# Olayinka David Vaughan

> Quant systems & full-stack engineer (Economics + Computer Science at Wesleyan University, declaring fall 2026). This portfolio renders as an interactive 3D "server farm": each of 15 projects is a server rack, grouped into 5 discipline clusters (quant, SWE, analyst, cybersec, and AI/ML). The scene is WebGL and invisible to text crawlers; this file is the machine-readable mirror of what it shows.

Built a limit-order-book matching engine at ~18M orders/sec in OCaml, a multi-agent reinforcement-learning economic OS in Python, and a neural network from scratch in C99. Economics + Computer Science at Wesleyan University (declaring fall 2026), based in Middletown, Connecticut.

Site: https://yinkavaughan.me/

## quant

Low-latency trading systems and from-scratch numerical code.

- [EconOS](https://econ-os.vercel.app): MARL economy · live shared mainframe — Multi-Agent Reinforcement Learning desktop environment for decentralized economic simulation. PPO-trained agents discover pricing, wage-setting, and consumption strategies inside a glassmorphic OS-style dashboard. (source: https://github.com/Builder106/EconOS)
- [OCaml LOB](https://ocaml-lob.vercel.app): ~18M orders/sec · p99 < 1µs — High-performance limit-order-book matching engine in OCaml 5. Allocation-free per-submit hot path, ~18M orders/sec, p99 latency under 1μs. Dream HTTP + SSE backend with a Bloomberg-terminal-style browser dashboard. (source: https://github.com/Builder106/ocaml_limit)
- [qforge](https://qforge-neural.vercel.app): Neural net in ~2k LoC C99 · zero deps — A neural network built from scratch in C99 — no TensorFlow, no PyTorch, no dependencies. Trains on market data, runs a DQN trading agent that outperforms buy-and-hold, ships as a WebAssembly demo. (source: https://github.com/Builder106/qforge)

## SWE

Shipped full-stack products with real users.

- [MicroMatch](https://trymicromatch.vercel.app): 123 tests passing · NGO ↔ volunteer marketplace — A micro-volunteering marketplace pairing NGOs with volunteers for 5–30 minute skill-building tasks. Volunteers browse the feed, claim missions, submit proof, and earn badges. NGOs post tasks and review submissions. (source: https://github.com/Builder106/MicroMatch)
- [STAIJA](https://staija.org): Live applicant flow · Nigeria's STEM scholars — Web platform for STAIJA's StepUp Scholars and Dynamerge programs — applicant tracking, mentorship workflow, and public content for Nigeria's STEM students. Application management, role-aware routing, and Mailgun-backed comms. (source: https://github.com/Builder106/STAIJA)
- [StudySprint](https://getstudysprint.vercel.app): Focus → plants → leaderboard — A study tracker that turns focus sessions into a growing garden. Run a focus timer, log sessions toward a daily goal, watch plants grow over time, and compare streaks on a public leaderboard. (source: https://github.com/Builder106/StudySprint)

## analyst

Statistical and data-pipeline work over real datasets.

- [CapitolAlpha](https://capitolalpha.vercel.app): 16,203 trades · +2.58% alpha (p < 0.05) — End-to-end Python pipeline auditing 16,203 disclosed Congressional stock trades from 2020–2024. Scrapes Senate and House Periodic Transaction Reports with Playwright + pdfplumber, computes Jensen's alpha against the S&P 500, and ships a Vercel findings page. Semester project for Wesleyan's QAC 420 — Data for Good. (source: https://github.com/Builder106/CapitolAlpha)
- [DataFest 2026](https://datafest-2026.vercel.app/): 3× ED-visit odds · n = 58,639 patients — ASA DataFest 2026 submission for Stormont Vail Health. R + DuckDB pipeline on a 7.6M-row EHR sample joined to a social-determinants questionnaire — patients reporting a transportation barrier show ~3× crude odds of ED visits and inpatient admits, independent of age. Wesleyan team 13. (source: https://github.com/Builder106/datafest-2026)
- [LinuxBenchHub](https://github.com/Builder106/LinuxBenchHub): Phoronix Test Suite · monthly CI captures — A benchmarking dataset and Rails 8 dashboard comparing Ubuntu, Fedora, and Debian under identical virtual hardware. Phoronix runs are captured monthly by GitHub Actions; R parsers and the dashboard consume the same composite XML, so the static analysis and the live UI never drift.

## cybersec

Supply-chain, agent, and on-chain economic security.

- [ClearHash](https://clear-hash.vercel.app): Source-rebuild gatekeeper · blocks supply-chain tampering — A pre-install gatekeeper that answers “is this binary actually a build of the source it claims?” Fetches a package, verifies its SLSA attestation through Sigstore + Rekor, rebuilds it from the attested source commit in an isolated Docker container, and blocks the install if the rebuilt file tree diverges from the registry artifact. Catches the event-stream / ua-parser-js / xz-utils class of attacks. (source: https://github.com/Builder106/ClearHash)
- [Halberd](https://halberd-keep.vercel.app): MCP firewall · p50 ≤ 200µs · 50k req/s — A JSON-RPC firewall that sits between an LLM agent and its Model Context Protocol servers. Every tools/call envelope is parsed, evaluated against a YAML policy bundle, and either forwarded or blocked with a synthetic error before a malicious payload reaches the host — defending against tool poisoning, argument injection, capability creep, and secret exfiltration. The policy engine compiles to WebAssembly for an in-browser playground. (source: https://github.com/Builder106/Halberd)
- [Quarry](https://quarry-mev.vercel.app): 188-byte Yul executor · 99.89% arb prediction — A bare-metal MEV arbitrage engine scoped to cross-DEX back-running. A TypeScript mempool scanner spots swaps about to land on Uniswap-V2-shaped pools, solves the optimal back-run input against post-victim reserves, and — if profit beats gas — packs calldata for a 188-byte Yul executor funded by an Aave V3 flashloan. No inventory; predatory sandwich and JIT strategies are explicitly out of scope. (source: https://github.com/Builder106/Quarry)

## AI/ML

Applied-LLM work with measured cost and accuracy per run.

- [Enclave](https://enclave-iota.vercel.app): 0 B egress on-device · privacy measured per run — An interactive workbench for clinical-document extraction that never phones home. Load a synthetic superbill, pick an extractor — a deterministic rules parser, a local on-device model (qwen2.5:3b via Ollama), or a hosted cloud model (Groq) — and watch a structured record fill in field-by-field against ground truth while a live gauge measures whether the document's bytes stayed on-device (0 B) or crossed to the cloud. Privacy isn't asserted, it's measured per run over 50 held-out synthetic superbills. (source: https://github.com/Builder106/Enclave)
- [Helm](https://helm-bridge.vercel.app): 99% invoice OCR · $0.0003/invoice · 15.4× faster — A Gemini 3.1 Flash Lite + MCP executive co-pilot for small-business operations. Four back-office workflows — AP-invoice OCR, creator-payout reconciliation, Tier-1 customer-service responses, and cross-company KPI Q&A — run end-to-end with measured cost and accuracy per task. AP-invoice OCR hits 99% parse / 91.9% field accuracy at $0.0003 per invoice (15.4× faster than a 6-min manual baseline); the payout reconciler exposes where an LLM reasons badly about multi-step arithmetic, so a deterministic re-computer disposes what the model proposes. (source: https://github.com/Builder106/Helm)
- [TradeTell](https://tradetell.streamlit.app): RAG over wiki + Discord + market data · writes Trader classes — A retrieval-augmented assistant for the IMC Prosperity trading competition. Ensemble retrieval over three weighted vector stores — competition wiki, community Discord exports, and historical market data — grounds answers about products, position limits, and strategy, and generates complete, ready-to-run Trader classes. Groq-backed generation (llama-3.3-70b-versatile) keeps inference fast inside a Streamlit chat UI that cites its sources per answer. (source: https://github.com/Builder106/IMC_Prosperity)

## Links

- [GitHub](https://github.com/Builder106)
- [LinkedIn](https://www.linkedin.com/in/yinka-vaughan/)
- [Devpost](https://devpost.com/olayinkav)
- [Resume (PDF)](https://yinkavaughan.me/Olayinka_Vaughan_Resume.pdf)
- [Email](mailto:vaughanolayinka@gmail.com)