Tamper-Proof Content Blocker & Device Protection App

System-Level Mobile Threat Protection & Local Network Filtering

The Challenge

Standard Android content filters are easy for users to bypass, disable, or uninstall, while third-party remote VPNs introduce severe latency and data privacy risks.

The Solution & Impact

Built a Kotlin-native Android application that enforces device-level content filtering and anti-tampering protection locally without sending user traffic to external servers.

Local VPN & DNS Filtering: Intercepts system-wide DNS packets via a local VpnService TUN interface to block prohibited domains locally with zero latency.

System-Level Security Hooks: Utilized Android Accessibility Services and Device Admin APIs to prevent unauthorized uninstallation, force-stopping, or setting bypasses.

Tamper-Proof Lock: Enforced settings locking via an encrypted, randomized password generator to ensure non-bypassable enforcement.

Tech Stack: Kotlin | Android SDK | VpnService | Accessibility APIs | DeviceAdminReceiver | Jetpack Compose

Keeping Device Admin scope minimal is the right call, Google's been rejecting anti-uninstall apps that request broad policies. Curious if you're watching the slow deprecation path there, since newer restriction APIs don't have a clean replacement yet for this exact use case.

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