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Built on Search Stack

Verdct: a film site with no database.

A film site you can browse by genre, year and certificate, search as you type, and review. Behind it: no database, no ORM, no search cluster, no schema — just two Search Stack lists. Delete them and there is nothing left to render.

Verdct's browse page filtered to science fiction, with genre and decade rails down the left, a count of 287 films across 12 pages, sort options, and a grid of posters.
Browsing by genre on theverdct.com. The rails, their counts, the ordering and the paging are one search call — there is no query in the app that assembles this.

No database at all

No persistence layer in the app. Every film, person and review is a record in a list; every page is one or more search calls.

The catalogue keeps itself

Films come from TMDB through an API feed we poll and stage. New releases appear, films that fall out are deactivated, and nobody re-imports anything by hand.

The search box isn't its code

Typeahead is our published npm widget calling suggest straight from the browser, across a group of two lists. Verdct's own server never sees a keystroke.

Every feature, and the call behind it

Each row is something you can click on theverdct.com, beside the documented Search Stack call that produces it.

On the site What Search Stack does
Browse, filter, sort and page the catalogue one search call with filters and ordering
The genre, year and certificate rails, with their counts facet values, asked for alongside the search
Suggestions as you type, over films and people at once suggest across a group of two lists
Typing a phrase from a review and finding the film the same query run over a second list
Finding a director or an actor by name a people list, plus the cast and director fields on each film
Posters, backdrops and trailers on every film resource fields — carried with the record, never matched as text
New films arriving as TMDB updates, old ones dropping out an API feed we poll and keep the list in step with
The median verdict and review count on each film stamped onto the record by that same feed
The search box itself the @searchstack/autocomplete widget, calling us straight from the browser
Typing "hitchcock" into Verdct's search box, with a dropdown holding Patricia, Michael and Alfred Hitchcock — each with a headshot and a role — followed by the films Rope, The Wrong Man and Strangers on a Train, each with a poster, year and genre.
One box, two lists: the people and the films come back together, ranked against each other. The suggestions go straight from us to the browser — Verdct's own server is not in this picture.

Reviews written in prose, not stars

  • Nobody picks a score. You describe the film, and a scoring guide turns that into a number on the same scale for everyone.
  • Each film carries the median of those scores, not the mean, so one furious reviewer can't move it.
  • The median is stamped onto the film's record by the same feed that keeps the catalogue fresh — a single record can never compute a figure across many.
Verdct's page for 12 Angry Men: poster, backdrop, tagline, a verdict of 92 described as Essential from the median of three scored reviews, and a breakdown of how those reviews landed.
One film page: poster, backdrop, synopsis, and a verdict of 92 that is the median of what three people said — all of it fields on a single record.

What it took to build

Two lists, one feed endpoint, and a front end. No migrations, no index tuning, no search server, and nothing to re-import when the data changes. Keeping the catalogue current, filtering it fast and searching it as you type are all configuration on a list.

If you want to build the same shape of thing with your own data, the search box lesson starts from an empty list and finishes with a working search box on a page.

Start free Visit theverdct.com
Free to 10,000 records. No credit card.
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