, most importantly KPIs and promises. So
S1: you could literally say, like I talked about it in
S1: the substrate video, you could say, look, here's how I'm
S1: measuring myself. Here are my assumptions. Here's what I believe
S1: the problems are. Here are my strategies for attacking those problems.
S1: And here are my specific projects that I'm going to
S1: do to implement those strategies. And here's how much it's
S1: going to cost. Here's how I'm measuring myself. And here's
S1: the promise if I don't move these numbers by X amount,
S1: you should fire me in two years or in four
S1: years or whatever the term is. So I think this
S1: is where leadership is heading. Transparent descriptions of vision, strategy, KPIs,
S1: and promises. Sean Ammirati Arathi, a professor at Carnegie Mellon,
S1: noticed a massive up leveling of progress in his entrepreneurship
S1: class this year, thanks to generative AI tools like ChatGPT,
S1: GitHub Copilot, and Flow wise, AI. So they basically the
S1: students use these tools for marketing, coding, product development, and
S1: recruiting early customers. This is what I've been talking about
S1: with AI automation. If you were competing with a 95
S1: out of 100 person before and they were a 95
S1: because they went to CMU, well, now you're competing with
S1: a 130 out of 100 because they went to CMU
S1: and they're using AI for everything. And for me, in
S1: my own life, I read better articles because of AI.
S1: I get better ideas because of AI. Therefore I build
S1: better stuff because of AI. And this is all feeding
S1: on itself, just okay. And this all feeds itself and
S1: just makes that whole top of funnel even better. And
S1: I believe that your options are to upgrade or lose. Basically,
S1: GM is cutting over 1000 software engineers to streamline its
S1: software services organization, streamlining by cutting out 1000 devs. The
S1: way I see this is they're actually just taking everyone
S1: out because they're like, this is a huge waste of time.
S1: We hired a bunch of duds. Let's start from scratch
S1: and only hire the best possible people who are probably
S1: also massively augmented with AI. Yeah, so I got a
S1: whole bunch of other content killer cult members. This is
S1: my new, um, way of framing. This whole thing is like,
S1: killer cult members is what companies are looking for. It's
S1: kind of obvious that this is what startups are looking for,
S1: but I think corporations are going to look for this
S1: as well. So people talk about, oh, toxic work culture.
S1: Guess what? Companies only want employees who have a toxic
S1: work culture. They want people who show up and are like,
S1: I am religiously dedicated to this. I will sleep under
S1: my desk. I will work as much time as I
S1: need to. I am fully dedicated to this mission. I
S1: wear all the swag in public. I talk about this.
S1: I think about this all day long. That is what
S1: people want. That is what hiring managers want. That is
S1: what corporations want. Because that behavior, especially if you're on site,
S1: which everyone is pulling everyone back to be on prem now,
S1: if you are surrounded by other cult members with that
S1: sort of energy and keep in mind, this also has
S1: toxic aspects to it. There are massive downsides to this
S1: type of culture, but the downsides are not to the company. Really.
S1: The downsides are mostly to the employees themselves as a
S1: trade off for doing this much risk to get much,
S1: a lot of a reward, potentially in equity or pay
S1: or whatever. So this culture is good for companies and
S1: that's why they are fostering it. That's why they're saying
S1: you must be on site. So for example, OpenAI OpenAI
S1: requires you to be on site. You have to work
S1: on site. There are some exceptions, very few. But in
S1: general they make a cool office. In a cool city,
S1: they require the best. They hire the best talent. They
S1: basically tell you, or it's naturally implied that you must
S1: be one of these killer cult members. And then that
S1: is that energy which is synergistic with itself, builds and
S1: builds and builds and it produces the best products that
S1: people want to buy, and they get massive valuations. And
S1: that's capitalism and that's what works. What doesn't work is, okay,
S1: let's not do that. Let's have a company culture that's
S1: good for employees, and let's make sure there's work life balance.
S1: And let's make sure, like all these different policies and
S1: it ends up with a lot of people not working,
S1: not doing good work. It ends up with A-players hiring
S1: B players. B players hire C players. You end up
S1: with a bunch of C players, a few B's and
S1: and some D players. And after a while, the corporation
S1: looks at their entire headcount, spend their entire human resources spend,
S1: and they're like, I'm not getting value from this. This
S1: is not worth it. And they just fire everyone. And
S1: so when I see somebody as firing a thousand software
S1: engineers to streamline the software, I don't know exactly what's
S1: happening here. I'm arguing that what I'm saying might be
S1: happening here is definitely happening all over the place. And
S1: what they'll do is they'll go to zero. Then they'll
S1: find a players who are killer cult members and only
S1: hire those from now on. And here's the crazy part.
S1: 100 of those people might be worth 1000 or 10,000
S1: C players who are interested in work life balance. Again,
S1: I'm not saying anything about like the total value of
S1: on society of work life balance or all of that.
S1: I've got completely separate ideas about that. Actually, in the
S1: essay We were Lied To About Work, you can see
S1: what I really feel about that whole thing. The point is,
S1: this is what companies are looking for. If you want
S1: to get hired, you have the answer. Meta is using
S1: AI to streamline system reliability investigations with a new root
S1: cause analysis system. System combines heuristic based retrieval and large
S1: language model based ranking, achieving 42% accuracy in identifying root
S1: causes at the investigation start. I didn't look to see
S1: or it wasn't in there, how that compares to humans
S1: and how fast, because that's the trick. Accuracy and speed
S1: and of course, cost. AI companies are shifting focus from
S1: creating godlike AI to building practical products. Who knew? So
S1: I don't think this is a bubble pop. I think
S1: it's a natural maturity of brand new tech that just
S1: came out. And because people are still figuring this stuff
S1: out and it's basically day one, like AI hasn't even
S1: gotten good yet, hasn't even started to get good. Canada
S1: is slapping 100% import tariffs on Chinese electric vehicles starting
S1: October 1st. We were just talking about that, former Google
S1: CEO Eric Schmidt predicts rapid advancements in AI, the potential
S1: to create significant apps like TikTok competitors in minutes within
S1: the next few years. I know what he's saying, but
S1: there's a difference between being able to run an app
S1: at scale versus creating it. Of course, he knows that
S1: better than I do or most people. But important point
S1: to make. Claude 3.5 can now create icalendar files from images.
S1: And this guy Greg's ramblings shows how you can use
S1: this feature to generate calendar entries by snapping a photo
S1: of a schedule or event flyer. AWS CEO Adam Selipsky
S1: predicts that within the next 24 months, most developers might
S1: not be coding anymore due to AI advancements. He says
S1: the real skill shift will be towards innovation and understanding
S1: customer needs, rather than writing code. 100% agree. Although most
S1: developers in 24 months. This is the type of thing
S1: where it happens way slower than you think and then
S1: way faster than you think. At the same time, most
S1: in 24 months will not be coding anymore. That is
S1: wildly off, wildly off. But people who are like, oh,
S1: it's going to take forever. It's going to take, you know,
S1: three years, five years, seven years, ten years, 15 years.
S1: They are also wildly off. Chinese companies have ramped up
S1: imports of chip production equipment. $26 billion in the first
S1: seven months of 24 on chip production equipment. They need
S1: to equip 18 new fabs expected to start operations in 24.
S1: There they are all in on this because they see
S1: the world turning against them and shutting them down. So
S1: they are all in on this. Question is, can they actually,
S1: even with all the fabs and the equipment and you know,
S1: the factories, do they have the know how to actually
S1: do what TSMC is doing? My current understanding from current
S1: knowledge combined with like the book chip. Wars know they
S1: don't have the knowledge. So even with all that stuff,
S1: it's not going to be enough humans. Cisco's laying off 7%
S1: of its workforce, around 6000 employees, as it pivots towards
S1: AI and cybersecurity. McKinsey's new study reveals that business leaders
S1: are missing the mark on why employees are quitting. They
S1: say companies are focusing on transactional perks like compensation and flexibility,
S1: but employees are actually seeking meaning. Belonging, holistic care and
S1: appreciation at work couldn't have been better timed with this
S1: week's essay. 24 brain samples collected in early 2424 brain
S1: samples in 2024 measured an average of 0.5% plastic by weight.
S1: So a brain is multiple pounds. Is it £3 or £2?
S1: I can't remember 3 or £4, something like that. It's heavy.
S1: You can feel this thing right. You can feel your head.
S1: It's heavy. Half a percent. Okay. Let's just type in here.
S1: What's the half a percent of the weight of an
S1: average brain? 1400 grams. Half a percent of that would
S1: be seven grams. Okay, I put 25g of coffee to
S1: brew when I brew coffee, so I roughly feel like
S1: I know that's about a third, about a third of
S1: the amount of coffee that I drink in the morning,
S1: which when you brew that thing, I mean, it's significant.
S1: This is a extremely non-trivial amount of plastic sitting inside
S1: the brain. And there's a lot of speculation right now
S1: which I don't put too much weight into it because,
S1: you know, how we have these health scares and like, oh,
S1: this is dangerous and that's dangerous or whatever, but seven
S1: grams of plastic, seven grams of plastic sitting inside of
S1: a brain. I feel like it can't be good unless
S1: it's like alien plastic that is like nanobots or something.
S1: But no, this is regular plastic. The question is, where
S1: is it coming from? How is it getting in there?
S1: Is it in all our foods? Is it in? Is
S1: it from drinking bottles? I'm actually super concerned about this
S1: because I drink energy drinks. That's why I switched to
S1: these because I think they have less. But I saw
S1: a crazy report about my favorite not energy drinks, protein drinks,
S1: my favorite protein drink. It was the core power one,
S1: and it was like off the charts in the amount
S1: of plastic, according to this one lab that ran it.
S1: Now who knows, maybe that lab was run by the
S1: counter product, which I went out and bought, by the way,
S1: which is Muscle Milk. Anyway, I'm concerned about this, but
S1: I'm also cautious because you don't know if it's a
S1: scare turn. That plastic could be completely inert and doesn't
S1: even matter. And the thing that's causing all this cancer
S1: and drop in testosterone and all this stuff is actually
S1: something else could be our sun exposure, which I think
S1: is a later. Yeah, it's two stories down. So anyway,
S1: lots of plastic in our brains. Not sure what that
S1: means yet. Gallup has released its 2023 Global Emotions Report,
S1: which measures the world's emotional temperature through Positive Experience Index.
S1: I'm opening this one because it is cool. Look at this.
S1: Experienced anger. Look at this. You got these country breakdowns.
S1: This is really cool. Then you got map views. And
S1: look at this. The map view. You can click on
S1: sadness stress worry pain. Look at that enjoyment. Okay. So
S1: dark is better right? Um. China. Yes. Yeah. Dark. Yep. Okay,
S1: so let's look at a light one. Yes, 100% dark
S1: is better because that's Afghanistan. Two thirds. No. Oh, man,
S1: that's so depressing. Afghanistan has two thirds. No enjoyment. However,
S1: the question was asked another one with that similar color turkey.
S1: I'm pretty sure that's turkey. Yeah that's Turkey. Uh, Ukraine.
S1: About half, for obvious reasons. What's this one? Morocco half.
S1: What's this one? Tunisia half. Roughly super happy over here.
S1: Somalia Why is Somalia so happy? See, I love these visualizations. Uzbekistan.
S1: Very happy. What have we got over here? Norway is happy.
S1: Keep forgetting. Norway is the far left one. What is
S1: this one? Over here. Estonia. Very happy. Iceland could have
S1: guessed that one. Mexico! Kicking total ass over here. Ireland seems. Yeah.
S1: Really happy. UK? Not so much. What are these? Over here. Indonesia. Indonesia.
S1: Very happy. Malaysia. Very happy anyway. Really cool stuff. Russian Federation.
S1: Pretty low score, actually. I think this is the actual
S1: worst one. Afghanistan, which is totally explainable. I mean, so
S1: this is one experience I've been having is I take
S1: a decent number of Ubers and I always talk to
S1: the person in the Bay area. My chances of getting
S1: someone from Afghanistan are like 80, 90%, and I talk
S1: to every single one of them for the entire duration
S1: of the trip. Usually they are interpreters, don't say translator,
S1: they are interpreters. And oftentimes they worked for the US government,
S1: which is how they got their visa to come over
S1: here and what they have back home if they brought
S1: their family. Most likely they didn't bring most of their family.
S1: Their family is in danger because they are here and
S1: it is going from bad to worse every single week.
S1: It just is so, so depressing. Anyway, I'm on a
S1: tangent here, but talk to your Uber drivers. Experience life
S1: through other people's eyes. Okay. Um. Data from surveys conducted
S1: in 142 countries. Mix of telephone, face to face, and
S1: some web stuff. About a thousand respondents per country. So
S1: that's not great, but I'm sure they're doing it scientifically,
S1: so it's a decent sample. Non-smokers who avoided the sun
S1: had a life expectancy similar to smokers who got the
S1: most sun. And this is nearly 30,000 Swedish women over
S1: 20 years, by the way, Scandinavia likes to smoke. And Germany,
S1: this is the worst thing about Europe and there are
S1: many things competing for that right now. But one of
S1: the worst things about Europe is smoking. I go there
S1: and I'm just like, what is going on? Yeah, Switzerland.
S1: I'm about to go back to Switzerland and everyone's going
S1: to be smoking inside the restaurant. They're smoking inside the restaurant.
S1: I'm just like, what is? I thought you guys were
S1: the advanced group. Okay, the research suggests that avoiding sun
S1: is as risky as smoking. So this needs more research, obviously.
S1: But like I said, damn. For me, I get sun
S1: in the morning. I got decent amount of sun this morning.
S1: Massive boost for me. I put on the Waking Up app,
S1: listen to Sam do a ten minute thing. I do
S1: a ten minute walk out there, I do some breathing.
S1: That's how I start my day. When I'm on a routine,
S1: which I should be and often not enough. But I
S1: did today and yesterday and the day before. All right.
S1: Stanford researchers have found that blocking the whatever blocking this
S1: pathway in the brain can reverse the metabolic disruptions caused
S1: by Alzheimer's disease, providing cognitive improving cognitive functions in mice.
S1: I'm starting to feel like we're about to make massive
S1: progress on both Alzheimer's and cancer. And honestly, it's making
S1: me want to invest in like, the top three drug
S1: companies I think I'm already in, which one I'm in,
S1: the one that does wegovy. I can't remember the name
S1: of that one. I'll think of it. It's right there.
S1: It's right there. Anyway, I'm in that one, but I'm
S1: not in the Lilly one. I think they're called Eli Lilly,
S1: but they're rebranding to Lilly. So I figure if I
S1: get into the two top competitors, one or both of
S1: them is going to do something with Alzheimer's and cancer,
S1: and we're already doing it with obesity. Like, that's a trifecta.
S1: All we need now is like balding and aging. Aging
S1: is the big one. Aging and cancer I would say,
S1: are the big ones. And then like obesity and hair loss,
S1: that's amazing. So not investment advice, but I'm damn sure
S1: getting in all right. Air purifiers and two Helsinki daycare
S1: centers reduced sick kids days by 30%. And I don't
S1: think this is Covid or flu or anything. I think
S1: they're just talking about like, all cause based on the
S1: the study parts that I saw, University of Missouri scientists
S1: have developed a liquid based solution that removes over 98%
S1: of nanoplastics from water. It uses water repelling solvents to
S1: absorb the particles, which are easily separated and removed. I
S1: assume you just run it through a filter and those
S1: things will be big globs, and they get stopped behind
S1: by the filter. I expect to see lots more of this.
S1: Can't wait for the Huberman episode on this, because I
S1: wanted to be able to do this like cheaply at home.
S1: Eli Lilly's weight loss drug Tripeptide, found in Zepp bound
S1: and Manjaro, reduced the risk of developing type two diabetes
S1: by 94% in obese or overweight adults with pre-diabetes, 94%.
S1: And Apple Podcasts is losing ground to YouTube and Spotify,
S1: so recent study put YouTube at 31%, Spotify at 21%,
S1: and Apple Podcasts at 12. I don't do Spotify, I
S1: do YouTube and Apple Podcasts. But all right. Ideas thought
S1: of a cool idea for fabric, Telos and substrate. Maintain
S1: a list of everything I've been really, really wrong about,
S1: which I'm already building that list, and then write a
S1: fabric pattern that looks at that list. By the way,
S1: I'm just going to have it all in my same
S1: Telos file as I talked about in the augmented course,
S1: but basically just have this list listed as all my
S1: biggest mistakes, cognitive errors or whatever listed in there. Then
S1: I also have another section inside that same telos file,
S1: which is like all my current beliefs, my model of
S1: the world. And then the fabric pattern basically says evaluate
S1: all my current beliefs, look at all the previous mistakes
S1: that I've made and look for patterns. Look for what
S1: in my current beliefs might be broken in a similar
S1: way as the way I was wrong about the other things.
S1: First of all, it's going to help me diagnose why
S1: I was wrong. Is it because I have a bias
S1: towards this one thing? It's like, oh, you're so pro
S1: I that you are wrong about this thing because you
S1: thought technology was a solution. So turns out your bias
S1: is thinking that tech can solve too many things in
S1: the world. Good to know. Like I'm already actively defending
S1: against that bias because I know it's there doesn't mean
S1: I'm properly or adequately defending against that bias. Point is,
S1: I want to see the bias. I want to see
S1: it call me out on it, and look for other
S1: evidence that my current beliefs are broken because of that problem.
S1: Discovery Foof I uses fluff and I yeah, that's why
S1: it's called that. To find more web hacking targets by
S1: Joseph Thacker, go fuzzy recursively looks at JavaScript files and
S1: finds endpoints that can be tested. Analyze interviewer techniques is
S1: a new fabric pattern that will capture the je NE
S1: sais quoi. By the way, this is spelled wrong. I
S1: don't know why I didn't just use AI and spell
S1: this correctly. I always spell this wrong. N e is
S1: n a I s and I think the other one
S1: might even be wrong too, but I think the quote
S1: is right. But anyway, it's n I s I believe
S1: is how you spell that. Anyway, someone French gave me
S1: the proper spelling again, more I should be part of this. Um,
S1: back to my bias of I being the problem, but
S1: I've been using this on Dwarkesh and Tyler Cowen content
S1: analyze interviewer techniques. It basically finds out, it figures out
S1: and tells you why they're such a good interviewer. Harness
S1: is a quick tool I put together to test the
S1: efficacy of one prompt versus another. It runs both against
S1: an input and then scores the output according to a
S1: third objective prompt that rates how well they followed the
S1: plot and actually executed the instructions of what the prompts
S1: were trying to do in the first place. So super useful.
S1: Stayed in time are the same thing by Hillel Wayne.
S1: Don't Force yourself to become a bug bounty Hunter by
S1: Sam Curry 67 years of RadioShack catalogs have been scanned
S1: and are now available online. MD RSS is a go
S1: based tool that converts markdown files to RSS feeds. You
S1: can write articles in a local folder, and it automatically
S1: formats them into an RSS compliant XML file. Super cool.
S1: No hello, no quick call, no meetings without an agenda.
S1: You already know that's good. Roger Penrose's book, The Emperor's
S1: New Mind explores the relationship between the human mind and computers,
S1: arguing that human consciousness cannot be replicated by machines. I
S1: have the opposite view, which is why I'm going to
S1: read this book collection of free public APIs that are
S1: tested daily and the recommendation of the week. Take the
S1: time to read this week's main essay. We've been lied
S1: to about work, but more than just read it. Think
S1: about what it means. If I am right, think about
S1: what that means for you and your career, but also
S1: all the young people you know and care about. So
S1: I didn't talk about the solution in the piece, but
S1: it's essentially human 3.0, and I'm going to be talking
S1: a lot more about that, but start thinking about it now.
S1: Definitely recommend that you read this piece. And the aphorism
S1: of the week to fear love is to fear life,
S1: and those who fear life are already three parts dead
S1: to fear. Love is to fear life, and those who
S1: fear life are already three parts dead. Bertrand Russell. Unsupervised
S1: learning is produced and edited by Daniel Miessler on a
S1: Neumann U87 AI microphone using Hindenburg. Intro and outro music
S1: is by zombie with a Y. And to get the
S1: text and links from this episode, sign up for the
S1: newsletter version of the show at Daniel miessler.com/newsletter. We'll see
S1: you next time.