HashRabbit

What was HashRabbit?

• HashRabbit was a startup focused on operational tooling for Bitcoin mining, built to give mining farm operators better visibility into the health and performance of their hardware.
• Mining farms at the time ran large numbers of GPU rigs and ASIC devices, and operators needed to monitor metrics like device temperature, hash rates, and overall rig status across potentially hundreds of machines.
• HashRabbit’s vision was to connect discrete mining devices into a shared network, aggregate their operational data, and present it through a modern web dashboard, replacing the legacy monitoring software many operators were still running on outdated Windows systems with limited functionality and no active maintenance.
• The longer-term ambition included enabling operators to pool compute across rigs and coordinate mining effort across clusters, but the first iteration focused on a specific client operating mining farms in China who needed embedded software for their rigs and a web interface for monitoring device health.

How I joined HashRabbit?

• I joined HashRabbit as the sole frontend engineer on a small startup team, responsible for building the web dashboard that would serve as the primary interface for monitoring mining operations.
• The role offered a high degree of independence. I spent most of my time working self-sufficiently, going deep on problems, connecting the pieces between the data layer and the presentation layer, and focusing on clean UI and faithful implementation of the design.

The Dashboard & Charting

• The core deliverable was a web-based operations dashboard where mining farm operators could monitor device health, hash rates, temperatures, and other metrics across their rigs.

  • The dashboard featured charting and data visualization built with D3 and React, designed to give operators a comfortable, modern interface for understanding the state of their hardware at a glance.
  • Beyond the charts, the interface included navigational elements for browsing devices and pages, but the primary value was the real-time operational visibility, replacing what had previously been a limited legacy experience.

Elm and JavaScript Co-Piloting

• One of the more interesting aspects of the project was the decision to build a significant portion of the application logic in Elm, a functional programming language centered on simplicity, type safety, and modeling side effects as data.

  • Elm’s effect system allowed us to write programs for receiving, transforming, and routing API data with a high degree of confidence that side effects were fully intentional. The type system made it possible to model the application’s data flows as complete, predictable pipelines from API response to presentation-ready state.
  • However, writing the entire application in Elm was not practical at the time. The Elm ecosystem did not yet have mature libraries for charting and data visualization, so the presentation layer, including the charts, UI chrome, and interactive elements, was written in JavaScript, React, HTML, and CSS.

• This created a hybrid architecture where Elm handled the core application logic, including API requests, data transformation, and local caching, while JavaScript handled the rendering.

  • The experiment explored how far Elm and JavaScript could co-pilot each other, with Elm providing stability and correctness guarantees for the data layer and React providing ecosystem support and flexibility for the visual layer.
  • The approach was consistent with what others in the Elm community were exploring at the time, and contributing to that community through real production use felt like a meaningful way to support the language’s development.

Observations

• HashRabbit was an early-career experience that reinforced the value of deep, independent work, spending extended time with a problem, crafting a solution with care, and seeing it through to a stable result.
• The project also offered early exposure to the trade-offs of hybrid language architectures, including when to lean on a more constrained language for its guarantees and when to reach for a broader ecosystem to fill gaps a younger language had not yet covered.