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About me
Software Engineer · Go & Elixir · Cloud-Native
I'm a passionate Software Development Engineer at Initializ with experience building scalable backend systems and cloud-native applications. I specialize in crafting robust, high-performance solutions that handle thousands of concurrent users.
My expertise lies in backend development with Go, Elixir, PostgreSQL, Redis, Kubernetes and AWS — focusing on distributed systems, microservices architectures, and secure APIs.
I write about things I learn while building: system design tradeoffs, Go internals, AWS infrastructure, and the occasional silly bug that cost me three hours. The goal is to capture genuine insights from real engineering work, not textbook theory.
Aug 2024–Now
Initializ.ai
Architecting and developing high-traffic lending platforms serving thousands of concurrent users. Building a RAG-based AI Assistant for the Initializ platform. Go, Elixir, Kubernetes, AWS.
Aug 2023–Jul 2024
Initializ.ai
Built secure client-side encryption modules and custom Kubernetes controllers for automated infrastructure management.
Mar 2023–Aug 2023
EMSEC
Engineered a secure and scalable backend system to manage SSL certificate data from over 100 websites.
My daily editor. Clean, fast, and the extension ecosystem covers everything I need.
AI-native IDE on top of VS Code — excellent for exploring unfamiliar codebases quickly.
Shell with sensible defaults, aliases, and plugins (git, zsh-autosuggestions, syntax highlighting).
Terminal emulator on macOS. Native splits, profiles per project, and solid colour themes.
Session management for long-running processes — invaluable when SSHing into remote servers.
My go-to (pun intended) for backend services. Compiled, fast, simple concurrency, and excellent tooling.
For anything touching the web layer — APIs, full-stack apps, tooling scripts.
Scripting, data processing, and quick prototypes. Hard to beat for one-off automation.
Runtime for TypeScript services and tooling when the JS ecosystem wins.
First choice for relational data. Reliable, feature-rich, and the JSONB support is underrated.
When the data model genuinely calls for documents. Used for content-heavy and flexible schemas.
Caching, pub/sub, rate limiting, and session storage. Always nearby in the stack.
When I need single-digit millisecond latency at scale and can design around its constraints.
Primary cloud. Mostly S3, Lambda, EC2, RDS, SNS/SQS, and IAM. Deep familiarity with the pricing model too.
Containers for local dev parity, CI pipelines, and deployment. docker-compose for multi-service local setups.
CI/CD pipelines. Simple YAML, good marketplace, free tier is generous.
Frontend and Next.js deployments. Zero-config, edge network, excellent DX.
All production servers run Linux. Ubuntu for predictability, documentation density, and package availability.
API development and testing. Bruno for local-first, git-friendly collections.
Database GUI that works with every database I use. Saves time on schema exploration.
Version control. Conventional commits, feature branches, squash merges. Nothing exotic.
Terminal UI for Kubernetes. Makes cluster inspection and log tailing much less painful.
JSON on the command line. Indispensable when working with API responses in scripts.
Framework. Server components, static generation, and API routes all in one place.
Utility-first CSS. Once you internalize the scale, the speed is unmatched.
View counts, upvotes, and reports are stored here. Client-only; no API in the loop.
Deployment. Every push to main deploys in ~30 seconds.
All posts are plain Markdown files in the repo. No CMS, no database for content.
Personal knowledge base and drafts. Local-first, Markdown, and the graph view is genuinely useful.
Issue tracking for projects. Fast, keyboard-driven, and doesn't get in the way.
Whiteboard for system design diagrams. Hand-drawn look makes it feel low-stakes and fast to iterate.
Team docs and wikis when collaborating. Not for personal notes (too slow), but good for shared knowledge.
I'm always happy to talk about system design, backend engineering, or interesting engineering problems.