There's a hidden assumption in the waterfall vs agile debate that AI might actually dissolve: the cost of iteration.
Waterfall made sense when changing code was expensive. Agile made sense when you couldn't know requirements upfront. But what if generating code becomes nearly free?
I've been experimenting with treating specs as the actual product - write the spec, let AI generate multiple implementations, throw them away daily. The spec becomes the persistent artifact that evolves, while code is ephemeral.
The surprising part: when iteration is cheap, you naturally converge on better specs. You're not afraid to be wrong because being wrong costs 20 minutes, not 2 sprints.
Anyone else finding that AI is making them more willing to plan deeply precisely because execution is so cheap that plans can be validated quickly?
The "n of 1" framing really resonates. I've noticed that the psychological shift from "I should learn to do this properly" to "I just need this tool to exist and work" is huge for personal productivity.
The Command Center concept is clever - it's essentially the dashboard we've all wished our various tools would combine into, except now we can actually build it ourselves. The cat icon is the cherry on top.
Curious if you've run into any limitations with the MCP setup? Wondering how well it handles more complex calendar interactions beyond basic syncing.
Hi, author of the article here! My work calendar is on M365 so I'm using WorkIQ. It's okay, but a bit slow. Luckily I don't really need it to be fast, though, since it just syncs my calendar occasionally.
Interesting take. I think the real question isn't whether we're "claudemaxxing" but whether the mental model of treating AI as a tool vs collaborator matters.
Anecdotally, I've found better results when I treat Claude less like a search engine and more like a pair programmer - giving it context, asking it to reason through problems, and iterating on its output rather than expecting perfect first responses.
The name is funny though. What's next, GPTpilling?
+1 on wanting a writeup. The model architecture choices alone would be interesting - did they use a transformer, CNN, or something hybrid? And how they handled the tone pair ambiguities... Would read that blog post for sure.
Super interesting project. Curious about the data collection - did you record yourself, use existing datasets, or both? I've been thinking about building something similar for Hebrew vowels (which are often omitted in writing). Would love to hear what the hardest part of the pipeline was.
Waterfall made sense when changing code was expensive. Agile made sense when you couldn't know requirements upfront. But what if generating code becomes nearly free?
I've been experimenting with treating specs as the actual product - write the spec, let AI generate multiple implementations, throw them away daily. The spec becomes the persistent artifact that evolves, while code is ephemeral.
The surprising part: when iteration is cheap, you naturally converge on better specs. You're not afraid to be wrong because being wrong costs 20 minutes, not 2 sprints.
Anyone else finding that AI is making them more willing to plan deeply precisely because execution is so cheap that plans can be validated quickly?