Question: How do you evaluate AI startup engineering signals?

Answer: Distinguish model training infrastructure (sporadic large commits, research-oriented) from product engineering (frequent small commits, customer-driven iteration). The strongest signal is a transition from research-style to product-style commit patterns, indicating the company is moving from experimentation to shipping.

AI Startup Engineering Signals in 2026: What Investors Should Watch


Question: What makes AI startups different on GitHub?

Answer: AI startups show the highest average commit velocity but also the highest noise of any sector. Open source experimentation, research-oriented commits, and community activity inflate the standard metrics. The key is separating product engineering (shipping features) from research exploration (running experiments).

AI Startup Engineering Signals in 2026: What Investors Should Watch


Question: What is the best AI startup investment signal?

Answer: The research-to-product transition. When an AI startup's commit pattern shifts from sporadic large commits (experiments, model checkpoints) to frequent small commits (API endpoints, deployment config, monitoring), the team is moving from 'does this work?' to 'let's ship this.' This transition often precedes a fundraise.

AI Startup Engineering Signals in 2026: What Investors Should Watch


Question: How long does this workflow take?

Answer: 30 minutes per week. The workflow is designed to fit into a Monday morning routine: check rankings, screen candidates, verify signals, and add qualified leads to your pipeline.

A Weekly Deal Sourcing Workflow Using Engineering Signals


Question: How many leads does this workflow typically produce?

Answer: 2-5 actionable leads per week, depending on how many sectors you track and how selective you are. The quality is high because engineering acceleration is a leading indicator – you are finding companies before they appear in traditional deal sourcing channels.

A Weekly Deal Sourcing Workflow Using Engineering Signals


Question: How do cybersecurity GitHub signals differ from other sectors?

Answer: Cybersecurity deploy frequency spikes often reflect CVE response rather than product iteration. Infrastructure buildout may indicate compliance infrastructure (SOC 2, ISO 27001). The strongest signal is sustained acceleration outside of incident-response cycles.

Cybersecurity Startup Signals: Reading GitHub Data for Security Deals


Question: How do you separate CVE response from real product acceleration?

Answer: Check the timing: does the velocity spike coincide with a major CVE disclosure? If the spike happens within days of a published vulnerability, it is likely reactive patching. Sustained acceleration over 2-3 weeks without an external trigger indicates genuine product momentum.

Cybersecurity Startup Signals: Reading GitHub Data for Security Deals


Question: What does compliance infrastructure signal in cybersecurity startups?

Answer: New repositories related to SOC 2 audit trails, ISO 27001 documentation, or penetration testing frameworks indicate a company preparing for enterprise sales. Most enterprise buyers require SOC 2 compliance, so this buildout is a positive investment signal – it requires capital and precedes revenue growth.

Cybersecurity Startup Signals: Reading GitHub Data for Security Deals


Question: Can you use GitHub data to evaluate climate tech startups?

Answer: Yes, but with nuance. Software-heavy climate companies (carbon accounting, energy trading) show standard engineering signals. Hardware-adjacent companies show lower commit velocity but meaningful infrastructure buildout when transitioning from R&D to deployment.

Climate Tech Engineering Signals: What GitHub Data Reveals About Green Startups


Question: What is the strongest climate tech investment signal?

Answer: The R&D-to-deployment transition. When a climate tech company's GitHub activity shifts from experimental (research notebooks, prototype code) to operational (deployment scripts, monitoring, CI/CD pipelines), the technology is moving from lab to field. This transition requires capital and often precedes fundraising.

Climate Tech Engineering Signals: What GitHub Data Reveals About Green Startups


Question: Which climate tech companies show up on GitHub?

Answer: Software-heavy climate companies appear most clearly: carbon accounting platforms, energy trading tools, grid optimization software, and ESG reporting systems. Hardware-adjacent companies building intelligence layers (battery management, sensor networks, predictive maintenance) also show meaningful GitHub signals.

Climate Tech Engineering Signals: What GitHub Data Reveals About Green Startups


Question: What are the biggest mistakes investors make with GitHub signals?

Answer: The five most common: confusing stars with traction, ignoring the private repo blind spot, overweighting absolute commit counts over acceleration, missing the context behind velocity spikes (bots, docs, migrations), and treating engineering signals as investment decisions rather than sourcing signals.

5 Mistakes Investors Make When Reading GitHub Signals


Question: Are GitHub signals reliable for investment decisions?

Answer: GitHub signals are reliable for deal sourcing – identifying interesting companies early. They are not reliable as standalone investment decisions. Engineering acceleration should be the first step in a diligence process, not the last. Always verify with direct founder conversations, product evaluation, and market analysis.

5 Mistakes Investors Make When Reading GitHub Signals


Question: How is the prediction calculated?

Answer: We compute a rolling 14-day window of commits per startup org, contributor count delta over 30 days, and new-repo creation rate. When all three accelerate inside the same two-week window, we classify the startup as 'accelerating'. Historical backtest shows ~70% of accelerating startups announce a fundraise within 6 weeks.

I Tracked 4,200 Startup GitHub Orgs for Six Months. Here's What Predicts a Series A.


Question: Why does this work better for non-AI startups?

Answer: AI startups commit constantly regardless of fundraise timing – the signal-to-noise ratio is poor. The pattern is most diagnostic in devtools, infrastructure, fintech, and cybersecurity, where engineering velocity tracks more closely with company stage and runway pressure.

I Tracked 4,200 Startup GitHub Orgs for Six Months. Here's What Predicts a Series A.


Question: Can I check my own startup's signal?

Answer: Yes. The free tool at /predict accepts any GitHub org name and returns the live signal classification (accelerating, steady, decelerating) plus the underlying commit and contributor numbers. No signup required.

I Tracked 4,200 Startup GitHub Orgs for Six Months. Here's What Predicts a Series A.


Question: How do I verify these predictions?

Answer: The /predicted page is a public, dated, snapshot watchlist. Bookmark it. Come back in 6 months and check how many of the 10 startups raised, were acquired, or had a major launch. Each card links to the underlying GitHub org so you can audit the signal yourself.

I Tracked 4,200 Startup GitHub Orgs for Six Months. Here's What Predicts a Series A.


Question: How many tools should an MCP server have?

Answer: There is no universal number, but the heuristic that works in practice is: one tool per distinct user intent, not one per REST endpoint. For @gitdealflow/mcp-signal, that came out to five tools mapping eight endpoints — three endpoints folded into multi-purpose tools, two were renamed for verb-noun clarity. Most teams ship with too many tools, not too few. Every tool in the menu costs the model reasoning bandwidth, costs the user latency, and increases the chance of a wrong-tool selection. Audit by logging what your users actually ask for in plain English, then reverse-engineering the smallest tool surface that covers those intents.

I cut my MCP server from 8 tools to 5 and the hallucinations stopped


Question: Why does MCP tool naming matter for accuracy?

Answer: The model selects tools by matching the user's prompt embedding against each tool description's embedding. When two tools share half their vocabulary — list_startups and get_startup, for example — the confidence between them collapses to a coin flip. Verb-noun names parse better than camelCase boundaries, and when the noun is a word the user actually says (startups, signals, sectors), you get a much cleaner lock. Renaming list_signals to get_startup_signal alone fixed selection on prompts that did not even contain the word signal, because the model parsed startup from context.

I cut my MCP server from 8 tools to 5 and the hallucinations stopped


Question: What is the MCP tool menu tax?

Answer: Every tool you expose adds its full schema to the model's context every single turn — description, parameter list, parameter types, return shape. With terse docstrings, that runs ~600 input tokens per tool. Eight tools is ~5,000 tokens of menu before the user has said anything. The tax is paid in three currencies: input tokens (cost), reasoning bandwidth (accuracy), and time-to-first-token (latency). Cutting tools you do not actually need reclaims all three.

I cut my MCP server from 8 tools to 5 and the hallucinations stopped


Question: Should each REST endpoint become an MCP tool?

Answer: No. REST APIs are designed around resources; MCP tools should be designed around user intents. Most APIs have more endpoints than they have distinct intents. Mapping one-to-one ships the extra endpoints as MCP tools that mostly get confused for one another by the model. The cleaner mental model is: list the conversational intents your users have (what would they say in plain English), then design the smallest tool set that covers them. Implementation detail like single-resource gets and list-with-filter pairs almost always collapses into one tool with optional parameters.

I cut my MCP server from 8 tools to 5 and the hallucinations stopped


Question: What is the Agent2Agent (A2A) protocol?

Answer: A2A is an open protocol from Google for agent-to-agent communication. An agent publishes a JSON AgentCard at /.well-known/agent-card.json describing its capabilities and exposes a JSON-RPC 2.0 endpoint that other agents call to send messages and receive task results. By April 2026 it had passed 22,000 GitHub stars and was supported by 150+ organizations including Microsoft, Salesforce, and SAP. It is complementary to MCP — MCP exposes tools to a single AI assistant, A2A lets agents call other agents across the network.

I made my VC deal flow callable by Claude this weekend. Here is what that actually means.


Question: How is the GitDealFlow A2A agent different from the existing MCP server?

Answer: Same five skills, different transport. The MCP server runs over stdio and is configured per-AI-assistant (Claude Desktop, Cursor, Windsurf). The A2A agent runs over HTTP/JSON-RPC and is configured per-agent-runtime (Google Agent Builder, LangChain, CrewAI, Mastra, Vercel AI SDK). MCP is for direct AI-to-tool calls. A2A is for agent-to-agent chains where another agent calls us as one node in a workflow. We ship both because the audiences are different.

I made my VC deal flow callable by Claude this weekend. Here is what that actually means.


Question: Do I need an API key?

Answer: No. The A2A endpoint at signals.gitdealflow.com/api/a2a is unauthenticated. There is no signup, no rate limit enforced at the application layer, and the upstream CDN absorbs typical agent traffic. The five free MCP tools and five free A2A skills are part of our distribution-magnet strategy and stay free forever. Paid features are scoped to /predict and the Insider Circle layer.

I made my VC deal flow callable by Claude this weekend. Here is what that actually means.


Question: What can the agent NOT do today?

Answer: The stub I shipped is read-only and synchronous. It does sync message/send returning a terminal Task, all five skills via text intent or structured data parts, CORS preflight, and JSON-RPC error codes. It does NOT do streaming via message/stream, task persistence with tasks/get, push notifications, authenticated extended cards, or per-user prediction skills. The first four are stubbed because no paying customer has asked for them. The fifth — predictions as an A2A skill — is the cliffhanger. Today /predict is browser-only. When it is callable by your AI, this gets interesting.

I made my VC deal flow callable by Claude this weekend. Here is what that actually means.


Question: How do I plug it into my agent runtime?

Answer: Drop the AgentCard URL — https://signals.gitdealflow.com/.well-known/agent-card.json — into your runtime's agent registry. Most runtimes (Google Agent Builder, LangChain, CrewAI, Mastra, Vercel AI SDK, Inkeep) auto-parse the card and expose the five skills as callable tools. The interactive playground at /a2a-demo lets you watch a live JSON-RPC request and response without any runtime configuration.

I made my VC deal flow callable by Claude this weekend. Here is what that actually means.


Question: What does Receipts actually do?

Answer: You paste a GitHub username. We fetch the user's public starred repos via the GitHub API (no login, no OAuth — starring history is public metadata). Then we cross-reference each starred repo against a curated database of ~75 validated unicorns: companies that hit a $1B+ valuation, raised a Series A or later, were acquired, or crossed 25K+ stars in the last five years. For every match, we measure the gap between when you starred the repo and when the validation event happened. The earlier you starred, the more points. Top 5 wins are summed and normalized to a 0-100 Scout Score, with a rank from Curious to Oracle.

Every dev has invested in unicorns. They just don't know it.


Question: Why backwards-looking? The Scout game on /predict is forwards.

Answer: /predict asks you to call a startup before they raise. The resolution window is six months. That works for taste validation but it has a virality ceiling — Twitter does not share things that pay off in Q4. Receipts inverts the timing: you get instant proof of taste from a database we already maintain. Same Scout ladder, same ranks, same brand. Receipts is the top-of-funnel; /predict is the conversion. Both feed the existing five-email Soap Opera onboarding sequence.

Every dev has invested in unicorns. They just don't know it.


Question: How is the Scout Score computed?

Answer: For each starred repo that matches a validated win, points = weight × min(months_early / 24, 1.0). Weight scales with the event: Series A = 50, Series B = 70, Series C+ or acquisition = 80-90, $1B+ valuation = 100. Twenty-four months early is a perfect multiplier — past that we cap because you cannot get more credit for being twenty years early. We dedupe to one win per company (you do not get points for starring three Vercel repos), then sum the top 5 and normalize so five perfect early calls equals 100. Scoring code is open-source at the route handler in the pseo-site repo.

Every dev has invested in unicorns. They just don't know it.


Question: Does Receipts read my private repos?

Answer: No. The GitHub API endpoint we hit (`GET /users/:username/starred`) only returns public starring data. We never see private repos, DMs, your follower graph, your contributions, your forks, or anything that requires user-scoped OAuth. The token we use server-side is a fine-grained PAT with no scopes — it exists only to raise our shared rate limit from 60 requests per hour to 5,000. Receipts works on any public GitHub username without that user's involvement.

Every dev has invested in unicorns. They just don't know it.


Question: Why these 75 wins specifically?

Answer: The list is biased toward developer-tools, AI infrastructure, and data/ops companies that have public GitHub presence and a clear validation event in the last five years (Vercel, Anthropic, LangChain, Hugging Face, Supabase, Linear, Cursor, Bun, Astro, OpenAI, Mistral, Modal, Pinecone, Stripe, Grafana, dbt, Airbyte, etc.). Closed-source unicorns without public repos cannot be in the database. The list will grow — every funded GitHub-native company is a candidate. If a company you think should be here is missing, the receipt fails to register a win and your score is lower than reality. That is a known false-negative.

Every dev has invested in unicorns. They just don't know it.


Question: What does the Scout Score badge actually show?

Answer: The current Scout Score (0-100) and rank (curious / scout / sharp / elite / oracle) for the GitHub user named in the URL. Score is computed live from the user's public starring history vs. our database of validated unicorns — same algorithm as /receipts. The badge re-fetches when the CDN cache expires, so a user's score on the badge keeps pace with their score on the receipts page within an hour.

Free Scout Score badges: shields.io for GitHub investing taste.


Question: What does the Commit Momentum badge show?

Answer: The current commit-velocity tier (cold / warming / hot / breakout) for any tracked GitHub org, plus the percent change. Tiers map to ranges of the 14-day velocity change versus the prior 14-day window: breakout is +200% or more, hot is +50% or more, warming is -30% or more, cold is below -30%. Untracked orgs render an 'untracked' pill so the badge degrades gracefully if a maintainer adds it before we are tracking that org.

Free Scout Score badges: shields.io for GitHub investing taste.


Question: Why ship a badge instead of a wider integration?

Answer: READMEs are the most-trafficked surface in open source. A vanity-driven SVG badge in a profile or repo README compounds: each render is a brand impression for our domain via GitHub's camo CDN, each click is a visitor on a branded GDF page. Codecov, WakaTime, GitHub Stats all proved the pattern. The badge is autonomous — once a maintainer pastes it, it self-distributes for as long as the repo or profile is public. Zero ongoing maintenance.

Free Scout Score badges: shields.io for GitHub investing taste.


Question: Will the badge slow down my README?

Answer: No. GitHub renders all README images through its camo proxy, which caches the SVG aggressively (24h on our CDN, with ETag revalidation hourly). The badge endpoint always returns 200 even on transient errors — a bad render is a neutral gray pill, never a broken-image icon. Cache miss is 1-4 seconds (the GitHub starring API is the slow leg); subsequent hits are sub-30 ms.

Free Scout Score badges: shields.io for GitHub investing taste.


Question: Can I customize the colors or labels?

Answer: Not yet. The Scout badge color reflects the user's current rank (curious=teal, scout=sky, sharp=purple, elite=amber, oracle=rose). The Momentum badge color reflects the tier. The label text is fixed. The point of locking these is that a casual reader scanning a README should be able to recognize a Scout badge from a Codecov badge from a WakaTime badge at a glance. We may add a color override later, but only after the visual identity is established.

Free Scout Score badges: shields.io for GitHub investing taste.


Question: Where can I find the full list of 30 research findings?

Answer: All 30 findings live at signals.gitdealflow.com/research. Each one has a dedicated sub-page at signals.gitdealflow.com/research/{slug} with full ScholarlyArticle JSON-LD, citation chain (SSRN, OpenAlex, Crossref, Zenodo), and a copy-paste citation block.

30 Research Findings, Now One Page Each: How to Cite GitHub Engineering Acceleration


Question: How should I cite an individual finding in a memo or research note?

Answer: Each sub-page carries a "How to cite" block. The canonical form is: The Data Nerd (2026). "{finding title}." Finding {n} of 30 in: A Longitudinal Panel of GitHub Engineering Velocity for Venture-Backed Startups. SSRN abstract=6606558. Retrieved from signals.gitdealflow.com/research/{slug}. The full cross-graph identity map is at signals.gitdealflow.com/citations.

30 Research Findings, Now One Page Each: How to Cite GitHub Engineering Acceleration


Question: What does engineering acceleration mean on this site, again?

Answer: Engineering acceleration is a quantitative GitHub momentum signal — code-side momentum measured from public commit-velocity data, contributor growth, and repository creation. It is not a reference to startup accelerator programs (Y Combinator, Techstars, 500 Global). Every finding page restates this disambiguation in its provenance block.

30 Research Findings, Now One Page Each: How to Cite GitHub Engineering Acceleration


Question: Is the underlying paper peer-reviewed?

Answer: Not yet. The methodology is openly published on SSRN (abstract=6606558), CC BY 4.0, and is auto-indexed by Crossref, OpenAlex (W7154916891), Semantic Scholar, Unpaywall, and DataCite. The dataset has a permanent DOI on Zenodo (10.5281/zenodo.19650920). Replication studies are welcome — signal@gitdealflow.com for co-authorship on funding-event joins.

30 Research Findings, Now One Page Each: How to Cite GitHub Engineering Acceleration


Question: What engineering signals are ai & machine learning startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 7 ai & machine learning startups with measurable GitHub engineering signals. 2 of 7 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 372 commits, with harvard-edge leading at 1132 commits (+55% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which ai & machine learning startup has the highest engineering acceleration in Q2 2026?

Answer: harvard-edge leads the ai & machine learning sector in Q2 2026 with 1132 commits over a 14-day window, representing a +55% change from the prior period. With 97 active contributors and 1 new repositories, harvard-edge is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active ai & machine learning engineering teams located?

Answer: Among the 7 ai & machine learning startups we track, US accounts for the highest concentration with 3 teams. Startups building AI/ML infrastructure, applications, and 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 ai & machine learning startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 6 ai & machine learning startups with measurable GitHub engineering signals. 3 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 221 commits, with paperless-ngx leading at 61 commits (+336% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which ai & machine learning startup has the highest engineering acceleration in Q1 2026?

Answer: paperless-ngx leads the ai & machine learning sector in Q1 2026 with 61 commits over a 14-day window, representing a +336% change from the prior period. With 100 active contributors, paperless-ngx 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 ai & machine learning engineering teams located?

Answer: Among the 6 ai & machine learning startups we track, US accounts for the highest concentration with 2 teams. Startups building AI/ML infrastructure, applications, and 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 ai & machine learning startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 6 ai & machine learning startups with measurable GitHub engineering signals. 1 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 150 commits, with photoprism leading at 202 commits (+24% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which ai & machine learning startup has the highest engineering acceleration in Q4 2025?

Answer: photoprism leads the ai & machine learning sector in Q4 2025 with 202 commits over a 14-day window, representing a +24% change from the prior period. With 100 active contributors, photoprism 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 ai & machine learning engineering teams located?

Answer: Among the 6 ai & machine learning startups we track, US accounts for the highest concentration with 2 teams. Startups building AI/ML infrastructure, applications, and 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 ai & machine learning startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 6 ai & machine learning startups with measurable GitHub engineering signals. 4 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 221 commits, with harvard-edge leading at 748 commits (+713% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which ai & machine learning startup has the highest engineering acceleration in Q3 2025?

Answer: harvard-edge leads the ai & machine learning sector in Q3 2025 with 748 commits over a 14-day window, representing a +713% change from the prior period. With 97 active contributors and 1 new repositories, harvard-edge is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active ai & machine learning engineering teams located?

Answer: Among the 6 ai & machine learning startups we track, US accounts for the highest concentration with 2 teams. Startups building AI/ML infrastructure, applications, and 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 climate tech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 4 climate tech startups with measurable GitHub engineering signals. 0 of 4 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 37 commits, with carbon-design-system leading at 79 commits (-1% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: carbon-design-system leads the climate tech sector in Q2 2026 with 79 commits over a 14-day window, representing a -1% change from the prior period. With 100 active contributors and 1 new repositories, carbon-design-system 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 climate tech engineering teams located?

Answer: Among the 4 climate tech startups we track, US accounts for the highest concentration with 2 teams. Startups developing clean energy, carbon monitoring, and climate adaptation technologies. 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 climate tech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 4 climate tech startups with measurable GitHub engineering signals. 4 of 4 show positive commit velocity growth. The most common signal type is "Engineering hiring burst", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 44 commits, with opennem leading at 31 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: opennem leads the climate tech sector in Q1 2026 with 31 commits over a 14-day window, representing a +999% change from the prior period. With 9 active contributors, opennem is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 4 climate tech startups we track, US accounts for the highest concentration with 2 teams. Startups developing clean energy, carbon monitoring, and climate adaptation technologies. 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 climate tech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 4 climate tech startups with measurable GitHub engineering signals. 2 of 4 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 35 commits, with carbon-design-system leading at 89 commits (+27% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: carbon-design-system leads the climate tech sector in Q4 2025 with 89 commits over a 14-day window, representing a +27% change from the prior period. With 100 active contributors and 1 new repositories, carbon-design-system 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 climate tech engineering teams located?

Answer: Among the 4 climate tech startups we track, US accounts for the highest concentration with 2 teams. Startups developing clean energy, carbon monitoring, and climate adaptation technologies. 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 climate tech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 4 climate tech startups with measurable GitHub engineering signals. 3 of 4 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 nco leading at 5 commits (+150% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: nco leads the climate tech sector in Q3 2025 with 5 commits over a 14-day window, representing a +150% change from the prior period. With 24 active contributors, nco is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 4 climate tech startups we track, US accounts for the highest concentration with 2 teams. Startups developing clean energy, carbon monitoring, and climate adaptation technologies. 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 developer tools startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 2 developer tools 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 48 commits, with OpenAPITools leading at 41 commits (-25% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: OpenAPITools leads the developer tools sector in Q2 2026 with 41 commits over a 14-day window, representing a -25% change from the prior period. With 100 active contributors, OpenAPITools 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 developer tools engineering teams located?

Answer: The developer tools startups we track are geographically distributed across multiple regions. Startups building tools and infrastructure for developers. We derive geography from GitHub organization profiles.


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

Answer: In Q1 2026, we are tracking 2 developer tools 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 185 commits, with OpenAPITools leading at 102 commits (+104% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: OpenAPITools leads the developer tools sector in Q1 2026 with 102 commits over a 14-day window, representing a +104% change from the prior period. With 100 active contributors, OpenAPITools 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 developer tools engineering teams located?

Answer: The developer tools startups we track are geographically distributed across multiple regions. Startups building tools and infrastructure for developers. We derive geography from GitHub organization profiles.


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

Answer: In Q4 2025, we are tracking 2 developer tools startups with measurable GitHub engineering signals. 1 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 49 commits, with nocobase leading at 60 commits (+67% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: nocobase leads the developer tools sector in Q4 2025 with 60 commits over a 14-day window, representing a +67% change from the prior period. With 98 active contributors and 1 new repositories, nocobase 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 developer tools engineering teams located?

Answer: The developer tools startups we track are geographically distributed across multiple regions. Startups building tools and infrastructure for developers. We derive geography from GitHub organization profiles.


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

Answer: In Q3 2025, we are tracking 2 developer tools startups with measurable GitHub engineering signals. 2 of 2 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 147 commits, with nocobase leading at 239 commits (+152% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: nocobase leads the developer tools sector in Q3 2025 with 239 commits over a 14-day window, representing a +152% change from the prior period. With 98 active contributors and 1 new repositories, nocobase 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 developer tools engineering teams located?

Answer: The developer tools startups we track are geographically distributed across multiple regions. Startups building tools and infrastructure for developers. We derive geography from GitHub organization profiles.


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

Answer: In Q2 2026, we are tracking 11 cybersecurity startups with measurable GitHub engineering signals. 6 of 11 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 8 of the tracked companies. The average 14-day commit velocity across the sector is 230 commits, with NewLifeX leading at 6 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: NewLifeX leads the cybersecurity sector in Q2 2026 with 6 commits over a 14-day window, representing a +999% change from the prior period. With 82 active contributors, NewLifeX 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 cybersecurity engineering teams located?

Answer: Among the 11 cybersecurity startups we track, US accounts for the highest concentration with 5 teams. Startups protecting systems, networks, and data from digital attacks. 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 cybersecurity startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 11 cybersecurity startups with measurable GitHub engineering signals. 8 of 11 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 6 of the tracked companies. The average 14-day commit velocity across the sector is 261 commits, with Infisical leading at 399 commits (+1496% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: Infisical leads the cybersecurity sector in Q1 2026 with 399 commits over a 14-day window, representing a +1496% change from the prior period. With 100 active contributors and 3 new repositories, Infisical is showing a "Infrastructure buildout" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 11 cybersecurity startups we track, US accounts for the highest concentration with 5 teams. Startups protecting systems, networks, and data from digital attacks. 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 cybersecurity startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 10 cybersecurity startups with measurable GitHub engineering signals. 5 of 10 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 8 of the tracked companies. The average 14-day commit velocity across the sector is 233 commits, with Infisical leading at 285 commits (+67% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: Infisical leads the cybersecurity sector in Q4 2025 with 285 commits over a 14-day window, representing a +67% change from the prior period. With 100 active contributors and 3 new repositories, Infisical is showing a "Infrastructure buildout" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 10 cybersecurity startups we track, US accounts for the highest concentration with 5 teams. Startups protecting systems, networks, and data from digital attacks. 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 cybersecurity startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 11 cybersecurity startups with measurable GitHub engineering signals. 3 of 11 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 9 of the tracked companies. The average 14-day commit velocity across the sector is 160 commits, with projectdiscovery leading at 469 commits (+31% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: projectdiscovery leads the cybersecurity sector in Q3 2025 with 469 commits over a 14-day window, representing a +31% change from the prior period. With 100 active contributors, projectdiscovery 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 cybersecurity engineering teams located?

Answer: Among the 11 cybersecurity startups we track, US accounts for the highest concentration with 5 teams. Startups protecting systems, networks, and data from digital attacks. 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 healthcare startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 6 healthcare startups with measurable GitHub engineering signals. 1 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 30 commits, with third-culture-software leading at 20 commits (+25% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: third-culture-software leads the healthcare sector in Q2 2026 with 20 commits over a 14-day window, representing a +25% change from the prior period. With 28 active contributors, third-culture-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 healthcare engineering teams located?

Answer: Among the 6 healthcare startups we track, APAC accounts for the highest concentration with 1 teams. Startups applying technology to patient care, health systems, and drug discovery. 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 healthcare startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 6 healthcare startups with measurable GitHub engineering signals. 5 of 6 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 124 commits, with andes leading at 4 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: andes leads the healthcare sector in Q1 2026 with 4 commits over a 14-day window, representing a +999% change from the prior period. With 44 active contributors, andes 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 healthcare engineering teams located?

Answer: Among the 6 healthcare startups we track, APAC accounts for the highest concentration with 1 teams. Startups applying technology to patient care, health systems, and drug discovery. 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 healthcare startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 6 healthcare startups with measurable GitHub engineering signals. 3 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 175 commits, with ballerina-platform leading at 414 commits (+124% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: ballerina-platform leads the healthcare sector in Q4 2025 with 414 commits over a 14-day window, representing a +124% change from the prior period. With 100 active contributors, ballerina-platform 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 healthcare engineering teams located?

Answer: Among the 6 healthcare startups we track, APAC accounts for the highest concentration with 1 teams. Startups applying technology to patient care, health systems, and drug discovery. 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 healthcare startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 6 healthcare startups with measurable GitHub engineering signals. 2 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 176 commits, with ballerina-platform leading at 360 commits (+134% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: ballerina-platform leads the healthcare sector in Q3 2025 with 360 commits over a 14-day window, representing a +134% change from the prior period. With 100 active contributors, ballerina-platform 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 healthcare engineering teams located?

Answer: Among the 6 healthcare startups we track, APAC accounts for the highest concentration with 1 teams. Startups applying technology to patient care, health systems, and drug discovery. 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 edtech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 10 edtech startups with measurable GitHub engineering signals. 1 of 10 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 7 of the tracked companies. The average 14-day commit velocity across the sector is 68 commits, with sonic-pi-net leading at 8 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: sonic-pi-net leads the edtech sector in Q2 2026 with 8 commits over a 14-day window, representing a +999% change from the prior period. With 100 active contributors, sonic-pi-net 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 edtech engineering teams located?

Answer: Among the 10 edtech startups we track, EU accounts for the highest concentration with 3 teams. Startups transforming education through adaptive learning and institutional software. 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 edtech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 8 edtech startups with measurable GitHub engineering signals. 6 of 8 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 137 commits, with hpi-schul-cloud leading at 5 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: hpi-schul-cloud leads the edtech sector in Q1 2026 with 5 commits over a 14-day window, representing a +999% change from the prior period. With 100 active contributors and 2 new repositories, hpi-schul-cloud 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 edtech engineering teams located?

Answer: Among the 8 edtech startups we track, EU accounts for the highest concentration with 3 teams. Startups transforming education through adaptive learning and institutional software. 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 edtech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 9 edtech startups with measurable GitHub engineering signals. 3 of 9 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 7 of the tracked companies. The average 14-day commit velocity across the sector is 115 commits, with mdn leading at 692 commits (+68% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: mdn leads the edtech sector in Q4 2025 with 692 commits over a 14-day window, representing a +68% change from the prior period. With 100 active contributors, mdn 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 edtech engineering teams located?

Answer: Among the 9 edtech startups we track, EU accounts for the highest concentration with 3 teams. Startups transforming education through adaptive learning and institutional software. 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 edtech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 9 edtech startups with measurable GitHub engineering signals. 6 of 9 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 7 of the tracked companies. The average 14-day commit velocity across the sector is 291 commits, with particify leading at 15 commits (+114% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: particify leads the edtech sector in Q3 2025 with 15 commits over a 14-day window, representing a +114% change from the prior period. With 16 active contributors, particify 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 edtech engineering teams located?

Answer: Among the 9 edtech startups we track, EU accounts for the highest concentration with 3 teams. Startups transforming education through adaptive learning and institutional software. 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 e-commerce infrastructure startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 4 e-commerce infrastructure startups with measurable GitHub engineering signals. 3 of 4 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 29 commits, with ishgroup leading at 7 commits (+75% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which e-commerce infrastructure startup has the highest engineering acceleration in Q2 2026?

Answer: ishgroup leads the e-commerce infrastructure sector in Q2 2026 with 7 commits over a 14-day window, representing a +75% change from the prior period. With 13 active contributors, ishgroup 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 e-commerce infrastructure engineering teams located?

Answer: Among the 4 e-commerce infrastructure startups we track, APAC accounts for the highest concentration with 1 teams. Startups building backend systems and APIs for online retail. 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 e-commerce infrastructure startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 4 e-commerce infrastructure startups with measurable GitHub engineering signals. 3 of 4 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 29 commits, with ishgroup leading at 6 commits (+50% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which e-commerce infrastructure startup has the highest engineering acceleration in Q1 2026?

Answer: ishgroup leads the e-commerce infrastructure sector in Q1 2026 with 6 commits over a 14-day window, representing a +50% change from the prior period. With 13 active contributors, ishgroup 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 e-commerce infrastructure engineering teams located?

Answer: Among the 4 e-commerce infrastructure startups we track, APAC accounts for the highest concentration with 1 teams. Startups building backend systems and APIs for online retail. 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 e-commerce infrastructure startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 4 e-commerce infrastructure startups with measurable GitHub engineering signals. 1 of 4 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 15 commits, with solidusio leading at 46 commits (+283% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which e-commerce infrastructure startup has the highest engineering acceleration in Q4 2025?

Answer: solidusio leads the e-commerce infrastructure sector in Q4 2025 with 46 commits over a 14-day window, representing a +283% change from the prior period. With 100 active contributors, solidusio 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 e-commerce infrastructure engineering teams located?

Answer: Among the 4 e-commerce infrastructure startups we track, APAC accounts for the highest concentration with 1 teams. Startups building backend systems and APIs for online retail. 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 e-commerce infrastructure startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 5 e-commerce infrastructure startups with measurable GitHub engineering signals. 2 of 5 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 14 commits, with vuestorefront leading at 1 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which e-commerce infrastructure startup has the highest engineering acceleration in Q3 2025?

Answer: vuestorefront leads the e-commerce infrastructure sector in Q3 2025 with 1 commits over a 14-day window, representing a +999% change from the prior period. With 100 active contributors, vuestorefront 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 e-commerce infrastructure engineering teams located?

Answer: Among the 5 e-commerce infrastructure startups we track, APAC accounts for the highest concentration with 1 teams. Startups building backend systems and APIs for online retail. 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 supply chain startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 1 supply chain 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 20 commits, with medulla-tech leading at 20 commits (-49% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: medulla-tech leads the supply chain sector in Q2 2026 with 20 commits over a 14-day window, representing a -49% change from the prior period. With 25 active contributors and 1 new repositories, medulla-tech 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 supply chain engineering teams located?

Answer: Among the 1 supply chain startups we track, EU accounts for the highest concentration with 1 teams. Startups digitizing logistics, procurement, and inventory 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 supply chain startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 1 supply chain startups with measurable GitHub engineering signals. 1 of 1 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 51 commits, with medulla-tech leading at 51 commits (+325% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: medulla-tech leads the supply chain sector in Q1 2026 with 51 commits over a 14-day window, representing a +325% change from the prior period. With 25 active contributors and 1 new repositories, medulla-tech 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 supply chain engineering teams located?

Answer: Among the 1 supply chain startups we track, EU accounts for the highest concentration with 1 teams. Startups digitizing logistics, procurement, and inventory 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 supply chain startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 1 supply chain 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 72 commits, with medulla-tech leading at 72 commits (+140% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: medulla-tech leads the supply chain sector in Q4 2025 with 72 commits over a 14-day window, representing a +140% change from the prior period. With 25 active contributors and 1 new repositories, medulla-tech 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 supply chain engineering teams located?

Answer: Among the 1 supply chain startups we track, EU accounts for the highest concentration with 1 teams. Startups digitizing logistics, procurement, and inventory 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 supply chain startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 1 supply chain 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 50 commits, with medulla-tech leading at 50 commits (+25% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: medulla-tech leads the supply chain sector in Q3 2025 with 50 commits over a 14-day window, representing a +25% change from the prior period. With 25 active contributors and 1 new repositories, medulla-tech 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 supply chain engineering teams located?

Answer: Among the 1 supply chain startups we track, EU accounts for the highest concentration with 1 teams. Startups digitizing logistics, procurement, and inventory 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 web3 startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 6 web3 startups with measurable GitHub engineering signals. 0 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 61 commits, with NethermindEth leading at 99 commits (-26% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which web3 startup has the highest engineering acceleration in Q2 2026?

Answer: NethermindEth leads the web3 sector in Q2 2026 with 99 commits over a 14-day window, representing a -26% change from the prior period. With 100 active contributors and 6 new repositories, NethermindEth is showing a "Infrastructure buildout" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active web3 engineering teams located?

Answer: Among the 6 web3 startups we track, US accounts for the highest concentration with 1 teams. Startups building decentralized applications and blockchain infrastructure. 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 web3 startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 6 web3 startups with measurable GitHub engineering signals. 6 of 6 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 133 commits, with IntersectMBO leading at 119 commits (+358% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which web3 startup has the highest engineering acceleration in Q1 2026?

Answer: IntersectMBO leads the web3 sector in Q1 2026 with 119 commits over a 14-day window, representing a +358% change from the prior period. With 100 active contributors, IntersectMBO 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 web3 engineering teams located?

Answer: Among the 6 web3 startups we track, US accounts for the highest concentration with 1 teams. Startups building decentralized applications and blockchain infrastructure. 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 web3 startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 6 web3 startups with measurable GitHub engineering signals. 3 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 5 of the tracked companies. The average 14-day commit velocity across the sector is 64 commits, with NethermindEth leading at 115 commits (+85% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which web3 startup has the highest engineering acceleration in Q4 2025?

Answer: NethermindEth leads the web3 sector in Q4 2025 with 115 commits over a 14-day window, representing a +85% change from the prior period. With 100 active contributors and 6 new repositories, NethermindEth is showing a "Infrastructure buildout" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


Question: Where are the most active web3 engineering teams located?

Answer: Among the 6 web3 startups we track, US accounts for the highest concentration with 1 teams. Startups building decentralized applications and blockchain infrastructure. 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 web3 startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 6 web3 startups with measurable GitHub engineering signals. 3 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 93 commits, with BibliothecaDAO leading at 23 commits (+156% change). These patterns have historically preceded fundraise announcements by three to six weeks.


Question: Which web3 startup has the highest engineering acceleration in Q3 2025?

Answer: BibliothecaDAO leads the web3 sector in Q3 2025 with 23 commits over a 14-day window, representing a +156% change from the prior period. With 30 active contributors, BibliothecaDAO 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 web3 engineering teams located?

Answer: Among the 6 web3 startups we track, US accounts for the highest concentration with 1 teams. Startups building decentralized applications and blockchain infrastructure. 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 enterprise saas startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 6 enterprise saas startups with measurable GitHub engineering signals. 0 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 6 of the tracked companies. The average 14-day commit velocity across the sector is 79 commits, with langchain-ai leading at 105 commits (-5% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: langchain-ai leads the enterprise saas sector in Q2 2026 with 105 commits over a 14-day window, representing a -5% change from the prior period. With 100 active contributors and 1 new repositories, langchain-ai 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 enterprise saas engineering teams located?

Answer: Among the 6 enterprise saas startups we track, US accounts for the highest concentration with 2 teams. Startups building vertical and horizontal B2B software. 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 enterprise saas startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 6 enterprise saas startups with measurable GitHub engineering signals. 6 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 124 commits, with polarsource leading at 289 commits (+456% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: polarsource leads the enterprise saas sector in Q1 2026 with 289 commits over a 14-day window, representing a +456% change from the prior period. With 100 active contributors, polarsource 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 enterprise saas engineering teams located?

Answer: Among the 6 enterprise saas startups we track, US accounts for the highest concentration with 2 teams. Startups building vertical and horizontal B2B software. 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 enterprise saas startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 6 enterprise saas startups with measurable GitHub engineering signals. 3 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 6 of the tracked companies. The average 14-day commit velocity across the sector is 102 commits, with langchain-ai leading at 226 commits (+42% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: langchain-ai leads the enterprise saas sector in Q4 2025 with 226 commits over a 14-day window, representing a +42% change from the prior period. With 100 active contributors and 1 new repositories, langchain-ai 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 enterprise saas engineering teams located?

Answer: Among the 6 enterprise saas startups we track, US accounts for the highest concentration with 2 teams. Startups building vertical and horizontal B2B software. 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 enterprise saas startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 6 enterprise saas startups with measurable GitHub engineering signals. 5 of 6 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 6 of the tracked companies. The average 14-day commit velocity across the sector is 81 commits, with langchain-ai leading at 161 commits (+55% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: langchain-ai leads the enterprise saas sector in Q3 2025 with 161 commits over a 14-day window, representing a +55% change from the prior period. With 100 active contributors and 1 new repositories, langchain-ai 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 enterprise saas engineering teams located?

Answer: Among the 6 enterprise saas startups we track, US accounts for the highest concentration with 2 teams. Startups building vertical and horizontal B2B software. 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 data infrastructure startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 10 data infrastructure startups with measurable GitHub engineering signals. 1 of 10 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 7 of the tracked companies. The average 14-day commit velocity across the sector is 328 commits, with VictoriaMetrics leading at 71 commits (+4% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: VictoriaMetrics leads the data infrastructure sector in Q2 2026 with 71 commits over a 14-day window, representing a +4% change from the prior period. With 100 active contributors, VictoriaMetrics 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 data infrastructure engineering teams located?

Answer: Among the 10 data infrastructure startups we track, US accounts for the highest concentration with 5 teams. Startups building pipelines, warehouses, and observability 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 data infrastructure startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 10 data infrastructure startups with measurable GitHub engineering signals. 8 of 10 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 4 of the tracked companies. The average 14-day commit velocity across the sector is 364 commits, with airbytehq leading at 559 commits (+1647% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: airbytehq leads the data infrastructure sector in Q1 2026 with 559 commits over a 14-day window, representing a +1647% change from the prior period. With 100 active contributors, airbytehq 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 data infrastructure engineering teams located?

Answer: Among the 10 data infrastructure startups we track, US accounts for the highest concentration with 5 teams. Startups building pipelines, warehouses, and observability 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 data infrastructure startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 10 data infrastructure startups with measurable GitHub engineering signals. 4 of 10 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 7 of the tracked companies. The average 14-day commit velocity across the sector is 391 commits, with dbt-labs leading at 573 commits (+60% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: dbt-labs leads the data infrastructure sector in Q4 2025 with 573 commits over a 14-day window, representing a +60% change from the prior period. With 100 active contributors and 2 new repositories, dbt-labs 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 data infrastructure engineering teams located?

Answer: Among the 10 data infrastructure startups we track, US accounts for the highest concentration with 5 teams. Startups building pipelines, warehouses, and observability 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 data infrastructure startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 9 data infrastructure startups with measurable GitHub engineering signals. 3 of 9 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 6 of the tracked companies. The average 14-day commit velocity across the sector is 377 commits, with opensearch-project leading at 52 commits (+24% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: opensearch-project leads the data infrastructure sector in Q3 2025 with 52 commits over a 14-day window, representing a +24% change from the prior period. With 100 active contributors, opensearch-project 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 data infrastructure engineering teams located?

Answer: Among the 9 data infrastructure startups we track, US accounts for the highest concentration with 5 teams. Startups building pipelines, warehouses, and observability 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 robotics startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 3 robotics 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 23 commits, with ihmcrobotics leading at 23 commits (+44% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: ihmcrobotics leads the robotics sector in Q2 2026 with 23 commits over a 14-day window, representing a +44% change from the prior period. With 79 active contributors, ihmcrobotics 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 robotics engineering teams located?

Answer: Among the 3 robotics startups we track, US accounts for the highest concentration with 1 teams. Startups building autonomous robots and robotic process automation. 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 robotics startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 3 robotics startups with measurable GitHub engineering signals. 2 of 3 show positive commit velocity growth. The most common signal type is "Deploy frequency spike", observed in 2 of the tracked companies. The average 14-day commit velocity across the sector is 47 commits, with StanfordVL leading at 12 commits (+1100% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: StanfordVL leads the robotics sector in Q1 2026 with 12 commits over a 14-day window, representing a +1100% change from the prior period. With 55 active contributors, StanfordVL 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 robotics engineering teams located?

Answer: Among the 3 robotics startups we track, US accounts for the highest concentration with 1 teams. Startups building autonomous robots and robotic process automation. 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 robotics startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 3 robotics startups with measurable GitHub engineering signals. 0 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 30 commits, with ihmcrobotics leading at 34 commits (-11% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: ihmcrobotics leads the robotics sector in Q4 2025 with 34 commits over a 14-day window, representing a -11% change from the prior period. With 79 active contributors, ihmcrobotics 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 robotics engineering teams located?

Answer: Among the 3 robotics startups we track, US accounts for the highest concentration with 1 teams. Startups building autonomous robots and robotic process automation. 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 robotics startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 3 robotics startups with measurable GitHub engineering signals. 0 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 28 commits, with StanfordVL leading at 54 commits (-7% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: StanfordVL leads the robotics sector in Q3 2025 with 54 commits over a 14-day window, representing a -7% change from the prior period. With 55 active contributors, StanfordVL 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 robotics engineering teams located?

Answer: Among the 3 robotics startups we track, US accounts for the highest concentration with 1 teams. Startups building autonomous robots and robotic process automation. 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 legal tech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 2 legal tech startups with measurable GitHub engineering signals. 1 of 2 show positive commit velocity growth. The most common signal type is "Engineering hiring burst", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 116 commits, with bridgecrewio leading at 17 commits (+55% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: bridgecrewio leads the legal tech sector in Q2 2026 with 17 commits over a 14-day window, representing a +55% change from the prior period. With 100 active contributors, bridgecrewio is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 2 legal tech startups we track, US accounts for the highest concentration with 2 teams. Startups automating legal workflows and compliance 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 legal tech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 1 legal tech 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 260 commits, with wazuh leading at 260 commits (+65% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: wazuh leads the legal tech sector in Q1 2026 with 260 commits over a 14-day window, representing a +65% change from the prior period. With 100 active contributors and 1 new repositories, wazuh 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 legal tech engineering teams located?

Answer: Among the 1 legal tech startups we track, US accounts for the highest concentration with 1 teams. Startups automating legal workflows and compliance 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 legal tech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 2 legal tech startups with measurable GitHub engineering signals. 0 of 2 show positive commit velocity growth. The most common signal type is "Engineering hiring burst", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 134 commits, with wazuh leading at 245 commits (-8% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: wazuh leads the legal tech sector in Q4 2025 with 245 commits over a 14-day window, representing a -8% change from the prior period. With 100 active contributors and 1 new repositories, wazuh 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 legal tech engineering teams located?

Answer: Among the 2 legal tech startups we track, US accounts for the highest concentration with 2 teams. Startups automating legal workflows and compliance 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 legal tech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 2 legal tech startups with measurable GitHub engineering signals. 1 of 2 show positive commit velocity growth. The most common signal type is "Engineering hiring burst", observed in 1 of the tracked companies. The average 14-day commit velocity across the sector is 98 commits, with bridgecrewio leading at 11 commits (+22% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: bridgecrewio leads the legal tech sector in Q3 2025 with 11 commits over a 14-day window, representing a +22% change from the prior period. With 100 active contributors, bridgecrewio is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 2 legal tech startups we track, US accounts for the highest concentration with 2 teams. Startups automating legal workflows and compliance 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 hr tech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 3 hr tech startups with measurable GitHub engineering signals. 0 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 689 commits, with PostHog leading at 907 commits (-40% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: PostHog leads the hr tech sector in Q2 2026 with 907 commits over a 14-day window, representing a -40% change from the prior period. With 100 active contributors and 4 new repositories, PostHog is showing a "Infrastructure buildout" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 3 hr tech startups we track, US accounts for the highest concentration with 3 teams. Startups building recruiting, people management, and workforce analytics 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 hr tech startups showing in Q1 2026?

Answer: In Q1 2026, we are tracking 3 hr 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 1 of the tracked companies. The average 14-day commit velocity across the sector is 372 commits, with ever-co leading at 77 commits (+88% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: ever-co leads the hr tech sector in Q1 2026 with 77 commits over a 14-day window, representing a +88% change from the prior period. With 79 active contributors and 1 new repositories, ever-co 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 hr tech engineering teams located?

Answer: Among the 3 hr tech startups we track, US accounts for the highest concentration with 3 teams. Startups building recruiting, people management, and workforce analytics 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 hr tech startups showing in Q4 2025?

Answer: In Q4 2025, we are tracking 3 hr tech 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 270 commits, with zapplyjobs leading at 43 commits (+999% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: zapplyjobs leads the hr tech sector in Q4 2025 with 43 commits over a 14-day window, representing a +999% change from the prior period. With 8 active contributors, zapplyjobs is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 3 hr tech startups we track, US accounts for the highest concentration with 3 teams. Startups building recruiting, people management, and workforce analytics 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 hr tech startups showing in Q3 2025?

Answer: In Q3 2025, we are tracking 3 hr 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 1 of the tracked companies. The average 14-day commit velocity across the sector is 270 commits, with zapplyjobs leading at 87 commits (+1143% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: zapplyjobs leads the hr tech sector in Q3 2025 with 87 commits over a 14-day window, representing a +1143% change from the prior period. With 8 active contributors, zapplyjobs is showing a "Engineering hiring burst" pattern — one of the more reliable leading indicators of a significant product milestone or fundraise.


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

Answer: Among the 3 hr tech startups we track, US accounts for the highest concentration with 3 teams. Startups building recruiting, people management, and workforce analytics tools. Geographic distribution matters for investors because engineering talent clusters correlate with sector-specific domain expertise and proximity to early adopter customers.