The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…
I've personally been facing this lately 5.6 at max effort and fable have done tasks for me that I previously that would be a nearly 6mo project and it took me a week. It also did it better than I would have. The task was to build a high performance classification model. It not only helped make an entire data capture pipeline but also made the sythetic data basline needed. Then it proceeded to build and test 100 diffe…
How can you know it will have failed? I don't think it's that hard, if you clearly define the goal well, and have a bit more compute available, and do some intermediary bookkeeping.