Lukas Murdock
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Building Geodetic Go

Planted 02025-06-29

The Hunt Begins

Late one night, a friend mentioned something that would consume me for the next day: survey marks.

Survey markers, also called survey marks, survey monuments, or geodetic marks, are objects placed to mark key survey points on the Earth’s surface. They are used in geodetic and land surveying. A benchmark is a type of survey marker that indicates elevation (vertical position). Horizontal position markers used for triangulation are also known as triangulation stations. Benchmarking is the hobby of “hunting” for these marks.

Since 1807, NOAA’s National Geodetic Survey (NGS) and its predecessor agencies have placed permanent survey marks or monuments throughout the United States so we can know exact locations and elevations on the surface of the Earth. A typical mark is a brass, bronze, or aluminum disk (or rod), but marks might also be prominent objects like water towers or church spires. The National Geodetic Survey’s database contains information on over 1.5 million survey disks, each with a detailed datasheet describing its exact position and physical characteristics.

The National Geodetic Survey Map is an ArcGIS Online Web Map Application that enables users to view multiple datasets provided by the National Geodetic Survey.

The Mark Recovery Dashboard displays mark recoveries that have been submitted to NGS.

An app about hunting these marks and tracking progress in a region would give my friends and I a reason to explore places — some nerds need nerdy nudges to navigate nature.

Geocaching.com seems to have had this feature at some point but seems to have removed the dataset (or maybe they just removed the mark page).

Benchmark Hunter is an iOS app to hunt for NGS Survey Marks released in 2021.

This seemed like the perfect excuse to answer a bigger question: In June 2025, what does AI-assisted development look like for a solo developer building something real?

With respect to Pokemon Go, I made a new directory geodetic-go.

Data Pipeline

Information about survey monuments (aka “marks”) stored in the National Geodetic Survey’s Integrated Database (NGS IDB) may be retrieved and displayed in a variety of methods. One standard is known as a datasheet, an ASCII text file consisting of rigorously formatted lines of 80 columns

— The DSDATA Format

The NGS provides datasheets at the state-level.

I chucked the DSDATA format spec into Google Gemini Chat and asked it to write a parser with a focus on extracting latitude, longitude, and market type. First I had it write TypeScript — as the rest of the codebase would be. However, it kept producing non-working stuff. Then, I asked it to do it without specifying a language and it started doing it in python but I don’t want to deal with the (venv) stuff. So I told it to write it in Go and it worked first try.

type Datasheet struct {
	PID               string `parquet:"pid"`
	Designation       string `parquet:"designation"`
	State             string `parquet:"state"`
	County            string `parquet:"county"`
	Latitude          string `parquet:"latitude"`
	Longitude         string `parquet:"longitude"`
	OrthometricHeight string `parquet:"orthometric_height"`
	EllipsoidHeight   string `parquet:"ellipsoid_height"`
	MarkerType        string `parquet:"marker_type"`
	RawText           string `parquet:"raw_text"`
}

I knew I wanted to parse and store the data in Cloudflare R2 because I enjoy the product and pricing. My first idea was to store the data in SQLite but after realizing the total text size when storing the raw text I want to display would be in the GBs — I didn’t want to send GBs down to a client — I realized I needed to change my approach. With the importance of data compression and the write-once, read-many style of data, I chose to use Apache Parquet files.

NGS provides DataSheets at the State level. However, to minimize data requirements I partitioned by county as well.

The pipeline is:

  1. Download DataSheet text files from NGS (these come as .zip files)
  2. Parse and partition into Parquet files
  3. Upload parquet files

To upload into R2, I prompted Claude Code to write a script to use Rclone.

Frontend

I knew I wanted to use React Router v7 SPA mode — I’ve used Remix for years and have used React Router v7 in various other projects. Vite comes with Tailwind support but I just had Claude Code write a STYLE.md file and it starts like this

Terminal Design Features

Visual Style:

  • Classic green-on-black terminal color scheme
  • JetBrains Mono monospace font throughout
  • Terminal-style borders and panels
  • Animated blinking cursor effect
  • CRT-style scan line animation
  • Subtle screen grain effect

[…]

I didn’t start with this though but early on told Claude Code to redesign the frontend in this style and it did a great job.

AI models are great at writing terrible React code. Terrible, terrible, React code. I imagine this will get better over time — and there is certainly prompting improvements I can do but wow is it annoying.

Backend

I knew I wanted to use Cloudflare Workers for the backend if I could — there are a lot of limitations if you choose workers runtime but when it works it’s great and the platform is great. I chose Hono as the web framework and copied the Hono Stacks markdown documentation for Claude Code to use.

Hono’s RPC feature allows you to share API specs with little change to your code. The client generated by hc will read the spec and access the endpoint type-safety.

— Hono Stacks

I love this feature but interestingly enough Claude Code wrote 100% of the backend and client API code in this project. Not without needing adjustments though.

Dev missteps

  • Storing data in SQLite » moved to Apache Parquet
  • Served data through API » query directly from frontend
    • parquetjs in workers runtime: cloudflare workers fs.stat not implemented yet » hyparquet

AI Review

I have yet to use Claude Code in an unchained manner — I either tell it exactly what to do or I tell it to think about how to do something, review that, and then tell it to do it and approve/deny every step of the way. With AI tools, you can adjust the input and in some cases you can poke the black-box a bit to change the output — but at the end of the day the output is still non-deterministic.

From what I’ve seen, if you do not technically understand the output you will immediately shoot yourself in the foot. If you cannot tell if the output is bad you cannot adjust it and you just dig a deeper hole in which shit is flung. Even in this project, by choosing React and deciding to move fast, and not having proper guides setup before hand, I let slide several useEffects and useStates of genuinely bad code! AI will produce many egregious suggestions but if you are knowledgeable about it you can fix it.

I used Repomix to pack the documentation for Hono.js and React Router into their own markdown files so I could tell Claude Code to search the file on how to use a certain thing. I also copy-paste specific documentation from Cloudflare into markdown files — their site has a copy-as-markdown button and it works beautifully — and tell Claude Code to read that file.

80% of the time I went Read [x] [y] [z] and think about how to implement [a]. There’s certainly better ways of going about it but this works pretty good.

We recommend using the word “think” to trigger extended thinking mode, which gives Claude additional computation time to evaluate alternatives more thoroughly. These specific phrases are mapped directly to increasing levels of thinking budget in the system: “think” < “think hard” < “think harder” < “ultrathink.” Each level allocates progressively more thinking budget for Claude to use.

— Claude Code Best Practices

AI amplifies specific development practices. Good practices become superpowers, bad practices become disasters. The two best practices you can do right now are

  1. Clear documentation and convention.
  2. Defined workflows, quality gates, and testing strategies.

Tools used

Packages structure

  • packages
    • backend - Hono/Cloudflare Workers API
    • datasheet-downloader - Go downloader for NGS DataSheets
    • datasheet-parser - Go parser for NGS DataSheets → Parquet files
    • frontend - React Router web application

Resources

Check out my AI tools & resources reference