Peter Yang
Batch-generating content with AI creates a soulless commodity, requiring creators to deploy agents for deep research and packaging while anchoring credibility in real-world product engineering.
AI-assisted summary of How I Run My 1.5M+ Follower Content Business With Codex | Riley Brown — original source. Check the original for full context.
Source published:
Key ideas
- Batch-producing content with AI is a trap that degrades audience trust; creators should instead treat agents as modular skills to accelerate deep research and build a defensible quality moat. (0:00)
- Using fast API scrapers like Supadata alongside parallel sub-agents allows creators to analyze an entire competitor video archive and reverse-engineer structural hooks in under 30 seconds. (7:14)
- Packaging should follow organic content rather than dictate it: recording unscripted, curiosity-driven insights first and automating hook and thumbnail packaging afterward protects creative energy. (15:09)
- Pairing voice dictation via Wispr Flow with automated Excalidraw skills and AI-native design tools like Paper accelerates storyboard creation and viral thumbnail face-swapping. (23:57)
- Active product development provides the technical substance required to produce credible AI tutorials, preventing creators from devolving into shallow news aggregators. (38:16)
Detailed notes
## Background Published by Peter Yang on YouTube on August 16, 2026, this video features AI creator Riley Brown breaking down how he runs his 1.5M+ follower content business using Codex. According to the description, Brown showcases his end-to-end production stack, demonstrating workflows for drafting viral video hooks, building animations, drawing diagrams, and designing thumbnails.
## TL;DR Riley Brown explains his philosophy of prioritizing sustained content quality over mass AI batching, demonstrating how he chains modular desktop skills within Codex to automate research, motion graphics, and thumbnail creation. He reveals his tactical toolchain—pairing Wispr Flow dictation with Excalidraw diagrams, using SerpAPI and Remotion for programmatic B-roll, and applying his BRENS framework to reverse-engineer high-retention hooks. Brown also showcases his multi-agent platform Chorus for collaborative Slack workflows and argues that continuing to build real software products is the ultimate defense against becoming a generic content aggregator.
## Atmosphere & style The interview feels like an intimate, over-the-shoulder screen-share between two technical peers, marked by an authentic workshop energy rather than rehearsed presentation. Riley speaks with casual, rapid-fire transparency, freely sharing live desktop tools, laughing about cosmetic thumbnail prompts, and openly detailing agent limitations. Peter provides a grounded, curious sounding board, fostering a conversational dynamic that balances hands-on technical tinkering with pragmatic reflections on creator burnout and craftsmanship.
## Bulletin - [00:00](https://www.youtube.com/watch?v=N34zz1-RSGw&t=0s) Batch-producing content with AI is a trap that degrades audience trust; creators should instead treat agents as modular skills to accelerate deep research and build a defensible quality moat. - [07:14](https://www.youtube.com/watch?v=N34zz1-RSGw&t=434s) Using fast API scrapers like Supadata alongside parallel sub-agents allows creators to analyze an entire competitor video archive and reverse-engineer structural hooks in under 30 seconds. - [15:09](https://www.youtube.com/watch?v=N34zz1-RSGw&t=909s) Packaging should follow organic content rather than dictate it: recording unscripted, curiosity-driven insights first and automating hook and thumbnail packaging afterward protects creative energy. - [23:57](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1437s) Pairing voice dictation via Wispr Flow with automated Excalidraw skills and AI-native design tools like Paper accelerates storyboard creation and viral thumbnail face-swapping. - [38:16](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2296s) Active product development provides the technical substance required to produce credible AI tutorials, preventing creators from devolving into shallow news aggregators.
## Key takeaways - [00:00](https://www.youtube.com/watch?v=N34zz1-RSGw&t=0s) Mass-batching content degrades creative soul and audience trust; the true moat for creators in the AI era is compounding quality over time. - [00:28](https://www.youtube.com/watch?v=N34zz1-RSGw&t=28s) Long-form video editing remains the single stage Riley keeps in human hands because automated AI video editing tools are not yet mature enough. - [03:16](https://www.youtube.com/watch?v=N34zz1-RSGw&t=196s) Local desktop agent environments like Codex outperform browser chatbots by directly accessing the native file system to produce automated B-roll via Remotion and SerpAPI. - [07:14](https://www.youtube.com/watch?v=N34zz1-RSGw&t=434s) Extracting video transcripts in real time via the Supadata API lets concurrent sub-agents map high-performing narrative structures onto new topics in seconds. - [07:14](https://www.youtube.com/watch?v=N34zz1-RSGw&t=434s) Rigid teleprompter scripts fail to build audience trust, making conversational expertise essential after locking in the opening 30-second retention hook. - [11:06](https://www.youtube.com/watch?v=N34zz1-RSGw&t=666s) Claude reliably handles transcript processing and content extraction, whereas GPT-based models frequently trigger copyright guardrails that substitute summaries for raw transcripts. - [13:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=804s) The BRENS framework (Big, Relatable, Easy, New, Safe) structures long-form hooks to immediately assure viewers that investing 30+ minutes will be worth their time. - [15:09](https://www.youtube.com/watch?v=N34zz1-RSGw&t=909s) Dictating packaging and thumbnails before filming forces creators into trend-chasing; shooting authentic tangents first and automating packaging afterward preserves originality. - [17:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1044s) Any repetitive task in Codex can be converted into a reusable skill, creating an integrated desktop workspace spanning Excalidraw, Notion, and Typefully. - [23:57](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1437s) Rambling stream-of-consciousness thoughts into Wispr Flow enables Codex to generate nine-slide Excalidraw storyboards that dictate roughly 80% of a video's narrative arc. - [25:41](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1541s) Programmatically controlling AI design tool Paper allows creators to swap their likeness onto proven thumbnail compositions rather than generating inconsistent images from scratch. - [28:49](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1729s) Visual prompting—selecting reference frames directly on an AI design canvas—replaces complex prompt engineering and manual text style guides. - [31:15](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1875s) Skills should be refined through conversational feedback and fresh-session testing rather than manually editing code or micro-optimizing fragile syntax. - [33:55](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2035s) Team-based multi-agent architectures are moving toward specialized domain colleagues within Slack rather than relying on a single monolithic system. - [38:16](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2296s) Building real-world software products provides the hands-on engineering experience needed to deliver educational content with genuine technical substance.
## Detailed walkthrough ### [00:00](https://www.youtube.com/watch?v=N34zz1-RSGw&t=0s) The Quality Moat vs. Batching Content
When creators first discover AI automation tools, their immediate instinct is almost always to scale production through sheer volume: churning out dozens of scripts, batch-generating posts, and flooding platforms. Riley Brown rejects this approach outright. While he demonstrates how easily an AI can scrape a hundred viral thumbnails and adapt them to his own brand, he emphasizes that treating AI as a factory for mass-produced assets is a trap. "What I want is better, because I think the moat is quality over a long period of time versus batching. Batching will make it soulless over time."
Instead of using AI to replace taste, Riley approaches agentic workflows as a composable library of distinct "skills." The real leverage comes when a creator learns to chain these skills together, combining modular capabilities—like competitive research, style matching, and graphic generation—to produce a single piece of high-craft content that cuts through the noise.
---
[00:28](https://www.youtube.com/watch?v=N34zz1-RSGw&t=28s) Inside Riley Brown's 1.5M+ Follower Content Engine
Peter Yang introduces Riley Brown, an AI educator who has grown an audience of nearly 1.7 million followers across platforms over the past three years. Beyond content, Riley’s ecosystem includes building software products like Vibecode and Chorus, as well as enterprise consulting to help businesses implement autonomous agents. Across all of this, content creation remains his primary craft, and AI agents assist with virtually every phase of his pipeline: ideation, competitive research, hook development, and thumbnail design.
The single exception in Riley's production line is long-form video editing. He still relies on human video editors because AI video editing simply "isn't there yet." His distribution strategy treats YouTube as an intellectual anchor, but he refuses to rely on lazy cross-posting. While he originally started on Instagram and TikTok, he prefers long-form storytelling and finds that in-depth videos perform just as strongly natively on X (Twitter) as they do on YouTube. Rather than relying heavily on automated clipping tools—a workflow he is only recently beginning to explore—he prefers crafting native formats tailored to each channel.
---
[03:16](https://www.youtube.com/watch?v=N34zz1-RSGw&t=196s) Local Control & Automated B-Roll with Remotion and SerpAPI
Opening his workspace, Riley highlights a fundamental distinction between browser-based AI chatbots and desktop agent environments like Codex or Claude Code. Traditional tools like ChatGPT run entirely in the cloud, requiring users to manually upload files into remote environments. Codex runs locally, interacting directly with the user’s operating system and native file system, and includes built-in computer-use tools.
```mermaid flowchart TD accTitle: Automated B-Roll and Motion Graphics Workflow accDescr: Shows how video transcripts are converted into animated B-roll using Codex skills, SerpAPI, and Remotion. subgraph S1["Asset Discovery"] A["Video Transcript"] --> B["Internet Image Puller Skill"] B --> C["SerpAPI Google Images"] C --> D["Relevant Logos & Assets"] end subgraph S2["Motion Generation"] D --> E["@remotion Plugin"] F["Remotion Best Practices Skill<br>(Brand Colors & Aesthetics)"] --> E E --> G["Custom Motion Graphic / B-Roll"] end ```
To create the polished animations that open his videos, Riley avoids cumbersome after-effects workflows by combining two specific Codex skills: - **@remotion Plugin**: A built-in code-to-video plugin that generates animated graphic overlays and dynamic flowcharts directly inside the environment. - **Remotion Best Practices**: A personalized brand skill that injects Riley's specific design aesthetic, layout rules, and signature color palette into the generated code so every animation matches his visual identity. - **Internet Image Puller**: A custom skill integrated with SerpAPI's Google Images endpoint. It analyzes Riley's draft script, determines which software logos or visual assets are needed for B-roll, fetches the correct high-res image files from the web, and feeds them directly into the Remotion composition.
By chaining these skills together in a single prompt, the agent autonomously inspects the script, pulls the required partner or platform logos, and exports a branded motion graphic ready to drop straight into the timeline.
---
[06:20](https://www.youtube.com/watch?v=N34zz1-RSGw&t=380s) Sponsor: Rapid Prototyping with Google AI Studio
Peter Yang highlights Google AI Studio as the fastest way to take an idea from prompt to a live prototype. To demonstrate, Peter describes using AI Studio to generate three distinct landing page concepts for a bakery called "Corgi Confections," using visuals produced by Google's built-in image models. Each concept explored a different marketing angle: one centered on artisanal fresh bread, another highlighted specialty cakes, and a third focused on the bakery's founder narrative.
With one click, AI Studio allows creators and product managers to publish a live, shareable URL to gather instant feedback from users and stakeholders, illustrating why it served as Peter's go-to sandbox during his career in product management.
---
[07:14](https://www.youtube.com/watch?v=N34zz1-RSGw&t=434s) Reverse-Engineering Viral Hooks with Supadata and Sub-Agents
Turning to ideation, Riley reveals his favorite research tool: a custom "YouTube Researcher" skill powered by the Supadata API. While standard open-source tools like `yt-dlp` require downloading massive video files locally just to extract an audio track and transcript, Supadata performs an instant API call that pulls a complete transcript in roughly one second.
Codex can also orchestrate concurrent sub-agents. By instructing the model to parallelize the task, Codex will spin up six sub-agents simultaneously, allowing a creator to scrape and synthesize the entire archive of a competitor’s channel in under 30 seconds.
Riley's philosophy on scripting centers on an asymmetry: the first 30 seconds demand obsessive structural discipline, while the rest of the video should remain loose and conversational: - **The "Hook Outline" Skill**: Riley feeds high-performing videos—such as an upload by Alex Hormozi—into his system to extract the structural mechanics of their opening hook and core narrative spine. The agent maps his own topic ideas directly onto that proven psychological blueprint. - **Why Full Scripts Fail in the AI Era**: Riley argues that fully scripted, teleprompter-style videos will lose audience trust as AI tools proliferate. Instead, the enduring moat for creators is showing authentic human passion and deep domain expertise off-the-cuff, while using AI strictly to nail the opening retention hook. - **Current Codex Limitations**: As he runs the live demo, Riley notes a frustrating issue where GPT-backed models in Codex occasionally stall or refuse to trigger the external transcript-pulling tool, a recurring reliability issue that has pushed him to explore alternative agent frameworks.
[11:06](https://www.youtube.com/watch?v=N34zz1-RSGw&t=666s) Model Guardrails, Educational Positioning, and the Substance-First Hook
When examining how different AI models handle YouTube transcripts and hooks, Riley explains why he prefers Claude over GPT models for reverse-engineering content. GPT models often refuse to reproduce transcripts directly, summarizing an intro instead due to strict copyright filters. Claude's models, by contrast, complete the task without hesitation, while open models like GLM-4 don't care at all and will process whatever text they are fed.
Peter observes that Riley's video intros take a noticeably educational, substance-heavy approach—calmly previewing what the video covers rather than relying on hyperactive "Codex is the most freaking amazing" hype. Riley confirms this is an intentional philosophical choice:
```mermaid flowchart TD accTitle: Substance-First Content Strategy accDescr: Visual flow showing how Riley designs content around transferable outcomes before packaging. subgraph Ideation["1. Topic Selection"] A["Focus on the core topic<br/>(e.g., Nine Codex Workflows)"] B["Ensure tool-agnostic value<br/>(works in Claude Code / Cowork)"] end subgraph Production["2. Execution & Hook"] C["State specific outcomes upfront"] D["Deliver deep, uncompromised substance"] end A --> B --> C --> D ```
- **Tool-Agnostic Value**: Riley builds videos around the underlying topic rather than locked-in tooling. Even in his top-performing Codex video—which garnered roughly 300,000 views covering nine practical use cases—he explicitly told viewers upfront that most techniques could be duplicated inside Claude Desktop or Claude Cowork. - **Outcome-Oriented Intros**: Instead of sensationalist hype, he leads by defining the concrete outcomes viewers will walk away with, establishing early value before diving into technical walk-throughs.
---
[13:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=804s) The BRENS Framework: Engineering Hooks That Keep Viewers Safe
Turning to tactical advice for creators, Peter and Riley discuss how to structure long-form video introductions. While Peter typically splices highlight clips from the interview to form a teaser before rolling into the guest introduction, Riley recommends systematically testing opening formats until finding a winning structure. He shares the core mental model he uses for hook architecture: the **BRENS** framework.
```mermaid flowchart LR accTitle: The BRENS Hook Framework accDescr: Five pillars of Riley Brown's BRENS hook methodology. B["Big<br/>(World-class / Massive impact)"] R["Relatable<br/>(Affects everyone's daily work)"] E["Easy<br/>(Clear framework / X steps)"] N["New<br/>(Novel models / Fresh tech)"] S["Safe<br/>(Respects viewer time)"] B --- R --- E --- N --- S ```
The acronym breaks down into five levers: - **Big**: Frame the development as massive (e.g., "A brand new model just dropped, and it's the best in the world"). - **Relatable**: Connect the advance directly to the audience's daily reality (e.g., "This is going to impact every single business and creator"). - **Easy**: Remove friction by introducing a digestible structure (e.g., "Here is a 6-step method or 9 simple tips that guarantee results"). - **New**: Leverage novelty, as audiences naturally gravitate toward fresh developments. - **Safe**: Riley highlights safety as the make-or-break factor for long-form videos over 30 minutes. Viewers need explicit reassurance that watching won't waste their time. The creator must establish authority and promise a high return on attention: *"By the way, this guy's an expert, he's going to talk about these things, and I promise you by the end of the video you will get value out of it."*
---
[15:09](https://www.youtube.com/watch?v=N34zz1-RSGw&t=909s) Gut-Driven Content vs. Soulless Packaging
Addressing the perennial YouTube debate—whether to design the thumbnail and title before filming or build them after—Riley pushes back against the conventional wisdom of packaging first. While many creators abandon video ideas if a thumbnail concept doesn't immediately look viral, Riley follows an organic, backwards-engineered workflow:
1. **Follow Curiosity First**: Riley picks topics purely by instinct ("I feel it in my heart, like my gut tells me what I should talk about"). About 90% of his process is getting deep into the technical weeds and documenting how he actually uses tools day to day. 2. **Accept Creative Tangents**: Often his best videos involve 30-minute unscripted tangents. Because the content evolves naturally during shooting, filming the hook beforehand would misrepresent the final piece. 3. **Automate the Packaging (The Final 10%)**: After filming, Riley feeds the raw video transcript into an AI agent running a custom YouTube researcher skill. The agent scrapes YouTube for high-performing formats, patterns, and structure, then generates an optimized hook tailored precisely to the tangents and takeaways actually covered. 4. **Preserving Creator Energy**: Riley emphasizes that letting packaging dictate what gets made turns content into a "soulless game." Making viral packaging the sole criterion leads inevitably to chasing trends for the sake of views: *"Then I'll end up making dance videos on TikTok, which is something I don't want to do."*
---
[17:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1044s) The Codex AI Stack: Dynamic Skills, In-App Browser, and Workflow Automation
Riley transitions back to his Codex setup to demonstrate how he automates daily operations. Rather than trying to design complex agents in the abstract, his rule of thumb is simple: whenever he catches himself doing a repetitive task with AI, he instructs Codex to *"turn this into a skill."*
```mermaid flowchart TD accTitle: Riley's Codex Skill and Automation Stack accDescr: Interconnected AI skills managed within the Codex platform. subgraph Skills["Codex Custom Skills"] E["Excalidraw Skill<br/>(Generates diagram links)"] T["Thumbnail Skill<br/>(Scrapes patterns & swaps faces)"] P["Typefully Skill<br/>(Drafts tweets from ideas)"] end subgraph Browser["Codex In-App Browser Workspace"] N["Notion Video Database<br/>(Short/Long-form scripts)"] G["Google Docs & Calendar<br/>(Schedule & document management)"] M["Email Automation<br/>(Composes draft replies)"] end Skills --> Browser ```
Riley highlights the core skills and workspace features that anchor his daily routine:
- **Excalidraw Diagrams**: When explaining complex AI workflows, Riley uses an Excalidraw skill inside Codex. He asks the agent for a visual diagram; Codex builds it and generates a clickable link that opens directly in Excalidraw with fully editable flowcharts. - **Thumbnail Swapping**: His thumbnail workflow scrapes top-performing thumbnails across the niche, replaces the subject with his own face using image models, and fine-tunes color grading to match his visual style. - **Typefully Twitter Integration**: Codex connects directly to Typefully. Whenever Riley encounters an interesting concept or tool, he commands the agent to draft a tweet. He lets drafts accumulate throughout the week, then logs into Typefully every Monday to review and schedule them. - **Frictionless In-App Browser & Notion**: Riley emphasizes Codex's built-in web browser as one of its most productive features, eliminating the context-switching penalty of jumping across browser tabs.
To demonstrate live, Riley prompts Codex: *"Hey, I want to make a short-form video on the latest Meta Ray-Bans. Please add a new short form in progress to Notion and give me the link."* The agent writes directly to his Notion video database, generates a link, and renders the Notion page right inside the Codex workspace. He then instructs it to draft an outline and a hook focusing on the massive opportunity for building third-party smart-glasses apps—handling scriptwriting, calendaring, and drafting emails without ever leaving the single desktop interface.
[22:10](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1330s) Quality Over Batching: Building a Moat Through Deeper Research
When creators first get access to powerful automation and AI tools, their initial instinct is almost always to batch produce mass quantities of content. Riley actively resists that temptation. For him, the real objective is not churning out volume, but making every piece noticeably better. Instead of writing dozens of mediocre scripts at once, he uses Codex as an always-on research assistant running in the background. While he writes, he expects Codex to open background tabs, bring up relevant documentation, fact-check every claim in his script line-by-line, or analyze competitor content. He will frequently tell Codex to review a creator who excels at video pacing, analyze why their retention works, and figure out how to weave those specific pacing techniques into the video at hand. Codex runs off for ten minutes, does the deep analysis, and returns with concrete recommendations.
Riley firmly believes that the true competitive advantage for creators is sustained high quality over time rather than pure volume, warning that "batching will make it soulless over time." By accelerating research by 10x, Codex simply allows a creator to pour far more total research and polish into a single video. When Peter asks about his current production targets, Riley shares that his ultimate cadence goal is four long-form videos and five high-quality short-form videos per week. Peter laughs that producing just one or two videos a week leaves him completely exhausted, to which Riley emphasizes that leaning heavily on Codex is the only reason such a schedule is sustainable and enjoyable for him.
- **Quality vs. Volume:** Riley warns against batching content just to fill a queue, emphasizing that high research density is what creates a defensible creator moat. - **Background Research Agents:** Codex operates asynchronously, spending five to ten minutes fact-checking claims, fetching source links, and reverse-engineering pacing structures from top creators. - **Publishing Goals:** Riley targets five high-polish short-form videos and four long-form videos per week, contrasting sharply with creators who burn out making one or two.
[23:57](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1437s) Dictating Structure: Wispr Flow, AirPods Max, and Excalidraw
Turning high-level concepts into a structured visual video outline does not start with typing at a keyboard. Riley reveals that he rarely types his thoughts; instead, he relies almost entirely on Wispr Flow for voice dictation. He slips on his AirPods Max specifically for their superior microphone clarity, starts Wispr Flow, and simply paces around his space for ten minutes rambling about whatever topic is on his mind. He talks through the overarching premise of the video, the key problems he has been pondering that week, and the specific visual diagrams he wants to explain.
During this stream-of-consciousness brain dump, Riley will explicitly instruct his custom Excalidraw skill to produce a nine-slide diagram deck structured into distinct logical sections. Once he finishes pacing and talking, he triggers the skill and walks away. When he returns, Codex has rendered clean Excalidraw diagrams that represent roughly 80% of what will appear in the final video. Rather than generating a script and then illustrating it, these visual slides serve as the structural anchor for the entire production, establishing the narrative arc before Riley spends 20 to 30 minutes fine-tuning the diagrams and shooting demos.
- **AirPods Max + Wispr Flow Setup:** Riley uses high-quality headphone mics to wander around hands-free while dictating continuous, unstructured thoughts into Wispr Flow. - **Visual-First Storyboarding:** Rather than writing scripts first, Riley prompts Codex to produce nine structured Excalidraw slides that dictate the pacing, demonstrations, and narrative beats of the video. - **80/20 Generation Efficiency:** The automated skill delivers roughly 80% finished visual diagrams, requiring only 20 to 30 minutes of manual human tweaking.
[25:41](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1541s) The Thumbnail Engine: Automating Concept Boards with Codex and Paper
Thumbnail design often stymies creators because generative image tools tend to distort facial expressions or mangle likenesses when prompted to change a smile into an exaggerated reaction. Riley circumvents this by using Paper, an AI-native design tool that functions like an AI-first alternative to Figma. Because Paper is designed from the ground up to integrate with AI models and external scripts, Codex has complete programmatic control over the canvas, creating a fluid bridge between competitor research and image editing.
```mermaid flowchart TD accTitle: "Thumbnail Creation Workflow with Codex and Paper" accDescr: "Flowchart showing how Riley scrapes inspiration, brings it into Paper, prompts visual changes, and iterates."
subgraph Research ["Inspiration & Ingestion"] A["Codex YouTube Scraper Skill"] -->|"Fetch Top Thumbnails"| B["Competitor References<br>(Hormozi, Martell, Jikh)"] B -->|"Auto-populate canvas"| C["Paper Canvas Board"] end
subgraph Generation ["Canvas Image Manipulation"] C --> D["Select Reference Thumbnail<br>& Creator Headshot"] D -->|"Prompt In-Painting Model"| E["Generate Batch Variations<br>(Face Swap & Styling)"] end
subgraph Refinement ["Visual Iteration & Testing"] E --> F["Canvas Tweaks:<br>Smoothing, Glow & Contrast"] F --> G["Human Designer Polish<br>& Continuous A/B Testing"] end ```
In a live walkthrough, Riley demonstrates a workflow from an upcoming video where he instructed Codex to scrape top-performing YouTube thumbnails from Alex Hormozi and Dan Martell and arrange them across a Paper board. With the competitor benchmarks laid out alongside transparent headshots of himself, Riley drags an Alex Hormozi thumbnail onto the workspace and uses Paper's built-in generative canvas tools to execute an in-place face swap. He types a plain-English prompt: "Please replace the guy with a beard with the guy that doesn't have a beard, just replace the guy, except make his shirt white, and make him wearing a chain." When Peter chuckles and asks why he specifically requested a chain, Riley jokes that because the podcast appearance is important, wearing a chain in the thumbnail is naturally essential.
Riley repeatedly taps Enter to generate seven rapid variations simultaneously. He points out his natural design progression across the board: taking a high-converting layout from Andrei Jikh or Hormozi, swapping in his own likeness, and letting the AI inherit the emotional composition of the original without having to build graphics manually from scratch. Once a decent baseline appears, he zooms in and issues localized refinement prompts—smoothing out the face while preserving character consistency, adding an orange-white rim glow to pop from the background, and boosting the brightness of accent elements.
- **Paper as AI-Native Figma:** Paper allows direct programmatic API access, letting Codex autonomously fetch image assets and populate working boards without manual file downloads. - **Face Swapping Over Fresh Generation:** Instead of prompting a thumbnail entirely from scratch, Riley maps his face onto proven, high-performing thumbnail templates from creators like Alex Hormozi and Andrei Jikh. - **Rapid In-Painting Iterations:** Riley generates seven variants in parallel, then applies targeted prompt edits to adjust rim lighting, facial smoothing, and color saturation.
[28:49](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1729s) Visual Prompting, Reference Boards, and Continuous A/B Testing
Rather than relying on tedious written style guides describing exact hex codes, fonts, and text placement, Riley relies entirely on "visual prompting." Modern multi-modal models (which Riley mentions under shorthand names like Nano Banana) allow creators to pass visual references directly into the prompt context. On his Paper canvas, Riley simply selects an existing thumbnail that already has his preferred visual styling alongside his working draft, prompting the tool to make the subject in image A look like image B, and borrow the typography treatment from image C. Pointing directly to visual assets replaces hundreds of words of descriptive prompt engineering.
Despite having an automated pipeline, Riley still works with a dedicated thumbnail designer. The AI outputs generated in Paper serve as high-fidelity concepts and starting assets that he hands off to his designer for final execution. Riley considers YouTube packaging critical enough to warrant extensive A/B testing: if a superior thumbnail doubles the click-through rate, the resulting distribution payoff dwarfs the cost of both the tools and human design support.
- **Visual Prompting Paradigms:** Creators no longer need complex style sheets; multi-modal tools allow users to select multiple visual reference frames on a canvas and tell the model to match their aesthetic and typography. - **Human-in-the-Loop Optimization:** Riley uses AI to generate strong foundational variants rapidly, which his thumbnail designer then polishes and loads into continuous A/B test splits. - **Payoff of Packaging:** Riley highlights that doubling a video's performance through aggressive thumbnail experimentation easily justifies paying for premium design workflows and external tools.
[31:15](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1875s) Stacking Skills and Natural Language Debugging
Riley explains that the true power of an AI-driven workflow emerges when seemingly unrelated skills are chained together. His thumbnail system was born out of an accidental realization: he originally built a YouTube research skill that downloaded thumbnails as raw image files to his computer, which he found cumbersome to inspect. Because he had already built a separate Paper skill to create software mockups, he wondered if he could instruct Codex to direct the output of the YouTube scraper straight onto a Paper canvas. Connecting those two separate skills immediately eliminated the friction of local file management and unlocked a whole new creative canvas workflow.
When Peter asks whether Riley ever opens and manually edits the underlying code or prompt instructions inside the skill files, Riley gives an emphatic "no." He treats skill creation as an entirely test-driven, conversational process. He asks the AI agent to perform an action using a skill. If the output succeeds, the skill remains as is; if the agent makes a mistake, Riley gives direct natural language feedback: "Hey, you didn't do a good job, please change the skill so that you don't do that again." The AI modifies its own instructions, and Riley starts a fresh session to confirm the fix works.
- **Skill Stacking:** Breakthrough workflows occur when disparate tools intersect, such as piping scraped YouTube assets straight into an AI canvas design tool. - **Zero Manual Code Editing:** Riley never writes or inspects the skill files directly, keeping the entire skill development and maintenance loop strictly in plain natural language. - **Conversational Test-Driven Development:** When an agent produces flawed output, Riley tells it what went wrong, instructs it to update its own instructions, and verifies the update in a fresh environment.
[32:28](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1948s) Testing Codex Skills and Outcome-Based Iteration
Riley explains his exact feedback loop for developing and refining skills in Codex. Rather than manually nitpicking or hardcoding instructions, the most effective process is to clear the chat context, spin up a fresh session, and test the agent again. If it executes the task successfully, the skill is solid; if it fails, you prompt the AI to correct the skill based on that failure. Riley dismisses rigid prescriptive advice from people claiming a skill must be written in one exact syntax to perform well, pointing out that such quirks are only true for a specific model on that specific day. Because frontier models update constantly, micro-optimizing prompt syntax is fragile. Instead, creators should remain strictly outcome-based: set a standard for the finished output, convert working logic into a skill, and continuously benchmark it against actual results.
Peter observes that this high level of personalization is precisely why custom skills are displacing traditional SaaS products—standard software simply cannot adapt to an individual user's exact creative preferences. Riley agrees, though he clarifies that his freewheeling approach applies specifically to creative workflows where the stakes are essentially zero: "Just realize that there's no risk, just let your AI cook and tell it to do a better job." By contrast, when his company handles high-stakes, mission-critical operations—such as handling client deliverables, payments, or sensitive data—they enforce a rigorous review protocol where team members analyze and audit skills before deployment.
[33:55](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2035s) Multiplayer Workflows and Slack-Based AI Agents
The conversation shifts to the current boundary of AI tooling: moving beyond individual workflows into collaborative team environments. Both hosts observe that while desktop environments like Codex excel at local, single-player productivity, they do not naturally scale into multiplayer experiences outside of traditional Git pull requests. Anthropic and OpenAI are both racing to solve this collaborative layer, with Anthropic heavily targeting Slack as the shared interface. Riley remains intrigued by Slack, though he admits he is not entirely sold on it being the ultimate UI.
```mermaid flowchart TD accTitle: AI Agent Architecture Comparison accDescr: Comparing Anthropic's centralized single-agent approach to the multi-agent specialist approach in Slack. subgraph Centralized ["Anthropic Model: Unified Agent"] U1["Team Member"] --> C1["Single Central Agent<br/>'Mini God' System"] C1 --> O1["Handles All Company Domains"] end subgraph Specialist ["Chorus Model: Team of Specialists"] U2["Team Member"] --> S1["@CMO Agent"] U2 --> S2["@Dev Agent"] U2 --> S3["@Content Man"] S1 --> D1["Marketing Context"] S2 --> D2["Code Context"] S3 --> D3["Ideation Context"] end ```
To run automations without needing his personal computer running around the clock, Riley avoids local Codex instances and instead relies on cloud-based Claude agents integrated directly into Slack. He reveals a dedicated Slack channel called `#my-agents`, which houses custom automated personas: - **Chief Marketing Officer (CMO) Agent:** Delivers regular marketing updates, metrics, and automated summaries. - **Content Man:** Continuously analyzes external creator content and spits out fresh concepts for short-form videos. - **Peter Yang Bot:** An agent Riley whipped together in five minutes right before the recording by feeding it transcripts of Peter's YouTube channel.
These agents run on Riley's own platform, Chorus, though he emphasizes that the multiplayer agent space remains an open problem. While Anthropic approaches the problem by constructing what Riley characterizes as a centralized "mini god that knows everything," platforms like Chorus treat agents as distinct colleagues. Team members mention specific specialist agents—tagging the CMO agent or company developer agent—with each bot adapting its tone and responses based on who is addressing it.
[37:01](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2221s) Cloud Automations, Browser Agents, and Local Trade-offs
When Peter asks how cloud-based Claude agents handle authentications or actions inside password-protected web apps, Riley predicts rapid progress. Driven by advancements in browser-use models, he anticipates that by the end of the year, agents will operate cloud browsers seamlessly while staying logged into a user's standard SaaS suite without requiring constant manual intervention. Major research labs are aggressively prioritizing autonomous browser control in the cloud specifically because it unlocks reliable cron jobs and automated web workflows.
Currently, running local automations involves frustrating trade-offs: - **Main desktop limitations:** Running automations locally through Codex requires keeping the primary computer powered on and awake 24/7. - **Dedicated hardware workarounds:** Riley keeps a headless Mac mini running local Codex automations, but Peter points out that a secondary machine lacks direct access to the user's primary files and credentials. - **Cloud isolation:** Cloud agents solve the uptime problem but struggle with local file access and authenticated session states.
Riley acknowledges that every agent paradigm currently carries frustrating trade-offs, describing the friction as a universal "pain in the ass" that the industry will spend the next six months attempting to iron out.
[38:16](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2296s) Staying in the Trenches: Why Building Products Trumps Pure Content
Peter brings up the broader creator business model. In the AI niche, high-margin revenue is abundant: brand sponsorships, mid-roll ads, and corporate consulting/training for enterprises desperate to adopt agents offer an easy path to wealth. Riley affirms that ad revenue and brand sponsorships alone form an "insane business," and that he could easily "ride off into the sunset and make a very good living" doing nothing but posting videos. However, he deliberately avoids that trap, warning that pure content creators inevitably run dry when they turn into glorified news aggregators.
Instead, Riley views real-world development as the engine that fuels compelling content. The only reason he possesses deep, practical knowledge about Codex is that he spends every day working alongside software engineers, picking up three or four technical lessons daily. By building products and deploying agents for clients, his tutorials deliver actionable substance rather than theoretical commentary. Peter agrees, citing indie hacker Pieter Levels as the archetype of someone whose content remains endlessly engaging precisely because he builds in public, launches products, and talks through real-world lessons—including the four dozen ventures that failed before finding hits. Riley’s long-term operational objective is to build a high-performing product team that handles the operational engineering, allowing his content to serve as the top-of-funnel engine for genuine business utility.
[41:02](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2462s) Chorus and the Future of Team-Based Agents
To conclude the interview, Peter invites Riley to share what he is building with Chorus. While Anthropic's native Claude Slack integration centers on skills tied directly to their proprietary models, Chorus takes an agnostic approach: - **Model Flexibility:** Connects to any underlying foundation model, not just Anthropic. - **Omnichannel Interfaces:** Lives where users actually communicate, including Slack, iMessage, and shared group messages. - **Group Dynamic Support:** Group chat integration has proven to be the single most popular feature among early users.
Riley explains that rather than aggressively pitching Chorus purely as self-serve SaaS software, the company pairs the platform with white-glove implementation services. Enterprise appetite for agent deployment is so strong that setting up Chorus to automate client operations has become a massive commercial driver. Peter wraps up by thanking Riley for his transparency across his entire technical stack, calling his workflows a genuine inspiration for modern builders.
## Notable quotes > [00:00](https://www.youtube.com/watch?v=N34zz1-RSGw&t=0s) What I want is better, because I think the moat is quality over a long period of time versus batching. Batching will make it soulless over time. — Riley Brown
> [13:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=804s) By the way, this guy's an expert, he's going to talk about these things, and I promise you by the end of the video you will get value out of it. — Riley Brown
> [15:09](https://www.youtube.com/watch?v=N34zz1-RSGw&t=909s) I feel it in my heart, like my gut tells me what I should talk about — Riley Brown
> [15:09](https://www.youtube.com/watch?v=N34zz1-RSGw&t=909s) Then I'll end up making dance videos on TikTok, which is something I don't want to do. — Riley Brown
> [17:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1044s) turn this into a skill. — Riley Brown
> [17:24](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1044s) Hey, I want to make a short-form video on the latest Meta Ray-Bans. Please add a new short form in progress to Notion and give me the link. — Riley Brown
> [25:41](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1541s) Please replace the guy with a beard with the guy that doesn't have a beard, just replace the guy, except make his shirt white, and make him wearing a chain. — Riley Brown
> [31:15](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1875s) Hey, you didn't do a good job, please change the skill so that you don't do that again. — Riley Brown
> [32:28](https://www.youtube.com/watch?v=N34zz1-RSGw&t=1948s) Just realize that there's no risk, just let your AI cook and tell it to do a better job. — Riley Brown
> [33:55](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2035s) mini god that knows everything — Riley Brown
> [38:16](https://www.youtube.com/watch?v=N34zz1-RSGw&t=2296s) ride off into the sunset and make a very good living — Riley Brown
## Mentioned Riley Brown, Peter Yang, Codex, Claude Code, Claude Desktop, Claude Cowork, Anthropic, OpenAI, GPT, GLM-4, Vibecode, Chorus, Remotion, SerpAPI, Google Images, Google AI Studio, Corgi Confections, Supadata, yt-dlp, Alex Hormozi, Dan Martell, Andrei Jikh, Pieter Levels, Excalidraw, Typefully, Notion, Google Docs, Google Calendar, Meta Ray-Bans, Wispr Flow, AirPods Max, Paper, Figma, Slack, iMessage, Mac mini
## Video description Copy my best AI workflows to save time and automate busywork: https://www.behindthecraft.com 🤝 Subscribe to my practical AI newsletter read by 150K+ professionals: https://creatoreconomy.so
Riley is a top AI creator who reaches 1.5M+ followers. In this episode, he showed me how he uses Codex to run his entire content business from generating engaging thumbnails to writing incredible video hooks. Riley also shared his full AI stack including some lesser known AI tools to edit videos, draw diagrams, and more.
We talked about: (00:00) How Codex runs Riley’s 1.5M+ follower content business (03:16) Demo: Using Codex to create animations and B-roll (07:28) Demo: How Codex can reverse-engineer a top YouTube hook (13:24) The BRENS framework to turn an idea into a strong hook (17:24) Demo: Riley’s AI stack for diagrams, thumbnails, and posts (22:01) How Riley creates so much content every week (25:41) Demo: Using Codex and Paper to create viral YouTube thumbnails (35:01) How to set up AI agents to find content ideas quickly
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📌 Get the takeaways: https://creatoreconomy.so/p/how-i-run-my-content-business-with-codex-riley-brown
Where to find Riley: X: https://x.com/rileybrown Website: https://agentnative.inc/
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[Source](https://www.youtube.com/watch?v=N34zz1-RSGw)