Solo founder and full-stack engineer with ~3 years applied AI/software experience. I design and ship end-to-end systems — from real-time detection pipelines to bonded-cellular hardware — and I'm comfortable reasoning about model behavior, prompts, and edge cases at the level a training pipeline needs.
Autonomous AI clipping tool for streamers. Detects hype moments from live chat and audio using an adaptive per-stream statistical baseline (z-scores/EWMA — not fixed thresholds), then triggers a local desktop agent to pull the real OBS replay buffer, transcode, and publish the clip to a web dashboard in seconds — fully autonomous, no manual editing.
View repo →Compact bonded-cellular streaming hardware: a wallet-sized encoder unit with four simultaneous carrier modems and a wireless HDMI transmitter dongle, built to replace $10k+ broadcast rigs at a fraction of the cost. Reached full v5 specification — firmware, bill of materials, and build documentation — using SRT with forward error correction for stream resilience.
Private AI backend-as-a-service platform built for enterprise clients: a self-hosted stack giving companies their own retrieval-augmented AI layer over internal data, with workflow automation and model routing built in.
Full production website for a pan-African tour operator, built and shipped end-to-end for a freelance client: dated-departure package browsing, a custom safari request flow with camp selection, an auto-advancing photo hero, a social-impact section, and a chat widget for FAQ handling. Live at idealafricasafaris.com.
View repo →Automated forex and crypto strategy execution, built from scratch and running on a live MT5 demo account. Implements London Breakout and New York session strategies on XAUUSD with custom lot-sizing logic, plus a separate BTC 4H breakout bot against the Binance API.
I'm a self-taught software/AI developer on top of a formal Electronics and Computer Engineering degree — currently pursuing a Master's alongside my own ventures. I learn by shipping full systems end-to-end rather than by working through courses, which is why my projects span real-time backend infra, applied AI, and hardware.
I spend a lot of time in the gap between "the model gives a plausible answer" and "the system is actually correct under real conditions" — debugging routing logic, race conditions, and edge cases that only show up once real data hits a real pipeline. That's the same muscle AI training work draws on: reading an output carefully, knowing why it's right or wrong, and being precise about the correction.
Comfortable tracing a bug or a bad output back to its exact root cause rather than patching symptoms — the ops notes across my projects are full of "root cause found," not guesses.
I document architecture, decisions, and trade-offs as I go, so handoffs and reviews are fast and unambiguous — a habit that transfers directly to writing clear evaluation feedback.
Solo founder across four concurrent projects — used to making scoped, defensible calls without waiting on a team, and to being direct about what's working and what isn't.