Question: What should an investor do after spotting a hiring burst signal?

Answer: Treat it as a diligence trigger, not a buy signal. Confirm the contributor growth is internal hires rather than external open-source contributors, check whether the company has already announced a round (in which case the signal is confirmation, not discovery), and time outreach for the next financing milestone.


Question: How is the Infrastructure Buildout signal detected?

Answer: A startup is classified as an infrastructure buildout when it creates three or more new public repositories within 30 days, expanding its technical surface area with new microservices, SDKs, or platform components.


Question: What can falsely trigger an infrastructure buildout signal?

Answer: Open-sourcing existing internal repositories, splitting a monorepo into multiple repos, or mirroring code for compliance can all create a burst of new repos without real platform investment. The methodology reads new-repo counts as a surface-area proxy, so a single repo split can inflate the signal if it is not weighed against velocity and contributor signals.


Question: Which funding stage does an infrastructure buildout usually indicate?

Answer: Infrastructure buildout is classic Series A/B behavior: the core product works, and the team is building the platform around it. The pattern requires capital and reflects confidence in the product direction, so it is a strong trajectory signal at later early-stage rounds.


Question: How does an infrastructure buildout relate to fundraise timing?

Answer: A buildout typically precedes a Series A or B by roughly three to six weeks, because a company expands its platform before telling the market it is scaling. Investors seeing a fresh buildout can often engage before the round is announced.


Question: How is an infrastructure buildout different from the other acceleration signal types?

Answer: An infrastructure buildout is driven by repo creation (3+ new repos in 30 days), whereas a hiring burst is driven by contributor growth, a deploy spike by commit-velocity change, and a framework migration by general acceleration. Buildout signals strategic technical investment rather than raw shipping speed.


Question: What should an investor do after spotting an infrastructure buildout signal?

Answer: Look at what the new repositories are: SDKs and platform components suggest a product-line expansion, while forks and mirrors suggest noise. Pair the buildout with the company's stated roadmap and recent hiring to judge whether the new surface area maps to a genuine growth thesis.


Question: How is the Deploy Frequency Spike signal detected?

Answer: A startup is classified as a deploy frequency spike when its commit velocity increases 150% or more versus baseline, the team is shipping code at an unusually high rate over the 14-day window.


Question: What can falsely trigger a deploy frequency spike signal?

Answer: Dependency-update and bot commits, mass refactoring, and CI/CD noise can all inflate commit counts without real feature work. The methodology filters known bot accounts and trivial-file commits, but a single large refactor can still look like a spike, which is why the signal requires the acceleration to persist into a second 14-day window.


Question: Which funding stage does a deploy frequency spike usually indicate?

Answer: Deploy spikes are most common at pre-seed and seed, where a small team ships hard toward a launch or product-market-fit iteration. They can also appear at later stages around a major product launch, but the raw-shipping signature is most diagnostic early.


Question: How does a deploy frequency spike relate to fundraise timing?

Answer: A deploy spike often indicates a product launch, rapid iteration on customer feedback, or a competitive response, all of which are common in the weeks immediately before a fundraise announcement. It is a near-term timing signal, sometimes visible as little as three weeks ahead.


Question: How is a deploy frequency spike different from the other acceleration signal types?

Answer: A deploy spike is the purest velocity signal: it is driven by commit-velocity change (+150% or more) with contributor count roughly flat. A hiring burst requires rising contributors, an infrastructure buildout requires new repos, and a framework migration is the residual catch-all for general acceleration.


Question: What should an investor do after spotting a deploy frequency spike signal?

Answer: Check whether the spike is broad-based (many contributors) or concentrated (one developer), since the 3.4× finding shows velocity-plus-diversity is far more predictive than velocity alone. Then look for what is being shipped, a launch, a competitive response, or a pivot, before acting.


Question: How is the Framework Migration signal detected?

Answer: A startup is classified as a framework migration when it shows general engineering acceleration that does not fit the hiring-burst, infrastructure-buildout, or deploy-spike rules, typically indicating a technology-stack transition from prototype to production infrastructure.


Question: What can falsely trigger a framework migration signal?

Answer: Because framework migration is the residual category, it can absorb mixed signals, a mild velocity bump plus a couple of new repos that individually fall under each threshold. The methodology accepts this imprecision deliberately: the category flags 'something is changing under the hood' and routes the investor to diligence rather than asserting a specific cause.


Question: Which funding stage does a framework migration usually indicate?

Answer: Framework migration most often maps to seed through Series A, where a startup transitions from prototype to production infrastructure, moving from exploration to exploitation, a milestone that frequently precedes fundraising. It is a slower, quarter-scale signal than the other acceleration types.


Question: How does a framework migration relate to fundraise timing?

Answer: Framework migrations move on a longer horizon than hiring bursts or deploy spikes, often one to two quarters ahead of a fundraise. They signal the shift from building to scaling, which is exactly the story a startup tells a Series A or B investor, but they are less useful for catching a round within weeks.


Question: How is a framework migration different from the other acceleration signal types?

Answer: Framework migration is the subtlest of the acceleration types and the only residual one: it is general acceleration that does not meet the contributor-growth, repo-creation, or velocity thresholds. Where the other acceleration types point at a specific mechanism (hiring, building, shipping), framework migration points at a phase change in the technology stack.


Question: What should an investor do after spotting a framework migration signal?

Answer: Treat it as an invitation to look deeper rather than a specific call. A framework migration suggests a startup is professionalizing its stack, which is worth a diligence conversation about architecture, technical debt, and whether the migration is enabling scale or hiding instability.


Question: How is the Deceleration signal detected?

Answer: A startup is classified as decelerating when its 14-day commit velocity falls below the prior 14-day window, a velocity ratio below 1.0. It is the inverse of the acceleration signals and is computed from the same public commit-activity endpoint.


Question: What can falsely trigger a deceleration signal?

Answer: Sprint-cycle lumpiness is the main false positive: a team that works in two-week sprints can post a quiet planning week that reads as deceleration. A single quiet window should be read as noise until it persists. Holidays, seasonal dips, and post-launch regrouping are common non-negative causes.


Question: Which funding stage does deceleration usually indicate?

Answer: Deceleration is not stage-specific. A post-launch slowdown is common at any stage, but a sustained drop paired with falling contributor count is most worrying at seed and Series A/B, where a shrinking team can signal runway pressure or a stalled roadmap.


Question: How does deceleration relate to fundraise timing?

Answer: Deceleration is usually a neutral or negative timing signal: a team that just shipped a milestone often slows down to regroup before the next push. It becomes a red flag only when it is sustained and paired with declining contributors, which can indicate turnover or a strategic stall ahead of a difficult round.


Question: How is deceleration different from the other four signal types?

Answer: Deceleration is the only cooling signal: the four acceleration types (hiring burst, infrastructure buildout, deploy frequency spike, framework migration) all flag rising activity, while deceleration flags falling commit velocity. It is read as the inverse axis and pairs most usefully with the contributor and repository signals to tell a finished push from a stalled team.


Question: What should an investor do after spotting a deceleration signal?

Answer: Do not treat it as a sell signal. Check whether the slowdown is isolated to commits or accompanied by falling contributors and a pause in new repositories. A finished milestone with a stable team is normal; a broad-based slowdown is worth a founder conversation about roadmap, runway, and whether the team is regrouping or stalling.


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

Answer: Weekly. Data refreshes every Monday. The most recent period is Q3 2026.


Question: How should this data be cited?

Answer: VC Deal Flow Signal (signals.gitdealflow.com), Q3 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.