Idler

Founded 2025

Train and evaluate models using reinforcement learning environments from real production work

You can use idler to train and evaluate advanced machine learning models using realistic reinforcement learning environments. It provides bespoke evaluation frameworks derived from real-world production coding tasks and valuable economic workflows. By testing models against complex real-world software scenarios, your team can measure practical capability rather than relying only on standard synthetic benchmarks. The service helps developers pinpoint specific model weaknesses, fine-tune frontier coding systems, and thoroughly verify performance on complex production tasks before deploying them directly to live software engineering pipelines.

What Idler does

Evaluate LLMs on real production engineering tasksRun reinforcement learning training across production environmentsBenchmark autonomous coding agent performanceDesign bespoke test suites for complex coding workflowsAssess model accuracy on economic development scenariosAnalyze failure modes of AI coding assistantsReinforcement learning environments built directly from real production engineering workBespoke model evaluation setups tailored to complex workflowsFrontier coding performance testing across realistic codebasesEconomically valuable workflow simulation for autonomous software agentsBenchmark tracking against frontier model capabilitiesRealistic task generation reflecting modern developer environments