The argument in one line.
Treating AI agents like employees rather than chatbots requires three connected systems: a shared task queue, a low-friction capture habit, and self-checking evals that catch bad output before it reaches you.
Read if. Skip if.
READ IF YOU ARE…
- A founder or team lead already using AI tools ad hoc who wants to move from prompting one task at a time to running a standing queue of agent-completed work.
- Someone comfortable with a project-management tool like Linear, Asana, or Trello who wants to bolt AI agents onto that existing workflow instead of adopting new software.
- A solo operator managing multiple businesses or clients who needs to delegate low-stakes tasks (research, drafts, thumbnails) without personally supervising each one.
SKIP IF…
- You're looking for a no-code, step-by-step build of the automation itself — this covers the underlying system, not the exact webhook/agent wiring.
- You don't yet have a real backlog of delegable tasks — the value only shows up once there's actual work to route through the system.
The full version, fast.
The video argues AI agents become genuinely useful only when moved out of a chat window into the same task system a team already uses. The mechanism: a shared AI-and-human workspace (built on Linear) where tasks flow through Inbox → Next → Doing → Waiting → Done, and tagging a task 'agent-ready' fires a webhook that hands it to an agent with full workspace context, a knowledge base, and stored credentials. A low-friction capture habit (hotkeys, phone shortcuts) keeps ideas flowing into that queue. Evals — standardized pass/fail checklists — make the agent re-iterate its own output until it clears quality bars before a human ever reviews it. The result: the operator's job shifts from doing work to scoping tasks and running quality control.
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00:00 – 00:48
01 · How the Most Productive People Work Now
Cold open + thesis: AI agents are changing daily workflow, but nobody's shown what that looks like in practice. States the business context — LeftClick, Clarvo, Maker School, $400K+/month.
00:48 – 02:04
02 · Part 1: A Shared AI + Human Workspace
Introduces Linear as the shared board where humans and AI agents work off the same queue, with statuses Inbox / Next / Doing / Waiting / Done.
02:04 – 03:01
03 · Tagging Tasks as AI-Ready (Live Demo)
Creates a real task ('Ideate 5 New YouTube Videos'), writes a plain-language brief, and tags it agent-ready to hand it off.
03:01 – 04:10
04 · An Agent Picks Up the Task
Drags the task into Doing; the agent posts a plan, starts working, and updates status autonomously without further prompting.
04:10 – 05:43
05 · Under the Hood: Webhook → Fable 5 → Knowledge Base
Diagrams the mechanism: a new task fires a webhook to his agent ('Fable 5'), which draws on workspace context, a knowledge base, and stored credentials to produce real output.
05:43 – 06:34
06 · Verifying Outputs With Human Taste
Reviews the agent's YouTube title/angle ideas, rejects the weak ones, picks a winner, and explains that human taste is the final filter over a forced ideation pool.
06:34 – 08:37
07 · What a Typical Day Looks Like
Shows rapid-fire task creation (chair research, CRM emails, immigration paperwork) and argues this removes context-switching by letting him operate 'at the speed of thought.'
08:37 – 09:22
08 · Part 2: Capture (Getting Things Done)
Introduces David Allen's 'capture' concept: a low-friction way to get any idea into the to-do system immediately.
09:22 – 10:46
09 · Desktop Capture: Hotkeys + Send to Agent
Demonstrates a bound hotkey that creates a Linear task instantly and sends it to the agent with a command-based shortcut.
10:46 – 11:36
10 · Phone Capture: The Action Button Trick
Shows a phone action-button shortcut that creates a task on the go, arguing lower friction means the system gets used more.
11:36 – 12:25
11 · Push Notifications When Agents Need You
Explains push notifications that surface only when an agent is blocked waiting on him, plus full task-history visibility.
12:25 – 13:37
12 · Part 3: Evals — AI That Checks Its Own Work
Defines evals as a standardized checklist run on every output before it reaches him, so quality bars are enforced automatically.
13:37 – 14:26
13 · Eval Example: Is This Render Usable?
Walks a real eval file (visual-asset-eval.md) checking generated renders/thumbnails for clean text, correct style, legibility, and correct dimensions.
14:26 – 15:33
14 · Eval Example: Was This Done the Way Nick Thinks?
Walks a second eval file (principles.eval.md) scoring output against first-principles reasoning, EV discipline, time-minimization, and 'verified not plausible.'
15:33 – 16:33
15 · Part 4: Q&A — You're the Manager Now
Reframes the founder's new role: scoping work and doing QA instead of producing it directly, comparing it to managing a team of writers.
16:33 – 18:27
16 · How to Recreate This With One Prompt
Tells viewers they can copy the video transcript into an agent (Fable or any model, e.g. GPT-5.6) to rebuild something similar for themselves.
18:27 – 19:21
17 · Free Resources + Maker School
Points to free 'Maker Zero' workflow files and pitches Maker School, his paid 90-day automation program with a money-back guarantee.
Lines worth screenshotting.
- Tagging a task 'agent-ready' in a shared workspace fires a webhook that hands the task to an AI agent with full workspace context, a knowledge base, and stored credentials attached.
- The most effective AI agent setups don't live in a chat window — they live inside the same task-management system, like Linear, that a human team already uses.
- A low-friction capture method — a hotkey or a phone action-button shortcut — matters because the harder it is to log a task, the less you'll trust the system with your real backlog.
- Evals are a standardized checklist an AI agent runs its own output through — legible, on-brief, correct dimensions, matches your reasoning — before a human ever sees the result.
- If an eval fails, the system doesn't hand the broken output to a person — it makes the agent iterate and retry automatically until the output clears the bar.
- As agent delegation scales, a founder's job shifts from doing the work to scoping it precisely enough that automated guardrails can catch failures without human intervention.
- Running dozens of AI agents in parallel doesn't remove the bottleneck — it just moves it from 'how much work can I do' to 'how quickly can I scope and verify work.'
- A capture-and-delegate system only pays off once verification is cheap; if checking an agent's output takes as long as doing the task yourself, the system has no value.
- Feeding an agent a personal knowledge base of past decisions lets it evaluate whether new output matches how you'd reason about a problem, not just whether it looks plausible.
- Push notifications for 'agent needs you' turn quality control into a queue cleared a few times a day instead of a channel that has to be babysat continuously.
Three systems turn AI agents into a standing workforce.
SYSTEM OVER PROMPTS
The upgrade from prompting a chatbot to running a fleet of agents isn't a better model — it's a shared task queue, a low-friction capture habit, and evals that catch bad output before you ever see it.
02Part 1: A Shared AI + Human Workspace
- Route AI agent work through the same task board your team already uses, not a separate chat window, so agents and humans work off one shared queue.
- Use a simple status pipeline — Inbox, Next, Doing, Waiting, Done — and a single 'agent-ready' tag to decide which tasks a human works versus an agent.
- Give an agent three inputs before it can produce real output: current workspace context, a knowledge base of past decisions, and the credentials it needs to act.
08Part 2: Capture (Getting Things Done)
- Make logging a new task as low-friction as possible — a hotkey, a phone shortcut — because friction at the capture step is what kills a delegation system.
- Route captured tasks straight into the same project-management queue instead of a separate notes app, so nothing captured needs a second manual step to become actionable.
- Use push notifications to surface only the moments an agent is blocked and needs input, so you check in a few times a day instead of babysitting constantly.
12Part 3: Evals — AI That Checks Its Own Work
- Write evals as standardized pass/fail checklists — not vague quality bars — so an agent can objectively grade its own output before a human ever reviews it.
- Let a failed eval trigger automatic retries instead of routing straight to a human, so most quality problems get fixed before they ever reach your review queue.
- Score outputs against explicit personal principles (first-principles reasoning, expected value, whose time gets saved) so 'quality' means matching how you actually think, not just looking polished.
15Part 4: Q&A — You're the Manager Now
- Expect your own role to shift from producing the work to scoping it precisely and then verifying the results — the same shift a manager makes with human staff.
- Treat verification speed as the real constraint on how much delegated work you can handle, not how many agents you can run in parallel.
- A one-person operation can run many parallel workstreams once tasks are well-scoped and evals are in place — the ceiling becomes your ability to check in and decide, not to execute.
Terms worth knowing.
- Agent-ready
- A label applied to a task in a project-management tool that tells an automated system the task is cleared for an AI agent to pick up and work on.
- Webhook
- An automated message sent from one piece of software to another when a specific event happens — here, tagging a task triggers a request that hands the task to an AI agent.
- Evals
- A standardized set of pass/fail checks run against an AI agent's output to determine whether it meets quality requirements before a person reviews it.
- Capture
- A low-friction method for logging a new task or idea the moment it occurs to you, from David Allen's Getting Things Done, so it enters a to-do system instead of being forgotten.
- Knowledge base
- A stored collection of background information — past decisions, preferences, strategy documents — that an AI agent references so its output matches how a specific person or team operates.
Things they pointed at.
00:00toolLinear
08:40bookGetting Things Done by David Allen
Lines you could clip.
00:00
“So the way the most productive people on Earth work today is very different from just maybe six months ago.”
07:06
“I no longer just have to sit down and then look at a terminal, wait for its outputs, and then finally when it gives me an output, I proceed. I'm capable of operating at like the speed of thought.”
14:26
“Was this done the way that Nick thinks?”
17:32
“I've sort of had to go up one level of abstraction and then be responsible for the fleet of managed agents, essentially.”
Word for word.
Read-along
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metaphorstory
00:00So the way the most productive people on Earth work today is very different from just maybe six months ago. And the entire reason is because of AI agents. But despite everybody and their mom talking about AI agents, using them in workflows, I don't think anybody's actually clarified what it looks like in a practical knowledge work style situation.
00:19I think Claude made some good headways with, you know, Claude integrated in Slack. But today, wanna show you guys how I am currently getting things done. My business is gonna do over $400,000 this month, and we work with a variety of different things.
00:31We work with media. We work with AI implementation. We do consulting.
00:35We have a variety of different, like, verticals and stacks. And I wanna show you how you can use AI agents to meaningfully improve both the quantity and then the quality of work that you do in an organization in a very simple and straightforward way.
00:48So the very first thing you need is you need a shared AI and human workspace. And to make a long story short, this is a place where you can have AI working on some task alongside people. Genuinely, you can use whatever platform or tool you want for this workspace.
01:06The thing that is more important than the specific, like, selection is just the shape of it. And I'm gonna run you through what ours looks like right now. I use a tool called Linear, which is just one of the many project management workspaces that you could use for this.
01:20And basically, the way linear is broken down is you have a variety of different statuses that denote where a task is in the pipeline. And so the statuses that we've set up, if I zoom in a little bit over here, is we have one called inbox. We then have another one called next.
01:37We have one called doing. And then we finally have one called waiting. And then there's also the done status, which is where we put tasks after they're done.
01:46Just so that if, you know, we need to take a look at how a task was completed or get some more context or move it back to next or doing, you know, we can do that pretty easily. So I'm not gonna talk all day about this setup because hopefully that's pretty standard. The key thing in here is this isn't just humans working on this to do list.
02:02We now have the ability to weave in AI agents. And rather than have to like prompt an AI agent constantly, all I'll do is I will make a task and I will tag it as AI ready and then I will send it off and actually have it do said task.
02:14So one of the tasks on my to do list today is I need to ideate, you know, another five videos. So I'm gonna press c, that's gonna open up a new issue. And then I'll say ideate five new YouTube videos.
02:25And then in the description, because keeping in mind that I'm not just gonna give this to a person, I'm gonna give this to an AI agent. I'm just going to, you know, ad lib what I want. Hey, I want you to use context based off videos that I've published before to come up with five new YouTube video ideas, including three title options for each, some angles for each, maybe even some brief outlines based off of current trending content on the Internet.
02:46After I'm done, I'm gonna head over here to create issue and click that button. Now, all I need to do is I just need to tag this. And this is a very slow, naive way of doing all this, by the way.
02:56I'll show you a much faster way. And once it's an agent ready, we can actually begin working on the task. So the way that, you know, I'm gonna do it is I'm just gonna drag it over to next.
03:04And essentially, occurs when that happens is this sends a request to a server where I have an AI agent living. This AI agent will pick up that task. It will then consult my knowledge base to learn kind of contextually how previous tasks have been completed, my preferences surrounding tasks, and so on and so forth.
03:21If we just click on this and scroll down, you can see the Nick OS agent run has actually started. So it's picking it up now, assessing the card, posting the plan, and so on and so forth. And then it's even actually writing stuff down.
03:32It's doing so just with my profile because I'm cheap and I don't want to spend, you know, a bunch more linear costs. But obviously, you could have this be like my fable agent. And then it's going through and actually doing the task for me right now.
03:43You know, if I exit out of this, you see we've now changed the status to doing. And so now this is this is occurring almost entirely autonomously. All I do is I'm just like the the project manager, if that makes sense.
03:53I add things to a queue, agents complete them according to my specifications, and or, you know, if there's a q and a step involved, which a task of this would probably have a q and a step involved, you know, the agent will wait for my approval before actually doing, let's say, the publishing or interacting with, like, the wider Internet, creating me a post on some profile.
04:10So while it's doing the task, let me just run you through essentially what's going on under the hood. You know, we start with the new task over here, and that's the one that I just created talking about, you know, the content that I wanted to make, the the YouTube titles and and angles. Then it's caught by a webhook.
04:25For those of you guys that don't know, that's just a place on the Internet that a request gets sent to. And the cool part about Linear and one of the reasons I like it is just because it has that functionality built in. You can just like send a request off when it's tagged with something.
04:36And now it goes to Fable five. The thing is Fable five has access to three things. It has access to workspace context.
04:41So this is everything that, you know, the we are currently using or doing in the workspace, all the files, all the other tasks, you know, our content pipeline and so on and so forth. It then has access to a knowledge base, which is a bunch of additional textual information that it can draw upon if necessary. And then finally, it has it has access to credentials.
04:59And these credentials are things like, you know, passwords to various services and platforms that maybe it needs to use Chrome Dev Tools to sign into Or, you know, API keys and so on and so forth. And all this is stored relatively securely to the point where Fable can use all of it in conjunction with its own gigantic to actually do the work.
05:17And you can kind of see that in the task. I mean, after the update, it's just confirmed that this is how it understands it. Make me some videos.
05:24Well, I've given it context as to my highest performing courses in the past. I've given it I've given it context as to my knowledge base. I've given it context as to like my strategy docs, so how I typically create ideas and angles and so on.
05:37And you can also see that it just actually changed the label to waiting, and changed the status to waiting. So let's take a look at what that looks like. Now, the thing to know about AI agents is, you know, their outputs aren't incredible.
05:48You can't just trust an AI agent to do everything entirely on its own. What you need to do is you need to verify its outputs. You need to essentially have some sort of final line in the sand where you will check on the outputs and then select the ones that you like the most.
06:01You'll basically force ideation, let's say, over a large solution space and then apply your human taste to selecting like the best winners. I just did that in my ads video that I published earlier.
06:12And you can see here that I don't really like all of these ideas. I don't think they're all brilliant. But this one down here, let AI agents run a YouTube channel for ninety days.
06:20This one here is pretty solid. You can see it's come up with some different title formats, a source trail.
06:25It's given me some reasonably good reasoning and rationale behind why something like that would work. And I think that might actually be a video now that I am going to do. In a typical day, my pipeline might look reasonably like this.
06:37I'll have a couple of tasks in next. I'll have a few in waiting. You know, let's say I make another one right off the top of my head.
06:45I want to sign up to Anthropic Partner Network, find out everything I need, and prewrite draft application.
06:54You know, I'll I'll just be rolling through here selecting agent ready on tasks that make sense, but then also adding tasks that maybe other people in my organization or I need to do. This is really the the idea behind a collaborative and shared workspace. And the main benefit there is I no longer just have to sit down and then look at a terminal, wait for its outputs, and then finally when it, you know, gives me an output, I proceed.
07:14I'm capable of operating at like the . I'm capable of going very fast here. You know, I can bang out 20 of these ideas, simultaneously have 20 different agents all operating on various tasks using my knowledge base.
07:26And then I can just check-in, you know, once or twice a day when they're done to, like, assess in batches. This basically solves context switching. Now once you're done, you can actually just give it some information like, hey, I like this one.
07:38I'd like to generate 10 thumbs for the video alongside adding this plus alternatives, plus everything into content pipeline.
07:49The reason why this is valuable is you just don't need a text box open all day, and you can actually work off of a single source of truth. I wanted to buy an ergonomic chair for my home setup. Well, I had to go through and just do a tremendous amount of research, cross referencing Reddit, and so on and so forth, to get me a bunch.
08:03Then I actually ended up buying one of these here, which took like thirty seconds. And you can do this for any task, know. Email rest of people in Clarivo's CRM.
08:10Cancel Roger's Internet for family. You can see that was back when I was trying to master my English accent. That didn't work very well.
08:17Look into how to achieve US resident status and fill out forms. The whole idea is you're basically taking the agent out of the chat box, and then you're actually integrating into the core of your business. But believe it or not, as it is, this isn't very valuable.
08:28I mean, we've done is we basically just created a way to dump ideas in, or we haven't really created a way to extend past that. And the thing that makes it valuable is when you combine that with capture. Now the first time I encountered the concept of capture was when I was reading through David Allen's Getting Things Done from forever ago.
08:44And to make a long story short, what capture means is it's just a simple and easy and low friction way of getting ideas into some to do list. Now, we actually have our to do list. Right?
08:55That to do list, in essence, is the project management shared human and AI workspace that I showed you earlier. Well, the valuable thing about having a low friction capture method is like a good project managers, Anything that comes to mind over the course of the day, any task that you need working on, any concept or idea for anything, you can get into that system extremely easily whether or not you're on the go.
09:16Let's say, like walking around your city or something like that, going from meeting to meeting, or sitting down at the computer. So obviously, a really low friction capture method is just using linear and hot keys. So maybe create Wikidata entry for Left Click AI Incorporated.
09:32And maybe this is something that, you know, I wanna tag as agent ready, create an issue for, and then actually have it proceed with the task like we just did a moment ago. Well, the simplest and easiest way of sorting this out is actually just by, you know, binding a hotkey or something, and then, you know, adding a task as follows.
09:47And so now I actually have this built into my computer. So maybe I'm out and about and, I don't know, I'm watching some YouTube videos for my daily updates. And I realized that the thumbnail sort of generation prompt is a little bit off.
09:58Well, now what I can do is I can actually kick off an AI agent workflow literally without having to stop a beat just by opening this and saying fix thumbnails for daily updates channel. Well, now this has actually been sent off to my linear. And if I wanted to tag this as AI agent ready, you know, I can actually send this to my agent with a brief.
10:15I just hold command and press g. So that's pretty easy. I can give it some additional information if I want.
10:20I can tell it. Right now, the thumbnail text seems to be a little bit small. At any point in time, I basically have woven in AI into my my day to day flow such that it exists with me next to me, and I can work on essentially whatever tasks.
10:34I mean, you think about it, I could fire off 50 of these simultaneously. I can now operate at the speed of thought. The bottleneck is no longer, you know, how much work can I do in a day?
10:43It's how quickly and accurately can I scope the work that I want done in a day? I just ran over here to get my phone so I could show you what this looks like as well. You could build a shortcut into your phone such that you can actually just hold this button, create a demo task in linear, And it'll actually go through, and I don't know if you guys could tell, but I just had a little chime noise.
11:03And then I can go back over to linear, and then you could see that I just I just added that in the system. You know, it's like tied to my action button. It's extraordinarily low friction.
11:10These little hacks, these aren't necessary to be clear. But the lower friction that you make something like this, the more you end up relying on sort of your to do list, your project manager, your shared AI, and human workspace. And it could also tie this in such that, you know, maybe if you click the action button twice, it automatically starts it as an agent task.
11:27Whereas if you click it once, you reserve that for a human task. You can also send notifications so that when, you know, an AI agent needs you for something, it'll actually pop a notification up in your screen. I did this a while back and it was super valuable.
11:38Having push notifications when agents are waiting on you, and then all you do is you click on it and then voice transcribe, like, that is pretty freaking sexy. And not only is it sexy, it's also obviously the future of work. If right now, our ability to assess and verify the outputs of agents is the bottleneck, we need to make our ability to do that as easy as humanly possible.
11:55What's cool too is you get total task visibility, so there's like a trail, and it's persistent. It doesn't disappear at the end of every prompt. So for instance, you could see here that I actually moved a task from waiting back to next.
12:06I removed a label that said waiting on Nick because, you know, I wanted to provide some more context to the task. And this is going to get picked up just like any other task. All the context is gonna be fed into this async runner and then, you know, things are just gonna kind of work on its own autonomously without my direct oversight.
12:21The third thing you need in a system like this is you need what are called evals. Now in case you guys didn't know, evals are a set of evaluations that you run an output through or a model through in order to determine whether or not it is giving you the sorts of things that you want.
12:38We're essentially assessing its performance. And for project management and modern AI based productivity, you know, I define evals as basically a standardized checklist steps that I run all outputs through before they give it to me. In that way, I know that, you know, we have my tone of voice on every project.
12:54We have all of the basic LLM isms taken out of the text, like em dashes and stuff like that. We have my reasoning applied to everything. So I fed in a big knowledge base, basically, based off my the way that I make decisions.
13:06And I was like, was this a sort of decision that I would reasonably have made under these circumstances? And the whole idea is rather than giving work off to AI completely and then just hoping it does a good job, what you do is you give it a set of guardrails that it can operate on. And then if it assesses that it's fallen out of those guardrails, it will just iterate and continuously retry the project until it eventually gets within the constraints.
13:27Once it's in the constraints, my actual work to, I don't know, touch it up or change something or, I don't know, pick better YouTube titles, pick better tasks, change the work, whatever is significantly reduced. So for instance, here's one on the left hand side here called visual asset. Is this render usable?
13:42This applies to demos, thumbnails, diagrams, and generated imagery before it reaches my folder. So, you know, in order for a render to be considered waiting on Nick, basically, in order before it even gets to the point where I'm ready to make a q and a check, The text has to render clean.
13:59It needs to like literally visually assess every output and determine that there's no garbled or melted characters. It needs to figure out, hey, you know, is this in the style Nick likes, which right now is this ink editorial thing, black ink on pure white. I I just love that.
14:12Is it legible at thumbnail size? Basically, is it like sufficiently zoomed in? Is it faithful to the brief?
14:18Does it have the right shape? AKA, you know, IGN my thumbnails at twelve eighty by seven twenty. You know, I do diagrams, it's at nine twenty nineteen twenty by ten eighty.
14:25Here's another one here. Was this done the way that Nick thinks? And this is loosely, you know, related to the knowledge base that I fed it in a while ago.
14:32But it's, you know, hey, there are five questions. You gotta score each of these zero to two.
14:37If the total is less than seven, a k if one of them is like a zero, then this whole project is a fail and we need to continuously iterate until it is good. So one of my principles is first principles. Did the work reason from the actual mechanics of the problem or did it just pattern match to what people usually do?
14:51EV discipline. Are the conclusions and choices high expected value? If so, then it's good.
14:57And if not, then it's not. Is Nick's time minimized? You know, if there's any part of this that could have been done without Nick in the loop, make sure to go back and do it according to this so that Nick doesn't have to do any additional work.
15:07Is it verified, not plausible? Is there a leverage check, you know, if there's a big lever ignored because I had a skill in my workspace, then I want you to rerun this. And so, mean, like, this is these are tokens.
15:18Right? This isn't free. But typically, this is worth far less than the time that I would spend getting an average task up to snuff.
15:25And the key is, you know, having a knowledge base of some kind that it consults to like get the the right shape, and then forcing that shape into that guardrail container that I talked about earlier. And finally, there's quality and assurance. And this is really where you come in as, you know, the final, as mentioned, gate to the task.
15:42The reality is the nature of work has changed a fair amount. You know, before, I was actually doing a lot of the work. But now, what I'm typically doing is I'm spending time scoping the work so that, you know, the guardrails are set really efficiently and effectively, as well as a clear definition of what it is that I want.
16:00And then I'm spending most of my time evaluating the results of the work, doing q and a. It's kind of a mind shift from back when I was actually like freelance writing in my content marketing agency. And then later on, I ended up managing a bunch of content writers.
16:14It's very similar. I'm just going one level up the abstraction chain. So I'm not actually directly involved with the deliverables.
16:19You know, my fingers and my hands aren't the the things that are doing the heavy lifting, moving the products and so on and so forth. It's my mind assessing the quality and ensuring that my taste sort of applied to everything that I want. And so obviously, you can take what I showed you today, you can apply it in any way, shape, and form.
16:33You could, for instance, go down to the description, expand it, show the transcript, go to the top right hand corner, copy and paste the entire thing, and put it into Fable. And it could actually recreate something like this fairly straightforwardly for you, probably without burning more than 25 to 50% of your session limit.
16:48You can do the same thing with GPT 5.6, so really any model. This is just my own architecture. But I just wanted to begin the the wider conversation of how to actually apply AI in a modern workplace.
16:57I think a lot of people here have sort of pieced together bits and tiny atomic habits from various workflows that they've seen on the Internet. But this is a pretty cohesive way to weave this into your organization. And I know a lot of you guys are probably going to look at what I've done and be we could make that better.
17:12And that was sort of sort of the idea. I'm sure there are many ways you can make this better, and I'm keen to see, you know, what you guys do with us. Since implementing this inside of LeftClick, Clarvo, and Maker School, you know, my day to day responsibility has also shifted a fair amount too.
17:26Rather than necessarily be involved with every little thing, I've sort of had to go up one level of abstraction and then be responsible for the the , essentially.
17:36I've also had to start thinking about things like my token budgets. These are things that I never used to think about before because I was typically constrained to operating within one little, like, terminal window, having some input go in and then waiting for the output to come out. I've also become significantly more productive because no longer do I have to just sit and wait for an output.
17:53You know, I can just fire off outputs basically about as fast as the speed of thought. And I think that's a major unlock. You have like a little ideation session.
18:01You sit down, ideate the top five tasks that need to be done, assign agents to as many of these as can be done. They do a bunch of pre lifting while you focus on something else. And then maybe three or four times throughout the day, every couple of hours, you check back in on the outputs.
18:14This sort of gets you out of just staring at the terminals all day. Actually gets you, I don't know, building relationships with key vendors, selling, which is obviously the number one lever that any founder or, you know, top level executive in a company really needs to be doing, and so on and so forth. Okay.
18:27If you like this sort of thing, I actually have a bunch of files down below in the description in the line that says maker zero, where you can just copy and paste everything that I write there to an agent, and you can have it do it just like that transcript idea earlier. I also wanna make it clear that this isn't prescriptive.
18:42I'm not telling you have to do it on linear. You have to do it in this particular way. But this is just how I'm managing it in my organization and the results that I'm seeing.
18:49And if you want to learn how to implement strategies like this into an AI and automation service business, basically, a business where you get paid to build things like this for other people, definitely check out Maker School as well. It's my ninety day automation roadmap where I actually guide you through everything you need to do to start as a total beginner and actually get your first client.
19:08And I guarantee you that first client in ninety days, or I give you all your money back. It's very straightforward. It's very tried and tested, and it's been finished by over 10,000 people to date.
19:17Thank you very much for watching. I'm looking forward to seeing all y'all in the next video.
The Hook
The bait, then the rug-pull.
Nick Saraev opens with a claim about how the most productive people now work, then spends nineteen minutes proving it with his own screen: a live Linear board where AI agents pick up tasks, get graded by automated evals, and hand back finished work — while he does almost none of the typing himself.
Named ideas worth stealing.
01:28model
The Task Pipeline (Inbox → Next → Doing → Waiting → Done)
- Inbox
- Next
- Doing
- Waiting
- Done
A five-status board where both humans and AI agents pull and hand off the same tasks; tagging a card 'agent-ready' is what lets an agent claim it.
Steal forany team running human + AI work through one board
04:10model
One Trigger. Full Context. Real Output.
- Workspace context
- Knowledge base
- Credentials
The three inputs an agent needs before a single webhook trigger can produce usable output: what's happening in the workspace right now, background knowledge/preferences, and access to the tools/logins required to act.
Steal forscoping what any agent needs before wiring it into a workflow
14:26list
Eval Principles
- First principles
- EV discipline
- Is Nick's time minimized
- Verified, not plausible
- Leverage check
Five yes/no questions scored to decide whether an agent's output matches how the founder actually reasons, not just whether it looks plausible.
Steal forwriting an eval rubric for your own agent outputs
13:37list
Visual Asset Eval Checklist
- Text renders clean (no garbled characters)
- Matches the intended visual style
- Legible at thumbnail size
- Faithful to the brief
- Correct dimensions/shape for the platform
The pass/fail bar a generated thumbnail, diagram, or render must clear before it's even considered ready for human review.
Steal forgating AI-generated images/thumbnails before they reach you
How they asked for the click.
VERBAL ASK
18:27product
“if you want to learn how to implement strategies like this into an AI and automation service business... definitely check out Maker School... I guarantee you that first client in ninety days, or I give you all your money back.”
Soft-pitches free resources first (Maker Zero workflow files), then transitions into the paid program with a money-back guarantee and a social-proof number (10,000+ students) — low-pressure, single ask at the very end.
MENTIONED ON CAMERA
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
OTHER LINKSAlso linked in the description.
Visual structure at a glance.
