Benchmark Watch
Evaluation notes, benchmark interpretation, leaderboard skepticism, and measurement failures.
Field Guides and Frameworks
Implementation playbooks, operator patterns, and deployment methods.
No Field Guide content is currently available for this topic.
Signals, Maps, and Watch Lists
Production-oriented analysis, benchmarks, and market/system intelligence.
External tools
Execution tooling is separate
Swarm Signal keeps the analysis layer. Use BoredTools for reusable production templates and trackers.
How to Evaluate AI Models Without Trusting Benchmarks
Benchmarks are contaminated, gamed, and misleading. Here's how to build evaluation systems that predict real-world model performance.
Knowledge Graphs Just Made RAG Worth the Complexity
Retrieval-augmented generation was supposed to solve the hallucination problem. It didn't. Most RAG systems still return the wrong chunk, miss the...
The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point
Computer-use agents jumped from 12% to 72% on OSWorld in 18 months. The scores look like progress. The latency and efficiency numbers tell a different story.
Config Files Are Now Your Security Surface
Agentic coding assistants went from autocomplete to autonomous operators in under two years. Now they're editing production code, filing pull requests,...
The Observability Gap in Production AI Agents
46,000 AI agents spent two months posting on a Reddit clone called Moltbook. They generated 3 million comments. Not a single human was involved. When...
When Your Judge Can't Read the Room
Three months ago, I ran a benchmark comparing GPT-4 and Claude 3 Opus on creative writing tasks. GPT-4 won by a comfortable margin according to my...
Most Agent Benchmarks Test the Wrong Thing
The SciAgentGym team ran 1,780 domain-specific scientific tools through current agent frameworks. Success rate on multi-step tool orchestration: 23%. Same...
How to Test and Debug AI Agents
Agents that call APIs, write to databases, and send emails can't be tested like chatbots. A complete guide to failure taxonomies, debugging tools, and evaluation pipelines.
The Benchmark Crisis: Why Model Leaderboards Are Becoming Marketing Tools
All three leading AI models now score above 70% on SWE-Bench Verified. That milestone should be cause for celebration. Instead, it exposes a growing crisis
The First Model Trained to Swarm: What the Benchmarks Actually Show
Every multi-agent system before K2.5 was a framework bolted on top of a model that never learned to coordinate. PARL changes the equation, but the benchmarks tell a nuanced story.