Local-first agent tooling · production AI · full-stack systems
I’m Alfredo Dela Peña III, engineering lead at Centervert and AI engineer at Boost Capital. I’m the guy you call when the job crosses frontend, backend, agents, evals, infra, documents, fraud checks, dashboards, memory, and whatever broke at 2AM. True harness engineer: I wire the tools, bend the workflow, and ship the system.
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└──────────────────────────────────┘At Boost I work on AI Parser Studio: OCR, multi-provider LLM extraction, field dictionaries, fraud scoring, monitoring, and failover around documents that feed onboarding decisions.
Memorantado started from a simple irritation: I wanted useful local memory without logging into something just to put rows in SQLite.
Tulay ng Mama Mo is a two-way communication bridge between Codex and Claude Code. The name is what happens when I name things too quickly.
I made my own local-first memory for me because I could not be bothered logging in just for some SQLite.
02I am notoriously bad at naming things, so I just thought of this. It is used for two-way communication between Codex and Claude Code.
03Forked terminal agent multiplexer. I care about running multiple agents without turning the terminal into a circus.
04Forked Plankton because Alex Fazio could not be bothered merging my PR. Ported it for Pi and added Elixir support.
05Logger library. The name happened because I was picking my nose for boogers while trying to think of one.
AI engineer on AI Parser Studio: a production FastAPI document-processing platform for OCR, structured extraction, fraud detection, batch parsing, monitoring, and multi-provider LLM failover.
Client via BoostClient work through Boost. Worked on document parsing and fraud-detection gaps, including payslip AI-edit/inpainting cases flagged by risk teams.
AI / full-stackWorked on AI evaluation and contract/property document analysis, plus product surfaces around the workflow.
Real estate productProperty product work around listings, recommendation surfaces, and real-estate UX.
Community productReal-time chat, community tooling, influencer workflows, event tracking, and responsive management interfaces.
Find the actual constraint before touching code.
Remove steps, services, and abstractions that do not carry weight.
Use evals, logs, and boring verification instead of vibes.
Prefer the smaller system that survives contact with users.
For practical AI systems