Couchbase

@couchbase · Santa Clara, CA · joined 12 Jan 2011

#Couchbase delivers Capella, the developer data platform for critical applications in our AI world ✨

135 794Followers

1 013Following

28 580Posts total

7.9KViews on collected posts

โพสต์ล่าสุด

Couchbase@couchbase1 Sep

Don't blame the model. Most AI agents fail because the data layer does — no memory, no live retrieval, no governance, no traceability. Bolting together separate vector, cache, and doc stores doesn't scale. See the AI Data Plane → https://t.co/XFwMStDgWh 509 views · 1 likes · 0 reposts · 0 replies Open on X →

Couchbase@couchbase27 Aug

Most AI pilots wow the board. Few ship. The gap is the data layer, not the model. Fix the stack before you swap it: https://t.co/73R7A6Dx9x #Couchbase #EnterpriseAI #AgenticAI https://t.co/471X4Vu8fw 393 views · 1 likes · 0 reposts · 0 replies Open on X →

Couchbase@couchbase26 Aug

There's a threshold where AI response latency stops being a technical metric and starts feeling like an awkward silence to a caller: <500ms. Agora hit that bar building real-time conversational AI agents on Couchbase. Read how: https://t.co/TexbAcLf6c https://t.co/lLT03nMeVi 373 views · 1 likes · 0 reposts · 0 replies Open on X →

Couchbase@couchbase20 Aug

Your GenAI assistant crushes it in testing. Then real customers hit it, and it breaks. The problem isn't the model. It's the data layer. Our CPSO, Barry Morris, explains in Forbes: https://t.co/STfvdk2vxR #AI #ODP4AI 519 views · 1 likes · 0 reposts · 0 replies Open on X →

Couchbase@couchbase20 Aug

@couchbase Could you help me with startups? 1 views · 0 likes · 0 reposts · 0 replies Open on X →

Couchbase@couchbase21 Oct 2025

Couchbase’s breakthrough vector technology delivers billion-scale performance with high recall accuracy and millisecond latency. With the introduction of three distinct vector indexing and retrieval capabilities that support a variety of diverse vector workloads, Couchbase 8.0 ht 6.1K views · 8 likes · 1 reposts · 4 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

5 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 100K–1M ผู้ติดตาม ต่ำกว่าบัญชีรุ่นเดียวกันทั้งการเข้าถึงและการมีส่วนร่วม.

ยอดดูมัธยฐาน393บัญชีนี้5 983ค่ามัธยฐานของ 100K–1M

การเข้าถึง, %0.29%บัญชีนี้1.49%ค่ามัธยฐานของ 100K–1M

การมีส่วนร่วม, %0.20%บัญชีนี้0.93%ค่ามัธยฐานของ 100K–1M

ตัวชี้วัด	บัญชีนี้	ค่ามัธยฐานของ 100K–1M	อัตราส่วน
ยอดดูมัธยฐานต่อโพสต์	393	5 983	0.07×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)	0.29%	1.49%	0.19×
อัตราการมีส่วนร่วม	0.20%	0.93%	0.21×

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

6.1K21 Oct

120 Aug

519

37326 Aug

39327 Aug

5091 Sep

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.25%21 Oct

0.00%20 Aug

0.19%

0.27%26 Aug

0.25%27 Aug

0.20%1 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.

What the audience does

Likes63.2%12 in total

Reposts5.3%1 in total

Replies21.1%4 in total

Quotes10.5%2 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.