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.

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.

Membership associations sometimes need more than a recommended waiver. A group insurance policy may require every member club to present the same approved language to its Participants.

Emailing a Word document to each club distributes the wording, but it does not keep that wording under control. A club can edit its copy, miss the next update, or keep collecting responses against an old version. Later, the association may have no reliable way to tell which document a Participant actually saw.

Curling IO v3 will connect those pieces. A membership association can publish one locked waiver, make it available to its member clubs, update it in one place, and report on which clubs have Participants responding to it.

Curling IO used to run on Debian configured with Ansible. That setup worked. It installed packages, configured Caddy and the firewall, created systemd services, and prepared our blue-green deployment slots.

The problem was not that Ansible couldn't describe the server. The problem was that a working, mutable server let us get away with an incomplete description. Old files remained under /etc. Build tools had been installed through a different path. Provider choices about disks and RAID lived outside the playbook. A provisioning run could succeed because an earlier run had already left the right thing behind.

NixOS is much less tolerant of these gaps. This strictness is occasionally annoying, but it is also the main benefit. It forces us to decide who owns a file, a service, a secret, a package, or a recovery input.

Moving from Ansible to NixOS taught us how much validation and cleanup the old approach had been missing. It exposed the difference between a server that continues to work and one we can recreate from declared inputs.

This is what we found useful while rebuilding an OVH bare-metal server from empty disks, restoring its state, and making the process safe enough to repeat.

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 sell much more than leagues and bonspiels. They run clinics, rent ice, assign lockers, host junior camps, sell banquet tables, and collect donations.

Today, each of those offerings has to fit one of Curling IO's predefined types. That works, but sometimes only because a club picks the closest available bucket. One active junior summer camp is stored as a Product, with its August dates written into the name. A full-sheet ice rental is stored as a Program because it needs a date, capacity, and a waitlist. A donation is stored as a Fee.

In Curling IO v3, the organization decides which product types it needs. A club can create Camps, Clinics, Ice Rentals, Locker Rentals, Fundraising, or any other category that fits its operation, then choose what each type can do.

We know that many of our clubs use WordPress or Joomla for their curling websites. These are popular platforms, and for good reason: they're flexible and there's no shortage of tutorials and plugins. But that popularity comes with a serious downside. General-purpose CMS platforms are big targets, and volunteer-run clubs often don't have anyone watching the security queue. Here's what you need to know.