https://heyq.com/blog Define your product once. Build everywhere. HeyQ manages product intent so every AI tool knows exactly what you're building. Stop re-explaining. Start shipping. Thu, 14 May 2026 15:14:06 GMT https://validator.w3.org/feed/docs/rss2.html heyQ Blog System en All rights reserved 2026, Q https://heyq.com/blog/the-hard-part-of-vibe-coding-isn-t-building https://heyq.com/blog/the-hard-part-of-vibe-coding-isn-t-building Thu, 30 Apr 2026 11:00:00 GMT https://heyq.com/blog/create-product-truth-not-more-tickets https://heyq.com/blog/create-product-truth-not-more-tickets Wed, 29 Apr 2026 10:17:00 GMT “Just write better tickets.” That sounds reasonable. If Cursor forgets what you are building, write a ticket. If Claude suggests something out of scope, write a better spec. If the team loses track of decisions, add more notes. If the product changes during the build, update the backlog. Simple. Also wrong. Not because tickets are useless. Tickets are fine for tracking work. But the core problem in vibe coding is not that builders lack tasks. The core problem is that AI tools do not have a durable understanding of the product. They do not know what is true now. They do not know what changed last week. They do not know which decisions still matter. They do not know which ideas belong in v1, later, or never. They do not know what the product is trying to become unless you explain it again. A ticket can say: > “Build onboarding flow.” Product truth says: > “This product is for solo AI-first builders. The first session should reach value fast. Team onboarding is out of scope for v1. The user should leave with a usable product truth, not a configured workspace.” Those are different artifacts. One tracks work. The other guides judgment. AI-first development needs the second one. --- ## Tickets were built for human coordination Tickets solve a real problem. A team needs to know: - What work exists - Who owns it - What state it is in - What needs to happen next - What has already shipped That is why Jira, Linear, Asana, and every other issue tracker exist. They are coordination systems. They were designed for a world where humans needed to hand work to other humans. A product manager wrote the requirement. Engineers estimated it. Someone assigned it. The work moved across a board. The ticket was the unit of coordination. That model still matters in many teams. But AI-first builders have a different problem. When you work with Cursor, Claude, Lovable, v0, or GitHub Copilot, the immediate issue is not usually “which human owns this card?” It is: - “Does this suggestion match the product?” - “Is this in scope?” - “What did we decide last time?” - “Why did we cut this from v1?” - “What should the AI know before proposing an implementation?” - “What changed after the last PR shipped?” Those are not ticket questions. They are product truth questions. A board can tell you whether work is in progress. It cannot, by itself, tell an AI tool what the product means. --- ## AI does not need a prettier backlog A lot of product software is responding to AI by adding generation on top of the old model. Generate a ticket. Generate acceptance criteria. Generate a summary. Generate a sprint plan. That can be useful. But it does not change the shape of the system. It still assumes the ticket is the center of gravity. For AI-first work, that is backwards. The most valuable thing you can give an AI tool is not a longer ticket. It is the product context that lets the AI make better decisions across many tickets, chats, PRs, and future sessions. The AI needs to know: - The user - The problem - The current scope - The explicit non-goals - The decisions already made - The constraints that should shape suggestions - The language the product uses - The difference between v1, v1.0.1, and v2 That information should not be trapped inside one issue. It should be reusable across every issue. That is product truth. --- ## Why Jira and Linear are the wrong shape Jira and Linear are excellent at what they were designed to do: track work. They are not designed to be a living source of product truth for AI tools. Their basic object is the issue. An issue is a container for a piece of work. It may contain context, but the context is usually local to that task. Once the issue is done, the context becomes historical. Useful if you search for it. Mostly invisible if you do not. AI-first development needs something more persistent. If you decide “team features are out of scope for v1,” that should not live inside one ticket about onboarding. It should become a scope boundary the AI checks every time it suggests a team-related feature. If you choose “speed over customization” as a product principle, that should not live inside one Linear comment. It should shape future implementation, copy, onboarding, and roadmap decisions. If a PR changes the product, that should not just close an issue. It should update what the product truth says is true now. Traditional PM tools can store these notes. But storing is not the same as guiding. The problem is not that Jira or Linear lack text fields. The problem is that their data model treats product knowledge as supporting detail around work, not as the primary artifact every AI tool should read from. For human teams, the board was the operating layer. For AI-first builders, product truth is the operating layer. --- ## Why Notion is too loose Notion is the natural fallback. If tickets feel too rigid, write everything in docs. Product brief, scope, decisions, roadmap, notes, launch copy, meeting summaries, random ideas. Put it all in Notion and call it a source of truth. This works for a while. Then the truth starts spreading. One page has the old positioning. Another page has the new user segment. A launch doc mentions a feature that moved to later. A decision is buried under a heading from three weeks ago. A teammate adds a note that contradicts the current scope. Claude can read one page, but not the whole system. Notion is flexible because it is freeform. That is also the problem. AI tools need structured, current, reusable context. They need to know which parts are authoritative, which are draft, which are superseded, which are decisions, which are scope boundaries, and which are just notes. A wiki can hold product truth. But it does not keep product truth alive. And unless you add structure, rules, review flows, and propagation, your AI tools are still guessing which note matters. --- ## Why raw chat history fails The worst source of truth is also the most common one: AI chat history. This is where a shocking amount of product thinking now happens. You ask Cursor to scope a feature. Claude suggests a smaller v1. You decide teams belong in v2. You explore three pricing models. You ask for launch copy. You accept a tradeoff. You make a decision. Then you close the tab. Technically, the information still exists. Practically, it is gone. Chat history is where product knowledge is generated. It is not where product knowledge should live. It is too long, too noisy, too session-bound, and too hard to reuse. The next AI chat does not automatically know which sentence mattered. Your teammate does not know which thread contains the decision. Your roadmap does not know that v2 idea exists. Your GitHub PR does not know which mission it connects to. The answer is not to save every transcript. The answer is to extract the product truth from the transcript. --- ## Product truth is the missing layer Product truth is the living model of what the product is, what it is not, what changed, and why. It includes things like: - Vision - Target user - Current scope - Out-of-scope boundaries - Later ideas - Product decisions - Rationale - Missions - Roadmap direction - Changelog-ready shipped knowledge - Language and positioning The important word is living. A static product brief is not enough. A launch plan written once is not enough. A backlog full of tickets is not enough. In AI-first development, the product changes while you build. You cut scope in a Claude chat. You accept a smaller implementation in Cursor. You ship a PR that changes the user flow. You discover a better v2 while finishing v1. You move a mission to done and realize the changelog should change. Product truth has to evolve with all of that. Otherwise it becomes another stale document your AI tools ignore. --- ## The comparison that matters The difference is not “HeyQ has AI and Jira does not.” That is too shallow. Everyone will have AI. The real difference is the primary artifact. ### Jira / Linear Primary artifact: the ticket. Best for: - Tracking work - Managing ownership - Running team workflows - Prioritizing backlogs - Seeing delivery status Weakness for AI-first builders: - Product context is fragmented across issues - Decisions are buried in comments - Scope boundaries do not automatically guide AI tools - Shipped work closes tickets but does not necessarily update the product truth ### Notion / docs Primary artifact: the page. Best for: - Freeform thinking - Writing specs - Collecting notes - Creating lightweight wikis Weakness for AI-first builders: - Too unstructured for reliable AI reuse - Multiple pages contradict each other over time - No clear review flow for truth changes - No native connection between AI chats, missions, PRs, and shipped product knowledge ### Raw AI chats Primary artifact: the conversation. Best for: - Exploration - Scoping - Coding help - Fast thinking Weakness for AI-first builders: - Session-bound - No durable product memory - Hard to search and reuse - Future plans, decisions, and rationale disappear into transcripts ### HeyQ Primary artifact: living product truth. Best for: - Giving AI tools reusable product context - Capturing decisions and scope as they emerge - Turning ideas into missions - Keeping missions connected to product knowledge - Linking PRs back to what they shipped - Making product changes reviewable before they become truth This is the category shift. Not tickets with better AI. Product truth that AI can actually use. --- ## A simple example Imagine you are building onboarding. In Cursor, you ask for a first version. Cursor suggests a setup wizard with team invites, workspace settings, profile completion, and integrations. You look at it and realize that is too much. You decide: - v1 should only create the first project - team invites move to v2 - integrations are later - the first session should end with usable product truth, not workspace configuration In a ticket-first system, you might update the onboarding ticket. That helps a little. But the decision is bigger than the ticket. It should affect future suggestions about onboarding, team features, activation, pricing, landing page copy, and roadmap scope. If that decision only lives in an issue comment, the next AI session can still suggest team invites again. In a product truth system, the decision becomes reusable: - The scope boundary updates - The mission reflects what v1 actually includes - The v2 idea becomes a future mission - Cursor checks the summarized product truth next time - Claude can explain why teams are not in launch scope - The GitHub Q Bot can connect the PR back to the mission - Q can suggest a changelog entry when the mission moves to done Same product decision. Completely different outcome. --- ## “Do I still need tickets?” Maybe. But tickets should not be the source of truth. For many builders, especially solo founders and small teams, the unit of work can be lighter: a mission with enough context to act, connected to the product truth that explains why the work matters. For larger teams, tickets may still exist. That is fine. The point is not to ban tickets. The point is to demote them. Tickets should be downstream of product truth, not a replacement for it. Product truth says what is true about the product. Missions and tickets say what work should happen next. When the work changes the product, the truth should update. That loop matters more than the format of the task. --- ## Why this matters more now Before AI, product context mostly moved through people. A PM explained the feature. An engineer asked questions. A designer knew the tradeoffs. A founder carried the vision in their head. It was messy, but humans are good at filling gaps. AI tools are different. They are powerful, but they are literal. They work from the context they can see. If the product truth is scattered, stale, or hidden in a closed chat, the AI will make plausible guesses. Plausible guesses are dangerous because they look productive. The AI will still generate the feature. It may even generate a good feature. Just not necessarily the feature you meant to build. That is why “ship what you meant” requires more than a backlog. It requires a living product truth that every AI tool can consult before it helps you move faster in the wrong direction. --- ## What HeyQ is building HeyQ is the PM tool for the vibe coding era because it starts from a different assumption. Traditional PM tools assume the work is the center. HeyQ assumes the product truth is the center. You can start from an idea, a doc, a PDF, a URL, a repo, a template, or a blank workspace. Q helps structure the product truth: vision, scope, decisions, boundaries, missions, and history. Then that truth becomes useful outside HeyQ. Cursor and Claude can access summarized product truth through MCP, Cursor rules, and skills. When an idea appears in an AI chat, you can ask the AI to scope it, add it to Q, or turn it into a mission. When a mission is finished, the AI can update it with actual product knowledge: what the user gains, what changed, and what decisions were made. When a PR ships, the GitHub Q Bot can connect the code back to the mission. When workspace activity creates an obvious next step, General-Q can suggest it: > “You moved this mission to Done. Should we draft a changelog entry?” That is not more ticket management. That is product truth staying alive as the product changes. --- ## The bottom line The future of AI-first product work is not a better backlog. It is not a prettier ticket. It is not a giant Notion wiki. It is not a folder full of chat transcripts. Those things can hold fragments of the product. But AI-first builders need the thing underneath them: a living product truth that explains what the product is, what changed, what matters, and what should happen next. Tickets track work. Product truth guides AI. And when AI is helping you build the product, the thing that guides AI becomes the most important artifact you have. --- **Want product truth instead of more tickets?** Start with whatever you have — an idea, a doc, a repo, or a shipped product. Q will help turn it into living product truth your AI tools can actually use.]]> https://heyq.com/blog/build-capture-reuse-the-workflow-for-ai-first-development https://heyq.com/blog/build-capture-reuse-the-workflow-for-ai-first-development Mon, 20 Apr 2026 10:11:00 GMT Build → capture → reuse. This is the practical version of Retroactive PM. It is how AI-first builders keep moving fast without letting every decision disappear into a chat thread. --- ## The problem with stopping at “build” Most vibe coding workflows have one step: > Build. That is the magic and the trap. You can go from thought to working software faster than ever. You can test ideas before you fully understand them. You can ask Cursor for the version you could not quite explain, then react to what it gives you. For builders, this feels like freedom. But a build-only workflow has a hidden failure mode: the product gets smarter, but the product memory does not. The app changes. The code changes. Your understanding changes. But the durable product layer stays blank. So the next time you open an AI tool, you are back to narrating the product from memory: - “We decided not to support teams yet.” - “Actually, onboarding changed last week.” - “The billing flow is weird because we cut subscriptions from v1.” - “There was a better approach Claude suggested, but I cannot find it.” - “Cursor gave me a v1.0.1 plan after the last feature discussion. Somewhere.” The build moved forward. The context did not. That is why build → capture → reuse matters. The goal is not to slow down the build. The goal is to make sure the build leaves memory behind. --- ## Step 1: Build Start where the energy is. Open Cursor. Open Claude. Open Lovable. Open v0. Start with the product truth you have, even if it is incomplete. This is important: build → capture → reuse does not mean “start with nothing.” You still want a baseline: - Who the product is for - What problem it solves - What is in scope now - What is explicitly not in scope - What decisions already exist - What constraints matter That baseline is your starting product truth. In HeyQ, that baseline is available to your AI tools through MCP, Cursor rules, and skills. Cursor or Claude can be instructed to check the summarized product truth before they help you scope or build. You should not have to paste your whole product backstory into every new chat. But the baseline does not need to be perfect before the build begins. In AI-first development, product truth often starts as a rough sketch and becomes sharper because you build. During the build, your job is not to preserve the original plan at all costs. Your job is to notice when the work teaches you something worth saving. Useful signals include: - Cursor suggests a simpler version than the one you planned - Claude explains a product tradeoff you had not considered - You cut a feature because it is not needed for launch - You add an unplanned behavior because it obviously improves the flow - A bug fix reveals a deeper product rule - A PR changes what the product actually does - You find yourself explaining the same context to AI for the third time Those are not distractions from the work. Those are the work producing product knowledge. --- ## Step 2: Capture Capture is the step most builders skip. Not because they are careless. Because the existing tools make capture feel like paperwork. If capturing means stopping the build, opening Jira, writing a ticket, choosing an assignee, setting a status, adding acceptance criteria, and pretending this was planned all along — of course builders will not do it. That is old-world capture. AI-first capture is different. You are not trying to document every keystroke. You are extracting the small number of things future-you, your teammate, or your next AI session will need to know. Capture the things that change the product truth. ### Capture decisions A decision is any choice that explains why the product is the way it is. Examples: - “Teams are out of scope for v1 because solo builders are the first target.” - “We chose Clerk because speed matters more than owning auth infrastructure.” - “We are keeping onboarding lightweight because the first session should reach value fast.” If the decision would help you avoid re-litigating the same question later, capture it. ### Capture scope changes Vibe coding constantly changes scope. Sometimes you cut. Sometimes you expand. Sometimes you move something to later. Examples: - “No team billing in v1.” - “GitHub PR summaries are now in launch scope.” - “Slack notifications move to later.” Scope changes matter because they are the rules your next AI session should obey. If they stay in your head, your AI will keep suggesting the wrong thing. ### Capture future work AI sessions are full of future plans. The problem is that they appear at the worst possible moment: while you are trying to finish something else. Examples: - “After this ships, add a better empty state.” - “v1.0.1 should improve import quality.” - “v2 should support shared workspaces.” These should not interrupt the current build. But they should survive it. If you and Claude decide on a v1 and v2, and you only ship v1, the v2 plan should not die in that chat. Ask the AI to write the follow-up as a mission in Q, or add the idea to the right “later” bucket. ### Capture what shipped When a PR merges, the product has changed. That change should not live only in git history. Capture: - What shipped - Why users care - Which user-facing behavior changed - Which product decisions were made - Which scope boundaries changed - Whether it creates a follow-up - Whether it is changelog-worthy This is not about dumping technical implementation details into Q. The code already lives in the repo. What Q needs is the product knowledge: what the user gains, what changed in the product, and what decisions the work created. This is where Retroactive PM becomes very literal: the work happened, then the product layer caught up. ### Capture open questions Not everything needs to become a decision immediately. Sometimes the useful capture is simply: - “We are not sure whether agencies are a primary segment.” - “Need to decide whether project templates are onboarding or growth.” - “Unclear whether this belongs in product truth or missions.” Open questions are better than invisible uncertainty. A product truth with known unknowns is healthier than a product truth pretending everything is settled. --- ## Step 3: Reuse Capture is only valuable if it changes the next build. Otherwise it is just nicer documentation. The point of living product truth is that it becomes reusable context for every tool and every session. Reuse means the next AI interaction starts smarter. Cursor checks the summarized product truth before suggesting an implementation. Claude knows teams are out of scope because that decision is in Q. A mission carries the user-facing reason for a feature, not just a technical task list. The GitHub Q Bot can connect a PR back to the mission it implements. A teammate can see the decision without asking you to retell the whole story. Your landing page reflects the product you actually shipped, not the product you vaguely remember planning. This is the compounding part of the loop. A single captured decision is useful. A hundred captured decisions, scope boundaries, missions, and shipped changes become an operating system for the product. Every future build gets the benefit of past builds. That is the entire point. --- ## The loop in practice Here is what build → capture → reuse looks like in a normal AI-first session. ### Before the session You start with a lightweight product truth: - The target user - The current goal - The relevant scope boundaries - The decisions that should constrain the work You AI tool gets that context through Q: MCP, Cursor rules, or skills tell the AI to check the product truth first. The goal is not to write a perfect spec. The goal is to give the build a memory before it starts. ### During the session You build. Cursor proposes a simpler implementation. You accept it. Claude points out that a feature you planned creates onboarding complexity. You cut it from v1. You and the AI decide that v1 should ship now, v1.0.1 should add a refinement, and v2 should handle the larger version. This is the moment where most product knowledge gets lost. Not because Q is secretly watching everything you do in Cursor or Claude. It is not. Because the AI tool you are already using is connected to Q, you can tell it what to do with the product knowledge: - “Scope this as v1 / v1.0.1 / v2.” - “Add this idea to Q.” - “Create a mission for the v2 follow-up.” - “Update the current mission with what we actually decided.” - “Summarize the user-facing product knowledge from this feature.” You do not stop to write a giant document. You turn the useful parts of the session into product truth while they are still fresh. ### After the session The product truth catches up: - The scope page gets a proposed update - A new mission is created for the follow-up - The current mission is updated with what actually shipped - A decision gets logged with rationale - The shipped PR is connected to a mission - or a mission is created if none exist yet - The changelog draft has the right language You review the changes as diffs. Accept what is true. Reject what is noise. Nothing changes silently. The product now remembers what the build taught you. ### Next session You do not start from zero. The captured truth is available to Q, Cursor, Claude, your teammates, and your future self. The loop closes. Build produced knowledge. Capture preserved it. Reuse makes it valuable. --- ## Capture can start from more than code Not every useful idea appears while you are actively coding. Sometimes you are thinking through pricing. Sometimes you are planning onboarding. Sometimes you are exploring a marketing idea. Sometimes you are trying to decide whether a feature belongs in v1 or v2. In those cases, the workflow is still build → capture → reuse, but “build” may mean building the product model rather than writing code. Open a separate Cursor or Claude chat and ask: - “Scope this idea against the current product truth.” - “Is this v1, later, or out of scope?” - “Turn this into a mission in Q.” - “Add this as a future idea, but do not change current scope.” - “Compare this idea to our existing positioning and decisions.” The point is the same: do the thinking where you naturally do the thinking, then capture the result in Q so it can be reused later. --- ## What to capture vs. what to ignore A common fear is that this becomes yet another documentation tax. It should not. The goal is not to capture everything. It is to capture the things with future value. A good filter: > Will this help the next build, the next explanation, or the next decision? If yes, capture it. If no, let it go. Capture: - Decisions with rationale - Scope boundaries - Meaningful product behavior changes - Future plans you actually want to revisit - Repeated explanations you are tired of giving - PRs that change user-facing behavior - Tradeoffs that would be expensive to rediscover - User-facing product knowledge discovered while building Ignore: - Temporary implementation details - Random prompt fragments - Dead-end explorations with no future relevance - Every tiny bug fix - Notes that only made sense in the moment - Documentation for the sake of looking organized The point is memory, not neatness. If a captured artifact will not make future work better, it is probably not product truth. --- ## Why “living” matters A lot of tools can store a plan. That is not enough anymore. The plan is only useful if it can evolve when the product evolves. In vibe coding, product truth is not a stone tablet. It is closer to a live model of what the product currently is, what it is becoming, and why. That means it has to accept updates from multiple directions: - From the founder's idea - From AI chats where scope gets decided - From missions - From PRs - From user feedback - From the live website - From the actual product as shipped If the product truth only reflects what you believed before building, it will become stale almost immediately. If it updates with every meaningful build, it becomes more valuable over time. This is the difference between a static source of truth and a living product truth. Static truth says: > “Here is what we planned.” Living truth says: > “Here is what is true now.” AI-first development needs the second one. --- ## The role of Q This is what Q is for. Q is not just there to help you write better tickets. That would miss the point. Q's job is to keep product truth reusable across the tools where AI-first builders already work. In the build → capture → reuse loop, Q helps in a few specific ways. ### Q gives AI tools the product truth Any AI toll can connect to Q through MCP, Cursor rules, and skills. That means they can check the product truth before helping you scope, plan, or build. They do not need you to re-explain the whole product every time. The product truth becomes the context layer your AI tools call first. ### Q gives the AI somewhere to put product knowledge When a useful idea appears in a chat, you can ask the AI to add it to Q. Not as raw transcript. As structured product knowledge: - A mission - A scope update - A decision - A future idea - A changelog-ready note - An update to an existing mission This is the capture step. Q does not need to watch everything. It needs to be the place where the AI can put the important parts when you ask it to. ### Q connects shipped work back to missions The GitHub Q Bot connects PRs with missions. That matters because a PR is where planned work becomes shipped product behavior. If the implementation changes the product, the mission and product truth should be able to catch up. Again, the goal is not to store code details in Q. The goal is to preserve the product knowledge created by the code: what shipped, why it matters, and what decisions were made. ### Q suggests the next product hygiene step There is also General-Q: the Q that understands what is happening inside your account and can suggest useful follow-ups. For example: > “Hey, you moved this mission to Done. Should we draft a changelog entry?” Or: > “You created three missions around onboarding. Should we group them into an operation?” This is not Q spying on your coding session. It is Q responding to actions inside your HeyQ workspace and helping you close the loop. The important part is control. Q should propose truth changes, not silently rewrite the product behind your back. Builders need speed, but they also need trust. Reviewable diffs are what make living truth safe. --- ## The workflow is the product moat At first, build → capture → reuse sounds simple. It is. That is why it matters. The hard part is not drawing the loop. The hard part is making it natural enough that builders actually do it. Traditional PM tools are built around the old loop. Their data model assumes the work is defined before it happens. A ticket is born in the backlog, travels through statuses, and ends at done. But AI-first work does not always start as a ticket. Sometimes it starts as a question. Sometimes it starts as a prompt. Sometimes it starts as a PR. Sometimes it starts as a half-built feature that needs to be understood after the fact. A product tool for this era has to accept all of those as valid entry points, then turn the useful parts into product truth. That is the shift. Not better backlog management. Better product memory. --- ## The bottom line AI-first builders do not need more process for its own sake. They need a loop that matches how they actually work. Build when building is the fastest way to think. Capture the decisions, scope changes, shipped behavior, and future plans that should survive the session. Reuse that product truth so the next build starts smarter than the last one. That is build → capture → reuse. Not a ceremony. A memory system for the vibe coding era. --- **Want to build this way?** Start with whatever product truth you have — an idea, a doc, a repo, or a rough plan. Connect your AI tools to Q, build the way you already build, and use Q to capture the product knowledge worth remembering.]]> https://heyq.com/blog/retroactive-pm-product-management-for-builders-who-build-first https://heyq.com/blog/retroactive-pm-product-management-for-builders-who-build-first Thu, 12 Mar 2026 10:43:00 GMT “How do we fully define the work before anyone starts?” The question is: > “How do we keep the knowledge generated while building from disappearing?” That is the job of Retroactive PM. --- ## Traditional PM is front-loaded Traditional PM is built around a front-loaded workflow: 1. Define the requirements 2. Break them into tickets 3. Prioritize the backlog 4. Assign the work 5. Build against the plan 6. Ship The plan sits upstream from the build. The work is supposed to conform to what was decided earlier. That workflow works best when you have multiple teams, expensive coordination, long delivery cycles, and high cost of change. If ten people need to stay aligned for six weeks, the planning layer matters because it prevents everyone from walking in different directions. But that is not how a lot of AI-first builders work. A solo founder does not always know the feature before opening Cursor. A two-person team does not always have a sprint plan. An indie hacker might discover the actual product by prompting, deleting, rewriting, and shipping three rough versions in a weekend. The old workflow says: > Plan → ticket → build. Vibe coding often looks more like: > Build → realize → decide → adjust → ship. The product thinking is still happening. It just moved. It moved into AI chats. Into half-finished Claude threads. Into Cursor suggestions. Into PR descriptions. Into the moment where you say, “Actually, v1 should not include teams,” and then forget you said it. Traditional PM assumes the important decisions happen before the work starts. Vibe coding proves that many of the important decisions happen because the work started. --- ## The build process generates product knowledge This is the part that is easy to miss. When you build with AI, you are not just producing code. You are producing knowledge about the product. Cursor suggests a smaller v1 than the one in your head. That is product knowledge. Claude explains two possible architectures and you choose the simpler one because speed matters more than control. That is product knowledge. You cut a settings page because it is not needed for launch. That is product knowledge. A PR ships an unplanned improvement that changes the onboarding flow. That is product knowledge. You decide that teams belong in v2, not v1. That is product knowledge. None of this looks like a traditional ticket. Most of it is not even a task. But it is the stuff that makes the product coherent. The problem is that the places where this knowledge appears are terrible places for it to live. AI chat is not a product memory. Git history is not a product memory. A closed Cursor tab is definitely not a product memory. They are places where knowledge is generated. They are not places where knowledge survives. That gap is what Retroactive PM is designed to close. --- ## What Retroactive PM means Retroactive PM is the practice of capturing what the build process reveals, turning it into durable product truth, and reusing it across future work. More simply: > Build first. Capture what matters. Reuse it next time. It is not “write tickets after the fact.” That would be pointless. Nobody needs a beautiful backlog of work that already happened. Retroactive PM is about capturing the product truth that the work exposed: - What shipped - Why it shipped that way - What changed from the original idea - What got cut - What moved to later - What decision the builder made mid-flow - What future plan appeared during the build - What the next AI session needs to know The artifact is not bureaucracy. The artifact is memory. A traditional ticket says: > “Do this work.” Retroactive product truth says: > “This is now true about the product.” That distinction matters. Because vibe coders are right to reject heavy planning rituals. They do not need to turn every idea into a ticket before they are allowed to explore. They do not need to stop the build so they can perform product management theater. But they do need the product to remember what the build taught them. --- ## The new loop: build → capture → reuse Retroactive PM has a different loop from traditional PM. Not: > Plan → ticket → build → close. But: > Build → capture → reuse. ### 1. Build You start where vibe coders naturally start: in the work. You open Cursor. You ask Claude. You try the idea. You generate the first version. You let the product become clearer by touching it. This is not a failure of discipline. This is the point. AI makes exploration cheap enough that building can become a form of thinking. ### 2. Capture As the build creates knowledge, the important parts get pulled out of the places where they would otherwise disappear. A v2 idea becomes a future mission. A scope decision becomes a product boundary. A shipped PR becomes a changelog-ready summary. A tradeoff becomes a decision log entry. A changed feature becomes updated product truth. The capture does not have to interrupt the builder. Ideally, it happens alongside the build — proposed, reviewed, and accepted when useful. ### 3. Reuse The captured truth becomes useful the next time you build. Cursor gets better context. Claude does not need the whole product re-explained. A teammate can understand why the feature exists. Your launch copy reflects what actually shipped. Your roadmap includes the v2 idea that would have died in a chat thread. This is where the value compounds. The first capture saves a note. The tenth capture creates memory. The hundredth capture becomes a living product model that every tool can work from. --- ## Retroactive does not mean careless There is an obvious objection here: > “Isn't this just an excuse to avoid planning?” No. Retroactive PM is not anti-planning. It is anti-fake-planning. Sometimes you know what needs to happen before you build. Great. Write the mission. Define the scope. Give your AI tool the context and let it execute. But sometimes you do not know yet. Sometimes the fastest way to understand the product is to build a rough version and see what breaks. Sometimes the best product decision appears halfway through a debugging session. A good product layer should support both modes. Plan ahead when the plan is real. Capture retroactively when the truth emerges from the work. The mistake is pretending all product thinking must happen before code exists. That was never completely true, and AI makes it obviously false. --- ## Why this matters more with AI Retroactive PM is not just a nicer workflow. It becomes necessary because AI accelerates both creation and forgetting. A human team might take two weeks to generate ten important product decisions. An AI-first builder can generate ten in an afternoon. The velocity is intoxicating. But the memory loss scales with it. Every new AI session starts colder than it should. Every tool switch drops context. Every unrecorded decision becomes something you have to rediscover later. Every shipped feature without rationale makes the product harder to explain, market, refactor, or hand off. AI makes the build faster. It does not automatically make the product more coherent. In fact, without a product memory, it can make the product less coherent faster. That is the hidden cost of vibe coding: not bad code, but lost truth. Retroactive PM is how you keep the speed without letting the product dissolve into chat history. --- ## What HeyQ is building This is the pattern HeyQ is built around. HeyQ gives your product a living truth: the vision, scope, decisions, boundaries, missions, and history that describe what the product is and why it is that way. Q keeps that truth current as you build. Not by forcing you to write perfect specs before touching code. Not by turning vibe coding into Jira with better branding. By capturing the knowledge your build already generates and making it reusable: - AI chats can become structured updates - PRs can become missions, decisions, and changelog-ready summaries - Scope changes can become reviewable diffs - Future plans can survive the session where they appeared - Cursor and other AI tools can read from the same product truth instead of starting from scratch The point is not to slow builders down. The point is to stop the build process from throwing away its own intelligence. --- ## The bottom line Traditional PM was designed for a world where planning came before building. Vibe coding creates a world where building often comes before planning is complete. That does not eliminate product management. It moves it. The new product layer has to sit beside the build, not in front of it. It has to capture what changed, what shipped, what was decided, and what should be remembered next time. That is Retroactive PM. Build first when that is how you work. But do not build without a memory. --- **Want to try Retroactive PM?** Start a HeyQ project from an idea, a doc, or your existing repo. Build however you build. Q will help turn what you discover into product truth that survives the next session.]]> https://heyq.com/blog/do-vibe-coders-even-need-a-pm-tool https://heyq.com/blog/do-vibe-coders-even-need-a-pm-tool Thu, 12 Feb 2026 13:11:00 GMT https://heyq.com/blog/vibe-coding-is-fast-vibe-coding-forgets https://heyq.com/blog/vibe-coding-is-fast-vibe-coding-forgets Mon, 09 Feb 2026 21:13:00 GMT