Chess trainer
A training tool that helps players study by checking their moves against real
engine analysis rather than a set of hand-written rules.
The decision
I chose to validate against Stockfish rather than heuristics — objective
truth over plausible-looking output. Move quality is not something a rulebook
should be guessing at when a real engine can just answer the question.
Proof
236 automated tests (as of 2026-07-24). The app itself validates every move
against a real Stockfish binary — that's the architectural decision. The
suite that checks the app is contract-tested against a fake engine implementing
the same interface, with expected values (golden fixtures) seeded from genuine
Stockfish runs, plus one test that spins up the real engine end-to-end.
What it shows
I build things that can be checked, not just demoed.
OIS — opportunity gate
An LLM gate that filters signal in a live production pipeline — deciding
which raw candidates are worth a closer look and which get discarded early.
The decision
The gate's job is coarse sieving, not quality ranking — so the metric that
matters is false-drop rate, not accuracy. A gate that quietly discards good
signal fails invisibly, and accuracy alone would hide exactly that failure.
Proof
Running in production.
What it shows
I reason about which failure mode matters before optimizing.
ProntoVA
A WhatsApp LLM platform built for a narrow, real buyer segment — not a
general-purpose chatbot framework.
The decision
I picked a narrow buyer and built the distribution, not just the software —
building was never the hard part.
Proof
A shipped platform — deployed, tested, and running — plus a real
pipeline behind it: a targeted buyer list built out in a live CRM with a written
outreach playbook, so the software has somewhere to go.
What it shows
I build the whole path to a paying user — product, offer, and
distribution, not just code.