tweetindex
FR

Abhinav Upadhyay ✓

@abhi9u · joined 13 Jul 2009

NetBSD Dev | Python Internals, AI, compilers, databases, & performance engineering | Join 16k+ other readers at https://t.co/OQfxW0443i

18 569Followers
1 834Following
14 905Posts total
202.2KViews on collected posts

Derniers posts

Writing leads to clear thinking. https://t.co/HCSsDr2EYz
4.1K views · 179 likes · 7 reposts · 0 replies Open on X →
@opdroid1234 Can we configure claude API key in kaho? I've API credits sitting unused :D 1.3K views · 2 likes · 0 reposts · 2 replies Open on X →
Excellent write-up on performance work in Numpy and CPython for the Free-Threaded build https://t.co/rcwqvRzzUF https://t.co/BUYGRf2ArV
4.1K views · 75 likes · 12 reposts · 1 replies Open on X →
uv revolutionized Python packaging with Rust, and it's exciting to see the revolution reaching plotting as well. I tried xy with nearly 5 million points, and it managed to plot in ~4 ms; Matplotlib took roughly 2 seconds on the same data. But you can't win over matplotlib http
26.2K views · 296 likes · 19 reposts · 4 replies Open on X →
I dreamt of writing a book for as long as I can remember, wrote many outlines, but none materialised. Then I started writing this article on virtual memory, which kept getting longer and longer and turned into an unplanned book. Based on all the positive feedback, I recently ht
30.5K views · 502 likes · 58 reposts · 10 replies Open on X →
@abhi9u systems books hit harder once the OS layer clicks. so many database decisions are just memory hierarchy problems wearing a blazer 2.6K views · 19 likes · 0 reposts · 2 replies Open on X →
It is also available as a beautifully typeset 60-page PDF to read offline: https://t.co/fbUy2atjVo A sample: https://t.co/PYx2SwOHjk
8.4K views · 97 likes · 6 reposts · 4 replies Open on X →
Read it here: https://t.co/ZzF3HzosyV 10.1K views · 171 likes · 14 reposts · 1 replies Open on X →
Before jumping into books like DDIA or Database Internals, it helps to understand the systems layer these designs are built on. A lot of the design of such data-intensive systems is based on virtual memory: page tables, page faults, mmap, the page cache, swapping, NUMA https://t
114.8K views · 1.4K likes · 172 reposts · 25 replies Open on X →

Face aux comptes de taille comparable

4 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé à plus de monde que les comptes de taille comparable.

Vues médianes4 100ce compte924médiane pour 10K–100K
Portée, %22.08%ce compte3.62%médiane pour 10K–100K
Engagement, %1.69%ce compte1.52%médiane pour 10K–100K
IndicateurCe compteMédiane pour 10K–100KRapport
Vues médianes par post4 1009244.44×
Portée (vues ÷ abonnés)22.08%3.62%6.10×
Taux d'engagement1.69%1.52%1.11×

Autres comptes de cette tranche →   Comparer avec un autre compte →   Comment ces repères sont établis →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

114.8K16 May
10.1K
8.4K
2.6K
30.5K29 Jun
26.2K28 Jul
4.1K5 Aug
1.3K21 Aug
4.1K23 Aug

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.42%16 May
1.84%
1.28%
0.80%
1.87%29 Jun
1.22%28 Jul
2.16%5 Aug
0.30%21 Aug
4.50%23 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes43.4%2 779 in total
Reposts4.5%288 in total
Replies0.8%49 in total
Bookmarks51.3%3 281 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Comptes similaires