It’s remarkable how often you need to be dramatically upgrading your AI architecture given the pace of progress in AI models right now.
If you’re building agents, you basically need to throw away large parts of previous work that you setup to compensate for model limitations every few quarters. The systems you built to mitigate context window limits aren’t useful anymore, and for many use-cases it’s easier just to throw more compute at a problem today in ways that wouldn’t have worked previously.
If you’re deploying agents in a workflow, you likely need to equally be rethinking your core systems at about that same frequency. The way you would deploy agents in an enterprise 18 months ago is entirely different from the best practices that you’d have today.
This is partly why everyone’s working so hard right now. Right as a best practice is solidified, models improve dramatically, and that old work is rendered obsolete. Unclear that this lets up anytime soon, which is why the it pays to be so wired in right now.
@robert_shaw I've built software to help team leaders create better visibility, alignment, and execution around strategic direction with @Replit
https://t.co/g7GE3pqEdJ
Bought https://t.co/F7G2Szx574 yesterday, to help fuel the democratization of software :)
Coming soon: almost any software you want for $1/mo each.
Builders: Want to join me in this new adventure?
Bought https://t.co/F7G2Szx574 yesterday, to help fuel the democratization of software :)
Coming soon: almost any software you want for $1/mo each.
Builders: Want to join me in this new adventure?
This is a dopamine loop, and it’s one of the most powerful ones humans have ever encountered.
Every time you prompt an AI and get a useful result back in seconds, your brain gets a hit. Variable-ratio reinforcement, same mechanism as slot machines, except the reward is real: actual output, actual progress, actual leverage on your ideas.
Traditional work follows a delayed-reward structure. You write code for 6 hours, maybe it compiles, maybe you get feedback in a week. The gap between effort and reward is wide enough that motivation decays constantly.
AI compresses that loop to seconds. Effort → reward → effort → reward. Your prefrontal cortex stays engaged because the next payoff is always one prompt away. This is why people describe it as “fun” when they’re actually working 14-hour days. The subjective experience of effort disappears when reward frequency is high enough.
The “harder than ever” part is real too. When your bottleneck shifts from execution to imagination, you run out of excuses to stop. There’s no “waiting on the build” or “blocked by review.” Every idea you have can be tested immediately, which means your brain never gets a natural stopping point.
People who thrive on this are selecting for a specific neurotype: high novelty-seeking, high conscientiousness, tolerance for rapid context-switching. That’s maybe 10-15% of the population.
The other 85% will experience the same tools as overwhelming, not energizing. And that split is going to define the next decade of who captures value from AI and who gets displaced by it.
Nearly every ambitious person I know who has dived into AI is working harder than ever, and longer hours than ever.
Fascinating dynamic tbh.
I have NEVER worked this hard, nor had this much fun with work.
I'm continuously astonished with the awesome capabilities of @Replit ! For those interested in learning how to vibe code in 2026, I would definitely recommend starting with @Replit . Here's a quick demo of a product I'm building with @sarahendline https://t.co/25vm1L1zK0
@ReplitSupport I want to have multiple devs working on the same app. Should we use replit projects (I see this is being changed in Mar?) or git tab branches? I'm noticing many differences between them. Pros/cons of each?
@mattyp I want to have multiple devs working on the same app. Should we use replit projects (I see this is being changed in Mar?) or git tab branches? I'm noticing many differences between them. Pros/cons of each?