Pipeline Activity
Daily event volume. Note the weekly cycle — activity drops on weekends.
What makes this data unique: Every chart below is derived from time-series data that only exists because a pipeline has been polling structured APIs every 15–60 minutes, 24/7, for 107 days. None of these insights can be reproduced by searching the web or asking a cloud AI model.
Daily event volume. Note the weekly cycle — activity drops on weekends.
Active postings over time. Rising = posting faster than filling. Only continuous polling reveals trajectory.
| # | Company | Peak Active |
|---|---|---|
| 1 | Anduril Industries | 2,129 |
| 2 | Datadog | 466 |
| 3 | Anthropic | 454 |
| 4 | MongoDB | 442 |
| 5 | Shield AI | 396 |
| 6 | Okta | 381 |
| 7 | Zscaler | 352 |
| 8 | Roblox | 323 |
| 9 | Verkada | 320 |
| 10 | Oscar Health | 301 |
| 11 | Remote.com | 295 |
| 12 | Airbnb | 264 |
| 13 | Brex | 262 |
| 14 | Unknown | 248 |
| 15 | Elastic | 233 |
Change in active posting count: first week avg vs last week avg. Based on snapshots, not events.
Titles posted by 3+ companies in 14 days. Signals market-wide demand, not one company's plan.
ATS postings that disappeared quickest (>1 day, excl. federal). Fast disappearance = high demand.
Salary bands from postings with compensation data. Over time, reveals market-rate shifts.
Stars gained across repos per topic. The slope reveals where developer attention is flowing.
% subscriber growth surfaces fast-growing niche communities invisible in absolute numbers.
Data collected via continuous polling of public APIs: Greenhouse, Lever, Ashby, Workable, Recruitee, USAJobs (job boards); GitHub Search API (repositories); Reddit /about.json (subreddits). Net hiring change calculated from daily active posting snapshots, not cumulative events.