Benchmarks
Measuring what AI can actually do. Which benchmarks matter, which are gamed, and why evaluation is harder than it looks.
Deep Dives and Frameworks
Implementation playbooks, operator patterns, and durable analysis.
No deep-dive content is currently available for this path.
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.
TerminalWorld Makes Agent Benchmarks Harder to Fake
TerminalWorld turns public terminal recordings into validated agent tasks. The signal is not a higher leaderboard score. It is a harder benchmark supply chain.
SMAC-Talk Shows Agent Chat Is Not Coordination
SMAC-Talk adds natural-language communication and deception to StarCraft-style multi-agent evaluation. The result is a useful warning: agent chat can expose coordination failure as easily as it fixes
Agent Benchmarking Doesn't Need Every Task
Efficient agent benchmarking points to a cheaper way to compare agents: run the tasks that still separate systems, not every task in the suite.
Browser-Use Agents After the Computer-Use Benchmarks
Browser-use agents look cleaner than desktop agents, but the benchmarks still hide drift, cost, auth, and recovery failure.
The Benchmark Trap: When High Scores Hide Low Readiness
AI benchmarks measure performance in sanitized environments that bear little resemblance to conditions where these systems will actually operate.