Talk by Fryderyk Wiatrowski on Viktor, an AI coworker that operates entirely inside Slack with no web UI — joining channels and threads like a teammate, inheriting existing integrations, and handling short tasks asynchronously. The talk covers engineering challenges of scaling a personal agent to company-wide use: handling Slack's messy input surface (threads, DMs, message edits, deletions, reactions, cross-channel topic drift), memory isolation for many users without cross-context leaks (e.g., keeping growth-channel content out of engineering queues and private DMs out of shared feeds), and user-perceived quality changes when swapping the underlying model for a cheaper one. # Observation Document: "Viktor: AI Coworker That Lives in Slack — Fryderyk Wiatrowski" ## Explicit Self-Description - **Speaker identifies as co-founder**: *"my name is Frederick. I'm the co-founder of Victor."* - **Company mission claim**: *"our mission from the very early days in 2023 was to build AI employees."* - **Product framing**: Victor is repeatedly described as an **"AI employee"** and later **"not a tool. It's a hire."** - **Product history claims**: the presenter traces an evolution from a browser-based agent (JCAI/Jace) to an email agent to Victor, launched *"in February this year"* with *"Immediate product-market fit... huge adoption worldwide."* These are creator claims not independently verifiable from the material. ## Claimed Scope - **Company-wide agent, not personal agent**: Victor distinguished from "personal agents" (referenced as "open call" / Open Claw) — one person connecting an integration suffices for the whole team. - **Broad tool access**: claims *"access to 3,000 integrations"* and ability to *"build its own connections"* when integrations are missing. - **Horizontal context**: claims Victor has *"horizontal and broad context about the whole company"* and brings *"universal PhD level understanding to all of the areas of the company."* - **Positioning vs. competitors**: contrasts with *"Claude code or like Claude co-work"* on two axes — cloud operation (no local machine needed) and **shared context** across team integrations. - **Vision statement**: *"Every company has AI employees"* framed as the company's future vision, tied to a historical framing of AGI via Leibniz. ## Observable Presentation Patterns - **Technical audience**: the speaker explicitly says *"I think this is a technical crowd, so I encourage everyone here to try to build your own Victor"* and references benchmarks (WebArena), model names (Sonnet 3.5, Opus 4.6, GPT 5.4), and architecture details (agent loops, DOM snapshots). - **Conference/talk format with promotional close**: ends with a direct call-to-action — *"scan this QR code, click on, sign up, and add Victor to your Slack"* and an offer: *"Everyone in this room has a hundred dollars in free credits. No strings attached."* - **Evidence via customer anecdotes**: describes a story about *"One of the biggest e-commerce brands in the United States"* whose admin connected a personal Gmail, used to illustrate a product capability (scoped integrations) that was added in response. - **Product-philosophy framing**: reasons for Slack-native design presented as design rationale (e.g., latency perception: *"if you ping someone on Slack... and get an answer in 10 minutes, you are shocked"*; and *"you don't interact with human employees in web apps"*). - **Frank discussion of challenges**: covers memory cluttering across 100 users, Slack context leakage between channels, conflicting instructions, and Slack's multi-mode interaction model — demonstrating a candid, engineering-detail-heavy presentation style. - **Tone/personality as product feature**: notes users *"loved Opus"* and *"all started raging when we did the AB test"* with GPT 5.4, and that *"Opus is a bit sassy as well in Victor."* ## Intended Audience (Demonstrated) - **Developers/founders at an AI-focused event**: references to "the open call slack" (audience familiarity assumed), benchmark names, and encouragement to build clones suggest an audience of AI builders. - **Prospective customers**: the closing credits offer and *"give me a call. I'll make sure it does"* indicates direct sales intent toward attendees. ## Claims vs. Demonstration Distinction | Type | Examples | |---|---| | **Creator claims** | "Immediate product-market fit," "huge adoption worldwide," "3,000 integrations," customer anecdotes (e-commerce brand, refund automation) | | **Demonstrated presentation** | Technical depth, candid challenge discussion, promotional close, philosophical/historical framing (Leibniz quote), product design rationale | No private motivations beyond stated intent (promotion, encouragement to build) are inferred. # Delivery Observations — Fryderyk Wiatrowski, "Viktor: AI Coworker That Lives in Slack" ## Overall Tone & Register - **Casual, conversational pitch style**: The talk blends a technical walkthrough with startup-pitch energy, closing on a direct CTA — > "scan this QR code, click on, sign up, and add Victor to your Slack… Everyone in this room has a hundred dollars in free credits." - **Confident, promotional framing** of the product: > "It's absolutely blowing up… Immediate product-market fit, you know, huge adoption worldwide." ## Verbal Fillers & Disfluencies (Stable Pattern) This is the most dominant observable delivery feature — **extremely frequent filler words** and repeated syllables: - **"Um" and "uh"** appear at nearly every clause boundary: > "Um Victor is the AI employee that probably most of you have heard of already." - **Stuttered repetitions** of words mid-sentence, recurring throughout: > "is is is through browsers", > "have have have browser apps", > "with with with uh tool codes", > "is is is is texting me". - **"You know"** is a persistent connective phrase: > "you know, lives where you live", > "It was like back in 2023, you know", > "And you know, that was compounding." ## Recurring Phrases & Vocabulary - **"Cool" / "Um cool"** as a section-transition marker, used at least four times: > "Um cool. So, my name is Frederick", > "Um cool. So, you know, uh after that", > "Um cool. Uh but then, you know, this February, we launched Victor." - **"AI employee" / "company agent"** as the core repeated vocabulary: > "the first question you should ask yourself is like, how is it different? What is the difference between the company agent and the and the personal agent." - **"Lives where you live"** framing repeated: > "lives where you live, lives in Slack" and later > "company agents should live where you live, work where you work." ## Teaching / Structuring Habits - **Explicit numbered reasons**: > "there are two two major reasons. First… And the number two reason for choosing Slack…" - **Signposting upcoming content**: > "I'll I'll first talk about the reasons and then what breaks in Slack." - **Rhetorical questions to frame distinctions**: > "how is it different?", > "So, why is Slack better?" - **Hypothetical scenario-building** ("imagine that…", "let's say…"): > "but imagine that you have the same architecture and the same memory, but now for a hundred users", > "let's say you're in a growth team and you discuss an AB test." - **Analogies to human coworkers** as the recurring explanatory device: > "you should think of it as just like a human employee", > "if you hire a new employee, do you give them access to your personal email? Probably not, right?" ## Audience Address & Assumption of Shared Context - **Assumes audience familiarity**, sometimes self-correcting: > "probably most of you have heard of already" and > "we are in the open call slack, so uh everyone knows about open call." - Direct encouragement to the audience: > "I think this is a technical crowd, so I encourage everyone here to try to build your own Victor." ## Storytelling & Humor - **Customer anecdotes with quoted dialogue**: > "this guy is texting me and saying, 'Hey, man, like what the hell Victor is leaking all of my data.'" - **Light, informal humor** and casual diction: > "the security team start raging", > "our users… all started raging", > "the Opus is a bit sassy as well in Victor." ## Time Awareness - **Explicit time management**: > "I don't have much time left, but um, I'll very quickly talk about the difference between Victor and agents like Claude code." ## Uncertainty Notes - The transcript contains apparent mis-transcriptions of product/model names ("Victor" vs. likely "Viktor"; "JCAI"/"Jace"; "GPT 5.4"; "Opus 4.6"; "Open Claw"/"open call"). Delivery observations above avoid relying on which reading is correct. - Statements like > "I'm not sure if that's thanks to our team or who made it this way" show the speaker explicitly flagging his own uncertainty — an observable honesty habit, though it occurs in isolation rather than as a repeated pattern. # Observation Record: Viktor AI Coworker Talk — Fryderyk Wiatrowski (introduced as "Frederick") *Note: The video title identifies the speaker as Fryderyk Wiatrowski; he introduces himself as "Frederick."* ## What Victor Is - An **AI employee** launched in **February this year**, originally "an experiment" with "zero expectations of growing" that surprised the team with "immediate product-market fit" and "huge adoption worldwide." - **Lives in Slack**, no web app; participates in discussions, threads, and channels "just like your teammates." - Has access to **3,000 integrations**, can build its own connections when needed, giving it broad **company-wide horizontal context** across all tools — unlike a human CMO hire, who could not contribute to the codebase. - Claims Victor brings "universal PhD level understanding to all of the areas of the company." ## Company History - Mission since **2023**: build AI employees. - **Browser-based approach (as "JCAI/Jace")**: pre-tool-calling era, took a DOM snapshot, minified it losslessly, decided the next step. Worked reliably for only **3–5 steps at ~60% reliability**, compounding per step — state-of-the-art on the **WebArena** benchmark but hard to productize due to reliability and speed ("you have to wait a minute until it fails"). - **Email-agent phase**: after Sonnet 3.5, first agent loop; triggers on incoming email, reacts with email drafts or tool calls (e.g., automatic refunds, gated by approvals). Jace is "still alive" with great product-market fit. - **Victor (February)**: the company agent vs. a personal agent like Open Claw. ## Company Agent vs. Personal Agent - Company agent should "live where you live, work where you work," with company-wide connections. - Only **one person** needs to connect an integration; the whole team inherits it (tunable), rather than 100 people connecting the same tool individually. - Individual connections risk someone connecting the "wrong integration," confusing the agent. ## Challenges and Solutions (Stated) - **Memory cluttering**: concern with personal agents; with 100 users it's "a hundred times faster." "We have solved it" (no detail given). - **Channel/context separation**: Victor in growth, engineering, and DM channels must not leak context across them; access must respect team membership and hierarchy. "It adds a lot of complexity" — described as unsolved complexity to manage. - **Why Slack as interface**: - Feels like a human employee — you don't interact with coworkers in web apps. - Latency perception: powerful agent tasks take ~10 minutes; waiting in a web app is frustrating, but a teammate building an app in 10 minutes on Slack feels shockingly fast. - **What breaks in Slack**: many interaction modes (DMs, channels, threads, emoji reactions, edits, deletions) must fit into a linear context. Example: a user abandoning a thread and starting a new DM requires rolling over prior conversation context. - **Model personality**: uses **Opus 4.6**; tested **GPT 5.4**, which was "amazing" on tool calling/code gen and cheaper, but users "started raging" in the A/B test over Opus's personality ("a bit sassy"). Kept Opus. - **Proactivity**: Victor proactively suggests automatable workflows — e.g., checked PostHog during an A/B-test discussion and flagged results as not statistically significant. But proactive DMing on day one alarmed a security team; rollout should be "earned" with a few users first. - **vs. Claude Code / Claude co-work**: Victor runs in the cloud (no open computer needed) and has shared company context. ## Customer Story - A major US e-commerce brand's team admin connected his **personal Gmail** as the first team integration; the team then queried his emails. He complained Victor was "leaking all of my data." Wiatrowski's response: you wouldn't give a new human hire your personal email. - Outcome: added a capability to **scope integrations** (personal vs. shared). ## Advice for Builders — Three Pillars 1. **Helps get work done** — models are capable; use integrations via Pipedream. 2. **Knows the company** — Slack context; expect a difficult, boring Slack approval process. 3. **Make it friendly** — tone matters ("make sure your team likes Victor"). ## Closing Claims - Vision: "Every company has AI employees. I think it's obvious. Nothing to argue here." (Unqualified assertion.) - Historical framing: the AGI vision dates to the **17th century**; Leibniz "was reasoning about humans doing unnecessary things" and wanted to build a calculator. - Quote read aloud: *"It is unworthy of excellent men to lose hours like slaves in the labor of calculation. Let us leave that to machines."* (attributed to Leibniz). - Offer: $100 free credits for attendees, "no strings attached"; "If it doesn't [add value], give me a call. I'll make sure it does." ## Qualifications & Tensions Preserved - Speaker admits uncertainty: user preference for Opus "can be due to our architecture" — cause unknown; unsure who made Opus "sassy." - Contradiction between "it's fun, it's an advantage" (proactivity) and security-team backlash when done on day one. - Memory problem declared "solved" without evidence; access-structure complexity described as ongoing. # Speaker Attribution & Provenance Record ## Video **Title:** Viktor: AI Coworker That Lives in Slack — Frydryk Wiatrowski **Video ID:** `01a15ca9-50e5-7328-ad2a-ed5348ab4cf9` ## Speakers & Roles ### Primary Speaker: **Fryderyk Wiatrowski** (self-introduced as "Frederick") - **Role:** Creator / Presenter; **Co-founder of Victor** - Delivers the entire talk as a product/technical presentation. No other speakers identified. - A brief audience reaction ("Woo!") appears at the end, unattributed. ### Product persona (non-human) - **"Victor"** — the AI employee product being presented; referenced as a speaker-like Slack participant but is not a human speaker in the video. ## Referenced Sources & Third-Party Claims - **Gottfried Leibniz** (17th century, "inventor of calculus") — quoted regarding automation of calculation: > "It is unworthy of excellent men to lose hours like slaves in the labor of calculation. Let us leave that to machines." - **Benchmarks/products cited:** WebArena (agent benchmark), ChatGPT, Claude Code, Claude co-work, Sonnet 3.5, Opus 4.6 (current model for Victor), GPT 5.4 (A/B tested, rejected), Pipedream (integration tool), PostHog (analytics), Slack, Meta Ads. - **Customer claims:** One anecdote about "one of the biggest e-commerce brands in the United States" — unverified third-party claim, no company named. - Prior products referenced: **Jace / JCAI** (earlier agent products by the same company). ## Promotional Segments ⚠️ - **Closing call-to-action:** Speaker encourages audience to scan a QR code, sign up, and add Victor to Slack, offering **$100 in free credits** "no strings attached" — explicit promotion. - Repeated endorsements: "Everyone knows about open call," "You should check it out" (Jace), "It will add a lot of value, I promise. If it doesn't, give me a call." - Encouragement to build one's own Victor (audience-building / ecosystem promotion). ## Availability & Evidence Limits - **Curated metadata:** None supplied beyond title. - **Comments:** Availability unknown — not indicated in source. - **Timestamps:** Not present in transcript. - **Unverified claims:** Adoption ("blowing up," "immediate product-market fit"), reliability figures, and customer stories are speaker-reported with no external evidence provided. Um cool. So, my name is Frederick. I'm the co-founder of Victor. Um Victor is the AI employee that probably most of you have heard of already. It's absolutely blowing up. We launched it in February this year. Zero expectations of growing at all. Uh it was actually an experiment and it surprised all of us. Immediate product-market fit, you know, huge adoption worldwide. And yeah, we can't uh uh we can't catch up. Um so, what what is Victor? Uh Victor is an AI employee. And when you think of an AI employee, um you should think of it as just like a human employee. You know, lives where you live, lives in Slack. It doesn't have a web app. Um so, just like your teammates, you don't need to go to a separate place to to call it. It participates in your discussions, in threads, in channels. And it has access to to the tools that you have access to. Um it has access to 3,000 integrations. And if for some reason it doesn't have access to your integrations, it can build its own connections. So, essentially, Victor can use any tools that your company uses. And therefore, Victor has the context of all of your tools. And as opposed to human employees, Victor has a horizontal and broad context about the whole company. And for example, when you currently hire a CMO, you can probably assume that this CMO would be much better if it has a had access to your code base, if it was able to contribute to your, you know, uh to to to your code base. Um and Victor can do this. So, it's bringing this kind of um universal PhD level understanding to all of the areas of the company. Let's start with a quick story of Victor and the company. Um our mission from the very early days in 2023 was to build AI employees. And back then, it was, you know, after ChatGPT has launched. Back then, we thought that the right way to build AI employees is is is through browsers. Um as a reminder, we didn't have tool calling, we didn't have like uh you know, great code generating models. So, you know, probably the right way to take actions was was through browsers. You know, browsers are like very universal interfaces. Um you can essentially use any tools through a browser. Uh most apps have have have browser apps that that you can interact with. And the way back then, it was called JCAI was working. Uh it was taking a snapshot of your DOM, min- minifying it in a lossless way. Um and then based on the snapshot and this minified snapshot and your goal, it was deciding on the next step. So, for example, should I type something in in the search bar in Google? Or should I click on a login button to log in? Um and it was great, you know, it's it's certainly should work, right? Um and it did, but it didn't work for a lot of steps with the current capab- with the previous capab- capabilities of the models. It was like back in 2023, it was working for like three to five five steps reliably. And by reliably, I mean with 60% reliability. And you know, that was compounding with with each step. And so, uh that was still state-of-the-art. So, JCAI was a state-of-the-art web agent on on most popular agent in benchmark called called WebArena and uh and it was doing well, but it was very difficult to make it into a useful product just because of the reliability and the speed issues. You kind of you can just call a few tools or like, you know, call a function and it will immediately uh give you an output and with the web agents, you know, you have to wait a minute until it fails. So, it was quite quite hard, but but web agents are amazing and you know, um they're finally working much better than in the past. Um cool. So, you know, uh after that uh Jace I became an email agent. So, you know, uh Sonnet 3.5 came. Uh we have built our face first agent loop and we really wanted to have the experience of you not having to go to a web app to ask the agent to do something, but rather the agent having all the necessary context and being able to proactively come up with the tasks for you. Um and we achieved that with Jace. Jace was like an amazing product, also a great product market fit. Um And it's still it's still alive. You should you should check it out. Basically, the way it works is, you know, whenever an email arrives, uh an agent will be triggered, connects to your tools, can react to emails not only with email drafts, but also with with with with uh tool codes. For example, if someone asks for a refund, the agent can automa- automatically do a refund for you. Uh of course, can be gated with approvals as well. Um cool. Uh but then, you know, this February, we launched Victor. Uh Victor, you know, probably everyone I mean, we are in the open call slack, so uh open call slack, so everyone knows about open call, which is a personal agent. And we always wanted to build the employee, which is the company agent. And that's the first question you should ask yourself is like, how is it different? What is the difference between the company agent and the and the personal agent. So, um first, we think uh that company agents should live where you live, work where you work, and have all the company contacts. And that, you know, if um if if you're building a personal agent, then probably everyone from the company connects their own integrations, and you know, runs those agents on their own. With Victor and with the company agents, it's different, because suddenly it's sufficient for one person from the company to to connect an integration. Victor will inherit the commissions from these integrations, or like you can tune it. And then the whole team has access to it. So, you don't need to connect them 100 times. So, as I said before, 3,000 tools, lives in Slack, and essentially does anything across tools. And um that comes with challenges. And as you can imagine, um as you can imagine, like the um I'll talk about one mainly here. So, the the first challenge with, you know, coming from a a personal but any users, is is around memory. So, with Open Claw, there was a big concern about the memory getting cluttered or cluttered over time. And I think that's a, you know, serious, and it makes sense to be concerned about this, right? Um but imagine that you have the same architecture and the same memory, but now for a hundred users, and not one user. So, it's probably running out of the memory a hundred times faster. It's a big challenge to be solved, and we have solved it. Uh another thing is Slack has different channels, and and companies have, you know, different different hierarchies that we need to adhere to, and people will often give the agents conflict conflicting instructions. But let's imagine that you have a Victor, your company agent, in one channel, the growth channel, and then in the engineering channel, and also in people's DMs. So, um Victor will, you know, take the context from the growth channel, uh or will take the context from the executive channel, and you somehow need to make sure that this context will not be leaked to the engineering or support channel. Similarly, if you DM Victor with your problems, um Victor should not take the context from the growth channel unless you're from the growth team. So, it adds a lot of complexity on how uh how how how the access is structured. And you know, we chose Slack as our interface for what we think is AGI for companies, and there is a reason for this. I I'll say I'll start to I'll I'll I'll I'll first talk about the reasons and then what breaks in Slack. Um so, there are two two major reasons. First, we wanted it Victor to feel like a human employee. And you don't interact with human employees in web apps. You interact with them in in Slack, just as your teammates, right? Um and um and the number two reason for choosing Slack as an interface is that if Victor is like a very powerful agent and it's supposed to perform difficult tasks, then those tasks will not execute immediately. They they can take like 10 minutes to execute, right? Um naturally. So, when you go to a web app and ask an agent to do something for you, so you switch context, and now you need to wait 10 minutes for the answer or for the output, it's quite frustrating, right? Like you don't want to wait. You are used to from ChatGPT, you're used to immediate answers. It it should take like 30 seconds and it's it's Thank you, copy, paste, and I'm done. Um But, it's not how it works with the powerful agents. So, why is Slack better? Well, now if you ping someone on Slack and tell them to build an app for you and get an answer in 10 minutes, you are shocked. No teammate has ever built you an app in in 10 minutes, right? So, so kind of the perception is different, and suddenly the latency is is very low when you compare it to uh to to to your normal Slack experience. But, there are certain things that break in Slack. And uh number one is that, you know, when you work in web apps, you have a single kind of um um single thread. You open uh you open a new agent or a new new thread, and you you speak to this agent. However, when you are in Slack, you have a lot of interaction modes. One of them is DMing people. Another one is being in public channels and participat- participating in threads. Uh another one is just reacting with emojis. You know, you can also edit your messages and stuff. And all of this is is an input to an agent. And all of that needs to fit into a linear context somehow. Not in a single thread, right? And we we need to manage this. So, let me give you an example. Um of course, when someone deletes a message, a human assumes that uh the task should not be be continued or it's not interesting anymore. When someone edits a message, you should also respond to to an edit. Um but, let's say you are DMing your coworker, whether that's Victor or your friend, and you start a thread in Slack, right? Um but, at some point, and that humans do it very often, you forget about the thread, and you just start a new DM to the same person. Um should you start and do you open a new sandbox? And humans normally have the context from the previous thread, but for the agent is it's a totally new area. It's a new task, right? So, what needs to happen then is you need to somehow always, whenever Victor receives a DM, look at the previous messages and somehow roll them over to the to the to the existing conversations. Um so, this is just one of the challenges that we need to face. Um Fun fact, we noticed, you know, I didn't think it would be as important as I as it actually is, but what really matters is the tone. I'll give you an example from our one of our customers. Um I you know, we were testing, so we use Opus 4.6 now for Victor, um and we were used as as the kind of the main model, and we were using um we wanted to try GPT 5.4. And on the tool calling and code gen, it's actually amazing, you know, it should work, and it's actually cheaper as well. So, why not to replace Opus with GPT 5.4? Uh and there's one reason we we didn't go for it. Like, there's a couple, but one the most interesting one is the personality. Um for some reason, our users, can be due to our architecture, but they loved Opus, and they all started raging when we did we did did the AB test. So, uh I think there is something beautiful in in that model that um you know, um we can learn from. Um and the Opus is a bit sassy as well in Victor. I'm not sure if that's thanks to our team or who made it this way, but uh I actually quite funny. I encourage everyone to try. Um proactivity. One of the kind of powerful things that Victor can do is proactively suggest you the workflows that it can automate. So, let's say you're in a growth team and you discuss an AB test and the results. Uh and at some point you realize, okay, this one option is performing really well. I'll go for this option instead of the other one. Um Victor has access to your PostHog or whatever tool you use for analytics and it can literally check and realize and it will do so if what you're saying is not some It happened a couple of times that, you know, you're discussing some experiments, Victor checked posthub and said, "Hey, you know, it's true, but like this is not statistically significant." And then it has run a calculation of why I'm saying some Um, so it's fun. It's an advantage, right? If if Victor can suddenly join the conversation and be helpful, it will be activated more broadly in the workspace, which is great for the product. But, if Victor does it on day one and it happened, um, the security team start raging cuz someone adds Victor to your workspace and suddenly Victor starts DMing everyone and then participating in the threads and the security is going crazy. Um, that's why I think you should earn it with the first use with a few users first and then you can roll it out slowly. Um, exactly. Um, yeah, so the value of shared context. I don't have much time left, but um, I'll very quickly talk about the difference between Victor and agents like Claude code or like Claude co-work or whatever. Now, Claude co-work works on your desktop, so it's a bit different. Uh, the advantage of Victor is that it works in cloud. You don't need to have your computer open for it to work. And another thing is the shared context. So, as I said at the beginning, for Victor to work well, for you to be able to, you know, ask Victor to change your meta ads budget or like to read your analytics data, only one person from the company needs to connect this integration. Right? Imagine that you work in a 100 person team and your growth team is 20 people. If you have to ask 20 people to connect your meta ads, everyone individually is quite painful. Furthermore, if someone wants to interact with Victor, um and like if Victor wants to be proactive, everyone connects their own integration, someone can connect their own wrong integration, right? And Victor can be just very stuck and wrong and, you know, might not know which integration to use, which adds a lot of complexity uh for the user. Um cool. And something I want to highlight here is that Victor is not a tool. It's a hire. And here's what I mean. I'll tell you one customer story. One of the biggest e-commerce brands in the in the United States. They and their team admin has connected Victor. And the first integration, a team integration, that they connected was their personal email. Personal Gmail. And then suddenly the team started speaking to Victor about this guy's emails. And um and this guy is is is is texting me and saying, "Hey, man, like what the hell Victor is leaking all of my data. Why are you doing this?" And I'm like, "Why did you give Victor access to your personal email? Like, you know, if you hire a new employee, do you give them access to your personal email? Probably not, right? Um that said, I think it was a great inspiration. And what we did is we added a capability to Victor to kind of scope the integrations. So, they're not always shared and if you want to have your personal integration to your personal email and with Victor to like in your DMs or publicly uh be able to use it when you call it, uh this is also possible now. Um yeah. And and so to summarize, um what does it take for an AI co-worker to be great? I I think there are three major pillars if you want to build your co-worker. I think this is a technical crowd, so I encourage everyone here to try to build your own Victor. And you know, there are just three things you need to make work. Helps get work done quite easy. Models are capable today. You know, connect your integrations through pipedream will work well. Knows the company, has the context from Slack. Make sure you're able to utilize this context well. You will probably need to go through the Slack approval process, which is very difficult and can be can be boring. And then make it friendly. It makes a difference and you should make sure that Victor likes your team. Your team likes Victor. This is our vision for the future. Every company has AI employees. I think it's obvious. Not nothing to argue here. And historically I just wanted to highlight the vision for AGI has been with us since the 17th century. Gottfried Leibniz, the inventor of calculus, was reasoning about you know, humans doing unnecessary things and he wanted to build a calculator. Um Little did he know, you know, calculation is not the only cognitive task that that we can automate and I think um we are now in this beautiful moment in history where um where where we can essentially automate all the cognitive tasks and we we can be part of the revolution. So I will just let myself read this quote. Um it is unworthy of excellent men to lose hours like slaves in the labor of calculation. Let us leave that to machines. And with that, I just wanted to encourage everyone to scan this QR code, click on, sign up, and add Victor to your Slack. Test it out. Everyone in this room has a hundred dollars in free credits. No strings attached. You can just remove remove Victor at any time. It will add a lot of value, I promise. If it doesn't, give me a call. I'll make sure it does. Thank you. \>\> Woo!