Introducing the Content Machine! This was the first time I walked through the mechanics of our anti-slop content system & how we drive 10,000,000+ impressions at @tenex_labs with a marketing team of...2. Thanks to @clairevo for having me on her show to share. Full-writeup & interview below... What is the Content Machine? A directory of daisy-chained skills that turn what you already say into publish-ready content. This is the key way to avoid turning into a slop cannon. It mines the places you already talk (Slack, Notion, Gmail, Linear, GitHub) plus what the internet is saying, finds the ideas worth writing, interviews you to extract the story, drafts it, and edits it to a 9/10 bar before you post. What are the principles of this system? 1) It is not a "write me a post" prompt. The core belief baked into it: the raw material must come from you. 2) The machine never invents your voice and never fabricates your insight. It does the research, the structure, and the editing, so your time goes only to the part only you can do. It focuses the human on the first & final mile of the content process. How is it structured? A two-layer split between the process & the person - The process layer is generic and shared: the pipeline, the content-type specs, the copywriting references, the onboarding flow. That is what lives in git and what gets shipped to other people. - The personal layer is yours and never leaves your machine: content-machine.config.md, creators// (profile, style guide, lessons), projects/, published/, oracle-reports/. All gitignored, and the desktop build script refuses to package any of it. What is the 10-step pipeline? 1) Creator Select. Multi-creator by design. It figures out who this run is for and loads their profile, style guide, and content lessons. Everything downstream is scoped to that person. - Onboarding (first run only). Scaffolds the workspace, auto-creates your Notion Vault, connects your sources, and builds your voice guide one of three ways: import a guide you already have, feed it writing samples, or sit for a short voice interview. 2) The Oracle. Two idea engines running in parallel: - Oracle scans what you wrote in the last 7 days across Slack, Notion, Gmail, Linear, and Git, hunting for "spikes," moments where you naturally said something worth expanding. - The Internet Reader scans what the world is saying, pulled only from the source list in your profile (handles, labs, outlets, keyword watchlist), plus a social sweep across Reddit, X, YouTube, Hacker News, and more (thanks /last30days & @mvanhorn). - Every idea is scored 0 to 10 (POV strength 25%, story potential 25%, emotional intensity 20%, lesson/framework 20%, depth 10%). Everything qualifying gets written to The Vault, a Notion database that is the durable idea bank. 2.5) Research. Before you get interviewed, a research agent builds a sourced brief: - key facts with links - current developments - what has already been said in-market - contrarian angles - open questions only you can answer. 3) Interview Panel. Six interviewer personas (Ferriss, Rogan, Larry King, Stern, Barbaro, Barbara Walters) ask you one question at a time, each chasing a different dimension: tactics, story, core truth, the hidden thing, clarity, emotional depth. It pushes back on vague answers and will not advance until it has 2 to 3 specific stories with real details. 4) Production. Your interview becomes a raw markdown file: stories, core insights, quotable moments, the emotional anchor, surprising reveals, the "so what." Your exact words are preserved. This file is the source of truth for everything that follows. 5) Refinement. Now it drafts, and only now. It must read your style guide, your content lessons, and the spec for the chosen format (LinkedIn post, X thread, long post, playbook, podcast promo, reaction post, article, and so on). 6) Writer's Council. Six reviewers score the draft: Morgan Housel (will this matter in 10 years), Tim Urban (is it confusing), Shaan Puri (would I stop scrolling, plus three alternate hooks), Greg Isenberg (what can someone steal), David Perell (is it personal, observational, playful), and a Slop Detector hunting AI tells. Each gives what's working, what needs work, a fix, and a score. 7) Revision Loop. Under 9/10 goes back around. The smart part: fixes get sorted into editorial (the machine rewrites it itself) and information gaps (only you have the answer), and information gaps route back to the Interview Panel with targeted questions rather than letting the machine make something up. Max 3 editorial cycles. At 9/10 the piece becomes the anchor. 8) Repurposing Engine. One anchor fans out into 10+ derivatives: X article, LinkedIn article, short X posts, short LinkedIn posts, a playbook if there is a framework in it. Each one is written native to its platform with a fresh hook, not cross-posted, and each runs the full council and revision loop to 9/10 on its own. This is the multiplier. 9) Distribution (optional, off by default). UTM tagging, a scheduled publishing queue, CRM capture of every touchpoint, attribution reporting back to pipeline, and marking the Vault row as Published. 10) The Learning Loop, always running. After you approve a piece it diffs your first draft against the final, extracts the pattern, and asks you to confirm it. Confirmed lessons go into content-lessons.md and override the style guide. Once a lesson proves out across a few projects it graduates into the style guide itself. Your first drafts get better over time instead of you re-explaining preferences. P.S. i'm thinking about opensourcing this. should i do it?

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Interesting call on the Google - $RDDT partnership with a former lead at Google, as well as why AI talent is leaving $GOOGL The read is that Reddit's negotiating position has deteriorated materially His framing is that the original agreement was a function of where large language models sat at that specific moment. Bard was becoming Gemini, hallucination rates were high, and the model had no grounding in current events. Training produces a model that is effectively six months stale by the time it ships. Reddit solved a narrow, acute problem: real-time human commentary at breadth and depth. The competitive set for that data was thin. X, Meta's properties and Threads are walled gardens aligned with rival frontier labs and were never gettable. Reddit was the one large corpus of unfiltered human language actually available for purchase, which is why both Google and OpenAI ended up there. The price reflects how little this mattered to Google's P&L. Roughly $60m: "For Google, $60 million to buy specific data is not a lot of money." His view is that the cash was the least interesting component. The valuable consideration was prompt data flowing back—what the user asked, and what they asked that led to a click through to Reddit. Both sides of the intent coin. He connects this to the trajectory of Reddit's advertising business, which has scaled well beyond what a new sales team and new infrastructure alone would explain. On the widely discussed point that AI Overviews traffic converts poorly, he broadly accepts it but argues the second-order effect dominates. Reddit held primacy in the citation slot, so volume was high even if quality was low, and the learning from that volume compounded. Google has since connected its search index and corpora more directly to the model layer, so the original grounding gap has largely closed. YouTube citation share has overtaken Reddit. Google News, Merchant Center and Places cover most of what Reddit was a shortcut to. His read on Reddit publicly signaling it might walk is that this is negotiation conducted through the press, and that it implies Google came back with worse terms—likely stripping preferences and the prompt data return rather than simply cutting the number. Google's standard posture on data rights is full and unrestricted use, and carve-outs on usage are not how Google contracts. "They probably don't need it. They probably want it." He expects a deal—the relationship is warm and mutually beneficial—but on terms that reset Reddit's expectations. Money is the secondary variable. The variable that matters to $RDDT holders is whether prompt-level signal keeps flowing. Asked whether Google would simply take the data if talks collapse, he says no on cultural grounds, that it is not in the corporate DNA to do that after the fact. More interesting is his description of how frontier labs behave when sued over training data: they do not settle, because a settlement establishes a price and invites every other rights holder. They litigate, spend, and drag it to a quiet resolution specifically to avoid setting precedent. Independent of the Google relationship, he identifies the harder issue: Reddit sits in the middle of a considered purchase journey with nothing to sell at the end of it. A user researches a bike on Reddit and buys it somewhere else. Two steps, and the second one is where the margin lives. The old funnel involved ten websites and fifty data points before purchase. That discovery phase is now collapsing into the chat interface, and the losers are the intermediaries that monetized the journey rather than the transaction. This is a Gemini problem, a Claude problem and an OpenAI problem simultaneously, not a Google-specific one. Search advertising worked because the system was deterministic—a tree you navigate from trunk to branch to leaf, ending in a transaction. Token predictors give wildly different answers to marginally different prompts, and the labs do not fully understand their own models' behavior post-training. Tuning that for advertiser ROAS is closer to dark arts than to keyword auction mechanics. The deeper constraint is grounding. Merchant Center is the largest product data repository in the world, Places the largest inventory of shops and locations, and every flight and hotel has been tuned within an inch of its life because advertisers were trained over two decades to feed that system. OpenAI has none of it. He is measured on the disruption question rather than dismissive. He cites Walmart attributing roughly 20% of traffic to OpenAI as evidence the relay is real, and he sees a credible alternative path: merchants exposing their own data through open interfaces that models come and fetch, rather than piping it into Merchant Center. That inverts the current architecture, but it needs an ROI case to bootstrap and there is a chicken-and-egg problem. His conclusion is share erosion and ad revenue siphoning, not collapse. On the suggestion that OpenAI should just acquire an ad tech engine, he is dismissive for the right reason: buying keyword-era infrastructure is buying an internal combustion engine in an electric vehicle world. A paragraph-long voice prompt carries vastly more intent than a three-word query, and the extraction method has to be built for that, not retrofitted. "Google has innovator's dilemma on steroids." A USD 250bn high-margin advertising business, powered by data that advertisers were trained to supply, cannot be hard-switched to Gemini. The chosen path is to infiltrate Search with AI and tolerate a messy middle until ROAS re-stabilizes on the new medium. He is candid that the current state is worse than Search at its peak, and he references the reported history of deliberately degrading search ad quality to increase monetization as evidence that the profit motive is explicit. On Google execution speed - Every objective must ladder from the most junior contributor up through director and VP. Search sits at the top of the tree, Android just behind, peripheral products have no influence. Strategy gets negotiated between silos, which is slow. "innovation is by definition outside of that data structure." Work outside the ladder is unsanctioned, and unsanctioned work costs you promotions and raises. He extends this to DeepMind, which he believes is now materially less isolated than it was and is being pulled into commercial delivery, and offers that as the explanation for researcher attrition to Anthropic and OpenAI. Not compensation—loss of research autonomy.

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