Hasan Halabi

Tag: product thinking

  • The Hidden Skill in Using ChatGPT: Turning Ambiguity into Next Actions

    The Hidden Skill in Using ChatGPT: Turning Ambiguity into Next Actions

    Most advice about using ChatGPT eventually becomes advice about prompts.

    Use this structure. Add this role. Say it in this order. Put this phrase at the end. Sprinkle a little magic dust on the prompt and wait for professional-grade results to arrive.

    I understand why people talk this way. Prompts matter. A clear request is better than a vague one. This is not exactly breaking news.

    But I think the obsession with “prompt engineering” misses the more important skill.

    In real work, the hard part is often not writing the perfect prompt. The hard part is that you do not yet know what you are asking for. The situation is messy. The requirement is unclear. The business problem is half-defined. The client request sounds simple until you start touching it. The product idea feels obvious in your head, then suddenly collapses the moment someone asks one reasonable question.

    This is where ChatGPT becomes useful in a much deeper way.

    Not as a content generator.

    Not as a magic answer machine.

    Not as a productivity toy wearing a suit.

    Its real value, at least in the way I use it, is helping me turn ambiguity into the next practical action.

    That may sound less exciting than “10 prompts that will change your life,” but it is much more useful. Also, it has the small advantage of being true.

    The real problem is usually not the prompt

    When people say they are bad at using ChatGPT, they often assume the problem is that they are bad at prompting.

    Sometimes that is true. Many people do give it vague, lazy, or incomplete instructions and then act surprised when the output is not useful. That is still the old rule: garbage in, garbage out. AI did not cancel that rule. It just made the garbage arrive faster and with better formatting.

    But in many cases, the deeper problem is not the wording of the prompt.

    The deeper problem is unclear thinking.

    A founder may say, “I need help improving onboarding.”

    A client may say, “We need to automate this process.”

    A manager may say, “The team is not aligned.”

    A consultant may say, “I need to prepare a proposal.”

    A product owner may say, “We need an AI feature.”

    These sound like tasks. They are not tasks yet. They are clouds.

    There may be a task hiding inside them, but it has not been extracted. The real issue may be unclear ownership, missing information, bad workflow design, weak product positioning, unrealistic scope, poor communication, or simply the fact that nobody has agreed what success looks like.

    If you treat that kind of statement as a prompt and ask ChatGPT to “solve it,” you will usually get something that looks helpful and feels slightly hollow.

    The better move is to use ChatGPT to help unpack the situation before asking for output.

    That is the part many people skip.

    Sometimes you do not know what output you need

    One of the most useful lessons I learned from working with ChatGPT is that I do not always know what the right output should be.

    This sounds obvious, but it matters.

    People often approach ChatGPT as if the format is already clear:

    “Write me an email.”

    “Create a checklist.”

    “Summarize this.”

    “Give me a plan.”

    Those are useful requests when you already understand the problem well enough to know what form the next step should take.

    But many real situations are not like that.

    Sometimes you are facing a new kind of project. Sometimes you are entering a business area you do not fully understand. Sometimes a client request touches technical, operational, and political issues at the same time. Sometimes you are dealing with a personal situation you have never encountered before, and the problem does not come with a clean label attached.

    In those moments, asking for “a plan” may be too early.

    You may not need a plan yet.

    You may need questions.

    You may need a map of the situation.

    You may need a list of assumptions.

    You may need to separate facts from opinions.

    You may need to identify what you do not know.

    You may need to define the decision before trying to make it.

    This is where ChatGPT is valuable as a thinking partner. It can help you figure out what kind of output is useful before you waste time producing the wrong one.

    That difference matters.

    A polished checklist for the wrong problem is not progress. It is stationery.

    The best use of ChatGPT is often the questions it asks back

    The single most useful habit I have developed with ChatGPT is very simple:

    I ask it to ask me questions when it does not understand me well enough.

    That is it.

    Not a secret mega-prompt. Not a framework with a dramatic name. Not something I need to sell in a course while standing in front of a rented bookshelf.

    Just this: if the situation is unclear, do not pretend it is clear. Ask me.

    This one habit changed the quality of the output more than most prompt tricks I have seen online.

    The reason is simple. Good questions force better thinking.

    A vague idea can survive inside your head for a long time because nobody is challenging it there. It sounds complete because you are familiar with it. You know what you mean, or at least you think you do.

    Then ChatGPT asks:

    Who is this for?

    What does success look like?

    What is the first version?

    What is out of scope?

    What happens if this fails?

    Who owns the decision?

    What information is missing?

    What assumption are you making here?

    Suddenly, the idea is not as complete as it felt ten minutes ago.

    That is not a bad thing. That is the work.

    The question exposes the missing part. Answering it forces you to think in a direction you may have avoided, ignored, or simply never noticed. Many times, the question is more valuable than the answer because it moves your attention to the right place.

    This is especially useful in unfamiliar territory.

    When you already understand a domain, you know where the traps usually are. You know which questions matter. You know which details are dangerous to ignore.

    But when the territory is new, you do not even know what to look for. You may be confident about the wrong things and blind to the important ones. In that situation, a thinking partner that keeps asking structured questions is extremely useful.

    Not because it replaces your judgment.

    Because it improves the conditions under which your judgment works.

    Ambiguity becomes useful when it turns into an artifact

    A good ChatGPT session should not end with a nice conversation.

    It should end with something useful.

    That does not always mean a finished document. Sometimes the useful output is small. But it should be concrete enough that you can do something with it.

    For example, ambiguity can become:

    • a list of decisions that need to be made
    • a product brief
    • a checklist
    • a set of acceptance criteria
    • a meeting agenda
    • a proposal outline
    • a risk list
    • a set of client questions
    • a first experiment
    • a workflow map
    • a ClickUp task list
    • a draft email
    • a comparison table
    • a clear “not now” list

    This is where the value becomes real.

    The conversation takes something foggy and turns it into an object you can review, edit, share, assign, test, or implement.

    That is the difference between using ChatGPT as entertainment and using it as part of serious work.

    I do not want to leave a session thinking, “That was interesting.”

    Interesting is nice. Actionable is better.

    If I start with a vague idea for a product, I want to leave with a clearer product definition.

    If I start with a confusing client request, I want to leave with a list of questions that will uncover the real requirement.

    If I start with an operational mess, I want to leave with a workflow breakdown and the next few decisions.

    If I start with a new area I do not understand, I want to leave with a learning path, unknowns, risks, and first experiments.

    The point is not that ChatGPT magically solves the whole thing.

    The point is that the fog has been reduced.

    Now there is something to hold.

    This is how I use it before product work

    This is also why I use ChatGPT before I move into implementation work.

    When I am building a product, I do not want the coding tool to invent the product for me. That is not its job. Before I go anywhere near implementation, I need the idea to become clearer.

    So I use ChatGPT to pressure-test the thinking.

    What is the product supposed to do?

    Who is it for?

    What is version one?

    What should wait?

    What are the constraints?

    What would make this fragile?

    What are the dangerous assumptions?

    What happens when something fails?

    By the time I move toward a PRD, a product brief, or a technical specification, the value has already started. The document is not just documentation. It is the result of thinking being forced into shape.

    This is why I do not see ChatGPT as something I use only to “generate content.” That is one small use case.

    The better use case is structured thinking.

    It helps me move from “I have an idea” to “this is the product I am actually building.”

    Those are not the same thing.

    An idea can be vague and still sound impressive. A product definition cannot hide as easily. It has to answer questions. It has to make tradeoffs. It has to say what is included and what is not.

    That is where ChatGPT is useful. It helps expose the distance between the idea and the thing that can actually be built.

    This is also how I use it in business work

    The same pattern applies outside product development.

    For example, when work becomes messy across tools, people, deadlines, and priorities, ChatGPT can help me think through the mess before I put structure around it.

    I may start with a rough description of what is happening:

    This project has too many moving parts.

    This client request is unclear.

    This workstream keeps getting delayed.

    I am not sure what the next right step is.

    That is not enough to produce a reliable plan. But it is enough to begin a useful conversation.

    The value comes when ChatGPT starts helping me separate the situation into parts:

    What are the facts?

    What are the assumptions?

    Who is waiting for whom?

    What decision is blocked?

    What is urgent but not important?

    What is important but still undefined?

    What can be turned into a task?

    What needs a conversation before it becomes a task?

    This is where a tool like ClickUp becomes useful after the thinking. ChatGPT helps me clarify, question, and organize. ClickUp helps me store the result in a structured way.

    That sequence matters.

    If I put unclear thinking into a task management system, I do not get clarity. I get organized confusion. Very neat. Very searchable. Still confusion.

    The thinking has to happen first.

    Then the structure becomes useful.

    The problem with prompt engineering culture

    This is why I am not very impressed by the online obsession with prompt engineering.

    Not because prompts are useless. Again, clear language matters.

    But a lot of what gets sold as prompt engineering feels like course-selling theater. It takes a real thing — the importance of clear instruction — and turns it into a performance. Suddenly every normal thinking habit needs a special name, a template, a secret formula, and ideally a checkout page.

    I do not think most people need that.

    Most people need to get better at explaining the situation, identifying what is unclear, answering hard questions, and turning the conversation into a usable next step.

    That is not as marketable as “copy this prompt and become 10x,” but it is far more practical.

    The best ChatGPT users I have seen are not necessarily people with fancy prompts. They are people who can think clearly with the tool.

    They know when to ask for options.

    They know when to ask for questions.

    They know when to challenge assumptions.

    They know when to turn the discussion into a checklist.

    They know when to stop generating and start deciding.

    They know when the answer sounds good but is still not grounded enough.

    This is not prompt engineering in the theatrical sense.

    It is thinking discipline.

    The tool is useful, but you still own the judgment

    There is an important boundary here.

    Using ChatGPT as a thinking partner does not mean outsourcing your judgment to it.

    That would be a mistake.

    The tool can ask useful questions. It can organize information. It can suggest options. It can help you see gaps. It can turn scattered thoughts into a first structure. It can make unfamiliar territory feel less chaotic.

    But it does not live with the consequences.

    You do.

    You still need to decide what is true, what matters, what is safe, what is appropriate, and what should happen next.

    This is especially important in business situations where context matters. A tool may produce a clean plan that ignores the politics of a client relationship. It may suggest an efficient workflow that does not fit the people who actually have to use it. It may make something sound simple because it does not understand the hidden cost of change.

    So I do not use ChatGPT as the decision-maker.

    I use it as the thinking environment.

    That distinction keeps the work grounded.

    The tool helps me think better. It does not absolve me from thinking.

    The real skill is moving from fog to next action

    The hidden skill in using ChatGPT is not having a perfect prompt library.

    It is knowing how to work with ambiguity.

    It is being able to start with something unclear and move toward something useful.

    Sometimes that means asking for a draft.

    Sometimes it means asking for a checklist.

    Sometimes it means asking for questions.

    Sometimes it means admitting that the next step is not a plan, but a better understanding of the problem.

    This is why ChatGPT can be useful for non-technical founders, technical operators, consultants, and anyone who deals with messy work. It gives you a way to think with pressure. It helps you slow down the right parts of the process before you speed up the wrong ones.

    That matters because most bad execution does not start as bad execution.

    It starts as unclear thinking that nobody challenged early enough.

    ChatGPT is valuable when it helps you challenge that thinking before it turns into tasks, code, commitments, proposals, or decisions.

    Used well, it does not just help you produce more.

    It helps you see what needs to be produced.

    And sometimes, that is the whole difference.

  • Why I Start Every AI-Built Product With a PRD

    Why I Start Every AI-Built Product With a PRD

    One of the stranger side effects of AI-assisted development is that it made some people think planning matters less.

    I had the opposite experience.

    The more useful AI became, the more I needed clarity before I asked it to build anything.

    That is why I start every AI-built product with a Product Requirements Document “PRD”. I will use the term once here, but what I really mean is something simpler: a clear product definition. A brief. A spec. A document that forces me to stop pretending the idea is finished when it is still half-baked and wearing confidence like a costume.

    I am not doing this because I enjoy documentation. I am definitely not doing it because I miss corporate ceremony. I am doing it because I want better output, fewer wrong turns, and a product that behaves like something I actually meant to build.

    In my workflow, this step happens before Codex.

    Always.

    I do not want the coding tool to invent the product for me

    When people talk about building with AI, a lot of the conversation goes straight to prompting the coding tool.

    That makes sense on the surface. If the tool writes code, then the obvious question is how to talk to it.

    But for me, that is already too late.

    Before any implementation starts, I need to know what I am asking for. Not in a vague “I have a direction” way. I mean clearly enough that the system is not quietly making important product decisions on my behalf.

    Because that is what happens when the brief is weak.

    The tool still produces output. Sometimes a lot of it. Sometimes surprisingly good-looking output. But under the surface, it is filling gaps with assumptions. It is choosing scope, making tradeoffs, inventing behavior, smoothing over ambiguity, and guessing what matters.

    That may be acceptable for a quick experiment.

    It is not how I want to build products.

    I do not want Codex, or any AI coding tool, deciding what version one should include, what edge cases matter, which features should wait, what happens when something fails, or how the product should behave under real constraints. Those are product decisions. I want to make them before I get to the implementation stage.

    That is the real reason I start with a product brief.

    The document is not the goal. Clarity is the goal.

    I think this is where people misunderstand the point.

    The goal is not to produce a polished document for its own sake. The goal is to reach a level of clarity that makes implementation reliable.

    Sometimes that clarity ends up in a clean PRD. Sometimes it is a technical spec. Sometimes it is a plain-language product brief with enough structure to remove confusion. I do not care too much what label it gets.

    What I care about is whether it answers the questions that would otherwise get pushed into the coding phase.

    What is the product?

    Who is it for?

    What problem is it solving?

    What does the first version include?

    What is explicitly out?

    What should happen when something fails?

    What constraints matter in the real environment?

    If those answers are still fuzzy, then the coding stage becomes more expensive than it looks. Not always in money, but definitely in time, rework, review effort, and product drift.

    You feel like you are moving fast because code is appearing quickly.

    Then you discover you have been building motion, not clarity.

    Why this matters even more with AI

    With human developers, unclear thinking is already expensive.

    With AI, it gets expensive faster.

    That is the part I learned very quickly.

    If the product definition is weak, AI does not pause and say, “This is still not thought through properly, maybe let us step back.” It usually goes ahead and builds something. That can create a very convincing illusion of progress.

    And honestly, that illusion is dangerous.

    Because now you have screens, flows, database structures, API logic, and implementation details growing around assumptions that were never properly decided. You are not just missing clarity anymore. You are missing clarity with momentum.

    That is why I do not see the product brief as optional overhead. I see it as protection against fast confusion.

    The clearer the product is, the more useful AI becomes.

    It guesses less.

    It drifts less.

    It makes fewer wrong assumptions.

    It becomes easier to review.

    It becomes easier to refine.

    And the gap between what I meant and what gets built becomes much smaller.

    That is a very practical payoff.

    I use ChatGPT first because I need pressure before I need code

    This is also why my workflow starts in ChatGPT, not in Codex.

    At that stage, I am not asking for implementation. I am asking for pressure.

    I want the idea challenged.

    I want missing decisions exposed.

    I want vague language forced into specific language.

    I want the comfortable illusion that “the idea is clear in my head” to be tested before the coding starts.

    That is what ChatGPT is useful for in this part of the process.

    I use it to discuss what already exists in the market. I use it to brainstorm the shape of the product. I use it to narrow scope. I use it to discover what I have not yet decided. And very often, the value is not in the answer itself. The value is in the next question it pushes back at me.

    That interaction helps me move from “I think I want this” to “this is what I am actually building.”

    Only after that do I want the coding tool involved.

    What I put inside my product brief

    I do not treat this like a giant enterprise artifact. I keep it practical.

    At a minimum, I want these things clear before I start implementation:

    1. The purpose
    What the product is supposed to do, in plain language.

    2. The user
    Who it is for and what real need it serves.

    3. The first version
    What version one includes, and just as importantly, what it does not include.

    4. Core flows
    What the user actually does and what the product needs to support.

    5. Constraints
    Technical realities, hosting limits, security concerns, operational conditions, or anything else that changes design decisions.

    6. Failure behavior
    What should happen when something breaks, times out, gets interrupted, or loses access.

    7. Boundaries
    What I am consciously postponing so the first version stays focused.

    That last part matters more than people think.

    A good product brief is not only a description of what I want to build. It is also a written record of what I am refusing to build right now.

    That saves a lot of pain later.

    A real example from my backup plugin

    One public example I can talk about is a WordPress backup plugin I am designing for Google Drive backups.

    If I had started directly in a coding tool, the request could have been one sentence long: build a WordPress plugin that backs up a site to Google Drive.

    That sounds reasonable until you realize how much product thinking is hiding inside that sentence.

    What exactly gets backed up?

    Should it create large temporary ZIP files locally, or should it upload directly?

    Should version one support restore?

    Should it support migration?

    Should it support multiple cloud providers?

    How should it behave on shared hosting with limited resources?

    Without a clear product definition, the coding tool would still start building. But it would be building around guesses.

    In my case, working through the brief changed the shape of the product in important ways.

    For example, I made an explicit decision to keep version one focused on Google Drive only, instead of trying to support multiple storage providers immediately. I also made a clear decision that automated restore would be out of scope for version one. That kept the first release much tighter and much more realistic.

    Those are not small details. They shape architecture, user expectations, support burden, and implementation complexity from the beginning.

    That is exactly why I want those decisions made before code starts, not discovered halfway through it.

    This step saves me from fake progress

    One reason I value this part of the workflow so much is that it protects me from fake progress.

    Fake progress is when implementation starts quickly, output appears quickly, and the whole thing feels productive, but the actual product is still not defined properly.

    That kind of speed is seductive.

    It feels efficient.

    It looks efficient.

    But later it usually turns into revisions, corrections, backtracking, and the awkward realization that the tool did not misunderstand me. I just had not finished understanding the product myself.

    The product brief helps me catch that earlier, when the cost is still low.

    It is easier to fix vagueness in a document than in a growing codebase.

    It is easier to challenge scope in a discussion than after features have already started multiplying.

    It is easier to decide what matters before implementation than while reviewing output shaped by assumptions I never meant to approve.

    I do not see this as documentation. I see it as leverage.

    That is probably the simplest way to put it.

    I am not writing a product brief because I enjoy preparing documents before the “real work” begins.

    For me, this is the real work.

    This is where the product becomes concrete enough to build well.

    This is where ambiguity gets reduced.

    This is where version one becomes realistic.

    This is where the coding tool becomes more useful, because it is no longer being asked to fill in the product thinking for me.

    The better this stage goes, the more value I get from AI later.

    That is why I start here every time.

    Not because it looks organized.

    Not because it sounds professional.

    Because it works.

    And when I am using AI to build real products, “it works” is a much better standard than “it felt fast at the beginning.”

  • Using AI to Build Products Is Serious Work, Not Vibe Coding

    Using AI to Build Products Is Serious Work, Not Vibe Coding

    I really dislike the term vibe coding.

    Not because it sounds silly, although it does. And not because people should not have fun with technology. They should. The problem is that the term quietly suggests something bigger: that building with AI is mostly casual, experimental, and a bit unserious. Like you are just throwing prompts at a machine, getting lucky sometimes, and calling it product development.

    That is not how I see it.

    For small businesses, freelancers, and solo founders, AI-assisted development is not a toy. It is not a side show. It is a serious way to build real products faster, with less overhead, and with much more independence than most people realize.

    But there is a catch.

    Using AI to build products only works well when you stop treating it like magic.

    I am not speaking theoretically here. I am speaking from how I work now. I stopped hiring developers in early 2025, and today I rely on Codex as the only solo developer across the projects I am handling. That does not mean I sit down, type a vague idea, and watch perfect software appear like a cooking show reveal. It means I changed the way I think about product development.

    And that is exactly where most of the confusion around “vibe coding” starts.

    Using AI is not the problem. Vagueness is.

    The first rule many of us learned when we started learning computer science in the late 1990s was simple: garbage in, garbage out.

    That rule did not disappear because AI arrived. If anything, it became more important.

    If you give an AI coding tool a half-idea, a blurry goal, and a pile of unmade decisions, it will still produce something. That is part of the danger. It is very easy now to get output that looks impressive before you realize it is built on fuzzy thinking.

    That is what I would call vibe coding.

    Not using AI.

    Not building quickly.

    Not shipping with AI assistance.

    Vibe coding, to me, is when someone gives AI a semi-idea and expects it to somehow fill in the product thinking, business logic, edge cases, user needs, and technical tradeoffs by itself. That may produce demos. It may even produce code that runs. But it usually does not produce a reliable product.

    The issue is not the intelligence of the tool. The issue is the laziness of the input.

    My workflow does not start with Codex

    This is the part that matters most.

    I do not start with Codex.

    I start with ChatGPT.

    That surprises some people because if the goal is to build software, the instinct is to go directly to the coding tool. But in my experience, that is too early. Before implementation, I need clarity. I need pressure. I need questions. I need to discover whether the idea in my head is actually a product or just a direction wearing a confident face.

    So I use ChatGPT first for thinking, not coding.

    I use it to discuss what already exists in the market. I use it to brainstorm approaches. I use it to challenge weak assumptions. I use it to force me into specifics when I am still speaking too generally. Sometimes the most useful thing it does is not answering me. It is asking me the questions I should have asked myself earlier.

    That stage is extremely important because most product ideas are incomplete when they first show up. They sound clear because they are familiar to the person thinking about them. But once you start discussing them properly, all the hidden ambiguity comes out.

    What exactly is the first version?

    What is essential and what is not?

    What should happen when something fails?

    What does the user actually need, not what sounds nice in a feature list?

    What should be postponed even if it looks attractive now?

    That is where the serious work starts.

    The goal is not a better prompt. The goal is a clear product.

    A lot of people talk about prompting as if the secret is finding the perfect sentence to unlock perfect software.

    I do not think that is the real game.

    The goal is not to write a clever prompt. The goal is to create a clear picture of the product before asking an AI coding tool to build it.

    Sometimes that ends up becoming a proper PRD. Sometimes it is a technical specification. Sometimes it is just a very well-structured breakdown of scope, flows, decisions, and constraints. The format matters less than the clarity.

    What matters is that by the time I move into implementation, I am no longer asking the AI tool to invent the product for me. I am asking it to execute against something I have already thought through.

    That changes everything.

    When the product is clear, the AI becomes dramatically more useful. It stops guessing as much. It makes fewer wrong assumptions. It can move faster without dragging the whole project into chaos. Review becomes easier. Iteration becomes sharper. The output becomes more reliable because the target is more reliable.

    This is why I do not see serious AI development as “prompting.” I see it as structured product thinking followed by accelerated execution.

    A small real example

    One public example I can talk about is a WordPress backup plugin I am designing.

    If I had gone straight to a coding tool with the raw idea, the prompt would have sounded something like this: build me a WordPress plugin that backs up a site to Google Drive.

    That sounds fine until you realize how many decisions are hidden inside that one sentence.

    What exactly gets backed up?

    Should it create large local zip files or upload directly?

    Does it support manual backups, scheduled backups, or both?

    What belongs in version one and what should wait?

    How should it behave on limited shared hosting?

    What errors deserve admin alerts?

    Should it support restore? Migration? Multiple cloud providers? Incremental backup?

    If you skip those questions and just start coding, the AI will still build something. But now the product is being shaped by whatever the model guesses, not by your actual priorities.

    That is not a development strategy. That is delegation by wishful thinking.

    Instead, I used ChatGPT first to work through the product properly. The discussion narrowed the scope, removed unnecessary features from the first version, and focused heavily on reliability over feature count. The result was a much clearer first release: what it should do, what it should not do, how it should behave, and what kind of architecture made sense for the real hosting environments it would run on.

    Only after that kind of clarification does a coding tool become truly powerful.

    That is the difference I keep trying to explain when people reduce AI development to “vibes.”

    My tool preference is personal. The method is broader.

    For my own work, I use Codex.

    I have also tried Google AI assistance and Google Firebase Studio, and in my own experience they did not come close to Codex for the way I work. I have not had the chance to try Claude Code yet, so I am not pretending to publish a grand ranking of all AI coding tools from a mountaintop.

    But honestly, that is not the most important part.

    The methodology matters more than the vendor.

    If you start with vague thinking, weak scope, and missing decisions, most AI development tools will give you unreliable results sooner or later. If you start with a clear product picture, a defined first version, and a realistic understanding of the problem, you give any strong AI development tool a much better chance of producing useful work.

    Tool choice matters, yes.

    But thought process matters more.

    AI did not remove the need for product thinking. It increased it.

    This is the part many people still miss.

    AI does not remove the need to think clearly. It increases the cost of not thinking clearly.

    When development becomes faster, confusion also becomes faster. Wrong assumptions spread quicker. Bad scope decisions show up earlier. Weak product thinking gets amplified instead of hidden behind longer timelines.

    That is why I reject the framing behind vibe coding.

    Using AI to build products is not unserious. In many cases, it is the opposite. It demands sharper thinking because the execution layer has become much faster. If you are careless, the tool will happily help you build the wrong thing efficiently. That is not innovation. That is just a quicker route to regret.

    Serious AI development, at least in the way I use it, starts before the first line of code. It starts with clarity. It starts with the discipline to define what you want, what you do not want, and why.

    That is not a vibe.

    That is product work.

    And if you do it properly, it can absolutely produce real products.