The new vibe coding interview round (00:00)
Aakash: I have just finished AI interviews at Uber, Atlassian, Cisco, Roblox, and a few other companies. One of the fastest growing AI PM interviews is the vibe coding interview, where you are essentially designing a product and taking it all the way to a prototype.
I’ve been hearing from more and more PMs I’m coaching that they’re encountering a new PM interview round. This isn’t the traditional product sense round, and it isn’t an engineering coding round either. It’s a hybrid of the two, and I’m calling it the vibe coding round.
AI PM roles at companies like Google, Meta, OpenAI, and Anthropic pay anywhere from 300,000 to north of a million dollars. I’ve been interviewing AI PM leaders like Jiona Zen, the CPO at Laurel AI, who literally ask you to screen share how you use AI in the interview. If you’re nervous about such an upcoming interview, then you need to watch today’s episode.
Every video out there on vibe coding and PM interviews is about building a from-scratch new product. But the questions you’re actually most likely to see are building features on existing products. Today’s episode will go through a mock interview for you to actually see what it’s like if you have to build an existing feature for a new product, and show off your AI prototyping and vibe coding skills in an interview live.
The prompt: build a feature for LinkedIn (01:20)
Interviewer: Very excited to be interviewing you today. We’ll be doing this interview for about 45 minutes, and for about 30 minutes of this, what I’d love for you to do is vibe code a feature or capability. I’ll be giving you a prompt, but would love to see how you go about the process of vibe coding this out.
Today we’re going to be building a feature for LinkedIn. So assume that you are a product manager at LinkedIn. LinkedIn really cares about you staying in touch with your network, and what we are also realizing is people are generally apprehensive of doing so. We’re hoping that AI can reduce some of these barriers. So help me build or conceptualize a feature, and then of course take it to a prototype where you, within the constraints of LinkedIn, are doing a better job of staying in touch with your network. Does that sound good?
Aakash: Let me understand this interview correctly. In about 30 minutes, you want to see how I actually vibe code and prototype a solution where we’re helping people stay in touch with their network with AI.
Interviewer: That is correct. This has to be AI-first, while at the same time, I would like to see you using AI to build a prototype.
Defining the goal and the user problem (02:31)
Aakash: What is the goal of this feature? Why are we building this feature versus any of the other things we could be building?
Interviewer: What we have found out is that users come to LinkedIn to consume content, but that is more of a passive activity. We’d want users to be actively engaged with the rest of their network, which involves a certain cognitive load or barrier. We are hoping to bring down this cognitive load and barrier for users so as to then increase retention and engagement with the platform in general.
Aakash: Just to repeat it back, as I understand the mission and vision of LinkedIn as a whole, you guys have talked a lot about how you want to create economic opportunity. People get economic opportunity by retaining on the platform and engaging with their network. And so the goal is to move them from content passive consumers to active engagers in their network.
Interviewer: Correct.
Aakash: I’m going to take like two to three minutes, if that’s okay, just to gather my thoughts. Actually, I don’t even think I needed two to three minutes. I’d rather just talk through this all together. Let me share my screen, since I know you wanted to see how I do all this.
I’m going to start in a low-fi way, without AI or anything, because I think it is important. The way I think about using AI is AI can do the middle 60% of the work, but the 20% before, the initial thinking, is still important to do as a human, and then the final refinement is really important to do as a human. So I want to start as a human.
We only have like 28 more minutes. I just want to spend five or six minutes almost speed running what would be a typical 30-minute product sense interview. Then I want to go to the screen share part. How I want to do that is think about what is the PRD for the solution we choose, prototype it across a couple different tools, choose a tool, iterate on that one, and maybe even build out backend at the end. Does that sound good?
Interviewer: That sounds reasonable. Yes.
Speed-running the product sense round (04:23)
Aakash: I haven’t thought about this any further than just putting this user tag, so let’s think about it out loud together. There’s sort of a 99% / 1% split of LinkedIn users like you said. 99% of people consume content, 1% write content. This point is kind of orthogonal even to how much they engage with network.
Another vector we might want to think about is that in any given month, some crazy percent, a little less than 99%, say 95% don’t engage with their network and 5% do. What we really want to do is think about, across those 95% who don’t engage, who are those people and who do we want to build for?
I’d start to think about the buckets we should put together for these people. An important and relevant dimension is going to be whether they’re job searching right now, which is probably only like 5%. The reason I’m putting numbers is just because I want to get a sense of how big these groups are. So 5% are probably job searching right now. Then 80% probably want to keep up their professional profile for when they job search, and that’s where networking might have a use case that’s different from job searching. And then there’s probably like 15% of people who couldn’t care less about their career.
On the low end, we have people who aren’t career optimizers. On the high end, we have people who are already too rich. I think the joke was that the new CEO of Apple didn’t even have a LinkedIn. So there’s people on both sides who couldn’t care less about their career.
What I’m seeing right away by putting some numbers on this is that the bucket of people who want to keep up with their professional life is going to be really interesting to play with, and the job searching bucket might end up being the easiest to capture. So I think we can focus on both these as focus areas.
Prioritizing needs across user groups (07:46)
Aakash: Job searching. These people are looking for referrals. Referral is how you get a modern job. They’re looking to create connections at target companies. They’re looking to talk to people ahead of them in their career. So maybe this person is a PM at Google and I really want to be a PM at Google. Even though I’m not targeting a referral, I want to talk to them so I can learn how they did things. They also might be trying to warm up their own network, their alumni and different things like that.
So I’m going to move on to the professional profile bucket. If you want to keep up your professional profile, it’s almost different, because you don’t care as much about referrals. You do care a lot about talking to people ahead of your career. Then we have to think about the other people who want to keep up with their professional profile. What I’m trying to do is think about the triggers, because as we build this product, there’s going to be some trigger or use case for why they’d care about keeping in touch with their network. So we see two overlapping needs across these groups already.
Interviewer: One of the reframes I can probably suggest here, Aakash, is I’d love for you to focus a little bit more on what are the hurdles that stop people from connecting to their network. I like where you’re spending time on the users, but if you could talk about what really stops people or makes it awkward for people to reach out. Fair to assume they are soft searching all the time. I’d love to hear how you really want to get into that problem statement here.
Aakash: We’ll focus on the soft job searchers, and thanks for keeping me going fast here on the users. What you’re asking next is what are the problems we want to overcome? One of the big things is you even forget how you knew someone. You forget why you might need to talk to someone. An example is you’re trying to do a deal with their company, or you’re going to a conference they’ll be at, or you’re solving a problem that they also solved in their job. There’s this whole amount of product friction we can explore, and that’s probably going to be a pretty fruitful area for prototype design.
Mapping friction in the current workflow (11:19)
Aakash: I think here we can just do an audit together. Let’s go look at the whole flow. If I hit my network tab, this takes me to invitations. Then there’s this catch-up feature. Look at this, we’ve got an empty state in our catch-up feature. As we go through the audit, this could be an interesting first area to attack. I’ve dropped it here into the Miro.
Then, job changes, birthdays. Because I’ve turned this off, so let me turn this on for a second. This is tied to our notifications. Why is our updates section tied to our notifications? That’s another audit we could do. I have to turn on in-app notifications in order to get access to job changes. Super odd. And then it ends up saying no recent updates, and grow your network drops you back here. So basically that button is just completely broken.
We can see the current workflow for the product has a lot of friction in even discovering who to talk to, and there’s probably a lot of fruit we can have just from optimizing that.
Let’s assume I figured out who to talk to. I’m connected to this guy, he’s a member of technical staff at Anthropic. When I click in, I don’t know how or why I’m connected to this guy, when we even connected. So I’m totally lacking in this information. There are almost two elements that have come out of the audit so far. Area one is that we aren’t recommending who to talk to very well. The second area is once you get to somebody, giving you the information to talk to them.
A word on Land Your Dream PM Job (13:36)
Aakash: I want to take a second to talk to you about the fourth cohort of Land PM Job. I trained 30 students in cohort 1, 50 students in cohort 2, and 75 students in cohort 3. And I am bringing back the program for cohort 4.
It starts in August and it lasts 3 months, where you’re going to have intense sessions. A Monday morning session where I go over your resume, behavioral interviews, LinkedIn. On top of that, Bart Jorski is going to be teaching you the PM fundamentals in 2026: how to write AI PRDs, how to AI prototype with Claude Code, all of the key skills you need to freshen up your knowledge for this market. Ang Vermani is going to be teaching you AI product management. He is an AI product manager at Uber, and he is going to teach you how to build AI features that actually work successfully. On top of that, Prasad Reddy is going to be doing one-on-ones with you for market reviews, LinkedIn review, candidate market fit review. So it is a full package. It is three courses in one for one low fee. So join at landpmjob.com.
Finding the AI writing gap (13:36)
Aakash: The next area where there might be friction is if I’m messaging somebody. If I go to message her, what happens when I hit write with AI? Introduce yourself. It takes quite a while, but we do have an AI feature. This is good. Part of it might be we have a pretty strong AI feature. What if we bring that forward and get it to people sooner?
Interviewer: A pretty strong number of em dashes in there do scare me though, Aakash. That is not at all humanized, plus it is not pulling in the right context from either your past interactions or your broader relationship with that person.
Aakash: I was thinking about some sort of past interaction public widget, so I’m glad you brought that up.
Interviewer: Just double click on that a little bit, Aakash. The thing we think with LinkedIn is that the true value of your network lies in relatively weak ties, which is why the person you selected here is someone you haven’t touched base with in a bit. Or in some cases, you don’t even remember who that person is. The problem is not that you forget, it’s that you don’t have a non-awkward reason to reach out.
Your audit definitely revealed this as a pretty big gap, which is you are being prompted, it’s almost a notification that you’re receiving, and that notification is meant to be an organic reason for you to reach out, but doesn’t do a great job. On top of that, cold outreach itself is hard and doesn’t pull in the contextuality. That’s the frame I’d love for you to carry through the interview. Both the fact that it’s awkward to reach out for a non-genuine reason, and that cold outreach is generally hard for people. That is what we have observed with our users.
Aakash: I’m glad we were thinking alike. Let’s do that. Let’s assume I was going to do a full exploration of the problem and solution space and prioritize, but it sounds like you’re saying let’s just move forward with a solution along the lines of reaching out to weak connections with context and a relevant message, easily.
Interviewer: I’d love to see more of the prototyping part, Aakash. That’s why I am sort of pushing in this direction.
Writing the PRD and the three non-negotiables (17:10)
Aakash: To build a good prototype, we need to clearly create the right context now. We can go into what the PRD for this looks like. Some of the key elements we need to put in are the overall goal, which is to drive economic opportunity by getting back together with your weak connections.
Then the non-goals first. The non-goals are spam messages, because people are going to leave our platform, or out-of-context messages, or messaging people you don’t know but are just connected to.
Then the metric of success. There’s going to be leading and lagging metrics. The leading metrics we’re going to care about are people read the message, people respond to the message. I don’t think we’re actually reading their messages, but if we were, we could even create an LLM judge that talks about did they do something together. Those would be the leading metrics. The lagging metric, like you already talked about, is ultimately we’re trying to drive retention.
So we’ve got the overall hypothesis, we’ve got the context, we’ve got the non-goals. These I think are the three non-negotiables to creating a good PRD. So now I think we’re ready to prototype. Do you think so?
Interviewer: Yes, I do think so. Just one quick note on the PRD itself. As you look into the experience, it would be great to define what a weak connection looks like, as well as, when you say you’re going to reach out with the right context, how you think about the context that’s already available on LinkedIn versus your broader relationship with the person.
Aakash: We can discover and tackle it later, but at the high level, what we want to define for the PRD is that a weak connection is somebody you’re first-degree connected to but haven’t messaged or commented on their content in the last 3 months. Something like that. And then context available on LinkedIn: any shared employers, academic institutions, locations, and then engagement data, and things like application data if you’re interested in the same field. Those would be the main things.
The curveball: what belongs in your harness (20:09)
Aakash: Let’s move into prototyping. As you can see, I spent a lot of time on the product journey before, because prototyping, if I look at when I was doing it 3 years ago versus now, a lot of the skill has actually dissolved. The models keep getting better, so the upfront definition is more what matters.
I’ve created a folder, just a blank folder, inside of Claude Code. What I’m going to do is build out the context for this folder.
Interviewer: Let me throw you a curveball question here. I know you’re assuming this is a blank folder, but if you had a magic wand and could have a harness here, what are the top three things you’d want to have in this harness so as to prototype much faster?
Aakash: I could even pull those, I can pull my own PM operating system. There’s three things: the design system, the overall strategy and prior features, and a prototyping skill. Those are the three things I’d want in here. We can just prompt it right now. Create a context library. Assuming you’re a PM at LinkedIn using publicly stated info, create an AI prototyping skill and create the LinkedIn design system. For that, I’ll give it a couple of screenshots, some of the ones we already took in Miro, so it doesn’t have to go looking for more.
I didn’t even place it super carefully in the prompt, because I found, especially Fable, which we’re using right now, it can handle it. So this will be my harness. That’s Claude Code.
Running the prompt across Magic Patterns, Lovable, and Claude Code (22:39)
Aakash: I actually wouldn’t only use Claude Code. What I found is that it can be useful to create one prompt and then use it across multiple other tools. One of the other tools I like to use is Magic Patterns. What I like about Magic Patterns is it doesn’t create a backend, and because it’s front-end only, it’s really fast.
The easiest way to write out this prompt for a tool like Magic Patterns when it doesn’t have a harness, in my opinion, is to go to Claude Chat and create the prompt for this together. Write a prompt for AI prototyping tools like Lovable and Bolt and Magic Patterns. Now it knows what we’re doing.
Then, how am I going to give it the context? I’m literally going to give it the Miro we just created together. I’ll zoom in on some of this information, paste some screenshots in so it really understands. We want to generate diverse solutions that surface weak connections with contextual messages that can help the user generate economic opportunity. Some of the surfaces we identified are three: the homepage, the grow your network page, and profiles of your first-degree connections. Then I’ll say write a PRD. The idea is that the AI prototyping tool can brainstorm different diverse solutions.
I’d honestly open up another tool too. My third favorite tool for this kind of work is Lovable. So now we’ve got a prompt being built in Claude, we’ve logged into Magic Patterns, we’ve got a harness being built inside Claude Code, and we’ve got Lovable. That’ll be our third tool for this parallel prototyping workflow.
Let’s go into Claude Code and see how it’s doing. It’s created all of this for us. I always like to sanity check this. How did it do with the design system? Pretty good. How did it do with the context library? Pretty good. For the context library, it looks like it needs a little bit more information. In this product section, it doesn’t have all the context we just talked about. One of the things that will help is giving it those Miros.
Let’s take a look at the Claude prompt we created. Generate five distinct UI concepts for a feature that helps professionals reconnect. Looks great.
What makes a good prototyping prompt (26:26)
Interviewer: Can you talk for a few seconds about what makes this a strong prompt? What distinguishes a good prompt for prototyping from a not-great prompt?
Aakash: It’s actually the same as any prompt, if I’m honest. There’s four things roughly. Number one, giving it a very clear definition of the steps of the task it should pursue. That’s why it starts with generate five distinct UI concepts. Number two, defining your context. It has really good definitions of the context. Number three, showing what you want the output to be. It has really good requirements and an output section. Number four, saying what you don’t want. So it has all four elements of a pretty good prompt. Sonnet did a decent job with this.
Interviewer: Do you not want to give it some design guidance on this being for LinkedIn?
Aakash: In the prompt itself, it says right at the top. My bad. It has some information, but it could be better. Honestly, you’re right. Let’s stop these prompts and iterate. A lot of times we do need to iterate on the prompt.
I’m also thinking about this: I don’t know if I really want the prototyping tool to generate the five diverse solutions. I don’t even want Sonnet to generate them, I want something like Fable to generate the five diverse solutions, and we’ll prototype one in each of the tools. So I’m going to go to a more powerful model now and do that brainstorming step. You could do it in a prototyping tool. It depends. Designers talk about are we code-first or canvas-first. I kind of trust Fable more, and I don’t want to give too much away to those tools that don’t even sometimes show me which model they have underneath.
Why you define the design system first (29:03)
Aakash: While that’s happening, let’s go back into Claude Code. This is where we had the design system already in place, so we can use the prompt we originally got. Now it becomes a little bit of a wait for Claude, and that’s why I like to have parallel systems.
One of the workflows I really like, since that prompt is taking some time, is to have it define the design system first before you give it any more information. What do I mean? Learn the design system of LinkedIn. Simple as that. Then we give it that one screenshot we created. I’ll give it the exact same prompt here in Magic Patterns. So now we have three AI agents working for us. Each of them is going to take some time.
Let’s go back to Claude, which was working on our five diverse solutions. The five concepts it created, it didn’t format these very well, but let’s keep moving for the sake of time. In the homepage feed, a warm signal guide. That’s the type of thing I want to prototype. A dedicated tab, grow your network like an inbox. Also cool. The user’s first-degree network rendered as a visual map. Also very cool.
Prioritizing the five concepts (30:48)
Aakash: Against these, what I want to do is quickly apply a framework. How innovative is this? How much is this really going to drive success? Does that surface get enough area? Will this drive our ultimate goal?
Immediately, we know the heat map is going to be a lot more innovative than the reconnect queue, but it’s going to roughly rate the same on the other two variables. Warm surfacing is going to get a lot of volume, so it’s really going to drive the outcome, but it’s not very innovative. A profile context layer just seems like it needs to be part of most of what we’re doing, so it can go in harmony with whatever we create between one and three. A homepage notification can also go in harmony. So four and five are surfaces for a feature. What we’re really thinking about is one and three, and this is the most innovative. So I want to do a combo of concepts, three through five. Create the final prompt for Lovable, Magic Patterns, Claude Code.
I know we’re almost at time. What’s going to happen next: we’re going to take this prompt, take like 30 seconds, paste it into the three, and see which one we like the most.
The back-end question (32:25)
Interviewer: While we’re waiting, I know you’ve shared the key tools you’d use for front-end design. If you also had to do a back-end design for this, could you talk about what tools you would use and how you’d go about that?
Aakash: I am a sucker for an IDE plus either Codex, Gemini, or Claude model. We were looking at Cursor plus Claude Code. What I’ll do, if I’m in Magic Patterns for instance, it allows you to export, or Lovable you can also publish to GitHub. So with Lovable I publish to GitHub, and with Magic Patterns I export, and I take it into an IDE. I’m a Claude fan over Codex or Gemini for 90% of cases. The only 10% I really use Codex for is very long-running coding tasks, like when I’m actually coding a backend.
For the front-end work, I tend to prefer the taste of Fable 5. So I’d be using Fable 5 for most of that. If I had some long backend tasks, I might use Codex for it. One, because it’s a little cheaper than Fable 5, and two, because I find it’s much better. It’s like a workhorse, it’ll work for a whole day. Fable wants to work for like an hour.
Evaluating the concepts (33:37)
Aakash: Let’s go into this final prototyping prompt. I agree with this prompt. Now I’m going to go into Magic Patterns. It had a question about whether it should scaffold the system. I said yes. This one did too, I said yes. Then I hit it with the prompts.
Now let’s look at Claude Code. If you recall, we actually gave it a little more autonomy. Claude Code came up with its own different concepts. It looks like it came up with five different concepts that it’s working on, and it’s developing these HTMLs for them. It looks like the first four concepts might be ready. Let’s go into concept one, that’s most likely to be fully ready. This would be warm threads. If we open this in browser, nice. I’m loving this.
Interviewer: Yeah.
Aakash: It did a pretty good job of keeping true to the design system and pulling in the right context. I’m pretty happy with this. What I’m saying is this is in the design system. It’s a real feature that will work.
Now we’re going to evaluate the other concepts. Reconnect in Q. Worth reaching out to this week, write with context, exactly the problem we were talking about. Home moments. The moments one, I feel like this is almost a P2 or P3 feature, because those other ones are hitting much more common surfaces and happening more often for more people. So I like those a little better. Here’s what a draft will look like. I like the draft, it’s actually using the context in a strong way. And then the last one, constellation. It may not be ready yet, but it looks good enough.
This is so interesting. When we were talking about this in chat, I thought the constellation would be the killer feature. Now I see it, and I’m like, maybe not. Based on everything I’m seeing, warm threads is really powerful, reconnect queue is super powerful, and then drafts first. Those three, based on my product sense and product taste, would be the most powerful.
How to finish the journey (36:29)
Aakash: I know we’re running out of time, so let me summarize what I’d do from here. I’d put these all together, keep it at front end, and then user test this a little bit with myself and my own team. Then I’d take it to a system like UserTesting or UserVoice where you can literally pay people to interact with a front end, and I’d get their feedback. Once I got the positive signal, I’d go into the back end and create this as a real live prototype, connect it into our GitHub, pull from our actual codebase and design system. From there, I think we might be ready to do like a 0.1% AB test.
Interviewer: That sounds great. Thank you so much. I know we’re at time, so I really appreciate you going through the flow, Aakash, and for taking all the curveball questions. How are you feeling about it?
Aakash: We good. I wish we could have seen Magic Patterns and Lovable finish up, so I’m glad you pushed me on the time. The big challenge here is always getting through everything in 30 minutes. But if we imagine a world where we’re surfacing up warm connections to you in your home feed and in your grow your network tab with in-context messages, we’ll for sure make a significant dent in leading metrics like how many messages are sent and responded to, and hopefully lagging metrics like retention.
Interviewer: It was very nice to see Fable pull both the importance of the direction we had given it, in terms of warmth and cold outreach, as well as stick to the design system so well. The fact that you established a harness right out of the gate, which then was able to pull in the design system, was quite crucial to that.
Self-assessment and the core trade-off (38:20)
Aakash: Overall, I’d give myself like an 8 out of 10. You rightly pushed me on let’s get to the actual prototyping and how do you prototype well, because I was doing too deep a job on the user and problem. If I were to do it over, I’d go even a little bit faster on user and problem so I could have more time and we could see the output.
Interviewer: You were doing a great job from a traditional product sense standpoint. I would have loved to see that level of depth and thoughtfulness in a traditional product sense interview. What’s more critical in a prototyping interview is you get that out of the way quickly so as to showcase your skills with the prototyping part. The trick is having attained the depth of thinking in a shorter time span, so as to have your prototype be deep and thoughtful, not just surface level. That’s the key trade-off you face. If you don’t spend sufficient time and thoughtfulness in the first part, then your prototype is not going to be great. The interviewer is not there just to see your process, they’re also assessing the outcome you land at. I think you were able to accomplish both quite well.
Aakash: So would this be a passing interview, do you think?
Interviewer: Oh, this would definitely be.
Feedback bucket by bucket (39:38)
Interviewer: Let me go through, I have my notes right here. Overall, you probably underrated yourself a little bit. I’d put you between an 8.5 and a 9. A very clear and confident pass for me, and it was genuinely just great to watch you tackle all the curveball questions.
Let’s break this down into the different buckets you use, because the framework you use, Aakash, is a great one for approaching any prototyping interviews.
On user, I’d put you at an 8.5 to 9. You segmented really well, everything from network engagers to job searchers, and putting numbers on each of the buckets was super helpful. That is generally a strong instinct, and the way you connected it back to the mission really anchors you in the user being critical to the company. It was crisp, it was correct. What would have been great to see was a little bit of a mid exploration on how you keep the professional profile bucket after I redirected you.
The second part is problem. This is where I’d put an 8 out of 10, because I had to push you a little bit to get to the core underlying problem of hurdle, cognitive load faster. The live product audit was the highlight of the section, and that’s probably better than 95% of candidates, who are going to theorize the entire experience rather than actually opening up the app. You found the empty catch-up state, the bizarre notification coupling. All of those were very real problems that the interviewer is going to say, ah yes, I see why this doesn’t work today. That leads you very clearly into laying out potential solutions.
On the solution, you landed pretty well. Once the concepts rendered, you were able to generate very clear five directional ideas. I’d put you in the 8.5 to 9 bucket. To me, the thing that would have made this even stronger would have been more specificity and more opinion on the solution itself. But given the time constraint, I’m not going to ding you against it. If you were in a product sense interview where you had a lot more time, that is where the taste and opinion piece plays a much bigger role.
On the PRD, you hit the non-negotiables pretty cleanly. You had a very clear, sharp set of goals, a set of non-goals, and a very clear leading and lagging metric split, which was pretty awesome. A solid 8 to 8.5.
On the prompt, even though your prompt was generated using AI, I don’t think that comes naturally to a lot of people. So despite the fact that Fable or Sonnet generated your prompt, I’m still going to give you a 9 to 9.5. The part where you were able to talk about what constitutes a good prompt really stood out. It’s not that you didn’t write the prompt well, but if you were given the time and the head space to write it, that makes me confident you’d be able to write a great prompt. Watching you use AI to write the prompt was the cherry on top.
Parallel tools and AI nativeness (43:25)
Interviewer: On parallelization, which is one of the very unique aspects of your framework, that is the part most people are going to underestimate or not leverage well. In a very short burst of time, you leveraged Magic Patterns, you leveraged Bolt, Claude Code, and you quickly talked about Codex and a couple of other tools. The way you were walking through it made me confident you had a good idea of what to use when, and even how to use it. You didn’t use Claude Code for idea X, you used it specifically for first building the harness and then jumping in. You used a very specific tool for generating your idea graph, and so on. Working in parallel with a pretty broad set of tools shows the AI nativeness that most interviewers are looking to solve for.
Finally, back end. We didn’t get to spend a whole lot of time on the back end, but talking about Codex and how you would go about completing the rest of the journey was a confident enough signal. I’d give you an eight out of 10. If you could have gotten a little bit more into the backend design, I’d probably have given you a 9 to 9.5, but again, very solid.
Overall, I think it’s a solid 8.5 to 9. Very solid score for an in-product evolution of an idea. To close this off, most people underestimate how hard it is to iterate on an existing and mature product than to come up with a new idea. It’s pretty easy to say, okay, build product X for a completely new set of users. But by asking you to evolve an existing product and its features, there are already constraints and assumptions in place that are often not surfaced or taken into consideration. That makes it much harder to play into. So kudos on cracking that well.
Aakash: Thank you.
The UPS PPPB framework and closing advice (45:41)
Aakash: Let me put together for you guys what we just showed in a visual. I wrote this piece for you all, and this is what we teach in the cohort about the vibe coding interview. This is normally for paid subscribers, so you guys are getting a bit of alpha in that we talk about this UPS PPPB framework, and that’s what I showed today.
You have to adjust this for the time. Sometimes backend won’t get 10 to 15 minutes. Sometimes these need to get less time. So think about your own interviewer, and when your interviewer gives you a solution, like I was basically given the solution, be willing to move forward. That was one of the things: this interview is still evolving. There’s no clear time frame of spend this amount, this amount. The way this interview evolved, I hadn’t seen the question before and I didn’t agree on any of the curveballs ahead of time. It was all live. You have to handle it as it comes for you, and you need to look at the timer.
So I would say practice your timing. Even mine could have been better. Practice your confidence with different workflows, different tools. That was one of the things I liked most about what I did. And then practice how you explain crisply: these are the problems, these are the solutions, this is the PRD. Once you put that all together, you’ll be ready to succeed at this interview.
Interviewer: Thank you again, Aakash.
Aakash: All right, guys. See you in the next episode.