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Achievement: Pull Shark

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Achievement: Pull Shark

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@hackingroom

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learnercys/README.md

Hi, I'm Carlos 👋

Senior Full Stack Engineer · 10+ years building backend systems that move money, data, and (lately) LLM tokens reliably.

I design distributed services in Go and Python: event-driven pipelines, multi-tenant APIs, and payment workflows. I run them on AWS and Kubernetes. Today I'm building microservices and AI-powered workflows. Before that I built data platforms for higher education, led banking and fintech projects, and worked on payments infrastructure.

🔧 What I work with

Languages: Go · Python · TypeScript Backend: gRPC · REST · FastAPI · PostgreSQL · Redis · SQS · event-driven architecture Cloud & Infra: AWS · Kubernetes · Terraform · Docker · GitHub Actions · OpenTelemetry AI: OpenAI & Anthropic APIs · LLM workflows & evals · MCP · Claude Code / Cursor Frontend: React · Next.js · React Native

🚀 Featured projects

Project What it shows
event-pipeline Payment-event pipeline in Go + Python: transactional outbox, SNS/SQS fan-out, idempotent consumers, and a chaos test (SIGKILLs, a Postgres restart, injected crashes) that proves every balance stays exact.
ledger Multi-tenant double-entry ledger in Go: gRPC + HTTP/JSON, Keycloak OIDC, Postgres row-level security, money rules enforced by the database, and property-based tests that prove money is never created, lost or overdrawn.
llm-gateway Provider-agnostic LLM gateway in Python: Claude + OpenAI fallback with circuit breakers, structured outputs, per-tenant budgets and rate limits, cost tracking, and a ticket-triage eval suite scored in CI.
platform Helm charts and a one-command local Kubernetes environment for all of the above, an OpenTelemetry pipeline with Grafana dashboards, and Terraform for AWS (EKS, RDS, SNS/SQS, Pod Identity), with security scans and an end-to-end deploy in CI.

Every project runs locally with one command (make up) and has CI that tests it end to end.

Fundamentals, for fun: mini-git (a Git implementation in Go) · monkey (an interpreter in Go)

💡 Things I care about

  • Correctness under failure. Retries, duplicates, and partial outages are the normal case, not edge cases.
  • Boring, observable systems. If it can't be traced, measured, and rolled back, it isn't done.
  • Pragmatic AI. LLMs are a component like any other: they need evals, budgets, and fallbacks.

📫 Find me

Website · LinkedIn

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  1. mini-git mini-git Public

    A small Git implementation in Go: content-addressable object store, trees, commits, branches, checkout, and fast-forward merge

    Go

  2. event-pipeline event-pipeline Public

    Payment-event pipeline in Go + Python: transactional outbox, SNS/SQS fan-out, idempotent consumers, and a chaos test that proves exactly-once effects

    Go

  3. ledger ledger Public

    Multi-tenant double-entry ledger in Go: gRPC + HTTP/JSON, Keycloak OIDC, Postgres row-level security, DB-enforced money invariants, and property-based tests

    Go

  4. llm-gateway llm-gateway Public

    Provider-agnostic LLM gateway in Python: Claude + OpenAI fallback with circuit breakers, per-tenant budgets and rate limits, cost tracking, and a ticket-triage eval suite

    Python

  5. monkey monkey Public

    Tree-walking interpreter in Go: lexer, Pratt parser, closures, arrays, hashes, and built-ins

    Go 1

  6. platform platform Public

    Helm charts, local Kubernetes (kind), OpenTelemetry observability and Terraform for AWS (EKS, RDS, SNS/SQS) for event-pipeline, ledger and llm-gateway

    HCL