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Scoured 187272 posts in 22.9 ms
Which one is more important: more parameters or more computation? (2021)  🔀Data Pipelines
parl.ai·6d·Hacker News
After $10B raise, Project Prometheus becomes one of the most valuable five-month-old startups ever funded  💥Chaos Engineering
techfundingnews.com
·6d
Why Culture Alone Doesn’t Move Customer Experience Scores  🏗️Software Architecture
cmswire.com·3d
Unified observability in Amazon OpenSearch Service: metrics, traces, and AI agent debugging in a single interface  🔀Data Pipelines
aws.amazon.com·2d
Commanding the Foul Shot: A New Ensemble of Free Throw Metrics  🌐Distributed Systems
arxiv.org·23h
Web Review, Week 2026-17  💥Chaos Engineering
ervin.ipsquad.net·6d
matthiasn/lotti 0.9.976+3984  🐝eBPF
github.com·4d
Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework  🐝eBPF
arxiv.org·23h
Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems  🏗️Software Architecture
arxiv.org·23h
Inferring bifurcation diagrams of two distinct chaotic systems by a single machine  💥Chaos Engineering
arxiv.org·23h
Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations  🌐Distributed Systems
arxiv.org·23h
On the Role of Time Series Clustering in Traffic Matrix Prediction  📐Capacity Planning
arxiv.org·23h
End-to-End Learning for Partially-Observed Time Series with PyPOTS  🔀Data Pipelines
arxiv.org·2d
EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming  🗃️ClickHouse
arxiv.org·1d
Time-Series Forecasting in Safety-Critical Environments: An EU-AI-Act-Compliant Open-Source Package / Zeitreihenprognose in sicherheitskritischen Umgebungen: Ein KI-VO-konformes Open-Source-Paket  💥Chaos Engineering
arxiv.org·2d
Beyond the Final Label: Exploiting the Untapped Potential of Classification Histories in Astronomical Light Curve Analysis  🔀Data Pipelines
arxiv.org·2d
Measuring the Unmeasurable: Markov Chain Reliability for LLM Agents  🌐Distributed Systems
arxiv.org·2d
Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts  🌐Distributed Systems
arxiv.org·2d
Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning  💥Chaos Engineering
arxiv.org·2d
MEG-RAG: Quantifying Multi-modal Evidence Grounding for Evidence Selection in RAG  🗃️ClickHouse
arxiv.org·2d
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