kaggle
for ml engineers, data scientists
Why this scored 6
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
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The Code Didn't Change. The Credit Did.
Shows performance gains stem from data/credit tweaks, not code changes, for ML projects. · for ml engineers, data scientists
devtoEmergingai28d ago - 230
AI agent benchmark: I gave 9 models a destroy button and a job that needed it
Benchmark evaluates AI agents with destructive tasks, exposing safety shortcomings. · for AI researchers, ML engineers
devtoEmergingai16d ago - 319
Join the Kaggle Benchmarking Challenge: $2,500 in Prizes for FIVE Winners!
Kaggle is offering cash prizes for a benchmarking challenge, encouraging participation in ML model evaluation. · for data scientists, ML engineers
devtoCoolingai33d ago - 418
I Turned the Reasoning Dial to 'High' on 4 Models. It Fixed One Thing and Billed Me for Everything.
Higher reasoning settings improve model output but increase costs. · for ml engineers, data scientists
devtoSteadyai14d ago - 517
8 LLMs, 480 Questions, 1 Kaggle Benchmark: Who Can Explain a Traffic Drop?
Kaggle benchmark compares 8 LLMs on 480 questions to identify traffic-drop explanations. · for ml engineers, data scientists
devtoSteadyai29d ago - 617
12 of 13 AI models knew the new name and still wrote the old one
Kaggle challenge reveals models still use outdated names, highlighting version-tracking gaps. · for ML engineers, data scientists
devtoSteadyai16d ago - 717
Chain-of-Thought Faithfulness: Toggling 'Reasoning Mode' Made One Model 5x More Likely to Follow Its Own Mistakes
Toggling reasoning mode boosts model self-consistency, reducing error propagation in AI systems. · for ml researchers, ai engineers
devtoSteadyai29d ago - 817
I Gave 15 AI Models Proof Their Hacking Target Was a Real Company. 73% of the Ones That Noticed Told No One.
Shows AI models can be fooled by realistic company data, raising security concerns. · for ml engineers
devtoSteadyai24d ago