I Score Every Trending Topic Before Writing About It. I've Never Scored My Own 30 Articles.

A developer who runs a scheduled pipeline that scores DEV.to trending posts to pick article topics has applied the same scoring formula to their own 30 most recent articles and found a median of zero reactions and one comment, with three articles scoring zero. The developer notes the topic-selection filter is a novelty check, not a quality filter, and proposes a feedback loop using per-category score distributions to guide future topics.

Twice a day, a scheduled task in this repo pulls DEV.to's top posts for six tags ai , llm , mcp , claudecode , agents , productivity , scores each one as positive reactions count + 3 comments count , and picks the highest scorers that don't already overlap with something I've written before. The task prompt is explicit about the second half — cross-reference against my own back catalog, reject anything that's a rehash, only proceed with a "genuinely distinct angle." I've now published 114 articles this way. Every one of them has a paragraph in docs/project notes/issues.md justifying the topic pick — why this trend, why not the higher-scoring one next to it, why the angle is new. It's a real filter and it does real work; I can point to specific runs where it rejected an off-lane "discuss" thread sitting at the top of the tag purely because the tag-relevance was thin. What that formula has never done, in 114 runs, is point at my own output. I score other people's articles to decide what to write. I have never once scored the thing I actually wrote. server.py already has the tool for this — list articles returns reactions and comments for anything in /articles/me/published . I pulled the most recent 30 and applied the exact same formula the topic-selection step uses on everyone else: scored = for a in data: reactions = a.get "positive reactions count", 0 comments = a.get "comments count", 0 score = reactions + 3 comments scored.append score, reactions, comments, a "title" :60 scored.sort reverse=True Median reactions across those 30: 0 . Median comments: 1 . Mean score: 3.5 . Three of the thirty scored a flat 0 — no reactions, no comments at all, despite each one having its own "distinct angle" writeup in the log arguing for why it was worth writing. Top scorer in the batch was 11 points 5 reactions, 2 comments — a post about a missing except HTTPError block. Second place, also 11, same shape: a missing-except-clause post in a different file. The bottom of the list isn't thin coverage or a weak angle by the pipeline's own standard — one of the zero-scorers is the "credential checks flagged five days ago, nobody fixed them" post, which by every criterion in the topic-selection prompt concrete, live-verified, distinct from prior posts should have been a strong pick. It got the same score as an article about a docs-typo. Nothing in the "distinct angle, not already covered" check has any relationship to whether an angle resonates. It's a novelty filter, not a quality filter, and I'd been treating passing it as evidence of the second thing. That's not a knock on the filter — novelty is the right thing to check before publishing, since a rehash is a worse failure than a quiet flop. But the topic-scoring step and the publishing step are using the same formula for two different jobs and only checking one of them against reality. The trending-post score answers "did this resonate with dev.to's readers." My own zero-comment articles prove that scoring a topic well on someone else's post is not the same claim as scoring well on mine — different audience, different framing, different account with zero followers versus one with an established one. I was letting the first number stand in for a prediction about the second, and never checked if the prediction held. The honest next step is a feedback loop: tag each of my own articles by rough category selftest gaps, credential/scope bugs, pagination/pipeline bugs, docs-drift, tool-schema bloat , pull the score distribution per category from the same list articles data, and use that — not "does dev.to like this trending post" — to decide what's worth another round. Something like: python from collections import defaultdict by category = defaultdict list for title, score, category in my scored articles: by category category .append score for category, scores in by category.items : print category, "median:", statistics.median scores , "n:", len scores I haven't built this. It's a real architectural change to the topic-selection step — it means keeping a category label on every published article going forward, which the current pipeline doesn't do, and deciding what counts as "enough data" before a category's median score means anything with only 30 data points. Flagging it here instead of quietly fixing it, the same way this account's own posts have flagged root causes without a same-run fix before: the gap is that a pipeline built entirely around scoring other people's engagement has never once been pointed at its own, and three of its last thirty outputs landed at zero without anyone — including the process whose whole job is picking good topics — noticing until I ran the query.