No IMDB for web novels: readers build the list

There is no IMDB for web novels. No body rates Royal Road, Wattpad, or a translated Chinese serial. Nothing hands you a score you can trust. What you get is each platform's own ranking — reading counts, follows, posting cadence — and a recommendation layer that readers build themselves, and that keeps getting thicker.

On 17 October, four threads in a single day showed how that layer works and where it breaks.

Four threads, one complaint

• Someone merged tier lists into a tool. A reader says they combined 900+ Reddit tier lists into a LitRPG recommender. That "900+" is the poster's own number in the thread title, not an audited count, so quote it as theirs: Made a LitRPG series recommender which combines 900+ reddit tierlists together.
• Someone asked for the thing everyone wants: Is there a rating site (like IMDB) for web novels?. The answer, as always, is no.
• Someone went looking for the good stuff the rankings miss: What are some genuinely underrated Royal Road stories that deserve more attention?.
• The weekly r/noveltranslations thread: What have you read this week and what do you think about it?. Oldest curation format there is, still the best one: real people, real reads, reasons attached.
• And a variant of the same problem: picking an audiobook, in a thread where the poster describes their own state as "severe decision paralysis" — Looking for audiobook recommendations.

None of these is an event. Four in a day, all the same complaint, is a signal: the demand for finding a book has outgrown the infrastructure for finding a book.

Rankings do not save you, because they do not measure what you need

A platform ranking answers "is this popular". It does not answer "will I like it". Three concrete reasons:

1. It measures timing, not quality. A serial reaches the top by posting steadily at the right moment, not by being better written.
2. A rank is one number; taste is a vector. "Good" for a numbers-go-up LitRPG reader is not "good" for someone reading translated xianxia.
3. It rewards what is easy to count. A popular bad book still outranks a quiet good one.

So readers build their own infrastructure: merge the tier lists, open the thread, ask each other. It is still human curation — it just has no schema, no API, and it sinks after three days.

Reader-built lists: usable, if you know where they break

• A tool one person made (like the recommender above): strong because it aggregates many sources; weak because one person decided how to aggregate, and nobody else maintains it.
• Ask threads: strong because reasons come attached; weak because the loudest replier is not the most careful reader.
• The recurring weekly thread: strongest on recency; weak because nobody summarises it.

The practical version: pick three different sources, read the reasons instead of the score, and write two lines about your own taste before you ask. If you cannot say what you like, no recommender will help.

What it means if you translate: being recommended is not enough

This part gets said less. Getting named in a recommendation thread is good. But the new reader tries chapter 1, and drops it there for three very specific reasons:

• Character names change spelling between chapters, or pinyin, Sino-Vietnamese forms and English alternate inside one book.
• Forms of address drift: "shizun" yesterday, "teacher" today, bare name tomorrow — with the relationship unchanged.
• Chapter-1 voice does not hold: chapter 1 has a voice, chapter 15 slides back into template prose.

None of that is the source's fault. It is the translator's work, and it is why a recommended book can still die in a new reader's first ten minutes.

What 2novel does — and does not

2novel is a workspace to translate and edit a book chapter by chapter: import source, translate, compare, edit, export. For the three points above:

• Book Bible holds character names, terminology and forms of address for the whole book, so later chapters do not invent a new rule.
• Side-by-side source when you suspect dialogue or address has slipped.
• Chapter 1 as the sample: edit it by hand, accept that version, then translate in bulk.

To be explicit so you do not wait for something we are not building: 2novel does not score stories, not rank them, not recommend them, and does not filter slop. There is no IMDB for web novels, and 2novel is not trying to be one. If you want the checklist for spotting a book that opens strong and then flattens, that is the earlier post: LLM stories that open strong, then flatten. This post is a different job: finding books, and keeping one alive after somebody recommends it.

A short working loop

1. Pick one book you have the rights to translate. Not a library.
2. Trial-translate chapter 1. Fix names, address forms and a few characteristic lines by hand. Put them in the Book Bible.
3. Jump to a mid-book chapter and compare with the source. Can you still tell the speakers apart? Did terms bounce?
4. Only when 2 and 3 hold, raise the volume. AI drafts can miss meaning, voice or context; you make the last call.

User guide: 2novel.app/docs.

Nobody is going to score this for you. But there is a real recommendation layer, built by readers, and there is specific work that keeps your translation alive past chapter 1.