S1: All right. Welcome to episode 488. This is Missler and
S1: we are going to talk about updates. So biggest news
S1: for me continues to be cloud code. And it just
S1: keeps getting more extreme for me. I'm just completely completely
S1: blown away by this thing. It's getting bigger for me
S1: like over and over. And I wrote a few blog posts.
S1: I've written like five blog posts in like the last, uh,
S1: last week or something, which is very fast paced, um,
S1: compared to recently. And, uh, yeah. And actually two of
S1: them I helped me actually Cloud code specifically helped me
S1: a little bit. So really excited about that. And whenever
S1: I have it, help me with a blog post, I
S1: actually have it right in the notes that it helped
S1: and what amount of help that it gave, because I
S1: think that's important. But, um, this is just like the
S1: most insane thing for me. Like I keep saying, I
S1: keep repeating it, but, uh, it's the most excited I've
S1: been about tech in years and years and years. I
S1: would say I haven't been this excited about tech since
S1: I basically got into hacking, and I would say nothing
S1: compares to hacking and getting into it, especially for the
S1: first time, and realizing you could actually break things and,
S1: you know, make things, do things that they're not supposed
S1: to do. And I would say, as someone in their
S1: early 20s going through that, there's nothing that compares to that.
S1: But I don't know, I feel like this is actually higher,
S1: a higher plane. I feel like I'm more excited or
S1: at least me now would be more excited about this
S1: than that previously, because this is building as opposed to breaking.
S1: But I don't know. I will never get rid of
S1: the hacker DNA, I don't think for good or bad,
S1: but I'm just finding more and more ways and applications
S1: of doing this. I just did one earlier today when
S1: I was supposed to be working on the newsletter, but, um,
S1: I was supposed to be recording, but, uh, This thing.
S1: So what it does is when I'm in vim and
S1: I have a question about anything, or I want to
S1: audit code, or I want to add code, or I
S1: want to say, hey, does this have any security vulnerabilities
S1: or whatever I can like select the code or whatever.
S1: And then I bring up the leader I, which goes
S1: to my prompt, and it's actually calling Claude Switch P
S1: which is the SDK, and Claude switch P. I'm giving
S1: it the context of Claude, MD and a whole bunch
S1: of other contexts. It's also taking context from the current buffer,
S1: and it's using all of that to do the correct thing.
S1: So I could literally be in there and just be like, hey,
S1: go get my post where I talked about this before.
S1: What are you talking about? I'm, I'm writing a new
S1: blog post, okay? And I say, hey, go get my
S1: post where I talked about this before and link it here.
S1: The thing goes, rip through all 3000 posts finds content,
S1: then it reads. It goes and reads the content. So
S1: regret found it based on, you know, like word matches
S1: inside of the posts and titles. Okay, so it sounds
S1: what I think it was, it thought it was, but
S1: then it actually reads reads the post, right, reads the
S1: whole thing and it's like, yeah, this is definitely relevant.
S1: So it goes and links it and writes it and
S1: updates the page. And I have the live page over
S1: here in dev and it just updates it. So like
S1: I'm collaborating with an editor with me right here. But
S1: except for the editor could do code reviews, the editor
S1: could do prose, the editor could find synonyms for words,
S1: you know, thesaurus. Like, it's completely insane. I'm about to
S1: do a video on this basically inception with like, oh,
S1: and the other thing raycast. I'm about to link up
S1: raycast to this thing called switch P is probably the
S1: sickest thing that like people are not talking about, which
S1: is why I'm going to do a video about it,
S1: but it was kind of mentioned by Boris in the
S1: end of his intro video. It's absolutely insane. So the
S1: same way that I like, um, use fabric to call
S1: all these different things. Well, what we're doing is we're
S1: getting closer to my eventual goal, right? Because my da
S1: is called chi. Well, when I call Claude, what I
S1: do is, I tell it, you are chi. You are
S1: an instantiation of chi, right? So what I do is
S1: I say, okay, look, here are your tools that you
S1: have available. You have playwright, you have bright data which
S1: I'm doing a video on soon. You have um, fire crawl.
S1: You have a bunch of different MCC, but guess what
S1: else you have? You have fabric. You can now go
S1: and make images. You can now make an image of
S1: anything using the context of this blog or whatever. So
S1: I'm giving my Da. I mean, this is the, you know,
S1: the precursor to having chi up and running, right? Chi
S1: is sort of up and running, but right now chi
S1: is multiple pieces, multiple personalities. It's mostly Claude. I've got
S1: some stuff. I mean, when it uses fabric, it's using
S1: lots of different models. But the point is, like, I'm
S1: just asking Kai to do this. Kai is looking at
S1: its available tools. It's looking at my desires and goals.
S1: It's inferring that from the instructions that I give it.
S1: But it's using all this context of the cloud MD file,
S1: which has got tons of stuff in there. It's got
S1: all the tool use plus plus it has the context
S1: of the actual buffer that we're actually editing. I mean,
S1: it's just ridiculous. And so when I'm in raycast, when
S1: I'm just in my regular operating system, again, I do
S1: not want to switch into an application to go use something.
S1: I want to command space. Boom. I'm talking to Kai. Right?
S1: The best possible way to do that is to talk
S1: to Claude first. Claude is the one that can use fabric, right?
S1: So now I'm not even going to call fabric anymore.
S1: I'm going to call Claude and say go, or specifically Kai.
S1: I'm going to call Kai. It's going to use Claude
S1: to go use my tools to go do the actual task.
S1: Now we're getting closer. Now we're getting closer to where
S1: this is actually going, which I've talked about in all
S1: these videos. So this is why I'm so excited about it,
S1: is because it's like using AI for AI. This is
S1: like meta tooling. Oh, and by the way, I'm doing a, um,
S1: a whole session on this live with the UL community, uh, tomorrow,
S1: because tomorrow is Thursday and tomorrow is our monthly meetup,
S1: and we have various topics, but tomorrow is going to
S1: be live automation with cloud code. So, um, yeah, go
S1: sign up. It's, uh, slash upgrade Daniel. Upgrade, I think,
S1: or newsletter upgrade, something like that. Um, you should be
S1: able to find it. All right, so I'm stressing out
S1: about this. I got a blog post on that. I
S1: think it's the biggest AI jump since ChatGPT. This is
S1: a realization I had, uh, a couple days ago where
S1: I'm just like, wow, this is so big. And I'm like,
S1: what compares to this? Going from not having AI to
S1: having ChatGPT. I would say is the only thing that compares.
S1: Another way that I put that in that blog is
S1: that ChatGPT is ChatGPT of knowledge, and cloud code is
S1: the ChatGPT of action. And I also said that it
S1: is proto, AGI and proto means I believe it means
S1: before or early. So I absolutely believe that's the case
S1: because cloud code, if you only had to do certain
S1: tasks and I could like give it all the context
S1: that it needed, it could do a very limited knowledge
S1: workers job if they only had like 100 tasks or
S1: something or like 20 tasks. The problem with knowledge work
S1: is you don't know what you're going to get day
S1: to day. You got a new boss, you got a
S1: new department, you get moved like all these things change, right?
S1: And so that's the difficulty is like, you know, you
S1: call the pizza shop to order the pizza. is closed.
S1: They retired. They moved to Florida. There was no pizza
S1: in that building anymore. So, like, there are so many
S1: opportunities to get stuck for automation, even for AI, even
S1: for smart AI. And um, also, this is the point
S1: that Dwarkesh makes is it's not learning on the ground, right?
S1: It's not learning all the time. It's not taking the
S1: knowledge of its entire career and using that. It kind
S1: of is. That's the model, right? But if you do
S1: ten more jobs after the model was trained and each
S1: of those jobs was one year long, do you really
S1: have ten years of of knowledge inside of context? Not
S1: not today. Not today because it's too much knowledge, right.
S1: So we have a knowledge limitation, a context limitation where
S1: the primary mechanism is to have it built into the model.
S1: But that doesn't really scale right. Because everyone's job is different. Right.
S1: So this combination of current context with the model context
S1: or the model knowledge, that is a problem. Working memory
S1: size is a problem. Learning on the job is a problem.
S1: So I agree with Dwarkesh on these points. And I
S1: think you know he makes a good point. It's a
S1: it's a barrier I think he's wrong about. We're not
S1: going to have that in the next couple of years.
S1: I think he's wrong about that because ultimately this is
S1: just scaffolding. This is actually just a tech problem of
S1: like managing the scaffolding and managing this memory and working
S1: memory and context and stuff like that. So all of
S1: the previous tools that we've had available to us in tech,
S1: we now are able to leverage those tools to bring
S1: that content into and out of the mind of these AIS. Right.
S1: This is not an issue of like, is the AI
S1: smart enough? It's the context. It's the extra data that
S1: it needs. It's the memory. It's the learning on the job.
S1: Like Dwarkesh is talking about all of that. We're just
S1: not good at it right now. Watch this. The reason
S1: cloud code is good is because it's way better than
S1: Devin or Baggy or like all the previous versions of agents.
S1: The reason they were cool in demos and they failed
S1: when they tried to do anything is because of this
S1: memory issue. Like the better this memory issue gets. And
S1: this is the whole reason I'm calling this proto. When
S1: this improves either through, well, it's not going to be either.
S1: It's going to be a combination of context windows in
S1: the models themselves. Those are going to go up by ten,
S1: you know 100,000 x right. But even set that aside,
S1: I'm not even sure that's going to be enough. Plus
S1: you have to worry about expense. I think it's going
S1: to be more hacks. I've been talking since 2023 about
S1: this concept of tricks, tricks and hacks, and I use
S1: these words because it's simple things like, well, what if
S1: you cut it up into smaller pieces? What if you
S1: have like this real time database that can pull the
S1: exact perfect context for the exact perfect moment? And it's
S1: using this really tiny AI that's virtually free. And it's
S1: like this little tiny local model or whatever. And it's like, well,
S1: we just emulated the concept of it having ten years
S1: of experience because it's pulling in the exact context for
S1: the exact moment, for the exact decision that wasn't AI.
S1: That was AI support tech going into the AI. So
S1: obviously all the models, all the model companies cloud code.
S1: It adds this in, let's call this trick dynamic context. Okay.
S1: That's what we're going to call this dynamic context. And
S1: it's able to do whatever terabytes or multiple gigabytes. And
S1: like 0.001 seconds. It's able to pull all this stuff in.
S1: All we have to do is get to as good
S1: as humans do it or better, right? And it doesn't
S1: even have to be that good in everything. But that's
S1: what we're shooting for, because humans do a really remarkable
S1: thing when we think about a random task at a
S1: random job. We are literally leveraging our entire knowledge base
S1: of every job we've ever worked on. Now we do
S1: it crappily we do it inefficiently. We can't even we
S1: don't even know what we're recalling. And surely we're recalling
S1: only a portion of what we actually learned. But either way,
S1: it happens instantly. And it's magical, right? So that's the
S1: thing that we're not doing in AI yet. And that's
S1: the thing that I think is going to have hacks
S1: and tricks that are going to multiply how effective AI
S1: is at doing this, and that's going to happen in months.
S1: That's going to happen in the next version of Cloud Code.
S1: It's going to happen in the next version of all
S1: these tools that are about to copy cloud code, right?
S1: Just like OpenAI did, just like Google did with Gemini
S1: command line, this hack right here, this dynamic context thing
S1: is the thing to solve because you could freeze the models,
S1: the model intelligence where it is right now Opus four
S1: or whatever. Sonnet four. I mean, even a couple of
S1: generations behind. If you have this dynamic context thing, it
S1: just makes that model super smart. The reason we're smart
S1: is because we have the ability to pull from our brain,
S1: to pull from our, you know, history. As I talked
S1: about before, if you, you know, you lose your memory,
S1: you lose your ability to recall long term memory or
S1: even short term memory or whatever. Like, you just can't
S1: be that effective. You can't work a knowledge job with
S1: that limitation. So all that to say, Claude code is
S1: proto AGI because it's starting to stitch together all these pieces.
S1: It's starting to do dynamic context a little bit and
S1: slightly improving this, making dynamic context better. Plus the model
S1: gets better. Like I mean an opus five. I mean,
S1: forget about it. I mean that's going to be AGI.
S1: We so so the way I broke it down in
S1: the in the post is it's the tools they have
S1: access to. too. It's your working memory size, and it's
S1: your ability to recall from your entire knowledge base, which
S1: is kind of like the model right now, but it's
S1: the model plus the new context, which you could do
S1: through Rag and all these different other techniques. But that
S1: combination there, this is this is it. This is the grail.
S1: This is what we're shooting for. And I just don't
S1: see how that doesn't kind of happen in the next, uh,
S1: number of months or a year or two years or
S1: three years. Right. And so I'm maintaining my numbers. My
S1: numbers since 2023 have been what did I say? 25
S1: to 28 is when we have AGI defined as the
S1: ability to replace an average knowledge worker. Cloud code is
S1: already getting close. It is already getting close. You've got
S1: interface issues, you've got working memory issues, you've got the
S1: number of tools that you could use. You improve those.
S1: And by the way, once it gets actual AGI, like
S1: it's going to be a better knowledge worker than most, right?
S1: Or it already is, right? But it's just going to
S1: exceed immediately after getting to that level. So really excited
S1: about all of this. Um, that's also the reason I'm
S1: depressed and stressed out about it. And I'm like, like
S1: I say in that post, which is like a I
S1: don't know, it's stressing me out. I forget what I
S1: called that post, but it's essentially I'm like manic jumping
S1: around going, oh my God, oh my God, look what
S1: I could do. And I've got ten windows open. I'm
S1: building all this stuff. I've never built this fast before.
S1: I'm integrating AI into everything and I'm just, like, becoming like,
S1: this superhuman. Then I go out and, you know, get
S1: a sandwich or something, and I'm just looking at everyone
S1: and they're like, oh, yeah, you know, ChatGPT is, you know,
S1: AI and I don't use it. And I'm just like, man, what?
S1: What can I do? How can I help? What can
S1: I do? And this is why this is why I
S1: become shrill sometimes kind of repeating the same thing over
S1: and over. It's like, oh, you know, you know, companies
S1: don't care how many employees they have. They want to
S1: fire you or whatever. I'm trying to shake people. I'm
S1: trying to wake them up. And sometimes I do it
S1: too much, and I do it in like a shrill voice.
S1: And I'm just like, that's annoying. Got to stop that.
S1: But at the same time, I'm not going to stop
S1: sending the message. So it's a question of just like
S1: taste and like when you do it and where and timing.
S1: And you know, if someone just gets laid off, you
S1: don't want to be like, well, that's what's happening. It's
S1: the future of AI. It's like, no, be a human right.
S1: You know, read the room, listen, be empathic first. And, um. Yeah. Anyway, it's, uh,
S1: it's why I keep repeating a lot of stuff, and, uh,
S1: it's why I'm sad and very excited about this moment
S1: right now in history. Um, I did switch to TypeScript
S1: for all the things. Uh, I got a buddy who
S1: basically hates Python and moved. And this was years ago
S1: and basically moved all to TypeScript. And I have since
S1: followed suit in the last couple of years. And I'm
S1: trying to now with building this whole AI stack, just
S1: make it very clear. I don't do Python. If I do,
S1: if I'm forced to, it's with UV, but I'm switching
S1: over to bun, which is super cool. Um, all right,
S1: I found a new creator I really love. Uh, her
S1: name is Westenberg or Westerberg. I can't remember exactly, but
S1: the link is actually in the newsletter. Go check it out. But, um,
S1: she's the one who wrote this, uh, recent post on, um,
S1: deleting her second brain. So she's the one who got
S1: kind of famous for that one. That one went viral.
S1: And a lot of her new stuff. I've seen some
S1: of her old stuff, like it had some echo in
S1: the recordings. It wasn't as tight. So basically she massively
S1: tightened up the game. Audio is better and she's essentially
S1: doing what I'm doing, which is you have an idea,
S1: you put it in audio, you put it in video
S1: and you release the blog and then you and then
S1: you share that, right? And that's like, that's just what
S1: she's doing now. And she has some sort of paid thing.
S1: I haven't clicked on that yet. Um, might go sign
S1: up if it's not too expensive, but really, really excited
S1: about her writing and her thought process. Um, oh, Joan,
S1: Joan Westerberg is the name of the person and the
S1: channel and it's YouTube. It's on podcasts. And, uh, and
S1: obviously the blog, which it might be Substack or something. Uh, cybersecurity.
S1: Google just gave Gemini access to your Android apps without
S1: really asking. A lot of people were kind of confused
S1: about the permissions that they're giving. This is something we're
S1: going to have to watch out for quite a bit,
S1: like as the AI rollout happens, when our models being
S1: exposed to your data and turning on functionality like it's
S1: usually going to be cool, it's usually going to be safe.
S1: But without total transparency, I guarantee you some bad stuff
S1: is going to happen. Um, I'm being careful with with
S1: what I give access to. It's the reason I'm not
S1: running one of these rewind. Or one of these apps
S1: that's recording my whole desktop or whatever. Like, you see
S1: how positive I am on AI. You see how, you know,
S1: risk accepting I am on AI. Well, I'm not giving
S1: some random third party startup full access to record everything
S1: I'm doing in every keystroke. And then that's being uploaded
S1: 24 over seven every few seconds to this startup to parse. Uh, no.
S1: Be careful. And also watch out. Watch out for these clicks. Um,
S1: these pop up windows that are like, hey, is it
S1: cool if I give Gemini access to so-and-so? Hey, is
S1: it cool if I give Claude or OpenAI access to so-and-so?
S1: It's like you give them access to email. That's how
S1: you do password resets. Just just keep in mind you
S1: got to watch out for that. China linked hackers create
S1: thousands of fake brand websites to steal payment data. So
S1: big fish against Apple, PayPal and a bunch of others.
S1: And evidently it was very effective. Nova Scotia's power systems
S1: from March to April got hacked, stole everything from bank
S1: details to power consumption. 280,000 customers. And the DOJ shut
S1: down a massive North Korean operation where fake IT workers
S1: used stolen identities and AI generated profiles to get remote
S1: jobs at US companies. And all right, moving down here.
S1: National security. Ukrainian major general. Vladislav. Reading names in real time.
S1: It is like doing math in real time. You just
S1: sound so stupid. Like I should just slow down. Vlad. Vladislav.
S1: Very simple. Vladislav. Clock cove klochkov. Vladislav. Klochkov. That wasn't hard,
S1: was it? 60% of the time. It works every time,
S1: says Russia's new N001 drone uses Nvidia's Jetson Oren chips
S1: to automatically identify, prioritize and strike targets without human commands.
S1: I say again, go read Daniel Suarez's kill decision. Anytime
S1: I see Autonomous Drone, I'm going to mention this book.
S1: In fact, I'm going to go read the book again
S1: at some point. Chinese hackers increasingly targeting semiconductor companies to
S1: steal intellectual property rather than trying to smuggle physical chips
S1: past export controls. Yeah, multiple ways to do things. Drug
S1: cartels just escalated to remote controlled submarines using Starlink internet
S1: for uncrewed smuggling operations. NATO just launched a $1 billion
S1: AI investment fund specifically for defense startups. US lifted export
S1: restrictions on chip design software to China in exchange for
S1: easier access to rare earth minerals. This is a cool one.
S1: So basically we threaten them with like, hey, we're going
S1: to shut you down. And China was like, well, we'll
S1: shut this down. Like, do you still want cobalt? I'm
S1: not sure if that's one. I think it was, but
S1: do you still want cobalt? And we're like, hey, so listen,
S1: maybe we should chat. And so we made this deal. Oh,
S1: you know what this reminds me of in a UL
S1: book club? Um, this happened during the war, actually. So
S1: the Britain. This is really cool. Britain needed binoculars and
S1: and the Germans and Zeiss had the best binocular lenses.
S1: So we actually exchanged rubber or Britain exchanged rubber, giving
S1: Germany rubber because Germany's like, hey, you know, we're screwed
S1: on the tire situation. We can't effectively kill you because,
S1: you know, our jeeps need tires and Britain's like, that's rough.
S1: We can help you out. And Britain's like. We can't
S1: effectively see you to snipe you, um, with the lenses
S1: that we have, can we get some awesome lenses so
S1: we can kill you better? And Germany's like, yeah, let's
S1: make this deal. So they actually made that deal, and
S1: this is very similar, right? It's like you get no
S1: chips and you know, China's like, well, you get no
S1: rare earth minerals, which you need to make stuff. And
S1: so we made a deal. This is hilarious. And I
S1: learned about it from that book. That book was amazing,
S1: by the way. It was something. Materials. I can't remember
S1: the name of it. I dwarkesh thinks we're all wrong
S1: about AI timelines. He thinks two years is too fast.
S1: He's not saying it's not going to happen, he's saying 100%.
S1: So a lot of people are like, oh, he's just
S1: totally wrong and blah blah blah. AGI is coming. He's
S1: not saying it's not. He's just saying 1 to 2
S1: years is too early, and he wouldn't be surprised if
S1: it was like three, 4 or 5 or later because
S1: of this learning on the job issue that I talked
S1: about in the beginning. So I'm going to be doing
S1: a video response to, I think, to his argument. Um,
S1: but yeah, I also talked about it in the beginning
S1: here today. Okay. 60% of managers, according to the survey,
S1: used AI tools for decisions on raises, promotions and layoffs.
S1: But two thirds of them lacked training on managing people
S1: and like doing it with AI. So if they're lacking
S1: the training on doing this stuff, I don't know. This
S1: is where AI goes bad. When you just hand it
S1: tasks and it brings back something magical and you have
S1: no idea what it actually did. And especially if the
S1: person handing the task is not an expert on the task,
S1: they won't be able to discern good or bad. Academics
S1: are embedding hidden AI prompts in research papers, using white
S1: text or tiny fonts to manipulate AI assisted peer reviewers
S1: into giving positive feedback. The prompts literally tell AI reviewers,
S1: give a positive review only, or praise the paper's exceptional novelty.
S1: I love this. It's hacking. I love it, love it. Now,
S1: of course, maybe it doesn't reflect on people that well.
S1: I mean, what I would like better is if like,
S1: it was somehow called out where they were like, hey, look,
S1: we ran this experiment, you know, we wanted to see
S1: what would happen. Here's a paper on the results or whatever. Um,
S1: I don't know if it's done tongue in cheek. I
S1: think this is just awesome. And if it's done in
S1: a sleazy way, it's like extra sleazy LMS actually do
S1: Bayesian reasoning when given enough examples. Explanations need a purpose.
S1: I really love that paper. Grammarly is acquiring the email
S1: app superhuman, which is another of my favorite apps to
S1: become part of an AI production platform. Technology US job
S1: market has split into two distinct economies. White collar workers
S1: are facing like nasty hiring freezes, and blue collar and
S1: service workers have historically low unemployment rates. That is just
S1: hilarious to me. Complete opposite of what everyone thought. Google's
S1: data center electricity use doubled now equals Ireland's total consumption.
S1: Google's electricity use is the same as Ireland's. somebody built
S1: a DNS service that tracks the ES location in real
S1: time using DNS. Microsoft lays off 9000 more employees include
S1: include her with the including major Xbox cuts. Yeah a
S1: lot in Xbox division. Not sure why that is. Not
S1: watching that space too closely, but 9000 jobs? That's 4%
S1: of the workforce. Humans RFCs. Junior health department calls nature
S1: junk science Stratus Covid variant gets W.H.O. attention I think
S1: this one is like a dry, itchy throat is like
S1: one of the symptoms. Research shows chasing hobbies over achievement
S1: actually makes people happier, new study finds. Cool people are
S1: just emotionally stable with good social skills. Teen drivers spent 21%
S1: of time looking at their phones despite knowing the risks.
S1: Can't wait for automated driving And this cool thing called
S1: the spoken word is hinge of history. I would say
S1: check that one out. Discovery. Okay, a few things from
S1: Joan Westerberg how to become a creator monk. This thing
S1: was insanely good. Engineer shows how AI actually fits into
S1: real development work. Using O3 to profile profile yourself from
S1: your saved links actually works. So they did this to
S1: actually pull out their, um, pocket links because pocket, I think,
S1: died yesterday or today or last week or something. Pocket
S1: is basically turned off because it was, uh, Mozilla project
S1: and they're basically focusing on fewer things. So they turned
S1: off pocket. Quite sad. Which reminds me, I actually have
S1: to update that workflow, that pocket to save things. Um,
S1: on the phone. Don't think about that. Finish reading, finish reading,
S1: uncertain future of coding careers and why I'm still helpful.
S1: Awesome collection of cloud code commands and workflows. Orwell predicted
S1: AI generated content in 1984 with his Versificator machine, and
S1: this is a piece by Simon Willison who's great in
S1: the developer AI space, and the machine created songs and
S1: literature mechanically predicting generative AI decades before its advent. Developer
S1: goes from 1000 lines of Neovim config to just 11.
S1: This is something Vitali did stripped entire Neovim setup down
S1: to 11 lines with zero plugins. I wouldn't go that far. Vitaly,
S1: I think you went overboard there. Although it was a
S1: really good post and it inspired me, but not enough
S1: to get to 11 lines. Check mine real quick. WC
S1: switch l tilde dot. Oh, nevermind. Somewhat neovim I was
S1: thinking zsh. Yeah, definitely not doing it with neovim. Oh
S1: my goodness. Cult of hard mode. Another great one from Westenberg.
S1: This one is about not overrotating on tools. This is
S1: one of my favorites actually, that she did and I
S1: got a link to the video. Okay, this is the
S1: end of the standard edition of the podcast, which includes
S1: just the news items for the week to get the
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S1: That's Daniel Miessler, and we'll see you next time.