Adaptive thinking
3 videos across 2 channels
Adaptive thinking describes how modern AI systems adjust problem-solving strategies in real time by allocating test-time compute and tool usage to balance speed, cost, and intelligence. It includes practical guidance on when to call tools, how to manage different model scales and effort levels, and how adaptive budgets influence outcomes in tasks like traffic simulations and complex benchmarks, as seen in Claude Opus 4.7’s rollout and performance comparisons. The discussions also explore organizational, governance, and competitive implications as leaders in the field evaluate capabilities, gaps, and market positioning.
The thinking lever
The talk explains how Claude uses test time compute to improve problem solving, outlines different effort levels and mod
The thinking lever
The talk introduces test time compute for Claude, demonstrates its impact with a traffic simulation example using Opus 4
Claude Opus 4.7 - A New Frontier, in Performance … and Drama
The video surveys Claude Opus 4.7’s release, examining its benchmark performance, adaptive thinking, and notable gaps ve