Speaker 1: Hey, folks, welcome back to another episode of the Ruby
Speaker 1: Rogues podcast. This week, on our panel we have Valentino Stole. Hey, now,
Speaker 1: I'm Charles Maxwoot from Top End Devs And yeah, it's
Speaker 1: been a while Valentino, we've kind of had a lot
Speaker 1: going on, just picking things back up. Do you want
Speaker 1: to give people a quick update as to where you're
Speaker 1: at and then I'll do the same thing and then
Speaker 1: we can dive into the rest of this topic for today.
Speaker 2: Yeah.
Speaker 3: Yeah, you know, earlier in the year I moved gigs,
Speaker 3: I was at doc Semity and I switched over to
Speaker 3: the Lovely team Agusta working on AI related stuff.
Speaker 2: Pretty wild thing's happening.
Speaker 1: Yeah, you know, lots of So when you say you're
Speaker 1: working on AI stuff for Gusto, are you integrating AI
Speaker 1: based features into what Gusto does or you know, well,
Speaker 1: what does that look like? I guess it seems like
Speaker 1: there's a wide world of things that people are doing
Speaker 1: with AI.
Speaker 3: There is a wide world of things. So they have
Speaker 3: a you know, Chapot Gus who's like the assistant for
Speaker 3: your business, your small business, and uh, you know, they're
Speaker 3: basically just integrating their entire ecosystem into the chato okay,
Speaker 3: amongst other things.
Speaker 2: There's a lot of other initiatives. That's that's the one
Speaker 2: I've been focused on in my op okay.
Speaker 3: So yeah, pretty much, anything that you would want to
Speaker 3: do as a small business, think about what an AI
Speaker 3: assistant might help out with that, especially when you have
Speaker 3: a small business platform you can plug into.
Speaker 1: It's it's kind of exciting, very very cool. I'd love
Speaker 1: to dig into that. It looks like a lot of fun.
Speaker 1: So yeah, So for me, about the same time you
Speaker 1: transition over to Gusto, I dropped my contract and went
Speaker 1: and worked for price Picks and so I've been at
Speaker 1: price Picks since March and I've spent quite a bit
Speaker 1: of time working on a lot of the social features.
Speaker 1: So if you're if you get into the app and
Speaker 1: you're you want to follow another member's profile or you
Speaker 1: want to they have the feeds where you can see
Speaker 1: what other people or wagering or stuff like that. You know,
Speaker 1: it kind of makes it fun and you know you're
Speaker 1: able to kind of see more of the things that
Speaker 1: other people are doing and you know kind of build
Speaker 1: stuff off of that. And anyway, I've I've been spending
Speaker 1: a lot of time on that and on the profile stuff.
Speaker 1: So that's where a lot of my work has come.
Speaker 1: It's actually kind of funny because my wife and I'll
Speaker 1: be sitting watching TV and a price picks that will
Speaker 1: come up, and it's, you know, it's like, hey, did
Speaker 1: you know that you could you know whatever, and I
Speaker 1: and I look at her and I go, yeah, I
Speaker 1: built that, right, at least I built them back end of.
Speaker 2: It, right, are you on there? You know, pick making
Speaker 2: your picks?
Speaker 1: You know?
Speaker 2: No.
Speaker 1: In fact, my track record on picking the right stuff
Speaker 1: to win is abysmal. I mean, you know, obviously as
Speaker 1: an employee, I can't actually play for real money, but
Speaker 1: you know, if I want to play, I can play,
Speaker 1: you know, and it's kind of fun. I just you know,
Speaker 1: I can't. I can't depositor withdraw money and so anyway,
Speaker 1: it's just it's just yeah, it's so so it's all
Speaker 1: in fun. But yeah, I almost never win.
Speaker 2: So it is funny to see, like, you know, ads
Speaker 2: pop up and you're like, hey, yeah, I know that.
Speaker 1: Yeah, yeah. But my deal is is I don't really
Speaker 1: follow NFL or NBA or you know, any of the
Speaker 1: big sports that people are are in there playing on
Speaker 1: So I think I think I'd have a better shot
Speaker 1: if I was like a die hard Eagles fan or something,
Speaker 1: you know. And so it's like, I'm going to put
Speaker 1: something in on every Eagles game, but I don't because
Speaker 1: I just don't care. Yeah, but yeah, So we were
Speaker 1: talking before the we got recording, and you said that
Speaker 1: you've been working on this autogenesis stuff and Gym.
Speaker 3: It's a term I came across called autogenetic, which basically
Speaker 3: just means self generating, and Okay, I've started to explore
Speaker 3: like what it might look like for these AI things
Speaker 3: to just like assemble the themselves things that let them
Speaker 3: do more rather than having me decide.
Speaker 2: What it should do more.
Speaker 1: Mhm.
Speaker 3: And that part of my exploration is this gem called
Speaker 3: it's a Ruby gem called Agentic. I started as just
Speaker 3: like I wanted a way to do plan and execution
Speaker 3: the workflows. So if you're not familiar with those, it's
Speaker 3: a way for you to have an AI thing an LLM,
Speaker 3: create a plan and then execute that plan. And so
Speaker 3: you can think cloud code right, is a perfect example
Speaker 3: of how this kind of started in a more practical way.
Speaker 3: There there were others before them, but you know, a
Speaker 3: plan execute is very like common pattern for AI stuff.
Speaker 2: And as I was.
Speaker 3: Building it, I had these like, you know, agents that
Speaker 3: I was like manually creating, and I thought, why am
Speaker 3: I creating these things? There has to be a that
Speaker 3: the plan could just be like, okay, what kinds of
Speaker 3: you know, what do I need in order to accomplish
Speaker 3: this goal?
Speaker 2: Right?
Speaker 3: And then I have you know, if I introduced concepts
Speaker 3: to the LLM that it knew how it could use
Speaker 3: them and how it could build them, could it do
Speaker 3: it effectively? And so the obvious one is agent, right,
Speaker 3: Like you have instructions and it can do things, it
Speaker 3: has tools available to it, and so can an LLM
Speaker 3: with just knowledge about like that construct build its own
Speaker 3: and assemble its own agents to accomplished tasks that it
Speaker 3: has to do.
Speaker 2: And so that's where I kind of like really.
Speaker 3: Dug into this agentic gem and started I created this
Speaker 3: like way to assemble agents based on just like instructions
Speaker 3: and giving it a role, and with that like primitive
Speaker 3: giving it a name, a role and instructions, throwing a
Speaker 3: task it and be like, hey, like assemble whatever agents
Speaker 3: you need to accomplish this task and give me the
Speaker 3: result of that.
Speaker 1: Right, So it reminds me a little bit of some
Speaker 1: of the conversations we've had with other people about AI
Speaker 1: agents and lms, where effectively, I think kind of the
Speaker 1: basic version is you have one agent or one program
Speaker 1: and you give it a task and it just kind
Speaker 1: of runs until it gets the stuff done right. And
Speaker 1: so then we've talked specifically, I'm thinking of when we
Speaker 1: talked to Obi about Ray where he said, you know,
Speaker 1: I've got multiple agents, right, So I've got like a
Speaker 1: calendar scheduler agent, I've got this agent, I've got that agent,
Speaker 1: I've got another agent, right, and so they all kind
Speaker 1: of specialize, and then he's got one or two that
Speaker 1: kind of orchestrate things at different levels and so and
Speaker 1: a lot of that was done deliberately, right. It's like,
Speaker 1: I'm going to have a calendar agent, I'm going to
Speaker 1: have a chat agent, I'm going to have a whatever
Speaker 1: else agent. And what you're saying is is in your case,
Speaker 1: you're saying, Okay, I just need this task done right,
Speaker 1: and so you make up what the agents are, right,
Speaker 1: and so you're giving that or that level of design
Speaker 1: and orchestration to the LLM as well.
Speaker 2: Exactly because.
Speaker 3: Because this object oriented, right, and all these types constructs
Speaker 3: are defined in code, I could save the artifacts of those, right.
Speaker 3: So like, as it's building out these agents to accomplish tasks,
Speaker 3: it's saving them off for use later.
Speaker 1: Right.
Speaker 2: So like if it goes.
Speaker 3: And if it has like a bigger plan that it's
Speaker 3: trying to get through and a goal is trying to accomplish,
Speaker 3: and it's creating all these subtasks and making agents and
Speaker 3: then reuse those agents as it goes through the plan,
Speaker 3: and so like let's say it's like just like a
Speaker 3: research agent that it decides to make, and it goes
Speaker 3: and it researches some content and then pulls back information
Speaker 3: and saves it in a file, and that's like what
Speaker 3: it was built to do. And then it comes across
Speaker 3: another task it is like I need to research about
Speaker 3: this new thing. You can just reuse that agent and
Speaker 3: because like it then becomes available to the system as like, hey,
Speaker 3: this is these are agents that are available right at
Speaker 3: every given turn in the task, you can choose to
Speaker 3: pick one of those existing ones or build a new one.
Speaker 3: And sometimes you know, if the research is if the
Speaker 3: task is not related enough to what is trying to
Speaker 3: do like let's say it was like just generic research
Speaker 3: on the web, but it needed to research in a
Speaker 3: specific file that it would create a new agent for
Speaker 3: file research. All right, So it's it's kind of interesting
Speaker 3: to watch this thing kind of bloom, right, uh huh
Speaker 3: as you give it different things to do.
Speaker 1: So one thing that I'm wondering about is it seemed
Speaker 1: like some of the sub jobs or subclasses of agent
Speaker 1: or however you want to look at, that they had
Speaker 1: pretty specific functions that they needed, right, So for example,
Speaker 1: talking to a calendar or you know, connecting to a
Speaker 1: particular service or things like that. Does it create those
Speaker 1: two and actually write the function code for those sometimes?
Speaker 2: Right? So that's the beauty of this is like it's
Speaker 2: an experiment and things that all.
Speaker 1: It really is coming from my job.
Speaker 3: Like, yeah, sometimes it does effectively do that, you know.
Speaker 3: Huh when it gets runs there are cases where it
Speaker 3: like you know, because I do have limitters on it
Speaker 3: that like don't let it like recursively you know, get
Speaker 3: to itself.
Speaker 2: So if it fails to.
Speaker 3: Do something, it doesn't get stuck and it could just
Speaker 3: move on and say I wasn't able to do that,
Speaker 3: And so it does do that, but yeah, I would
Speaker 3: say the more complicated things, it tries to just break
Speaker 3: it apart into smaller things and try and accomplish those.
Speaker 3: And so I haven't really pushed the limits.
Speaker 1: Of it yet, right, So, I guess one thing that
Speaker 1: I'm curious about with some of this, because I guess
Speaker 1: you could also just say, you know, here are some
Speaker 1: functions that I just have available, right, and then it
Speaker 1: could build the agents around those however it wants. But
Speaker 1: I guess I'm wondering, you know, how granular does it get?
Speaker 1: And I'm also curious what the implications are for Let's
Speaker 1: say I want to actually build and design something like
Speaker 1: this on my own. I don't want to do the
Speaker 1: auto genetic stuff. I just want to, you know, I
Speaker 1: want to decide where the boundaries are for for my agents.
Speaker 1: You know, does this inform those decisions for you? But
Speaker 1: let's let's back up real quick, like how how granular
Speaker 1: does it get? And how you know, how does it
Speaker 1: handle some of that stuff? Because I'm imagining, for example,
Speaker 1: let's say it did need to do some scheduling or
Speaker 1: you know, calendar management, right, it could have a connect
Speaker 1: to Google Calendar, and I have these six functions that
Speaker 1: it can do, or it could say, you know, I'm
Speaker 1: just a busy checker and I'm an appointment updater and
Speaker 1: I'm right, And so you could end up with agents
Speaker 1: that do a bunch of different things as opposed to
Speaker 1: one that just kind of generically and it is Google Calendar.
Speaker 2: Yeah, I get that.
Speaker 3: I mean I built this in a very modular way
Speaker 3: because I wanted the ability to have like agents that
Speaker 3: I specifically made with certain things. And so you can
Speaker 3: build your own agent, and there's even like you can
Speaker 3: give it like specifications if you don't want to fill
Speaker 3: in all the details, so you can create like kind
Speaker 3: of a spec for how the.
Speaker 2: Agent should be built.
Speaker 3: Okay, but yeah, there's like a register, so you could
Speaker 3: like register your agents and then it would make use
Speaker 3: of those like in the normal process as it's looking
Speaker 3: to accomplish tasks, and you can bypass all have to
Speaker 3: use Yeah you don't have to use self assembly.
Speaker 1: Yeah right, but but what how how far does the
Speaker 1: self assembly go?
Speaker 2: It goes pretty deep? Yeah, I mean it.
Speaker 3: So I built like a capability system I call it,
Speaker 3: where like let's say, you know, a capability could be
Speaker 3: most similar to like tools, I guess in the modern realm,
Speaker 3: but like I think we'd more of like okay, be
Speaker 3: able to search the web, like read a file, generate
Speaker 3: a pdf. Like these are all capabilities, right, And so
Speaker 3: I made like kind of an agent capability system that
Speaker 3: you can register with the agentic system and just say
Speaker 3: okay you have and it's like all defined abstractly with okay,
Speaker 3: you have this capability, and here's the implementation function and
Speaker 3: it takes inputs and it generates a normalized response.
Speaker 2: And these like.
Speaker 3: Specifications that you're generating for the capability help inform the
Speaker 3: agentic system as well what it can do, right as
Speaker 3: it's building agents too, So like you can basically define
Speaker 3: kind of like what what is capable for agents to
Speaker 3: be built with, right, And so it's not just gonna
Speaker 3: go in like say oh I can go and like
Speaker 3: connect Google Calendar and like automate a huge pipeline. There's
Speaker 3: only so many capability is that you can register at
Speaker 3: a time. And so like I made it kind of
Speaker 3: with that informed decision of like I don't want to
Speaker 3: just make stuff up.
Speaker 2: Because it was at first.
Speaker 3: Not really doing too great, but as soon as you
Speaker 3: put some like kind of guardrails around it and give
Speaker 3: it like, oh, you can only.
Speaker 2: Kind of do these things. It performs really well.
Speaker 3: And so like when you especially for you know, it's
Speaker 3: funny now that like research a deep research is a thing, right,
Speaker 3: Like that's like the easiest thing to do with all
Speaker 3: of these agents. It's just like right, the other report
Speaker 3: and like search the web, right, like so easy to do, right, yeah,
Speaker 3: And so like what happens when you want to do
Speaker 3: like next things and have it like yeah, like you said,
Speaker 3: talk to APIs or things like that.
Speaker 2: It's yeah, it's interesting to see how it evolves.
Speaker 1: Yeah, it does? Does it ever evolve? I'm gonna I,
Speaker 1: you know, I'm going to kind of build my own
Speaker 1: tools or my own capabilities or do you just lock
Speaker 1: that down? So it's like you have to ask.
Speaker 3: So I've tried, uh my, my preliminited. My initial goal
Speaker 3: was could I get it to build itself?
Speaker 2: Right? So, like if I I through just like a
Speaker 2: plan a goal at it, like hey, I want.
Speaker 3: To create like a self assembling agent system in Ruby, right,
Speaker 3: and could I.
Speaker 2: Get that to actually happen?
Speaker 1: Right?
Speaker 2: And what right? Like what agents would it built?
Speaker 1: You know?
Speaker 3: I created a coding agent for Ruby. Right, it created
Speaker 3: like uh, you know a I don't know, PM like agents,
Speaker 3: like managed a project. Right, It did all these things,
Speaker 3: and it got like.
Speaker 1: It got to a.
Speaker 2: Certain point where it was just like, all right, it
Speaker 2: just spent way too long, and it like it said, oh,
Speaker 2: I've gone through too many turns, and now I can't
Speaker 2: like I say it was decent. You know, it wasn't
Speaker 2: what I.
Speaker 3: Would code, right, but it's a little rising how far
Speaker 3: it did get and the agents that it built, and
Speaker 3: so I've actually reused and repurposed some of the agents
Speaker 3: that it made in my own interesting, which is kind
Speaker 3: of fun. And you know, the probably the biggest benefit
Speaker 3: to me personally is I got it to use Olama locally,
Speaker 3: so I could just use starcoder or something like that
Speaker 3: to run on my own machines and not have to
Speaker 3: spend any more. Yeah, but I'm trying to evolve it.
Speaker 3: It has this like extension system that I'm trying to
Speaker 3: like adapt where you can give it a domain, so
Speaker 3: like if you want it to be scoped on healthcare,
Speaker 3: then you can add like specific knowledge to that domain
Speaker 3: and have it use it in its assembly and execution.
Speaker 3: And I have like this initial like self learning system
Speaker 3: that I've started to to explore, where it keeps track
Speaker 3: of records of itself and tries to create patterns and
Speaker 3: like strategies based on how you use it, so it'll
Speaker 3: like slowly evolve in different ways based on how you
Speaker 3: like train it ultimately or tell it how to learn.
Speaker 3: And so I've been exploring That's probably where I want
Speaker 3: to spend more of my time. Next is kind of
Speaker 3: getting it to recognize things that it does well and
Speaker 3: doesn't and how do I tell it and inform it
Speaker 3: when it's doing something that it shouldn't be doing or not.
Speaker 3: I haven't really figured out that like human intervention aspect
Speaker 3: right at the moment. It's kind of just like making
Speaker 3: that up on its own.
Speaker 2: So it's kind of interesting.
Speaker 3: To explore what models do and what they learn on
Speaker 3: their own because they are all different. It's kind of
Speaker 3: fun to rerun the same experiments with different models and
Speaker 3: see how it evolves differently.
Speaker 1: Yeah, I bet so. I'm a little curious, you know,
Speaker 1: as you get into this, like what is it showing
Speaker 1: you or telling you about using agents or building agents?
Speaker 1: Like what has it taught you any lessons that it's
Speaker 1: like when I do this on this other project, I
Speaker 1: ought to build my agents more this way or that way.
Speaker 3: It's yeah, I would say the biggest thing I've kind
Speaker 3: of taken away is that lms are not good at
Speaker 3: like managing their own artifacts, right, Like if you tell
Speaker 3: it to like do something and like keep track of something,
Speaker 3: it's going to do it differently almost every time, and
Speaker 3: like you might see some normalization, but like it's going
Speaker 3: to take a lot of prompting to get it to
Speaker 3: like be any like deterministic at all.
Speaker 2: And so I've I've found that like trying to trying to.
Speaker 3: Help it avoid maybe some of those pathways to generate
Speaker 3: specific things in certain formats to just avoid those. Getting
Speaker 3: back to the lessons, it's hard to tell what is
Speaker 3: a good lesson or not right because so much of
Speaker 3: it is like the LM's deciding what to do. And
Speaker 3: I guess what I found is the more struck sure
Speaker 3: and reils that you give to llms, the more deterministic
Speaker 3: you can make what it does. And so it doesn't
Speaker 3: know anything, but if you if you can help kind
Speaker 3: of visualize things for it, it seems.
Speaker 2: To produce much better results.
Speaker 3: What I mean by that is like you know it
Speaker 3: has a ton of training data, so you know you
Speaker 3: kind of know, like you have a good idea and
Speaker 3: understanding of what training data has just by asking you
Speaker 3: a bunch of questions, right, and but you can get
Speaker 3: an idea for like what concepts it has too, right,
Speaker 3: And so I like to like poke the models all
Speaker 3: the time on what concepts it can recognize, right, And
Speaker 3: if you give it a new concept, can it like
Speaker 3: continue to like reading about that concept and like you know,
Speaker 3: mutate it at as like you would right, Like if
Speaker 3: you're going to create like a user object in rails, right,
Speaker 3: like and you wanted to add a new attribute to it?
Speaker 2: Is like that straightforward?
Speaker 3: Like would you be like, well, why would I add, right,
Speaker 3: like a shopping cart to a user? Maybe you would, right,
Speaker 3: But like would you would you add like a I
Speaker 3: don't know something super unrelated like a I don't know
Speaker 3: what Maybe that's a bad example. Let's say you had
Speaker 3: a school, would you add like a grocery store to it?
Speaker 1: Right? Right? Uh?
Speaker 2: Can the LLM like know that kind of thing?
Speaker 3: The answer is yes, the lms are very good at
Speaker 3: those connections of things, right, And so like, uh, the
Speaker 3: further away that the concepts are actually better to the genet,
Speaker 3: like the generations will get right, because that's what it's doing.
Speaker 3: It's like trying to hone in on very like similar things,
Speaker 3: right and like what the next things are. And so
Speaker 3: if you can give it more like funneling going back
Speaker 3: to Obi's like you know, narrowing the path, right, the
Speaker 3: more you can do that, even on a conceptual level,
Speaker 3: the better, right. And so that's where I've kind of
Speaker 3: like taken, like been blown away by how well this works.
Speaker 2: Is like I can just give it more and more.
Speaker 3: Concrete concepts for it to reason about, and it can
Speaker 3: figure out how they work together, right, And then okay, well,
Speaker 3: if you then help it, like tell it how those
Speaker 3: things connect and work together, it doesn't even better. And
Speaker 3: so like and the more things that you can like
Speaker 3: you know, glue together and firm up, like, the better
Speaker 3: that it performs, and the more determinists.
Speaker 2: That you can get it. And so I guess that's
Speaker 2: kind of what I've.
Speaker 3: Taken, is like if you can decouple that idea and
Speaker 3: like make a bunch of things so that you can
Speaker 3: define those like concepts and how they work together, then
Speaker 3: you'll get the best result.
Speaker 1: From all of this, gotcha, So which is the most
Speaker 1: important to master? Then? Is is it the prompt? Is
Speaker 1: it the definition of the problem? Is it more affinement
Speaker 1: on the tool?
Speaker 2: It's always the definition of the realm.
Speaker 3: I mean helm's are dumb, right, Like they can't come
Speaker 3: up with their own stuff, right, Like you could try
Speaker 3: your best and like tell it to create the best
Speaker 3: company in the world. It's not gonna make any money, right,
Speaker 3: Like it has no desires, you know, like it's missing
Speaker 3: a lot of the things that it takes to like
Speaker 3: create something substantial, like to create something that's meaningful to you,
Speaker 3: because it doesn't really care about you.
Speaker 2: It doesn't care about anyway.
Speaker 3: So you know, it's always you know, that's why you
Speaker 3: know spectrum in development if you've heard that, Like that's
Speaker 3: become so popular because like really this how what you're
Speaker 3: telling it to make and do is like the most
Speaker 3: important part.
Speaker 2: Yeah, and so the more Yeah.
Speaker 1: It sounds a little bit like So we had a
Speaker 1: conversation on JavaScript Jabber with Eric Kens anyway, he works
Speaker 1: for Amazon, and he was on talking about their Kiro editor.
Speaker 1: That that was kind of we talked about it literally
Speaker 1: like two weeks later. Curser added, their killer feature, which
Speaker 1: was the plan. Right, So you had agent, you had
Speaker 1: asking you. Now you have a plan and so it'll
Speaker 1: actually pull together the entire plan and things like that.
Speaker 1: And so it sounds like what you're talking about is
Speaker 1: the better your plan is and the better you can
Speaker 1: specify what you want, the better the tool works.
Speaker 2: Yeah, totally, and the better the plan works.
Speaker 3: Yeah, you know, forget the tools on their own, Like
Speaker 3: the tools are just actions that can be taken, right,
Speaker 3: and so it's it's all about the plan. It's all
Speaker 3: about what you really want to do, right, Like if
Speaker 3: I tell my son to go outside and like clean up,
Speaker 3: I wouldn't expect anything to happen, you know, Like what
Speaker 3: does that mean?
Speaker 2: You know, it wouldn't mean anything for me if I
Speaker 2: was asked to go outside and clean up, I guess
Speaker 2: like rape, Like I don't.
Speaker 1: Yeah, so I guess. Part of my question then is,
Speaker 1: you know, it seems like AI is evolving so fast, right,
Speaker 1: and so, like you were talking about this autogenetic AI
Speaker 1: agent stuff and I was like, I mean, you explained
Speaker 1: it to me in like two sentences and I understood
Speaker 1: what it was, but I don't know if it's something
Speaker 1: that I would have dreamed up on my own. And
Speaker 1: it seems like, you know that this is just another
Speaker 1: step along the way toward wherever we wind up with AI.
Speaker 1: And so part of me is wondering, is this is
Speaker 1: this a tool or a technique that people are going
Speaker 1: to start adopting now, and where where does it lead
Speaker 1: us to over the next six months to a year.
Speaker 3: I think we're already starting to see that where like
Speaker 3: the where you know, where I first saw the style
Speaker 3: of AI use was like air handling. Some air happens
Speaker 3: while you're developing and you ask an LM, can you
Speaker 3: fix this?
Speaker 2: Can you handle this case?
Speaker 1: You know?
Speaker 3: And a lot of times, especially for JavaScript errors, right,
Speaker 3: that was like the classic example back in the day
Speaker 3: when you're using an LM and you want you know,
Speaker 3: JSON back and it gave you improperly format a JSON
Speaker 3: and you ask the LM again, hey fix this JSON,
Speaker 3: and ninety nine percent of the time it'll fix that, Jason,
Speaker 3: so you don't have an issue, and this goes and stuff.
Speaker 1: I feel lazy when I do that, but I do it.
Speaker 3: All the time, all the time, right, And so like
Speaker 3: this makes a lot of sense, and this is a
Speaker 3: little bit like autogenetic, right, Like you're getting the system
Speaker 3: like the self healing aspects is like autogenetic, like you're
Speaker 3: getting the system itself to generate, you know, Like and
Speaker 3: I think it's going like we're starting to see you know,
Speaker 3: products crop up around this kind of thing like security analysis,
Speaker 3: security prevention and right, like being able to get ahead
Speaker 3: of like these issues ahead of time, having something like
Speaker 3: we're we're kind of seeing the ambient response so far
Speaker 3: of things that are just latent sitting in your system
Speaker 3: that are then analyzing and then getting actions to set
Speaker 3: up to do later, which is less like of this
Speaker 3: you know maybe generative aspect of itself, but it is
Speaker 3: a little bit like you know, still that self feeling
Speaker 3: self like you know, improving nature, which are along the
Speaker 3: same lines in my mind. And so I'm wondering if
Speaker 3: we're more like getting to that, like you know, Nvidia
Speaker 3: is trying to make their own like kernel right where
Speaker 3: an LM sits on the kernel, which is really interesting
Speaker 3: And what what does it look like when the system
Speaker 3: that you're using in general, right is like an agent, right,
Speaker 3: and it's it's a little that's like kind of the
Speaker 3: mind bend the right, Like yeah, okay, Like if you
Speaker 3: just log on a computer, you start talking to it
Speaker 3: and it can do stuff for you, like kind of wild, right,
Speaker 3: Like what does that do to apps?
Speaker 2: Right when?
Speaker 3: And you can be like, all right, well create slack.
Speaker 3: I want to communicate with my friends, right, and like,
Speaker 3: right it goes I was.
Speaker 1: Going to ask how far down the rabbit hole does
Speaker 1: it go? Right where it's it's okay, Well, I'm going
Speaker 1: to generate myself because you know, initially I could see
Speaker 1: it where it's okay, I've got a browser, I've gotta this,
Speaker 1: I've got that. But yeah, eventually it just gets to
Speaker 1: the point where it's like I'm going to generate a
Speaker 1: windowing system, right, I'm going to generate a something I
Speaker 1: can make API calls with. And so you know, my
Speaker 1: interface to the world through my operating system is completely
Speaker 1: different from yours because it built something around how I
Speaker 1: think about how I communicate with the world and how
Speaker 1: I research things and how I approach different things as
Speaker 1: opposed to you. And yeah, and so I don't use
Speaker 1: the standard app. I use the custom whatever that it knows.
Speaker 3: I like, I mean, I think Google is onto the
Speaker 3: right thing with their agent agent protocols. But I think
Speaker 3: I think they were just too early, right, Like we
Speaker 3: need we need more protocols, like everybody like trashes MCP
Speaker 3: for being like, oh, it's just like a wrap around
Speaker 3: open API, which maybe it is, but like I think
Speaker 3: the definitions of the specifications are the value, right, like
Speaker 3: defining what it means to be a tool, how how
Speaker 3: that tool interacts, how it can like respond, how it's defined,
Speaker 3: and then you know, going further, what are the protocols
Speaker 3: that can communicate with it?
Speaker 2: And so like all of that stuff is very important,
Speaker 2: and like.
Speaker 3: That is kind of like what is missing and what
Speaker 3: will be most important if we move to this world
Speaker 3: of like an operating system level, right, is like it'll
Speaker 3: just be a you know, huge like pool of protocols
Speaker 3: that like, okay, if you're creating an app like on
Speaker 3: this new system, it the artifact of that is the
Speaker 3: protocol of how to use that that then other things
Speaker 3: can inspect and then build for right, And so I
Speaker 3: see this kind of like and it's a little funny
Speaker 3: because like the path to get there is like a
Speaker 3: bunch of trash, right, Like the paths again, where we
Speaker 3: are today is like currently trash. You know, so like
Speaker 3: we had to get through all of this like bad
Speaker 3: generation before to be like confident enough now where it's
Speaker 3: like we're really good and you're kind of like impressed
Speaker 3: by it, but like to get to that next level again,
Speaker 3: you have to get through all of the trash work.
Speaker 3: And we're like we're already seeing that with like AI
Speaker 3: slop right, like that's the new thing.
Speaker 1: We We've always done this, right, It's just it feels
Speaker 1: like the pace of iteration is so much faster, right,
Speaker 1: I mean, you know, go back through rails right, you know,
Speaker 1: we we had some people had some other great ideas.
Speaker 1: They put them into MERB. We went through that heinous
Speaker 1: upgrade you know, to Rails three, which was super painful,
Speaker 1: the asset pipeline situation right where we went from sprockets
Speaker 1: to webpacker out to currently prop shaft, which is better,
Speaker 1: but it's still not like super intuitive friendly whatever you
Speaker 1: know fixes all my issues, right, it's just way more
Speaker 1: approachable than webpacker, right, And so we're going through the
Speaker 1: same thing here where I mean I remember having conversations
Speaker 1: with just regular people like my father in law where
Speaker 1: he's like, well, never trust AI because it hallucinates all
Speaker 1: the time. And I said, well, give it a couple
Speaker 1: of years because they're going to keep improving it, right,
Speaker 1: And so does it still hallucinate? Yeah, but does it
Speaker 1: do it a lot less and does it have a
Speaker 1: lot more tools at disposal? Absolutely? And so again, like
Speaker 1: you're saying, I mean, we're just going to kind of
Speaker 1: have to see how this evolves. It'll be really interesting
Speaker 1: to see what gets picked up and what gets dropped
Speaker 1: and if we find a better way. I mean, one
Speaker 1: of the issues I have with things like MCP is
Speaker 1: that it takes up a whole ton of my context
Speaker 1: to tell it all the things it can do, and
Speaker 1: you know, and it's like, no, I want the context
Speaker 1: to be here's all the stuff that Chuck cares about, right,
Speaker 1: figure the f out right? And so.
Speaker 2: Yeah, it's interesting. A concept was brought up at.
Speaker 3: The AI Engineering Code Conference our Code Summit recently in
Speaker 3: New York.
Speaker 2: Concept called progressive disclosure.
Speaker 3: And it's an interaction design like principle of just like
Speaker 3: incrementally like disclosing new like information to a user right
Speaker 3: or to an interface. And this is now being applied
Speaker 3: a lot to like coding agents because of this exact
Speaker 3: problem you're saying, where all right, you have like I
Speaker 3: don't know, thousands of MCP servers and tools and documents,
Speaker 3: and you know, like there's no way it can keep
Speaker 3: all that in its context. How do you like surface
Speaker 3: the right things at the right time? Like it's a
Speaker 3: common like current problem, and this seems to be like
Speaker 3: the best solution that I've seen so far of like
Speaker 3: basically creating like smaller chunked like summarizations of what the
Speaker 3: different things are that can be surfaced, and then providing
Speaker 3: it a you know, a link ultimately to find out
Speaker 3: more right, right, like demand pages right, Like man, you
Speaker 3: know that's why like CLI tools will always work better
Speaker 3: than any MCP server you set up, because like the
Speaker 3: help mechanisms are so like token conscious yep, and also
Speaker 3: like available and straightforward, like it's all normalized, right, Like
Speaker 3: there's a protocol that like it follows right and gives
Speaker 3: you the information that is needed in order to learn
Speaker 3: how to use it. But also you just be like,
Speaker 3: you know, show me all the commands I have, right,
Speaker 3: and it doesn't give you like pages of text. It
Speaker 3: gives you like a one liner of like all the commands, right, yeah,
Speaker 3: And like that's that is very valuable to like nlll
Speaker 3: F right.
Speaker 2: Yeah, it's like this kind of concept.
Speaker 3: I think it's being more and more just like bod
Speaker 3: skills are a perfect example of this progressive disclosure where
Speaker 3: you have like this made a data the front matter
Speaker 3: they call it, where you could say, hey, this is
Speaker 3: like information about what the skill does and is and
Speaker 3: the things that can be used with it and the
Speaker 3: tools that it uses, and like just surface this right
Speaker 3: to all the other ones and it does like an
Speaker 3: incredible job.
Speaker 2: Yeah, way better, yeah, way better than the tools.
Speaker 1: Yeah.
Speaker 3: Not that not that tools aren't valuable, but I think
Speaker 3: we're like kind of moving away from the idea of
Speaker 3: these like actionable things needing to be dumped at the
Speaker 3: m all the time.
Speaker 1: Right, So I kind of want to spin a little
Speaker 1: bit back toward the autogenetic AI team. You know that
Speaker 1: you you it kind of spins up its own set
Speaker 1: of agents to do the work. Are you using this
Speaker 1: anywhere in production or experimenting with this anywhere where it's
Speaker 1: likely to go into production?
Speaker 2: No? No yet, I do have.
Speaker 3: I do have some experiments in the works of using
Speaker 3: it within a rails app uh that I that I
Speaker 3: hope to get to production. It's it's hard because it's
Speaker 3: like the models still aren't quite there. Right, they're better, right,
Speaker 3: you know, the best performance I've seen is from Opus
Speaker 3: four five.
Speaker 2: But it's just like so it's a.
Speaker 3: Little less extensive now, but like right, yeah, not not
Speaker 3: worthwhile to like introduce in a you know, in any
Speaker 3: official capacity on my own, so to be cost effective.
Speaker 1: Yeah, So I guess the other question related to that
Speaker 1: then is and it sounds like the answer to this
Speaker 1: qu question is going to be yeah, that's correct. But
Speaker 1: so the main limitation to this is the limitations on
Speaker 1: the capabilities of the models. And so if we had
Speaker 1: stronger models that were able to write better code or
Speaker 1: make better decisions, or maybe had a larger context window
Speaker 1: so you could it could figure out more stuff and
Speaker 1: remember more stuff. Those those kinds of things would make
Speaker 1: this a much more effective approach. Yeah.
Speaker 3: I mean it's interesting because I see production changing, right,
Speaker 3: Like when people say, well is this in production? You
Speaker 3: know your local system is ultimately becoming production now, right
Speaker 3: with all of these coding agents. Like if I wanted
Speaker 3: to like for my job personally, you know, maybe I'm
Speaker 3: biased because like I'm a programmer, right, but like if
Speaker 3: part of my job is to like review code, right,
Speaker 3: And so I'm a coding agent looks up to the
Speaker 3: getthub cli and can review the prs that are open
Speaker 3: that I'm assigned to and give me a breakdown and
Speaker 3: have some actionable comments like right, and so like all
Speaker 3: that stuff is kind of like your local machine is
Speaker 3: becoming more and more of a production system that you
Speaker 3: can run more and more things on, right, and especially
Speaker 3: as we start, like as I start to see this
Speaker 3: stuff coming, like I feel like the production line is
Speaker 3: going to blend with local so much because of what
Speaker 3: you can produce on your local machine now where you
Speaker 3: don't need to offload that work in the cloud anymore
Speaker 3: because you can just generate a quick ashumel page and
Speaker 3: whether or not it like persists or not, like that's fine,
Speaker 3: because it was just to show you something, right, or
Speaker 3: to do something and like create that interface, and that
Speaker 3: those like the interfaces are becoming temporary, right, like and
Speaker 3: at least for our work, right, but I can see
Speaker 3: I see it more and more, right, and so like
Speaker 3: you could do like a big question is always like, well,
Speaker 3: well what can't.
Speaker 2: You do with cloud code? Right?
Speaker 3: What can't you do with chat GPT? Right, what can't
Speaker 3: you do with these things on their own? And there's
Speaker 3: the answer to that becomes less and less things.
Speaker 2: Right.
Speaker 3: And so if you're running all of this on your
Speaker 3: machine anyway, like where is production right if you're doing
Speaker 3: more and more work on these things. And so I
Speaker 3: see it as like your local environment kind of like
Speaker 3: taking on more of the chunk out of that production.
Speaker 3: And I use this library a lot on my local
Speaker 3: machine to do different tasks that I know it does well, right,
Speaker 3: And so like, really most of the exploration that we're
Speaker 3: seeing is well, what does it work well doing?
Speaker 2: And the answer to that is like more things.
Speaker 3: Because the models are getting better, right, But it's also
Speaker 3: like go use it, try it out, and if it
Speaker 3: works for you, like just keep using it.
Speaker 1: Yeah, Like I think if I can restate what you're saying,
Speaker 1: because whenever we think of production, like you know, if
Speaker 1: you work a company that you know has like this
Speaker 1: giant rails app or you know, micro services or what however,
Speaker 1: however your architected, however, your architecture, production is is that
Speaker 1: set of code that you deploy out to the web
Speaker 1: servers so that people can interact. But what you're saying
Speaker 1: is is production anymore is wherever the work's getting done.
Speaker 1: And so if you know, in that case, then you're using,
Speaker 1: for example, the auto autogenetic tool to get work done
Speaker 1: locally because that's where the work is done, you know.
Speaker 1: And similarly, in a lot of these other cases, the
Speaker 1: line's going to get blurred from is it on the
Speaker 1: production to server to you know, where am I getting
Speaker 1: the work done? Right? So is it going to work
Speaker 1: on my machine and then connect to some quote unquote
Speaker 1: production system out there in the cloud or you know,
Speaker 1: and how much of this is going to live back
Speaker 1: here with wherever I'm at and wherever I'm doing the
Speaker 1: work and wherever I'm interacting with it?
Speaker 3: Yeah, you know, we had we had Dave kamer on
Speaker 3: at one point where he was talking about his local
Speaker 3: server setup, which is just mind bogging if you if
Speaker 3: you ever get to talk to Dave, ask him, ask
Speaker 3: him about his local service setup. But I guess right,
Speaker 3: but it this was years ago to now, right, But
Speaker 3: he had to a point where you know, he could
Speaker 3: basically send a a web request to this server with
Speaker 3: a job to do, right, like almost like a job
Speaker 3: in Q but to his local machine right right, And
Speaker 3: I see, like more like from a system that like
Speaker 3: you know, was had an app and everything.
Speaker 1: I think it was.
Speaker 3: This was for transcribing the videos on Drifting Drifting Ruby, right,
Speaker 3: And he had this job that was like okay, like
Speaker 3: in queue the transcription process and it would like basically
Speaker 3: trigger back home at his computer and run it through
Speaker 3: some local models so we wouldn't have to pay for
Speaker 3: any of it, and then transrib it right, and then
Speaker 3: send the request back up. So again like perfect example
Speaker 3: of like where is production? Right? Like right, Granted he
Speaker 3: he has his own like legit production set up at home.
Speaker 1: Yeah, he has a server rack and all kinds of
Speaker 1: stuff at home.
Speaker 3: But you know, like it's still the same. You know,
Speaker 3: it's still the same, like where's the work getting done?
Speaker 3: I feel like more people don't want it to get
Speaker 3: done in the cloud, you know, like all of the
Speaker 3: DH stuff you know has been pushing back against the
Speaker 3: cloud and having you know, machines running in your closet.
Speaker 1: Right.
Speaker 2: I feel like that's going to become more and more popular.
Speaker 3: And like what does that mean then for production if
Speaker 3: you're distributing it like that?
Speaker 1: Yeah, And that's really interesting too write because yeah, it
Speaker 1: seems like the way you're talking about this, Yeah, a
Speaker 1: lot of that becomes a lot more not only possible,
Speaker 1: but convenient. Right where I have more control, I have
Speaker 1: more capabilities, it's more personalized, and it's all because it's
Speaker 1: right here in front of me. And so then the
Speaker 1: services in the cloud become less about oh what can
Speaker 1: I do with the user interface and a lot more
Speaker 1: about hey, what can you do for me in whatever
Speaker 1: thing I'm trying to accomplish?
Speaker 2: Yeah, exactly.
Speaker 3: I mean imagine the day where you can run like
Speaker 3: a GPT five level model, like just on a computer
Speaker 3: in the background, right, Like eventually it'll get there. So like,
Speaker 3: what does what does that mean if you're like no
Speaker 3: longer need like the internet to do that kind of
Speaker 3: computation right right, And like there was slowly the internets.
Speaker 3: We like that's how we communicate. We still have to
Speaker 3: like communicate to like solve any real problem. Right, But yeah, again,
Speaker 3: it like turns more into like the protocol and communication
Speaker 3: layers more than anything. I just see that aspect of
Speaker 3: things eating more of this than anything.
Speaker 1: So one last question I guess I have on this,
Speaker 1: and it's it's somewhat related to something you said before,
Speaker 1: but also related to this approach that you've got here
Speaker 1: where you've got it kind of autogenerating agents. And before
Speaker 1: what you said was and I don't remember if it
Speaker 1: was before the call or not, but you mentioned that
Speaker 1: you're not writing as much code, right, And so my
Speaker 1: question is is I guess there are two parts of this,
Speaker 1: So I ask the first one is this has changed
Speaker 1: the way that you do your job? Right?
Speaker 2: Yes?
Speaker 3: And no, I mean this particular project. I have another
Speaker 3: project called the AI Software Architect that is like literally
Speaker 3: just markdown files and that does most of my job
Speaker 3: for me, and it's great.
Speaker 2: And I would say yes, like a you.
Speaker 3: Know, it's hard because like coming if you're new, right
Speaker 3: coming into the industry, like you still need to have
Speaker 3: like the knowledge and experience of like how to develop
Speaker 3: and practice software and systems, how systems work.
Speaker 2: Maybe the you don't have to know.
Speaker 3: About like semicolon's and where to put like syntax stuff more,
Speaker 3: but you still need to know how to build systems
Speaker 3: and how how to integrate and the concept building.
Speaker 2: I see that becoming more prevalent and more.
Speaker 3: Maybe something that people should focus on is that conceptual
Speaker 3: like conceptualization and compression, and like you know, managing and
Speaker 3: thinking through those concepts, because that is ultimately what what
Speaker 3: your job becomes when you just lean heavy into all
Speaker 3: these agents. Is like you're just conveying concepts that you
Speaker 3: want to create that don't exist, right, and like it
Speaker 3: can't do that because it's not trained on those concepts, right,
Speaker 3: And so it's like really just comes down to like
Speaker 3: and again I hate to keep like mentioning DHH, but
Speaker 3: like you know, all of his old stuff coming up
Speaker 3: is coming up again, where it's like, you know, software
Speaker 3: is writing, right, Like when you're writing software, you're really authored,
Speaker 3: like you're you know, it's a communication like thing. So
Speaker 3: like my job is still the same, and I still
Speaker 3: need to like communicate in very specific ways to get
Speaker 3: what I want out of it. And the better I communicate,
Speaker 3: the better the things work right in the long run
Speaker 3: and maintain better, right, and people can like work with
Speaker 3: it too, right, And so nothing has really changed, I guess,
Speaker 3: is what I'm saying.
Speaker 1: Yeah, except it seems like you're operating at a different
Speaker 1: level where you're actually now telling systems what you want
Speaker 1: instead of getting in and telling the system what to do.
Speaker 1: If that makes sense, right, Where you're writing more prompts
Speaker 1: and less code.
Speaker 2: Yeah, that's true. Yeah, I feel like that's true of
Speaker 2: most people at this point.
Speaker 1: Yeah.
Speaker 2: Yeah, Well if not, I would like to hear from you, you.
Speaker 1: Know, right, well yeah, and I yeah, I'm just thinking
Speaker 1: of all the reasons why you may end up in
Speaker 1: a slightly different situation. But I would be speculating, and
Speaker 1: I don't know how useful that is. I guess that.
Speaker 1: The second part of this question, then, is is, so
Speaker 1: let's imagine that I'm I've been doing this for a while, right,
Speaker 1: you know, I've been writing rails for twenty years now,
Speaker 1: you know, but I talk to other people, you know,
Speaker 1: it's ten years or fifteen years or five years or whatever,
Speaker 1: you know. So I'm out there, you know, I'm building
Speaker 1: apps in Ruby, and I'm looking at things like this
Speaker 1: and saying, okay, well, now you have code writing code,
Speaker 1: you have models writing code. A lot of this is
Speaker 1: more prompt engineering than software engineering, but you have to
Speaker 1: understand the software engineering. So what does my job look like,
Speaker 1: you know, within the next year, two years, three years,
Speaker 1: four years, And what do I need to be paying
Speaker 1: attention to and learning so that I'm not just a
Speaker 1: repository for I can generate code because it looks like
Speaker 1: a machine's going to be able to do that soon.
Speaker 3: Yeah, I mean the thing is like the machines still
Speaker 3: generate things that people need to know about, right, Like
Speaker 3: it's not like it's creating novel things. It's not creating HTTP, right,
Speaker 3: Like it's not creating its own protocols, which maybe will
Speaker 3: be the another future, right maybe, but so like people
Speaker 3: still need to understand those concepts, right, how does HTTP work?
Speaker 3: Like if you're making something that you want to put
Speaker 3: on the Internet, Like the Internet isn't going.
Speaker 2: To change in the next ten years.
Speaker 3: Maybe it will, but like unlikely, right, And so like
Speaker 3: you still have these protocols that are used that you
Speaker 3: need to understand. You know, you need to understand you
Speaker 3: know HDML, you know maybe maybe not so much that
Speaker 3: HTML like aspects of things, but like how it's communicated and.
Speaker 2: Served, right and how users see it.
Speaker 3: If you're like doing dynamic content and programmatic like flows,
Speaker 3: like you need to understand like system processing and architecture
Speaker 3: and you know, best practices for like how services.
Speaker 2: Communicate to each other.
Speaker 3: Right, Like there's a bunch of bunch of fundamentals that
Speaker 3: are still applicable, Like in traditional computer science, and maybe
Speaker 3: like the whole testing aspect of things is changing, like
Speaker 3: how you test and what you test. Like, I feel
Speaker 3: like we're still not, you know, all in agreement on
Speaker 3: that those aspects of things anyway, right, but it's still there.
Speaker 3: People still like I wouldn't trust delivering anything to people
Speaker 3: that pay me money, right if I didn't test it,
Speaker 3: because I don't want to have to like be a
Speaker 3: customer support person, right right, and like also like a
Speaker 3: little bit shame on you, right like right.
Speaker 1: So, so it seems like though that the skill set
Speaker 1: has shifted from being able to sit down and actually
Speaker 1: like crank the code out to being able to ask
Speaker 1: for what you want. But it also seems like what
Speaker 1: you're saying is is you still have to conceptually understand
Speaker 1: how software goes together and how the systems work so
Speaker 1: that you can intelligently ask for what you want and
Speaker 1: you can also intelligently validate what you get.
Speaker 3: Right, Yeah, you have to know the concepts, I mean
Speaker 3: circling back to that, right, like, yeah, you may not
Speaker 3: need to know get, but you have to know how
Speaker 3: code changes flow works, right, yeah, like even if it
Speaker 3: uses SVN or you know, God forbid us SVN, but like,
Speaker 3: you know, can you use some other like you know,
Speaker 3: code management tool? Right, Like you need to know how
Speaker 3: those things work just fundamentally so that you could be
Speaker 3: like what change right, Like so you know how to
Speaker 3: ask the thing for the very specific things. You know,
Speaker 3: maybe it can like make to add the changes that
Speaker 3: you need and like you know, revert things when they
Speaker 3: go wrong. Right, But you need to know those keywords, right,
Speaker 3: You need to know that those are possible, and you
Speaker 3: need to know the concept So again back to the concepts.
Speaker 3: They're just like a bunch of concepts you need to
Speaker 3: like know right, so that you can like really max
Speaker 3: out with these coding agents.
Speaker 1: Right. So I guess the final question I'll ask, because
Speaker 1: I think this is just getting us to an interesting place.
Speaker 1: Is the way I learned a lot of the stuff
Speaker 1: that you're talking about having to know is that I
Speaker 1: had to do it on my own for a long time.
Speaker 1: And then you've got some of these newer folks that
Speaker 1: are you know, graduating from college or coming up through
Speaker 1: the boot camps or self teaching that haven't done it
Speaker 1: as zillion times like I have. So how do they
Speaker 1: learn this stuff? Like is it a different avenue do
Speaker 1: the AI systems llms actually help them learn how to
Speaker 1: do this? I mean, what do you do for people
Speaker 1: who are new that haven't built, you know, twenty years
Speaker 1: of muscle memory on on how to build web apps
Speaker 1: or how to use command line or things like that.
Speaker 3: Process is still the same, Like you know, go learn
Speaker 3: to yourself and use these tools to your advantage. Right,
Speaker 3: when something doesn't go as you expect or you don't
Speaker 3: know something, ask right, I guess that makes sense. Yeah,
Speaker 3: it's the harder part is like you know, okay, knowing
Speaker 3: what to ask right, and that I feel like there's
Speaker 3: still a lot of room for traditional like courses, right,
Speaker 3: is like there's going to be a lot of times
Speaker 3: where you're just not going to know what to ask
Speaker 3: and you'll never get there. And traditional like coursewear and
Speaker 3: workloads like that teach very specific things. Will it help
Speaker 3: you introduce that? Introduce that to you, right? And like
Speaker 3: maybe once you've visualized like what all the like the
Speaker 3: course material are, then maybe you could dig in like
Speaker 3: maybe this whole like mi T open course where like
Speaker 3: you know, Stanford ree learning stuff. You know, that could
Speaker 3: be the future because it just provides all of the
Speaker 3: concepts and material that you would need to know for
Speaker 3: very specific things, and then the agents can help you,
Speaker 3: like because you could create plans, right, a learning plan,
Speaker 3: like help me learn how to do this thing. Here's
Speaker 3: the course material, right, and like why do you need
Speaker 3: the course? But at the same time, you know, like
Speaker 3: it's the concepts and the material and like all these
Speaker 3: things that.
Speaker 2: Are going to be like kind of the value.
Speaker 3: And so don't go out there exploiting uh, you know,
Speaker 3: open courses course please.
Speaker 2: You know, it's a.
Speaker 3: Great platform, lots of great learning out there, and if
Speaker 3: you want, you can earn your own.
Speaker 2: Degrees out of it. From what I've seen.
Speaker 1: Kind of yep, makes sense, all right, Well, is there
Speaker 1: anything else that people ought to know about any of
Speaker 1: this stuff? Kind of meander through a bunch of stuff
Speaker 1: beyond autogenetic stuff, but I think it's helpful for people
Speaker 1: to understand what it's doing.
Speaker 3: Yeah, I mean, if you're working on something, let me know.
Speaker 3: I'm the code name v on on Twitter. This stuff
Speaker 3: is just really I'm working on another project now on
Speaker 3: exploring the idea of letting letting lllms pave their own
Speaker 3: path with their own memories. Actually, one of my Uh.
Speaker 3: One of my coworkers, Martin, he like made this prompt
Speaker 3: where you basically just like ask an LLM to like
Speaker 3: generate a profile about itself, right, and like what is
Speaker 3: it like almost like to try and get its like
Speaker 3: soul out of itself. And it's really interesting, Like yes,
Speaker 3: the models all different, you know, all the different models
Speaker 3: the same thing, and like the results are wildly different.
Speaker 3: It kind of gives you some like insight into like
Speaker 3: how the models operate just like holistically I think. But yeah,
Speaker 3: I have another project. I'm calling it seed box, but
Speaker 3: like just asking an LM. All right, here's what you
Speaker 3: did last time. What do you want to do next?
Speaker 3: You know, more experimentation like this, We need more like
Speaker 3: you know, stuff out there to experiment with and just
Speaker 3: like see what these things are capable of, because we're
Speaker 3: trying to come up with ideas for what they're good at,
Speaker 3: and like is that like the best?
Speaker 2: Right?
Speaker 3: Like shouldn't they also be coming up with ideas so
Speaker 3: what they're good at?
Speaker 2: Right? What does that mean?
Speaker 3: You know, there's a lot, there's so much exploration I'm
Speaker 3: excited to see.
Speaker 1: Yeah.
Speaker 2: Funny now very cool?
Speaker 1: All right, Well let's go and do some picks. You
Speaker 1: have some picks.
Speaker 2: Yeah, we were talking before the show. There's a project
Speaker 2: called open code.
Speaker 3: It's like a cloud code alternative, fully open source works
Speaker 3: on your act Windows, Linux. It's really impressive. I've been
Speaker 3: using it lately and yeah, it's fantastic.
Speaker 2: Check it out.
Speaker 1: Nice. I'm gonna throw out a board game pick, as
Speaker 1: I am wont to do, and then I will man
Speaker 1: I sound sold fashion when I say it that way anyway,
Speaker 1: and then and then I'll probably throw out something else. So, yeah,
Speaker 1: the game that we've been playing lately is that we
Speaker 1: played last time I got together with my friends was Infiltrators.
Speaker 1: And so Infiltrators is you have a bunch of suspects
Speaker 1: and you're trying to figure out who they are, and
Speaker 1: it's just a color, a number, and so usually on
Speaker 1: your first turn, everybody nabs a suspect, and so you
Speaker 1: know you have information that nobody else has because you
Speaker 1: can't tell people who your suspect is, but you know
Speaker 1: who your suspect is. And so then you play cards
Speaker 1: on other people or on yourself in order to figure
Speaker 1: out who the suspects are. And so it's a process
Speaker 1: of elimination, right because you when you play a card
Speaker 1: on a suspect, it either has something in common with
Speaker 1: it or nothing in common with it, and so it's
Speaker 1: just a process of elimination to figure out what it is.
Speaker 1: And then it has kind of the concept that you
Speaker 1: get out of the Crew where it has multiple missions,
Speaker 1: and so it'll say, use these colors, use these numbers.
Speaker 1: You have so many bullets, right because you're executing your
Speaker 1: suspects when you know who they are. And so anyway,
Speaker 1: it's it's pretty fun place, pretty fast. You play up
Speaker 1: to five people, I think, and anyway, really really enjoyed it.
Speaker 1: If you don't want the gun and bullet aspect, you
Speaker 1: can track that. However, you're going to track that. You know,
Speaker 1: it's basically you have so many tries and then you know,
Speaker 1: you try not to run out of cards and things
Speaker 1: like that. So anyway, super duper fun. So I'm gonna
Speaker 1: pick that, and then I'm trying to think what else
Speaker 1: to pick. I mean, lately, I've just been using the
Speaker 1: plan feature on Cursor and then right I go look
Speaker 1: at what it did and tell it what it did wrong,
Speaker 1: and it cleans it up and it's pretty nice, you know,
Speaker 1: And like I said, for my full time job, I'm
Speaker 1: copilot and you know, works more or less the same
Speaker 1: way and does a great job, and so liking those,
Speaker 1: so I guess I'll all shout those out just kind
Speaker 1: of as where I'm sitting now. And then one last
Speaker 1: thing I'm going to mention is so I've been putting
Speaker 1: together two things and one of them is and I'm
Speaker 1: going to do an episode on it. I'm not sure
Speaker 1: if I'm going to release this episode first for that one,
Speaker 1: So if you've already heard the whole episode on it, great,
Speaker 1: But I'm a big fan of the seventy five Hard
Speaker 1: Challenge by Andy Frazella. You know, I've lost a bunch
Speaker 1: of weight doing seventy five Hard. I feel like I've
Speaker 1: leveled up as a person doing seventy five Hard, and
Speaker 1: so I thought, well, what if there was something like
Speaker 1: this for code right, And so I'm putting together a
Speaker 1: code Forge seventy five Challenge. It's based very heavily on
Speaker 1: seventy five Hard, except instead of workouts, you're you know
Speaker 1: you're writing software. He has you read a book for
Speaker 1: ten minutes a day. I adopted the same thing. Right,
Speaker 1: go find a tech book on something you want to learn,
Speaker 1: you know. So it's that kind of thing. So I'll
Speaker 1: walk through all the different pieces on the other on
Speaker 1: the other episode, I'm looking to expand it to be
Speaker 1: a so he expanded it out to be a year
Speaker 1: long challenge. So you know, seventy five Hards the first
Speaker 1: phase of the challenge, and so I'm looking to do
Speaker 1: that too, because I want to encourage people to speak
Speaker 1: at an event, whether it's a meetup or a conference.
Speaker 1: I want to encourage people to you know, generate content
Speaker 1: and things like that because I think it helps your career.
Speaker 1: And so there will be other phases to the program
Speaker 1: similar to what seventy five Hard does, and I'm gonna
Speaker 1: I'm planning on putting together a little tracking app with
Speaker 1: rails and hot wire Native so that you can track
Speaker 1: your progress right and then if you miss one of
Speaker 1: the items, then you have to start over anyway. So
Speaker 1: I'm putting that together and then the other thing I'm
Speaker 1: putting together. Both of them are relevant to what we've
Speaker 1: been talking about today. I've been finding as I talk
Speaker 1: to people, and Valentino I think made the case for this, honestly, Yeah,
Speaker 1: because I work with people and talk to people, there
Speaker 1: are folks that have major gaps in their knowledge in
Speaker 1: some areas, and I do too, honestly with regards to
Speaker 1: Ruby or Rails. Right, there are things that it's like, oh,
Speaker 1: I didn't know it did that, or oh I didn't
Speaker 1: know that. You know. The way that we do things
Speaker 1: now is we architect it this way so that way, right,
Speaker 1: because I have a life and so I don't, you know,
Speaker 1: I don't get into all the nitty gritty of what
Speaker 1: came out in some point too, and how do I
Speaker 1: use it and what's the best way around that? And
Speaker 1: so if you want to do the Code for seventy
Speaker 1: five challenge and you want to level up on Rails,
Speaker 1: then I'm putting together what I'm calling Ruby Geniuses, and
Speaker 1: it'll have stuff or actually it's Rails Geniuses, but it'll
Speaker 1: have Ruby and Rails content and there will be that
Speaker 1: daily level up kind of thing, and then we're going
Speaker 1: to have there'll be different membership levels. So if you
Speaker 1: just want the tutorials and stuff, great, and then if
Speaker 1: you want to be part of the training and things
Speaker 1: like that that we're doing every week in the book club,
Speaker 1: then you can get a higher level. And then I'm
Speaker 1: doing the same thing for AI, and that's also going
Speaker 1: to be focused in the tools, which is part of
Speaker 1: what we talk today, and then also building AI agents
Speaker 1: and AI enabled features, free applications. That stuff's just moving
Speaker 1: so fast. I feel like, you know, having a group
Speaker 1: that gets together on a regular basis and looks at
Speaker 1: it and talks about it and says, hey, have you
Speaker 1: seen this is very very helpful and handy, And so
Speaker 1: you can sign up for one or the other or both.
Speaker 1: There will be a discount if you sign up for both.
Speaker 1: But I plan to address this, you know, we'll we'll
Speaker 1: have weekly meetups and book clubs and things like that.
Speaker 1: I'm trying to figure out exactly how to do the
Speaker 1: book clubs so that you're kind of getting more of
Speaker 1: the evergreen stuff that's not changing as fast with the
Speaker 1: AI approach and tools and things like that. But we'll
Speaker 1: figure that out and we'll go along as we go.
Speaker 1: But I feel like if you're leveling up in these areas,
Speaker 1: then you're going to put yourself in a position where,
Speaker 1: no matter how much the models do for you, or
Speaker 1: how different companies approach their workforce or things like that,
Speaker 1: you'll always have a competitive edge because you know you
Speaker 1: understand the architecture of what you're building or what you're
Speaker 1: getting help building, and then you're also going to understand
Speaker 1: the tools and the architecture of what the capabilities are
Speaker 1: when you need to build something with AI. Anyway, you
Speaker 1: can go check those out. It's going to be Railsgeniuses
Speaker 1: dot com and Aidevgeniuses dot com. So I'm just gonna
Speaker 1: put those out there. You'll get a whole lot more
Speaker 1: explanation on the code for seventy five episode if you
Speaker 1: get a chance to listen to that. And then I
Speaker 1: am going to be offering a launch discount through the
Speaker 1: fifth of January. And the reason I'm going through the
Speaker 1: fifth of January is that as I've talked to people,
Speaker 1: some people want to expense this to work, and some
Speaker 1: people have used all their budget for twenty twenty five
Speaker 1: and want to expense it in twenty twenty six. Oh,
Speaker 1: I will give you the opportunity to do so with
Speaker 1: the discount, And if you want to use up the
Speaker 1: rest of your twenty twenty five budget and things like that,
Speaker 1: then you can do that too. And if you need
Speaker 1: some kind of arrangement where it's like, well, I only
Speaker 1: have so much twenty twenty five budget, and I want
Speaker 1: to use some my twenty twenty six budget and reach
Speaker 1: out to me and we'll figure it out. But I'm
Speaker 1: not going to hard pitch it. If it sounds like
Speaker 1: something you want, I'm putting it together because it's something
Speaker 1: that I wanted. I was like, I am missing stuff,
Speaker 1: and I feel like if I get people together in
Speaker 1: a group, then I will miss less stuff. And then
Speaker 1: it also gives me an excuse to go and learn
Speaker 1: things that, you know, maybe apply beyond what I get
Speaker 1: in my full time job. So anyway, that's what we're doing.
Speaker 1: And yeah, I just I want to give you the
Speaker 1: tools so that no matter where any of this goes,
Speaker 1: you know, you have the skills, you have the knowledge,
Speaker 1: and you can you can go and kind of build
Speaker 1: whatever kind of life and career you want. And I
Speaker 1: guess we'll wrap it up here until next time.
Speaker 2: Next out