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Operator of an 8-figure family ecommerce brand

349 following7k followers

The Entrepreneur

DTC Prophet is the hands-on operator behind an 8-figure family ecommerce brand who turns marketing chaos into repeatable systems. He tweets high-utility playbooks, tech stacks, and automation blueprints with the blunt confidence of someone who’s earned the numbers. Expect revenue case studies, ad teardown lists, and a suspiciously productive Mac mini.

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Calls himself a 'Prophet' but mostly predicts spreadsheets, he won’t part the seas, he’ll just automate 30 ad creatives before brunch and tell you which one will actually sell.

Built and operates an 8-figure family ecommerce brand after scaling from early years of ~$30k to $1.5M/year, and built an automated ad-generation pipeline that yields 30, 40 launch-ready creatives daily.

To systematize direct-to-consumer growth so independent brands can scale predictably, by turning creative work into automated, repeatable machines and sharing the practical playbooks that actually move the needle.

Data over ego, systems over heroics, and craft over shortcuts. He distrusts shallow AI outputs that look paid-for, believes good creative comes from quality inputs and iteration, and values family-first business longevity rather than short-term hype.

Tactical operator who builds systems: strong at automation, ad creative scale, tech stack optimization, and curating competitive creative examples. High signal-to-noise content, practical, copyable steps rather than vague motivation.

Can be blunt to the point of polarizing (hot takes on AI and platforms), sometimes favors automation over nuance (risking cookie-cutter creatives), and hasn’t fully converted tactical authority into a larger personal brand community on X.

Grow on X by turning tactical wins into storytelling pipelines: 1) Post regular teardown threads (one brand/ad per thread) with before/after metrics and creative calls-to-action. 2) Share short behind-the-scenes clips of the automation workflow (screen recordings of the Mac mini run, naming conventions, etc.). 3) Create a pinned 'DTC Starter Pack' tweet linking your tech-stack + templates and collect emails for a newsletter. 4) Run weekly micro-series (e.g., 'Ad Teardown Tuesday') so followers know when to return. 5) Use Spaces or AMAs to convert followers into loyal community members and interview other DTC operators. 6) Boost 1, 2 top-performing tweets with a small ad spend to widen reach, then turn replies into tweet threads and resources. 7) Publish downloadable templates/cheat sheets (ad prompt templates, brand DNA checklist) to convert engagement into subscribers.

Fun fact: he runs an ad-generation automation that cranks out 30+ high-quality static ads overnight on a Mac mini. Follower count: 7,756; Tweets: ~2,981. He publicly documented a climb from $30k/year to $1.5M/year and currently runs an 8-figure family ecommerce operation.

Top tweets of DTC Prophet

You can pretty easily automate this with Nano Banana 2 right in Claude Code to generate 30-40 very high quality static ad creatives daily Almost every single creative is ready to launch into our account and we can easily 2-3x the volume of it I previously had it as a Claude Project but realized it could be fully automated in code given how good the output ads are To build it in Claude Code, you first need to create or gather all of these assets: - Brand overview/DNA doc - List of ~50 template ad generate prompts - Folder of customer reviews, PPS responses, persona docs, marketing materials, etc - Product source images (product renders, PDP images) - 5-6 existing winning static ads - 5-6 existing losing static ads The output ads you get are a function of the inputs you give it, you should spend a lot of time building these and I highly suggest not defaulting to AI to build them, especially the brand DNA doc Give it bad info and you’ll get bad ads in return For the original ad prompt template sheet, I found it useful to have them be 30 completely different styles and designs (offer ad, big headline, UGC/native, bullet point benefits, comparisons, etc...) Then it’s very simply to build actually. The flow is: 1. Read all source docs 2. Read existing winning/losing ads 3. Load product reference images 4. Load ad gen prompt template 5. Send to gemini text model 6. Gemini builds 30-40 new ad prompts 7. Run through nano banana 2 with reference images 8. Save all assets Below is an image of the step by step automation in Claude code, you can probably just ask it to build this for you The only external thing you’ll need to get is a Gemini API key for the text and image models Then the script will run whenever you schedule it, create 30-40 unique ad generation prompts for you, generate the static ads through nano banana and then save them wherever you want I have everything running on a mac mini so it runs before I wake up and I have 30+ new ads to look at first thing in the AM I also built in our ad naming conventions into the script so that it automatically names our ads in the file name, so I can load them into Adnova and launch them very fast (2-3 mins) The only non-automated step in this is getting the image files from local device into the platform media library or ad launcher tool but that takes like 60 seconds per batch It costs ~$1/day and takes 10-15 minutes

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Most engaged tweets of DTC Prophet

I am starting to think about creative testing on Meta a bit differently now I’m not sure how much sense it really makes to be testing individual ads in total isolation anymore It’s the entire portfolio of your ads that performs and not 1 individual unit out of a 1,000+ units (with 50k+ sequence variations) If Meta’s models and systems (gem, andromeda, etc) are all built against sequencing and creating thousands of unique funnels for unique users with hundreds of different problems/reasons to buy, then it doesn’t really make sense to analyze and test individual creatives I’m finding it pretty hard to analyze creatives in isolation with intellectual honesty and finding any sort of truth about what the individual creative is really doing How do you know where it fits in customer sequences? If it gets a 50% higher CPA than your baseline, how do you know the impressions it drove didn’t cause your offer ad to get a 15% lower CPA than baseline at more spend than before? How do you really know who it’s getting served to? How are you supposed to honestly assess when to turn an ad off if you have no idea the value of the impressions it drives if you aren’t aware of where it fits into your average sequence? Etc… Pretending to know these answers is very misleading and can lead to bad decisions, both in the account and creative strategy/production I’m starting to build campaigns/ad sets with this in mind, and thinking about creative testing from perspective of adding to sequencing versus trying to test individual things Instead of trying to find single creatives that massively outperform baseline, I want to be building ads that fill in holes in the portfolio that make the entire thing better And only judging CPA at campaign or ad set level now (wherever budget is set)

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We are about to launch hundreds (soon thousands) of app/tool comparisons on 1800d2c.com. While they'll start out scrappy, we think they'll become a main feature for our users as our database, taxonomy and functionality improves. We are an objective source of information, but if a SaaS has shady policies, absurd/hidden pricing or anything of the like, we will certainly bring it to light here! Also a big programmatic seo play for the site.

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