#egf_ogf
Live, measured metrics for the hashtag #egf_ogf from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
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Day-by-day usage
measured · fosstodon.org (Mastodon public tags API) · fetched 2026-07-28 18:13 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · fosstodon.org (Mastodon public search API) · fetched 2026-07-28 18:13 UTCNo related tags with measured usage found for #egf_ogf.
Live pulse
measured · fosstodon.org (Mastodon tag timeline) · fetched 2026-07-28 18:13 UTCEverything below is measured over the latest 7 public posts (spanning ~4443 hours).
Posting hours (UTC)
Languages: English (5) · German (2)
Avg boosts / post: 0.1
Used together with
No co-used tags in the sample.
Top of the latest posts
#EGF_OGF Of course, \( [P(r)] \) could have been applied to the left. Both change the polynomial value. But we can change the basis and keep the polynomial value. \[ \left[\begin{array}{ccccc} a_{0} & a_{1} & a_{2} & \cdots & a_{n}\end{arra
@rrogers Just Latex practice: Utility of Pascal's Matrix \[ \left[\begin{array}{ccccc} a_{0} & a_{1} & a_{2} & \cdots & a_{n}\end{array}\right]\left[\begin{array}{c} P(r)\end{array}\right]\left[\begin{array}{c} 1\\ x\\ x^{2}\\ \cdots\\ x^{n
@zbMATH Actually, I developed a direct mathematical interpretation. You replace Roman's t-> ln(P) where P is Pascal's Matrix and things make more sense. (Hint: ln(p) when applied is differentiation). Some examples are noted under #EGF_OGF I
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