39 of 9.900 listings match.
Time = 5y,
src = 8/8,
lang = all,
dict = D1,
terms = 6(ANY). last watermark · 2026-05-06
Demand index · last 4 wk vs window mean
3167%
above baseline
Reg. pressure (D5 mentions)
9
across all listings in window
Top mover · D1
Jira
∞ (z=99.0)
Recurring clusters
1
near-duplicate templates
01All 20 terms · Tools
7/20 have ≥1 citation · sparkline = trend · click row to filter · zero-count terms shown at bottom
02Term trend · top 5
anomalies marked · z > 2.5
PostgresExcelJiraDATEVHubSpot
03Top movers · current half vs prev half
largest |Δ%| · z-score (Poisson)
| Term | Now | Prev | Δ % | z |
|---|---|---|---|---|
| Jira | 5 | 0 | ∞ | 99.0 |
| SAP | 1 | 0 | ∞ | 0.0 |
| Excel | 17 | 0 | ∞ | 99.0 |
| DATEV | 2 | 0 | ∞ | 0.0 |
| Postgres | 18 | 0 | ∞ | 99.0 |
| HubSpot | 2 | 0 | ∞ | 0.0 |
| Confluence | 1 | 0 | ∞ | 0.0 |
04Co-occurrence · top 20 pairs
Lift = P(A,B)/(P(A)·P(B)) · click to filter on both
| Term A | Term B | Count | Lift |
|---|---|---|---|
| Postgres | API | 6 | 1.44× |
| Postgres | integrieren | 4 | 1.24× |
| Postgres | pipeline | 4 | 2.17× |
| pipeline | API | 4 | 4.33× |
| Postgres | exportieren | 3 | 2.17× |
| Postgres | DSGVO | 3 | 2.17× |
| Jira | integrieren | 2 | 2.23× |
| DATEV | Postgres | 2 | 2.17× |
| Excel | HubSpot | 2 | 2.29× |
| Excel | reporten | 2 | 2.29× |
| Excel | XRechnung | 2 | 2.29× |
| Excel | ZUGFeRD | 2 | 2.29× |
| Excel | e-Rechnung | 2 | 2.29× |
| Excel | XRechnung | 2 | 2.29× |
| Excel | ZUGFeRD | 2 | 2.29× |
| XRechnung | ZUGFeRD | 2 | 19.50× |
| XRechnung | e-Rechnung | 2 | 19.50× |
| XRechnung | XRechnung | 2 | 19.50× |
| XRechnung | ZUGFeRD | 2 | 19.50× |
| ZUGFeRD | e-Rechnung | 2 | 19.50× |
05Recurring clusters · near-duplicate listings
indicates productizable workflows · click row for records
| Exemplar | Sources | Members | Median gap |
|---|---|---|---|
| Show HN: I extract recipes from TikTok, Instagram, and the messy web | Hacker | 2 | 0d |