gaasher

Use when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and…

Use when the user wants an iterative, self-checking exploratory analysis of a dataset — surfacing findings that are each verified by…

Use when the user wants the LLM to do its own ML research: a fully-autonomous loop that hacks the training code, runs it, and keeps…

Use when a loop needs scholarly literature — paper discovery, novelty checks, full-text snippet search, citation-graph traversal…

Use when the user wants an autonomous ML research loop that does more than blindly try changes.

Use when the user wants to iteratively improve an artifact under a hard correctness bound while minimizing a measured cost — refactoring a…