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MLOps

MLOps news and analysis on Web Pulse: 1300 curated articles tracking the latest MLOps developments, tools, and research, updated continuously from vetted sources.

1300 articles page 5 of 65 0 sources 30 min sync cycle updated 2026-08-22

// latest articles 1300 indexed

14:47
2026-08-21
gist.github.com
large-language-models · · neu

claude cost optimization instructions

Anthropic has published a set of cost-optimization instructions for Claude, emphasizing a plan-first gate, delegation of exploration to subagents, and defaulting to the cheaper Haiku model for subagent tasks. The guidanc…

13:34
2026-08-21
dev.to
ai-agents · · neu

Your AI Agent Returned HTTP 200. Why Did the Workflow Still Fail?

A 58-day deployment of 78 agents produced 6,768 failed outputs, all returning HTTP 200 and appearing fluent, yet failing due to shape mismatches such as missing fields or wrong language. The report urges treating model r…

11:26
2026-08-21
dev.to
artificial-intelligence · · neu

What actually breaks when you run LLM agents unattended for 58 days

An organization running 78 autonomous LLM agents for back-office tasks published a dataset of 6,768 failures recorded over 58 days, finding that 43% of errors were 'boring' shape mismatches—such as missing required headi…

09:40
2026-08-21
dev.to
ai-agents · · neu

Detecting Tool + Schema Drift in a Remote MCP Server

An engineer has detailed a method for detecting tool and schema drift in remote MCP servers, a failure mode invisible to conventional uptime checks. The approach involves fingerprinting a server's advertised contract—inc…

mcp
03:01
2026-08-21
dev.to
machine-learning · · neu

PCA Deletes Your Quietest Signals First

In the seventh part of a series on classic machine learning from an SRE perspective, an engineer explains that PCA can delete quiet but critical signals, such as a client health metric stuck at 2/10, because it prioritiz…

00:54
2026-08-21
dev.to
large-language-models · · neu

RAG - Hallucination Detection

A developer explains how to detect hallucinations in retrieval-augmented generation (RAG) systems, where an LLM generates responses not supported by the retrieved context. Techniques include comparing embeddings, using t…

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