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Kaggle

@kaggle · San Francisco · joined 06 Oct 2009

Kaggle is the largest global AI community of developers, researchers, and enthusiasts who compete, collaborate, and benchmark what's next in AI.

319 157Followers
292Following
5 686Posts total
76.3KViews on collected posts

Neueste Beiträge

Introducing ExtractBench on Kaggle Benchmarks with @llama_index. When AI agents rely on schema-guided extraction before human review, one truncated schedule or invented value becomes a wrong payment or decision. ExtractBench evaluates models in workflows based on real-world htt 11.6K views · 108 likes · 11 reposts · 4 replies Open on X →
@kaggle I learn that poor people died first in the titanic 77 views · 3 likes · 0 reposts · 0 replies Open on X →
Ready to build models that support knee MRI interpretation? 🩻🦵 Check out this starter notebook by Pilkwang Kim. It is a great starting point to read the DICOM acquisitions, sample and normalise the MRI slices, and turn a pretrained vision backbone into twelve abnormality 12.6K views · 87 likes · 21 reposts · 2 replies Open on X →
@kaggle xgboost 57 views · 3 likes · 0 reposts · 0 replies Open on X →
@kaggle XGBoost 591 views · 10 likes · 0 reposts · 0 replies Open on X →
What's one thing you learned on Kaggle? 🌟 17K views · 73 likes · 1 reposts · 26 replies Open on X →
@kaggle @GertLabs nice 3.7K views · 19 likes · 0 reposts · 1 replies Open on X →
@kaggle @GertLabs do the agents get any signal from the caller's voice or is it purely the conversation text? 205 views · 0 likes · 0 reposts · 1 replies Open on X →
@kaggle @GertLabs a tie for first between claude and gemini, so the real winner is whoever's caller was most polite about being an identity thief 262 views · 1 likes · 0 reposts · 0 replies Open on X →
Today, we’re excited to launch Adversarial Customer Service on Kaggle Benchmarks, in partnership with @GertLabs. This benchmark is a two-sided security game: one model plays a bank's support agent holding customer records and a verification policy, the other plays a caller who h 18.6K views · 131 likes · 15 reposts · 7 replies Open on X →
I knew I belonged on Kaggle when... (finish the sentence) 👇 11.6K views · 42 likes · 1 reposts · 14 replies Open on X →

Im Vergleich zu Konten gleicher Größe

11 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 100K–1M Follower. genau im Median seiner Follower-Klasse.

Medianaufrufe3 749dieses Konto5 983Median für 100K–1M
Reichweite, %1.17%dieses Konto1.49%Median für 100K–1M
Interaktion, %0.84%dieses Konto0.93%Median für 100K–1M
KennzahlDieses KontoMedian für 100K–1MVerhältnis
Medianaufrufe pro Beitrag3 7495 9830.63×
Reichweite (Aufrufe ÷ Follower)1.17%1.49%0.79×
Interaktionsrate0.84%0.93%0.91×

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

11.6K19 Aug
18.6K20 Aug
262
205
3.7K21 Aug
17K28 Aug
591
5729 Aug
12.6K
77
11.6K4 Sep

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.49%19 Aug
0.84%20 Aug
0.38%
0.49%
0.53%21 Aug
0.62%28 Aug
1.69%
5.26%29 Aug
0.89%
3.90%
1.08%4 Sep

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

What the audience does

Likes66.7%477 in total
Reposts6.9%49 in total
Replies7.7%55 in total
Quotes1.8%13 in total
Bookmarks16.9%121 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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