What rises in r/MachineLearning.
Older ranked records represented before 2026-03-07. Useful for prominence and memory—not total activity.
Newest records from 2026-03-07 to 2026-08-08. The boundary is inferred because original retrieval provenance was not stored.
Core views require at least 1,500 total records and 500 canon records; this corpus has 1,764 and 764. Topic comparison is withheld because this corpus has not been topic-enriched.
Historical material available
764 ranked historical records span 12 represented years. Topic evolution is not shown until this corpus has enriched topic assignments.
What is earning attention now.
The newest-post pool is filtered within age bands, then ranked by score and reply-count percentiles. Posts newer than 7 days are withheld when enough mature candidates exist.
Candidate index is relative to this recent pool, not a prediction of future quality or final Reddit rank.
Read the recent pool without contaminating the canon.
Every lens below uses only the inferred newest-post cohort from 2026-03-07 onward. They describe patterns inside that selected pool—not subreddit-wide prevalence.
What the community keeps saying
Distinct-post frequency across 1,000 titles and 985 available bodies. High-information two-word frames lead; normalized domain terms remain expandable.
Show recurring domain terms
Inspect language emerging within the recent pool
What started appearing more often
Per-post prevalence in 500 earlier versus 500 later posts · 985 bodies available. Minimum support removes one-off spikes.
Show more emerging patterns
Which language markers became more visible
Boundary-aware phrase-marker rates across 1,000 titles and 985 available post bodies. Categories may overlap and describe language patterns—not author emotion.
View marker dictionary
curiosity: why, how, what, explain, understand, learn, curious, wonder, anyone know, thoughts on
frustration: broken, fails, failed, error, issue, problem, stuck, doesn't work, not working, frustrated, worse, unusable, bug
uncertainty: maybe, might, could, unsure, not sure, uncertain, possibly, seems, apparently, i think
excitement: amazing, awesome, excited, love, can't wait, incredible, finally, great, impressive, game changer, wow
urgency: urgent, asap, right now, immediately, today, deadline, breaking, just released, now available
appreciation: thanks, thank you, appreciate, helpful, grateful, shoutout, well done, great work, kudos
| Month | Curiosity | Frustration | Uncertainty | Excitement | Urgency | Appreciation |
|---|---|---|---|---|---|---|
| 2026-03 | 63.1% | 27.7% | 20.8% | 10.0% | 6.2% | 11.5% |
| 2026-04 | 55.3% | 20.7% | 33.2% | 11.5% | 10.6% | 12.9% |
| 2026-05 | 59.9% | 21.6% | 30.8% | 9.3% | 14.1% | 11.9% |
| 2026-06 | 61.7% | 18.9% | 28.0% | 11.4% | 13.1% | 16.0% |
| 2026-07 | 63.5% | 20.8% | 29.2% | 5.7% | 10.9% | 18.2% |
| 2026-08 | 66.1% | 15.3% | 39.0% | 10.2% | 16.9% | 10.2% |
Which formats carry the conversation—and when
Flair is the available proxy for content format. Monthly share shows when each label became more or less common; volume and platform response remain separate.
Where recent platform attention accumulated
Posts in the inferred recent candidate pool are binned by Reddit score and upvote ratio. Brighter cells contain more selected posts; score uses a logarithmic scale.
1,000 recent candidates represented · relative platform response, not quality or subreddit-wide reach.
| Score band | Upvote-ratio band | Posts |
|---|---|---|
| 0–0 | 0–10% | 2 |
| 0–0 | 10–20% | 10 |
| 0–0 | 20–30% | 21 |
| 0–0 | 30–40% | 36 |
| 0–0 | 40–50% | 40 |
| 0–0 | 50–60% | 29 |
| 1–1 | 50–60% | 21 |
| 2–2 | 50–60% | 11 |
| 2–3 | 50–60% | 1 |
| 3–4 | 50–60% | 1 |
| 4–6 | 50–60% | 2 |
| 7–10 | 50–60% | 1 |
| 1–1 | 60–70% | 14 |
| 2–2 | 60–70% | 18 |
| 2–3 | 60–70% | 17 |
| 3–4 | 60–70% | 4 |
| 4–6 | 60–70% | 9 |
| 6–7 | 60–70% | 11 |
| 7–10 | 60–70% | 3 |
| 10–12 | 60–70% | 5 |
| 16–20 | 60–70% | 2 |
| 20–26 | 60–70% | 2 |
| 26–33 | 60–70% | 1 |
| 455–577 | 60–70% | 1 |
| 2–2 | 70–80% | 12 |
| 2–3 | 70–80% | 8 |
| 3–4 | 70–80% | 12 |
| 4–6 | 70–80% | 18 |
| 6–7 | 70–80% | 14 |
| 7–10 | 70–80% | 20 |
| 10–12 | 70–80% | 8 |
| 12–16 | 70–80% | 10 |
| 16–20 | 70–80% | 14 |
| 20–26 | 70–80% | 6 |
| 26–33 | 70–80% | 7 |
| 33–42 | 70–80% | 3 |
| 54–68 | 70–80% | 1 |
| 68–87 | 70–80% | 4 |
| 2–3 | 80–90% | 4 |
| 3–4 | 80–90% | 9 |
| 4–6 | 80–90% | 8 |
| 6–7 | 80–90% | 22 |
| 7–10 | 80–90% | 15 |
| 10–12 | 80–90% | 20 |
| 12–16 | 80–90% | 16 |
| 16–20 | 80–90% | 25 |
| 20–26 | 80–90% | 25 |
| 26–33 | 80–90% | 12 |
| 33–42 | 80–90% | 24 |
| 42–54 | 80–90% | 18 |
| 54–68 | 80–90% | 16 |
| 68–87 | 80–90% | 7 |
| 87–110 | 80–90% | 4 |
| 110–140 | 80–90% | 1 |
| 140–177 | 80–90% | 2 |
| 177–224 | 80–90% | 4 |
| 224–284 | 80–90% | 2 |
| 1–1 | 90–100% | 9 |
| 2–2 | 90–100% | 9 |
| 2–3 | 90–100% | 10 |
| 3–4 | 90–100% | 2 |
| 4–6 | 90–100% | 3 |
| 6–7 | 90–100% | 8 |
| 7–10 | 90–100% | 15 |
| 10–12 | 90–100% | 14 |
| 12–16 | 90–100% | 17 |
| 16–20 | 90–100% | 33 |
| 20–26 | 90–100% | 24 |
| 26–33 | 90–100% | 22 |
| 33–42 | 90–100% | 19 |
| 42–54 | 90–100% | 24 |
| 54–68 | 90–100% | 25 |
| 68–87 | 90–100% | 24 |
| 87–110 | 90–100% | 24 |
| 110–140 | 90–100% | 27 |
| 140–177 | 90–100% | 21 |
| 177–224 | 90–100% | 10 |
| 224–284 | 90–100% | 12 |
| 284–360 | 90–100% | 6 |
| 360–455 | 90–100% | 5 |
| 577–730 | 90–100% | 4 |
Inspect available source posts.
Open the posts behind the interpretation. The score distribution is a diagnostic of this selected source set—not a model of the subreddit as a whole.
Inspect collected-score distribution
How attention is distributed
Collected posts grouped into Reddit score bands.
Available posts by Reddit score
20 availableSampling and methodology
Collected records: 1,764
Represented range: 2015-04-23 → 2026-08-08
The corpus combines a broad newest-post listing with bounded top/year and top/all listings. The interface infers the recent boundary from the newest 1,000 records because original retrieval provenance was not stored.
Older records are analyzed as a ranked historical canon. They can show what was prominent and repeatedly surfaced within ranked retrieval; they cannot estimate total monthly activity, publishing habits, or whole-conversation prevalence.
Recent contender ranking compares posts within age bands using score and reply-count percentiles. Platform attention is not evidence quality, accuracy, representativeness, or future performance.