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

Posts recentes

MLOps Community@mlopscommunity24 Sep

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 →

MLOps Community@mlopscommunity18 Sep

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 →

MLOps Community@mlopscommunity14 Sep

"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 →

MLOps Community@mlopscommunity13 Sep

@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 →

MLOps Community@mlopscommunity4 Sep

Full conversation here: https://t.co/u2xQpOpi7i Spotify: https://t.co/bN4gHCIU61 109 views · 0 likes · 0 reposts · 0 replies Open on X →

MLOps Community@mlopscommunity4 Sep

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 →

MLOps Community@mlopscommunity30 Jan

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 →

Em comparação com contas do mesmo porte

6 posts dos últimos 90 dias, ao lado da faixa de 10K–100K seguidores. alcance comum para o seu porte, reação mais fraca que a maioria.

Mediana de visualizações334esta conta924mediana para 10K–100K

Alcance, %2.93%esta conta3.62%mediana para 10K–100K

Engajamento, %0.44%esta conta1.52%mediana para 10K–100K

Métrica	Esta conta	Mediana para 10K–100K	Proporção
Mediana de visualizações por post	334	924	0.36×
Alcance (visualizações ÷ seguidores)	2.93%	3.62%	0.81×
Taxa de engajamento	0.44%	1.52%	0.29×

Outras contas desta faixa → Comparar com outra conta → Como estas referências são construídas →

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

4464 Sep

109

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

0.67%4 Sep

0.00%

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.