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Redis

@Redisinc · joined 27 Jan 2014

See how fast feels

44 120Followers
2 990Following
14 192Posts total
7.9KViews on collected posts

Neueste Beiträge

@Redisinc @databricks Nice pattern. Real-time state is great, but as that state keeps changing, being able to understand where the current state came from becomes just as important as serving it fast. Is that right? 11 views · 0 likes · 0 reposts · 0 replies Open on X →
@Redisinc Appreciate the post — and your partnership, as always! 156 views · 4 likes · 0 reposts · 0 replies Open on X →
Two systems, one job: continuous compute and instant serving. @Databricks Real-Time Mode computes state as events happen. Redis serves it in sub-millisecond time. Full pattern and code walkthrough here: https://t.co/FBhLf3YjLk https://t.co/rhPPnc43aH
2.1K views · 13 likes · 3 reposts · 2 replies Open on X →
@Redisinc Use https://t.co/YrCQ7j2QpW for Redis Observability 12 views · 0 likes · 0 reposts · 0 replies Open on X →
Join our 5-part AI tech talk series and fix this, one 15-minute session at a time, building a context-aware app on Redis Iris: https://t.co/B8zY2pFtBh 294 views · 4 likes · 0 reposts · 1 replies Open on X →
Your agent forgets everything between sessions, retrieval slows down as your data grows, state goes stale, and the LLM bill keeps climbing. Sound familiar?🧵 1.5K views · 4 likes · 1 reposts · 2 replies Open on X →
Vector search is one piece of a production RAG system. Most standalone vector databases only solve that piece. Semantic caching and agent memory usually end up bolted on separately, often meaning Redis gets added anyway. 🧵 https://t.co/2PBSRtHibT
1.3K views · 4 likes · 2 reposts · 2 replies Open on X →
Redis Iris gives a ReAct loop a fast, consolidated context layer instead of three separate systems for memory, retrieval, and caching. ReAct agents reason, act, and observe in a loop. Simple to build, but every tool call re-sends the growing history, and costs stack up fast. 🧵 ht
1.4K views · 3 likes · 2 reposts · 1 replies Open on X →
The response to Redis certifications has been 🔥 Haven't started yet? Don't worry, you can still get 50% off your first certification with code Earlyaccess50. Start your journey here: https://t.co/Ma2njhWcDb https://t.co/1ezfKJ45uS
1.2K views · 4 likes · 2 reposts · 0 replies Open on X →

Im Vergleich zu Konten gleicher Größe

9 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. übliche Reichweite für diese Größe, schwächere Reaktion als bei den meisten.

Medianaufrufe1 200dieses Konto924Median für 10K–100K
Reichweite, %2.72%dieses Konto3.62%Median für 10K–100K
Interaktion, %0.50%dieses Konto1.52%Median für 10K–100K
KennzahlDieses KontoMedian für 10K–100KVerhältnis
Medianaufrufe pro Beitrag1 2009241.30×
Reichweite (Aufrufe ÷ Follower)2.72%3.62%0.75×
Interaktionsrate0.50%1.52%0.33×

Weitere Konten dieser Größe →   Mit einem anderen Konto vergleichen →   Wie diese Vergleichswerte entstehen →

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

1.2K4 Sep
1.4K8 Sep
1.3K9 Sep
1.5K10 Sep
294
1211 Sep
2.1K
156
1112 Sep

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

0.50%4 Sep
0.42%8 Sep
0.63%9 Sep
0.48%10 Sep
1.70%
0.00%11 Sep
0.87%
2.56%
0.00%12 Sep

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

What the audience does

Likes52.9%36 in total
Reposts14.7%10 in total
Replies11.8%8 in total
Bookmarks20.6%14 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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