This post reads like someone who just discovered the OSI model and tried to shoehorn it into neurobiology.
The idea that the "revolution" is a hardware layer that just plugs into the brain and expands it with new neurons assumes a very naive model of how neural integration works. Brains don’t just recognize foreign neurons like USB devices. Synaptic plasticity, metabolic compatibility, glial interactions, all of that matters a lot more than signal translation.
Also, calling it a "data layer" glosses over the fact that neurons don't pass around clean, structured data. There’s no JSON over axons, information in the brain is messy, noisy, and deeply contextual—less like a protocol stack, more like a wet, self-rewriting spaghetti code.
So, if the core insight is "just add more neurons and treat it like hardware expansion," then the real challenge is being understated by several orders of complexity.
One thing people often miss: Gmail’s search isn’t broken because it’s technically hard. It’s broken because it's not a priority.
Accurate, user-respecting search doesn’t drive engagement or ad revenue. Nudging you toward the Promotions tab does. So over time, product decisions optimize for "glanceable convenience" rather than depth or control.
it’s disinterest, bordering on contempt. The infra could support proper search. But letting power users mass-delete or filter with precision is not part of the funnel.
I’m mostly web/backend so not deep in iOS, but SwiftUI does look like yet another case of abstracting away control in the name of “ease.” Same pattern with React: move logic to declarative bindings, then spend hours debugging state sync and lifecycle quirks.
UIKit with didSet {} or NotificationCenter might be verbose, but at least you can see what’s going on. SwiftUI’s “magic” feels like trading simplicity for opacity.
And yeah, Interface Builder, VB, even Delphi gave visual control without hiding everything. Declarative UIs sound elegant until you need to trace why a view is re-rendering 7 times in a scroll.
Switching apps isn't the root problem, switching contexts is.
If tools are well-integrated and mentally compartmentalized (e.g., Slack for fast input, Notion for synthesis, Jira for logistics), switching shouldn't feel painful. But when decisions get scattered and nothing enforces consolidation, yeah, it becomes chaos by slow erosion.
Ironically, the more we try to unify everything into one tool, the worse the mental clutter gets. The problem isn't that we have too many tools, it's that we try to make each one do more than it's supposed to.
So, It's not app switching that kills productivity. It's app misusing.
The idea reminds me of how data -> information -> knowledge -> wisdom has been discussed for decades. INK feels like a rebrand of that chain, just trimmed. Not saying it's bad, just not sure what's actually new here
The idea that the "revolution" is a hardware layer that just plugs into the brain and expands it with new neurons assumes a very naive model of how neural integration works. Brains don’t just recognize foreign neurons like USB devices. Synaptic plasticity, metabolic compatibility, glial interactions, all of that matters a lot more than signal translation.
Also, calling it a "data layer" glosses over the fact that neurons don't pass around clean, structured data. There’s no JSON over axons, information in the brain is messy, noisy, and deeply contextual—less like a protocol stack, more like a wet, self-rewriting spaghetti code.
So, if the core insight is "just add more neurons and treat it like hardware expansion," then the real challenge is being understated by several orders of complexity.