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.

Use Free Agents to find curlers who want to join a team in your league or bonspiel. It adds an opt-in to registration and gives your managers a list of people to contact.

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.

A member wants to spread their season fee over a few months. Collecting those payments by e-transfer and tracking them in a spreadsheet can be challenging.

Payment plans let you split selected purchases into scheduled installments. Members choose a plan at checkout, the installments are collected automatically, and every payment stays connected to the member's order in 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.

If your club uses a content management system (CMS), this might sound familiar. A staff member or volunteer sets up the website, but when they leave, no one else knows how to update it. Updates get put off, and old information stays online.

We built Website Studio to make it as easy as possible for club staff to keep their website up to date. Your website lives in Curling IO alongside your club’s programs, events, and registration. You don’t need to manage a complicated third-party website, like WordPress, or pay another provider just for website hosting.

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.

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 optional in-app assistance for most tasks a club manager does. It appears within the section where the work is happening and uses the context needed for that work.

An assistant can investigate a problem, explain what it finds, and prepare an operation for review. It performs that operation only after an administrator explicitly approves 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.

Clubs often need to send purchase information to systems outside Curling IO. That might mean updating a spreadsheet, notifying staff, or starting a workflow in another service.

Rentals are one example. Someone may need to prepare the lounge, reserve tables, arrange catering, or add the booking to a staff spreadsheet.

Curling IO v3 webhooks can send that product purchase to Zapier, n8n, or another HTTPS endpoint when it is submitted, paid, cancelled, or rescheduled.

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.

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 clubs use rentals for practice ice, lounges, meeting rooms, and other bookable resources they define. A rental needs a product and price, but it also needs a schedule, one or more resources, public availability, and a booking record after checkout.

In Curling IO v3, an administrator defines the rental schedule and resources in the admin area. Available time slots appear on the public calendar, where curlers can add one to the cart and complete checkout. This post explains the rental options being added and how they fit into the calendar.