Jev’s founder, Diogo Almeida, gives a FULL masterclass on the thinking behind Jev. bookmark this if you want to put it to work with an AI employee. pair Jev with Viktor and build a workflow that > takes your goal > does the work > evaluates it against your criteria > and sends fixable issues back for revision watch the talk, then follow the full Jev + Viktor setup in the article below:

you should spend some time connecting jev to an AI employee like viktor... the goal is to get back work that needs fewer corrections from you. here's the setup: -> give viktor a goal and the source material. he figures out the steps, researches, uses your connected tools and produces the draft -> jev handles the evaluation. give it a specific requirement, the relevant evidence and a section of the draft. it returns a structured judgment with probabilities and confidence -> work that meets your criteria comes back for your review. fixable issues go back to viktor with the requirement he needs to address, then through the checks again -> uncertain judgments and missing evidence come to you. those are the decisions worth your attention instead of another round of “please check everything” -> apply it to your content... does the opening promise something useful, do the five posts actually say different things, did the draft invent a claim about something you tested -> if the source only supports three useful posts, the system should flag that instead of inventing two more. cap the revision rounds so it knows when to stop and ask -> save the corrections you approve in viktor’s working guide. the next assignment starts with those instructions already in place viktor creates. jev evaluates. your feedback improves the next assignment. the full setup is in the article below.

Jev’s founder, Diogo Almeida, gives a FULL masterclass on the thinking behind Jev. bookmark this if you want to put it to work with an AI employee. pair Jev with Viktor and build a workflow that > takes your goal > does the work > evaluates it against your criteria > and sends fixable issues back for revision watch the talk, then follow the full Jev + Viktor setup in the article below:

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McKinsey wrote a whole report about the AI-native marketing team. most good research gets consumed once and then disappears into the content void. for growth marketers, that feels like a waste. the same idea can travel a lot further when you rethink the format, not just the message. so I gave it to @framer's Agent and asked it to make the report interactive. it turned the research into a visual field guide for what an agentic marketing org could actually look like all built + iterated directly inside Framer feels like a much more interesting way to consume research than another 20-page PDF

you should spend an afternoon building a second brain with jev... here's how i'd set it up: > create one inbox for saved posts, articles, meeting notes and ideas... give your agent access to it > give jev your active projects and interests so it can sort incoming material into categories that mean something to you > have your agent find similar notes, then use jev to flag duplicates and spot which ones add something new > save everything with its original link and date so you can always check where it came from > whenever you start a task, have your agent search the archive and let jev score which notes are relevant > feed those into your writing agent before it starts working... your customer conversations, examples and previous decisions are already there writing a proposal? bring back what the customer said on the call and the case study that answers their objection. working on a post? pull up the idea you saved three weeks ago alongside your own notes on the topic. start with one project and the material you've already collected for it. ask your coding agent to build that workflow, then check what it brings back on your next real task.

McKinsey wrote a whole report about the AI-native marketing team. most good research gets consumed once and then disappears into the content void. for growth marketers, that feels like a waste. the same idea can travel a lot further when you rethink the format, not just the message. so I gave it to @framer's Agent and asked it to make the report interactive. it turned the research into a visual field guide for what an agentic marketing org could actually look like all built + iterated directly inside Framer feels like a much more interesting way to consume research than another 20-page PDF

recommended reading this week: @alexeheath / @finkd - Mark Zuckerberg on Muse, Meta’s biggest AI bet yet: why Meta is giving away personal agents, how Zuckerberg plans to make money from them, and his bet on privacy. interview + transcript. @thedankoe - You need to be delusional if you want to succeed: on choosing goals you actually want, how other people’s expectations limit your ambition, and being busy without getting anywhere. @OpenAI - Reimagining advertising with AI: sponsored agents, Shopify integration and managing campaigns inside ChatGPT. useful for understanding what businesses can start testing as conversational ads develop. @joshelman - Product Management is Still All About Telling Stories: how to explain who your product is for and why they should care. especially relevant when building something is getting easier than deciding what’s worth building. @SemiAnalysis_ - Everyone Says Datacenter Moratoriums Are Killing the US Buildout. We Mapped All 300 of Them: which restrictions actually delay construction, and why counting bans gives you a very different picture from checking individual projects. @benthompson - Salesforce AI Force, Agents as UI, The Race to Headless: why Salesforce is giving up its interface as a competitive advantage, and what that means for software businesses. subscriber-only. essential reading: the Zuckerberg interview + Dan Koe + Product Management is Still All About Telling Stories

you should spend an afternoon building a second brain with jev... here's how i'd set it up: > create one inbox for saved posts, articles, meeting notes and ideas... give your agent access to it > give jev your active projects and interests so it can sort incoming material into categories that mean something to you > have your agent find similar notes, then use jev to flag duplicates and spot which ones add something new > save everything with its original link and date so you can always check where it came from > whenever you start a task, have your agent search the archive and let jev score which notes are relevant > feed those into your writing agent before it starts working... your customer conversations, examples and previous decisions are already there writing a proposal? bring back what the customer said on the call and the case study that answers their objection. working on a post? pull up the idea you saved three weeks ago alongside your own notes on the topic. start with one project and the material you've already collected for it. ask your coding agent to build that workflow, then check what it brings back on your next real task.

recommended reading this week: @alexeheath / @finkd - Mark Zuckerberg on Muse, Meta’s biggest AI bet yet: why Meta is giving away personal agents, how Zuckerberg plans to make money from them, and his bet on privacy. interview + transcript. @thedankoe - You need to be delusional if you want to succeed: on choosing goals you actually want, how other people’s expectations limit your ambition, and being busy without getting anywhere. @OpenAI - Reimagining advertising with AI: sponsored agents, Shopify integration and managing campaigns inside ChatGPT. useful for understanding what businesses can start testing as conversational ads develop. @joshelman - Product Management is Still All About Telling Stories: how to explain who your product is for and why they should care. especially relevant when building something is getting easier than deciding what’s worth building. @SemiAnalysis_ - Everyone Says Datacenter Moratoriums Are Killing the US Buildout. We Mapped All 300 of Them: which restrictions actually delay construction, and why counting bans gives you a very different picture from checking individual projects. @benthompson - Salesforce AI Force, Agents as UI, The Race to Headless: why Salesforce is giving up its interface as a competitive advantage, and what that means for software businesses. subscriber-only. essential reading: the Zuckerberg interview + Dan Koe + Product Management is Still All About Telling Stories

Sam Altman has a pretty wild prediction: the first legitimate one-person billion-dollar company may already exist. content is one of the clearest places to see how that kind of leverage gets built. one creator can now direct the roles that previously required an entire media team: research, strategy, writing, editing, distribution, and performance analysis. i built those roles as six Hermes bots sharing one Obsidian content brain. every campaign moves from the first signal to the finished article, platform distribution, editorial review, and the lessons that improve the next run. one person remains the publisher. the system supplies the operating capacity of the company around them. here is the complete build:

Mark Zuckerberg just described the foundation for an AI content team that actually thinks like its creator. creators will train AI systems on their content, values, and business objectives. that accumulated context becomes the shared brain behind the entire operation. give six Hermes bots access to it and every specialist inherits the same understanding of your audience, offers, proof, voice, and previous performance. the researcher knows your evidence bar. the strategist knows which outcomes matter. the writer knows how you develop an argument. the distribution bot knows how each platform should enter the idea. every campaign begins with years of accumulated judgment instead of another empty prompt. i included the complete creator-brain structure and six-bot role map below: