Software Engineer

Alexis Alulema

Software Engineer · Builder

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

Read the blog View projects

Try it live

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

See all projects →
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
Try the live demo →
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
Try the live demo →
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
Try the live demo →
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.

Start with part 1 →
  1. Part 1How Does an Agent Learn? Teaching a Car to Drive
  2. Part 2How Do We Improve a Policy Without Breaking It? PPO Explained from Scratch
  3. Part 3Can We Teach by Imitation? Behavioral Cloning Explained from Scratch
  4. Part 4Can We Learn to Behave Like an Expert? GAIL Explained from Scratch
  5. Part 5Can We Combine Imitation and RL? BC, GAIL, and PPO as One System

Latest posts

See all posts →
19 min read

What Should an Agent Be Able to See? Observation Design Explained from Scratch

Part 7 of the Reinforcement Learning from scratch series with Agentic Racing. How you decide what information an agent gets, going one by one through the project pilot's 42 real observations: scaling, anticipation, memory, what can let a policy memorize a circuit, what it doesn't see (the rivals), the bugs that sent empty observations, and why the pilot, the expert, and the LLM strategist deliberately see different things.

reinforcement-learningmachine-learningagentsunity
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
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