GitDealFlow Network

Discover related tools across our portfolio ecosystem

GitDealFlow is part of a portfolio of 10 specialized platforms serving different industries and use cases. Below are the most closely related tools, each built with the same engineering standards and AI-native architecture.

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Why This Page Exists

GitDealFlow is a public deal flow signal dataset: 350+ startup GitHub organizations across 15 sectors, refreshed weekly, with breakout teams surfacing 21 to 47 days before their round is announced. This page makes one part of that system legible: what it measures, how it is computed, and how to use it in a live sourcing workflow. The method is published end to end and falsifiable by design, with the working paper on SSRN and the dataset downloadable under CC BY 4.0.

Start Here

A practical read-through of GitDealFlow Network: the dataset behind this page refreshes weekly across 350+ organizations and 15 sectors, and every figure shown traces to a public GitHub REST API pull. That matters for two reasons. Reproducibility: any number here can be re-derived from primary sources, which is the standard the published methodology sets for itself. Timeliness: engineering acceleration precedes announcements, so this page follows the data cadence rather than the news cycle, and the freshness endpoint always reports the exact pull date.

If GitDealFlow Network is your entry point, the fastest next steps are fixed: skim the glossary for the three or four terms that anchor the topic, open the research dataset to see the raw weekly snapshots behind the summary numbers, and run one live query against the free momentum checker with a company you already know well. Seeing the signal fire on a familiar name is the quickest way to judge whether code-side sourcing belongs in your own workflow.

One caveat worth stating plainly on GitDealFlow Network: momentum is a leading indicator, not a verdict. A repository can accelerate for reasons that never become a fundraise, and a quiet quarter does not mean a team is failing. The disciplined use of this page is as one input in a stack, a way to rank where scarce diligence time goes, and a way to notice change early. The methodology page documents every limitation, including the bot filter, the two-period confirmation rule, and the sectors where coverage is thinnest.