AI Progress Benchmarking and Historical Tracking Platform
P6/10May 19, 2026
WhatA structured timeline database that tracks LLM capabilities over time with dated benchmarks, letting teams objectively measure whether newer models actually improve their specific workflows.
SignalThe post itself addresses a gap everyone feels — the field moves so fast that nobody can keep track of what actually changed and when, making it impossible to evaluate progress objectively.
Why NowThe LLM market has fragmented into dozens of competing models releasing weekly, and enterprises are struggling to decide which model to use for which task without a reliable historical record.
MarketEnterprise AI teams and developers choosing between models; $500M+ TAM in AI evaluation/observability tooling; competitors like Artificial Analysis cover benchmarks but not historical capability tracking tied to real-world use cases.
MoatProprietary longitudinal dataset of model capabilities over time becomes more valuable as it accumulates — no one can backfill this data once the moment passes.
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