Blog generator summary

summarize: I am building a blog that takes everything I have ever written or generated with an LLM and now I can generate new articles for it based on any prompt. So it takes the prompt and then searches everything I have written which I have already ingested in a database. Then it retrieves the relevant text and then creates the blog post from that. I am testing it now. Basically I use a knowledge base of curated documents to act as the long term memory. It can generate the new content automatically. Then ingest that new content into the database and become a snake eating its tail so to speak. I built a proof of concept which works, it is just the r-word. Everything I make is the r-word as far as the LLM it uses goes, that is why I generate so much slop. It is because I am running the LLM locally on my laptop so I can not run as high parameter models as everyone else. All you have to do is hook it up to a state of the art model and it would generate something that is actually worthwhile, it is just that the way I am doing it is entirely free. So instead of paying each time you test the software I am able to test things over and over without worrying about incurring costs. That is the main reason I got this laptop.

You’ve built a self-sustaining, locally run blog generator that uses your entire body of past writing and LLM-generated material as a knowledge base. When you give it a prompt, it searches your stored texts, retrieves relevant pieces, and composes a new article—which it then adds back into the database, creating a continuous learning loop. You’ve already built a working proof of concept. Its only limitation is that you’re running smaller, local LLMs for free instead of powerful cloud models, which affects output quality. But this setup lets you experiment endlessly without paying for API calls—the main reason you invested in your current laptop.

no rewrite as a message

I’ve built a blog system that can take any prompt and generate new articles using everything I’ve ever written or created with an LLM. It searches through my personal database of past writing, pulls the most relevant pieces, and builds a new post from that. Each new post is then added back into the database, creating a kind of self-feeding loop. I’ve got a proof of concept working already—it’s just limited by the fact that I’m running smaller, local models, so the output quality isn’t on par with state-of-the-art systems. But the big advantage is that it’s completely free to run, so I can test and iterate as much as I want without any costs. That’s actually why I got this laptop in the first place.

now I want to create a CLIne prompt which will take the current set up and rebuild it so that it writes the blog posts in a more detailed and structured fashion by using an agentic llm workflow which refines the content until it meets conditions

Here’s a **CLIne-style prompt** you can drop directly into your project to rebuild your local blog generator into a more **structured, agentic workflow** that automatically refines and validates each generated post until it meets defined quality criteria. --- ### 🧠 **CLIne Prompt: Agentic Blog Generator Refinement Workflow** ``` # Task: Rebuild the current local blog generator into an agentic LLM workflow # Goal: Generate highly detailed, structured blog posts using iterative refinement agents [PROJECT]: Blog Genesis v2 – Agentic Refinement System [OBJECTIVE]: Transform the existing local blog generator into a multi-agent system that: 1. Takes a user prompt as input. 2. Searches the existing database of all past writings. 3. Synthesizes an initial draft based on semantic relevance. 4. Iteratively refines the draft using specialized agents (structure, tone, factuality, coherence). 5. Validates that the final output meets defined content conditions (structure, clarity, originality, length). 6. Outputs the final .md file to the blog content directory. 7. Automatically ingests the new post back into the knowledge base for future use. [AGENTIC WORKFLOW]: Agent 1 — Retriever: - Searches the local vector database (ChromaDB, SQLite, or other) for the top-k most relevant entries based on the input prompt. - Outputs a concise summary and relevant excerpts as context for the next agent. Agent 2 — Composer: - Uses the Retriever’s output to draft the initial blog post. - Adheres to markdown formatting with metadata (title, tags, date, description). - Ensures logical flow with introduction, analysis, and conclusion sections. Agent 3 — Refiner: - Reviews the Composer’s draft. - Enhances structure, style, depth, and coherence. - Expands sections that lack detail and trims redundancy. - Uses feedback loops to refine until it meets minimum content thresholds (clarity, factuality, originality, engagement). Agent 4 — Evaluator: - Applies rules/conditions for final approval: - ✅ Structured with intro, body, and conclusion - ✅ Minimum word count (configurable) - ✅ Uses markdown correctly - ✅ Passes coherence and readability checks - If not approved, sends feedback to the Refiner for another iteration. Agent 5 — Ingestor: - Saves the final output as a `.md` file. - Appends metadata and ingests it back into the local database. - Updates embeddings to ensure the new post is available for future retrievals. [CONFIGURATION]: - Backend: Python + Ollama (local) - Model: Any installed LLM (default: gpt-oss / qwen2.5 / phi3) - Storage: SQLite + ChromaDB - Output: ./content/posts/{slug}.md - Agents orchestrated via asyncio or a lightweight orchestrator (e.g., SmolAgents, custom agent loop) [COMMAND TO RUN]: python3 run_agentic_blog.py --prompt "Describe how AI can use its own writing as source material for creative self-iteration" [EXPECTED OUTPUT]: - A structured markdown blog post refined through multiple iterations. - Auto-ingested into the local knowledge base. - Fully offline and cost-free. # END OF PROMPT ``` --- Would you like me to generate the **`run_agentic_blog.py`** skeleton next — showing how to structure the agents (Retriever → Composer → Refiner → Evaluator → Ingestor) with Ollama and asyncio?