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MLOps Community

@mlopscommunity · Worldwide · joined 06 Mar 2020

The MLOps community is an open and transparent community where all are welcome to participate. It is a place where MLOps practitioners can collaborate and share

11 412Followers
400Following
5 864Posts total
5.4KViews on collected posts

Ultimi post

A server that lists its skills and serves them as resources, so tools ship with instructions for using them. Ola Hungerford of @Nordstrom on Skills over MCP, dependency gaps, and the context bloat that returns at hundreds of skills. https://t.co/KEU9vzKPZz
249 views · 0 likes · 0 reposts · 1 replies Open on X →
WebMCP moves tool calling into the web page, scoped to the page you are on and running in your logged-in session, instead of an agent screenshotting and searching the accessibility tree. Angie Jones on why that matters and what still blocks adoption. https://t.co/uONnOxau89
420 views · 1 likes · 0 reposts · 1 replies Open on X →
"As a company we are spending $2 per million token. What does it mean? It doesn't mean anything." Kuntal Patel and Abhinav Lad of Palo Alto Networks on why per-token cost is a weak KPI, and what they measure once agents start looping on their own. https://t.co/SbQTf0dk3s
424 views · 1 likes · 0 reposts · 3 replies Open on X →
@mlopscommunity @Pinterest Five layers and the first two immutable once bought — that's the trap. No amount of quantization saves a bad hardware order. 1 views · 0 likes · 0 reposts · 0 replies Open on X →
Ambud Sharma of @Pinterest maps AI efficiency to five layers: hardware, capacity, inference engine, model and quantization, and routing and governance. The first two are immutable once bought, so no tuning higher up recovers a wrong order. https://t.co/Jmgw9m50fk
446 views · 1 likes · 0 reposts · 2 replies Open on X →
Full conversation here: https://t.co/u2xQpOpi7i Spotify: https://t.co/bN4gHCIU61 109 views · 0 likes · 0 reposts · 0 replies Open on X →
Coding Agents Conference. March 3rd at the Computer History Museum. https://t.co/i1HyBy4Agp https://t.co/fAj1vjjrMj
3.7K views · 17 likes · 3 reposts · 1 replies Open on X →

Rispetto ad account della stessa dimensione

6 post degli ultimi 90 giorni, accanto alla fascia di 10K–100K follower. copertura nella norma per la sua dimensione, reazione più debole della media.

Visualizzazioni mediane334questo account924mediana per 10K–100K
Copertura, %2.93%questo account3.62%mediana per 10K–100K
Interazione, %0.44%questo account1.52%mediana per 10K–100K
MetricaQuesto accountMediana per 10K–100KRapporto
Visualizzazioni mediane per post3349240.36×
Copertura (visualizzazioni ÷ follower)2.93%3.62%0.81×
Tasso di interazione0.44%1.52%0.29×

Altri account di questa fascia →   Confronta con un altro account →   Come sono costruiti questi parametri →

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

3.7K30 Jan
1094 Sep
446
113 Sep
42414 Sep
42018 Sep
24924 Sep

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.62%30 Jan
0.00%4 Sep
0.67%
0.00%13 Sep
0.94%14 Sep
0.48%18 Sep
0.40%24 Sep

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

What the audience does

Likes51.3%20 in total
Reposts7.7%3 in total
Replies20.5%8 in total
Quotes5.1%2 in total
Bookmarks15.4%6 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.

Account simili