#online evaluation
290 related articles
·3 min
How to Evaluate AI Agents in Production: Four Online Evaluation Methods Explained
Offline tests passing doesn't mean production reliability. This guide breaks down Online Evals for AI Agents: rule checks, LLM-as-a-Judge, user feedback, and human review.
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·3 min
Why Is Self-Hosting European Open Source Projects So Hard? Root Causes and Recommendations
Exploring why European open source projects are hard to self-host: architectural complexity, digital sovereignty compliance, documentation gaps, and practical advice for users and maintainers.
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·4 min
Transitioning from DevOps to MLOps: A Complete Learning Roadmap
How can DevOps engineers efficiently transition to MLOps? This guide covers MLOps core concepts, standard workflows, essential tools, and Azure practices with a progressive learning roadmap.
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·2 min
Transformer²: Achieving Co-Design of Robot Morphology and Control with a Unified Architecture
Deep dive into how Transformer² uses a unified Transformer architecture to integrate robot morphology design and motion control into one model, enabling task-driven end-to-end co-design for embodied AI.
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·3 min
From Musk to Jefferson: Beware the Cognitive Trap of Cross-Domain Experts
Why do geniuses in one field often become overconfident in others? From Musk's controversial interview to Jefferson's blind spots, an exploration of cross-domain cognitive arrogance.
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·3 min
From Demo to Production: The Real Challenges of Deploying LLM Systems
Deep analysis of deploying LLM systems from prototype to production: a real-world AI incident investigation assistant case study revealing key engineering challenges beyond the model.
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·3 min
From Demo to Production: The Real Challenges of Deploying LLM Systems
Deep analysis of deploying LLM systems from prototype to production: a real-world AI incident investigation assistant case revealing critical engineering challenges beyond the model.
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·2 min
Can 45 Yes/No Questions Let an LLM Identify 16 Cards? An Information Theory Perspective on AI Reasoning
Analyzing whether LLMs can identify 16 cards through 45 yes/no questions from an information theory perspective. Exploring AI reasoning capabilities in constraint-based multi-turn tasks.
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·2 min
Why Is the Gemini Pro Model Taking So Long? The Gap Between Community Expectations and Release Timelines
Reddit community expectations for Google Gemini Pro shifted from June optimism to July anxiety. We analyze the technical realities behind delayed AI model releases and the challenge of expectation management.
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·5 min
Loop Engineering Explained: The New Programming Paradigm Where AI Prompts Itself
Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·2 min
Enterprise RAG in Practice: A Complete Guide to the Core Challenges from Setup to Production Optimization
A deep dive into enterprise RAG from setup to production, covering document chunking, vector search, query rewrite, reranking, and quality evaluation frameworks.
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·5 min
A Beginner's Guide to Prompt Engineering: From One-Off Instructions to a Systematic Methodology
A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.
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·3 min
Netflix Trust Exercise Turns Into Firing Trap: Where Are the Boundaries of Corporate Trust?
A Netflix employee was fired after sharing private info in a trust exercise. We analyze the risks of corporate trust exercises and how employees can protect themselves.
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·3 min
Google Launches Gemini 4 Pre-training: Its Most Ambitious AI Model Training Yet
Google DeepMind announces Gemini 4 pre-training has begun, calling it their most ambitious training yet. A deep dive into its technical direction, compute scale, multimodal breakthroughs, and competitive impact.
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·3 min
Netflix Trust Exercise Turns Into a Firing Trap: Where Are the Boundaries of Corporate Trust?
A Netflix employee was fired after sharing personal information in a team trust exercise. This article analyzes the risks of corporate trust exercises and how employees can balance candor with self-protection.
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·2 min
Getting Started with Codex: A Practical Guide to OpenAI's AI Programming Assistant with Tips to Avoid Common Pitfalls
A detailed guide to OpenAI Codex's core features and setup, comparing it with Claude Code, plus practical tips for configuring DeepSeek and avoiding common pitfalls.
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·2 min
Interview Prep Strategy for Senior Data Scientists: How to Prepare Efficiently Without Burning Out
Senior data scientist interviews are broad and multi-round. Learn an efficient evergreen fundamentals + targeted sprint strategy covering ML, SQL, system design, and mindset tips.
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·5 min
The Five Stages of AI Agent Architecture Evolution: The Real Path from Model Calls to Production Runtime
Deep dive into the five evolution stages of AI Agent architecture: model calls, tool calls, workflows, Agent loops, and production runtime. Learn the responsibility boundaries and design principles.
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·5 min
Complete Breakdown of Core Competencies for AI Agent Engineers: From Demo to Production-Grade Systems
Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.
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