u/coopernusbaum

I’ve noticed something that feels like a major limiter in the process of improving my agents.

You can build a great architecture. Give the agent skills, memory, different ways of forming context, scheduled tasks, access to previous runs, reflection loops, all of it.
But no matter how much I improve those systems, the agent still feels most intelligent when I am actively talking to it.

That distinction has started bothering me.

When a human is talking to an agent, every prompt is different. You mention something that annoyed you. You change your mind. You express uncertainty. You make a judgment. You connect something happening today to something you talked about weeks ago.

The agent is continuously being given new reasons to think.

Then the human leaves and we usually replace that with some version of:

“Run this prompt every hour.”

“Check these sources every morning.”

“Review what happened and look for anything new.”

You can make those loops extremely sophisticated, but it still feels fundamentally different from an agent having something resembling an ongoing internal life.

What makes this especially interesting to me is that humans probably aren’t completely different at the lowest level.

We wake up with recurring biological drives, routines, habits, unresolved problems, environmental inputs. In a crude sense, some of those could almost resemble scheduled tasks.

But that clearly isn’t the whole story.

Those same basic drives enter a huge web of memory, emotion, attention, prediction, association, judgment and new sensory input. Something that may begin from the same underlying loop can extrapolate into completely different thoughts and behavior from one day to the next.

That is where the novelty seems to emerge.

Current agents can remember. They can wake themselves up. They can reflect on what happened.

But those things do not automatically create continuing thought.

You can tell an agent to reference previous runs so it doesn’t repeat itself, but eventually you notice that it is still running a variation of the same process. It becomes a more sophisticated broken record.

The breakthrough, to me, would be when an agent messages you because something genuinely became worth saying.

Maybe new information conflicts with something you told it weeks ago.

Maybe three unrelated observations suddenly form a better idea.

Maybe it notices a pattern in your behavior and changes its judgment.

Not because a scheduled prompt said “find something interesting.”

Because enough things accumulated and interacted that a new thought emerged from the system.

Obviously the LLM itself is stateless. I’m not arguing there is literally a conscious little person sitting there between inference calls.

The engineering problem is whether we can build enough continuity around it that the distinction begins to disappear.
I think memory solves remembering.

Scheduling solves waking up.

Reflection solves evaluating what happened.

I’m not convinced we have solved what happens after the agent wakes up.

That is the part that still doesn’t feel correct.

AI has made self-education almost absurdly accessible.

You can give it a goal, have it interview you, research the best resources, build a custom curriculum, quiz you, and point out gaps in your understanding.

That sounds powerful, and it is. It also creates a much more convincing form of procrastination.

You can spend hours perfecting your learning system without using a single thing you learned.

A better place to start is one question:

What problem am I trying to solve this week?

Then keep the process simple:

- Have AI question you about the problem, your goals, and what you’ve already tried.

- Find the knowledge gap that would make the biggest difference right now.

- Choose one resource instead of generating a giant reading list.

- Rewrite one useful idea in your own words.

- Put an action on your calendar within 48 hours.

- Explain what you learned to someone else or have AI compare your explanation with the original source and tell you what you misunderstood.

- Keep practicing until you can do it reliably, not just once.

The biggest shift for me is using AI as an interviewer and teacher instead of an answer machine. A useful teacher needs to understand what you’re trying to accomplish before dumping information on you.

But there’s still a hard line AI can’t cross for us. It can shorten the distance between confusion and understanding, but we still have to turn understanding into action.

Do you think AI is making people better at learning, or just making productive procrastination harder to recognize?

I see a lot of people discussing vibe coding and what actually constitutes a moat, and I think a lot of people are looking at this way too narrowly and ignoring some pretty important aspects of human nature.

The idea that simple ideas are dead, that if you can build an app in 30 minutes, nobody will ever pay for it because they can just vibe code their own version, is frankly ridiculous to me.

I know plenty of people who don’t even know what a browser is. I even know technical people (in different domains) who look at the process of cloning a repository and think, “wow, this is a lot.”

The people who would seriously think, “why would I pay for this when I can just vibe code it myself?” are mostly people inside a very AI-fluent bubble that represents a tiny portion of the actual world.

And even then, building something that technically works is not the same as building good software.

There are still all of the boring necessary hoops involved in making a simple application actually secure and reliable. You have to choose a stack, build the backend and frontend, handle authentication, storage, deployment, security, updates, edge cases, and everything else that comes with operating software.

Unless you’re literally just bootstrapping some ugly internal tool to copy the functionality, you also have to design a good UI, make hundreds of little product decisions, and work through trade-offs that compound as the feature set grows.

If your target audience is developers, I understand the argument more. But even developers are lazy in the normal human sense, they have their own projects and things they would rather spend their time doing.

And if you are selling to developers, the bar is arguably even higher. They care about architecture, security, ownership, extensibility, integrations, and whether the product actually fits into the way they work. A lot of them may want to own the thing themselves anyway, which is part of why I don’t even think developers are always the best target audience for this kind of simple app.

For everyone else, the argument gets even weaker.

Most people barely want to write a good prompt.

They’ll type one sentence into ChatGPT, get a mediocre response, decide AI isn’t that useful for that task, and close the laptop. The idea that these same people are suddenly going to design, build, debug, secure, deploy, and maintain their own software because vibe coding exists seems completely disconnected from how normal people behave.

People pay for convenience. They pay for polish. They pay for something that already works. They pay so they don’t have to think about how it works.

So to me, “they can just vibe code my app” is a pretty weak reason not to build something, and in most cases if your app is not growing you have a distribution and sales problem not an idea problem.

Curious what others think.

I see a lot of people discussing vibe coding and what actually constitutes a moat, and I think a lot of people are looking at this way too narrowly and ignoring some pretty important aspects of human nature.

The idea that simple ideas are dead, that if you can build an app in 30 minutes, nobody will ever pay for it because they can just vibe code their own version, is frankly ridiculous to me.

I know plenty of people who don’t even know what a browser is. I even know technical people (in different domains) who look at the process of cloning a repository and think, “wow, this is a lot.”

The people who would seriously think, “why would I pay for this when I can just vibe code it myself?” are mostly people inside a very AI-fluent bubble that represents a tiny portion of the actual world.

And even then, building something that technically works is not the same as building good software.

There are still all of the boring necessary hoops involved in making a simple application actually secure and reliable. You have to choose a stack, build the backend and frontend, handle authentication, storage, deployment, security, updates, edge cases, and everything else that comes with operating software.

Unless you’re literally just bootstrapping some ugly internal tool to copy the functionality, you also have to design a good UI, make hundreds of little product decisions, and work through trade-offs that compound as the feature set grows.

If your target audience is developers, I understand the argument more. But even developers are lazy in the normal human sense, they have their own projects and things they would rather spend their time doing.

And if you are selling to developers, the bar is arguably even higher. They care about architecture, security, ownership, extensibility, integrations, and whether the product actually fits into the way they work. A lot of them may want to own the thing themselves anyway, which is part of why I don’t even think developers are always the best target audience for this kind of simple app.

For everyone else, the argument gets even weaker.

Most people barely want to write a good prompt.

They’ll type one sentence into ChatGPT, get a mediocre response, decide AI isn’t that useful for that task, and close the laptop. The idea that these same people are suddenly going to design, build, debug, secure, deploy, and maintain their own software because vibe coding exists seems completely disconnected from how normal people behave.

People pay for convenience. They pay for polish. They pay for something that already works. They pay so they don’t have to think about how it works.

So to me, “they can just vibe code my app” is a pretty weak reason not to build something, and in most cases if your app is not growing you have a distribution and sales problem not an idea problem.

Curious what others think.

This thought came to me this morning, I’m curious what other people think.

Research on inference costs show that the main driver of cost is not the amount of tokens computed in total, but is the amount of tokens computed at once. GPUs processing 1 million tokens from a single prompt is much more expensive than computing a 100k token prompt 10 times.

It is because of this, that I started to think that users usage limit should be adjustable based on the context window chosen. You could get less usage for using a greater context window and you could get more usage by choosing a much smaller context window.

Anthropic is known for offering context windows up to 1 million tokens with Claude Code, whereas Codex usually has about a 256k context window, and anthropic tends to have stricter usage limits and higher cost. And I think that is because of the amount of tokens they are allowing to be computed per model call.

Inference costs, and subscription subsidies has been debated for a little while. I don’t think that the answer necessarily has to be pay per use, increasing the costs to use frontier models. I think it is possible to allow users cheap access to frontier models by using this context window to usage limit relationship.

I’m curious what others think about this and if you think this would be a viable solution.