Big Idea

The argument in one line.

Jev, a non-conversational classifier from TypeSafe AI, sits in front of existing agent harnesses like Codex or Grok Bot to score, filter, and route high-volume work before it reaches a frontier model or a human.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…

You already run agent harnesses like Codex, Claude Code, or Grok Bot for marketing, sales, or recruiting work and want a faster pre-filter before those agents spend tokens.
You handle repetitive classification decisions at volume: screening applicants, scoring inbound leads, triaging a shared inbox, or deciding which content idea duplicates something already live.
You're comfortable writing explicit pass/fail or scored criteria for a task, since a classifier's value depends entirely on how well you define what counts as good.

SKIP IF…

You want a tool you can chat with or that executes tasks directly. Jev only classifies; it hands the routing decision to another agent or a human.
You're looking for proof the '100x' headline claim is real. The video's own on-screen caption during the benchmark reads 'One benchmark. Not a 100x result,' and every workflow dashboard shown is labeled fictional, illustration-only data.

TL;DR

The full version, fast.

Jev is a classifier, not a chat model, that TypeSafe AI is positioning to sit in front of whatever agent harness you already use (Codex, Grok Bot, Hermes) so it can score, filter, and route high-volume decisions before a frontier model spends tokens on them. Eric Siu applies a repeatable Inbox/Leads to Classify to Route pattern across recruiting screens, content and clip selection, CRM triage, customer voice analysis, sponsorship replies, outbound testing, and duplicate-content checks. The one real proof point is a vendor benchmark where Jev finished a Wikipedia classification task faster and cheaper than several LLM competitors, explicitly flagged on screen as a single vendor demo, not a 100x result. Every dashboard used to illustrate the workflows is labeled fictional.

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Chapters

Where the time goes.

00:00 – 00:34

01 · Cold open: add Jev to your existing agents

States the thesis: Jev supercharges the harnesses you already use rather than replacing them, illustrated with a 40-topics-to-10 screening example.

00:34 – 01:53

02 · What Jev actually is, proven on a vendor benchmark

Defines Jev as a classifier (not an LLM you chat with) and shows TypeSafe AI's own benchmark: 0.827s, 4.26x faster and 2.6x cheaper than competitors, explicitly flagged as one vendor demo, not a 100x result.

01:53 – 02:42

03 · Start with your own workflows: content and clip selection

Recommends auditing your existing workflows for the highest-priority slot; demos a proposed workflow doc and a content-engine use case for shortlisting strong moments and flagging repetition.

02:42 – 03:39

04 · Recruiting: score applicants against explicit criteria

Applies the classifier to a recruiting pipeline: applications scored against criteria like two promotions and three-year average tenure, with a confidence score and human review at the end.

03:39 – 04:05

05 · Sponsor break: Single Brain

Mid-roll pitch for Single Brain, an AI marketing implementation agency.

04:05 – 04:52

06 · The repeatable pattern and an inbox triage dashboard

Names the reusable Inbox/Leads to Classify to Route pattern, then shows a fictional inbox-triage dashboard as illustration.

04:52 – 05:50

07 · CRM, revenue, and customer voice dashboards

Fictional dashboards illustrating pipeline risk flags and customer-call theme mining (from tools like Granola or Gong) to guide messaging and sales.

05:50 – 06:49

08 · Sponsorships and outbound review dashboards

Fictional dashboards for sorting inbound sponsorship requests by budget and audience fit, and for deciding when to test a new outbound variable based on underperformance.

06:49 – 07:23

09 · Search and AI visibility dashboard

Fictional SEO/AEO dashboard cross-referencing organic clicks and AI citations against customer-voice themes to prioritize content topics.

07:23 – 07:52

10 · Sponsor break: Single Brain Gateway

Pitches the free Single Brain Gateway for connecting marketing, sales, and revenue tool accounts to agents in one place.

07:52 – 09:23

11 · Choosing clips and stopping duplicate content

Demos picking the best moments from long recordings, then a three-gate duplicate-content check: is the topic already live, what's the closest existing page, and is the new idea meaningfully better.

09:23 – 10:20

12 · Define your criteria, keep human judgment

Reflects that quality is subjective, so the fix is writing down your own guidelines clearly enough for a classifier to apply consistently, with a human still deciding.

10:20 – 11:00

13 · Where to start

Closing advice: add the classifier's API key to whichever harness you already use (Codex, Grok Bot, Hermes) and ask what it can do there first.

Atomic Insights

Lines worth screenshotting.

Jev is a classifier, not a chatbot: it can't hold a conversation or execute tasks, it only scores and routes.
In TypeSafe AI's own benchmark, Jev finished a Wikipedia-page classification task in 0.827 seconds, 4.26x faster and 2.6x cheaper than the next fastest competitor tested.
The video's own on-screen caption during that benchmark reads 'VENDOR DEMO. One benchmark. Not a 100x result,' directly undercutting its own '100x' title.
Every workflow dashboard shown (inbox triage, CRM, sponsorships, outbound, SEO/AEO) is labeled 'FICTIONAL DATA. Illustration only. No live Jev run.'
The repeatable pattern behind every use case is three steps: Inbox/Leads, Classify, Route. One repeatable decision, one useful handoff.
Screening 100 job applicants by hand doesn't scale; scoring each against explicit criteria (e.g., two promotions across two companies, three-year average tenure) plus a confidence score lets a human review a shortlist instead of the full pool.
Adding a classifier layer doesn't reduce token spend; it increases it slightly, since it's an extra model call. Its value is speed and consistency at volume, not lower cost.
The workflow for stopping duplicate content is a three-gate check: is the topic already live, what's the closest existing page, and is the new idea meaningfully better.
A shortlist from a classifier is a decision aid, not an accuracy test. Human review stays in the loop at the end of every workflow shown.
The suggested rollout is to pick the one harness you already use, find its highest-leverage workflow, and add the classifier's API key to that rather than building something new.

Takeaway

A classifier that scores your queue before an agent works it.

WHAT TO LEARN

Any time a decision boils down to a repeatable yes/no or a score against explicit criteria, a lightweight classifier layer can screen it faster than routing everything through a full agent, as long as a human still makes the final call.

01Cold open: add Jev to your existing agents

A classification layer doesn't replace the agent harness you already use. It's meant to sit in front of it and pre-filter what reaches it.
The pitch to evaluate any 'add this tool' claim: does it change how you make money, not just what it can technically do.

02What Jev actually is, proven on a vendor benchmark

A classifier only scores, sorts, or flags input against defined criteria. It can't hold a conversation or execute open-ended tasks the way a chat model can.
The one real evidence shown is a single vendor benchmark, a Wikipedia classification task finishing in 0.827 seconds faster and cheaper than several LLM competitors, and the video itself flags that as one benchmark, not proof of a 100x claim.

03Start with your own workflows: content and clip selection

Before adding a new tool, list your existing workflows and rank them by priority. The highest-value slot for a classifier is usually the step where a human currently reviews more volume than they can keep up with.
For content review, a classifier's job is narrow: shortlist strong moments and flag repetition, not write or edit anything.

04Recruiting: score applicants against explicit criteria

Screening 100 applicants by hand doesn't scale. Scoring them against explicit, pre-defined criteria plus a confidence score lets a human review a shortlist instead of the full pool.
Missing evidence in an application isn't treated as an automatic rejection. It's flagged for a human to decide, which keeps the classifier from silently making judgment calls it isn't equipped to make.

06The repeatable pattern and an inbox triage dashboard

Every dashboard example in this video is explicitly labeled fictional, illustration-only data. Treat a vendor's product demo the same way: as a mockup of the idea, not proof it works on real data.

07CRM, revenue, and customer voice dashboards

Customer call transcripts can be mined for recurring themes by deal outcome, which is a concrete way to turn call recordings into marketing and sales language.

08Sponsorships and outbound review dashboards

A classifier applied to inbound requests can pre-sort by stated criteria like budget or fit so a human replies only to the ones worth a reply.

09Search and AI visibility dashboard

Cross-referencing what customers say in calls against what content is or isn't ranking is a way to prioritize which topics are worth creating more content around.

11Choosing clips and stopping duplicate content

A three-gate check before publishing new content: is the topic already live, what's the closest existing page, and is the new piece meaningfully better than what's already there.

12Define your criteria, keep human judgment

Definition of quality is subjective, so the fix isn't finding an objectively correct classifier. It's writing your own guidelines down clearly enough that a model can apply them consistently.
Human judgment stays in the loop at the end of every workflow shown here. The classifier's output is a shortlist or a score, never the final decision.

13Where to start

The suggested starting point for any new AI tool: don't build something new. Find the highest-leverage workflow you're already running and see if the tool improves that one step.

Glossary

Terms worth knowing.

Classifier: A model that only scores, sorts, or labels input against defined criteria. It cannot converse or execute open-ended tasks the way a chat-based LLM can.
Overlap gate: A check that compares a proposed content idea against everything already published to catch duplicates before publishing.
Confidence score: A number a classifier attaches to its own decision, indicating how certain it is, used to decide whether a case needs human review.
Agent harness: The tool or environment, such as Codex, Claude Code, or Grok Bot, that a person uses to run AI agents on their actual work.

Resources

Things they pointed at.

00:00toolCodex

01:19toolTypeSafe AI (Jev benchmark)

03:39productSingle Brain ↗

07:23productSingle Brain Gateway ↗

10:28toolGrok Bot

10:28toolHermes

Quotables

Lines you could clip.

00:00

“Jev is an absolute game changer when it comes to doing work.”

cold-open line that states the whole thesis in one breath→ TikTok hook↗ Tweet quote

00:45

“What is Jev actually? It is actually a classifier. That's the easiest way to think about this.”

plain definition that cuts through the hype framing→ IG reel cold open↗ Tweet quote

10:00

“The anxiety that I was having before was, I don't know if I can put this out there because I don't know if it's duplicate, I don't know if it's quality.”

honest, relatable admission of the actual problem being solved→ newsletter pull-quote↗ Tweet quote

10:28

“It's not an LLM, and not necessarily to make games. It's to get real work done and drive real revenue for your business.”

closing thesis restated, doubles as a CTA line→ TikTok hook↗ Tweet quote

The Script

Word for word.

Read-along

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metaphorstory

Jev is an absolute game changer when it comes to doing work. So marketing, sales, coding, recruiting, whatever it is exactly. And what I've been doing is I've actually been upgrading a lot of my harnesses with this because Jev is something that actually sits in between all the different harnesses that you're using, right?

So it doesn't necessarily replace them, but it does supercharge them. So basically what it's saying is like, hey, Originally, this bot said we should go for these 40 topics.

But Jeff says, hey, only these 10 clear the overlap gate. And when you think about it, is it necessarily a new large language model? Can you chat with it?

Can it actually execute on stuff for you? No, it can't. But even though it can't, it's extremely valuable.

And a lot of other creators right now are talking about how it's great for playing Doom and things like that. And that's cool. But I care about how this is going to affect.

business and how people can make more money from this ultimately. So when you think about it, what is Jeff actually, it is actually a classifier. Okay.

That's the easiest way to think about this. And when you think about work, a lot of your work actually needs to be classified. So a hundred people apply to during a company.

Now the workflow in the past was that you'd have to have a human review every single one of those hundred. And chances are, they probably wouldn't even get to it because we're limited on human capacity. Right now, the reality today is that Jeff can get through it very quickly.

And what you see on my screen over here is. it competing for Wikipedia page classification.

So Jeff is up here, right? You finish it so fast. Look at all these other ones are so slow, right?

This one on the bottom left is still taking forever. And then when you look at the results over here, you can see that 0 .827 seconds, right? It's 4 .26x faster than the latest or the next fastest one, 2 .6x cheaper, right?

When you think about doing millions of calls, it starts to add up over time, right? And so this is just, I'm really positioning this for you to start to think about how that's going to apply for your business, right? So I think one simple thing that you can do is think about all your workflows, the main harness that you use.

So for example, I use Codex over here and you can see on my screen. I said, hey, I want you to think about the workflows where we can implement Jev. So we have this leveling up marketing school content engine.

And so it says, hey, this is like a 93 out of 100 from a priority standpoint, which I always like doing. And to say, hey, this is where Jev helps. So after a transcription, so after it pulls from like a podcast that I do or like a piece of content like this one, it's basically going to.

Short list of strong moments, flag repetition, check complete takeaways, and review proposed edit. So why is this important? So Jeff is good at seeing the definition of done, right?

You can see multiple choice, like is it yes or no? Did it actually fit this? It can score.

And so you have three different levels that you have to define when you're working with Jeff. And it's not that hard to set up. And so when you have a lead come in, right, or maybe you're reaching out to someone from a recruiting standpoint, maybe you want them to have two promotions at two different companies, and you want to see an average tenure of three.

So it can actually score and it can also give a confidence score as well. So once it can just classify these things quickly for you, then it can shortlist a lot faster versus having to have your frontier tokens being spent on all that time and all that thinking over there. And so with Jeff, again, it's much faster and it's quickly looking at, hey, does it actually fit the verifiable options that you're looking for when it comes to whatever it is you're trying to accomplish?

And then from there, you can make a call on whether you want to move forward with the classification or, or whether you want to move forward with the task or not. So like I mentioned, number two, which over here is on the recruiting pipeline pieces.

Okay. So after replies arrive, classify intent, identify missing prescreen evidence and flag contradictions for review. By the way, if you're enjoying this right now, you got to check out single brain.

So single brain is where we help people with AI implementation. More importantly. marketing implementation.

And so whether it's helping people build agents around AEO, SEO, whether it's helping people scale their cold outbound systems, whether it's helping people scale their creative, this is the new way of doing marketing. We believe that the future of marketing agencies is this. Just go to single brain, single brain with a B and we'll see you on the other side.

But anything that needs classification, this is something to apply this to, especially when you need classification. Really, I would say classification at volume. And you need reliable classification, right?

And a lot of the work that we do is classification. Again, think about all the outreach that you're doing, recruiting, sales, marketing. All these things that you're doing, this will help you increase the speed significantly, and you'll ultimately reclaim a lot more time and not have to spend a lot more money.

I do think this does increase your cost a little bit when it comes to tokens overall, but I do think this is the future in terms of how we work. And this shows you the implications of how work is going to look, right? So as an example here, it's like, oh, from an inbox standpoint, here's the mix.

These are the prospects reaching out to you, customer, sponsorship, cold pitch, internal, newsletter, right? You can see revenue as well. This might be your open pipeline.

This might be connected to your CRM. Pipeline that's flagged as high risk or idle for seven plus days. So you can quickly get to what is important.

And you can have this handle the classifying instead of all the people on your team handling the classifying, right? Or how about for customer voice? So let's say it's pulling from Granola or Gong, which might be, you know, your call recorders, right?

So you can say, oh, you know, hey, what should your marketing say? These are customer, 24 customer excerpts over here. What customers, what are customers bringing up?

They want to see more business impact, faster onboarding, clear pricing, right? You know, they have themes by deal outcome, you know. And you can see by the deals that you won, the deals that you lost.

And so this can help guide your messaging on your website. This can help guide your sales as well. This can help guide your case studies too.

So you want to be hitting these objections all the time. And so when you see this marketing language over here, you can attack it, right? And all I'm using over here is Codex.

Any harness that you're using can probably put this together for you. You can look at sponsorships. So I get a lot of people reaching out for sponsorships, right?

So, hey, which sponsorships? deserve a reply. So 40 .5 K, these are stated budget, strong audience fit over here.

Who should you actually talk to? And then what it can do is you could have Jeff then route to an agent to then handle the rest for you. So It's really helping you make that, again, it's classifying for you so then you can make the right decisions.

You can have the right agents for the right jobs based on what the classification is, right? From an outbound standpoint, okay, here's what the funnel looks like. By the way, I didn't ask it to do all this stuff, right?

Here's what was delivered. Here's what was replied to. You might be using Apollo.

You might be using like a HeyReach, for example. You might be using like for LinkedIn outreach, you might be using whatever, HubSpot, right? Based on how an email.

how an email sequence is performing for you maybe you need to make some iterations and that way it's classifying it for you quickly it's looking at the data so it's saying hey okay it's time for us to run another test based on how poorly this has been performing or maybe how stale you know the creative is as an example so you can run this for ads as well right where's visibility growing or slipping so this is from like an aeo seo standpoint so you can see oh you know organic clicks are you know 5800 ai citations are this pages losing clicks are you know four but you can see hey organic clicks by topic over here do you want to be do you want to actually be ranking for these topics and then you can probably even cross -reference this with what your customers are saying with this customer voice section over here um because If customers are saying this and then you can see themes by deal outcome, then you probably should be creating more content, search and AI content around the stuff that is winning you deals around that language, right?

One more thing. If you're enjoying this right now, you got to check out the Single Brain Gateway. So this is completely free.

If you just go to singlebrain .com slash gateway, you can get access to our gateway, which allows you to connect all of your marketing, your sales, your revenue connectors, all of your APIs into one area where it's secure and give your agents access to it. it, give your people access to it. So you don't have to worry about provisioning individually.

You don't have to worry about everything being spread out, right? So just go to singlebrain .com slash gateway, and we'll see you on the other side. When you think about, um, if, when I record a video such as this, maybe this video goes for 60 minutes or a podcast goes for 80 minutes.

What are the absolute best moments? Um, based on what I've defined to be good, right?

And so, you know, do we have a very strong hook in the very beginning? And so right here, it's like, okay, here are the candidate moments that you have over here. Here's a suggested shortlist.

Here's a shortlist duration over here. So, you know, the one field every workflow needs, candidate placement in the episode, like all these things over here. These are helpful to look at for the human, but just keep in mind, your job here after this is to determine the workflows that need to be set up from an agentic standpoint.

So I have Grokbot pulled up now, and I'm going to show you another example. Jeff is great for doing AEO and SEO work because you can see on my screen over here, it says that it's looking at the content that should be produced on my website using my autonomous engine optimizer. Okay.

So what are the gates over here? Is the topic already live? So if it's already live, like the hardest number goes over here, that means we probably shouldn't publish it because we don't want more duplicate content.

So guess what? Those of you thinking, oh my God, I don't want to publish a bunch of slop on my website that is like duplicate content. By the way.

Jeff can help eliminate slop. The other thing is it can already spot what is duplicate on your website because it can quickly look at what you already have on your website, right? So that's one piece.

The closest live. So what is the closest living content that's tied to this one over here? It's like, oh, okay, this one.

Maybe you can create similar -ish content, but you don't want it to be too close, right? And so... is this meaningfully better?

And so you can tweak the eval. So that's the definition of success with Jeff. And so again, for sales, it might be, does this fit within our ICP?

Yes or no? And how do you score it based on that? And you can also set levels for it too, like one, two, three, four, five, like how much of it is a fit?

So I already had this AEO SEO bot running where it basically ingests from my content. It looks for the content spikes and it'll give me ideas every day. And I can say yes or no.

But the anxiety that i was having before was like you know what i don't know if i can put this out there because i don't know if it's duplicate i don't know if it's quality um and definition of quality subjective but we have our own guidelines and so you can set those guidelines with jeff and then after that um you can decide you can set more guidelines afterwards in terms of what other agents would be doing and then you can have a jeff tied to those agents as well uh to kind of continue to evaluate right and then you know ultimately we're just getting a lot closer um to doing a lot of the doing a lot of these rote tasks at scale while being able to maintain quality.

And that's what I think the power of this API is. And again, I think anything that you're using, whether you're using Grokbot, you're using Hermes, whether you're using Codex, you just add this Jev into it. that API key into everything that you're doing and you say, hey, I've just added this.

What are the highest leverage things I can add this to right now where it's going to make the biggest impact on the work that I'm doing? And that's a great place to start because it already knows how you work and you don't necessarily need to be technical or learn how to code or anything like that. You just ask it to do it and then you're good to go.

It's not as an LLM and not necessarily to make games or anything like that or play games. It's to get real work done and to drive real revenue for your business. So hope you...

enjoyed this video and we'll catch you in the next one.

The Hook

The bait, then the rug-pull.

Eric Siu opens by calling Jev a game changer for marketing, sales, coding, and recruiting work, then immediately narrows the claim: it doesn't replace the agent harness you already use, it sits in between your existing tools as a classifier.

Frameworks

Named ideas worth stealing.

04:11model

Inbox/Leads to Classify to Route

Inbox / Leads
Classify
Route

The one repeatable pattern applied across every use case in the video: start with a known queue, apply clear criteria plus evidence, then hand off to the right next step.

Steal forany high-volume decision queue: applications, inbound leads, support tickets, sponsorship requests

02:49model

Shortlist with evidence

Applications
Your criteria
Human review

For recruiting or any screening task: score against relevant experience and fit criteria, but treat missing evidence as a flag for human review, not an automatic rejection.

Steal forrecruiting pipelines, lead qualification

07:01model

Check before creating

Content idea
Overlap check
Review

Before publishing new content, check whether the topic is already live, find the closest existing page, and confirm the new piece is meaningfully better.

Steal forSEO/content calendars, avoiding duplicate publishing

CTA Breakdown

How they asked for the click.

VERBAL ASK

07:23product

“go to singlebrain.com slash gateway”

Soft mid-roll pitch woven into the workflow list rather than a hard sell; positions the free gateway as a natural next step right after showing the dashboards.

MENTIONED ON CAMERA

03:39productSingle Brain ↗

07:23productSingle Brain Gateway ↗

FROM THE DESCRIPTION

PRIMARY CTAWhere the creator wants you to go next.

OTHER LINKSAlso linked in the description.