Precomputing: Keeps Dashboards Fast Without Slowing Development Cycles
Teams that ship features fast need dashboards that answer questions instantly. The usual pattern is to recalculate answers on every refresh, or to ship all your data to a hosted analytics service and wait for it to ingest. Both approaches have the same problem: the dashboard either waits for the query, or you pay for scale you don’t need most of the time. In either case, answering a question about your product becomes a friction point.
Precomputing inverts the model. Instead of calculating answers on demand, you calculate them once, as the data comes in. You write a short policy that names the answers you want: requests per endpoint, month-to-date usage, error rates by status code. You say how long each level of detail should live. Precomputing compiles that policy to SQLite triggers, so every INSERT keeps those answers fresh, and reading an answer is a lookup.
For agile teams, the appeal is obvious. A sprint starts and someone asks: “How many requests did we get on that endpoint last week?” The answer is instant. You’re making decisions about the next feature based on real data without waiting for a query. Dashboards that answer in milliseconds instead of seconds change how a team works. You don’t skip checking the numbers because the check is too slow.
The tool is still just SQLite. Any SQLite tool opens the file and queries it directly. Your dashboard doesn’t need a separate API or service. You deploy the triggers once, they run invisibly, and the answers stay current. When triggers get too slow, you swap in a Go engine that runs the same policy and writes the same file, byte for byte. Your dashboard code doesn’t change.
The same policy language drives other things teams need. A usage meter for billing where a retried request counts once and a closed month stays locked. A log reducer that keeps every line on your machine but sends upstream only what a dashboard needs. An AI agent can ask the precomputed file over MCP instead of reading raw rows and doing the math itself.
Precomputing is version 0.1, tested hard on simulated data and not yet deployed to production traffic. The platform page covers the full architecture. The Policy Language walks through a policy line by line, showing how to define answers and set retention rules. The SQL demo puts three hours of simulated API traffic through a compiled policy in SQLite’s WebAssembly build, right in your browser, so you can see how answers stay current as traffic flows in.
For teams running their own infrastructure with SQLite on the backend, precomputing trades a one-time policy for the ability to answer questions at machine speed. No separate service. No API calls. No waiting. Your observability doesn’t slow down the development cycle, and dashboards that answer fast change how your team makes decisions.
The value proposition for agile development is that dashboards become a tool that accelerates sprints instead of slowing them down. You stop skipping the metrics check because it takes too long. You make decisions faster because the data is there. For teams that iterate fast and depend on feedback loops, instant answers are the difference between guessing and knowing.