Draft newsletters and social posts in your own voice
Send this URL to Cursor/Claude and ask it to build this for you. Task: Send this URL to Cursor/Claude and ask it to build this for you. It takes raw thoughts and ships newsletters, LinkedIn posts, and tweets in your own voice. tbh, I was being lazy around writing my newsletter. Every week, I'd sit down to write and think "there has to be a better way to do this." So instead of just pushing through like most people would, I did what any PMM worth their salt would do: I built a system (at scale) to solve it. Not just any system. A complete AI content creation workflow that takes my raw thoughts (which again I don’t type anymore) and turns them into newsletters, LinkedIn posts, and Twitter threads that sound exactly like me. And I'm going to show you exactly how I built it - and give you everything you need to build it yourself. What I Actually Built Here's the setup I created: Input: I just type my thinking into Cursor - doesn't matter if it's messy, stream-of-consciousness, or half-baked thoughts. it generally is my view on how I think it should be or how things Process: My system analyzes my input, learns from my existing content, scrapes industry insights from all popular marketing and GTM leaders (Emily Kramer, Kyle Poyar, Kyle Coleman) and generates content in my authentic voice. Output: Complete content package ready to publish across Reddit, LinkedIn, Twitter (X). The entire workflow runs inside Cursor. No external tools (except browser automation to crawl LinkedIn), no switching between apps, no complicated setups. I literally just input my thinking and it creates newsletters that write like me, think like me, and sound exactly like something I would actually publish (which I do though). How the System Actually Works Let me walk you through the real workflow: Step 1: Raw Input Processing I save my thoughts as a simple .txt file in my Cursor workspace. Could be 200 words, could be 1000 words. The system handles both. Example: I literally just typed "I was being lazy around writing my newsletter" and the system expanded that into this entire piece you're reading. Step 2: Industry Intelligence Gathering The system automatically: Scrapes latest LinkedIn posts of (Emily Kramer, Kyle Poyar, Kyle Coleman) for marketing insights and viral patterns Scrapes latest LinkedIn posts of (Emily Kramer, Kyle Poyar, Kyle Coleman) for marketing insights and viral patterns Analyzes top newsletters from Growth Unhinged, MKT1 Analyzes top newsletters from Growth Unhinged, MKT1 Extracts engagement triggers and high-performing content structures Extracts engagement triggers and high-performing content structures Updates my writing style based on my latest 32+ LinkedIn posts Updates my writing style based on my latest 32+ LinkedIn posts Step 3: Content Generation Using all that data, it generates: Newsletter: Expands my thinking to 2000-3000 words with my voice and structure Newsletter: Expands my thinking to 2000-3000 words with my voice and structure LinkedIn Posts: 2-3 optimized posts with engagement predictions LinkedIn Posts: 2-3 optimized posts with engagement predictions Twitter Thread: 5-8 tweets with proper threading and CTAs Twitter Thread: 5-8 tweets with proper threading and CTAs Step 4: Cursor Integration Everything stays in my development environment (all local). Generated content appears in organized folders which are pushed to Git. I can edit, refine, and publish directly from Cursor. No switching between tools. No copy-pasting. Pure workflow efficiency. Thanks for reading QBack: AI Quarterback for GTM! Subscribe for free to receive new posts The Technical Stack (For the PMMs Who Code) This isn't just prompt or context engineering. It's a complete system: Python Scripts: Automated content analysis and generationSelenium Integration: LinkedIn scraping for (Emily Kramer, Kyle Poyar, Kyle Coleman) insightsFeed Parsing: Newsletter analysis from top marketing sourcesStyle Learning: AI that adapts to my writing patterns over timeCursor Workflow: Everything integrated into my daily coding environment The system learns from: My existing LinkedIn posts (32+ posts analyzed for voice patterns) My existing LinkedIn posts (32+ posts analyzed for voice patterns) My newsletter content (automatically updated daily) My newsletter content (automatically updated daily) My Twitter threads and engagement patterns My Twitter threads and engagement patterns High-performing content structures of others High-performing content structures of others Industry leaders' viral hooks and frameworks; tools: Cursor. Human role: The original post does not say how a person stayed involved.; limitations: The workflow relies on browser automation (Selenium) to scrape LinkedIn, which may violate platform terms of service or break due to layout changes. It requires technical proficiency in Python and Cursor configuration. The source does not specify costs for API usage or maintenance, nor does it detail how the system handles factual inaccuracies or hallucinations in the generated content..
Tools
Published on Agent Work Atlas:
Overview
Send this URL to Cursor/Claude and ask it to build this for you.
Workflow
Send this URL to Cursor/Claude and ask it to build this for you. It takes raw thoughts and ships newsletters, LinkedIn posts, and tweets in your own voice. tbh, I was being lazy around writing my newsletter. Every week, I'd sit down to write and think "there has to be a better way to do this." So instead of just pushing through like most people would, I did what any PMM worth their salt would do: I built a system (at scale) to solve it. Not just any system. A complete AI content creation workflow that takes my raw thoughts (which again I don’t type anymore) and turns them into newsletters, LinkedIn posts, and Twitter threads that sound exactly like me. And I'm going to show you exactly how I built it - and give you everything you need to build it yourself. What I Actually Built Here's the setup I created: Input: I just type my thinking into Cursor - doesn't matter if it's messy, stream-of-consciousness, or half-baked thoughts. it generally is my view on how I think it should be or how things Process: My system analyzes my input, learns from my existing content, scrapes industry insights from all popular marketing and GTM leaders (Emily Kramer, Kyle Poyar, Kyle Coleman) and generates content in my authentic voice. Output: Complete content package ready to publish across Reddit, LinkedIn, Twitter (X). The entire workflow runs inside Cursor. No external tools (except browser automation to crawl LinkedIn), no switching between apps, no complicated setups. I literally just input my thinking and it creates newsletters that write like me, think like me, and sound exactly like something I would actually publish (which I do though). How the System Actually Works Let me walk you through the real workflow: Step 1: Raw Input Processing I save my thoughts as a simple .txt file in my Cursor workspace. Could be 200 words, could be 1000 words. The system handles both. Example: I literally just typed "I was being lazy around writing my newsletter" and the system expanded that into this entire piece you're reading. Step 2: Industry Intelligence Gathering The system automatically: Scrapes latest LinkedIn posts of (Emily Kramer, Kyle Poyar, Kyle Coleman) for marketing insights and viral patterns Scrapes latest LinkedIn posts of (Emily Kramer, Kyle Poyar, Kyle Coleman) for marketing insights and viral patterns Analyzes top newsletters from Growth Unhinged, MKT1 Analyzes top newsletters from Growth Unhinged, MKT1 Extracts engagement triggers and high-performing content structures Extracts engagement triggers and high-performing content structures Updates my writing style based on my latest 32+ LinkedIn posts Updates my writing style based on my latest 32+ LinkedIn posts Step 3: Content Generation Using all that data, it generates: Newsletter: Expands my thinking to 2000-3000 words with my voice and structure Newsletter: Expands my thinking to 2000-3000 words with my voice and structure LinkedIn Posts: 2-3 optimized posts with engagement predictions LinkedIn Posts: 2-3 optimized posts with engagement predictions Twitter Thread: 5-8 tweets with proper threading and CTAs Twitter Thread: 5-8 tweets with proper threading and CTAs Step 4: Cursor Integration Everything stays in my development environment (all local). Generated content appears in organized folders which are pushed to Git. I can edit, refine, and publish directly from Cursor. No switching between tools. No copy-pasting. Pure workflow efficiency. Thanks for reading QBack: AI Quarterback for GTM! Subscribe for free to receive new posts The Technical Stack (For the PMMs Who Code) This isn't just prompt or context engineering. It's a complete system: Python Scripts: Automated content analysis and generationSelenium Integration: LinkedIn scraping for (Emily Kramer, Kyle Poyar, Kyle Coleman) insightsFeed Parsing: Newsletter analysis from top marketing sourcesStyle Learning: AI that adapts to my writing patterns over timeCursor Workflow: Everything integrated into my daily coding environment The system learns from: My existing LinkedIn posts (32+ posts analyzed for voice patterns) My existing LinkedIn posts (32+ posts analyzed for voice patterns) My newsletter content (automatically updated daily) My newsletter content (automatically updated daily) My Twitter threads and engagement patterns My Twitter threads and engagement patterns High-performing content structures of others High-performing content structures of others Industry leaders' viral hooks and frameworks
Reported result
The source claims the system produces content that sounds exactly like the author and is indistinguishable from human writing. However, no quantitative metrics, engagement rates, or time-saving statistics are provided to verify these outcomes. The author states the newsletter itself was generated by this system, serving as a qualitative proof of concept rather than a measured business result.
Human role
The original post does not say how a person stayed involved.
Limitations
The workflow relies on browser automation (Selenium) to scrape LinkedIn, which may violate platform terms of service or break due to layout changes. It requires technical proficiency in Python and Cursor configuration. The source does not specify costs for API usage or maintenance, nor does it detail how the system handles factual inaccuracies or hallucinations in the generated content.
Try it
Act as a technical writer and developer. I need to replicate a content generation workflow using Cursor. Inputs: 1. My raw thoughts on [Topic] (provided as a .txt file or pasted text). 2. A sample of my previous writing style (3-5 paragraphs). 3. A list of industry leaders to monitor for insights: [Names/URLs]. Steps: 1. Analyze my writing style to identify tone, structure, and vocabulary patterns. 2. Simulate the gathering of industry insights by summarizing recent public posts from the listed leaders (do not use actual scraping tools if not available; use provided summaries or web search if permitted). 3. Draft a newsletter (2000-3000 words), 2 LinkedIn posts, and a 5-tweet thread based on my raw thoughts, ensuring the voice matches my samples. 4. Save these drafts in a folder structure: /output/newsletter.md, /output/linkedin.md, /output/twitter.md. Constraints: - Do not publish or send anything. - Flag any factual claims that need verification. - Ask me for clarification if the raw input is too vague. Deliverable: Provide the three drafted content pieces and a brief report on how you adapted my voice. I will review and approve before any further action.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.