Question: What engineering signals are proptech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 1 proptech startups with measurable GitHub engineering signals. 0 of 1 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 46 commits, with open-condo-software leading at 46 commits (-6% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which proptech startup has the highest engineering acceleration in Q2 2026?

Answer: open-condo-software leads the proptech sector in Q2 2026 with 46 commits over a 14-day window, representing a -6% change from the prior period. With 51 active contributors, open-condo-software is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active proptech engineering teams located?

Answer: Among the 1 proptech startups we track, LATAM accounts for the highest concentration with 1 teams. Startups applying technology to real estate and property management. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are proptech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 1 proptech startups with measurable GitHub engineering signals. 1 of 1 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 23 commits, with open-condo-software leading at 23 commits (+15% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which proptech startup has the highest engineering acceleration in Q1 2026?

Answer: open-condo-software leads the proptech sector in Q1 2026 with 23 commits over a 14-day window, representing a +15% change from the prior period. With 51 active contributors, open-condo-software is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active proptech engineering teams located?

Answer: Among the 1 proptech startups we track, LATAM accounts for the highest concentration with 1 teams. Startups applying technology to real estate and property management. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are proptech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 1 proptech startups with measurable GitHub engineering signals. 0 of 1 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 68 commits, with open-condo-software leading at 68 commits (-3% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which proptech startup has the highest engineering acceleration in Q4 2025?

Answer: open-condo-software leads the proptech sector in Q4 2025 with 68 commits over a 14-day window, representing a -3% change from the prior period. With 51 active contributors, open-condo-software is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active proptech engineering teams located?

Answer: Among the 1 proptech startups we track, LATAM accounts for the highest concentration with 1 teams. Startups applying technology to real estate and property management. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are proptech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 1 proptech startups with measurable GitHub engineering signals. 1 of 1 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 64 commits, with open-condo-software leading at 64 commits (+100% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which proptech startup has the highest engineering acceleration in Q3 2025?

Answer: open-condo-software leads the proptech sector in Q3 2025 with 64 commits over a 14-day window, representing a +100% change from the prior period. With 51 active contributors, open-condo-software is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active proptech engineering teams located?

Answer: Among the 1 proptech startups we track, LATAM accounts for the highest concentration with 1 teams. Startups applying technology to real estate and property management. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are agtech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 2 agtech startups with measurable GitHub engineering signals. 0 of 2 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 50 commits, with betagouv leading at 16 commits (-24% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which agtech startup has the highest engineering acceleration in Q2 2026?

Answer: betagouv leads the agtech sector in Q2 2026 with 16 commits over a 14-day window, representing a -24% change from the prior period. With 100 active contributors and 1 new repositories, betagouv is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active agtech engineering teams located?

Answer: Among the 2 agtech startups we track, Canada accounts for the highest concentration with 1 teams. Startups applying technology to agriculture and precision farming. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are agtech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 2 agtech startups with measurable GitHub engineering signals. 2 of 2 show positive commit velocity growth. The most common signal type is "Deploy frequency spike", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 48 commits, with LiteFarmOrg leading at 83 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which agtech startup has the highest engineering acceleration in Q1 2026?

Answer: LiteFarmOrg leads the agtech sector in Q1 2026 with 83 commits over a 14-day window, representing a +999% change from the prior period. With 65 active contributors, LiteFarmOrg is showing a "Deploy frequency spike" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active agtech engineering teams located?

Answer: Among the 2 agtech startups we track, Canada accounts for the highest concentration with 1 teams. Startups applying technology to agriculture and precision farming. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are agtech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 2 agtech startups with measurable GitHub engineering signals. 2 of 2 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 77 commits, with betagouv leading at 10 commits (+43% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which agtech startup has the highest engineering acceleration in Q4 2025?

Answer: betagouv leads the agtech sector in Q4 2025 with 10 commits over a 14-day window, representing a +43% change from the prior period. With 100 active contributors and 1 new repositories, betagouv is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active agtech engineering teams located?

Answer: Among the 2 agtech startups we track, Canada accounts for the highest concentration with 1 teams. Startups applying technology to agriculture and precision farming. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are agtech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 2 agtech startups with measurable GitHub engineering signals. 2 of 2 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 38 commits, with betagouv leading at 10 commits (+100% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which agtech startup has the highest engineering acceleration in Q3 2025?

Answer: betagouv leads the agtech sector in Q3 2025 with 10 commits over a 14-day window, representing a +100% change from the prior period. With 100 active contributors and 1 new repositories, betagouv is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active agtech engineering teams located?

Answer: Among the 2 agtech startups we track, Canada accounts for the highest concentration with 1 teams. Startups applying technology to agriculture and precision farming. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are gaming startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 3 gaming startups with measurable GitHub engineering signals. 1 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 41 commits, with castle-engine leading at 40 commits (+344% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which gaming startup has the highest engineering acceleration in Q2 2026?

Answer: castle-engine leads the gaming sector in Q2 2026 with 40 commits over a 14-day window, representing a +344% change from the prior period. With 24 active contributors, castle-engine is showing a "Deploy frequency spike" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active gaming engineering teams located?

Answer: The gaming startups we track are geographically distributed across multiple regions. Startups building game engines, multiplayer infrastructure, and gaming analytics. We derive geography from GitHub organization profiles.


Question: What engineering signals are gaming startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 3 gaming startups with measurable GitHub engineering signals. 3 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 66 commits, with castle-engine leading at 84 commits (+282% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which gaming startup has the highest engineering acceleration in Q1 2026?

Answer: castle-engine leads the gaming sector in Q1 2026 with 84 commits over a 14-day window, representing a +282% change from the prior period. With 24 active contributors, castle-engine is showing a "Deploy frequency spike" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active gaming engineering teams located?

Answer: The gaming startups we track are geographically distributed across multiple regions. Startups building game engines, multiplayer infrastructure, and gaming analytics. We derive geography from GitHub organization profiles.


Question: What engineering signals are gaming startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 3 gaming startups with measurable GitHub engineering signals. 2 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 49 commits, with castle-engine leading at 37 commits (+106% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which gaming startup has the highest engineering acceleration in Q4 2025?

Answer: castle-engine leads the gaming sector in Q4 2025 with 37 commits over a 14-day window, representing a +106% change from the prior period. With 24 active contributors, castle-engine is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active gaming engineering teams located?

Answer: The gaming startups we track are geographically distributed across multiple regions. Startups building game engines, multiplayer infrastructure, and gaming analytics. We derive geography from GitHub organization profiles.


Question: What engineering signals are gaming startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 3 gaming startups with measurable GitHub engineering signals. 1 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 53 commits, with castle-engine leading at 96 commits (+52% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which gaming startup has the highest engineering acceleration in Q3 2025?

Answer: castle-engine leads the gaming sector in Q3 2025 with 96 commits over a 14-day window, representing a +52% change from the prior period. With 24 active contributors, castle-engine is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active gaming engineering teams located?

Answer: The gaming startups we track are geographically distributed across multiple regions. Startups building game engines, multiplayer infrastructure, and gaming analytics. We derive geography from GitHub organization profiles.


Question: What engineering signals are space tech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 3 space tech startups with measurable GitHub engineering signals. 1 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 63 commits, with orbiternassp leading at 30 commits (+329% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which space tech startup has the highest engineering acceleration in Q2 2026?

Answer: orbiternassp leads the space tech sector in Q2 2026 with 30 commits over a 14-day window, representing a +329% change from the prior period. With 37 active contributors, orbiternassp is showing a "Deploy frequency spike" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active space tech engineering teams located?

Answer: Among the 3 space tech startups we track, US accounts for the highest concentration with 1 teams. Startups building launch vehicles, satellites, and space data platforms. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are space tech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 3 space tech startups with measurable GitHub engineering signals. 2 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 3 of the tracked companies. The average 14-day commit velocity across the sector is 130 commits, with orbiternassp leading at 46 commits (+12% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which space tech startup has the highest engineering acceleration in Q1 2026?

Answer: orbiternassp leads the space tech sector in Q1 2026 with 46 commits over a 14-day window, representing a +12% change from the prior period. With 37 active contributors, orbiternassp is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active space tech engineering teams located?

Answer: Among the 3 space tech startups we track, US accounts for the highest concentration with 1 teams. Startups building launch vehicles, satellites, and space data platforms. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are space tech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 3 space tech startups with measurable GitHub engineering signals. 2 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 3 of the tracked companies. The average 14-day commit velocity across the sector is 77 commits, with orbiternassp leading at 50 commits (+138% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which space tech startup has the highest engineering acceleration in Q4 2025?

Answer: orbiternassp leads the space tech sector in Q4 2025 with 50 commits over a 14-day window, representing a +138% change from the prior period. With 37 active contributors, orbiternassp is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active space tech engineering teams located?

Answer: Among the 3 space tech startups we track, US accounts for the highest concentration with 1 teams. Startups building launch vehicles, satellites, and space data platforms. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are space tech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 3 space tech startups with measurable GitHub engineering signals. 2 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 3 of the tracked companies. The average 14-day commit velocity across the sector is 131 commits, with OpenC3 leading at 57 commits (+39% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which space tech startup has the highest engineering acceleration in Q3 2025?

Answer: OpenC3 leads the space tech sector in Q3 2025 with 57 commits over a 14-day window, representing a +39% change from the prior period. With 45 active contributors, OpenC3 is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active space tech engineering teams located?

Answer: Among the 3 space tech startups we track, US accounts for the highest concentration with 1 teams. Startups building launch vehicles, satellites, and space data platforms. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are social & community startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 3 social & community startups with measurable GitHub engineering signals. 1 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 3 of the tracked companies. The average 14-day commit velocity across the sector is 230 commits, with bakaphp leading at 598 commits (+32% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which social & community startup has the highest engineering acceleration in Q2 2026?

Answer: bakaphp leads the social & community sector in Q2 2026 with 598 commits over a 14-day window, representing a +32% change from the prior period. With 14 active contributors and 1 new repositories, bakaphp is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active social & community engineering teams located?

Answer: Among the 3 social & community startups we track, LATAM accounts for the highest concentration with 1 teams. Startups building social networks, community platforms, and creator tools. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are social & community startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 3 social & community startups with measurable GitHub engineering signals. 2 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 175 commits, with nextcloud leading at 53 commits (+430% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which social & community startup has the highest engineering acceleration in Q1 2026?

Answer: nextcloud leads the social & community sector in Q1 2026 with 53 commits over a 14-day window, representing a +430% change from the prior period. With 100 active contributors, nextcloud is showing a "Deploy frequency spike" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active social & community engineering teams located?

Answer: Among the 3 social & community startups we track, LATAM accounts for the highest concentration with 1 teams. Startups building social networks, community platforms, and creator tools. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are social & community startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 3 social & community startups with measurable GitHub engineering signals. 1 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 3 of the tracked companies. The average 14-day commit velocity across the sector is 229 commits, with nextcloud leading at 132 commits (+6% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which social & community startup has the highest engineering acceleration in Q4 2025?

Answer: nextcloud leads the social & community sector in Q4 2025 with 132 commits over a 14-day window, representing a +6% change from the prior period. With 100 active contributors, nextcloud is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active social & community engineering teams located?

Answer: Among the 3 social & community startups we track, LATAM accounts for the highest concentration with 1 teams. Startups building social networks, community platforms, and creator tools. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What engineering signals are social & community startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 3 social & community startups with measurable GitHub engineering signals. 3 of 3 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 3 of the tracked companies. The average 14-day commit velocity across the sector is 220 commits, with unacms leading at 57 commits (+138% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which social & community startup has the highest engineering acceleration in Q3 2025?

Answer: unacms leads the social & community sector in Q3 2025 with 57 commits over a 14-day window, representing a +138% change from the prior period. With 11 active contributors, unacms is showing a "Framework migration" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active social & community engineering teams located?

Answer: Among the 3 social & community startups we track, LATAM accounts for the highest concentration with 1 teams. Startups building social networks, community platforms, and creator tools. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.


Question: What is the "Engineering Hiring Burst" signal?

Answer: Startups whose contributor growth rate exceeds 50%, indicating rapid team expansion — often following a recent funding round.


Question: What should investors look for in a "Engineering Hiring Burst" signal?

Answer: If you are seeing a hiring burst signal, you may be too late for the current round but well-positioned for the next one. The team expansion suggests the company has capital to deploy and is building toward a product milestone.


Question: What is the "Infrastructure Buildout" signal?

Answer: Startups that created 3+ new public repositories in 30 days, expanding their technical surface area with new microservices, SDKs, or platform components.


Question: What should investors look for in a "Infrastructure Buildout" signal?

Answer: Infrastructure buildout is classic Series A behavior: the core product works, and the team is building the platform around it. This pattern requires capital and reflects confidence in the product direction.


Question: What is the "Deploy Frequency Spike" signal?

Answer: Startups whose commit velocity increased 150%+ versus baseline — the team is shipping code at an unusually high rate.


Question: What should investors look for in a "Deploy Frequency Spike" signal?

Answer: Deploy frequency spikes indicate a product launch, rapid iteration on customer feedback, or a competitive response. All are potential indicators of product-market fit and interesting timing for investors.


Question: What is the "Framework Migration" signal?

Answer: Startups showing general engineering acceleration that indicates a technology stack transition — moving from prototype to production infrastructure.


Question: What should investors look for in a "Framework Migration" signal?

Answer: 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.


Question: How often is VC Deal Flow Signal data updated?

Answer: Weekly. Data refreshes every Monday. The most recent period is Q2 2026.


Question: How should this data be cited?

Answer: VC Deal Flow Signal (signals.gitdealflow.com), Q2 2026 data. DOI / paper: https://ssrn.com/abstract=6606558. License: CC BY 4.0.


Question: Median 14-day commit velocity for VC-backed startups: 71 commits?

Answer: The 14-day commit-velocity median across 55 venture-backed startups is 71 commits. A single number that anchors what 'normal' looks like for venture-backed engineering. Compare your portfolio against it. Source: §4.2 Velocity distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Mean commit velocity is 173 — over 2.4× the median?

Answer: Mean commit velocity is 173 — over 2.4× the median, indicating a heavy upper tail. Mean ≠ median is the signature of skewed distributions. VCs need the median, not the average. Source: §4.2 Velocity distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Top decile commit velocity: 392 commits per 14 days?

Answer: The 90th percentile commit velocity is 392 commits per 14 days. What 'top decile' looks like quantitatively. Test where your portfolio sits. Source: §4.2 Velocity distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Quarterly velocity change ranges from −94% to +1,647%?

Answer: Quarter-over-quarter velocity change ranges from −94% to +1,647%. The +1,647% number is a hook. Pre-launch sprints are visible in commit-velocity data. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Only 49% of VC-backed startups show positive velocity growth?

Answer: 49% of observations show positive velocity growth. Counterintuitive. Most assume 'all venture-backed startups grow fast.' Half do, half don't — even at this stage. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Framework migration dominates: 75% of venture-backed startup GitHub signals?

Answer: Framework migration is the dominant signal type — 75% of observations (165 of 219). Counter-narrative to 'engineering velocity = hiring.' The dominant pattern is rewrites, not headcount growth. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Engineering hiring bursts: only 9% of VC-backed startup signals?

Answer: Engineering hiring bursts represent only 9% of observations (20 of 219). Refutes the dominant VC heuristic that 'more contributors = momentum.' It's the rarest meaningful signal type. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Infrastructure buildouts are even rarer: 4% of observations?

Answer: Infrastructure buildouts are even rarer — 4% of observations (8 of 219). When you see infrastructure buildout, treat it as an outlier event. Possible platform pivot or enterprise launch. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Deploy frequency spikes: 12% of VC-backed startup signals?

Answer: Deploy frequency spikes are 12% of observations (26 of 219). Small teams sprinting toward a milestone are about 1 in 8. Often correlates with launch dates. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: US share of VC-backed open-source-active orgs: 56%?

Answer: Among observations with identifiable geography (108 of 219, 49%), US accounts for 60. US dominance in venture-backed open-source-active orgs is 56%. Lower than people guess for VC-backed. Source: §4.2 Geography, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: EU underrepresented in VC-backed open-source-active orgs (22%)?

Answer: EU venture-backed orgs in the panel: 24 (22% of identified geography). EU is meaningfully under-represented in venture-backed open-source-active orgs vs population baseline. Source: §4.2 Geography, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: LATAM punches above weight in VC-backed open-source-active orgs?

Answer: LATAM venture-backed orgs in the panel: 12 (11% of identified geography). LATAM punches above weight in venture-backed open-source-active. Under-priced sourcing surface. Source: §4.2 Geography, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Sector sample size: 1 (Legal Tech) to 8 (Data Infra/Cybersecurity)?

Answer: Sector sample size ranges from 1 (Legal Tech) to 8 (Data Infrastructure / Cybersecurity). Real-world heterogeneity in density of venture-backed open-source-first startups. Source: §4.2 Sectors, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Highest velocity change in latest period: castle-engine +344%, orbiternassp +329%?

Answer: The two highest-velocity-change observations in the most recent period are castle-engine (+344%) and orbiternassp (+329%). Specific, falsifiable, public. Anyone can verify on GitHub. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Extreme positive velocity outliers cluster in Gaming and Space Tech?

Answer: Extreme positive velocity-change outliers cluster in two sectors: Gaming and Space Tech. Both are under-covered by traditional VC alt-data tools. Sourcing edge for the right fund. Source: §4.2 Velocity change, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Framework-migration share is stable: varies <5 percentage points period-to-period?

Answer: Signal-mix stability: framework-migration share varies <5 percentage points period-to-period. The classification scheme produces stable distributions, suggesting the heuristics capture real structure (not noise). Source: §4.2 Signal type distribution, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: First public 5-quarter longitudinal panel for VC-backed startups (Q2 2025–Q2 2026)?

Answer: The dataset spans 5 quarters (Q2 2025 through Q2 2026). First public longitudinal panel at organizational level for venture-backed startups. Source: §1, abstract, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: GitHub-signal classifier is fully deterministic — no ML, no black-box?

Answer: The classifier is fully deterministic — no ML, no black-box. Auditable and replicable. Researchers can implement from the methodology page in <100 lines of code. Source: §3.3 Signal classification, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Why 14-day observation window: justified by Mockus, Fielding, and Herbsleb (2002)?

Answer: The 14-day observation window is justified by Mockus, Fielding, and Herbsleb (2002). Concrete academic anchor — empirical SE literature establishes 2-week windows smooth weekend/holiday noise. Source: §2 Related work, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Dataset under CC BY 4.0 with no restrictions on commercial use?

Answer: The dataset is distributed under CC BY 4.0 with no restrictions on commercial use. No academic-only license trap. Anyone can build a competing product on this data. Source: §7 Data availability, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Sampling rule: most-active repository per organization in trailing 14-day window?

Answer: Each observation is taken on the most-active repository per organization in the trailing 14-day window ending the first day of the quarter. Reproducible. Every researcher can implement this and check our numbers. Source: §3.2 Collection pipeline, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Panel structure: 219 observations across 55 unique startups?

Answer: The dataset is 219 startup-period observations, not 219 unique startups. Panel structure (longitudinal). 55 unique startups × ~4 quarters each = 219 observations. Permits fixed-effects regressions. Source: §4.1 Structure, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Dataset structure: 3 CSV files (startup_signals, sector_aggregates, signal_type_timeseries)?

Answer: The dataset is 3 CSV files: startup_signals (219 rows), sector_aggregates (72), signal_type_timeseries (15). Frictionless Data schema means it's plug-and-play for academic notebooks. Source: §4.1 Structure, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Why we don't pre-report statistical tests on cross-sectional questions?

Answer: We deliberately do not pre-report statistical tests on cross-sectional questions. Epistemic discipline. The paper is data + methodology, not pre-cooked findings to defend. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Selection bias: dataset over-represents sectors where open-source is conventional?

Answer: The dataset over-represents sectors where open-source work is conventional and under-represents consumer apps and many fintechs. Honest about selection bias. Cross-sector comparisons must account for it. Source: §5 Limitations, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Seed list excludes public companies and non-VC-backed open-source projects?

Answer: The seed list excludes public companies and non-VC-backed open-source projects. Targets the specific population of interest to early-stage investors. Source: §3.1 Seed list, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Dataset mirrored on Kaggle, Data.world, Zenodo, and canonical live API?

Answer: The data is mirrored on Kaggle, Data.world, Zenodo, and the canonical live API. Multiple distribution surfaces — institutional and indie researchers have a path. Source: §7 Data availability, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Open question: Do hiring-burst signals lead or lag framework-migration signals?

Answer: Open question: Do hiring-burst signals lead or lag framework-migration signals? Useful for VCs trying to time outreach. Pre-announcement vs post-announcement signal. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Open question: Is velocity change sector-mean-reverting?

Answer: Open question: Is velocity change sector-mean-reverting? Determines whether velocity is signal or noise. Panel structure permits the test. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.


Question: Open question: Why do US and EU signal-mixes differ (hiring vs framework migration)?

Answer: Open question: US observations skew toward hiring-burst and deploy-frequency-spike. EU skews toward framework-migration. Geography × signal-type interaction. Suggests different 'kinds of momentum' by region. Source: §4.3 Heterogeneity, "A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups" by The Data Nerd, SSRN abstract=6606558, CC BY 4.0.