Next.js frontends · Node.js backends · GitHub automation
The stack I reach for to ship fast, maintainable, production-ready apps.
Portfolio
Each entry covers the problem, approach, and outcome. Open a card for the full case study.
Hi, I'm Wahb — a self-taught full-stack developer who started coding just over a year ago and hasn't stopped shipping since. I build practical tools: web apps, backend services, and AI-powered systems. Most recently I've placed in two consecutive international hackathons — 3rd out of 775 participants at Hack for Humanity 2026 with EcoLens, and Technical Award winner at Hackonomics 2026 out of 1481 participants. Both solo builds. I care about making things that actually work — fast, reliable, and grounded in real-world needs.
Learned the basics of how websites are built and styled.
Started building small interactive projects and learning how logic and code work together.
Built complete web applications with modern tools and real features like dashboards and authentication.
Launched multiple real projects and ran them on my own Linux servers.
Deepened focus on performance, Core Web Vitals, and production-grade code quality.
🏆 Placed 3rd at Hack for Humanity 2026 — built EcoLens solo, an AI-powered waste classifier with gamified impact tracking, competing against 775 participants internationally.
🏆 Won the Technical Award at Hackonomics 2026 — built EconoQuest solo, an economics simulation game with a microservices backend, competing against 1481 participants internationally.
Going deep on DSA, mathematics, and ML fundamentals — building the foundation for serious AI/ML research.
Building an end-to-end ML audio pipeline for speaker diarization and publishing original research.
Full-stack engineer. Production-ready products, shipped fast.
Tell me what you're building.
Everything clients and recruiters typically want to know — answered upfront.
Still have a question not covered here?
Reach out directly
Full-Stack Engineer, AI/RAG Architect & Simulation Designer
Built a browser-based economics simulation with a full RAG advisory pipeline, Socratic AI advisor, and real-time WebSocket streaming — solo, on free infrastructure, for Hackonomics 2026. Players govern a nation across 7–8 fiscal years, adjusting real policy levers and living with the compound consequences — guided by a Socratic AI that asks pointed questions using their actual numbers and never gives answers.
April 2026
Economics education describes consequences; it doesn't produce them. Reading about the Weimar Republic gives you a fact. Making a money-printing decision at round 3 and watching it compress real salaries by round 5 gives you intuition. EconoQuest was designed to be the second kind of learning — a simulation where early policy decisions constrain your options for years, and every outcome is traceable to a choice you made.
Three-tier architecture on free infrastructure. React frontend communicates with a Fastify gateway via HTTP (authenticated flows) and WebSocket (streaming advisory responses). The gateway handles auth (same-domain cookie proxying for the browser session, JWT-in-URL for the WebSocket connection), LRU caching keyed by state hash and prompt version, and load balancing across two HuggingFace Spaces per service. Advisory requests flow through the conflict detector first — pure Python, zero tokens — then into the RAG pipeline: game state is embedded, pgvector in Supabase retrieves the most relevant of 99 knowledge chunks, a situated prompt is constructed, and Llama 3.3 70B via Groq streams the response back over WebSocket. KV caching keeps the streaming latency at ~5 seconds rather than 35. The archetype system and mandate timeline run entirely client-side at game end with no additional inference cost.
Submitted to Hackonomics 2026 as a solo build — zero cloud spend, production-grade architecture. The Socratic advisor generates genuinely situated hints using real game state: players get told about Weimar Germany because their specific inflation and money supply figures matched that RAG chunk, not because it was a random example. The WebSocket streaming pipeline held under demo conditions. The conflict detector eliminated the most dangerous false-positive advisor hints without touching the LLM.
Want to see the full RAG pipeline and simulation engine in action, or discuss production AI advisory systems built on free infrastructure? Try the live demo or reach out directly.
Full-Stack Engineer & ML Integrator
Built and shipped an AI-powered waste classification app solo in one month — placed 3rd internationally at Hack for Humanity 2026 out of 775 participants. EcoLens turns a photo of any waste item into an AI label, estimated CO₂/water/energy impact, and gamified points — bridging the gap between environmental awareness and measurable daily action.
March 2026
Most people want to recycle responsibly but face two blockers: they don't know what material something is, and they have no sense of whether their effort matters at scale. Existing recycling apps either give dry information with no feedback loop, or require manual input. EcoLens was built to solve both: real-time AI classification plus cumulative impact that compounds over time.
Fully serverless within Next.js — no separate backend process. Client sends a base64 image to POST /api/predict. The route proxies to the HuggingFace endpoint, receives labels + confidence scores, maps the top label to one of six categories (plastic, paper, glass, metal, organic, other), computes points and impact using estimated weight ranges per category, writes the scan to MongoDB, updates the user's cumulative stats and achievement state, then returns the enriched result to the client in a single response. Low-confidence results and 'unknown' labels are rejected before the write step — keeping impact data honest. Auth is handled via /api/user: OTP is generated and emailed on registration, verified on the next call, and a JWT is issued into an HTTP-only cookie with rotation on every subsequent authenticated request.
Placed 3rd internationally at Hack for Humanity 2026 — solo build, 775 participants. Judges scored the project on impact, innovation, design, and presentation. The predict → persist → notify pipeline held up under demo conditions, auth edge cases were handled, and the leaderboard and achievements gave the project a live, working feel rather than a static prototype.
Want to see the full pipeline in action or discuss how to integrate ML into a production web app? Check the live demo or reach out directly.