Snorkel AI vs tl;dv

Snorkel's output now reads as an evaluation lab rather than a labeling platform. The feed is dominated by benchmarks it builds or co-maintains — Terminal-Bench, Senior SWE-Bench, GDPval+ within the Snorkel Data Series, Agents' Last Exam with Berkeley RDI — plus frontier-model scorecards on Opus 5 and Grok 4.5. The through-line is expert-curated tasks with verifiable outcomes, deliberately kept partly private to resist contamination.

The work is moving from scoring single answers toward measuring long-horizon agent behavior: milestone-based evaluation, enterprise environments where an agent must call tools, query a simulated user, and respect approval rules. Snorkel's argument is that a correct final answer says little about whether the process was sound. With Terminal-Bench 4.0 that stance extends to the benchmarks themselves — they must be continuously maintained or they saturate and lose value.

Expect more milestone-scored, environment-based agent benchmarks aimed at specific enterprise workflows, and continued rapid scorecards each time a frontier model ships. The continuous-QA framing suggests recurring benchmark maintenance becomes a named offering.