I Used AI to Build a System for Using AI to Document Building AI Systems (And My Brain Hasn’t Recovered)

The Recursive Rabbit Hole That Changed Everything

Right, so yesterday I had one of those "hold on, what the actual hell am I doing?" moments that either signals genius or complete madness. I was using AI to design a system for using AI to journal about using AI for development.

Let that marinate for a second.

I'm a developer at Collab365, wearing roughly 47 different hats (slight exaggeration, but only slight), and I was drowning in my own productivity. Spending entire days solving complex problems, having breakthrough moments, learning expensive lessons - then forgetting everything by evening because I had zero time to document any of it.

Sound familiar? You're not alone!

 🎯 The Problem: Content Creation Hell for Developers

Here's the brutal reality of being a developer building enterprise software: Every day is packed with learnings worth sharing, but documenting them feels like choosing between shipping features and building an audience.

The daily struggle looked like this:*

• ✅  Spent 4 hours fixing enterprise authentication
• ✅  Had three "holy shit, this actually works!" moments
• ✅  Learned expensive lessons about API rate limits
• ❌  Documented exactly none of it
• ❌  Shared zero insights with other developers
• ❌  Forgot the solution by next Tuesday

Meanwhile, I'm watching other solo developers struggle with identical challenges I'd already solved. But my "solution" was buried in some random Discord message or lost in the void of my brain.

The cost of this approach:

• Lost content opportunities: ~5 potential blog posts weekly
• Repeated mistakes: Solving the same auth bug three times
• Zero audience building despite having genuinely useful insights
• Constant context switching when starting new AI chats

🔧 Enter Taskmaster: The Context Game-Changer

First breakthrough came when I discovered Taskmaster - an AI task management system that lets you write detailed PRDs and auto-generates structured tasks with dependencies. Suddenly my AI could read the entire project context instead of me explaining "I'm building playlist management software" for the 847th time.

What this solved:

• ✅ AI always understands full project scope
• ✅ Complex features broken into manageable subtasks
• ✅ No more "refresh context every new chat" hell
• ✅ Proper dependency management (can't deploy before testing, revolutionary stuff)

The cost: 2+ hours learning the system

The savings: 10+ hours weekly on task management

But here's where it gets interesting...

 🤯 The Meta Moment: Journaling About Building Systems

While using Taskmaster to manage my playlist management development, I realized I was having the exact content capture problem that thousands of other developers face. So I did what any reasonable person would do: I used AI to design a system for using AI to capture the experience of using AI for development.

The recursive nature of this was either brilliant or completely unhinged. Possibly both.

I created two complementary prompts:

1. Journal Entry Capture → Dramatic story structure (mission, struggle, plot twist, human moments). No one reads boring do they?
2. Blog transformation → Convert journal entries into viral developer content

📝 The Journal System: Capturing Stories Worth Sharing

The journal prompt forces you to think in story structure rather than corporate documentation:

• THE MISSION: What were you actually trying to accomplish?
• THE STRUGGLE: What went wrong and why it mattered?
• THE APPROACH: Your specific solution with tools/costs
• THE PLOT TWIST: The unexpected breakthrough moment
• THE COST: Real numbers - time, money, API bills, sanity
• THE HUMAN MOMENT: Your actual reaction when it worked
• THE INSIGHT: The lesson that others can copy
• THE CLIFFHANGER: What you're testing next

First test result: Corporate snoozefest material. Technically accurate, emotionally dead.

I'd built a sophisticated cure for insomnia.

🎢 The Redesign: From Documentation to Blog Content

That cringe moment reading my first journal entry taught me everything. I wasn't capturing stories - I was creating corporate documentation disguised as "authentic journaling."

The fix required:

- ✅ Multiple blog formats (single story, themed collection, case study, meta narrative)

- ✅ British humor guidelines (because American corporate speak makes me die inside)

- ✅ Viral headline templates that actually make people click

- ✅ Quality checklists focused on "would I share this?" not "is this technically correct?"

Format examples:

• Single Story: One major breakthrough with emotional journey
• Themed Collection: 3-5 related insights around one theme
• Case Study: Deep technical walkthrough with copy-paste code
• Meta Story: What you're reading right now

🎨 The Image Consistency Breakthrough

Just when I thought I'd solved everything, my perfectionist founder brain attacked the featured image problem. Detailed prompts for AI image generation are inconsistent as hell - same prompt, completely different results.

Funnily enough a fellow AI-Dev-Geek "Texted me" this (he's not one for "words") 😀 

ChatGPT

Upload an image then ask:

‘Give me an advanced json context profile for this image’

The solution that blew my mind:

Upload a reference image → Ask for "advanced JSON context profile" → Get exact technical specs of every visual element.

Instead of hoping AI interprets "dark mode with neon accents" consistently, I now have:

• Exact hex codes (#1B263B, #1DA1F2, #FF6B00)
• Precise composition ratios
• Specific lighting models
• Detailed style parameters

It's like giving AI the exact Pantone color instead of saying "paint something blue-ish."

💸 The Real Costs (Because Transparency Matters)

Time Investment:

• Initial system design: 1 hour
• Prompt engineering: 2 hoursish
• Testing and refinement: This is my first test 😀 
• Total: 3 hours of intensive work

Ongoing Costs:

• AI API calls: ~£5-10 monthly
• Image generation: ~£2-5 monthly
• But saves: 10+ hours weekly on task management. In all honesty, this is my only hope as I just can't do it all manually!
• Plus generates: 3-5 blog posts weekly from captured work

🚀 The Unexpected Meta-Product

Here's the beautiful irony: By solving my own content creation problem, I accidentally built something other developers probably need if they ever need to tell another human being what they're doing.

The prompts themselves have become shareable resources.

What started as personal productivity became:

• ✅ Copy-paste journaling system for developers
• ✅ Blog transformation framework
• ✅ Consistent image generation method
• ✅ Complete content creation pipeline

🔥 Why This Actually Matters

Every solo developer/founder/pay-the-bills person faces the same impossible choice: ship features or build audience. This system eliminates that choice by automating content creation from the work you're already doing.

The bigger picture:

• More developers sharing authentic building stories
• Less "perfect founder" facade, more real experiences
• Actual useful content instead of generic "10 Tips" posts
• Community learning from each other's expensive mistakes

🔄 The Daily Workflow: How This Actually Works

Here's the dead simple process I use every day to capture development work and transform it into blog content:

Step 1: Capture Development Sessions

Throughout the day, I either:

Summarize AI chats: Copy interesting development conversations where I solved problems

Voice dictate insights: Use WisprFlow.ai to speak my thoughts directly to AI when I have breakthrough moments. You HAVE TO TO TRY THIS AS IT SAVES SOOO MUCH IN TYPING!

The beauty of voice dictation: I can capture insights (or waffle like a good'n) while walking, during coffee breaks, or right after solving a tricky problem when the solution is fresh. No typing, no context switching - just speak and the AI structures it. Because Flow works in any text box (on a Mac), I can talk straight into Cursor, ChatGPT, Claude etc. It's epic!

Step 2: Run the Journal Capture Prompt

I have one master prompt that transforms any raw input (chat summaries, voice notes, random thoughts, waffle) into structured journal entries using the story framework:

• THE MISSION: [What I was trying to accomplish]
• THE STRUGGLE: [What went wrong and why it mattered]
• THE APPROACH: [My specific solution with tools/costs]
• THE PLOT TWIST: [The unexpected breakthrough moment]
• THE COST: [Real numbers - time, money, API bills]
• THE HUMAN MOMENT: [My actual reaction when it worked]
• THE INSIGHT: [The lesson others can copy]
• THE CLIFFHANGER: [What I'm testing next]

All entries get appended to one big daily journal file (`todays_journal.md`) - no complex organization, just chronological story capture.

Here's an example of a journal entry i cranked out: 

**TIME:** 2:30 PM

**THE MISSION:** Fix our broken daily digest automation that serves 20,000 Microsoft 365 subscribers through Collab365 Today - because when Feedly doesn't send full article content, our AI can't create proper summaries, making our newsletter look amateur compared to the polished content our enterprise audience expects.

**THE STRUGGLE:** Half our newsletter content wasn't allowed in because Feedly would send incomplete RSS article information. YouTube videos had promotional descriptions that told readers nothing useful. Blog posts came through as title-only with no context. Our automation would fail, leaving our team manually writing summaries for 20+ articles daily, or worse - sending out newsletters with no descriptions at all. For a newsletter reaching 20,000 people, this was embarrassing.

**THE APPROACH:** Completely rebuilt our Make.com automation flow with intelligent content detection. If Feedly sends incomplete content, we trigger Appify to scrape the full article. If it's a YouTube video, Appify grabs the transcript instead. Then we feed the complete content into AI for proper 2-sentence summaries. Created a multi-path workflow: Feedly → Make.com → Content detection → Appify scraping → AI summarization → SharePoint → Power Automate → Newsletter distribution.

**THE PLOT TWIST:** The automation actually got smarter than our manual process! Now we can handle ANY content type - articles, videos, PDFs, even content behind soft paywalls. The AI summaries are consistently better than what we used to write manually, and the whole thing runs completely hands-off while we focus on curation rather than content extraction.

**THE COST:** 4+ hours of intensive Make.com scenario building, testing different content types, and debugging edge cases. Plus the mental gymnastics of mapping out content flows that could handle every possible RSS feed variation. But this saves our team 2+ hours daily and dramatically improves newsletter quality.

**THE HUMAN MOMENT:** That moment when I tested it with a random YouTube video and watched Make.com automatically detect it was video content, grab the transcript, generate a perfect summary, and post it to SharePoint - all in under 30 seconds. Felt like watching a perfectly choreographed automation dance that I'd been trying to build for months.

**THE INSIGHT:** Don't just automate the happy path - build intelligent automation that handles edge cases better than humans do. The key was content type detection at the Make.com level, then routing to appropriate scraping strategies. Most people try to force one solution for all content types, but smart automation adapts to what it's given.

**THE CLIFFHANGER:** Now I'm wondering what other "manual because it's complicated" processes I could automate with this content-aware approach. Could this same pattern work for social media automation? And what happens when our newsletter quality jumps so much that subscriber engagement goes through the roof?

Step 3: Transform Into Blog Content

At the end of the day (or when I have 3-5 strong entries), I run the blog transformation prompt that:

• Analyzes all journal entries for themes and audience fit
• Recommends the best content strategy (single post vs. multiple posts)
• Creates viral-ready blog posts with proper structure, headlines, and CTAs
• Generates consistent featured image prompts

The entire daily workflow:

1. ⏱️ Throughout day: 30 seconds to dictate insights via WisprFlow
2. ⏱️  End of session: 2 minutes to run journal capture prompt
3. ⏱️  End of day: 5 minutes to generate blog post(s)
4. ⏱️  Total time: ~10 minutes for professional blog content

Why This Two-Prompt System Works

Journal Prompt = Raw Capture: Gets the story and emotion while it's fresh, doesn't worry about audience or polish

Blog Prompt = Strategic Transformation: Analyzes captured stories and creates targeted content for specific audiences with viral potential

The separation means I never lose authentic moments trying to make them "blog-ready" in real-time. Capture first, polish later.

✅ Your Copy-Paste Action Plan

Want to build your own AI journaling system? Here's exactly what to do:

1.  Set up voice dictation via WisprFlow.ai or similar tool
2. Create your journal capture prompt with the story structure framework above
3. Set up daily journal file (one big markdown file per day)
4. Design blog transformation prompt with multiple format options
5. Generate JSON image profile for visual consistency
6. Test with one day's work and refine based on results. This is day 1 for me 😀

The time investment: ~1-2 hours initial setup

The ongoing effort: 10 minutes daily (seriously!)

The output: Professional blog content automatically generated from your real work

🎭 The Beautiful Absurdity

I'm now journaling about redesigning the journaling system using the journaling system I just created.

It's recursive, slightly mad, and somehow exactly what the developer community needs.

The meta-documentation loop is either the future of authentic content creation or evidence that I've completely lost the plot. Possibly both.

What's next? Testing this enhanced system on real playlist management development work. Can I capture the messy reality of building enterprise features alone? Will this become the content breakthrough that lets me share my solo SaaS journey while actually building the damn thing?

And the ultimate question: Will other developers copy these prompts and start sharing their own authentic building stories?

The recursive experiment continues...