Charity Majors ✓

@mipsytipsy · San Francisco · joined 16 Nov 2009

honeycomb, observability, the second edition of "Observability Engineering" is coming out soon. find me on substack, bsky, LI, and occasionally here. 🐝🏳️‍🌈🦄

81 906Followers

590Following

64 154Posts total

78.5KViews on collected posts

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Charity Majors ✓@mipsytipsy20 Aug

the first in a 3-part series on @honeycombio's AI norms and values is up, called "How We Do Business at Honeycomb." https://t.co/3BnBkOwiya values are not supposed to be aspirational. values should codify how things actually work. this is how it works at our company. 5.2K views · 43 likes · 2 reposts · 4 replies Open on X →

Charity Majors ✓@mipsytipsy5 Aug

AGREE. 9.2K views · 26 likes · 0 reposts · 1 replies Open on X →

Charity Majors ✓@mipsytipsy10 Jul

Observability is a topic that I think could use more rigor and less fluff. Many people, including myself, are muddy on the topic. And we're in the middle of a big paradigm shift. Digging into some great resources from @mipsytipsy to get a better understanding myself. Will share 10.2K views · 10 likes · 0 reposts · 2 replies Open on X →

Charity Majors ✓@mipsytipsy9 Jul

@mipsytipsy Yeah def not a mistake that datadog bought quick wit folks recently. Surely they know. (Hiiii btw!!) 362 views · 2 likes · 0 reposts · 1 replies Open on X →

Charity Majors ✓@mipsytipsy9 Jul

@mipsytipsy How does Honycomb's storage engine compare to Clickhouse? 380 views · 1 likes · 0 reposts · 1 replies Open on X →

Charity Majors ✓@mipsytipsy9 Jul

@mipsytipsy the bit about vendors selling "datadog but cheaper" instead of building something different is the whole story. i know a couple people building the right way — unified columnar, not three pillars in a trenchcoat. the gap is real 1.2K views · 4 likes · 0 reposts · 1 replies Open on X →

Charity Majors ✓@mipsytipsy9 Jul

Years ago, I predicted that columnar storage would remake the landscape of observability tools. I thought we'd see a wave of new columnar-backed observability tools grow up to displace the old, creaky, three pillars model with tech that is faster, easier, cheaper, and better in 28.6K views · 145 likes · 8 reposts · 10 replies Open on X →

Charity Majors ✓@mipsytipsy7 Jul

@mipsytipsy @ClickHouseDB i really like this framing from Mats article https://t.co/Fo4yvqRjml 256 views · 4 likes · 0 reposts · 1 replies Open on X →

Charity Majors ✓@mipsytipsy7 Jul

@mipsytipsy @ClickHouseDB The architecture mutating with scale draws a weird parallel with how orgs work too and I keep realising how many of these patterns rear their head everywhere (like not just in tech)... It's almost like some universal set of rules :P 164 views · 1 likes · 0 reposts · 0 replies Open on X →

Charity Majors ✓@mipsytipsy7 Jul

@mipsytipsy @ClickHouseDB 🫶 309 views · 1 likes · 0 reposts · 1 replies Open on X →

Charity Majors ✓@mipsytipsy7 Jul

Go read this post from Mat Duggan right now -- "@ClickhouseDB is Winning the Observability Wars". Go! I'll wait. https://t.co/jLunSZrSDl "Every other observability backend I've worked with mutates as it grows... ClickHouse at 10 TB a day looks like ClickHouse at 1 TB a day htt 19.9K views · 88 likes · 9 reposts · 6 replies Open on X →

Charity Majors ✓@mipsytipsy2 Jul

Everybody hates AI mandates -- unsurprisingly, since most of them are terrible and poorly resourced. But they don't have to be terrible. A mandate plus enablement is the most honest and effective way to fund a lot of change on a tight timeline. https://t.co/4uTYtIPolO https://t 2.8K views · 19 likes · 2 reposts · 1 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

1 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.

ยอดดูมัธยฐาน5 239บัญชีนี้924ค่ามัธยฐานของ 10K–100K

การเข้าถึง, %6.40%บัญชีนี้3.62%ค่ามัธยฐานของ 10K–100K

การมีส่วนร่วม, %0.94%บัญชีนี้1.52%ค่ามัธยฐานของ 10K–100K

ตัวชี้วัด	บัญชีนี้	ค่ามัธยฐานของ 10K–100K	อัตราส่วน
ยอดดูมัธยฐานต่อโพสต์	5 239	924	5.67×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)	6.40%	3.62%	1.77×
อัตราการมีส่วนร่วม	0.94%	1.52%	0.61×

บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →

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

2.8K2 Jul

19.9K7 Jul

309

164

256

28.6K9 Jul

1.2K

380

362

10.2K10 Jul

9.2K5 Aug

5.2K20 Aug

Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.81%2 Jul

0.54%7 Jul

0.65%

0.61%

1.95%

0.59%9 Jul

0.42%

0.53%

0.83%

0.13%10 Jul

0.30%5 Aug

0.94%20 Aug

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

What the audience does

Likes51.6%344 in total

Reposts3.1%21 in total

Replies4.3%29 in total

Quotes1.8%12 in total

Bookmarks39.1%261 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.

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เทียบกับบัญชีขนาดเดียวกัน

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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