You scroll past this and assume the hard part is already solved because the star count is huge. If you mostly use chat-style AI and are just starting to follow new tools, that shortcut is exactly how you burn time, money, and attention.
On the public GitHub pages for mukul975, the GitHub Sponsors page shows a goal of 15 monthly sponsors with 0% visible progress [S001]. On the same profile, the featured Anthropic-Cybersecurity-Skills project shows 18.3k stars, and cve-mcp-server shows 1k stars [S002]. The gap is the point. Sponsors convert budget authority, not stars.
That does not prove who does or does not sponsor. Public pages cannot tell us whether star-givers lacked budget, interest, or timing. But the repo mix still matters: one public project is framed around GDPR, the EU AI Act, HIPAA, PIPL, and the DPDP Act, which are enterprise compliance topics [S004]. That makes it reasonable to read sponsorship here as closer to team spending power than general popularity.
This is the line worth sharing: don't judge an update by how many features or stars it lists. Judge it by whether it changes your next move. If you're evaluating open source tools, stop treating stars as a funding metric. If you're building one, stop assuming popularity will naturally become sponsor revenue. Share this with anyone who still reads stars as proof of business backing.