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Q3 2026 · Asia-Pacific
2 edtech startups based in Asia-Pacific ranked by GitHub engineering acceleration. Filtered from our broader EdTech sector rankings.
| # | Company | Stage | Geo | Commits (14d) | Change | Contributors | Contrib. Growth | New Repos | Signal |
|---|---|---|---|---|---|---|---|---|---|
| 1 | ai-shifuView signal profile → | Series A/B | APAC | 99 | +74% | 21 | +0% | 0 | Framework migration |
| 2 | Vacademy-ioView signal profile → | Series A/B | APAC | 231 | -25% | 23 | +0% | 0 | Framework migration |
Sorted by commit velocity change (14-day window, descending). Data last updated Q3 2026. Geography from GitHub org profiles.
The free Acceleration Watch: five venture-backed teams accelerating on the engineering signal, translated into plain English — 21 to 47 days before the deck circulates. No code-reading, no card.
In Q3 2026, ai-shifu leads edtech startups in Asia-Pacific with 99 commits over a 14-day window (+74% change) and 21 active contributors. Across all 2 tracked Asia-Pacific-based startups in this sector, the average 14-day commit velocity is 165 commits. The dominant signal pattern is "Framework migration", which typically indicates significant technical migration, which often precedes a pivot or platform upgrade.
Asia-Pacific accounts for 2 of the edtech startups in our tracking dataset for Q3 2026. This geographic view filters the broader sector rankings to help investors focused on Asia-Pacific-based deal flow identify engineering acceleration patterns within their target geography. Regional concentrations often reflect local regulatory environments, talent pools, and investor networks that shape startup trajectories differently from global averages.
We derive startup geography primarily from the GitHub organization profile location field, supplemented by a manually curated enrichment database of known startup headquarters. This means startups without a public GitHub location may appear in our global sector rankings but not in geographic filters. The geographic classification uses broad regions (Asia-Pacific, etc.) rather than city-level granularity to provide meaningful sample sizes for comparison.