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Raven: AI-powered knowledge management app with notes, tasks, and built-in streaming chat. 34 components, 25 composables, 13 Supabase PostgreSQL tables with Row Level Security policies on every table. 12 AI functions including streaming responses, voice input, and automatic conversation titles via Gemini.
Full i18n (EN/ES) with offline support and sync queue for unreliable connections. Real-time CRUD, folder organization, and workspace-based content separation. Auth via Supabase with RLS enforcing data isolation between workspaces.
Nuxt 4, TypeScript strict mode, Supabase (PostgreSQL, Auth, Storage). Custom instance deployed for a private client with ongoing development.
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AI-Powered Outreach Automation (n8n)
Built a workflow that turns outreach into a structured pipeline.
The system:
• Pulls verified leads from Supabase
• Uses AI to generate personalized emails
• Sends outreach via Gmail
• Monitors replies and analyzes sentiment
• Routes responses automatically (book meeting, review, or archive)
• Sends follow-ups if there’s no reply after 48 hours
Result: a fully automated pipeline that moves leads → conversations → booked meetings.
Designed for creators, agencies, and teams running outbound outreach at scale.
Nice workflow. Using AI to personalize outreach and track replies in one pipeline is really useful.
Build a Social Canvas with Google Gemini specially for a client who couldn't afford my services anymore.
I gave them a couple pre-build presets, which they can load into the canvas easily.
No design skills needed or subscriptions.
They where very happy https://www.winsbits.nl/canvas
Do you think something like this is scalable or would it be just a cool tool?
I don't know, I kind of feel like this is something you can offer to your client once they can't afford you anymore for any reason. Still valuable?
Wow!
Built a full-stack AI automation system that transforms how the company responds to RFPs.
Users upload RFP documents (PDF/Excel) through a web portal. An n8n pipeline processes the upload: AI parses the document to extract key fields (issuer, scope, budget, deadline), classifies the service line, matches content from an Airtable library (team bios, references, portfolio), and generates 80-90% complete proposal drafts with cover letter, company overview, technical approach, and more. Output is a formatted Word document ready for review.
Results:
✅ Reduced proposal creation from 4-6 hours to under 30 minutes
✅ 90% auto-complete rate on proposal sections
✅ 14+ RFPs processed through the pipeline
✅ Full proposal dashboard with status tracking
Tech: n8n, OpenAI, Airtable, Vercel, Webhooks
This is a really interesting automation workflow, Peter. Using AI to parse RFPs and streamline proposals is a smart use case.
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