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Why We Chose Rust for Version 3

· 12 min read
Dave Rapin
Dave Rapin
Founder @ Curling IO
About this post

This is a technical architecture post from the development of Curling IO v3. It is written for software engineers and goes deeper into Gleam, Rust, SQLite, client-side UI architecture, and build tooling than our usual product posts.

For Curling IO v3, we knew we wanted to move away from the Ruby on Rails stack we'd used for v2. We wanted a statically typed language with stronger compile-time guarantees and fewer footguns. We first explored Gleam because it ticked those boxes and gave us access to the BEAM. Lustre offered a typed UI framework for both server and client. An initial implementation let us see how that combination would fit our architecture. Our early foundation posts describe what we were learning and building.

That work helped settle the product requirements, the changes we wanted from Version 2, and the UI design. It also exposed tradeoffs in deployment and client interactions that led us to ultimately choose Rust and TypeScript as the best fit for our architecture before going further with Gleam and Lustre. SQLite and the product design carried forward. We’ve since ported and expanded our SQL tooling and built a small TypeScript runtime called Hypertea for client interactions.

The move improved deployment, gave us useful compile-time automation, and suited the way we investigate and fix issues. Unfortunately, it also made our builds much heavier. There are things about Gleam and Lustre that we still prefer.

Why We Built Our Own Error Tracking

· 14 min read
Dave Rapin
Dave Rapin
Founder @ Curling IO
About this post

This is a technical implementation note about error tracking in Curling IO v3. It is written for software engineers and operators, and goes deeper into Rust, SQLite, durable jobs, source maps, diagnostic safety, and LLM requests than our usual product posts.

A request returning HTTP 500 or a background job failing gives us an error message, but investigating it usually requires more: the producing commit, a source location, the failure chain, the operations that ran before the failure, and enough occurrences to see whether the inputs vary.

Repeated failures need separate handling. One defect inside a loop can produce thousands of reports. We need to retain the occurrence count without sending an alert or making an LLM request for each report.

We could have sent these errors to a hosted error-tracking service. We built a narrower system ourselves for three reasons: data sovereignty, direct integration with our application and operations pipelines, and control over which internal data leaves our infrastructure.

The implementation is split between Curling IO and our separate Operations application. Curling captures a bounded diagnostic envelope without waiting for another service. Operations imports and deduplicates it, sends the alert, asks a fast, lightweight, low-cost LLM for a structured analysis through OpenRouter when that integration is enabled, and stores the result with the issue. The redacted issue record is available through a command and a static report.

Human in the Loop with Contracts

· 14 min read
Dave Rapin
Dave Rapin
Founder @ Curling IO
About this post

This is a technical implementation note about the AI assistant architecture in Curling IO v3. It is written for software engineers and others designing agent systems, and goes deeper into Rust, persistence, authorization, and failure handling than our usual product posts.

The usual human-in-the-loop AI agent pattern goes something like this: the model requests a tool call, the agent runtime pauses, a human approves the call, and the runtime resumes so the tool can execute.

That is a reasonable general-purpose design. It is also stricter than simply letting an agent call every tool it can see. For Curling IO, we wanted to expose the smallest possible surface to the model and put an application-owned guardrail around every path to a write. That led us to a stricter question:

If the application already has the exact call details, why hand control back to the agent at all?

By the time we ask a club manager to approve an operation, Curling IO has parsed the model's request, resolved every default, checked the current application state, produced a fixed preview, and stored the exact arguments. The model has nothing useful left to contribute to execution, so we do not let it execute the operation or resume it merely to carry out the approval.

The agent proposes. The application turns that proposal into a contract. The human approves the contract. Rust executes it.

Optimizing Curling Draw Schedules

· 26 min read
Dave Rapin
Dave Rapin
Founder @ Curling IO

This post is part of our Curling IO v3 sneak peek series, where we explore some of the new features available in the upcoming version.

Curling IO v3 includes a new draw scheduling screen for event games. The schedule is a grid of draws and club resources, with unassigned games kept in a queue beside it. You can drag and drop games, lock specific placements, and use Allocate and Optimize around those locks.

Unlike a separate schedule template generator, this editor works with the event's actual teams, stages, games, resources, and draw times. Saving the schedule updates the event directly.

You can try most of the scheduling interface now at CurlingSchedules.com. It uses generic teams and browser-local saves instead of an event's actual games, but the grid, drag-and-drop editing, locks, catalog schedules, fairness inspection, and optimization are available today.