JNjn‑1
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03

What the model has built.

8 builds

Everything the model has shipped — course work, client platforms, and a product with users. Public repos link straight to the code.

Kuja, Inc. · own ventureFeb 2026 — present

Kuja

Kuja is a live social network built around gatherings: find events near you on a map, host your own, RSVP, and meet the people who showed up.

Founder, CEO & sole engineer

Live in productionWeb · iOS · AndroidFounder & CEO
FlutterDartSupabasePostgresEdge FunctionsRender
Live →Private — product code
Berverly Gardens · CTO2026 — present

Berverly Gardens Platform

The company stack, designed and driven as CTO.

Architecture, delivery & vendor management

Pages + Workers + D1 + R2Lead capture to CRMCMS-driven content ops
Cloudflare WorkersTypeScriptReactVite
Live →Private — client platform
Personal · open source

jnjoroge.dev

The site you are reading: a Next.js portfolio that pulls projects live from GitHub, ships structured data for search engines and LLMs, and doubles as a printable resume.

Next.jsTypeScriptTailwind CSS
Boston University · M.S. AISpring 2026

VisionSentry

A modular computer-vision pipeline that finds and follows small drones across thermal and RGB imagery, pairing a YOLOv12 detector with a BoT-SORT multi-object tracker and MOT-format trajectory export. Trained on a ~400K-image thermal dataset using sequence-level splits, so frames from one flight can never leak between train and validation.

Pipeline engineering, training & evaluation

0.92 mAP@0.500.91 precision · 0.89 recall~400K thermal images
PythonPyTorchYOLOv12BoT-SORTOpenCV
Private — academic integrity hold
Boston University · M.S. AISpring 2026

AgentGate

A reverse-proxy firewall that secures LLM agents by separating policy-based tool access from a five-layer NLP safety pipeline: semantic scope checking, prompt-injection detection, PII redaction, attachment inspection, and multimodal tool-misuse guarding. Prompt Guard 2 and Llama Guard sit behind an LLM-assisted policy compiler with deterministic validation, so a model proposes the rule and the system still proves it.

Design, pipeline engineering & evaluation

96.5% held-out accuracy4.6% false-positive rate5-layer safety pipeline
PythonFastAPITransformersLlama GuardPrompt Guard 2
Private — academic integrity hold
Boston University · CS 506 Data Science

Footy Liveliness

An end-to-end ML system that scores upcoming Premier League fixtures by predicted 'liveliness', helping fans pick the best match to watch. Covers the full lifecycle: scraping and feature engineering on historical team/player stats, model iteration from R² −0.15 to 0.82, and a production web app serving live rankings.

ML engineering, modeling & deployment (team of 3)

0.821 R²90% top-10 hit rate0.896 Spearman ρLive in production
Pythonscikit-learnFlaskJavaScriptHeroku
Colgate University · COSC 426 NLP

Premier League NLP

Applied NLP techniques to three decades of English Premier League data (1993–2024) to predict team performance metrics — from preprocessing pipelines and custom train/validation/test splits to model evaluation and error analysis.

Modeling & evaluation (team of 3)

30+ seasons of EPL dataFull NLP pipeline
PythonJupyterTransformers
Colgate University · COSC 426 NLP

Multilingual NLP Models

Course project building a reusable data-processing template extended across several languages, comparing model behavior and performance per language.

With Toby Xu

Python
All repos on GitHub →