Modern Tools Make Ambitious Software Feasible

Projects like pgrust encounter skepticism because the software landscape includes abundant low-quality AI-generated work. Yet pgrust’s extensive testing uncovered real Postgres bugs—numerical issues with SP-GiST operations, format cache corruption in JSONPath, pgcrypto failures, memory safety problems in the dictionary loader, and a money overflow bug—showing that ambitious projects can be executed to substantial quality when properly engineered.

The decisive factor is compression of development time. The author implemented several projects in low single-digit hours that would have required a year or longer previously: a regex engine, an optimized ripgrep fork, and Rust compilation improvements. An Azul AI required approximately 5 hours to reach superhuman performance and 20 hours to become the strongest version at that time. Gabriel Cangussu’s competing AI took substantially less time, attributed to better performance from newer models like Fable 5.1 and Opus 5.5 that understand board game AI techniques more effectively.

Yet few people attempt projects now feasible because they unconsciously operate under old cost assumptions. Programmers maintain an “aversion” to ambitious technical projects “even though it is now actually reasonable.” Common complaints like slow Rust compilation are solvable “without a lot of time or effort now,” but historical assumptions about effort requirements still govern behavior.