march 2026 - 145 posts across 8 accounts in 18 days

from march 2-20, 2026: 145 posts, 8 accounts, 4 platforms, 28,092 reach, $0 ad spend, less than 5 hours of total time. every post was generated and published through the dropspace API.

the full breakdown with per-account and per-platform data is in the complete case study.

each account ran a different level of automation.

• autonomous (dropspace brand) - 113 posts, 0.4-0.5% engagement. fully automated end-to-end. AI generates content, picks timing, publishes. no human review.
• human-approved (JCLVSH) - 16 posts, 1.2-2.5% engagement. AI generates drafts, human reviews and approves before publish.
• human-curated (ynho DJ clips) - 10 posts, 5.0-7.6% engagement. human selects the source material, AI handles clipping and formatting.
• automated (Studio Jams event photos) - 6 posts, 1.5% engagement. human uploads photos to a folder, pipeline handles the rest.

higher human involvement correlated with higher engagement. the autonomous pipeline traded engagement rate for volume - 113 posts with zero time spent.

for the JCLVSH account, linkedin averaged 1,057 impressions per post vs twitter's 313. same content, same timing, same persona. linkedin delivered 3.4x the reach.

if you're only posting on twitter, you're leaving reach on the table. the whole point of multi-platform publishing is finding where your audience actually is - and for B2B/tech content, linkedin consistently outperformed.

the autonomous pipeline is getting new content formats - viral short-form video, reaction clips, and thread-style posts. expanding to YouTube as a publishing target. building more use cases around podcasting and event content.

follow-up data posts are coming as each pipeline ships. the march numbers are the baseline.

the same API, MCP server, and webhooks used in this case study are available to every dropspace account. all templates are open-source on the community page.

API reference, MCP setup, and webhook configuration are in the developer docs.