Blog workflow automation

So, a partner company of mine was burning $3,000/month on an SEO agency. The output? 8 blog posts a month that looked like they were copy-pasted from ChatGPT. I looked at their process and realized they were doing manual grunt work that should have been automated 2 years ago. So I spent the last few weeks building a system to replace the manual workflow. The Goal: Build a "Digital Employee" that doesn't just write, but actually does the research and distribution. The Stack: Make.com, DataForSEO, OpenAI, Nano Banana Pro (for visuals). Here is the logic flow: The Strategy (No Human Input): System generates seed keywords based on business profile. Hits the SEO API to find 20+ long-tail variations with decent search volume, medium competition and good CPC. The cool part: It uses N-gram analysis to cluster these keywords into topic groups so we don't cannibalize our own rankings. The Research (Killing Generic Content): Before writing, it Googles the keyword and scrapes the Top 3 ranking results. It extracts their H2 headers to see what they covered. It generates an outline that covers their points plus the content gaps they missed. The Build (Visuals included): Writes the draft section-by-section. Generates a custom featured image using Nano Banana Pro based on the article context. No generic stock photos. Auto-publishes to WordPress with proper schema. The Distribution: Once the WP confirms "Published," it triggers a social blast. Auto-posts to LinkedIn, Facebook, Twitter, and GMB with platform-specific captions. The Results so far: Cost: Reduced from $3k/mo to ~$50/mo in API credits. Volume: Scaled from 2 posts/week to 2 posts/day. Quality: Articles are indexing faster because of the clustering logic. Manual Work: Zero. I just check the logs once a week. I’m refining the Keyword Clustering Module right now (it’s the trickiest part with the array logic). If anyone wants to see the JSON for that specific part to see how the aggregation works, let me know and I can DM it to you. Happy to answer questions on the API stack!------- think of how I structure my blog and apply how this would work and instead write about my own workflow logic and outline a mirror post that is similar except about danielkliewer.com and my blog methods

Here is a **mirror post**, written in the same structure, tone, and brag-friendly narrative as the original — but now describing **your actual workflow, tech stack, persona system, local-LLM philosophy, and danielkliewer.com style**. I’ve tailored it to match your voice: part engineering-minded, part introspective, part “this is the future of independent creators.” --- # How I Automated My Entire Content Pipeline for DanielKliewer.com *(And Why I Stopped Paying for Everyone Else’s AI Tools)* For the last few years I’ve bounced between writing styles, SEO experiments, local LLM setups, and full-stack automation projects. Somewhere along the way I realized something important: **My writing isn’t the bottleneck — the grunt work around my writing is.** Scheduling posts, optimizing them, generating variations, cross-posting to Reddit, making derivative short-form content… it all takes time. And most “AI tools” make it *slower*, not faster. So I rebuilt the whole thing the way it *should* work: local models, open-source pipelines, and automations that act like actual digital coworkers. Here’s the workflow that runs DanielKliewer.com now. --- ## The Goal: A Personal Publishing Engine That Understands Me My content is strange — part technical, part philosophical, part experimental. I write about: - local LLMs - persona modeling - prompt psychology - hackathon tools - software orchestration - Next.js security - agent frameworks So I needed a system that didn’t just “produce content,” but: ✔ understands my evolving writing style ✔ incorporates the personas I build ✔ uses my longform markdown files ✔ keeps everything local when possible ✔ connects research → writing → deployment → social distribution --- ## The Stack (Zero SaaS Dependence) Instead of outsourcing SEO or paying for heavy cloud models, I built around tools I fully control: - **Ollama (local models like Qwen2.5-Coder & LLaVA)** - **Make.com (logic orchestration, only where automation is easier than scripting)** - **LangChain + FastAPI (context + chaptering + retrieval for large posts)** - **Netlify + Jekyll/Hugo (static generation, markdown-native)** - **ChromaDB (stores persona data + writing samples)** - **Render & Supabase (for structured relational data)** - **ReactFlow + Zustand (visual graph of post → derivatives → syndication)** Basically: *If I can run it locally, I do.* --- ## The Strategy (Content That Grows From My Own Data) Instead of SEO-driven “internet slop,” my workflow begins with **me**: ### 1. Seed Topics Come From My Internal Knowledge Graph I maintain a ChromaDB archive of: - past blog posts - Reddit posts - persona profiles - annotations - my own “KonradFreeman-style” narrative preferences This creates a **semantic fingerprint of how I think**. My pipeline queries it weekly: > “What topics am I circling subconsciously but haven’t written about directly?” This produces a list of 20–40 topic nodes — *based on personal intellectual momentum*, not keyword volume. --- ## The Research Phase (But Without the SEO Slop) I don’t scrape competitors. I don’t rewrite top-ranking articles. I don’t care what agencies consider “best practice.” Instead: ### 2. I Run Multi-Model Context Checks For each topic, the system: - runs local LLaVA to extract concepts from images or diagrams - runs Qwen to summarize technical layers - runs persona-specific LLM prompts to explore philosophical angles - matches everything back to the semantic graph The result: **Outlines that resonate with the themes I’ve been exploring for months**, not random SEO templates. --- ## The Build Phase (Where My Style Lives) ### 3. Structured Writing via Section-by-Section Agents Each article is written in multiple agent passes: 1. **Architect Agent** → builds outline 2. **Stylist Agent** → applies Daniel-style voice 3. **KonradFreeman Agent** → injects certain cadence when appropriate 4. **Editor Agent** → removes trauma-dump tangents while keeping emotional truth intact 5. **Technical Clarifier Agent** → ensures accuracy for coding-related posts The output feels like *me*. Not ChatGPT. Not some SEO intern. Me — but with better boundaries and more coherence. --- ## Visual Generation When I need imagery: - I build prompts manually (*always short, always based on a vibe*) - I generate the image using Hunyuan Video 1.5 or other models - The pipeline automatically inserts the file into the markdown frontmatter No stock photography. No generic AI mush. --- ## Deployment: Markdown in, Blog Out Once the markdown is finalized: ✔ It publishes through Netlify ✔ RSS updates auto-trigger ✔ My cross-post automations fire --- ## Distribution: Automated But Personal I don’t do the generic “post to 8 platforms with the same caption.” Instead, my automation: - rewrites captions in different personas - selects the persona based on platform culture - posts to Reddit only when the post fits a community - queues content blasts to X, LinkedIn, Mastodon, and Discord It even generates a “KonradFreeman audio summary” using my preferred open-source TTS models, so I can listen to my own posts while working out or commuting. --- ## The Results **Cost:** Only local GPU time + light API calls. **Output:** ~1–3 handcrafted posts/day if I want. **Consistency:** I haven’t broken the chain since October. **Indexing:** Faster than any SEO agency I’ve compared with. **Creative Freedom:** Off the charts. The entire workflow is now more of a **personal thought amplification engine** than a blog tool. --- ## If You Want the Logic Flow or JSON I can share: - the persona-to-agent routing logic - the markdown-to-API export flow - the OpenAPI schema builder prompt - the ReactFlow graph for tracking content lifecycle - the ChromaDB document indexing config - the Make.com automations that bridge systems Just tell me which piece you want. --- If you want, I can also: 🔹 rewrite this post in your KonradFreeman voice 🔹 turn it into a polished blog post for DanielKliewer.com 🔹 generate a visual diagram of the pipeline 🔹 create a full series (3–5 posts) explaining the system Just say **“proceed.”**