What this is
A formula written with just three letters — y = x + F(x) — has underpinned nearly all visual AI since 2015. Developer "Lianqi" is serializing a tutorial titled Hand-Building an Industrial-Grade Rotated Object Detection Network from Scratch on Juejin, and the latest installment takes this single building block apart for readers. So-called residual connections are simply a network's "original-route return" shortcut: after processing the input, the network adds the original signal x straight back to the output. This 2015 ResNet invention is the reason nearly all visual AI today is trainable at all.
Industry view
Supporters argue these "hand-built" tutorials have real value — they strip the mystery off AI vision and show that the core technology isn't black magic. We notice that the developer community's "implement from scratch" trend continues. Counterarguments exist: reproducing operators and shipping industrial projects are two different things. Self-built frameworks suit learning and research, but 99% of companies are fine with off-the-shelf frameworks like Ultralytics and MMDetection — the ROI of reinventing the wheel isn't high.
Impact on regular people
For consumer markets: industrial vision AI (defect detection, drone inspection, etc.) is getting cheaper and is no longer exclusive to large companies.
For careers: understanding fundamentals like residual connections gives you ground-level judgment when talking to AI vendors — vendor jargon won't fool you as easily.
For enterprise IT: if you're considering building in-house vision AI capabilities, first assess whether your team can actually build from scratch; in most cases, buying a mature framework is the more realistic path.