Ep. 21 | What the Heck Is an AI Agent?  ## Metadata - Author: [[Overcommitted]] - Full Title: Ep. 21 | What the Heck Is an AI Agent? - Category: #podcasts - URL: https://share.snipd.com/episode/1f2357e4-22b6-470c-aa79-5b09d0f2bef8 ## Document Notes ## Highlights - **Agents As A New Software Paradigm** - AI agents open a new paradigm for interacting with software beyond traditional UI primitives like tables and infinite scrolls. - Domain-specific agents can deeply solve niche problems by combining models, retrieval, and tool access. Transcript: Jonathan Tamsut And today we're going to talk about a pretty trendy topic, AI agents. So I think this is something that we're all kind of exploring, and I think is kind of due to the industry. And yeah, we just want to kind of, I guess, learn. And I think this will maybe be like a learn in public type conversation. We'll kind of see where this goes. So I guess to kind of start us off, why are AI agents exciting to you? Why are they interesting to you? I mean, I think there's like, kind of like the obvious reason, I think for me is like, you know, we have these tools, these large language models, and they're capable of sort of information Retrieval and generating text and reasoning and i think that um there's a bunch of jobs that humans do that are boring and that humans don't want to do and i think it's just interesting To see you know how can you how can you take these sort of this these sort of like uh of building blocks of ai tooling to develop um sort of these you know agents or whatever these these entities That kind of are able to kind of perform these tasks and i think like you know kind of you know kind of in my dream like a lot of these tasks like it's like human out of loop like humans aren't Involved at all but um but yeah i guess yeah like i don't know yeah why why are ai agents interesting to you like when you guys hear the word ai agent what kind of what about that excites you Brittany Ellich I'm just curious kind of a vague question but i can go for it i so for full disclosure the extent of my experience with ai agents is ([Time 0:00:42](https://share.snipd.com/snip/58ebabde-4551-433c-acd7-299b75884cfb)) - **Start Agents From Existing Workflows** - Try building agents to offload repetitive cognitive tasks like triaging or summarizing information. - Start with familiar workflows and embed LLM calls into existing programmatic loops to iterate quickly. Transcript: Brittany Ellich Can go for it i so for full disclosure the extent of my experience with ai agents is really using tools that are already pre-built for this like the GitHub Copilot Coding Agent. That one was really nice to use because I can just like start using it with a GitHub issue. And that was not outside of my comfort zone in terms of things that I was already doing. But I am very interested in learning how to build my own agent. And I think I find it really interesting because I think it's a new way to think about solving problems. You know, previously, it's about like, oh, you're going to build this deterministic route of like this to the API to this to like gather stuff, but then you're going to have to do a lot Of thinking about the data that you get back and decide what to do with it. And the idea is with an AI agent, it can theoretically offload some of that thinking. So thinking about this episode and preparing for it, I was thinking about, okay, what is a practical problem that I could try to solve with an agent? And I came up with a whole bunch of them. And so now I'm really excited to learn how to build them and learn what to do with them so that I can actually partake in this trend. ([Time 0:02:15](https://share.snipd.com/snip/32c44e6f-424f-4690-8346-a81e45e6b44c)) - **Agent Core Loop And Guardrails** - Agents operate as perception → planning → acting loops often augmented with external tools and retrieval. - Guardrails and tool APIs are essential primitives to keep agent actions safe and useful. Transcript: Jonathan Tamsut So, you know, agent is like, yeah like in general an agent is is is it's kind of like a term that is i think existed in artificial intelligence for a while but in our context it's like there's Kind of three things an agent does perceives some inputs you know an agent can either receive text or can make an api call or whatever uses that to to plan its next action. It acts. So that action can be, you know, calling a tool or, you know, firing off, you know, doing something, you know, or like responding to a user or something. And then you kind of have this like chain of like perception, deciding what to do, acting, and then kind of starting over and over again. There's talked about previously, agents often have the ability to use tools. And I think agent is kind of an overloaded term. Like the quote unquote agents that I've kind of experimented with have been just like basically continuously you know, the OpenAI API. So like, you know, like I built an agent, you know, or I built like a Python script to get my emails and like help me decide, you know, how to like, you know, respond, you know, help sort my Emails in terms of, you know, priority items. I also send myself like to-do items via my email. So I wanted the LLM to like grab my to-do items, kind of sort them between personal and work and list them. And they're sort of like unstructured. ([Time 0:08:03](https://share.snipd.com/snip/0a1a80a3-bb73-4362-9421-6b5aee455c12)) - **Inject Domain Knowledge With Retrieval** - Use retrieval-augmented generation and embeddings to inject domain knowledge into agents. - Give agents curated corpora and tool access so they can look up facts and act reliably. Transcript: Jonathan Tamsut So like, there's this thing called retrieval augmented generation. So you can have, you can kind of give these models a corpus of text to kind of read and look up. There's these things, you know, and they rely on these things called embeddings, which we can talk about. And then, you know, models also have access to tools. And then another kind of big thing that a lot of the sort of agent frameworks out are some notion of guardrails. So that they have API primitives for limiting the actions an agent can take. ([Time 0:10:46](https://share.snipd.com/snip/072ca735-96c4-496f-bc65-d606171645e4)) - **Curate Newsletters With Embeddings** - Build an embeddings pipeline to cluster and rank blog posts by topical relevance for curated newsletters. - Combine labeled examples and iterative feedback (RLHF-style) to improve ranking over time. Transcript: Brittany Ellich So i have this idea to build an agent i want this to help me right now i write a newsletter where i go through and read a bunch of blog posts written by software developers and sort of categorize Them around the balanced engineer newsletter every week right now i'm really dependent upon rss feeds and just getting whatever information is pushed to me through those feeds. I would love to build an agent though that could take that list of RSS feeds and like one, go add to that list by finding more RSS feeds from other developers that are doing something similar. And two, would like search the entirety of an RSS feed and find which articles are actually the most applicable to look at or most, you know, fit those categories to look at. And like what are the best ones to look at? Because a lot of the things that people have written, like their most recent thing isn't necessarily the best thing they've written. So I'd like to look through the body of work within somebody's blog and say like, all right, how about you read X, Y, and Z post because they seem like the best ones to look at. Erika So how would I build that? Please tell me. Your metrics for success, where I'm curious how you come up with the topics that you want to fit these posts or blogs or whatever into, and then how you determine what the best fit is currently. Yeah. Brittany Ellich So I actually have a list of different topics that have some examples that go along with them. So for example, one of them is technical excellence, which looks at things like engineering blogs or ADRs or case studies and things that are about like actually building skills. And then I have another topic that would be communication and collaboration, where it's more about, you know, like management and leadership and team dynamics and things like that. And so I have this list of topics already. So I feel like I have that spot taken care of. And I also have an RSS link, a list of RSS feeds that I currently look at. I could always add to it. And shout out to anybody who writes things on the internet, please send me your RSS feed and I will include it in my list. But it's mostly a list that I've gathered over time and I'm currently subscribed to. But with that comes this very standardized way to look through this list of blog posts through RSS, which should make this theoretically pretty easy because that's already like programmatically Driven. And there's a way that it's set up through XML that it should be easy to look through and look through all of the content in each blog post and find like which ones are the most relevant to Each of those categories. Jonathan Tamsut Yeah. You know, it's interesting. So like, right. So you have some corpus of articles, right? You know, so you have, and then like, I think there's kind of a few problems here. What is you kind of, you kind of want to cluster articles based on these topics. And I think that's like very doable you know i talked about this thing called like an embedding if you guys an embedding model is basically a neural network model that um it it what it basically Does is you can think of it plotting um text in like in some like hyperdimensional space you know you can think of like 3d space and then um you know so there's 3d space and there's a bunch Of points that correspond to like text and you can you know you can embed tokens you can also embed like full documents and what an embedding model does is you can train it so that points That are close to one another are similar to one another so like if you have you know like the word like king and queen will be similar to one another and then there's also kind of there's Basically like vector math you can do on on so so you know each one of these points you can think of as like a vector from like the origin to the point and then you can do like basic vector math So you can take like king minus female or minus male equals queen you know so there's kind of like there's kind of like vector math you could do for sort of mapping concept semantics from One concept to another and so like yeah and embeddings are used for kind you can like you can like create an embeddings model for you know a bunch of documents and then you can you know basically Use vector distance to cluster so like but but this is all to say i think clustering documents on like is like doable i i do think you know you mentioned like you want to find the highest Quality articles um and that's that i i question i i don't know how you do um you know like like yeah how do you how do you rank is it like how much the article is cited by other articles is it Brittany Ellich Its relevance to Yeah, I'd say probably like the relevance to the topic. I'd probably have to build up like a list of keywords or something like that. Sort of like a search engine, I suppose, to like try to rank them together to see which one would be the most accurate. But I mean, I don't care about it being like totally accurate. I just want it to help me like parse through these and be like, yeah, this one looks like it's related to that. Erika This seems like one of those self-training opportunities where you could have it iteratively improve and not necessarily like explicitly define what best is at the beginning but Give it some set of instructions and then as it gives you back input it will learn what you consider the best yeah yeah i don't even... Jonathan Tamsut Oh, sorry. Go ahead. Oh, yeah. No, I was literally about to ask the same question you were about to ask, Brittany. Brittany Ellich Yeah. Yeah. What tools exist to do this? Is there... I mean, where do you even start with something like this? Yeah. Jonathan Tamsut Well, I know. So there are like embeddings, document embedding APIs that you can use where you basically upload the documents then you can you can assign labels to the documents and it'll essentially Create the embedding space so when you that's kind of like the it's like a supervised training algorithm so you have documents you have labels so when you upload a new document it'll Sort of take that document and give it the kind of label and embedding space the second thing yeah is like yeah like determining the yeah i mean so i think there are like relevance. I mean, I wonder, I wonder if you could, yeah, I mean, in terms of, in terms of finding the best article, like maybe this is just like an embeddings problem where you like categorize any Article, but you said you want to find like, want to find the best articles, but you said most, most relevant. So maybe, maybe it still has an embeddings problem where you find essentially the most relevant article, which I think like it would push back on whether that's the best way to do that. That maybe will reduce the likelihood that you'll come across like really like articles were like really novel ideas. Brittany Ellich That's a good point. Yeah. I mean, and I don't really necessarily care that it's the best ones. I just want to make sure that like I look through this list of articles and like consider the entire body of something that somebody wrote within their blog instead of just whatever is The newest thing that comes out. Yeah. Whether or not it's the best doesn't necessarily matter, but maybe just categorize it into the list of categories that I already have. So you're saying that I would need to go through and basically take a ([Time 0:12:24](https://share.snipd.com/snip/e1bf25d8-ca6e-45fe-b6f9-6854fcf145b4)) - **Newsletter Curation Use Case** - Brittany described her newsletter workflow and desire for an agent to expand RSS sources and rank posts by topic. - She already maintains topic labels and an RSS feed list, making this a practical agent project. Transcript: Erika Would I build that? Please tell me. Your metrics for success, where I'm curious how you come up with the topics that you want to fit these posts or blogs or whatever into, and then how you determine what the best fit is currently. Yeah. Brittany Ellich So I actually have a list of different topics that have some examples that go along with them. So for example, one of them is technical excellence, which looks at things like engineering blogs or ADRs or case studies and things that are about like actually building skills. And then I have another topic that would be communication and collaboration, where it's more about, you know, like management and leadership and team dynamics and things like that. And so I have this list of topics already. So I feel like I have that spot taken care of. And I also have an RSS link, a list of RSS feeds that I currently look at. I could always add to it. And shout out to anybody who writes things on the internet, please send me your RSS feed and I will include it in my list. But it's mostly a list that I've gathered over time and I'm currently subscribed to. But with that comes this very standardized way to look through this list of blog posts through RSS, which should make this theoretically pretty easy because that's already like programmatically Driven. And there's a way that it's set up through XML that it should be easy to look through and look through all of the content in each blog post and find like which ones are the most relevant to Each of those categories. Jonathan Tamsut Yeah. You know, it's interesting. So like, right. So you have some corpus of articles, right? You know, so you have, and then like, I think there's kind of a few problems here. What is you kind of, you kind of want to cluster articles based on these topics. And I think that's like very doable you know i talked about this thing called like an embedding if you guys an embedding model is basically a neural network model that um it it what it basically Does is you can think of it plotting um text in like in some like hyperdimensional space you know you can think of like 3d space and then um you know so there's 3d space and there's a bunch Of points that correspond to like text and you can you know you can embed tokens you can also embed like full documents and what an embedding model does is you can train it so that points That are close to one another are similar to one another so like if you have you know like the word like king and queen will be similar to one another and then there's also kind of there's Basically like vector math you can do on on so so you know each one of these points you can think of as like a vector from like the origin to the point and then you can do like basic vector math So you can take like king minus female or minus male equals queen you know so there's kind of like there's kind of like vector math you could do for sort of mapping concept semantics from One concept to another and so like yeah and embeddings are used for kind you can like you can like create an embeddings model for you know a bunch of documents and then you can you know basically Use vector distance to cluster so like but but this is all to say i think clustering documents on like is like doable i i do think you know you mentioned like you want to find the highest Quality articles um and that's that i i question i i don't know how you do um you know like like yeah how do you how do you rank is it like how much the article is cited by other articles is it Brittany Ellich Its relevance to Yeah, I'd say probably like the relevance to the topic. I'd probably have to build up like a list of keywords or something like that. Sort of like a search engine, I suppose, to like try to rank them together to see which one would be the most accurate. But I mean, I don't care about it being like totally accurate. I just want it to help me like parse through these and be like, yeah, this one looks like it's related to that. Erika This seems like one of those self-training opportunities where you could have it iteratively improve and not necessarily like explicitly define what best is at the beginning but Give it some set of instructions and then as it gives you back input it will learn what you consider the best yeah yeah i don't even... Jonathan Tamsut Oh, sorry. Go ahead. Oh, yeah. No, I was literally about to ask the same question you were about to ask, Brittany. Brittany Ellich Yeah. Yeah. What tools exist to do this? Is there... I mean, where do you even start with something like this? Yeah. Jonathan Tamsut Well, I know. So there are like embeddings, document embedding APIs that you can use where you basically upload the documents then you can you can assign labels to the documents and it'll essentially Create the embedding space so when you that's kind of like the it's like a supervised training algorithm so you have documents you have labels so when you upload a new document it'll Sort of take that document and give it the kind of label and embedding space the second thing yeah is like yeah like determining the yeah i mean so i think there are like relevance. I mean, I wonder, I wonder if you could, yeah, I mean, in terms of, in terms of finding the best article, like maybe this is just like an embeddings problem where you like categorize any Article, but you said you want to find like, want to find the best articles, but you said most, most relevant. So maybe, maybe it still has an embeddings problem where you find essentially the most relevant article, which I think like it would push back on whether that's the best way to do that. That maybe will reduce the likelihood that you'll come across like really like articles were like really novel ideas. Brittany Ellich That's a good point. Yeah. I mean, and I don't really necessarily care that it's the best ones. I just want to make sure that like I look through this list of articles and like consider the entire body of something that somebody wrote within their blog instead of just whatever is The newest thing that comes out. Yeah. Whether or not it's the best doesn't necessarily matter, but maybe just categorize it into the list of categories that I already have. ([Time 0:13:30](https://share.snipd.com/snip/cafe1205-97e0-4dc3-8c67-25c597afc74a)) - **Define Ranking Goals Explicitly** - Different ranking goals (relevance, novelty, citation) produce distinct curated outputs and trade-offs. - Comparing multiple objectives helps surface diverse articles rather than search-optimized noise. Transcript: Erika Be interesting to see what behavior you get with maybe two or three different approaches or goals in mind. Can think of you've mentioned wanting a diverse set of opinions and viewpoints to get a holistic sense of a topic we've talked about like best best articles like however you view that As like you know well written or readable or like interesting and um or like I think the third thing we talked about was like most cited yeah so I'd be kind of curious to see what what output You get um with even two different goals in mind but like comparing comparing um yeah comparing training goals uh with what what comes out of it yeah there is there is this like you know Jonathan Tamsut So all these models use like this concept of like rl hf if you've heard of it like the reinforcement learning through human feedback and it's basically right like it's when chat gpt shows You two responses and then asks you like which one's better right so the model is is um you know generating output and then you're telling it which one's better so that's kind of it's like Training loss it knows okay like i want to generate answers that are more similar to this so you could do like an rl hf thing where like i mean this is a lot of work like where you essentially Begin to rank um you know articles that you really like or maybe you rank all articles by like how much you really like how high quality they are and and maybe there is some sort of learnable Sort of implicit structure to like what makes a good article versus a bad article but yeah yeah um but yeah so yeah so yeah ranking yeah ranking yeah i feel like you know ranking articles I mean it's funny because like this is kind of just with how google works right like google yeah google just like ranks articles and returns them for relevancy and you know i think there's A bunch of there's a bunch of ways relevancy can be scored you know this is kind of like the fundamental problem like information retrieval like given a query and a corpus of documents Return the top k documents that are most relevant to um yeah in which case google already exists i'm not sure an agent is required um no that's fair but there's also a lot of downsides i Brittany Ellich Think to search engine optimization because a lot of that is really built on like the number of keywords that are present in an article and just because an article is stuffed with a bunch Of keywords doesn't necessarily mean it's like good yeah totally it's the same issue where you know if you're looking at articles that are pushed to you through something like linkedin Or some other social platform you're gonna see whatever the most popular one is not necessarily the one that's like the most relevant to what you're interested in yeah and a lot of times Those algorithms end up pushing things that are like the most you know the spiciest or the ones that get the most you know negative feedback or whatever and that's also not a thing that I really care about personally i just want to learn from other people and what their yeah experiences are yeah it's it's definitely very appealing taking back the control of the algorithm Jonathan Tamsut Yeah yeah that's what i want can i just i just want my own algorithm yeah that is actually like an interesting idea like we all like i mean a google i think customizes per user but like i think To an extent like what would i uh what what would what would a like an information retrieval system that you can truly customize and really well-defined, really clearly define relevancy? Maybe that's our next startup idea, truly customizable search engines. Although I think most people just want the same information. ([Time 0:19:50](https://share.snipd.com/snip/bf8ab84a-1208-42e1-8b15-c178fc90b784)) - **Protect Your Second Brain Locally** - Use plugins or local models (e.g., Obsidian Copilot or a Llama server) to keep private notes on-device. - Prefer local or self-hosted LLM backends when handling proprietary or sensitive data. Transcript: Brittany Ellich Pretty yeah i was gonna say I think that there are some plugins you can use that have some LLM capabilities built in that you can like feed your entire set of notes to it. I use one. Oh, okay. What is it? Jonathan Tamsut Yeah, it is called, if I can't find it in the next four seconds, then I'm just going to tell you. It's called Obsidian Copilot. Erika Yeah, I've been wary of using any plugins because it's like proprietary data i do have my obsidian in a private like github owned or github owned repo so i can play around with it there Jonathan Tamsut Yeah that's smart that's fair yeah this is probably um you know in violation of some corporate policy to use. Brittany Ellich I think some of them are built on like a local model, though, too, which would be, I mean, as long as it's not sending data out to, you know, some server for another company somewhere. Jonathan Tamsut Yeah, you could run like your own Llama server and use that as a back end with this, I think. ([Time 0:24:30](https://share.snipd.com/snip/6bfd4005-59e0-4d95-84aa-7fb1ba127569)) - **Career Agents Could Fix Job Matching** - Hiring and job search are broken and ripe for automation, but human bias and unclear job definitions complicate automation. - Career agents could serve as personal tutors that track skills, suggest learning steps, and match roles better than current processes. Transcript: Jonathan Tamsut I don't know operations that is that is there's like a subreddit called um like do like i think it's called like they did the math or do the math and it's where people make comments like That and then someone actually does the math i actually am curious what the math math is like everyone's emails who are older than two years how many terabytes of storage is that and then Like how you know how you know how much carbon had to be you know emitted into the atmosphere to ever produce that i think this is this is also a subject but i feel like this this is we're we're Having a wacky podcast episode i like it um this is another subject change but uh it's kind of related to agents but i actually think one one thing that's totally broken is looking for A job and it's broken for a number of ways i think the way we interview candidates is just is totally dumb i also think that you're experiencing this because you're going through it right Now yeah yeah yeah And not to yet, yeah. But I mean, I just think, right, like, you know, asking like a simple algorithm challenge and then spending a few hours with a candidate, asking them standard business questions, I Just feel like isn't a good predictor of whether they will be successful in that role. And I think that there's also kind of a related problem of like i really do believe this of like a human capitalization problem so i think there's a lot of really smart capable people that Are just not their skills and their intellect are not being deployed on like problems that really matter and i think like a some type of like omnipotent job you know like i'm like in the Limit if if there existed some type of like omnipotent job search engine that would like match you with a job that you would a like and be successful on and see would be like a good use of Your skills for society would be like an incredibly useful tool it's sort of this like social tool um and i wish it existed and uh but you know i don't know what are your guys thoughts on That what do you there's there's also like some like maybe downsides to like you know having a centralized thing for that but um i think you just invented communism yeah i fall on the complete Erika End of your perspective where i feel like the broken part of like the broken part of hiring is not necessarily like um not necessarily like the automated part like it's like uh it's like The people skills I feel like they're software engineering jobs there's definitely like the technical aspect of like you have these skills do you know how to do this and like there are Ways to test for that but i feel like the most important predictor is your like culture fit and like your personality like um and that's 100% not something that can be automated like i Think of this and like um like the answer that I would want to ask like the answer that I would want to find as an interviewer is like is this person resilient will this person grow in this Role and like you know continue to um provide value like um and like that's not in any way something that can be answered by a machine because people change and people grow and like you're Looking for those like raw human qualities and like like I said like the culture fit and the the skills fit for the job that you're looking for right now but you're also looking for the Potential of the future and yeah I like I feel like automating that to any degree is sort of, how do I say this? Like looking at the, from my perspective, like looking at the sort of like problem. Like that's not the, from my perspective, it's looking at the wrong problem. Yeah. Brittany Ellich I think also that would be expecting companies to like be able to accurately define what the job is and like what their culture is. Like, I don't know if you've, I mean, sure you've read some job descriptions lately. A lot of them are very grandiose compared to like what the actual work is. Yeah. So that also is like the other side of that. I think it's expecting people to know exactly what a job is. It's hard, actually. Jonathan Tamsut I totally agree with everything you said. Yeah, employers don't know what they want. I think like slope matters, then why intercept, you know, basically. I do think, you know, there's also, you know, humans are really biased. And there's an incredible amount of bias. There's just bias in the interview process. You know, hurts candidates depending on what the bias is. Erika So i actually i feel like i i'm sorry i'm cutting you off but go ahead i think where you might be going is like that's maybe the problem that we want to solve is like pointing out biased decision Jonathan Tamsut Making i don't think it's the problem but it is a problem and like maybe maybe yeah maybe it's not the most important problem but it is it is a problem i think that's like frustrating to Brittany Ellich Experience i think when i think back sorry when i think back to my experience too i think that humans are also biased towards like assimilating to whatever environment that they're In too like i don't most people i've worked with are people that i've really loved working with i think you hear that from everybody like when they leave a company they're like oh it was The people that really made this happen so like maybe there's not as much risk as of hiring you know the wrong hire as people think there is it seems like there's a lot of fear of saying like Jonathan Tamsut Oh what if i hire the wrong person but like usually most people turn out fine well i've i've heard the opposite i've heard i've literally heard people say you know we've hired too many B players you know so you know i think um you know yeah i mean to your point britney it's like what is what even is a good person at a company who knows there's not one definition companies Don't even know what they want but what should a company even be doing if a company's mission is stupid then it doesn't matter if you hire the best people they're just gonna be working On a stupid thing i mean there's just like infinite infinite turtles all the way down here but that's true though i i do find myself reaching like even as an employed person for like some Erika Kind of answer bank or like, like way to understand how leadership and like, you know, management is looking at success. Um, and like, I feel like if we're talking about, you know, agents that we would want in the world, like that is something that I would personally find helpful. Something I could go to and be like you know this is my like you would do like quarterly self-evaluations or whatever right like you know stick that into an agent and be like hey like is This aligning with what my company is valuing like what are some of your pointers because so much of our feedback comes filtered and like i don't know maybe there's like stuff happening Behind the scenes maybe there's stuff that like people wouldn't want to say to my face but like you know is actually ([Time 0:26:40](https://share.snipd.com/snip/fe8f7edb-a4a1-4853-ad80-2ac431960752)) - **Evaluate People, Not Just Code Tests** - When hiring, focus beyond algorithmic tests and evaluate resilience, growth potential, and cultural alignment. - Use human-centered criteria because personality and adaptability predict long-term success more than isolated technical tasks. Transcript: Erika Also think that you're experiencing this because you're going through it right now yeah yeah yeah And not to yet, yeah. Jonathan Tamsut But I mean, I just think, right, like, you know, asking like a simple algorithm challenge and then spending a few hours with a candidate, asking them standard business questions, I Just feel like isn't a good predictor of whether they will be successful in that role. And I think that there's also kind of a related problem of like i really do believe this of like a human capitalization problem so i think there's a lot of really smart capable people that Are just not their skills and their intellect are not being deployed on like problems that really matter and i think like a some type of like omnipotent job you know like i'm like in the Limit if if there existed some type of like omnipotent job search engine that would like match you with a job that you would a like and be successful on and see would be like a good use of Your skills for society would be like an incredibly useful tool it's sort of this like social tool um and i wish it existed and uh but you know i don't know what are your guys thoughts on That what do you there's there's also like some like maybe downsides to like you know having a centralized thing for that but um i think you just invented communism yeah i fall on the complete Erika End of your perspective where i feel like the broken part of like the broken part of hiring is not necessarily like um not necessarily like the automated part like it's like uh it's like The people skills I feel like they're software engineering jobs there's definitely like the technical aspect of like you have these skills do you know how to do this and like there are Ways to test for that but i feel like the most important predictor is your like culture fit and like your personality like um and that's 100% not something that can be automated like i Think of this and like um like the answer that I would want to ask like the answer that I would want to find as an interviewer is like is this person resilient will this person grow in this Role and like you know continue to um provide value like um and like that's not in any way something that can be answered by a machine because people change and people grow and like you're Looking for those like raw human qualities and like like I said like the culture fit and the the skills fit for the job that you're looking for right now but you're also looking for the Potential of the future and yeah I like I feel like automating that to any degree is sort of, how do I say this? Like looking at the, from my perspective, like looking at the sort of like problem. Like that's not the, from my perspective, it's looking at the wrong problem. Yeah. Brittany Ellich I think also that would be expecting companies to like be able to accurately define what the job is and like what their culture is. Like, I don't know if you've, I mean, sure you've read some job descriptions lately. A lot of them are very grandiose compared to like what the actual work is. Yeah. So that also is like the other side of that. I think it's expecting people to know exactly what a job is. It's hard, actually. Jonathan Tamsut I totally agree with everything you said. Yeah, employers don't know what they want. I think like slope matters, then why intercept, you know, basically. I do think, you know, there's also, you know, humans are really biased. And there's an incredible amount of bias. There's just bias in the interview process. 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