Building something like ARC-AGI-3 is not clean, linear progress. It’s cycles of false clarity and sudden collapse. Early phases feel deceptively simple. You wire components together, define abstractions, convince yourself the architecture is “general.” Small benchmarks pass. Patterns emerge. There’s a brief window where it feels like intelligence is just scaling away. Then it breaks. Not loudly. Subtly. Edge cases accumulate. Generalization fails in places that should be trivial. Systems that looked elegant turn brittle under distribution shift. You realize you didn’t build intelligence you built a narrow illusion of it. The middle phase is the hardest. Everything becomes ambiguous. You question whether the failure is in data, architecture, training dynamics, or your own assumptions about cognition. You rip apart modules that took weeks to design. You rebuild them differently, sometimes worse, sometimes better, usually just different. Iteration speed becomes survival. Long feedback loops kill progress. Short loops expose flaws faster but force you to confront them constantly. There’s no stable ground only temporary configurations that “work” until they don’t. The intensity comes from compression. Weeks of confusion collapse into a single insight. A structural change suddenly unlocks behavior that seemed impossible before. Not full generality never that but a shift. Enough to keep going. The “ups” are not success. They’re alignment moments where the system behaves in a way that suggests you’re closer to the right abstraction. The “downs” are everything else. You learn to stop trusting surface performance. You start looking for invariants: what holds across tasks, what transfers, what breaks cleanly versus catastrophically. Most designs fail this test. By the later stages, the work becomes less about building and more about removing. Stripping unnecessary complexity. Collapsing redundant pathways. Forcing the system into constraints that reveal whether it actually learned anything general. There’s no final moment where it’s “done.” Just diminishing returns and a shifting definition of what counts as progress. The process is not fun in a casual sense. It’s absorbing, exhausting, and occasionally sharp enough to feel like discovery.past 1.5 to 2 years on my planet a quick view my arc agi 3 score card and some other things i’ve done its the tip of the iceberg submitted by /u/-SLOW-MO-JOHN-D
Originally posted by u/-SLOW-MO-JOHN-D on r/ArtificialInteligence