Cool AI Projects That Failed: The File Integrity Gap (opens in new tab)

We ship tools that verify software artifacts. We deal with hashes, checksums, and provenance every day. But looking at the local AI landscape, there is a specific failure mode we see repeatedly: hype cycles that ignore the messy reality of file integrity in unstructured model dumps. Teams announce "cool" projects—agents, local reasoning loops, specialized inference stacks—but those initiatives collapse when they hit the first non-standard artifact. The gap isn't in the algorithm; it's in the ...