Model selection determines the available intelligence, while effort level controls how intensively Claude works—such as how many files are read, tests executed, and steps processed.
As model capability increases, prompting strategies and the economics of software development change, but not the fundamental requirements for value generation.
Dangerous AI errors often arise not from technical failures, but from hidden data problems and model deviations that only become visible once business-critical decisions have already been made.
A training-inference mismatch in LLM-RL leads to persistent off-policyness; MIPU resolves this through selective acceptance of policy updates based on inference-side improvements.
Claude Opus 4.8 and Sonnet 5 frequently invent non-existent parameters when using editing tools, causing third-party development environments like Pi to fail.
Claude Sonnet 5 with 1M token context becomes the default model in Claude Code 2.1.197 and costs $2 per 1M input tokens and $10 per 1M output tokens during the promotional phase.
FlashMorph converts Transformers into hybrid attention models by optimally determining which layers require full attention and which can be replaced with linear attention.
A 4B-parameter compiler translates natural-language function descriptions into compact, locally executable adapters that control a 0.6B-parameter interpreter instance, replacing API prompts from 32B models.
A modified Transformer with two independent computation streams for state management and token prediction reduces required resources and improves performance by 2–3 percentage points on downstream tasks.
