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Developed a Flutter-based Driver Mobile Application as part of an IoT-Based Smart Waste Management System to streamline waste collection operations. The application enables drivers to receive collection assignments, view smart bin locations, navigate optimized routes, update collection status, and monitor assigned tasks in real time.
The application communicates with the Laravel backend through REST APIs and synchronizes with IoT-enabled smart bins, allowing drivers to receive real-time updates on bin status and collection requests. The system was designed to improve operational efficiency, reduce collection delays, and support smart city waste management initiatives.
Key Features
• Secure Driver Authentication
• Real-Time Collection Task Assignment
• Interactive Map & Navigation
• Smart Bin Location Tracking
• Collection Status Updates
• Task History
• Profile Management
• Push Notifications
• REST API Integration
• Responsive Flutter User Interface
Technologies Used
Flutter, Dart, Laravel, PHP, REST APIs, MySQL, Firebase, Leaflet Maps, Git.
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AgentFlow10 — AI Agent & Automation Platform
Lead Engineer · Self-hosted AI agent platform with a visual workflow builder
The problem
Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.
There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.
What I built
AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.
The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.
What I did
I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.
Notable pieces of engineering:
A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent
LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text
A self-hosting path that a non-specialist can actually deploy
Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows
Why it matters for clients
This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.
Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker
🧠 I built an AI that reads my mechanic's receipts so I never have to remember my car's maintenance history again.
Meet OtoCare — my entry for the @Bubble Free For All challenge on @Contra. 🏁
Most of us can't answer: When did I last change the oil? What was the mileage? How much did I spend this year? The answers live in crumpled paper receipts and memory.
OtoCare fixes that with one simple loop:
📷 Snap a service receipt → AI extracts date, mileage, service type, parts & cost → you confirm → it's saved
⏰ Smart reminders tell you when maintenance is due (mileage + time based)
❤️ A transparent Vehicle Health Score (0–100) from your real records
🧠 OtoAI Copilot answers "What should I service next?" — using your actual data, never inventing anything
One focused workflow, built entirely nocode with Bubble + AI. From paper receipt to living digital service book in under 60 seconds.
You drive. You service. OtoCare remembers. 🚗✨
#BubbleFreeForAll #BuiltWithBubble #NoCode #Bubble #AI #VehicleMaintenance #AutoTech #IndoTech #NoCodeDev #Contra #OtoCare
indraseptianto19-97242.bubbleapps.io
Check out my Bubble app
Bubble is the only platform that lets you switch between AI prompting and visual editing to go beyond prototypes and launch real apps — no code required.
🔥 TalentFilter AI — AI-Powered Candidate Screening
TalentFilter AI is an AI-powered hiring application designed to help recruiters and hiring teams evaluate candidates faster and focus on the people who best match each role.
In Phase 1, I built the core hiring workflow: users can create and manage job openings, add candidates and CVs, automatically analyze candidate profiles with AI, generate match scores and hiring recommendations, and quickly identify the strongest candidates through a ranked dashboard and AI hiring insights.
The goal is simple: turn a pile of resumes into a clear, actionable shortlist.
Phase 2 will expand TalentFilter AI into a more complete recruiting workspace, including deeper candidate profiles, improved AI-generated interview questions and hiring insights, candidate pipeline/status management, stronger analytics, workflow automation, and a more polished end-to-end recruiter experience.
Built with: Bubble, AI integrations, workflow automation, dynamic data, and responsive UI.
