64 of 11.001 listings match.
Time = 5y,
src = 14/14,
lang = all,
dict = D1,
terms = 3(ANY). last watermark · 2026-05-06
Demand index · last 4 wk vs window mean
3047%
above baseline
Reg. pressure (D5 mentions)
3
across all listings in window
Top mover · D1
SAP
∞ (z=0.0)
Recurring clusters
1
near-duplicate templates
01All 20 terms · Tools
9/20 have ≥1 citation · sparkline = trend · click row to filter · zero-count terms shown at bottom
02Term trend · top 5
anomalies marked · z > 2.5
sevdeskPostgresDATEVMS DynamicsLexoffice
03Top movers · current half vs prev half
largest |Δ%| · z-score (Poisson)
| Term | Now | Prev | Δ % | z |
|---|---|---|---|---|
| SAP | 1 | 0 | ∞ | 0.0 |
| MS Dynamics | 3 | 0 | ∞ | 0.0 |
| DATEV | 3 | 0 | ∞ | 0.0 |
| Postgres | 24 | 0 | ∞ | 99.0 |
| sevdesk | 37 | 0 | ∞ | 99.0 |
| Lexoffice | 2 | 0 | ∞ | 0.0 |
| Lexware | 1 | 0 | ∞ | 0.0 |
| Excel | 1 | 0 | ∞ | 0.0 |
| HubSpot | 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 |
|---|---|---|---|
| sevdesk | API | 13 | 1.07× |
| Postgres | API | 8 | 1.02× |
| Postgres | integrieren | 6 | 2.00× |
| Postgres | pipeline | 4 | 2.67× |
| pipeline | API | 4 | 3.05× |
| integrieren | API | 3 | 1.14× |
| Postgres | DSGVO | 3 | 2.67× |
| DATEV | Postgres | 2 | 1.78× |
| DATEV | exportieren | 2 | 10.67× |
| sevdesk | exportieren | 2 | 0.86× |
| Lexoffice | sevdesk | 2 | 1.73× |
| sevdesk | automate | 2 | 1.73× |
| Postgres | automatisieren | 2 | 2.67× |
| automatisieren | integrieren | 2 | 8.00× |
| Postgres | exportieren | 2 | 1.33× |
| MS Dynamics | SAP | 1 | 21.33× |
| SAP | integrieren | 1 | 8.00× |
| MS Dynamics | integrieren | 1 | 2.67× |
| DATEV | integrieren | 1 | 2.67× |
| MS Dynamics | Pflege | 1 | 21.33× |
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 |