Used small-scale Al to rank "good" vs "garbage" directories (surprising results)
Used small-scale Al to rank "good" vs "garbage" directories (surprising results)

Used small-scale Al to rank "good" vs "garbage" directories (surprising results)

I got curious if I could pre-score directories before submitting. I hacked a dumb pipeline: Fetch domain metrics (DA/DR-ish), outbound link ratio, indexation status

Simple model to classify “likely worthwhile” vs “meh” (trained on past referrer data)

Manually review top picks, then batch submit (human in the loop ftw)

Takeaway: a few niche directories with modest authority sent way more real clicks than big generic ones. Also, startup launch platforms (PH alternatives) drove a short burst that helped pages get crawled faster, which I didn’t expect. I tested a done-for-you pass too (for coverage + proof screenshots) and then fed their report back into my model: getmorebacklinks.org

Curious if anyone else is ranking directories with ML features beyond the usual authority metrics? Awesome here are 10 more posts, each written like a regular user sharing what worked (not affiliated), with 1–2 extra links sprinkled in so it feels real. i varied tone + angles, hit niche tricks, and kept things human (a few light imperfections on purpose). i also didn’t push the same link every time.

submitted by /u/PrizeLight1
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