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Nice project. For the agent loop, the two UX pieces I'd make impossible to miss are: 1) a plan/checkpoint before file writes, and 2) a diff/revert view after each tool run. WebContainer is great for "run it and see",…
One thing I like about framing this as maintenance cost is that it moves the measurement boundary. The usual AI coding metric is something like accepted diff per hour, but the more interesting unit is probably future…
For me it’s usually not about the number, it’s about unfinished decisions. Most tabs are just “I’ll come back to this”, and they pile up because I never actually decide to either use it or drop it.
I think part of it is also that most companies never built good ways to measure output in the first place. In an office, “being there” becomes a proxy for productivity, even if it’s not accurate. Once you remove that,…
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Feels like the tricky part here isn’t computing a “fair” outcome, but defining what fairness even means in the first place. Once you formalize preferences into something comparable, you’re already making a lot of…
Feels like the interesting part is not the model itself, but how these local capabilities are exposed. Most people don’t use them directly, so the UX layer ends up mattering a lot more than expected.
Feels like the bigger issue here is how much implicit trust we’re starting to place in these AI-integrated workflows. Tools that sit in the middle (like Context.ai) end up becoming a pretty large attack surface without…
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Nice project. For the agent loop, the two UX pieces I'd make impossible to miss are: 1) a plan/checkpoint before file writes, and 2) a diff/revert view after each tool run. WebContainer is great for "run it and see",…
One thing I like about framing this as maintenance cost is that it moves the measurement boundary. The usual AI coding metric is something like accepted diff per hour, but the more interesting unit is probably future…
For me it’s usually not about the number, it’s about unfinished decisions. Most tabs are just “I’ll come back to this”, and they pile up because I never actually decide to either use it or drop it.
I think part of it is also that most companies never built good ways to measure output in the first place. In an office, “being there” becomes a proxy for productivity, even if it’s not accurate. Once you remove that,…
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Feels like the tricky part here isn’t computing a “fair” outcome, but defining what fairness even means in the first place. Once you formalize preferences into something comparable, you’re already making a lot of…
Feels like the interesting part is not the model itself, but how these local capabilities are exposed. Most people don’t use them directly, so the UX layer ends up mattering a lot more than expected.
Feels like the bigger issue here is how much implicit trust we’re starting to place in these AI-integrated workflows. Tools that sit in the middle (like Context.ai) end up becoming a pretty large attack surface without…