I'm 33 and I think Claude Code is melting my brain.
For 6 months straight I've had 5-6 terminals open at once, waiting on responses just to smash "enter" 90% of the time. That's the whole job now.
And it's doing something to me. A few friends and I keep circling back to the same thing in conversations: none of us feel as sharp as we used to.
Maybe it's just us. But I keep wondering how many other people in their 30s feel it too.
(And yeah: this is a me problem, how I lean on the tool, not the tool itself. Doesn't make the effect any less real.)
@Davidstrolder Absolutely awesome way to use AI. It’s one thing’s to read a book and take notes but to actually talk through with AI as the thinking partner, love it. I think it’s better as a post read tool🔥
this is the real tax of building with agents right now. potential is obvious but every “just ship X” turns into 5 sub-problems you didn’t know existed (for someone who isn’t technical anyways). the learning curve has a learning curve.
Sorry to anyone who thought AI would mean we’d work less (at least for now). AI makes it easy to explore more than you did before, and so you start doing far more as a result.
I regularly have seemingly small things that end up quickly consuming 3 hours because the agent made it easy to get started, but you still have to do the rest of the work to complete the project.
This is work that I wouldn’t previously have handed out to anyone else, it’s just stuff that never got done because it took too long to do fully manually. And, counterintuitively, for some of these tasks as AI gets good enough at doing them, it even becomes economically worth it to hire someone to do it on an ongoing basis with agents. But until you could try doing them at a low cost you would never have tried.
This is why AI won’t automatically reduce work in the way we imagine because work isn’t static. Most companies have far more they can do than they have today, it was just hard to get started on it all because of the natural constraints of time and labor availability.
Everyone saying a model feels dumb recently or it has degraded you're either not managing your context properly or your harness is a mess.
not deeply technical myself, but here's what made it click:
i run Claude Code on two machines. my MacBook Pro is the main one tons of MCPs, a CLAUDE.md i've iterated on for months, heavy memory accumulation from daily use. my Mac Mini barely gets touched simple strategic CLAUDE.md, clean setup, minimal tooling.
same model. same prompts. the Mac Mini gives me 10x better results. every time.
every MCP you bolt on, every memory you let accumulate, every tweak you stack onto CLAUDE.md is more noise the model has to push through before it even gets to your actual problem. it's gotten to the point i'm considering nuking my whole main setup and starting fresh.
worst part when i try to fix it, i just get agreeable answers. Claude trying to please me instead of surfacing what's actually broken.
moral of the story: don't blame the model. diagnose yourself and your harness. that's 100% the issue.
wow HUGE win for Google. they’ll own 20%+ of anthropic while gemini hits #1
let me explain:
- google is funding $5 billion into anthropic’s new data center but…
- guess what they’ll use to train Claude? Google’s TPUs of course :)
- Claude 5 will run on google’s ai chips = more $$$ + FREE UPGRADE FOR GEMINI
- every chip improvement google makes to upgrade claude… will be used to train gemini = gemini becomes smarter
anthropic basically funds the R&D cost for geminis next upgrade 😂
- google already owns 17% of anthropic. this new funding will increase this to a massive stake (eg 20%)
win-win for google. if gemini loses, they own anthropic 🙂 if anthropic can’t pay google - gemini gains market share
genius play $GOOG
If you think Claude killed Openclaw you clearly don’t understand Openclaw.
People don’t use Openclaw because it can perform tasks for them autonomously across devices. They use Openclaw so they can post about it online.
i spent hours inside OpenClaw and its alternatives so you don't have to...
here's the truth nobody wants to hear:
for the vast majority of people, it's a pure waste of time
> 20+ hours/week setting up and maintaining it
> constant memory loss
> debugging that never ends
> and you'll probably never get past the most basic use cases
it's not a toy and it's definitely not plug-and-play... it's a factory you have to run yourself
what i'd do instead:
take that time and split it
- half goes into building the same workflows in Claude Code or Codex
- half goes into getting dangerously good at one skill
more leverage, surely less automated, but 10x the results
(remember that eventually Anthropic, OpenAI and Google will implement OpenClaw's features in their agentic platforms)
The biggest mistake I made with AI wasn't using the wrong tools.
It was asking AI to do things I didn't understand myself.
I was stuck in an endless loop sending prompts, getting outputs, adjusting prompts, getting worse outputs. Hours of it. Blaming the model. Thinking if I just found the right tool, the right setup, something would finally click.
It didn't.
Then it hit me. I was that kid in school who copied everyone else's work and couldn't figure out why the tests kept going badly.
Same energy. Copying AI outputs from my own terrible inputs and wondering why nothing was working. Except now there's no teacher to catch me. No accountability. And the model trained to please will validate my bad thinking every single time. Even my stupidity gets a confident, well-formatted answer.
That's the trap a lot of people are walking into right now.
Outsource everything to AI. Cut overhead. Scale 10x. Move fast.
But if your knowledge of a domain is thin — no SOP, no real understanding of how the work should be done AI doesn't fill that gap. It makes the gap invisible.
The outputs look complete, sound confident, arrive instantly. You don't realise you're moving in the wrong direction until you've gone too far.
Motion without direction isn't progress.
This is a skill problem. Specifically: the skill of learning.
Grit — the passion and perseverance for long-term goals is the actual differentiator. Not which model you're using. Not your setup. The willingness to spend 2-3 hours learning
something before you try to automate it. To understand a domain well enough to give AI real direction. To capture what you learn, apply it, fail, and improve.
AI fluency is non-negotiable now. But fluency means understanding not just usage.
Learn first. Then use AI to go further than you could alone.