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PersonalRAGVault — search your own documents
An independent NextEleven software project for importing documents, searching a personal knowledge vault, and inspecting retrieved source material. This screenshot uses sample data. It demonstrates a practical AI workflow and a usable document-search interface.
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Product first or brand first? 🤔
We're cutting the promo reel for our case study and can't agree on its start. ✨
Which product ad opening would make you stay past the first 3 seconds?
Vote and tell why! 👇
Product DesignSoftware DevelopmentAdobe After EffectsCinema 4DFigmaBrand DesigncreativeaiworkflowUX Design
15 voted
25%
46 voted
75%
61 votes
Closed
I think both is important, but good product first.
AI Rent & Arrears Follow-Up Automation: Property Management Dashboard
A working demo of an arrears management dashboard for property managers. It detects overdue tenants, ranks who to follow up first, and drafts safe, factual tenant messages from stored data.
Chasing late rent is slow and inconsistent. Managers often don't know whom to contact first, and messages are written from scratch each time. I built a demo system that turns rent and payment data into a clear daily workflow:
Rent data → arrears detection → priority → follow-up message → escalation → resolution
What it does:
Dashboard: total rent due, collected, outstanding, overdue tenants, high-risk accounts and follow-ups due today.
Arrears queue: every overdue tenant with property, amount, days overdue, priority and a suggested action.
AI Arrears Prioritization: automatically ranks who the manager should contact first, based on days overdue, amount owed and past payment behaviour.
Account analysis: shows the amount outstanding, days overdue, payment history, communication log and a recommended next action for any tenant.
Tenant message generation: drafts a polite reminder, follow-up or escalation message matched to the account's stage.
Manual stage control: the manager moves each account through Reminder, Follow-up, Escalation and Resolved.
Tech stack: Python, Flask, SQLite, HTML, CSS, JavaScript, with optional local AI through Ollama and a rule-based fallback. It needs no paid APIs and runs fully offline.
Demo data: 5 properties, 20 units and tenants, six months of payment history and prior follow-up records, so the dashboard looks like a live system.
Honest framing: I'd label it a "working demo" or "prototype" rather than a production system. It uses seeded sample data and has no real SMS or email sending or login yet. Those would be the natural next steps for a client.
I built a free tool that tailors your resume to ANY job posting. Here's how it works 👇
Paste a job link.
That's it. One link.
Behind the scenes, it:
→ Reads the entire job posting
→ Pulls out what they're really asking for
→ Rewrites your resume to match it
→ Writes a custom cover letter (no generic fluff)
→ Saves both to a Google Doc, ready to send
No fake experience. No made-up achievements. Just your real work, presented the way each job wants to hear it.
If you're job hunting (or know someone who is), this could save hours.
Try it, break it, tell me what's missing. I'm building in public and want the feedback. 👇
Would love to hear what you think in the comments.
#AI #Automation #JobSearch #ResumeTips #CareerAdvice
The part where you just drop a single job link and it pulls the key requirements out is a neat way to cut the parsing step.