Underrated thought: you don’t need the latest AI model for your app. In fact, defaulting to the newest model can make you less profitable. It is usually the most expensive, and when you are just starting, every cent matters. Better approach: - try simpler models first - test Chinese models - use mini models where possible - reduce unnecessary thinking The thinking should be: what's cheapest model that gets the job done well.

if i had to start from ZERO, here’s how i’d get to $10k mrr with a mobile app 👇 1. choosing the app niche looksmaxxing, fitness, wealth - anything that appeals to core human desires is a MUST 2. designing the app for ads a viral screenshot-able feature like cal ai’s “ai calorie logging” - this will allow me to make ads really easily without making the user feel sold to (as i can just show the screenshot of the feature briefly) 3. getting ad-ready SUPER IMPORTANT: add an MMP to my app, for conversion tracking later when i run ads - either appsflyer, singular… 4. making the ads study competitors on instagram / tiktok - see what hooks their using and translate their formats to my own app - i’ll make 12-30 variants of 1-2 formats ive found 5. running the ads i prefer tiktok ads for mobile apps but meta does fine too - one campaign, one ad set, all my ads in the ad set - $50 a day budget to find a winner; then scale up spend once i’ve got a profitable ad that’s literally it. i wish id known this earlier because i spent thousands of $ on ugc and influencers that didnt work for me the most important part: DESIGN YOUR APP FOR ADS FROM THE START

I built a content machine. It turned me into a one-person media company, has driven tens of millions in pipeline for @tenex_labs, and is allergic to AI-slop. It has also turned all of my employees into content creators. I may opensource the whole thing, but for now, I'm going to share how I built it & how it works. Feel free to copy & paste the steps to Claude/Codex if you want to build your own content machine. Step 1: Map out the process In order to make any of your work AI-native, you need to understand the way in which it's been done historically. This is why business context & domain expertise REALLY matters, even in a post-AI world. Content has been my bread & butter for the last decade, so I started by pulling out an 8.5x11 sheet of printer paper and drawing the traditional process. 1) Look for inspiration 2) Pick a 10x content idea 3) Research the idea 4) Brain dump all of my thoughts about the idea 5) Decide the post format I want to create 6) Create a draft of the post 7) Edit the post 8) Create derivative versions of the post 9) Go live 10) Track performance Step 2: Where am I needed vs. not needed? I am needed for the first & final mile: First mile: picking the idea/direction & providing all of the necessary context Final mile: going through the final draft with a fine tooth comb & giving final sign-off. AI can handle the rest: Looking for inspiration, researching the idea, pulling my thoughts out, writing the post, doing a first edit, creating derivative content, and tracking performance. Step 3: Build the Content Machine The machine is one pipeline, run end-to-end or step-by-step. It is a directory of skills that mimic the steps in the content process that I've delegated. 1) The Oracle [AI] Mines my Slack, Notion, call transcripts and Gmail for spikes, moments I naturally said something worth expanding, while the Internet Reader curates an external feed of X accounts & websites I've selected. Qualifying ideas (≥6/10) are written to The Vault (a notion database of content ideas). 2) Select the idea from The Vault [Human] 3) The Researcher [AI] Before any interview, build a sourced research-report.md: TL;DR, key facts with links, current developments, what's already been said, contrarian angles, and open questions for the interview. Claims are adversarially checked; fact is separated from opinion. 4) Interview Panel [AI Human] Six world-class interviewers (Joe Rogan, Howard Stern, Michael Barbaro, etc) ask 12–15 questions, one at a time, each pushing a different dimension...and never satisfied with vague answers. Won't advance without 2–3 specific stories, real numbers, and emotional specificity. 5) Production [AI] The interview becomes a raw .md file: transcript, key stories, core insights, quotable moments, emotional anchor, surprising reveals, and the "so what." This raw file is sacred: my exact words, never paraphrased away. 6) Refinement [AI Human] I tell the machine what content type I want to create. It reads my custom style guide past feedback lessons content-type spec, then drafts in my voice...pulling real stories and quotes from the raw file. The #1 rule: write like you're texting a friend. Supports long posts, LinkedIn, X threads, and more. 7) Writer's Council [AI] Six expert reviewers (Shaan Puri, Morgan Housel, David Perell, etc) score the draft through their own lens, splitting fixes into editorial (the machine can rewrite) and information gaps (only the creator can answer...these route back to the interview panel). 8) Revision Loop [AI] Iterate until council scores 9/10. 9) Repurposing Engine [AI] One anchor → 10 natively-formatted derivatives, each re-hooked for its platform and each held to the same full Council → revision bar of 9/10. This is how two people produce like a hundred. 10) Final revision [Human] 11) Learning Loop [AI] After approval, the machine compares first draft vs. final, extracts confirmed lessons, and saves them to that creator's content-lessons.md. Every future first draft starts smarter. Lessons override the style guide when they conflict. Feel free to steal the machine & ask me any questions about how it works!

Making my SaaS AI-first: ✅ Open all API endpoints ✅ llms.txt & markdown docs ✅ CLI (new 🌟) ⬜️ MCP ⬜️ Generative UI ⬜️ Onboarding w/ paywall

Does posting & scheduling through the API kill reach? Short answer from my tests: yes. I’ve tried a few different ways of posting tiktok content. 1. Manual upload 2. Posting through the API (the worst) 3. Today: transferring to drafts API posting consistently performed worse for me. Testing drafts now.