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I’m building an automated content engine for a client using n8n and the Claude API. Every week it produces blog articles and social posts for eight brands. The client already has a blog with [number] articles, and we want the output to match that voice. What I’ve tried: [e.g. a tone-of-voice description in the system prompt / a few example articles in the prompt]. What I get is text that’s fine but sounds AI-written and slightly translated. It doesn’t read like a native Swedish copywriter wrote it. What I’m weighing: Is it better to extract a tone-of-voice guide from the blog (rules, word choices, sentence length, do’s and don’ts) or to pass example texts as few-shot? How many examples do I need, and should they be full articles or excerpts? Is a separate editor step that only reviews and adjusts tone worth it? How do you handle one shared base voice across eight brands that each have their own variant?

Maintaining an AI-built property finance app: proven Codex testing, release and handoff workflows? I’m building a small family property-management app using ChatGPT for requirements and Codex desktop on Linux for implementation, with TypeScript, PostgreSQL/Supabase and Vercel. It covers renovation forecasts, quotes, costs and payments, operating income and expenses, and family loans. The app is already in use. We have automated checks, isolated synthetic data, financial reconciliation and controlled releases. I’m looking for practical experience from people who have maintained an AI-built financial or admin app beyond its prototype: 1. How do you keep requirements, acceptance tests, candidate versions and deployed releases aligned across long Codex threads and workspaces? 2. What browser-testing and release-evidence workflow has caught failures that builds and unit tests missed? 3. Which GitHub skills or agent packages have improved your results without conflicting instructions or excessive process? 4. What works well for translating approved mockup images into maintainable application code, including screenshot comparison and mobile behaviour? 5. What broke after deployment, and what did you change? Links to maintained repositories, runbooks, example acceptance records or fictional-data screen recordings would be especially useful. The aim is to improve the existing application and workflow while keeping everyday tasks understandable for non-technical family users.

The content, business plan, and trajectory is established. Coding and execution is the precise matter that is required. I lack coding skills, and have yet to acquire a proper coder to fit the context.

I have tried many AI Tools however - I feel the time I Input data and effort into schedulign and resourcing is the problem - I need a Fix or a rubust reliable AI workflow so i can buy my time back. Any suggestions?

Learning baselines of app development, mainly coding. The concept and business model is fully intact, now the requirement is solely leaning on execution. The tools for execution are lacking.

I’m behind and self taught when it comes to prompt engineering and would like some guidance. I use terms like “exact replica” of image, “do not alter features”

I tried using Claude and it lost context of the whole project quickly and became a nightmare of human in the loop meat proxying. It made the exercise far from autonomous.

I record calls with Granola and paste the transcript into Claude, but the action items it pulls are hit or miss. Sometimes it skips things that were clearly decided and sometimes it invents owners. I tried asking for a table with owner and due date but it still guessed. Looking for a prompt or workflow that's reliable enough I can trust it without rereading the whole transcript.

I built an automation workflow with n8n and the OpenAI API to summarize AI news. I learned that prompt templates matter a lot, and chunking documents improved my results. My question for the community is about handling OpenAI API rate limits in n8n workflows. Is anyone else building AI news digest automations with n8n and ChatGPT prompts? 1. I built an AI news summarization workflow with n8n and the OpenAI API. 2. I used prompt templates and found that they had a significant impact on the results. 3. I added document chunking, which improved the summaries. 4. I’m looking for advice from others who have handled OpenAI API rate limits in n8n workflows.