Software Engineer

Alexis Alulema

Software Engineer · Builder

Software Engineer with 20+ years turning complex problems into scalable solutions — across software, cloud, AI and hardware.

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Real AI projects you can spin up on demand — each one gets its own ephemeral environment.

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Live demo

Trip Planner (Chain of Agents)

Type a trip request and watch a chain of specialised agents hand the work along in real time until they deliver a day-by-day itinerary checked against your budget. A LangGraph orchestrator owns the control flow; a local Qwen 2.5 writes only the prose, while typed decisions and plain arithmetic handle the rest. When the plan goes over budget, a conflict-resolution agent renegotiates it without a human. Local LLM (Qwen 2.5 via Ollama) and no API keys; live weather, places and exchange rates come from open data sources.

PythonFastAPILangGraph

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Live demo

Agentic Racing

Six identical cars race a closed circuit in the browser. Each car is a two-tier agent: a pilot that drives every frame, and an independent LLM team boss that reads live telemetry and radios back a strategy — attack, defend, conserve — F1 team-radio style, on screen. The race never waits on the model: a fixed heuristic takes over the instant it is slow, off, or replies invalid JSON. Fully self-hosted: a local llama3.2:3b behind a proxy with its own circuit breaker, rate limit and concurrency gate.

Unity WebGLPythonFastAPI

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Live demo

RAG Chatbot over Blog Posts

An interactive retrieval-augmented generation chatbot that answers questions using my blog posts as a knowledge base. Fully self-hosted: semantic vector search (pgvector) with multilingual embeddings and a local Qwen 2.5 LLM served by Ollama — no external LLM APIs.

PythonFastAPIpgvector

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Series · 5

Reinforcement Learning from scratch

Five articles that teach RL using a real project — Agentic Racing — as the lab: from what an agent is, to PPO, imitation and how to combine them.

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Latest posts

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October 10, 2026 26 min read

How Do We Tell an Agent What We Want? Reward Engineering Explained from Scratch

Part 6 of the Reinforcement Learning from scratch series with Agentic Racing. How a human goal gets translated into a reward, using the project's real RL pilot reward as a case study: scales, units, dense and sparse rewards, terminations that act as rewards, reward shaping, and why seven variants of the reward couldn't fix a problem that wasn't about the reward.

reinforcement-learningmachine-learningagentsunity

September 28, 2026 12 min read

A Trip Planner That Reasons Without Generating: Chain-of-Agents with a 1.5B Model

I took the Chain-of-Agents pattern from chapter 7 of 30 Agents Every AI Engineer Must Build and turned it into a real demo: a local 1.5B model that writes only what it must, a JEV-style decision engine that answers with probabilities instead of text, and a budget-conflict loop that resolves without generating a single token. What worked, what the model made up, and how the code ended up writing the honest part.

agentsllmlanggraphollamapythonfastapi

September 16, 2026 18 min read

Agentic Racing: A Pilot That Never Learned to Drive, a Team Boss That Did Learn to Reason

I built a racing demo where a reactive pilot obeys a team-boss LLM that reasons per event. Eight attempts at training the pilot with reinforcement learning didn't work — the track was broken before the algorithm ever had a chance — and measuring how much the strategist actually contributes turned into the more interesting experiment: seven runs, real and partially fixable reasoning biases, and a lesson about trusting the full benchmark over the quick desk-check.

reinforcement-learningllmunityagentsollamapython