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This article explores common reasons why AI agents fail shortly after being deployed in production, highlighting pitfalls and lessons learned.
A discussion on the challenges and successful strategies for deploying AI agents in production at scale, covering common pain points and effective solutions.
This paper presents a systematic analysis of evaluation pitfalls in multimedia event extraction, identifying issues such as inconsistent data processing, inconsistent task assumptions, and overly relaxed evaluation settings that can lead to overestimated performance.
A tweet highlighting advanced HTML Canvas challenges and a comparison of GLM 5.1 and GLM 5.2, with demos including ink diffusion, energy-blade duel, and more, all using pure canvas without libraries.
Discusses key challenges facing AI voice agents in real-world customer interactions, such as accent handling, latency, and integration, and invites experiences from businesses.
This article explains how AI agents in 2026 collect data from websites and APIs, and discusses key challenges like rate limits, CAPTCHAs, and IP blocking.
A developer shares a month-long experience building an LLM-powered wiki based on Andrej Karpathy's idea, discovering that while setup is easy, ongoing maintenance—like handling stale sources, cost, and integration—poses the real challenge.
In AI-assisted programming (vibe coding), the threshold for creating demo-level products is already very low, but building stable and reliable services still faces huge challenges, and this threshold has not been lowered.
Discusses the persistent challenges that prevent AI agents from reliably handling real-world tasks, such as changing websites and inconsistent workflows, despite progress in task execution.
The article discusses how interaction models pose new challenges for AI model inference engines, with a focus on the vLLM project's solution as covered in a podcast episode featuring Woosuk Kwon.