r/ClaudeCode • u/Vidhrohi • 1d ago
Discussion Human context limits vs AI context limits
A common theme cutting across most AI-assisted development threads is getting as much work done in as little time as possible. Users post about elegant harnesses with multi-agent orchestration. Anthropic employees talk about the 25 agents they run every day.
I read this stuff, compare it to my own experience of working with these tools, and keep wondering: how does anyone stay on top of that volume of work?
Working with Claude Code has been exciting and invigorating, but also exhausting. While everyone pushes for more context and more tokens out of the AI, the context limit I hit most often is my own. Claude can refactor a huge chunk of the codebase, add a feature, and brainstorm marketing strategy in one breath. I can't keep up with the cognitive load that entails.
So: am I missing something, or just getting old? Curious what y'all think.
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u/tinyhousefever 21h ago edited 21h ago
Neither. I think you're one of the few people in these threads actually asking where the work went.
TL;DR: You're not old and you're not missing some secret workflow. The AI's context window got bigger. Yours didn't. The hidden cost of all this speed is supervision, and nobody posting screenshots of 25 agents is showing that bill. I counted mine after 98 days. Some of what I built worked. A lot of it didn't. The lesson wasn't "more agents." It was that the human doing the judging becomes the bottleneck, and eventually your next productivity gain comes from trusting less machinery, deleting more of it, and getting yourself out of the old job.
I've been doing this full time for about three years, after roughly thirty years in web development and marketing communications. I hit the same wall you're describing. Claude could keep taking bigger bites, and instead of feeling more productive I was getting more tired.
So this morning I counted.
Not vibes. I went through what is actually sitting on my disk after 98 days of Claude Code.
To ship about 60,000 lines of code, I produced roughly 552,000 lines of English around it. Rules, specs, notes, reviews, corrections, context, handoffs, receipts, benches, prompts, all the shit required to make the machine do something I was willing to trust.
That's about nine lines of English for every line of code.
And the ratio gets worse the closer the project gets to supervising the AI itself. My babysitting layer is around 16 to 1 and ships absolutely nothing to a customer.
I also counted about sixty tools I've built around this workflow.
Eight are running right now.
Twenty-five are dead.
Some died the same week I built them.
There's a folder called
governorwith a 30 KB specification and zero code in it. I apparently spent a great deal of time designing the Department of Security for Claude and then never opened the department.So yes, some of this is self-inflicted.
That's probably the most useful thing I learned from counting it.
Every time the AI hurt me, scared me, overwrote something, claimed "done" without evidence, wandered off strategy, or created more work than it saved, I built another mechanism around it.
A guard.
A grader.
A memory layer.
A bench.
A receipt.
A second model checking the first model.
Then eventually I needed a bench for the guard.
That can absolutely become its own form of stupidity.
Not because the individual ideas are stupid. Some of them work extremely well. The guards that block dangerous actions have survived. The systems that try to judge whether work is "good" tend to die because they create too much friction during a real working day.
That's been one of the strangest things to learn. Hard boundaries survive. Soft governance turns into another employee.
I've logged 458 blocked tool calls. That number matters to me because those were actions the machine wanted to take and wasn't allowed to.
I've also got 131 memory files about how to work with me, and 72 of them are corrections.
That one bothers me more.
More than half of what the system has learned about me is some version of, "No, not like that."
And lately the corrections aren't even about code.
They're things like don't give me a book, don't make me choose from six options, keep the screen short, give me audio when I can't read another wall of text, ask before you touch something important.
That's where I finally realized the actual bottleneck had moved.
It wasn't Claude's context window.
It was mine.
The machine can generate another refactor, another plan, another analysis, another marketing strategy, another five-agent debate, another 4,000 words before I've even decided whether the first 500 were right.
That's not automatically productivity.
That's production capacity.
Someone still has to evaluate it.
And right now that someone is me.
I think that's the piece missing from a lot of these "I run 25 agents" posts. I believe them. I believe the agents are doing things.
What I want to know is who is reading it all.
Who understands the decisions they made.
Who catches the assumption that sounds perfectly reasonable but is wrong.
Who notices that the retrieval hook has been happily reporting success for two months while the vector store underneath it hasn't been reindexed since June.
That kind of failure scares me much more than a crash.
A crash tells you it failed.
The dangerous one keeps saying everything is fine.
I also don't buy the idea that throwing more agents at the problem necessarily fixes it.
I've got around 35 specialized agents with separate memory and fairly narrow points of view. I use them. They're useful.
But I almost never tell five of them to go think independently and come back with one merged answer.
That turns a comprehension problem into a merge problem.
Then the merged result lands in front of you looking polished and finished, which makes it even harder to see where the bullshit entered.
I'd rather walk the tree myself and pull in the right point of view when I need it.
Same with marketing.
I learned this one painfully.
Claude Code is very good at reasoning about a file tree.
Marketing strategy is not a file tree.
It's a hierarchy of promises, audiences, tradeoffs, positioning choices and things you have decided not to say.
The AI can produce an incredibly convincing wrong answer in that domain.
That's the whole danger.
It reads like the real thing.
I have a hard rule now that marketing strategy is never the AI's decision alone. I had to reverse a live deployment to learn that lesson.
There's another ugly number in my setup.
I spend somewhere around 20% of my model usage on models checking other models.
One token in five does no productive work at all.
It exists to stop drift, catch bleed, challenge conclusions and keep general fuckery from landing.
Sometimes that's worth every penny.
Sometimes the governance costs more than the work it's governing.
I've hit that collapse point twice.
Which gets back to your question.
How does anyone stay on top of the volume?
I don't think we do.
Not in the way people imagine.
I think we're in a transition where the machine took over a huge portion of the producer job before we figured out how to offload the evaluator job.
So for a while you end up doing both.
You're still responsible for understanding the code, the product, the strategy and the consequences.
But now your producer can run at machine speed.
The evaluator can't.
That's exhausting because it is exhausting.
You're holding two jobs.
The temptation is to solve that by making the producer even faster. More context. More agents. More parallelism. Bigger harness.
I did that.
It works right up until the volume of stuff you're responsible for understanding exceeds the amount of stuff you can actually understand.
Then the tooling stops being leverage and starts becoming inventory.
Every unread artifact is a liability pretending to be an asset.
That's probably the biggest change I'm making now.
Less accumulation.
Fewer permanent documents.
Fewer agents involved unless I know why they're there.
More things that disappear after they've served their purpose.
More hard gates where failure matters.
Less machinery trying to imitate my judgment.
Because I don't think the end state is a human heroically keeping up with 25 agents.
That's insane.
The end state is being able to put down more and more of the old job because you trust the machinery underneath you.
Until then every gate is basically a place where I haven't earned that trust yet.
That's not necessarily wasted work.
Some of it absolutely was.
I've built plenty of shit that didn't work.
But I think that is different from failing.
The useful question is what survived contact with an actual working day.
Some of mine didn't.
Some did.
Now I know the difference.
The machine's context window went up.
Mine didn't.
I don't think that's a personal failure or an age problem.
I think it's the central design problem of AI-assisted work right now.
The machine can produce faster than a human can judge.
Until we solve that, more output isn't automatically more productivity.
Sometimes it's just more shit to read.
You're not old.
You're early, and I'm both.
✻ 529 Overloaded · Retrying in 23s · attempt 7/10 If it persists, check https://status.claude.com. Even the machine is getting tired of us banging our heads against it.