11 posts tagged with "architecture"

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.

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.

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.

Curling IO's admin panel should feel instant when a club manager is working through a season setup. Toggle a setting, save a discount, move between product sections: the page should respond without a full reload.

Version 2 works, but every form submission reloads the page. Version 3's admin is a single Lustre server component running on the BEAM. One WebSocket connection, one long-lived Erlang process per session. Every interaction goes over that WebSocket and comes back as a DOM patch. The page never reloads, and there's no client-side JavaScript framework.

Fair warning: this post contains some opinions that are going to be controversial and may not age well. Here be dragons.

AI coding agents like Claude Code, OpenAI Codex, and Google Gemini can write code, run it, read the errors, and try again. That loop is the whole game. The faster and more informative that loop is, the more useful the agent becomes. After building Curling IO Version 3 in Gleam alongside AI coding agents, I'm convinced Gleam is the best language for this workflow. Agents don't write better Gleam - there's less training data. But Gleam's compiler lets agents self-correct without waiting for a human.

While writing the previous post about our per-test SQLite databases, I was describing how each test gets its own in-memory database, no shared connections, no shared state. And I thought: wait, if nothing is shared, can we just run them all at the same time?

Turns out we could, and our server test suite went from ~4 seconds to ~0.85 seconds for around 800 tests. Zero code changes to the tests themselves. One 25-line Erlang module.

Curling IO's tests don't need a shared test database, cleanup hooks, or transaction tricks. Each test gets its own database, so a test can pass alone or in the full suite for the same reason: nothing else can touch its data.

That falls out of one Version 3 choice: SQLite runs in-process. Each test gets a completely independent in-memory SQLite database, cloned from a template in microseconds using SQLite's backup API.

Version 3 should be cheaper to operate, easier to restore, and still fast during peak registration and live scoring. That pushed us toward a choice we didn't expect: SQLite.

We assumed PostgreSQL at first. After a decade running Postgres in production, we knew the tooling, the failure modes, and the operational playbook. Then we compared self-hosting Postgres with Litestream-backed SQLite and changed our minds.

When a curler asks for a login email, the page should respond immediately. They shouldn't wait on Postmark, and we shouldn't run a separate worker fleet just to send a message in the background.

Curling IO Version 3 runs background jobs inside the same BEAM runtime as the web app. No Redis. No separate worker. No additional deployment. The queue is durable because pending jobs live in SQLite, not memory.

English and French support has to be boring. A club in Quebec shouldn't find a half-translated registration page, and an organization running national competitions shouldn't need a support ticket to catch a missing label.

Curling IO has been bilingual since Version 2, but after a decade of maintaining around 10,000 YAML translation keys, we've hit the limits of what that approach can catch: missing keys, missing translations, and unused keys that accumulate silently over time. Version 3 keeps the bilingual experience and makes more of those mistakes impossible to ship.