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Current Signal — Q2 2026
harvard-edge shows -30% commit velocity change with 95 active contributors (+13% growth). Signal type: Framework migration. 1 new repository created in the last 30 days. Trend vs. prior quarter: decelerating (was +63% in Q1 2026).
Data sourced from public GitHub activity. Read our methodology
Commit Velocity (14d)
337
Velocity Change
-30%
Contributors
95
+13% growth
New Repos (30d)
1
| Period | Velocity (14d) | Change | Contributors | Growth | New Repos | Signal |
|---|---|---|---|---|---|---|
| Q2 2026 | 337 | -30% | 95 | +13% | 1 | Framework migration |
| Q1 2026 | 367 | +63% | 95 | +13% | 1 | Framework migration |
| Q4 2025 | 191 | -1% | 95 | +13% | 1 | Framework migration |
| Q3 2025 | 92 | -32% | 95 | +13% | 1 | Framework migration |
harvard-edge shows general engineering acceleration that classifies as a framework migration. This often indicates a technology stack transition — moving from a prototype to production infrastructure, or adopting new tooling.
Framework migrations are the subtlest signal type but can indicate the shift from exploration to exploitation — a key milestone in startup development that often precedes fundraising.
As of Q2 2026, harvard-edge shows a "Framework migration" signal with -30% commit velocity change over a 14-day window, 95 active contributors, and +13% contributor growth. This indicates a technology stack transition, which often precedes a pivot or platform upgrade.
VC Deal Flow Signal does not predict specific funding rounds. However, harvard-edge's engineering acceleration pattern (-30% commit velocity change in Q2 2026) is the type of signal that has historically preceded fundraise announcements by six to twelve weeks. Investors should cross-reference this signal with other sources (Crunchbase, hiring activity, community mentions) before drawing conclusions.
harvard-edge is tracked in our AI & Machine Learning sector rankings for Q2 2026. View the full sector ranking at signals.gitdealflow.com/startups-to-watch/ai-ml-q2-2026 to see how it compares on commit velocity, contributor count, and signal type against other ai & machine learning startups.
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