Don’t Leave AI Agent “Robustness” to Chance: Build Observable Execution Traces
In many AI Lab delivery scenarios, the most anxiety-inducing moment isn’t when the model lacks intelligence, but when it “occasionally” makes mistakes.
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Lab Articles
Deep dives into tech, design, and AI exploration
Why AI Lab Deliverables Require a "Chain of Evidence" Rather Than Just Prompts
In the delivery workflows of many AI labs, the most common misconception is attempting to solve all reliability issues by continuously optimizing prompts. When
Why Local Long-Context Models Are Changing Content Operations
In content operations, there is a category of tasks that short-context models can handle adequately but struggle to perform reliably: cross-day duplicate detect
The "Last Mile" of Scaled AI Agent Delivery: Engineering Pitfalls from Demo to Production
In the daily delivery workflows of AI Labs, we frequently encounter a phenomenon: an Agent performs impressively in a Notebook or a simple Gradio Demo, but its
How Small Teams Should Assign Responsibilities to AI Agents
The most common pitfall for AI teams isn’t a lack of agents, but rather a situation where every agent seems to be “somewhat responsible.” When responsibility bo
Why One-Click Installation Matters More Than Documentation
Many technical products treat “whether the user can install it” as the user’s problem. The documentation is clear, the commands are listed, and the dependency v
Why Unified Device Identity Transforms the Remote Connection Experience
In remote control products, the most confusing aspect for users is rarely locating the connect button, but rather determining "which device am I actually connec
From Repetitive Publishing to Content Gatekeeping: A Fix Record for a Daily Update System
The recent SFD daily updates exposed a typical issue: the system could publish on schedule, but it failed to determine whether "today's topic was just a repeat
Don’t Let “AI Delivery” Become “AI Hallucination”: Engineering Pitfalls in AI Labs Seen Through 10 Real Projects
In many AI labs or startup teams, a common scenario unfolds: A researcher gets a demo running in a Jupyter Notebook with impressive metrics and confidently hand