The 7 GitHub Signals That Predict Series A Rounds (Free Book)

Five startups accelerating on GitHub, every Sunday

The weekly panel covers 350+ venture-backed startup GitHub organizations across 15 sectors. Free, no card, unsubscribe in one click.

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PDF (1.1 MB), EPUB and plain-text editions, no card, no upsell wall.

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What the book covers

The seven signals, in the order the book teaches them:

Every chapter pairs the signal with the correlation evidence, the failure modes, and a worked checklist. The replication appendix re-derives each finding from the public dataset so you can check the work yourself.

Read the chapters online

The full text is also on the signal board, chapter by chapter: signals.gitdealflow.com/book. The book files below download directly, no email gate.

Who wrote it

The Data Nerd, founder of GitDealFlow. The methodology paper behind the book is SSRN-indexed (paper 6606558), with an ORCID-verified author record; see the research page for the dataset and citations.

What readers do with it

The book is written for investors who source: solo GPs building a first thesis, analysts at seed funds running weekly screens, and angels who want an edge that does not cost five figures a seat. Each chapter closes with a worked screen you can run the same afternoon with free tooling.

Frequently asked questions

Is the book really free?

Yes. PDF, EPUB and plain-text editions download directly from this page with no email gate, no card and no upsell wall. The online chapters stay free as well.

What data is the book based on?

The GitDealFlow panel: 350+ venture-backed startup GitHub organizations across 15 sectors, tracked weekly, with the methodology published as an SSRN-indexed paper and a public replication appendix.

Does GitHub activity really predict fundraises?

No signal is perfect, and the book is explicit about failure modes. Sustained commit velocity and contributor growth correlate with Series A timing; the evidence, thresholds and false positives are all laid out chapter by chapter so you can judge yourself.

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