AI didn't remove the thinking from research. It moved it to the front.
The effort I used to spend searching now goes into defining the question. I think that's a better use of it.
Keep the handoff visible. If you can see what someone actually asked the agent for, you can tell whether the brief failed or the execution did.
Without that, every "the agent got it wrong" turns into opinion against opinion.
Feeling the echo doesn't mean it's your storm.
When someone near you is going through something hard, the heaviness arrives and starts steering. The line doesn't push it away. It names it. Naming is the whole technique.
@jasonfried This is the gap I notice most. With kids, most evening events don't happen for me, so a weekly call sounds like the realistic way to stay close to people a stage ahead.
@tibo_maker Not a full solution, but the rule I try to keep: parallel sessions are fine as long as they're all on the same project. A bug, some pages and a refactor in three unrelated places at once is the part that fries my brain.
"Envy is the one sin that isn't even fun." Charlie Munger.
It came back after a run of podcasts about other apps and their revenue. Podcasts feature winners, and winners share the highlight reel. Measuring my Tuesday against someone's best quarter isn't information. It's a mood.
A question that fires in my head a dozen times a day now: how could this be automated?
Not in planning sessions. Before almost any task, as a reflex. The biggest surprises aren't the work tasks. They're the ordinary ones.
My co-founder's attention is scarcer than my time.
Not more valuable. Scarcer. His is split across projects and hard to schedule. Mine is predictable, which means I can move it. That difference changed how I handle days that don't go to plan.
My AI coach has a standing instruction: disagree with me.
The default is agreement, and agreement teaches you nothing. So: challenge weak assumptions, question optimistic conclusions, don't fold to be agreeable.
One bad Tuesday is not evidence.
At work I'd never call a landing page wrong after one bad day. At home I do it all the time. One hard conversation: "we don't communicate". One slow month: "the market's gone".
Count the data points first. If it's one, try again next week.
For the husband who swears he's not dressing up this Halloween. The Dude from The Big Lebowski. Bathrobe, sunglasses, sandals. So, his school drop-off outfit.
https://t.co/s9Wo4WQLNy
A rule from my own notes: learned pessimism is as dangerous as blind optimism. When results come slowly it's easy to decide nothing works, and then you quietly drop the things that were working.
You don't have to commit your entire life to optimism, just try it on for a week. Start thinking "what if it all worked out?" and "what a time to be alive!". Then compare your state of mind to the week before filled with pessimism and doomer nonsense.
@skirano Agree, and it lands hardest on product-led teams, where the product is also the marketing. If what people try for free can be recreated in a week, where do you put the steps ahead?
@businessbarista Fitting pick, since Spider-Man's famous line is about responsibility as much as optimism, and from those quotes @dhh has both covered.
@tdinh_me Curious how long hands-on mode stays hands-on. My weak spot is when the output starts looking clean and I catch myself approving instead of checking.
People blame AI agents for their own mistakes.
I catch myself reaching for it too. Something comes back wrong and the first instinct is "the agent got it wrong", not "I briefed it badly" or "I didn't check".
The tools changed this year. Who makes the mistakes didn't.
Small tasks steal focus from big ones, quietly, every day.
What finally worked for me: protected time blocks, and a "parking lot" for everything else. I get back to it in a dedicated slot later. Obvious. Also the hardest habit I've built this year.
@levie At our size the first eval is a plain note on what each agent is for, including what it can't do. Most of our "the agent is bad" moments were someone asking it for a thing it was never set up to do.
One very important thing that even the smartest people seem to have to learn from experience is taste in problems. There are juicy problems that tend to lead to interesting discoveries, and nasty problems that tend not to.