DEV Community: Mejzun

Devlog #1: I built a backlog tracker because I kept forgetting what to play

Mejzun — Mon, 19 Oct 2026 17:41:52 +0000

I kept forgetting which games I planned to play. So I built a simple backlog: what I'm playing, what's next, what I've finished.

That was the whole idea. Here's how it grew into NXTO, and what I learned on the way.

Step 1: A list that works

The first version did one thing: three lists. Playing, planned, finished. No accounts to connect, no clever features. Just somewhere to put games so I'd stop losing them in notes, wishlists and my own memory.

It solved the problem. But once I was tracking finished games, a new question showed up.

Step 2: "What should I play next?"

Having a history of what I'd finished made recommendations the obvious next feature. And I had a specific reason to build my own: I own games on several platforms, and Steam's recommendations can't see what I played elsewhere. Every store only knows its own corner of my library.

So NXTO recommends based on everything I've played, with my ratings as the signal. (I wrote up the technical side of that, including why my first version suggested junk, here.)

Step 3: Sharing it

Once I had this working for myself, I wanted to share it. I put it online on a random domain I already owned, nothing polished, just a link I could send to people.

Step 4: People signed up

To my surprise, people started registering. That meant it needed a proper home, so I bought nxto.app.

Hosting costs money, so I looked for a way to cover it without putting up a paywall. I added a Buy Me a Coffee link, and a price comparison for game keys: open a game and NXTO shows where it's cheapest (Eneba, G2A, Instant Gaming, Kinguin), converted to your currency. Some of those links earn me a small commission, and you never pay more.

Honest numbers

Since the first signup in May:

79 accounts, 64 of them have added games
about 10,400 games tracked in total
a catalog of around 150,000 games to search from

I'm building this in the open, with a public ideas page where you can tell me what to build next: nxto.app/ideas.

If you track a backlog, what's the one feature you'd want? And how do you decide what to play next?

Nxto story #1: I kept forgetting which games i planned to play. So i build a simple backlog to track what i'm playnig, what i plan to play and what i've finished. #buildinginpublic #indiedev #gamingbacklog

Mejzun — Mon, 19 Oct 2026 17:23:31 +0000

Why my game recommender kept suggesting junk (and how IDF weighting fixed it)

Mejzun — Mon, 19 Oct 2026 17:22:12 +0000

I play on several platforms, but every store only recommends games based on what I did there. Steam had no idea what I'd finished elsewhere, so its suggestions kept missing. I wanted one place that knows my whole library, so I built it: NXTO, a backlog tracker with its own recommendations.

The stack is boring on purpose: Vue 3, Express, SQLite via better-sqlite3. Game metadata and tags come from RAWG. The interesting part was making the recommendations not suck.

First attempt: Jaccard on tags

The obvious approach: each game has tags, and two games are similar if their tag sets overlap. Jaccard similarity does exactly that.

It looked fine until I read the results. Games matched on filler tags like 2d, atmospheric and rpg, which are shared by thousands of titles. The tags that actually define a game, like farming or souls-like, were drowned out.

Fix 1: IDF weighting

Borrowing from search: a rare tag should count more than a common one. I weight every tag by inverse document frequency and compare games as vectors with cosine similarity. Two games sharing souls-like now look far more alike than two games sharing 2d.

Fix 2: the long tail of junk tags

IDF created a new problem. The catalog has about 48k distinct tags, and 90% of them occur fewer than 10 times: typos, one-off labels, joke tags. Under plain IDF those rare tags score higher than any real tag. One tag with a document frequency of 1 got a weight of 8.5 and hijacked the cosine.

The fix is a minimum document frequency (MIN_DF = 5). Anything below the floor is dropped from the vocabulary entirely.

Fix 3: ratings are signal, not just "finished"

Finishing a game doesn't mean you liked it. I turn the user's rating into a taste weight:

Rating	Weight
5★	+2.0
4★	+1.5
3★	+0.6
2★	-1.0
1★	-2.0
unrated	+0.8

3★ means "fine, not a signal", and 1-2★ actively push the taste vector away from similar games. A user's profile is the weighted sum of their games' tag vectors.

I also only rank games with at least 10 ratings. Below that, the catalog is full of unrated jam entries that no ranking can order honestly.

Caching without serving stale results

Scoring a whole catalog per request is expensive, so I cache the sorted pool per user. The key includes MAX(updated_at) and the row count for the user's games. Both are needed. updated_at has one-second resolution, and deleting a game doesn't move the maximum at all, so a timestamp-only key would keep serving a pool that still contains the game you just removed.

I also bump an ALGO_VERSION in the key whenever scoring changes, so a deploy never serves pools built by the old algorithm.

What I'd do differently

Look at the output early. The tag problem was invisible in the code and obvious in the results.
Treat the vocabulary as data to clean, not just math to tune.

If you track a backlog, I'd love to know what it recommends for you. And if you've built a recommender, what did you do about noisy tags?

Try it: nxto.app

ive made game recommendation app nxto.app, it base on your game backlog and ur rating

Mejzun — Sat, 03 Oct 2026 11:43:50 +0000