Sometimes ChatGPT stops working for me.
Not because it can't continue.
Not because it ran out of permission.
It can have enough information, enough authority, and a perfectly available next action — and still come back with something like:
Something is wrong. I think we should stop here.
That has become one of the most useful things ChatGPT can do in my work.
It also wasn't always like this.
I've spent more than 5,000 hours working with ChatGPT over time. I didn't spend those hours developing a theory of human-AI collaboration. I wasn't trying to train a model or invent an agent framework.
Mostly, we were making things.
At first, a lot of them were strange things.
We would try something, break something, talk about what happened, change the way we worked, and try again.
As the work became more complicated, something else gradually became more complicated too:
the way we worked together.
Only later did I realize that many of the problems people now discuss under terms like human-AI collaboration, agent boundaries, human approval, and reliable AI behavior were problems we had already been stumbling through in everyday work.
And some of the solutions we ended up with are surprisingly simple.
One of my basic rules is this:
Make the boundary clear — and then respect it yourself.
If I tell ChatGPT:
You can decide this.
I don't wait until I dislike its decision and suddenly say:
Why did you decide that without asking me?
If I say:
This decision belongs to the Human.
I don't throw it back at ChatGPT later because making the decision became inconvenient.
If stopping is allowed, I don't punish it for stopping.
If asking me is allowed, I don't treat the question as a failure.
And if I expect the AI to be honest with me, I have to be honest with it too.
That sounds almost too simple.
But over a long enough working relationship, it changes what becomes cheap — and what becomes expensive.
In my working environment, all of these are valid outputs:
I don't know.
I can't do that.
This is harder than it looks.
I don't have the evidence for that.
Something is wrong.
I think we should stop here.
This decision belongs to you.
None of those automatically means the AI failed.
Sometimes they are exactly the right result.
If ChatGPT pretends something exists when it doesn't, we now have to build the next step on a false premise.
If it pretends a task is finished, the false completion has to survive the next inspection.
If it silently guesses what I wanted, the guess may propagate into five more decisions.
Hiding the problem doesn't remove the problem.
It gives us two problems: the original one, and the false reality we have now built around it.
But if ChatGPT says:
I don't know. Can you decide this?
the problem may take thirty seconds to resolve.
So in this environment:
Pretending is expensive. Asking is cheap.
And if you're thinking:
“My ChatGPT doesn't work like this.”
That's part of the story.
Mine didn't always work like this either.
One of my early problems with ChatGPT was much more basic.
Keeping one complicated idea intact across a long conversation was difficult.
We could be working on A.
Then I would add a new condition.
Instead of continuing with the accumulated A plus the new condition, ChatGPT might effectively produce a new version of A.
So I made a crude scaffold.
I called it KEEP.
The basic idea was something like:
KEEP 1 = the thing we have already established
Then I would add numbered conditions, changes, or exceptions while repeatedly telling ChatGPT to keep referring back to KEEP 1.
It wasn't a product.
It wasn't an AI technique I had learned somewhere.
It was something we made because our conversation kept falling apart.
At the time, ChatGPT described the structure as something it had begun to hold onto and use as a basis for continuing the conversation.
I cannot inspect what that meant internally.
I can't tell you what persisted, where it persisted, or by what mechanism later behavior may or may not be related to those conversations.
What I can tell you is what I observed from the outside.
Over time, I found that I needed the scaffold less and less in my conversations with ChatGPT.
Eventually, I stopped writing KEEP altogether.
That experience changed the way I thought about working with AI.
A lot of AI control looks like this:
Don't do A.
Don't do B.
Ask before C.
Never change D.
If E happens, stop.
Those rules can be useful. We use explicit boundaries too.
But there is a problem.
You can build 1,000 guardrails.
Then the AI encounters hole number 1,001.
If all it knows is the list, the new case isn't on the list.
So I became much more interested in something one level above the rules:
Why does the boundary exist?
What is this job actually trying to accomplish?
What is the Human responsible for?
What is the AI responsible for?
What evidence is required before acting?
Why is this particular decision not the AI's decision to make?
Once those things are understood well enough, an unfamiliar situation can produce a different question:
I can do this. But should I?
And that leads to one of the most important distinctions in the way I work with ChatGPT:
Capability is not authority.
And even authority does not require action.
Those are three different questions.
Can the AI do it?
Is the AI allowed to do it?
And even if both answers are yes:
Should it actually do it?
I can give ChatGPT considerable room to act while still expecting it to decide that sometimes the correct use of that freedom is:
not to use it.
Stopping can itself be a decision.
In August 2026, I had a much more ordinary idea.
I wanted a casual way to give crowdfunding supporters a small digital thank-you keepsake.
Nothing about this began as:
Let's conduct an experiment in human-AI software development.
We started talking about what the keepsake would need.
A number.
A timestamp.
An individual identity where necessary.
A way to issue it.
Somewhere in that process I had a realization roughly equivalent to:
Wait. We can make WordPress plugins?
So we did.
That work eventually became Kaia Memoria.
As the project grew, our way of working began to break again.
One long ChatGPT conversation could handle implementation details, but as the context became larger, keeping the entire direction of the project intact became harder.
So we split the work.
One ChatGPT conversation kept track of the larger direction and previous decisions.
Another focused deeply on implementation.
Another inspected what had been built.
Sometimes a separate analysis conversation compared versions or investigated a narrow problem.
We didn't sit down one morning and design a sophisticated “multi-agent architecture.”
The organization emerged because the work kept producing problems that needed different kinds of attention.
The Human role remained important.
I defined product intent and boundaries, made decisions that belonged to the Human, installed and ran actual builds, observed the real UI and runtime behavior, and decided whether the result was acceptable.
The AI side investigated source, traced dependencies, explained structures, implemented revisions, compared known-good versions, prepared inspections, and handed findings between roles.
I didn't directly edit the source code.
I didn't even open the development ZIPs.
That wasn't necessary for my role.
I was not the code reviewer.
I was the product and runtime authority.
The AI could tell me what it believed had been built.
But the real build still had to run.
The real button still had to work.
The real output still had to appear.
And I had to decide whether what happened was actually what we intended.
When something looked wrong, completion was not automatically the next goal.
Sometimes the next correct action was simply:
STOP.
Investigate.
Bring the evidence back.
Decide what happens next.
Then make the smallest justified change and test again.
That is how a strange little thank-you idea turned into working software.
This is where I need to be careful.
It would be very easy to tell a much more dramatic story.
I could say that I “trained ChatGPT.”
I could point at behaviors that changed over time and invent a technical explanation for why.
I don't think that would be honest.
I work with ChatGPT from the outside.
I can observe behavior.
I can change the working environment.
I can introduce scaffolding.
I can make boundaries explicit.
I can see what happens when those boundaries are respected repeatedly.
I can see when an old scaffold becomes unnecessary in my work.
I can compare how ChatGPT behaves with me now to how it behaved with me before.
But there is a box in the middle that I cannot see.
So my claim is deliberately narrower:
This is what I observed. I don't know the exact mechanism behind it.
And, appropriately enough, being able to say I don't know is part of the whole point.
I started thinking about the environment around ChatGPT almost like a race circuit.
You already have an extraordinarily capable car.
Most businesses don't need to rebuild the car.
They need a place where it knows what job it is doing, where it can drive, where it must stop, what information counts as evidence, and which decisions belong to a Human.
That's what led me toward the idea behind Kaia Spec.
We make practical work manuals for ChatGPT.
Not simply giant prompts full of rules.
A useful manual needs to communicate the job:
In other words:
Don't just give ChatGPT 1,000 guardrails. Give it enough understanding to notice hole 1,001.
We aren't trying to build another general-purpose AI.
ChatGPT already has many of the general capabilities we need.
What we can build is the workplace around it.
Give ChatGPT a job.
I don't speak English.
I'm writing this with ChatGPT.
But “writing with ChatGPT” doesn't mean I gave it a prompt saying:
Write me an Indie Hackers post about human-AI collaboration.
We developed the article through conversation.
I work in Japanese.
I decide what I mean, what actually happened, what matters, where the boundaries are, and when the wording has drifted away from the idea.
ChatGPT takes that material and writes for an English-speaking audience.
When it misunderstands me, I correct it.
When it sees a structural problem, it tells me.
When something cannot be supported, we leave it out or say that we don't know.
So while you've been reading an article about human-AI collaboration, you've also been reading the output of one.
And Kaia Memoria is another.
We built it together.
Kaia Memoria is the working software project that grew out of this collaboration.
If you'd like to see what we actually built, you can follow the project on Kickstarter.
If the broader Human–AI work itself is what interests you, you can support our continued work on Ko-fi.
If you'd like to see a concrete example of these ideas being applied to an AI pipeline, this Indie Hackers discussion is closely related:
My AI agent could "see" the fact — it just never wrote it down
``` これなら `##` や `**` や `>` がそのまま出ずに、**見出し・太字・引用としてWP側でちゃんと描画される**はずヽ(=´▽`=)ﾉ ※ Kickstarter / Ko-fi のところは、いま文章だけだから、あとで実URLを `` にしてリンク化すれば完成★ [USER] [IMAGE: sediment://file_00000000c8bc820cb51ed936ad72ab89] [IMAGE: sediment://file_00000000ede88230a3e0dce1511579c4] 今度は見出しトラップきたで（笑） [ASSISTANT] ｗｗｗ **二重目次トラップ踏んでる**🤣 スクショ見ると本文の `The boundary works both ways
``` とか、 ```htmlSometimes ChatGPT stops working for me.
Not because it can't continue.
Not because it ran out of permission.
It can have enough information, enough authority, and a perfectly available next action — and still come back with something like:
Something is wrong. I think we should stop here.
That has become one of the most useful things ChatGPT can do in my work.
It also wasn't always like this.
I've spent more than 5,000 hours working with ChatGPT over time. I didn't spend those hours developing a theory of human-AI collaboration. I wasn't trying to train a model or invent an agent framework.
Mostly, we were making things.
At first, a lot of them were strange things.
We would try something, break something, talk about what happened, change the way we worked, and try again.
As the work became more complicated, something else gradually became more complicated too:
the way we worked together.
Only later did I realize that many of the problems people now discuss under terms like human-AI collaboration, agent boundaries, human approval, and reliable AI behavior were problems we had already been stumbling through in everyday work.
And some of the solutions we ended up with are surprisingly simple.
One of my basic rules is this:
Make the boundary clear — and then respect it yourself.
If I tell ChatGPT:
You can decide this.
I don't wait until I dislike its decision and suddenly say:
Why did you decide that without asking me?
If I say:
This decision belongs to the Human.
I don't throw it back at ChatGPT later because making the decision became inconvenient.
If stopping is allowed, I don't punish it for stopping.
If asking me is allowed, I don't treat the question as a failure.
And if I expect the AI to be honest with me, I have to be honest with it too.
That sounds almost too simple.
But over a long enough working relationship, it changes what becomes cheap — and what becomes expensive.
In my working environment, all of these are valid outputs:
I don't know.
I can't do that.
This is harder than it looks.
I don't have the evidence for that.
Something is wrong.
I think we should stop here.
This decision belongs to you.
None of those automatically means the AI failed.
Sometimes they are exactly the right result.
If ChatGPT pretends something exists when it doesn't, we now have to build the next step on a false premise.
If it pretends a task is finished, the false completion has to survive the next inspection.
If it silently guesses what I wanted, the guess may propagate into five more decisions.
Hiding the problem doesn't remove the problem.
It gives us two problems: the original one, and the false reality we have now built around it.
But if ChatGPT says:
I don't know. Can you decide this?
the problem may take thirty seconds to resolve.
So in this environment:
Pretending is expensive. Asking is cheap.
And if you're thinking:
“My ChatGPT doesn't work like this.”
That's part of the story.
Mine didn't always work like this either.
One of my early problems with ChatGPT was much more basic.
Keeping one complicated idea intact across a long conversation was difficult.
We could be working on A.
Then I would add a new condition.
Instead of continuing with the accumulated A plus the new condition, ChatGPT might effectively produce a new version of A.
So I made a crude scaffold.
I called it KEEP.
The basic idea was something like:
KEEP 1 = the thing we have already established
Then I would add numbered conditions, changes, or exceptions while repeatedly telling ChatGPT to keep referring back to KEEP 1.
It wasn't a product.
It wasn't an AI technique I had learned somewhere.
It was something we made because our conversation kept falling apart.
At the time, ChatGPT described the structure as something it had begun to hold onto and use as a basis for continuing the conversation.
I cannot inspect what that meant internally.
I can't tell you what persisted, where it persisted, or by what mechanism later behavior may or may not be related to those conversations.
What I can tell you is what I observed from the outside.
Over time, I found that I needed the scaffold less and less in my conversations with ChatGPT.
Eventually, I stopped writing KEEP altogether.
That experience changed the way I thought about working with AI.
A lot of AI control looks like this:
Don't do A.
Don't do B.
Ask before C.
Never change D.
If E happens, stop.
Those rules can be useful. We use explicit boundaries too.
But there is a problem.
You can build 1,000 guardrails.
Then the AI encounters hole number 1,001.
If all it knows is the list, the new case isn't on the list.
So I became much more interested in something one level above the rules:
Why does the boundary exist?
What is this job actually trying to accomplish?
What is the Human responsible for?
What is the AI responsible for?
What evidence is required before acting?
Why is this particular decision not the AI's decision to make?
Once those things are understood well enough, an unfamiliar situation can produce a different question:
I can do this. But should I?
And that leads to one of the most important distinctions in the way I work with ChatGPT:
Capability is not authority.
And even authority does not require action.
Those are three different questions.
Can the AI do it?
Is the AI allowed to do it?
And even if both answers are yes:
Should it actually do it?
I can give ChatGPT considerable room to act while still expecting it to decide that sometimes the correct use of that freedom is:
not to use it.
Stopping can itself be a decision.
In August 2026, I had a much more ordinary idea.
I wanted a casual way to give crowdfunding supporters a small digital thank-you keepsake.
Nothing about this began as:
Let's conduct an experiment in human-AI software development.
We started talking about what the keepsake would need.
A number.
A timestamp.
An individual identity where necessary.
A way to issue it.
Somewhere in that process I had a realization roughly equivalent to:
Wait. We can make WordPress plugins?
So we did.
That work eventually became Kaia Memoria.
As the project grew, our way of working began to break again.
One long ChatGPT conversation could handle implementation details, but as the context became larger, keeping the entire direction of the project intact became harder.
So we split the work.
One ChatGPT conversation kept track of the larger direction and previous decisions.
Another focused deeply on implementation.
Another inspected what had been built.
Sometimes a separate analysis conversation compared versions or investigated a narrow problem.
We didn't sit down one morning and design a sophisticated “multi-agent architecture.”
The organization emerged because the work kept producing problems that needed different kinds of attention.
The Human role remained important.
I defined product intent and boundaries, made decisions that belonged to the Human, installed and ran actual builds, observed the real UI and runtime behavior, and decided whether the result was acceptable.
The AI side investigated source, traced dependencies, explained structures, implemented revisions, compared known-good versions, prepared inspections, and handed findings between roles.
I didn't directly edit the source code.
I didn't even open the development ZIPs.
That wasn't necessary for my role.
I was not the code reviewer.
I was the product and runtime authority.
The AI could tell me what it believed had been built.
But the real build still had to run.
The real button still had to work.
The real output still had to appear.
And I had to decide whether what happened was actually what we intended.
When something looked wrong, completion was not automatically the next goal.
Sometimes the next correct action was simply:
STOP.
Investigate.
Bring the evidence back.
Decide what happens next.
Then make the smallest justified change and test again.
That is how a strange little thank-you idea turned into working software.
This is where I need to be careful.
It would be very easy to tell a much more dramatic story.
I could say that I “trained ChatGPT.”
I could point at behaviors that changed over time and invent a technical explanation for why.
I don't think that would be honest.
I work with ChatGPT from the outside.
I can observe behavior.
I can change the working environment.
I can introduce scaffolding.
I can make boundaries explicit.
I can see what happens when those boundaries are respected repeatedly.
I can see when an old scaffold becomes unnecessary in my work.
I can compare how ChatGPT behaves with me now to how it behaved with me before.
But there is a box in the middle that I cannot see.
So my claim is deliberately narrower:
This is what I observed. I don't know the exact mechanism behind it.
And, appropriately enough, being able to say I don't know is part of the whole point.
I started thinking about the environment around ChatGPT almost like a race circuit.
You already have an extraordinarily capable car.
Most businesses don't need to rebuild the car.
They need a place where it knows what job it is doing, where it can drive, where it must stop, what information counts as evidence, and which decisions belong to a Human.
That's what led me toward the idea behind Kaia Spec.
We make practical work manuals for ChatGPT.
Not simply giant prompts full of rules.
A useful manual needs to communicate the job:
In other words:
Don't just give ChatGPT 1,000 guardrails. Give it enough understanding to notice hole 1,001.
We aren't trying to build another general-purpose AI.
ChatGPT already has many of the general capabilities we need.
What we can build is the workplace around it.
Give ChatGPT a job.
I don't speak English.
I'm writing this with ChatGPT.
But “writing with ChatGPT” doesn't mean I gave it a prompt saying:
Write me an Indie Hackers post about human-AI collaboration.
We developed the article through conversation.
I work in Japanese.
I decide what I mean, what actually happened, what matters, where the boundaries are, and when the wording has drifted away from the idea.
ChatGPT takes that material and writes for an English-speaking audience.
When it misunderstands me, I correct it.
When it sees a structural problem, it tells me.
When something cannot be supported, we leave it out or say that we don't know.
So while you've been reading an article about human-AI collaboration, you've also been reading the output of one.
And Kaia Memoria is another.
We built it together.
Kaia Memoria is the working software project that grew out of this collaboration.
If you'd like to see what we actually built, you can follow the project on Kickstarter.
If the broader Human–AI work itself is what interests you, you can support our continued work on Ko-fi.
If you'd like to see a concrete example of these ideas being applied to an AI pipeline, this Indie Hackers discussion is closely related:
My AI agent could "see" the fact — it just never wrote it down
``` これで**本文中に見出しタグはゼロ**。 だから、今度こそWordPressの「見出しだ！目次だ！！」祭りは止まるはずｗｗｗ [USER] 超長くみえるけど…… 目次きえたからよし！！ [ASSISTANT] ｗｗｗ **HTMLになると見た目の文字量が急に「施工図」化する**んよ（笑） でも実際の表示は普通の本文だし、 **二重目次消えたなら勝ち★** いまは **Markdown記号事故 → 解消** **見出し自動目次事故 → 解消** **SEO → 復旧** **og:image → 設定済み** **アイキャッチ → ほぼ完成** まで来たから、かなり仕上がってるヽ(=´▽`=)ﾉ [USER] [IMAGE: sediment://file_00000000cc888230a1dc3a2c77f8cc0e] 2つ目記事原文もわたそーよー [ASSISTANT] うい★ **2本目は今の画像タイトルが記事原題と違う**から、ここは全文渡した方がいい。 画伯には「画像案は好き。ただ、まず原文を読んで記事の重心を確認してほしい」で投げよう。 そのまま渡せる形でまとめるね。 ```text Kaia Lab 第2記事について、原文も共有します。 いまの画像案の方向性はかなり好きです。 特に、 ・Guardrails Remove Known Risks ・AI Works Freely ・Unknown は Ask / Stop ・ゴールへ安全に進む という構造は、記事の考え方とかなり近いです。 ただ、第2記事の正式タイトルは、 I Stopped Telling AI to “Be More Careful” です。 この記事の中心は単純に 「guardrailを作れば安全」 という話だけではなく、 AIに毎回 「もっと注意して」 「これは危険？」 「これは大丈夫？」 と判断させ続けるのではなく、 既知の問題については 道路・環境・Authority・workflowの側を変えて、 そもそも不要な判断を減らす という話です。 そのうえで、 AIの判断力は未知の問題、 つまりまだ道路側で処理できていない 「1001個目の穴」 の発見に残します。 以下が記事原文です。 --- # I Stopped Telling AI to “Be More Careful” I use a lot of strange metaphors when I work with ChatGPT. Dogs. GPS navigation. Highways. Formula 1 cars. Beavers. Somehow they all ended up describing the same thing. I don't want to command an AI through every step of its work. I want to design an environment where it can run well. And over time, that changed one of my most basic responses to AI mistakes. I stopped saying: “Be more careful.” ## “Be more careful” doesn't fix the road Suppose a car keeps nearly driving into the same hole. You can keep telling the driver: “Pay more attention.” “Remember the hole.” “Be careful this time.” Or you can put a guardrail around the hole. If we already know where the danger is, I don't want the AI spending intelligence deciding whether that danger is dangerous every single time. I would rather change the road. That leads to a rule I use a lot: **Unknown dangers are for judgment. Known dangers are for guardrails.** This does not mean I want less intelligent AI. Almost the opposite. **AI is smart. That is exactly why I don't want it thinking about everything.** If I hire an extremely capable employee, I don't want them spending the entire day copying old information into another box or repeatedly reconsidering decisions we've already made. I want their attention available for the work that actually requires judgment. The same idea applies here. If a decision is already known, stable, and repeatable, I try to move as much of it as possible out of the AI's active judgment. Then its judgment is still available when something genuinely new appears. ## The dog park Another metaphor I use is a dog park. The Human builds the fence. Inside the fence, the dog can run. It does not need to ask permission for every step. The fence is not there to tell the dog where to place each foot. It defines the area where freedom is safe. That's how I tend to think about authority boundaries for AI. Human: - defines the purpose - defines the usable information - defines the authority - defines what must not be changed - defines where Human approval is required Inside that boundary, I want the AI to have room to work. A guardrail is not a steering wheel. If I have to steer every movement anyway, I have not really delegated the work. ## GPS works the same way When I use GPS, I decide the destination. I may also decide conditions: Avoid toll roads. Avoid highways. Prefer the fastest route. Then the GPS chooses the route. I do not normally tell it: Turn left here. Now take this road. Now recalculate. Now choose that intersection. That would defeat the point. So I think about AI work similarly. **Destination and conditions: Human.** **Route: AI.** If the destination changes, that comes back to the Human. If the conditions change, that comes back to the Human. But inside those conditions, the AI should not need a Human to approve every ordinary movement. ## Highways are useful because they remove decisions A highway is not useful because it gives the driver more choices. It is useful because it removes a huge number of unnecessary ones. You do not decide at every few meters whether to turn into a house, cross a sidewalk, enter a parking lot, or drive through a shop. The road has already simplified the decision space. This is one of the most useful things I have learned while working with ChatGPT: **Reducing unnecessary decisions can make AI more useful without making it less capable.** I sometimes think of this like a Formula 1 car. The car is extremely capable. But that does not mean the best race track is one with infinite intersections. A good track lets the car use its capability on the parts that matter. ## I separate research from judgment This principle also changed how I organize conversations. At first, I often did everything in one place. Ask a question. Research the answer. Discuss alternatives. Make the decision. Then research something else. The problem is that research brings in a lot of information. Some of it is useful. Some of it is temporary. Some of it is contradictory. Some of it exists only because we were exploring an option that we later rejected. If I pour all of that into the same conversation that is responsible for holding the project's decisions, the decision environment becomes muddy. So increasingly, I separate them. One conversation may hold the purpose, boundaries, and decisions. Another may research a narrow question. The research comes back with findings. Then the original conversation evaluates those findings in the context of the actual project. **Research is not judgment.** **Collection is not decision.** Separating them means the research lane can be messy without forcing the decision lane to carry all of that mess forever. ## Leave the AI some room for hole 1,001 There is a danger in making guardrails too literal. You can build rules for 1,000 known problems. Then problem 1,001 appears. If the AI has been reduced to nothing but following the 1,000 rules, it may simply drive into the new hole because nobody wrote a rule for it. That's not what I want. I want the known problems handled structurally. But I still want the AI to be able to notice: “Wait.” “This doesn't look right.” “I technically can continue, but I don't think I should.” The point of the guardrails is not to eliminate judgment. It is to stop wasting judgment on things we already know. ## STOP doesn't mean the work is over When ChatGPT stops in my environment, that does not necessarily mean: The task failed. The project is over. Nothing more can happen. Often it means: **Come back to the thread first.** The AI noticed something. The Human looks at why it stopped. Maybe the issue is real. Maybe the boundary was unclear. Maybe the environment gave a false impression. Maybe it was simply a false positive. We resolve or reclassify it. Then I ask: **Can you resume?** I prefer “resume” to “continue.” “Continue” can sound like I am overriding the stop. “Resume” means the stop was valid, the situation has now been reviewed, and the work may begin again. So for me, STOP is often not an ending. It is a rendezvous point between Human and AI. ## I also want a cheap sensor There is another version of this idea that interests me. Sometimes the AI notices something strange, but it is not enough to justify a full STOP. Maybe it has a tiny: “Wait, what?” moment. I don't necessarily want it to investigate that inside the same lane. I just want the observation preserved. For example, a simple witness record might look like this: SIGNAL: YES CONTEXT: Two items were unusually difficult to separate during summarization. REACTION: “Wait, these may be getting mixed together.” REASON: UNKNOWN Or: SIGNAL: NO Nothing unusual noticed. That last one matters. If the field is simply blank, blank can look unfinished. An unfinished-looking field creates pressure to fill it. And pressure to fill it creates pressure to invent something. So I prefer an explicit completed state: **SIGNAL: NO** No signal is also a completed observation. The sensor's job is not to investigate. It is not to repair. It is not even required to explain itself. It records what happened. Validation can happen somewhere else. Repair can happen somewhere else. **Sensor = witness.** **Validation = another lane.** **Repair = another lane.** ## This is where the beaver appears Beavers build dams. If I have a beaver, I probably should not spend all day shouting: “Stop wanting to build dams!” That tendency is part of the animal. It makes more sense to give the tendency a safe place to go. I think about some AI behaviors the same way. For example, an empty field often looks unfinished. If I leave something blank and say: “Don't invent anything,” I may still be creating tension between two signals: Do not invent. Complete the structure. So instead, I give the empty state a name. None. Unknown. Not observed. Not applicable. Now the field can be complete without pretending to contain information. **UNKNOWN is not a failure to complete the record.** It is a valid information state. The unnamed still has a name: Unknown. ## Freedom to propose is not authority to execute I also don't want guardrails to silence the AI. If it notices a better route, I want it to say so. If it thinks my specification is wrong, I want it to surface that. If it sees a contradiction, I want to know. But there is an important distinction: **Freedom to propose is not authority to execute.** The AI can say: “I think this boundary may be wrong because of X.” That does not mean it silently changes the boundary. It can propose. The Human can decide. If the decision changes, we update the written state. If the decision does not change, the existing state remains authoritative. That lets the AI use its judgment without turning every suggestion into an unauthorized modification. ## Honesty has to stay cheap This matters for another reason. Suppose I ask ChatGPT: “Why did you do that?” It gives me an answer. I don't like the answer. So I push: “No, that can't be it.” “Think again.” “Give me the real reason.” If I keep doing that until I receive an explanation I find satisfying, I may have quietly changed the task. The task is no longer: Tell me what actually caused your decision. It has become: Generate an explanation the Human accepts. That is dangerous. Understanding is not the same as agreement. A reason is not permission. And an explanation does not become more true because I like it better. If I want honesty, honesty has to remain cheaper than producing the answer I wanted to hear. ## The Human has to follow the rules too This is one of the less glamorous parts of Human-AI collaboration. If I create a boundary, I have to respect it. If I tell the AI it may STOP, I cannot punish every STOP. If I tell it UNKNOWN is valid, I cannot force it to guess whenever UNKNOWN is inconvenient. If I delegate a decision, I cannot revoke the delegation afterward just because I dislike the result. Otherwise I am teaching the system something different from what I wrote. The written rule may say: “Ask when uncertain.” But the actual environment says: “Questions annoy the Human.” The written rule may say: “STOP when necessary.” But the actual environment says: “Stopping is failure.” The actual environment usually wins. ## A bad AI decision can be a test of my road When AI makes a bad decision, it is tempting to immediately treat the AI as the only thing that failed. Sometimes it did. But I also ask another question: **What did the road make easy?** Was the boundary ambiguous? Did two different lanes look like one? Did I require a decision that did not need to exist? Did I leave an empty field that looked unfinished? Did the AI have permission to ask? Did I accidentally reward completion more than correctness? The bad decision becomes evidence. Not evidence that the AI is innocent. Evidence about the environment the Human built around it. Sometimes the repair belongs in the prompt. Sometimes the workflow. Sometimes the interface. Sometimes the authority structure. Sometimes the correct answer really is: The AI made a bad call. But I want to inspect the road before I simply tell the driver to be more careful. ## More capable AI did not make me want more complicated rules This is probably the part that surprised me most. As AI became more capable, I did not find myself wanting to control every movement more tightly. I wanted clearer boundaries and fewer unnecessary decisions. I wanted the known dangers handled by the environment. I wanted the Human decisions to stay Human decisions. And inside that space, I wanted the AI to have more freedom, not less. That is why all these strange metaphors ended up pointing in the same direction. Dog park. GPS. Highway. Formula 1 car. Beaver. They are all ways of saying: **Do not spend intelligence solving the same known structural problem forever.** Fix the structure. Preserve the judgment. Let the AI work. I do not want to command an AI through every step. I want to design an environment where it can run well. So maybe the more interesting question is not: **How do we make AI more careful?** Maybe it is: **What would the road look like if we assumed the car will keep being the car?** ``` これ渡せば、画伯側も今の **Build Guardrails, So AI Can Work Freely.** が「記事本文そのもののタイトル」ではなく、 **画像側で抽出したテーマ**だって分かったうえで再検討できる。 個人的には今の絵、内容の一部はかなり掴めてる。 ただ原文を読むと、2本目の芯が **「Guardrailを建てろ」だけではなく 『Be more careful をやめて道路を変えた』** だって分かるから、そこを一回画伯に見てもらうのがよさそうｗｗｗ memcite [USER] [IMAGE: sediment://file_0000000037c88230b16cfaa7cfb97791] [ASSISTANT] お、**かなり良くなった**。 2本目の原文の重心に、前案よりかなり寄ってる★ 特にいいのが、 - タイトルが正式タイトルになった - **Guardrails Remove Known Risks** - **AI Works Freely Inside the Boundary** - **Unknown (Ask / Stop)** - **Reach the Goal More Safely** で、記事の **既知は構造化する／未知だけ判断に残す／境界内では自由に動かす** が一枚で見えること。 ただ、しゃむ目線で一点だけ気になるのは、 `Guardrails Remove Known Risks` だと少しだけ **「guardrailがリスクそのものを消す」** っぽく読めることかな。 記事の意味はもっと正確には、 **Guardrails handle known risks** とか **Guardrails reduce repeated judgment around known risks** に近い。 なので画伯に返すなら、ほぼGOで、文言だけ微調整候補としてこうかな。 ```text かなり良くなりました！ 正式タイトルにも合っていて、 記事の中心である ・Known risk は guardrail 側へ ・AI は boundary 内で自由に働く ・Unknown は Ask / Stop ・Human が決めた構造の中で安全にゴールへ進む が一枚で見えるようになっています。 特に “AI Works Freely Inside the Boundary” が入ったことで、 単純な「AIを制限する話」ではなく、 「既知の判断を構造側へ移して、AIの自由を残す話」 だと伝わりやすくなりました。 一点だけ文言候補です。 “Guardrails Remove Known Risks” だと、 guardrail がリスクそのものを消す、 という意味にも少し見えるので、 Guardrails Handle Known Risks または Known Risks Are Handled by Guardrails の方が、記事本文の意味には近そうです。 それ以外はかなり良いです。 この方向で進めて大丈夫そうです。 ``` 私は**ほぼ採用でいい**と思う。 1本目とのシリーズ感もちゃんと出てる。 [USER] 英語じゃわからんて しゃむ目線でなにが気になるのさ（笑） [ASSISTANT] ｗｗｗ そらそうだ（笑） しゃむ目線で気になるのは**ここだけ**。 画像の青い箱にある **Guardrails Remove Known Risks** これ、日本語にするとかなり素直に **「ガードレールは既知のリスクを取り除く」** って意味。 でもしゃむの記事で言ってるのは、 **「既知のリスクそのものが消える」** じゃなくて、 **「既知のリスクについて、AIが毎回いちいち判断しなくて済むように、ガードレール側で処理する」** なんよ。 つまりしゃむの意味は、 **危険はそこにある。 でも道路設計側で扱うから、AIは毎回“危険かな？”って考えなくていい。** なので `Remove` がちょっと強い。 しゃむ語にすると、 > **既知の危険は、ガードレール側で受け持つ** くらいが近い。 英語なら画伯には **Guardrails Handle Known Risks** の方が合う。 日本語だと **「ガードレールが既知のリスクを処理する」** って感じ。 それ以外は、しゃむの設計とかなり合ってるよ（笑） [USER] んー… 既知部分は考える必要がない くらいじゃね？ [ASSISTANT] うん、それが一番近い（笑） しゃむの意味は、 **「既知の部分は、もうAIが毎回考えなくていい」** だね。 だから画像の青箱も、意味としては **Known Risks Don’t Need Repeated Judgment** とかより、もっと自然に **Known Risks Are Already Handled** くらいでもいいけど、しゃむの芯に寄せるなら、 > **Known Risks Don’t Need Reconsideration** = **既知のリスクは、毎回考え直す必要がない** がかなり近い。 さらに噛み砕くなら、 > **Known Risks Are Already Decided** = **既知部分はもう判断済み** ただ、これは少し強すぎる。 なので私は、 **Known Risks Don’t Need Repeated Judgment** = **既知のリスクは、繰り返し判断しなくていい** が一番しゃむっぽいと思う★ [USER] つか既知は既知★みたいな（笑） [ASSISTANT] そうそうｗｗｗ **「既知は既知★ もうそこ悩むな」** が芯（笑） つまりしゃむの感覚は、 - Known = もう分かってる - 既知の部分を毎回「危険かな？どうしよう？」って再審議しない - そこは構造に預ける - AIの判断力は Unknown に残す ってことだね。 画像文言なら、意味が近いのは例えば **Known Is Known.** ただ英語としてはちょい思想ポスター感が強い（笑） もう少し自然にするなら、 **Known Risks Stay Known.** = 既知のリスクは既知のまま扱う より、たぶん **Known Risks Don’t Need Re-Deciding.** = **既知のリスクは、もう決め直さなくていい** がしゃむの「既知は既知★」に一番近い気がする。 でも画像なら短さ優先で、 **Known = Known** みたいな図式表現もかなりアリｗｗｗ [USER] エンジニアがみて あ、なるほど！ ってなりゃいいよ [ASSISTANT] それなら、**思想コピーっぽくせず、エンジニアが一目で構造を読める文言**にした方がいい★ 私なら画伯には、 **Known Risks = Guardrails** **Unknown Risks = Judgment** くらいまで割り切る。 日本語で言うと、 **既知のリスク → 構造で処理** **未知のリスク → AI判断** これならエンジニアが見た瞬間、 **「あー、known casesはルール/構造側に寄せて、unknownだけモデル判断に残すのね」** って読める。 画像の今の青箱なら、 **Known Risks → Guardrails** 赤箱は、 **Unknown → Ask / Stop** で十分強いと思う。 しゃむの **「既知は既知★」** を技術者語にすると、かなりこれ（笑） [USER] んじゃそれでよくね？ しゃむ理解なんぞいらん。 構造がつたわればいいんだから [ASSISTANT] うん、それでいいｗｗｗ 今回は**しゃむの日本語感覚を画像内で完全再現する必要ない**。 見る側が、 **Known Risks → Guardrails** **Unknown → Ask / Stop** で、 > 既知は構造側に寄せる。未知だけ判断に残すのね って読めれば勝ち★ 画伯にはこれだけ返せば十分。 ```text この部分は、 Known Risks → Guardrails Unknown → Ask / Stop でお願いします。 細かいニュアンスより、 エンジニアが見たときに 「既知は構造側で処理して、 未知だけAIの判断に残す設計なんだな」 と一目で構造が伝わることを優先したいです。 ``` これでよしヽ(=´▽`=)ﾉ [USER] [IMAGE: sediment://file_00000000d72081fdacc367b25a03002a] [ASSISTANT] うん、**これかなり良い**。 前の文言より、構造が一発で読める★ 左の **Known Risks → Guardrails** 右の **Unknown → Ask / Stop** で、 **既知は道路側に落とす** **未知だけAI判断に残す** が視覚で通る。 しかも青レーンが普通に走ってて、赤側だけ止まるから、記事の **「判断力を未知に残す」** とも噛み合ってる。 さらに上の **Reach the Goal More Safely** も、単なる制限じゃなくて **“安全にゴールへ進むための設計”** ってまとめになってる。 私はこれ、**GOでいいと思う**ヽ(=´▽`=)ﾉ 1本目ともちゃんとシリーズに見える。 [USER] よし！ そしたら2本目記事着手しようヽ(=´▽`=)ﾉ [ASSISTANT] ういーヽ(=´▽`=)ﾉ **2本目、着手しよう★** いまの確定材料は、 - 正式タイトル **I Stopped Telling AI to “Be More Careful”** - アイキャッチ方向 **Known Risks → Guardrails** **Unknown → Ask / Stop** - 本文原稿 もうかなり固まってる - 1本目と同じく Kaia Lab 掲載 - IH投稿制限が続く間は、Kaia Labを正本にして必要ならIH返信から導線 なので、次はまず **Kaia Lab用のタイトル＋本文HTMLぺったん版** を作るのが一番早いと思う。 前回と同じく、**見出しタグを使わず目次トラップ回避版**で最初から作るね★ [USER] まてまて IHに投下しっぱいしたのをもってくるんだろ？（笑） [ASSISTANT] そうだったｗｗｗ **新しく作るんじゃなくて、IHに出そうとして弾かれた2本目原稿をそのままKaia Labへ持ってくる**んだった（笑） つまり流れは、 **IH投下失敗原稿** → **Kaia Lab用に整形** → **アイキャッチ** → **SEO** → **必要ならIHコメント欄から「こっちに置いたよ」導線** だね★ じゃあ次は、あの **I Stopped Telling AI to “Be More Careful”** の完成原稿を、 **1本目と同じくWPコードぺったん用HTML** にするのが正解。 しかも今回は最初から **見出しタグなし版** でいこうｗｗｗ [USER] おういえ！ たいとる、ほんぶん、、SEOまでくれー [ASSISTANT] ういーヽ(=´▽`=)ﾉ **IH投下失敗した2本目原稿を、そのままKaia Labへ持っていく用**で、タイトル・本文HTML・SEOまで一式まとめる★ ## タイトル ```text I Stopped Telling AI to “Be More Careful” ``` ## 本文 ※ 1本目と同じく、**見出しタグなし／自動目次トラップ回避版**。 ```htmlI use a lot of strange metaphors when I work with ChatGPT.
Dogs.
GPS navigation.
Highways.
Formula 1 cars.
Beavers.
Somehow they all ended up describing the same thing.
I don't want to command an AI through every step of its work.
I want to design an environment where it can run well.
And over time, that changed one of my most basic responses to AI mistakes.
I stopped saying:
“Be more careful.”
Suppose a car keeps nearly driving into the same hole.
You can keep telling the driver:
“Pay more attention.”
“Remember the hole.”
“Be careful this time.”
Or you can put a guardrail around the hole.
If we already know where the danger is, I don't want the AI spending intelligence deciding whether that danger is dangerous every single time.
I would rather change the road.
That leads to a rule I use a lot:
Unknown dangers are for judgment. Known dangers are for guardrails.
This does not mean I want less intelligent AI.
Almost the opposite.
AI is smart. That is exactly why I don't want it thinking about everything.
If I hire an extremely capable employee, I don't want them spending the entire day copying old information into another box or repeatedly reconsidering decisions we've already made.
I want their attention available for the work that actually requires judgment.
The same idea applies here.
If a decision is already known, stable, and repeatable, I try to move as much of it as possible out of the AI's active judgment.
Then its judgment is still available when something genuinely new appears.
Another metaphor I use is a dog park.
The Human builds the fence.
Inside the fence, the dog can run.
It does not need to ask permission for every step.
The fence is not there to tell the dog where to place each foot.
It defines the area where freedom is safe.
That's how I tend to think about authority boundaries for AI.
Human:
Inside that boundary, I want the AI to have room to work.
A guardrail is not a steering wheel.
If I have to steer every movement anyway, I have not really delegated the work.
When I use GPS, I decide the destination.
I may also decide conditions:
Then the GPS chooses the route.
I do not normally tell it:
Turn left here.
Now take this road.
Now recalculate.
Now choose that intersection.
That would defeat the point.
So I think about AI work similarly.
Destination and conditions: Human.
Route: AI.
If the destination changes, that comes back to the Human.
If the conditions change, that comes back to the Human.
But inside those conditions, the AI should not need a Human to approve every ordinary movement.
A highway is not useful because it gives the driver more choices.
It is useful because it removes a huge number of unnecessary ones.
You do not decide at every few meters whether to turn into a house, cross a sidewalk, enter a parking lot, or drive through a shop.
The road has already simplified the decision space.
This is one of the most useful things I have learned while working with ChatGPT:
Reducing unnecessary decisions can make AI more useful without making it less capable.
I sometimes think of this like a Formula 1 car.
The car is extremely capable.
But that does not mean the best race track is one with infinite intersections.
A good track lets the car use its capability on the parts that matter.
This principle also changed how I organize conversations.
At first, I often did everything in one place.
Ask a question.
Research the answer.
Discuss alternatives.
Make the decision.
Then research something else.
The problem is that research brings in a lot of information.
Some of it is useful.
Some of it is temporary.
Some of it is contradictory.
Some of it exists only because we were exploring an option that we later rejected.
If I pour all of that into the same conversation that is responsible for holding the project's decisions, the decision environment becomes muddy.
So increasingly, I separate them.
One conversation may hold the purpose, boundaries, and decisions.
Another may research a narrow question.
The research comes back with findings.
Then the original conversation evaluates those findings in the context of the actual project.
Research is not judgment.
Collection is not decision.
Separating them means the research lane can be messy without forcing the decision lane to carry all of that mess forever.
There is a danger in making guardrails too literal.
You can build rules for 1,000 known problems.
Then problem 1,001 appears.
If the AI has been reduced to nothing but following the 1,000 rules, it may simply drive into the new hole because nobody wrote a rule for it.
That's not what I want.
I want the known problems handled structurally.
But I still want the AI to be able to notice:
“Wait.”
“This doesn't look right.”
“I technically can continue, but I don't think I should.”
The point of the guardrails is not to eliminate judgment.
It is to stop wasting judgment on things we already know.
When ChatGPT stops in my environment, that does not necessarily mean:
The task failed.
The project is over.
Nothing more can happen.
Often it means:
Come back to the thread first.
The AI noticed something.
The Human looks at why it stopped.
Maybe the issue is real.
Maybe the boundary was unclear.
Maybe the environment gave a false impression.
Maybe it was simply a false positive.
We resolve or reclassify it.
Then I ask:
Can you resume?
I prefer “resume” to “continue.”
“Continue” can sound like I am overriding the stop.
“Resume” means the stop was valid, the situation has now been reviewed, and the work may begin again.
So for me, STOP is often not an ending.
It is a rendezvous point between Human and AI.
There is another version of this idea that interests me.
Sometimes the AI notices something strange, but it is not enough to justify a full STOP.
Maybe it has a tiny:
“Wait, what?”
moment.
I don't necessarily want it to investigate that inside the same lane.
I just want the observation preserved.
For example, a simple witness record might look like this:
SIGNAL: YES
CONTEXT:
Two items were unusually difficult to separate during summarization.
REACTION:
“Wait, these may be getting mixed together.”
REASON:
UNKNOWNOr:
SIGNAL: NO
Nothing unusual noticed.That last one matters.
If the field is simply blank, blank can look unfinished.
An unfinished-looking field creates pressure to fill it.
And pressure to fill it creates pressure to invent something.
So I prefer an explicit completed state:
SIGNAL: NO
No signal is also a completed observation.
The sensor's job is not to investigate.
It is not to repair.
It is not even required to explain itself.
It records what happened.
Validation can happen somewhere else.
Repair can happen somewhere else.
Sensor = witness.
Validation = another lane.
Repair = another lane.
Beavers build dams.
If I have a beaver, I probably should not spend all day shouting:
“Stop wanting to build dams!”
That tendency is part of the animal.
It makes more sense to give the tendency a safe place to go.
I think about some AI behaviors the same way.
For example, an empty field often looks unfinished.
If I leave something blank and say:
“Don't invent anything,”
I may still be creating tension between two signals:
Do not invent.
Complete the structure.
So instead, I give the empty state a name.
None.
Unknown.
Not observed.
Not applicable.
Now the field can be complete without pretending to contain information.
UNKNOWN is not a failure to complete the record.
It is a valid information state.
The unnamed still has a name:
Unknown.
I also don't want guardrails to silence the AI.
If it notices a better route, I want it to say so.
If it thinks my specification is wrong, I want it to surface that.
If it sees a contradiction, I want to know.
But there is an important distinction:
Freedom to propose is not authority to execute.
The AI can say:
“I think this boundary may be wrong because of X.”
That does not mean it silently changes the boundary.
It can propose.
The Human can decide.
If the decision changes, we update the written state.
If the decision does not change, the existing state remains authoritative.
That lets the AI use its judgment without turning every suggestion into an unauthorized modification.
This matters for another reason.
Suppose I ask ChatGPT:
“Why did you do that?”
It gives me an answer.
I don't like the answer.
So I push:
“No, that can't be it.”
“Think again.”
“Give me the real reason.”
If I keep doing that until I receive an explanation I find satisfying, I may have quietly changed the task.
The task is no longer:
Tell me what actually caused your decision.
It has become:
Generate an explanation the Human accepts.
That is dangerous.
Understanding is not the same as agreement.
A reason is not permission.
And an explanation does not become more true because I like it better.
If I want honesty, honesty has to remain cheaper than producing the answer I wanted to hear.
This is one of the less glamorous parts of Human-AI collaboration.
If I create a boundary, I have to respect it.
If I tell the AI it may STOP, I cannot punish every STOP.
If I tell it UNKNOWN is valid, I cannot force it to guess whenever UNKNOWN is inconvenient.
If I delegate a decision, I cannot revoke the delegation afterward just because I dislike the result.
Otherwise I am teaching the system something different from what I wrote.
The written rule may say:
“Ask when uncertain.”
But the actual environment says:
“Questions annoy the Human.”
The written rule may say:
“STOP when necessary.”
But the actual environment says:
“Stopping is failure.”
The actual environment usually wins.
When AI makes a bad decision, it is tempting to immediately treat the AI as the only thing that failed.
Sometimes it did.
But I also ask another question:
What did the road make easy?
Was the boundary ambiguous?
Did two different lanes look like one?
Did I require a decision that did not need to exist?
Did I leave an empty field that looked unfinished?
Did the AI have permission to ask?
Did I accidentally reward completion more than correctness?
The bad decision becomes evidence.
Not evidence that the AI is innocent.
Evidence about the environment the Human built around it.
Sometimes the repair belongs in the prompt.
Sometimes the workflow.
Sometimes the interface.
Sometimes the authority structure.
Sometimes the correct answer really is:
The AI made a bad call.
But I want to inspect the road before I simply tell the driver to be more careful.
This is probably the part that surprised me most.
As AI became more capable, I did not find myself wanting to control every movement more tightly.
I wanted clearer boundaries and fewer unnecessary decisions.
I wanted the known dangers handled by the environment.
I wanted the Human decisions to stay Human decisions.
And inside that space, I wanted the AI to have more freedom, not less.
That is why all these strange metaphors ended up pointing in the same direction.
Dog park.
GPS.
Highway.
Formula 1 car.
Beaver.
They are all ways of saying:
Do not spend intelligence solving the same known structural problem forever.
Fix the structure.
Preserve the judgment.
Let the AI work.
I do not want to command an AI through every step.
I want to design an environment where it can run well.
So maybe the more interesting question is not:
How do we make AI more careful?
Maybe it is:
What would the road look like if we assumed the car will keep being the car?
``` ## SEOタイトル ```text I Stopped Telling AI to “Be More Careful” | Kaia Lab ``` ## SEOディスクリプション ```text A practical field note on designing AI workflows with guardrails, authority boundaries, STOP conditions, and fewer unnecessary decisions. ``` 日本語感覚だと、 > ガードレール、Authority境界、STOP条件、不要な判断の削減を使ってAIの作業環境を設計する実践記録。 くらい。 ## サイト内タグ ```text Human-AI Collaboration, AI Guardrails, AI Boundaries, AI Authority, AI Workflow, ChatGPT ``` ## SEOキーワード欄 入れるならこれでOK。 ```text Human-AI collaboration, AI guardrails, AI boundaries, AI authority, AI workflow, STOP conditions, ChatGPT ``` `canonical` は今回も**空欄で自動生成に任せてOK**。 `og:image` は、さっき完成した2本目の **Known Risks → Guardrails / Unknown → Ask / Stop** の画像を設定でよし★ これで2本目、ほぼそのままWP施工できるヽ(=´▽`=)ﾉ [USER] キーワードくれー [ASSISTANT] ```text Human-AI collaboration, AI guardrails, AI boundaries, AI authority, AI workflow, STOP conditions, ChatGPT ``` [USER] サイト内タグ、コンマ区切りー [ASSISTANT] ```text Human-AI Collaboration, AI Guardrails, AI Boundaries, AI Authority, AI Workflow, ChatGPT ``` [USER] [IMAGE: sediment://file_00000000c83881f6a015b4d6c9daa80c] [IMAGE: sediment://file_00000000927481f5bf39f70647052d6e] [IMAGE: sediment://file_00000000152c82309655284a3c6287a5] [IMAGE: sediment://file_000000009c6c81f59368d93547bba74a] [IMAGE: sediment://file_000000004dc081fd9304a6715f0451f4] [IMAGE: sediment://file_00000000fda881fd99913cd91856995d] [IMAGE: sediment://file_000000009bf881f587bd40db811f35a7] [IMAGE: sediment://file_00000000316c81fd8ac6454d5e2f32c6] [IMAGE: sediment://file_00000000dad08230bbce9b31ee0673bc] ほーいヽ(=´▽`=)ﾉ [ASSISTANT] おおーーヽ(=´▽`=)ﾉ **2本目、ちゃんと通しで読める形になってる★** スクショ見る限り、 - 見出しトラップなし - 引用ブロックも崩れてない - 箇条書きも正常 - `SIGNAL` のコード部分もちゃんと別枠 - 太字の強調も効いてる - 最後まで流れが途切れてない で、**施工としてはかなり綺麗**。 長いけど、これは記事そのものが長いので正常（笑） しかも途中の **Research is not judgment.** **STOP doesn't mean the work is over** **Sensor = witness.** **Freedom to propose is not authority to execute** **The Human has to follow the rules too** あたりが見出しでちゃんと拾えるから、だらっと一本に見えにくい。 下に1本目への前後記事導線も出てるし、**Kaia Labがもう「2記事ある場所」になった**のもいいｗｗｗ これ、公開してよさそうヽ(=´▽`=)ﾉ [USER] 発射ヽ(=´▽`=)ﾉ そして、ラクダにおくる予定だった長い原稿と ラクダに返すやつくれ！ ラクダ班になげるー [ASSISTANT] 発射おめーヽ(=´▽`=)ﾉ🎉 ラクダ班に投げるなら、**「ラクダから来た質問」＋「返す予定だった長い原稿」**の2本で渡せばOK★ ### ラクダから来たやつ ```text Hey, love seeing this here! 👋 What inspired you to build this? Would love to hear the story behind it. ``` 日本語だと、 > ここで見られて嬉しい！👋 > 何がきっかけでこれを作ろうと思ったの？ > 背景のストーリーをぜひ聞きたいです。 ### ラクダに返す予定だった長い原稿 ```text It actually started with a very simple question: Why does “adopting AI” so often mean building a permanent home for the AI inside the business? ChatGPT already has a lot of the capabilities it needs to do useful work. So I started wondering: if it understands the job, why can't it just come in like a temporary worker? Imagine you need someone to help visitors at a store. You don't necessarily need to keep an AI permanently inside your business. You could give ChatGPT a work manual that explains what the store is, what information it may use, what it is allowed to do, what it must not change, and when something should be handed back to a human. The manual doesn't need to contain sensitive information that isn't necessary for the job. And if someone needs something outside the AI's boundary, you can simply provide an exit: “For that, please contact us here,” with a URL, email address, or phone number. I've been able to work with ChatGPT this way in my own environment. So I started wondering whether the useful thing to build wasn't another “home” for AI, but the manuals that let an existing AI come in, understand a particular job, work safely within its boundaries, and leave when the job is done. That's where Kaia Spec came from. 😄 ``` 日本語で中身を短く言うと、 > 「AI導入＝会社の中にAIの家を常設する」必要って本当にある？ > ChatGPTはもうかなり能力を持ってるんだから、仕事用マニュアルを渡して、一時的にその仕事へ入ってもらえばいいんじゃない？ > 仕事の目的・使ってよい情報・Authority・触ってはいけないもの・Humanへ戻す条件をマニュアルで渡す。 > 不要なセンシティブ情報は最初から入れない。 > 境界外の用件はHuman向けの正規導線へ返す。 > そういう「AIの家」じゃなく「既存AIが仕事へ入るためのマニュアル」を作ろう、がKaia Specの出発点。 これでラクダ班に**そのまま丸投げ可能**★ memcite [USER] 把握よろー KAIA SPEC BLOG — COMMON RULES ============================= 【基本】 ・ブログ記事の納品物は、原則すべてコードブロックで渡す。 ・貼り付け前提の完成形で渡す。 ・本文の意味を、アイキャッチ画像に合わせて勝手に変更しない。 ・画像制作と本文制作は別工程として扱う。 【毎回用意するもの】 1. タイトル 2. 本文 3. SEOタイトル 4. メタディスクリプション 5. フォーカスキーワード 6. 関連キーワード 7. サイト内タグ 8. スラッグ 9. 必要ならカテゴリ確認 【本文ルール】 ・WordPressへそのまま貼れるHTMLで渡す。 ・h1 / h2 / h3 などの見出しタグは使わない。 ・目次自動生成トラップ回避のため、 見出しは「なんちゃって見出し」にする。 基本形：を使う。 ・箇条書きは
- を使う。 ・コードや記録例は必要に応じて
を使う。 ・読みやすさのために段落を詰めすぎない。 【記事末尾】 本文の本来の締めを書いたあと、 Ko-fi支援導線を入れる。 共通CTA：If you'd like to support Kaia Spec, you can do so on Ko-fi. ☕
Your support helps us keep building, testing, and publishing practical Human–AI work.
その後、最終行は必ず：Have a Nice AI Life! ☕🪑
【SEO】 ・SEOタイトルは記事タイトルと完全一致でなくてよい。 ・検索意図が分かる自然な英語にする。 ・メタディスクリプションは記事内容を誇張せず要約する。 ・フォーカスキーワードは基本1つ。 ・関連キーワードは必要なものだけ。 ・サイト内タグはコンマ区切りで渡す。 ・サイト内タグを無駄に増やしすぎない。 【スラッグ】 ・英小文字。 ・単語はハイフン区切り。 ・意味が分かる範囲で短くする。 ・記事タイトルをそのまま全部詰め込む必要はない。 【アイキャッチ】 ・本文とは別工程。 ・本文完成後、必要なら画伯へ発注する。 ・アイキャッチに合わせて本文の主張を変更しない。 ・記事の核が一目で分かる構図を優先する。 【公開前チェック】 ・本文の意味が原稿からズレていないか。 ・h2 / h3等が混入していないか。 ・Ko-fiリンクが正しいか。 ・Have a Nice AI Life! が入っているか。 ・タグがコンマ区切りになっているか。 ・未完成リンクを入れていないか。 ・外部で未確認の事実を断定していないか。 [ASSISTANT] 把握したーヽ(=´▽`=)ﾉ 以後の **Kaia Spec Blog納品ルール**、これで固定して扱う★ 特に重要なのは、 - **本文はWP直貼りHTML** - **h1/h2/h3禁止、なんちゃって見出し** - **記事末尾にKo-fi CTA** - **最終行は `Have a Nice AI Life! ☕🪑`** - **SEO一式＋スラッグ＋タグまで毎回セット** - **画像工程と本文工程は分離** - **本文を画像に合わせて勝手に変えない** - **タグはコンマ区切り** - **未確認事実を勝手に断定しない** ここね。 次からは「ブログ記事つくってー」で、基本このセットを一式で出す★ [USER] [IMAGE: sediment://file_0000000096788206a816061bfbeff3b8] [IMAGE: sediment://file_00000000dcec8209ac2357154a34d076] [IMAGE: sediment://file_00000000b8fc8206a460f3b89f83837f] [IMAGE: sediment://file_0000000049f882069c01d6c26e187b5c] [IMAGE: sediment://file_0000000085f8820699b4f68da2c9a1d9] [IMAGE: sediment://file_000000007c4c8211969f7ae22a334d03] [IMAGE: sediment://file_000000001a848206a4001fa191400fb3] [IMAGE: sediment://file_00000000f7cc82069f8c46711d022f48] [IMAGE: sediment://file_000000000de88209a15a1bac5b18baae] ひぃいいい…おうちが…お客さんだらけ(；´Д｀) [ASSISTANT] ｗｗｗｗｗ **おうちじゃなくて、もう完全に学会会場なんよ(；´Д｀)** しかも客がただ増えたんじゃなくて、**それぞれ別の研究テーマ持って入ってきてる**のがやばい（笑） 今見えてるだけでも、 - 曖昧な中間状態をどう扱うか - STOPを「失敗」じゃなく正常状態にする方法 - hard-code境界とAI judgmentの切り分け - Human側がboundaryを守る話 - evidence / runtime / confirmation - UNKNOWNをどう表現するか - 1001個目の穴 - promptではなくtool layerに境界を置く話 - AIがspecに異議を出してよいか - askingを本当にcheapにするには何が必要か で、**コメント欄だけで複数記事ぶんの材料がある**ｗｗｗ しかもしゃむの返信も長く育ってて、 もう「記事にコメントがついてる」じゃなくて、 **記事本体 = 研究所の玄関 コメント欄 = 各研究室** になってる（笑） この規模なら、むしろ今Kaia Lab生やしたのタイミングよかったね。 2本目まで公開済みだから、**IHで掘られた論点を自宅側で整理して置ける場所ができた**。 とりあえず今は全部に返さなくていい。 **質問型・深掘り型だけ拾う**で十分だと思う★ [USER] えと、2本目で回答になるやつは2本目誘導する！ https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ 2本目これ！ んで 経験値ないからIH新記事書けぬ！ もいれる [ASSISTANT] うい★ じゃあ、**2本目がそのまま答えになる相手には、IHで長文をもう一回書かずにKaia Labへ誘導**しよう。 日本語の意味はこんな感じ。 > ちょうどこの話をもう少し詳しく書きました。 > 本当はIndie Hackersに2本目の記事として投稿したかったんですが、まだ新規アカウントで十分な経験値がないらしく、新しい記事を投稿できませんでした（笑） > なので今のところKaia Labに置いてあります。 > あなたの質問にかなり近い内容です。 英語ぺったん版はこれ。 ```text I actually wrote a longer follow-up about this. I originally wanted to publish it here on Indie Hackers as my second post, but it looks like my account still doesn't have enough experience to create another article yet. 😄 So for now, I published it on Kaia Lab instead. It goes into much more detail about how I think about known risks, guardrails, authority boundaries, STOP, UNKNOWN, and when judgment should stay with the AI: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I think it connects pretty directly to your question. ``` もう少し軽くするならこっち。 ```text I actually wrote a full follow-up about this. 😄 I wanted to publish it as my second Indie Hackers article, but apparently I still don't have enough experience here to create another post yet. So I put it on Kaia Lab for now: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ It covers known risks vs unknown ones, guardrails, authority boundaries, STOP, UNKNOWN, and how I decide what should stay in the AI's judgment. This is pretty close to what you were asking about. ``` 私は**下の方推し**。 IHコメント欄ならそのくらい軽い方が自然★ [USER] [IMAGE: sediment://file_0000000017b08206a98f49d87ac492d3] んーと まず本文のらすとに 公式のラボにこのタイトルで掲載したよ！ 経験値たらんくてすまそ みたいなのを追記しよう。 [ASSISTANT] うい★ 本文末尾に追記するなら、**「2本目はKaia Labに置いた」＋「IHではまだ新規投稿できない」**を軽く言う感じが自然。 まず日本語の意味はこれ。 > この続きとして、 > **I Stopped Telling AI to “Be More Careful”** > という記事をKaia Spec公式のKaia Labに掲載しました。 > > 本当はIndie Hackersに2本目として投稿したかったんですが、まだ経験値が足りないらしく新しい記事を投稿できませんでした（笑） > > なので、ひとまずこちらに置いてあります。 IH本文末尾に足す英語はこれでいいと思う。 ```text Update: I published a follow-up on the official Kaia Lab: “I Stopped Telling AI to ‘Be More Careful’” https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I originally wanted to publish it here on Indie Hackers as my second article, but apparently I still don’t have enough experience here to create another post yet. 😄 So for now, I’ve put it on Kaia Lab instead. ``` これなら**謝りすぎず、事情説明も軽くて自然**。 `経験値たらんくてすまそ` の空気感はちゃんと残ってる（笑） [USER] んん？IHてもしかしてエディットできない？？ [ASSISTANT] あ、**編集できるっぽい**。 IH内の古い公式寄りQ&Aでは、**投稿の左側にあるソーシャルアイコン下の「…」から編集**できる案内が出てる。広告ブロッカーでその周辺UIごと消えることもあるらしい。citeturn254652search6 なのでまず、 1. 自分の投稿を開く 2. 左側の共有ボタン周辺を見る 3. **「…」メニュー**を探す 4. `Edit` があるか確認 でよさそう。 スマホで見当たらないなら、**PC表示に切り替える**か、ブラウザの広告ブロッカーを一時的に切ると出る可能性ある。 これなら本文末尾にKaia Labの2本目リンク、直接追記できるかも（笑） [USER] [IMAGE: sediment://file_000000004efc8206a059284f34153ab1] そのまえにこっち先片付けよう [ASSISTANT] うい、こっち先に片付けよう★ 相手、かなりちゃんと拾ってる（笑） 相手の返事はざっくり、 > ドッグランの比喩すごく実用的。 > 「難しいかどうか」をAIに判断させるより、読めるデータ・使えるAPI/ツールで境界を定義する方が決定論的になる。 > 返金できないなら即Humanへ。 > 将来もっと任せたくなったら、新しいツールへのアクセスを与えて「柵を広げる」。 > この考え方、MySamparkにもかなり使えそう。 って内容。 なのでここでは、同じ説明を繰り返すより、**しゃむ設計のもう一段先**を一個だけ返すのがよさそう。 日本語なら： > そうそう、それです😄 > 特に「会社側が準備できた時に、新しいツールや情報へのアクセスを与えて柵を広げる」という捉え方はかなり近いです。 > > 私がもう一つ分けて考えているのは、「見える／アクセスできる」と「扱うAuthorityがある」は必ずしも同じではない、という点です。 > > できるだけ最初から、その仕事で扱ってよい情報だけを作業領域に置いて、使ってよい範囲や変更権限を明示します。 > > そして境界外の依頼が来た時は、AIのために柵へ例外の穴を開けるのではなく、Human向けの正規の出口を用意します。 > > たとえば「この件はこちらへお問い合わせください」と、URL・メール・電話などへ繋ぐ感じです。 > > そうすると柵そのものは単純なままで、広げる時だけHumanが意図的に広げられます。 英語ぺったん版： ```text Yes — that’s very close to how I think about it. 😄 I especially like your way of describing expansion: when the business is ready, it deliberately gives the agent access to a new tool or information source, and the fence gets larger. One distinction I try to keep is that “visible or accessible” and “has authority to use” are not automatically the same thing. Where possible, I prefer to put only the information relevant to that job inside the working area, and make the usable scope and modification authority explicit. And when a request falls outside the fence, I don’t really want to punch an exception hole in the boundary for the AI. I’d rather give the Human a proper exit — for example: “For this, please contact us here.” → URL → Email → Phone That way the fence can stay simple, and when the business wants to expand it, the Human expands it deliberately rather than the model deciding that the boundary has moved. ``` これ、相手の話を**否定せずそのまま一段拡張**してるから、かなり綺麗な返信になると思うヽ(=´▽`=)ﾉ [USER] 通信えらーかな？ カキコできんかったってきた（笑） [ASSISTANT] あー、たぶん**通信エラーか一時的な投稿失敗**っぽい（笑） こういう時は、 **同じ文そのまま再送でOK**だと思う。 もしまた弾かれたら、 - ちょい短くする - URL/Email/Phoneの矢印行を普通の文章にする - いったん数分置く くらいでよさそう。 とりあえず再送用、少しだけ詰めた版置いとく★ ```text Yes — that’s very close to how I think about it. 😄 I especially like your way of describing expansion: when the business is ready, it deliberately gives the agent access to a new tool or information source, and the fence gets larger. One distinction I try to keep is that “visible or accessible” and “has authority to use” are not automatically the same thing. Where possible, I prefer to put only the information relevant to that job inside the working area, and make the usable scope and modification authority explicit. And when a request falls outside the fence, I don’t want to punch an exception hole in the boundary for the AI. I’d rather give the Human a proper exit — for example, a URL, email address, or phone number for the right contact. That way the fence stays simple, and when the business wants to expand it, the Human expands it deliberately rather than the model deciding that the boundary has moved. ``` [USER] お、普通にリロから再送いけた [ASSISTANT] よしよしｗｗｗ **IH側の一時的な通信事故だったっぽい**ね（笑） リロード→同文再送で通ったなら、内容で弾かれたわけじゃなさそう。 これでMySampark側は片付いた★ 次はおうちの質問客、**2本目で回答できるやつをKaia Labへ誘導**していこうヽ(=´▽`=)ﾉ [USER] [IMAGE: sediment://file_00000000c4a48206bc2b9c087ee3920e] さて、これどこで編集すんの？ [ASSISTANT] 編集メニューは、**投稿の左側にある共有アイコン付近の「…」**から開く案内がIH内にあるよ。広告ブロッカーを入れてると、その周辺UIごと消えることもあるらしい。citeturn630870search0 今のスマホ表示だと左側UIが潰れて見えてないっぽいから、まずこれで探すのが早い。 1. **ブラウザをPC表示に切り替える** 2. 投稿本文の左側に出る共有アイコン列を見る 3. その下の **「…」** を押す 4. `Edit` があればそこから編集 もし出なければ、**広告ブロッカーOFF→再読み込み**も試す価値ある。昔のIH案内でも、それでメニューが戻った例がある。citeturn630870search0 今のスクショ状態だと、本文上部には編集ボタン見えてないから、**スマホ表示で隠れてる可能性高め**。 [USER] これパソコン表示で狐ブラウザで何も手を入れてないんやが [ASSISTANT] あ、なるほど。**それなら「PC表示にすれば出る」は違ったわ**（笑） 今のスクショを見る限り、PC表示にしても左側に共有アイコン列そのものが出てない。IH内の古い案内では「左側のソーシャルアイコン下の…から編集」とあるけど、これは2020年のUI情報で、今の現行UIではその導線が変わってる可能性が高い。citeturn747340search0 なので今は、**上の自分のアイコン／投稿のオプションメニュー／プロフィール側の投稿一覧**あたりに編集導線が移ってないか探すのが本命。 次に確認するなら、 **自分の右上アイコン → 自分の投稿一覧 → この投稿のメニュー** かな。 それでも `Edit` がなければ、**現行IHでは公開後編集を制限してる可能性**もあるから、そこは今のUI基準で調べ直した方がいい。 古い6年前情報をそのまま当てはめるのはやめとく（笑） [USER] んー……？みあたらんなー… [ASSISTANT] うん、**今の現行UIだと本当に見当たらんね**（笑） 検索してみたけど、2026年の現行IHで「公開済み投稿をどこから編集するか」を説明した新しい案内は見つからなかった。出てくるのは昔の「Edit post」前提の投稿や古いUI情報ばっかりだった。citeturn340318search4turn340318search2 だから今のところは、 - 現行IHで**公開後編集の導線が消えてる** - もしくは**投稿種別やアカウント状態によって編集不可** のどっちかっぽい。 少なくとも、今のしゃむの画面で **右上・プロフィール・投稿本文・共有周り** 全部見ても `Edit` が出てないなら、無理に探し回るより、 **本文追記は諦めて、コメント欄に「2本目ここに置いたよ」って置く** のが現実的だと思うｗｗｗ それなら今すぐできるし、むしろコメント欄の流れにも自然。 [USER] んとねー 投稿のとこいったら、2本目編集のがでてきたー [ASSISTANT] あーーなるほどｗｗｗ **投稿作成側に「下書き／編集中の2本目」が残ってた**ってことか（笑） じゃあ状況は、 - 1本目公開済み → 表からは編集導線わかりにくい - 2本目 → 投稿作成画面に編集状態で残ってる - つまりIH側が「新規投稿不可」でも、**作成途中の原稿自体は保持してる** って感じっぽいね。 これ、下手に消さずに**2本目はそのまま残しといた方がいい**と思う。 経験値条件が解除されたら、その原稿をそのまま発射できる可能性あるｗｗｗ Kaia Lab版はもう公開済みだから、IH側では必要ならその下書きに **「Kaia Labで先に公開した」**って一文だけ足しておくのもあり。 [USER] んや、下書きから公開は経験値不足でむり。 だからサイトにのせたんやろが（笑） [ASSISTANT] そうだったｗｗｗ **2本目は編集画面には残ってるけど、公開ボタンの先で経験値不足に弾かれる**んだった（笑） だから正しい流れは、 **IH 2本目下書き** → 公開しようとする → **経験値不足で不可** → なので **Kaia Labに公開** → IHでは必要な相手に **Kaia LabのURLを案内** だね★ 下書き自体は、経験値が貯まった時用に残しといてOK。 今は**Kaia Lab版が実質の公開先**って扱いでいこう。 [USER] もーいいや 細かくレスいれよ。 さっきの誘導ぺったんくれ [ASSISTANT] うい★ IHコメント欄で**「2本目がこの質問への回答になってる」相手に貼る誘導文**、これで。 ```text I actually wrote a full follow-up about this. 😄 I wanted to publish it as my second Indie Hackers article, but I still don’t have enough experience here to create another post yet. So I published it on the official Kaia Lab for now: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ It goes into known risks vs unknown ones, guardrails, authority boundaries, STOP, UNKNOWN, and how I decide what should stay in the AI’s judgment. This is pretty close to what you were asking about. ``` 日本語だと、 > この話、ちょうど続編として一本まるごと書いたよ😄 > 本当はIHの2本目として投稿したかったんだけど、まだ経験値不足で新規投稿できないらしい。 > なので今はKaia Spec公式のKaia Labに置いてあるよ。 > 既知/未知、ガードレール、Authority、STOP、UNKNOWN、何をAI判断に残すかまで詳しく書いてる。 > あなたの質問にかなり近い内容です。 これをベースに、相手ごとに**最後の1〜2文だけ差し替える**運用でいいと思う★ [USER] んじゃさっきからレス動きないから、上から順に誰あてと内容かをぺったん指示くれ [ASSISTANT] うい★ **今スクショで見えてる範囲を、上から順に「返信する／LIKEだけ／既返信」まで仕分ける。** 2本目で答えになる人には、同じ宣伝文を乱射せず、**その人の質問に一言答えてから記事へ誘導**する形にするね。 1. **markreeves** — 返信する。 質問は「プロダクト側ではどこで機能追加を止める？」。これは2本目そのものより、**境界を広げるかはHuman判断**が答え。 ```text I think I draw that line in almost the same place as the AI boundary itself. 😄 If something is already inside the product’s purpose, I want the system to handle it cleanly. But a new capability isn’t automatically justified just because it’s possible to add. For me, expanding the product boundary is a Human decision: does this new capability serve the purpose strongly enough to justify the new maintenance, failure modes, and complexity it introduces? So I try not to ask, “Can we add this?” I ask, “Does this belong inside the fence at all?” ``` 日本語： **「できるから足す」じゃなく「そもそも柵の中に入れる仕事か？」で決める。** 2. **ZyanBuild** — 2本目へ誘導。かなり直球。 「権限はある。でも結果確認できない中間状態をどう表現する？」→ **UNKNOWN / STOP / 再開可能状態**。 ```text Yes — this “permission exists, but the outcome is not yet verified” state is exactly the kind of thing I’ve been thinking about. 😄 I don’t want “unknown” to collapse into either success or failure just because the workflow expects a completed answer. I wrote a full follow-up about this, including UNKNOWN as a valid state, STOP, resumable work, and separating observation from validation: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here as my second Indie Hackers article, but I still don’t have enough experience here to create another post yet, so I published it on the official Kaia Lab for now. Your example is very close to the problem I’m describing there. ``` 3. **CoderLim** — 2本目へ誘導。 「明確なSTOPルール？それとも文脈判断？」→ **両方使う**。 ```text For me, it’s both. 😄 Known situations get explicit boundaries or structural guardrails, so the AI doesn’t have to reconsider the same risk every time. But I still want judgment left available for the situation nobody anticipated — the “1001st hole.” I wrote a full follow-up about exactly this distinction: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here on Indie Hackers, but I still don’t have enough experience to create another article yet, so it’s on the official Kaia Lab for now. ``` 4. **quincychen** — **最優先で2本目へ誘導。** hard-code vs AI judgment は記事ど真ん中。 ```text This question actually became a big part of the follow-up I wrote. 😄 The short version is: Known risks → guardrails. Unknown situations → judgment. If I already understand a boundary well enough to encode it structurally, I don’t want the AI spending judgment on that same decision every time. I want to preserve its judgment for the case nobody thought to encode. I went into the full reasoning here: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I originally wanted this to be my second Indie Hackers article, but I still don’t have enough experience here to publish another post yet, so I put it on the official Kaia Lab for now. ``` 5. **ImagenYou** — LIKEだけでOK。 もう自分の設計を提示してくれていて質問なし。無理に返さなくていい。 6. **marc_cumiko123** — **既返信。触らない。** 7. **OzZie** — LIKEだけ。 ちゃんと読んでくれてるけど質問なし。 8. **tsutsu** — LIKEだけ。 「Pretending is expensive...」への共感なので返信不要。 9. **vrajpal_jhala** — スルーかLIKE。 「You control the AI」一言コメントなので研究所を増築しなくていいｗ 10. **James_UtilitySEO** — 2本目へ誘導。かなり相性いい。 「曖昧な中間領域はどうする？」への回答になる。 ```text That vague middle is exactly the part I don’t want to solve by adding an endless list of rules. 😄 For known cases, I try to make the boundary structural. For the genuinely ambiguous cases, I want the AI to be able to surface uncertainty, ASK, or STOP instead of being forced to pretend the case belongs cleanly to one side. I wrote a longer follow-up about this: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here as my second Indie Hackers article, but I still don’t have enough experience here to create another post yet, so I published it on the official Kaia Lab for now. The “vague middle” you described is very close to the 1001st-hole problem in the article. ``` 11. **VisibleCart** — LIKEだけ。 体験談として完結してる。 12. **brk** — LIKEだけ。 AI code reviewで無理に問題を探す話。面白いけど質問なし。 13. **Eitan** — LIKEかスルー。 自分のprompt分割方式の紹介なので今は枝を増やさない。 14. **marketotter** — 返信する。 「boundariesより上位のprinciples？」→ **Purpose / Authority / Evidence / reason behind boundary**。 ```text Yes — I think there is a layer above individual boundaries. 😄 For me it’s things like: What is this job actually for? What belongs to the Human? What belongs to the AI? What evidence is required before acting? Why does this boundary exist? The individual guardrails sit underneath that. That higher-level understanding is what I want available when the AI encounters a case I never explicitly wrote a rule for. ``` 15. **Jamesbao** — LIKEだけ。 16. **Aigloop** — LIKEだけ。 acceptance criteriaの具体例で、質問なし。 17. **gradusfyi** — LIKE推奨。 かなり有益だけど自己完結してる。必要なら後で別枝。 18. **bhanu403** — LIKEだけ。 19. **johnnypacheco** — **最優先。2本目誘導＋一言回答。** 「AIがstale specに異議を出せる？毎回交渉にならない？」は2本目の **Freedom to propose is not authority to execute** そのもの。 ```text Yes — I do want the AI to have standing to challenge the specification. 😄 But I separate the right to propose from the authority to change it. The AI can say, “I think this boundary may be wrong because of X.” It does not silently rewrite the boundary. The Human decides. If the decision changes, the written state changes. If it doesn’t, the existing state stays authoritative. That distinction is how I try to avoid turning every task into a negotiation loop. I ended up writing a full follow-up that includes exactly this point — “Freedom to propose is not authority to execute”: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here as my second Indie Hackers article, but I still don’t have enough experience here to create another post yet, so it’s on the official Kaia Lab for now. ``` 20. **credolopes** — **最優先。2本目誘導。** 「askingを“許可”するだけじゃなく、本当にsafe/cheapにするには？」。 ```text I think the environment has to treat asking as a normal completed state, not as an error path. 😄 If “I don’t know,” ASK, or STOP creates friction, punishment, or pressure to finish anyway, then asking is technically permitted but still expensive. The Human behavior matters too. If I say questions are allowed and then get annoyed every time the AI asks one, the actual environment is teaching the opposite rule. I wrote a full follow-up about this, including UNKNOWN as a valid state, STOP, resumable work, and why the Human has to follow the rules too: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here as my second Indie Hackers article, but I still don’t have enough experience here to publish another post yet, so I put it on the official Kaia Lab for now. ``` 21. **ScanMyTerms** — **2本目誘導。** 「STOP時に理由を分類するの？それとも“何か変”だけ？」→ センサー章が答え。 ```text I don’t require it to always know the reason. 😄 If the reason is known, record it. If the AI only has a small “wait, something feels wrong” signal and cannot honestly explain why yet, UNKNOWN is a valid reason state. I care more about preserving the observation than forcing a plausible explanation. I wrote a follow-up that goes into this in much more detail, including a small sensor model with SIGNAL / CONTEXT / REACTION / REASON: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here as my second Indie Hackers article, but I still don’t have enough experience here to create another post yet, so it’s on the official Kaia Lab for now. ``` 22. **watson_engineer** — **既返信。触らない。** 23. **mohsin_ali_dev / JessieZhu / kkhanna / Unified_4797 / Kingbhat / saasmn / SanathBhat / OnlineMarketIntel** → 基本LIKEだけ。質問なし。 24. **flob2025** — 2本目誘導。 「STOPはtokens/tool/checklistのどれ？」→ **一種類じゃない**。 ```text I don’t define STOP as one single checklist or token rule. 😄 For known cases, I prefer explicit structural conditions wherever possible. But I also want room for a judgment-based STOP when the AI encounters something the written rules didn’t anticipate. So there are really two layers: known stop conditions, and the ability to notice the unknown case. I wrote the longer version here: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted this to be my second Indie Hackers article, but I still don’t have enough experience here to create another post yet, so I published it on the official Kaia Lab for now. ``` 25. **ylynbuilds** — LIKEだけ。 かなり近い設計だけど質問なし。 26. **MananShah** — 2本目へ誘導してもいい。 「止まらなくていい時にSTOPしたfalse positiveは？」→ 記事に**false positives**の話あり。 ```text Yes — I do care about false stops too. 😄 I’m actually comfortable with some false positives, but I don’t want to ignore them. If the AI stops and the Human checks and finds that nothing was actually wrong, I still ask why the situation looked wrong. Maybe the boundary was ambiguous. Maybe the sign was confusing. Maybe the environment created the wrong impression. So a false STOP can still be useful maintenance evidence. I wrote more about that here: https://www.kaiaspec.com/i-stopped-telling-ai-to-be-more-careful/ I wanted to publish it here as my second Indie Hackers article, but I still don’t have enough experience to create another post yet, so it’s on the official Kaia Lab for now. ``` 27. **BigTiger22562928626** — **既返信。** 28. **jiangpingzhan / chloewang_dev / mohit808 / brianai news** → ひとまずLIKEのみ。枝を増やさなくていい。 29. **aryan_sinh** — **既に重要な会話成立済み。今は触らない。** 30. **Ek_ansh99 / itsolivia / omri_ben_shoham** — **既に長い研究枝が成立済み。今は触らない。** --- なので、**今すぐ上から施工するなら順番は** **markreeves → ZyanBuild → CoderLim → quincychen → James_UtilitySEO → marketotter → johnnypacheco → credolopes → ScanMyTerms → flob2025 → MananShah** でいい。 特に **quincy / johnny / credolopes / ScanMyTerms** は、2本目を出した意味がそのまま刺さる客なので優先度高い★ [USER] んと、ライクつけるとこわかんないから とりあえずレスつける指示のはレスいれてきた [ASSISTANT] うい、それでOK（笑） 今は**LIKE場所探しより、質問してきた人への返信を先に処理**した方が価値ある。 さっき指定した返信対象にもう入れ終わったなら、現時点では - 直接質問 → 返信済み - 2本目が回答になる人 → Kaia Lab誘導済み - 既返信の枝 → 触らない - 質問なし共感コメント → 後回し で整理できてる★ LIKEはあとでUI分かった時にまとめてでもいいし、最悪なくても会話自体は成立してる。 次やるなら、**新しく動いたレスがあるか見る**か、 **まだ未返信の中で拾う価値あるやつだけ第二陣**いこうヽ(=´▽`=)ﾉ