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Stockpile is an AI inventory workflow system built to automate stock operations with tighter control over writes, costs, and long-running tasks.
I built it to validate each action before execution, gate AI behavior through explicit rules, and handle inventory workflows in a way that stays fail-proof in production.
The system was built with fail-closed outputs, auto-repair paths, and async processing so the workflow stays stable under repeated use and heavier job volume.
This sounds incredibly solid. Building in fail-closed outputs and explicit rule gates is exactly how you make AI actually dependable in production. Love the focus on stability under load.
Thanks. I appreciate it 🤝
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This is my first real vibe coding project.
Aguante started as a marketplace idea for football shirt collectors.
The original goal was to connect buyers and sellers of rare, vintage and historic football shirts in a dedicated platform focused on collectors culture. But during the research process, interviews and competitive analysis, we realized something interesting:
The real pain wasn’t only buying or selling. It was discovering.
Collectors spend hours searching through Mercado Livre, OLX, stores, Instagram pages and random marketplaces trying to find rare pieces, often with poor organization and low trust.
That insight changed the direction of the project.
Instead of starting as a marketplace, Aguante evolved into a discovery platform - a search engine focused on collectible football shirts.
The platform monitors multiple sources daily, indexing public listings and helping collectors discover rare, used and historic shirts in a cleaner and more curated experience.
This project also became my first real “vibe coding” experience.
The entire MVP is being designed and structured using tools like:
- Claude Code
- OpenAI Codex
- GitHub Actions
- scraping automations
- AI-assisted workflows
The process has been really interesting because it mixes:
UX/UI design, product thinking, scraping pipelines, SEO strategy, collector behavior research and AI-assisted development.
In just two weeks running, Aguante is already indexing more than 12,000 football shirt listings across multiple stores and marketplaces automatically.
Right now, the project is still in validation phase, but it’s exciting to see an idea that started as a simple niche marketplace slowly turning into something much bigger.
If you enjoy football culture, vintage shirts or collectible products, feel free to explore the project:
aguante.com.br
More than the stands, it’s collecting.
Nice work
Spent some free time building LUMA Homes, a premium proptech concept exploring a calmer way to discover properties through guided curation.
Built with Next.js, React, TypeScript, TailwindCSS, Framer Motion, and i18n-ready architecture.
Trying to push deeper into product-led UI, stronger frontend systems, and startup-grade user experiences.
Nice!
Title: AI Lead Enrichment and Outreach Workflow
Description: Built an n8n workflow that reads a lead list from Google Sheets, visits each company website using an HTTP request node, and uses Groq LLaMA to generate a personalised cold email for each lead. The finished emails are written back into the spreadsheet automatically. Stack: n8n, Groq, Google Sheets.
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