Question: What GitHub patterns predict startup fundraises?

Answer: Five GitHub patterns reliably precede fundraise announcements: (1) The Contributor Step Function - a sudden 50%+ jump in unique contributors, indicating post-round hiring, (2) The Infrastructure Explosion - 3-5 new repos in a month, signaling platform buildout, (3) The Weekend Surge - sustained 7-day commit patterns from multiple contributors, (4) The Documentation Sprint - proactive documentation suggesting preparation for scrutiny, and (5) The Velocity Regime Change - commit velocity exceeding the 6-month average by 100%+.

5 GitHub Patterns That Predict Startup Fundraises


Question: How reliable are GitHub-based fundraise predictions?

Answer: GitHub patterns are leading indicators, not guarantees. They appear with enough regularity to be useful - particularly when multiple patterns overlap - but not all engineering acceleration leads to fundraising. Some acceleration reflects product-market fit, pivots, or hackathon activity. The patterns are most reliable when a startup shows two or more signals simultaneously, such as contributor growth combined with a velocity regime change.

5 GitHub Patterns That Predict Startup Fundraises


Question: Which combination of GitHub patterns is the strongest fundraise signal?

Answer: The strongest combination is Pattern 1 (contributor step function - sudden team growth) plus Pattern 5 (velocity regime change - sustained doubling of commit velocity). When both appear simultaneously, the startup has almost certainly either just closed a round or is in the middle of one. The new hires are shipping code at an accelerated pace, and the compound signal is very difficult to produce without real organizational change.

5 GitHub Patterns That Predict Startup Fundraises


Question: What is alternative data in venture capital?

Answer: Alternative data in venture capital is any dataset that reveals startup traction before it appears through conventional deal sourcing channels. The main categories include engineering activity from GitHub (commit velocity, contributor growth), hiring signals from job boards, web traffic from analytics tools, social mentions from platforms like Twitter and Hacker News, and patent filings. Unlike traditional deal flow data (funding announcements, press, warm intros), alternative data provides a leading rather than lagging indicator.

Alternative Data for Venture Capital: Why GitHub Is the Most Underused Signal


Question: Why is GitHub data considered the most underused signal for VCs?

Answer: GitHub data stands out among alternative data sources because it is continuous (updated daily, not monthly), free and public (no scraping or paid tools required), hard to fake (commits represent real engineering work), and reveals intent (the type of activity tells you what phase the company is in). Despite these properties, almost no investor monitors GitHub systematically - creating an information asymmetry for those who do.

Alternative Data for Venture Capital: Why GitHub Is the Most Underused Signal


Question: How do hedge funds and quant investors use alternative data?

Answer: Quantitative investment firms have used alternative data in public markets for over a decade - satellite imagery of parking lots, credit card transactions, app downloads. The edge comes not from exclusive data but from reading what others ignore, faster and more consistently. The same principle applies to venture capital: every investor has access to GitHub, but almost none monitor it systematically. Building a workflow around engineering signals creates a structural timing advantage.

Alternative Data for Venture Capital: Why GitHub Is the Most Underused Signal


Question: How can investors find startup deals before Crunchbase?

Answer: Investors can find deals before Crunchbase using three signal types: (1) GitHub engineering signals - the earliest indicator, detecting commit velocity spikes 6-12 weeks before fundraise announcements, (2) community signals from Hacker News, Product Hunt, and Indie Hackers - variable lead time, wide coverage, and (3) hiring signals from job boards and LinkedIn - 4-8 weeks lead time. Combining all three with Crunchbase for verification gives both timing advantage and diligence depth.

How to Source Startup Deals Before They Appear on Crunchbase


Question: What is the earliest public signal of startup momentum?

Answer: GitHub engineering acceleration is the earliest publicly available signal of startup momentum. The logic is straightforward: engineering acceleration precedes product milestones, which precede fundraise decisions, which precede Crunchbase entries. When a startup's commit velocity doubles in a two-week window and the change is sustained, the underlying cause - post-fundraise scaling, product-market fit, or launch preparation - is already in motion 6-12 weeks before any public announcement.

How to Source Startup Deals Before They Appear on Crunchbase


Question: How much time does GitHub signal data give you over Crunchbase alerts?

Answer: GitHub engineering signals provide 6-12 weeks of lead time over Crunchbase alerts. Crunchbase alerts trigger on fundraise announcements, which are published after the round closes - zero lead time. GitHub signals detect acceleration patterns while the round is still in progress or before fundraising even begins. The top movers in VC Deal Flow Signal's weekly rankings consistently include companies that announce raises 4-8 weeks later.

How to Source Startup Deals Before They Appear on Crunchbase


Question: What engineering metrics should startup investors track?

Answer: Investors should track seven engineering metrics from public GitHub data: (1) commit velocity - 14-day rolling commit count, (2) commit velocity change - the percentage change vs. prior period (the primary signal), (3) contributor count - proxy for team size, (4) contributor growth rate - indicates hiring bursts, (5) new repository count - signals infrastructure buildout, (6) weekend commit ratio - indicates deadline pressure, and (7) language/framework distribution - reveals technical maturity and stack choices.

7 Startup Engineering Metrics Every Investor Should Track


Question: What is the most important GitHub metric for venture capital deal sourcing?

Answer: Commit velocity change - the percentage change in 14-day commit count compared to the prior window - is the single most useful engineering metric for investors. It measures acceleration rather than absolute volume, which makes it comparable across startups of different sizes. A sustained velocity change above +50% for 3 or more consecutive windows is a meaningful signal. Above +100% is a regime change that has historically preceded fundraise announcements.

7 Startup Engineering Metrics Every Investor Should Track


Question: How can investors quickly screen startups using GitHub data?

Answer: A quick screening checklist: (1) Is commit velocity change positive and above 50%? (2) Has contributor count grown recently? (3) Are there new repos in the last 30 days? (4) Is the activity product-related, not just docs or CI/CD? (5) Does the tech stack match the company's pitch? If a startup passes all five checks, it deserves a deeper look. If it fails the first two, the engineering signal is not there. VC Deal Flow Signal automates checks 1-4 across 15 sectors weekly.

7 Startup Engineering Metrics Every Investor Should Track


Question: What is engineering acceleration?

Answer: Engineering acceleration measures the rate of change in a startup's engineering output relative to its own historical baseline. It is calculated as the percentage change in 14-day GitHub commit velocity versus the prior period. A +100% acceleration means the team doubled its commit rate. The metric is computed per startup, not across the population, which means a small team and a large team are measured against their own historical pace rather than each other.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: Is engineering acceleration the same as a startup accelerator program?

Answer: No, they are unrelated concepts that share a word. A startup accelerator (Y Combinator, Techstars, 500 Global) is a fixed-term program founders join for mentorship, capital, and networking. Engineering acceleration, as defined at VC Deal Flow Signal, is a quantitative signal computed entirely from a startup's public GitHub activity. Throughout this site, the term refers exclusively to code-side momentum: GitHub commit velocity, contributor growth, repository creation. It has nothing to do with program participation.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: Why does engineering acceleration matter for investors?

Answer: Engineering acceleration is a leading indicator of startup momentum. When a team accelerates its engineering output, the cause is usually post-fundraise scaling, product-market fit iteration, or launch preparation, all of which precede the public signals (press coverage, Crunchbase entries, hiring announcements) that most investors rely on. Catching the change at the GitHub layer typically gives investors a 3 to 6 week lead time over the press cycle.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: How is engineering acceleration different from DORA metrics?

Answer: DORA metrics (deployment frequency, lead time for changes, change failure rate, time to restore) measure engineering process quality, how reliably a team ships. Engineering acceleration measures output momentum, whether the team is speeding up. DORA requires internal CI/CD access; acceleration is computed from public GitHub data, making it useful as an external investment signal. The two are complementary: DORA helps engineering managers; acceleration helps investors source.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: What threshold counts as a meaningful acceleration?

Answer: A useful working threshold is +100% sustained over two consecutive 14-day windows. One-period spikes are usually noise, a hackathon, a single contributor onboarding, a documentation push. The two-period confirmation rule filters most of that noise. Pre-seed teams with very low absolute volume require larger percentage moves (often +200% or more) to clear the noise floor; later-stage teams can show meaningful signals at +50% because their absolute output is large.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: What are the four signal types?

Answer: Acceleration patterns sort into four operational types. The hiring burst combines rising commit velocity with rising contributor count, the strongest fundraise predictor. The shipping sprint shows velocity rising while contributor count stays flat, typical of launch preparation. The infrastructure buildout shows new repository creation accelerating, a structural investment, often platform migration. The platform migration shows language mix shifting between primary languages, the slowest-moving but most strategically significant signal. Each pattern implies a different diligence question.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: Can engineering acceleration be gamed by founders?

Answer: In theory yes; in practice it is expensive and easy to detect. A team can pad commit counts with mechanical edits, but contributor growth, repository creation, and language-mix changes are harder to fake. Most importantly, gaming the signal requires sustained effort from multiple contributors over weeks, which is itself a form of real engineering activity. Detection looks at commit size variance, file diversity, and contributor recency, checks any careful investor performs anyway.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: Where does engineering acceleration fit relative to other alternative data?

Answer: Engineering acceleration is the longest-lead-time signal in the public alternative-data stack. Hiring data (LinkedIn, Wellfound) is upstream of engineering output but noisier, many job postings never close. Web traffic (SimilarWeb, Specter) and social signals (Twitter, Hacker News) are typically downstream of engineering activity. The strongest sourcing stacks layer all three: engineering acceleration for early discovery, hiring for validation, web/social for downstream cross-validation. Each catches a different point in the startup's development arc.

What Is Engineering Acceleration? The Metric VCs Are Starting to Track


Question: What is commit velocity?

Answer: Commit velocity is the total number of commits to a startup's most active public GitHub repository over a rolling 14-day window. It measures the raw volume of engineering output.

Commit Velocity Explained: What Investors Need to Know


Question: Is high commit velocity always a good sign?

Answer: Not necessarily. High absolute commit velocity can reflect automated commits, documentation updates, or CI/CD activity rather than meaningful product development. What matters more is commit velocity change - whether the rate is accelerating.

Commit Velocity Explained: What Investors Need to Know


Question: What is a good commit velocity for a startup?

Answer: There is no universal benchmark - commit velocity depends on team size, commit granularity, and workflow conventions. A solo founder with 50 commits/week and a 10-person team with 200 commits/week may have equivalent per-engineer output. The useful metric is velocity change relative to the company's own baseline, not absolute counts.

Commit Velocity Explained: What Investors Need to Know


Question: Can you find pre-seed startups on GitHub?

Answer: Yes. Pre-seed startups often have public GitHub activity before they have any other public presence. Look for organizations with 1-3 contributors showing rapid commit acceleration from a low base - this pattern indicates early product development that often precedes a first fundraise.

Pre-Seed Deal Sourcing with GitHub Data: A Practical Guide


Question: What does pre-seed engineering activity look like on GitHub?

Answer: Pre-seed activity typically shows 1-7 contributors, commit velocity under 100 per 14 days, but with high acceleration rates (+200% or more). New repository creation (infrastructure buildout) is common as founders move from prototype to more structured development.

Pre-Seed Deal Sourcing with GitHub Data: A Practical Guide


Question: How do you find pre-seed startups before they raise?

Answer: Filter sector rankings for startups with 1-7 contributors showing +200% or higher velocity change. These disproportionate acceleration rates from a small base indicate a product breakthrough or first-fundraise preparation. Then verify on GitHub: is the activity product-related? Check Hacker News and Twitter for founder activity.

Pre-Seed Deal Sourcing with GitHub Data: A Practical Guide


Question: What GitHub patterns indicate a Series A startup?

Answer: Series A startups typically show 20-49 contributors, infrastructure buildout (3+ new repos in 30 days), and increasing repository specialization. The dominant signal type is 'infrastructure buildout' - the team is building the platform around a working core product.

Series A Signals: What GitHub Data Reveals About Growth-Stage Startups


Question: What is infrastructure buildout in startup engineering?

Answer: Infrastructure buildout means a startup created 3 or more new public repositories in 30 days. At Series A, these typically include API client libraries, SDK packages, CLI tools, and deployment infrastructure - signs that the team is building a platform around a working core product.

Series A Signals: What GitHub Data Reveals About Growth-Stage Startups


Question: How does contributor growth signal a funding round?

Answer: When contributor count jumps 50%+ in a short window (e.g., from 12 to 20 contributors), the company has likely closed a round and is scaling. This appears in GitHub data within weeks of new hires joining, but the Crunchbase entry may lag by 6-12 weeks.

Series A Signals: What GitHub Data Reveals About Growth-Stage Startups


Question: How do you evaluate open source startups with GitHub data?

Answer: Focus on the company-owned organization (not community forks), track core maintainer growth rather than total contributors, and look for commercial infrastructure signals - new repos for enterprise features, billing, or deployment tooling. Community star velocity is a social signal; commit velocity in the core product is the engineering signal.

Open Source Startups: An Investor's Guide to GitHub Signal Analysis


Question: What is the strongest open source investment signal?

Answer: Simultaneous community growth and commercial acceleration. When the open source project is gaining stars and contributors while the company organization is building enterprise infrastructure (billing, auth, deployment tooling), the open source flywheel is working - community traction is converting into commercial opportunity.

Open Source Startups: An Investor's Guide to GitHub Signal Analysis


Question: Do GitHub stars matter for startup investing?

Answer: Stars measure social interest, not engineering traction or commercial viability. A repository with 10,000 stars may have zero revenue. Stars can indicate developer mindshare, but commit velocity in the company's own repositories is a more reliable signal of engineering momentum.

Open Source Startups: An Investor's Guide to GitHub Signal Analysis


Question: Which predicts fundraises better: GitHub signals or hiring data?

Answer: GitHub signals provide earlier lead time (6-12 weeks vs 4-8 weeks for hiring) because engineering acceleration precedes hiring decisions. Hiring data is more explicit about growth type. The combination of both is stronger than either alone - GitHub for timing, hiring for confirmation.

GitHub Signals vs Hiring Data: Which Predicts Fundraises Better?


Question: How much lead time do GitHub signals give over hiring data?

Answer: GitHub engineering signals typically provide 6-12 weeks of lead time before fundraise announcements, compared to 4-8 weeks for hiring data. The gap exists because engineering acceleration (more commits, faster shipping) precedes the hiring decisions that follow. By the time a job posting appears, the engineering acceleration has been visible for weeks.

GitHub Signals vs Hiring Data: Which Predicts Fundraises Better?


Question: Should investors use GitHub signals or hiring data?

Answer: Both - sequentially. Use GitHub signals for early detection (which companies are accelerating?) then hiring data for confirmation and growth-type classification (are they hiring engineers, sales, or marketing?). The combination provides both timing advantage and strategic context.

GitHub Signals vs Hiring Data: Which Predicts Fundraises Better?


Question: What makes fintech GitHub signals different from other sectors?

Answer: Fintech engineering signals are influenced by regulatory requirements. Infrastructure buildout often indicates compliance infrastructure (KYC, AML, audit logging) rather than product expansion. Deploy frequency spikes may reflect regulatory deadline-driven development rather than customer-driven iteration.

Fintech Startup Engineering Signals: What the GitHub Data Shows


Question: What is the strongest fintech investment signal on GitHub?

Answer: Simultaneous product acceleration and compliance buildout. When a fintech company is shipping product features and building compliance infrastructure (KYC, audit logging, encryption) at the same time, it is preparing for a regulated launch - which requires significant capital and often precedes fundraising.

Fintech Startup Engineering Signals: What the GitHub Data Shows


Question: Can you find fintech startups using GitHub data?

Answer: Yes, but with sector-specific interpretation. Fintech companies with public repos typically focus on developer-facing products (payment APIs, banking-as-a-service, trading infrastructure). Consumer fintech companies are less likely to have public GitHub activity. Check the Fintech sector rankings for current data.

Fintech Startup Engineering Signals: What the GitHub Data Shows


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 350+ 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 350+ 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 350+ 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 350+ 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 six 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 welcome 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, signals@gitdealflow.com for co-authorship on funding-event joins.

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


Question: Are the GitDealFlow Chrome extensions really free?

Answer: Yes. Both extensions, the Crunchbase/Wellfound signal badge and the GitHub hover lookup, are free in perpetuity, with no account, no API key, and no tracking. The paid tiers (€7 First Look, €49/month Dashboard, €197/month Insider Circle) sit on top of the same public data with deeper rankings and weekly analysis; the extensions themselves never gate.

The 10 Best Chrome Extensions for VC Deal Flow (2026)


Question: What data do the GitDealFlow extensions collect?

Answer: Almost none by design. The Crunchbase/Wellfound badge reads only the company slug from the URL of the page you are on and makes one outbound request to signals.gitdealflow.com per profile load. There is no analytics, no host-page content collection, and no account. The GitHub lookup works the same way against GitHub org and repo URLs.

The 10 Best Chrome Extensions for VC Deal Flow (2026)


Question: Which Chrome extensions should a solo angel install first?

Answer: Start with the free five: both GitDealFlow extensions for the engineering-momentum signal on Crunchbase and GitHub, Hunter's free tier for founder emails, Save to Notion for capturing companies into a pipeline, and Grammarly free for outreach. That stack covers the full loop, discover, research, capture, reach out, at zero cost, and you can layer Affinity or Sales Navigator later if you raise a fund.

The 10 Best Chrome Extensions for VC Deal Flow (2026)


Question: Do browser extensions replace Crunchbase or PitchBook?

Answer: No, they complement them. Databases like Crunchbase record what already happened: the last round, the announced valuation. Extensions layer live context on top of those pages. The GitDealFlow badge specifically adds a leading indicator (engineering acceleration from public GitHub activity) to the lagging database you are already reading, which is a different job than replacing the database itself.

The 10 Best Chrome Extensions for VC Deal Flow (2026)


Question: What is the right order to run startup due diligence?

Answer: Cheapest-first. Start with the public, zero-cost layer (website, team, GitHub, web presence), then the qualitative layer (reference calls, customer calls), then the quantitative layer (financials, cap table, legal). Running legal diligence first spends the most money before you have filtered out the deals that fail the cheap checks.

The Startup Due Diligence Checklist: What to Check Before You Write the Check


Question: Where does engineering data fit in a due diligence checklist?

Answer: Between the public and qualitative layers. Public GitHub activity is free to check, needs no data room access, and answers a question that reference calls can only approximate: is the team shipping at an accelerating or decelerating pace. The GitDealFlow methodology reads commit velocity, contributor growth, and repository expansion to surface acceleration 21-47 days before a fundraise is announced [1].

The Startup Due Diligence Checklist: What to Check Before You Write the Check


Question: What is the single most-skipped due diligence check at seed stage?

Answer: Verifying shipping pace against the founder's own claims. Most seed diligence verifies credentials, market, and references, but does not independently confirm whether the team is actually building at the rate the pitch implies. Public commit and contributor data is the cheapest independent check for this.

The Startup Due Diligence Checklist: What to Check Before You Write the Check


Question: Do I need the full legal stack for a pre-seed check?

Answer: No. Pre-seed diligence should be proportionally lighter: team, market, product, and a basic cap-table sanity check. Full NVCA-model legal work is for priced rounds with real money at stake [3].

The Startup Due Diligence Checklist: What to Check Before You Write the Check


Question: How long should early-stage diligence take?

Answer: A focused seed-stage diligence pass can run one to two weeks when the cheap checks are front-loaded. The goal is not to eliminate risk but to find the deals that survive the public layer and are worth spending reference-call time on.

The Startup Due Diligence Checklist: What to Check Before You Write the Check


Question: Can I do technical due diligence without a data room?

Answer: Yes, partially. The public layer of GitHub gives you commit velocity, contributor growth, repository expansion, and signal type without any access request. What it cannot give you is private-repository activity, code quality, and security posture, which still need the data room or a direct conversation.

Technical Due Diligence With Public GitHub Data: Reading Engineering Health Before the Data Room


Question: What is the most important single metric to read first?

Answer: Trajectory, not level. A team accelerating from 20 to 40 commits per 14-day window is more informative than a team flat at 60. Commit velocity change is the leading indicator; absolute commit count is context.

Technical Due Diligence With Public GitHub Data: Reading Engineering Health Before the Data Room


Question: What signal types should I look for?

Answer: Four dominate: engineering hiring bursts, infrastructure buildout, framework migration, and deploy-frequency spikes. Each maps to a different company event, from raising and deploying capital to a product-platform bet.

Technical Due Diligence With Public GitHub Data: Reading Engineering Health Before the Data Room


Question: When is public GitHub data a false signal?

Answer: When activity is concentrated in configuration, policy, or compliance repositories rather than product code, or when a tiny contributor base creates a misleading percentage jump off a low baseline. Repository segmentation and a minimum contributor threshold both reduce these false positives.

Technical Due Diligence With Public GitHub Data: Reading Engineering Health Before the Data Room


Question: Does this replace a traditional technical review?

Answer: No. It moves earlier and filters which companies deserve a traditional review. A company that looks strong on the public layer still needs a code and architecture review before a priced round; a company that looks stalled on the public layer can often be deprioritized without one.

Technical Due Diligence With Public GitHub Data: Reading Engineering Health Before the Data Room


Question: Do I need a CRM to manage deal flow?

Answer: Not at first. A spreadsheet with four columns per stage works until you are tracking more deals than you can hold in your head. The system matters more than the tool; adopt a tool only when the spreadsheet's friction is actually costing you deals.

Deal Flow Management for Early-Stage Investors: Capture, Triage, Score, Prioritize


Question: What is the single biggest deal flow mistake?

Answer: Treating every inbound deal as equal. The pipeline exists to apply different levels of scrutiny to different deals, and the triage stage is where that differentiation happens. Without triage, every deal gets the same expensive attention and the best ones get buried.

Deal Flow Management for Early-Stage Investors: Capture, Triage, Score, Prioritize


Question: How do I score a startup before meeting the team?

Answer: Score on what is public first: market, product, traction, and shipping trajectory. Public GitHub activity is a free, objective input for the trajectory dimension, since it reads acceleration without any pitch involved [1].

Deal Flow Management for Early-Stage Investors: Capture, Triage, Score, Prioritize


Question: How often should I review the pipeline?

Answer: Weekly, at a fixed time. A weekly review keeps deals from going stale and surfaces the ones that changed status since last week. It is the difference between a pipeline and a list of companies you once looked at.

Deal Flow Management for Early-Stage Investors: Capture, Triage, Score, Prioritize


Question: What does good triage look like in practice?

Answer: A single pass that answers one question per deal: does this clear the bar for a real look, yes or no, in under a minute. Everything that clears moves to scoring; everything else is archived with a one-line reason.

Deal Flow Management for Early-Stage Investors: Capture, Triage, Score, Prioritize


Question: Why score at all instead of just reading the deal?

Answer: Because scores are comparable and readable later. A score forces you to separate the dimensions instead of collapsing everything into a gut feel, and a written scorecard is what lets you go back and see where your judgment was systematically wrong.

A Deal Flow Scoring Framework: Rank Inbound Startups Without a Full Partner Meeting


Question: How many factors should a scorecard have?

Answer: Four to five. More factors and the scoring takes too long to be worth it; fewer and you lose the ability to see which dimension is driving the decision. Team, market, product, and traction are the core four; engineering velocity is the optional fifth.

A Deal Flow Scoring Framework: Rank Inbound Startups Without a Full Partner Meeting


Question: How is engineering velocity scored?

Answer: Read the public trajectory: is commit velocity and contributor growth accelerating, flat, or decelerating. Acceleration maps to a high score, flat to the middle, deceleration to the low end. The signal precedes fundraises by 21 to 47 days in the backtest, so it is a leading, not lagging, input [1].

A Deal Flow Scoring Framework: Rank Inbound Startups Without a Full Partner Meeting


Question: Do all factors get equal weight?

Answer: No. At seed and pre-seed, team weight dominates. As the round gets later, traction and market weight rise. Set the weights explicitly for your stage and write them down, so the score reflects a real policy instead of a mood.

A Deal Flow Scoring Framework: Rank Inbound Startups Without a Full Partner Meeting


Question: How do I know my scores are calibrated?

Answer: Track outcomes. Go back after six months and check whether high-scored deals actually raised and performed better than low-scored ones. Calibration is the difference between a scorecard and a superstition.

A Deal Flow Scoring Framework: Rank Inbound Startups Without a Full Partner Meeting


Question: What is the difference between a scout and an angel?

Answer: A scout typically sources and refers deals on behalf of a fund or scout program and earns carry or a referral fee on closed deals, while an angel writes their own checks. The sourcing discipline is the same; the capital and economics differ.

Venture Scouting: How Scouts and Angels Source Deals Before the Databases


Question: How do scout programs work?

Answer: A fund gives a scout a small allocation to deploy, and the scout sources deals, usually in exchange for carry on what they bring in. The scout's edge is reach into a network or community the fund cannot easily cover itself.

Venture Scouting: How Scouts and Angels Source Deals Before the Databases


Question: Where do the best scouts find deals?

Answer: In communities and networks databases do not index: technical communities, founder circles, and the public engineering activity of startups before they announce. The common thread is finding the company before the round is public.

Venture Scouting: How Scouts and Angels Source Deals Before the Databases


Question: Why do databases lag deal discovery?

Answer: A database like Crunchbase records what has already happened: a round that was announced. The scouting opportunity is the 3 to 6 weeks before the announcement, when engineering activity is already accelerating but nothing is public yet [1].

Venture Scouting: How Scouts and Angels Source Deals Before the Databases


Question: Is there a free tool for scouting by engineering signal?

Answer: Yes. The GitDealFlow MCP server exposes six free, read-only tools including trending startups, sector search, startup lookup, and scout receipts, usable inside any MCP-compatible agent runtime [3].

Venture Scouting: How Scouts and Angels Source Deals Before the Databases


Question: Can GitHub signals discover pre-seed startups cold?

Answer: Rarely. Pre-seed teams are two to four engineers often working partly in private repositories, so the public footprint is thin. The practical use is verification, confirming that a founder you met through another channel is actually building, not cold discovery.

Pre-Seed Scouting With GitHub Signals: Finding Startups Before Their First Announcement


Question: What should I look for on a pre-seed founder's GitHub?

Answer: Consistency over volume. A founder who ships regularly, even at low absolute commit counts, is a stronger signal than a burst of activity followed by silence. Contributor breadth and repository activity both help confirm the team is real.

Pre-Seed Scouting With GitHub Signals: Finding Startups Before Their First Announcement


Question: Why is pre-seed the hardest stage to scout?

Answer: Because there is almost no public record yet: no funding announcement, no press, and a thin code footprint. The signal that works at Series A is too weak to be discovery-grade at pre-seed.

Pre-Seed Scouting With GitHub Signals: Finding Startups Before Their First Announcement


Question: What is the right pre-seed scouting workflow?

Answer: Source through communities, referrals, and accelerators, then use public GitHub activity as a free verification pass on the founders before you spend a meeting. It confirms shipping pace and team reality at zero cost.

Pre-Seed Scouting With GitHub Signals: Finding Startups Before Their First Announcement


Question: Is a pre-seed signal worth tracking at all?

Answer: Yes, as a verification layer and an early-warning system. A pre-seed team that begins accelerating is often deploying capital or approaching a round, which is exactly when a scout wants to be in touch.

Pre-Seed Scouting With GitHub Signals: Finding Startups Before Their First Announcement


Question: What is a good commit velocity benchmark?

Answer: There is no single number, because baselines differ by sector and stage. Enterprise SaaS teams commit 35 to 60 percent less than AI-tools teams at the same stage. The useful benchmark is a team against its own prior pace, which is what the velocity-change signal measures.

Engineering Velocity Benchmarks: What Fast Looks Like on GitHub, by Stage and Sector


Question: What threshold does GitDealFlow use to screen for acceleration?

Answer: The weekly Acceleration Watch cohort uses a transparent threshold of at least 40 commits per 14-day window and at least 5 contributors, and excludes percentage jumps off a near-zero baseline. The threshold exists to suppress low-base artifacts rather than to define fast in absolute terms.

Engineering Velocity Benchmarks: What Fast Looks Like on GitHub, by Stage and Sector


Question: How do sectors differ in the panel?

Answer: The current Q3 2026 panel spans 15 sectors. Web3 is the largest at 42 startups and agtech the smallest at 11. Raw size reflects where public GitHub activity is most observable, not where the best companies are.

Engineering Velocity Benchmarks: What Fast Looks Like on GitHub, by Stage and Sector


Question: Why is a team's own baseline the best benchmark?

Answer: Because acceleration is relative. A move from 18 to 36 commits per 14 days is a top-decile acceleration within enterprise SaaS even though the absolute number looks modest. Comparing against a sector distribution instead of the whole panel is the correct default [1].

Engineering Velocity Benchmarks: What Fast Looks Like on GitHub, by Stage and Sector


Question: Does a faster team mean a better company?

Answer: Not by itself. Velocity is a shipping-pace signal, not a quality signal. It predicts capital events, not outcomes. Use it as one leading indicator among several, never as a standalone ranking.

Engineering Velocity Benchmarks: What Fast Looks Like on GitHub, by Stage and Sector


Question: What exactly does commit velocity count?

Answer: The number of commits pushed to a startup's public repositories within a rolling observation window, read through the public GitHub statistics API [3]. It is a proxy for shipping pace, not a measure of code quality.

Commit Velocity Benchmark Numbers: The Thresholds Behind a Startup Acceleration Signal


Question: Why are there two observation windows?

Answer: The 14-day window is the default read, but enterprise and regulated teams work in two-week sprints that make a 14-day window lumpy. The 28-day window smooths that sprint artifact and gives a cleaner read of whether the team's overall pace is changing [4].

Commit Velocity Benchmark Numbers: The Thresholds Behind a Startup Acceleration Signal


Question: Why does a percentage change need a contributor floor?

Answer: Because a jump from 1 to 4 commits is a 300 percent change that means nothing. A minimum contributor count, and an absolute commit floor, suppresses the low-base artifacts that would otherwise flood the signal with noise.

Commit Velocity Benchmark Numbers: The Thresholds Behind a Startup Acceleration Signal


Question: What counts as a meaningful velocity change?

Answer: A sustained, non-artifactual move against the team's own baseline, ideally confirmed across both the 14-day and 28-day windows. A one-window spike is worth a look; a two-window trend is worth a call.

Commit Velocity Benchmark Numbers: The Thresholds Behind a Startup Acceleration Signal


Question: Is commit velocity the only number that matters?

Answer: No. It is one of three primary signals alongside contributor growth and repository expansion, and it is the raw-throughput input, not the conclusion. The composite is what predicts [1].

Commit Velocity Benchmark Numbers: The Thresholds Behind a Startup Acceleration Signal


Question: What matters most when evaluating a seed-stage founder?

Answer: Execution, not credentials. A founder's track record matters, but the best predictor of whether they can deliver is whether they are delivering right now, which is readable in their public shipping record.

How to Evaluate Startup Founders: Signals That Predict Execution


Question: How do I assess a technical founder's capability?

Answer: Read their work. Public commit history is a direct record of what a technical founder actually builds, at what pace, and with how many collaborators. It is the cheapest and least gameable founder check available [2].

How to Evaluate Startup Founders: Signals That Predict Execution


Question: What are the founder red flags that override everything?

Answer: A mismatch between the pitch and the public record, a shipping pace that contradicts the claimed traction, and a founder who cannot name the specific problem their product solves. Any one of these is worth walking away over.

How to Evaluate Startup Founders: Signals That Predict Execution


Question: Does a founder's network matter?

Answer: Less than it used to. Network helps with the first meeting, but a founder who ships in public can be discovered and verified without any network. Execution is the equalizer [1].

How to Evaluate Startup Founders: Signals That Predict Execution


Question: Can I evaluate a founder I have never met?

Answer: Partially. You can verify capability and shipping pace from the public record, but judgment and coachability still require a conversation. Use the public record to decide who earns the conversation.

How to Evaluate Startup Founders: Signals That Predict Execution


Question: Why is GitHub the most honest founder resume?

Answer: Because it cannot be rehearsed. A founder can curate a LinkedIn headline, but their commit history is a running record of what they actually build, at what pace, and with whom, generated daily without intent to impress.

Assessing Technical Founders From Their GitHub: Shipping Discipline as a Quality Signal


Question: What should I look for in a technical founder's GitHub?

Answer: Shipping discipline first: consistent commits over time, not a burst followed by silence. Then contributor patterns: does the founder build alone or attract collaborators. Then the specific repositories, to see whether the code matches the product story.

Assessing Technical Founders From Their GitHub: Shipping Discipline as a Quality Signal


Question: What is the Scout Score?

Answer: A 0 to 100 grade of a GitHub user's star history against roughly 75 validated unicorns. It measures whether a founder has been watching breakout companies early, a proxy for their eye for what is about to work [2].

Assessing Technical Founders From Their GitHub: Shipping Discipline as a Quality Signal


Question: Does a high Scout Score mean a good founder?

Answer: Not on its own. It measures one dimension, the ability to spot breakout companies early, which correlates with judgment. It is a useful signal alongside shipping discipline, not a standalone verdict.

Assessing Technical Founders From Their GitHub: Shipping Discipline as a Quality Signal


Question: How do I check a founder's shipping discipline quickly?

Answer: Look at the trailing activity: is there consistent, recent work, or a spike followed by silence. Consistency at any absolute volume is the signal; a founder who ships steadily is a founder who executes.

Assessing Technical Founders From Their GitHub: Shipping Discipline as a Quality Signal


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

Answer: In Q2 2026, we are tracking 16 ai & machine learning startups with measurable GitHub engineering signals. 7 of 16 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 13 of the tracked companies. The average 14-day commit velocity across the sector is 322 commits, with photoprism leading at 121 commits (+109% 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: photoprism leads the ai & machine learning sector in Q2 2026 with 121 commits over a 14-day window, representing a +109% 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 16 ai & machine learning startups we track, US accounts for the highest concentration with 5 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 16 ai & machine learning startups with measurable GitHub engineering signals. 9 of 16 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 12 of the tracked companies. The average 14-day commit velocity across the sector is 208 commits, with zapplyjobs leading at 8 commits (+999% 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: zapplyjobs leads the ai & machine learning sector in Q1 2026 with 8 commits over a 14-day window, representing a +999% change from the prior period. With 3 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 ai & machine learning engineering teams located?

Answer: Among the 16 ai & machine learning startups we track, US accounts for the highest concentration with 5 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 15 ai & machine learning startups with measurable GitHub engineering signals. 6 of 15 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 13 of the tracked companies. The average 14-day commit velocity across the sector is 183 commits, with huggingface leading at 165 commits (+67% 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: huggingface leads the ai & machine learning sector in Q4 2025 with 165 commits over a 14-day window, representing a +67% change from the prior period. With 100 active contributors and 3 new repositories, huggingface 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 ai & machine learning engineering teams located?

Answer: Among the 15 ai & machine learning startups we track, US accounts for the highest concentration with 4 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 15 ai & machine learning startups with measurable GitHub engineering signals. 9 of 15 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 13 of the tracked companies. The average 14-day commit velocity across the sector is 210 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 15 ai & machine learning startups we track, US accounts for the highest concentration with 4 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 fintech startups showing in Q2 2026?

Answer: In Q2 2026, we are tracking 1 fintech 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 2 commits, with finos leading at 2 commits (-87% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: finos leads the fintech sector in Q2 2026 with 2 commits over a 14-day window, representing a -87% change from the prior period. With 45 active contributors and 1 new repositories, finos 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 fintech engineering teams located?

Answer: The fintech startups we track are geographically distributed across multiple regions. Startups disrupting financial services through technology. We derive geography from GitHub organization profiles.


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

Answer: In Q1 2026, we are tracking 1 fintech 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 47 commits, with finos leading at 47 commits (-16% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: finos leads the fintech sector in Q1 2026 with 47 commits over a 14-day window, representing a -16% change from the prior period. With 45 active contributors and 1 new repositories, finos 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 fintech engineering teams located?

Answer: The fintech startups we track are geographically distributed across multiple regions. Startups disrupting financial services through technology. We derive geography from GitHub organization profiles.


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

Answer: In Q4 2025, we are tracking 1 fintech 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 67 commits, with finos leading at 67 commits (+139% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: finos leads the fintech sector in Q4 2025 with 67 commits over a 14-day window, representing a +139% change from the prior period. With 45 active contributors and 1 new repositories, finos 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 fintech engineering teams located?

Answer: The fintech startups we track are geographically distributed across multiple regions. Startups disrupting financial services through technology. We derive geography from GitHub organization profiles.


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

Answer: In Q3 2025, we are tracking 1 fintech 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 79 commits, with finos leading at 79 commits (+193% change). These patterns have historically preceded fundraise announcements by three to six weeks.


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

Answer: finos leads the fintech sector in Q3 2025 with 79 commits over a 14-day window, representing a +193% change from the prior period. With 45 active contributors and 1 new repositories, finos 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 fintech engineering teams located?

Answer: The fintech startups we track are geographically distributed across multiple regions. Startups disrupting financial services through technology. We derive geography from GitHub organization profiles.


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

Answer: In Q2 2026, we are tracking 5 climate tech startups with measurable GitHub engineering signals. 0 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 31 commits, with carbon-design-system leading at 97 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 97 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 5 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 5 climate tech startups with measurable GitHub engineering signals. 3 of 5 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 56 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 5 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 5 climate tech startups with measurable GitHub engineering signals. 3 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 50 commits, with CliMA leading at 44 commits (+100% 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: CliMA leads the climate tech sector in Q4 2025 with 44 commits over a 14-day window, representing a +100% change from the prior period. With 81 active contributors, CliMA 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 5 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 5 climate tech startups with measurable GitHub engineering signals. 2 of 5 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 45 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 5 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 4 developer tools startups with measurable GitHub engineering signals. 2 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 699 commits, with daintreehq leading at 2596 commits (+92% 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: daintreehq leads the developer tools sector in Q2 2026 with 2596 commits over a 14-day window, representing a +92% change from the prior period. With 5 active contributors and 1 new repositories, daintreehq 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 5 developer tools startups with measurable GitHub engineering signals. 3 of 5 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 228 commits, with daintreehq leading at 475 commits (+95% 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: daintreehq leads the developer tools sector in Q1 2026 with 475 commits over a 14-day window, representing a +95% change from the prior period. With 5 active contributors and 1 new repositories, daintreehq 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: Among the 5 developer tools startups we track, US accounts for the highest concentration with 1 teams. Startups building tools and infrastructure for developers. 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 Q4 2025?

Answer: In Q4 2025, we are tracking 4 developer tools startups with measurable GitHub engineering signals. 1 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 91 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: Among the 4 developer tools startups we track, US accounts for the highest concentration with 1 teams. Startups building tools and infrastructure for developers. 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 Q3 2025?

Answer: In Q3 2025, we are tracking 4 developer tools startups with measurable GitHub engineering signals. 3 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 345 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: Among the 4 developer tools startups we track, US accounts for the highest concentration with 1 teams. Startups building tools and infrastructure for developers. 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 Q2 2026?

Answer: In Q2 2026, we are tracking 23 cybersecurity startups with measurable GitHub engineering signals. 9 of 23 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 15 of the tracked companies. The average 14-day commit velocity across the sector is 152 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 23 cybersecurity startups we track, US accounts for the highest concentration with 8 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 23 cybersecurity startups with measurable GitHub engineering signals. 14 of 23 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 15 of the tracked companies. The average 14-day commit velocity across the sector is 193 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 23 cybersecurity startups we track, US accounts for the highest concentration with 8 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 22 cybersecurity startups with measurable GitHub engineering signals. 12 of 22 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 15 of the tracked companies. The average 14-day commit velocity across the sector is 179 commits, with lf-edge leading at 100 commits (+285% 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: lf-edge leads the cybersecurity sector in Q4 2025 with 100 commits over a 14-day window, representing a +285% change from the prior period. With 91 active contributors, lf-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 cybersecurity engineering teams located?

Answer: Among the 22 cybersecurity startups we track, US accounts for the highest concentration with 8 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 23 cybersecurity startups with measurable GitHub engineering signals. 10 of 23 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 15 of the tracked companies. The average 14-day commit velocity across the sector is 158 commits, with bytebase leading at 856 commits (+152% 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: bytebase leads the cybersecurity sector in Q3 2025 with 856 commits over a 14-day window, representing a +152% change from the prior period. With 100 active contributors, bytebase 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 23 cybersecurity startups we track, US accounts for the highest concentration with 8 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 Q3 2026?

Answer: In Q3 2026, we are tracking 26 healthcare startups with measurable GitHub engineering signals. 8 of 26 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 12 of the tracked companies. The average 14-day commit velocity across the sector is 53 commits, with aphp leading at 1 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 Q3 2026?

Answer: aphp leads the healthcare sector in Q3 2026 with 1 commits over a 14-day window, representing a +999% change from the prior period. With 3 active contributors and 3 new repositories, aphp 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 healthcare engineering teams located?

Answer: Among the 26 healthcare startups we track, EU accounts for the highest concentration with 6 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 Q2 2026?

Answer: In Q2 2026, we are tracking 28 healthcare startups with measurable GitHub engineering signals. 14 of 28 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 18 of the tracked companies. The average 14-day commit velocity across the sector is 84 commits, with Intelehealth leading at 3 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 Q2 2026?

Answer: Intelehealth leads the healthcare sector in Q2 2026 with 3 commits over a 14-day window, representing a +999% change from the prior period. With 14 active contributors and 4 new repositories, Intelehealth 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 healthcare engineering teams located?

Answer: Among the 28 healthcare startups we track, US accounts for the highest concentration with 6 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 27 healthcare startups with measurable GitHub engineering signals. 17 of 27 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 12 of the tracked companies. The average 14-day commit velocity across the sector is 40 commits, with healthchainai 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: healthchainai leads the healthcare sector in Q1 2026 with 4 commits over a 14-day window, representing a +999% change from the prior period. With 14 active contributors and 1 new repositories, healthchainai 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 healthcare engineering teams located?

Answer: Among the 27 healthcare startups we track, US accounts for the highest concentration with 6 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 28 healthcare startups with measurable GitHub engineering signals. 22 of 28 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 10 of the tracked companies. The average 14-day commit velocity across the sector is 42 commits, with andes leading at 14 commits (+1300% 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: andes leads the healthcare sector in Q4 2025 with 14 commits over a 14-day window, representing a +1300% 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 28 healthcare startups we track, EU accounts for the highest concentration with 6 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 18 healthcare startups with measurable GitHub engineering signals. 6 of 18 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 13 of the tracked companies. The average 14-day commit velocity across the sector is 70 commits, with hpi-studyu leading at 140 commits (+1456% 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: hpi-studyu leads the healthcare sector in Q3 2025 with 140 commits over a 14-day window, representing a +1456% change from the prior period. With 18 active contributors, hpi-studyu 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 healthcare engineering teams located?

Answer: Among the 18 healthcare startups we track, EU accounts for the highest concentration with 5 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 Q3 2026?

Answer: In Q3 2026, we are tracking 37 edtech startups with measurable GitHub engineering signals. 11 of 37 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 22 of the tracked companies. The average 14-day commit velocity across the sector is 52 commits, with ucfopen leading at 1 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 Q3 2026?

Answer: ucfopen leads the edtech sector in Q3 2026 with 1 commits over a 14-day window, representing a +999% change from the prior period. With 4 active contributors, ucfopen 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 37 edtech startups we track, US accounts for the highest concentration with 8 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 Q2 2026?

Answer: In Q2 2026, we are tracking 43 edtech startups with measurable GitHub engineering signals. 23 of 43 show positive commit velocity growth. The most common signal type is "Framework migration", observed in 25 of the tracked companies. The average 14-day commit velocity across the sector is 53 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 "Engineering hiring burst" pattern, one of the more reliable leading indicators of a significant product milestone or fundraise.