lucasnad27 / go.geo

Geometry/geography library in Go

Operating Instructions for Lucas

Below you'll find a collection of technical principles, tools, and frameworks. If you're interested in learning more about my experience and skills, I'd recommend checking out https://electricocean.io

First Principles for Good Software

1. Simple
2. Tractable
3. Repeat yourself -- don't build hasty abstractions
4. Simple, seriously.

Organizational Tools

• Omnifocus - A tool born out of the necessity of managing multiple people & projects
• Readwise - Surfaces daily highlights from books I've read int he past. Has become one of the favorite parts of my day.
• Goodreads - Seems to be rotting away after being acquired by Amazon, but still the best way for me to track my book reading.

Developer Tools

• VS Code - I always come back to VS Code, even if it is an electron app.
• VSpaceCode - The power of mnemonic prefixes and discoverability of SpaceMacs, as a VSCode Extension.
• Magit - Well-crafted UI abstraction for the git CLI, integrates with Emacs & VSpaceCode.
• Starship - First bash-it, then ohmyzsh, and now Starship. Each one required less configuration and similar efficiencies. I don't use a terminal without it.
• Codeium - Preferred AI coding assistant. Crazy fast. Constantly improving. Generour free tier for solo developers.

Frontend Awesome

A collection of my go-to tools for frontend development

• Svelte + SvelteKit - Managing state and updating the dom just make sense with this no nonsense framework.
• Tailwind CSS - Utility-first CSS framework that reduces my urge to retreat to backend development when building UIs.

Backend / Data Science Awesome

A combination of backend and data science resources

• Advanced Python Testing - I come back to this article again & again when building test pipelines and best practices.
• Black - Reduces the need for 80% of code comments (python).
• Dramatiq - Preferred async task processing library (python).
• Hugging Face - Github for machine learning

Operations Tools

I've worked with AWS, GCP, and Azure. Cloudflare provides a subset of these functionalities, but for most work loads, the gap is closing fast. And it's usually cheaper and easier to reason about. Here's the stack I used when re-building my B2B SaaS company, EasyLocates, with Cloudflare tooling.

• Object Storage: Cloudflare R2
• Image Pipeline: Cloudflare Images
• Compute: Cloudflare Workers/Pages -- combine with a meta-framework like SvelteKit and you have a redundant, self-healing node backend with zero configuration
• CI/CD: Integrates directly with Github
• Integration Testing: Github Actions continues to make my old operational skills obsolete. And I'm ok with that.
• AI: You can quickly run AI experiements with Cloudflare's built-in models. And their in-house vector DB, Vectorize, allows you to build RAG prototypes.
• DB: Postgres continues to be the go-to option for RDB data workloads.