I’ve been mentoring and reviewing resumes for aspiring PMs recently. The resumes are fine: internships, coursework, degrees, projects. Solid on paper. But the most important part is almost always missing: two or three sentences at the top explaining who this person actually is and why someone should hire them specifically. The reason you’d genuinely remember them, and not just their accomplishments.

When I give that feedback, something interesting happens. They come back with every other edit done - reformatted bullets, stronger verbs, cleaner layout - but those sentences either summarize everything already on the page or are still blank.

The problem? Students genuinely don’t know how to put their personality on a professional document. Those two versions of themselves live in separate rooms.

Work is about output - what I can do, what I’ve shipped, what I’m capable of. Then there’s the real them - curious, warm, the person their friends describe as someone who lights up a room or connects with a stranger in two minutes. They can’t put the second person on a resume because nobody told them that person belongs at work.

There's something deeper happening here. A resume isn't just a document, it's a system. And the system teaches you what counts: bullet points, action verbs, quantified impact. Over time, by writing yourself in outputs, you start seeing yourself that way. The format shapes the identity. I studied this in college through a lens called humanomics - the idea that the systems we build quietly shape the humans inside them. Resumes are a perfect example. They compress a whole person into a market-legible signal, and somewhere in the compression, the most interesting parts get lost.

I recognized this pattern because I lived it.

When I first became a PM and joined Walmart, I went all in on output. More hours, more work, more shipping. I thought that was the game. And the work did matter, but what actually helped me grow was how I showed up. How I made the team laugh on hard days. How people wanted to work with me because I cared about the problem and the people around it. I didn’t plan that, I just couldn’t help being myself eventually, and the career responded.

When I was in my job transition, it was who I truly was that resonated with the person on the other side of the screen. The way I told my story, thought about problems, and the energy I brought. That’s what made me memorable and that’s what got me hired.

So this time around at Adobe, I’ve gone personality first. I bring my curiosity, my humanomics lens, my creative instincts, my humor - all the things I used to think were separate from being a PM, and it works. Primarily because I’ve stopped splitting myself in two.

AI continues making execution cheaper by the day. The ability to write code, analyze data, generate content, build prototypes - is being commoditized fast. Which means “I’m a fast learner who delivers quality work” is no longer a differentiator, it’s the baseline. The traits these students are reserving for their “friend self” - how they think, how they connect, how they see the world - are becoming the most valuable professional skills they have.

And yet we’re still training people to split themselves. Work mode from 9 to 5. Real self on evenings and weekends. Grind through the week, escape on Saturday. Five days of performing a role to earn two days of being yourself.

There’s a show called Severance where employees literally have their work memories separated from their personal ones. Two selves, cleanly divided. It’s dystopian on screen. Most people are doing a softer version of that every day and then wondering why they feel unfulfilled.

The push I’m making — to the people I mentor, to myself, to anyone reading this — is to stop treating your personality as something you pack away at 9 AM and unpack at 6 PM. The curiosity, the honesty, the way you connect with people, the weird intersection of interests that makes you you, that’s not a distraction from your career. It can become the foundation for it.

Those blank sentences at the top of the resume? They’re blank because we’ve been taught that work is about output and everything else is personal. Over time, I’ve realized those two things aren’t separate. They never were. So write the personality sentences. Build the artifacts that show your taste - a website, a video, a portfolio, a newsletter. Let people discover who you are, not just what you’ve done.

The person your friends would describe is the one your career is waiting for.

— Akash

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There was a moment last Tuesday night where a Notion engineer dragged a task across a kanban board and something clicked for me.

The task moved into a column called “ready for agent.” A coding agent picked it up, read the spec, gathered context, started building, verified its own work, generated documentation explaining what it did and why, and handed everything back for a human to review. Accept or reject.

It was a familiar workflow, but performed by an agent.

I was at an evening called Shipping with Codex, co-hosted by Notion and OpenAI at Notion’s office in SoHo. Engineers from both teams showing how they build with coding agents. I was invited by Notion’s marketing team. I’ve been building enterprise agentic workflows at Adobe as well as personal agents for myself, so the excitement was high.

The Notion engineering lead, Varun, opened with a line that I wrote down immediately.

The agent’s job is not done until it has proven to you that it did the right thing.

Not just done the right thing, but proven it, to you - the human.

That distinction changes how you design everything. If the agent just writes good code but can’t show you what it changed and why, sure you’re building faster, but you’re also outsourcing risk, while keeping the accountability.

So Varun’s entire workflow is built around proof. Agents verify their own work through a spectrum of tools before a human ever sees it. And when the human does see it, they don’t just get code, they get artifacts. Documentation, interactive demos, a conversation thread where they can interrogate the agent’s reasoning. In one example, the agent generated a lightweight HTML prototype of the PR, complete with edge cases you could click through and quizzes to test your own understanding.

The system Notion built around this, which they call Factories, is basically agile for agentic workflows:

That’s the full loop: the spec is the input, proof is built in as a step, and the human stays at the decision point. Accept or reject.

The reject column was still longer than the accept column - Varun was honest about that. But every rejection sharpens the specs, improves the skills, and teaches the system what the team actually expects. The models will get better, workflow is what makes them usable.

Here’s where my brain went -

I’ve vibe-coded apps on a Saturday afternoon and written about how fun that is. It is fun. The creative energy of going from idea to deployed in an hour is something I think everyone should experience and keep exploring.

But there’s a gap between that and shipping something at scale to actual users. Something with stakes - where the code needs to be understood, reviewed, and trusted by other people or even by future you.

What I saw in that room was the bridge, and it’s a workflow any builder can adopt, whether you’re shipping to millions of users or building something for yourself that you want to actually maintain.

I keep coming back to why this felt so significant to me, and I think it connects to something I care about beyond product or engineering.

We’re in a moment where the dominant narrative around AI is about removing friction, removing steps, removing humans. Speed as the measure of everything.

But what I noticed was an engineer who had deliberately added steps. Added proof, explainability, and structure. The result was a system that was both faster and more trustworthy.

This feels like a pattern worth paying attention to; even beyond software, in how we think about any system where humans and machines need to work together.

The agent has to prove it, and we have to build the system that demands the proof.

— Akash

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With everything going on, I thought simply logging into Codex and shipping my first vibe-coded app would finally scratch the itch.

It did. And then it created a whole new obsession.

Once you launch something that’s yours, the will to improve it just takes over. At least for me. It needed to make sense to me and feel right to me. So I kept going back, the way you go back to an art project because you know it isn’t finished yet.

Art is not a one-time session.

Vibe-coding is not a one-time session either (depending on your session length and token limit, of course).

When you build something for the first time, your only job is proving it can exist. You want the core idea to work in its simplest form. Ship it before you make it perfect. That’s the whole point of the arts and crafts class energy I wrote about last week - lower the stakes, just make the thing.

But the second version is a different act entirely. The second version is personal.

Between v1 and v2, something shifted. I stopped thinking like someone building a thing and started thinking like someone who actually uses it. Which, in this case, I am. The Tamagotchi now lives on my desktop while I code. I look at it often.

And as that user, the list of things that felt wrong was immediate and obvious:

• I didn’t want to scroll, everything should live on one screen.

• The buttons needed to feel interactive, not just functional.

• I wanted to control the refresh rate myself instead of being stuck at 12 seconds.

• I wanted the back and forward buttons to move through quotes in order, not randomly (sometimes I copy and paste the quotes to friends).

• I wanted to understand why I’d pick Rick Rubin over Steve Jobs before I had to choose.

• I wanted a focus mode - everything stripped away, full screen, just the Tamagotchi.

None of that came from a research phase. No PRD, no user interviews. It came from using the thing and feeling what was off.

That’s what this whole iteration taught me about taste in practice. You don’t surface it by reading about design. You surface it by using something you made and paying attention to the friction.

The first version doesn’t create enough friction to feel that. It just needs to exist. The second version is where the friction starts talking.

You need the agency to actually go back. Shipping v1 is one kind of courage. Returning to it, deciding it deserves more, and rebuilding it because you think it should be better - that’s a different kind. Nobody asked me to do v2. No metric justified the sprint. I just knew it wasn’t done.

The gap between idea and artifact is so small right now that you can feel your way into a product instead of planning your way into one. When building was expensive, you had to specify everything upfront because iteration cost too much. Now it doesn’t. Which means your taste can show up in real time, grounded in actual use instead of speculation.

First version: does it exist?

Second version: does it deserve to?

TaMaGoTchi Inspires v2 is live. It’s not a startup. Maybe a few friends use it. But it’s finally the thing I actually imagined when I had the idea, not just the version I could ship in 60 minutes. That’s a different bar, and I couldn’t have cleared it without clearing the first one.

Try v2: here

If you haven’t shipped anything yet, that’s your v1 waiting. If you have, you already know what needs fixing.

— Akash

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My job is building products at scale.

At Walmart, I worked on search and personalization - the kind of system where a single bad decision affects millions of people looking for something at 11pm. At Adobe, I’m building AI agents for enterprise marketers, coordinating across engineering teams, infrastructure, compliance, and customers whose entire data operations run on the platform.

I know how software gets made. I live in that world every day. Yet, I had never shipped something just for myself. No PRD, no stakeholders, no users to research. Just me, an idea, and a question I’d been avoiding: what would I actually build for fun if I could build anything?

That question sat unanswered for a while because everything I imagined building felt too small to justify the effort. I was used to shipping things that mattered at scale. What’s the point of a tiny app that a hundred people might use?

This weekend I figured out that was the wrong question entirely.

I joined a live session run by - a sharp builder and thinker whose whole mission right now is simple: get people to make something, ship it, see what happens. The event was called makesomething. The premise was even simpler: build an app in 60 minutes, put it on the internet, done.

I showed up thinking this would be a forcing function. I know how these systems work and I’ve used AI prototyping tools before. What I didn’t expect was who else was in the room.

The YouTube chat was full of people from Canada, India, Brazil, Netherlands, all over - many of whom had never opened a code editor. Questions like “what’s localhost:3000” and “what’s git?” were flying by in real time. Farza and his co-host Julian were answering them without condescension, like this was exactly the right place to ask.

Something shifted in me when I saw that. I work in an environment where I’m usually the person who understands the system. Here, I made a deliberate choice: treat this like a beginner - don’t try to build the smartest thing or the most useful thing, build the thing you actually want to see exist.

The tool the session uses is Codex - an AI coding agent from OpenAI. You describe what you want in plain English, it writes the code, you watch it come alive in your browser. No syntax to memorize. No stack decisions to make upfront.

Once setup was done, it asked me a surprisingly human question: what are you into these days, like what do you spend your time on?

So I answered honestly without thinking much. I’ve been obsessed with analog things lately because the digital world is exhausting. I bought Tamagotchis for me and a few friends - grown adults, completely losing it over a pixelated egg. At the same time, I’ve been deep in Rick Rubin’s The Creative Act, quoting it constantly. His whole philosophy, strip things down until the truth shows up, has been running in the background of how I think.

Codex asked. I answered. And somewhere in that answer, an idea showed up:

What if those two things were one thing?

TaMaGoTchi Inspires is exactly what it sounds like. A giant retro Tamagotchi on a webpage. You pick a shell color. You pick a voice: Rick Rubin, Casey Neistat, Farza, Elon Musk, Steve Jobs. You tap the device and it drops a line of wisdom in that person’s tone. It auto-refreshes every 12 seconds. Left button goes back. Right button goes forward. Center button sparks something new.

I downloaded Tamagotchi images from Google to give Codex visual references. I pulled examples from arcade-y websites I liked. Then I just kept prompting until the look and feel matched what I had in mind:

Each prompt executed in under 5 minutes. At the 60-minute mark, I typed $deploy. Codex pushed it to Vercel. A real URL appeared. The app was on the internet.

Here’s what I want to talk about because this is the part nobody writes about when they write about vibe coding: the whole session felt like an arts and crafts class.

In the best possible way - there was no critique, nowhere to be, no stakeholder asking why this Tamagotchi needed to exist. No alignment meeting. No metric to justify the build. Just a room full of people making small weird things because they wanted to see them come alive.

For decades, software building felt serious because the cost of building was high. AI tools are quietly making experimentation playful again.

We need more of that. Spaces where the output doesn’t have to justify itself. Where the act of making is enough.

I’ve been noticing that the best creative moments in my life have always had this quality - a constraint on time, an absence of pressure, and permission to just try something. The arts and crafts class. The side conversation at a hackathon. The sketch you do on a napkin because there’s nothing at stake.

Vibe coding, in that environment, felt like that.

Now, let’s talk about ‘having good taste’. Because every builder, AI writer, and online guru right now is saying taste is the most important thing in the AI era. And I want to be direct: most of them are using it as a placeholder for something they can’t quite explain.

So let me actually say what it meant in this session.

The reason my Tamagotchi turned out the way it did wasn’t because I have superior aesthetic judgment. It’s because I grew up in the 90s and remember what those devices felt like. It’s because I bought one as an adult and noticed the specific feeling of holding a thing that only does one thing. It’s because I’ve been quoting Rubin long enough that his voice is somewhere in my head.

That’s not taste in the abstract. That’s lived preference. The specific imprint of experiences you’ve actually had.

My mother’s cooking tastes like home not because the recipe is objectively good, but because of everything attached to it. That’s what taste is. It’s not a skill you develop by reading essays about aesthetics. It’s what accumulates when you pay attention to the things you love (and the things others love too).

That’s what I was drawing on when I described what I wanted to Codex.

Here’s the realization that took me by surprise.

I’ve spent three years thinking my first shipped app needed to clear some bar - useful enough, polished enough, worth enough. Product managers spend years building that instinct. Does this move the metric? Does this justify the sprint?

But that calculus only makes sense when you’re building for millions of people. When you’re building for yourself, the bar is different. The bar is simply: does it exist?

If that sounds obvious, it wasn’t obvious to me. The habits of building at scale - the PRD, the research phase, the alignment - are genuinely useful at scale and genuinely in the way when you’re just making something because you want to see it.

Farza’s advice was simple: build for yourself first. Don’t worry about users yet. If you actually want to use it, you already understand the experience. You don’t need a research phase.

That single shift made shipping possible in 60 minutes.

The biggest thing I came away with isn’t about Codex or AI or building in public. It’s about the gap between the kind of building I do professionally and the kind that was always available to me personally. I had permission to start long before last weekend. I just never gave it to myself.

My new goal is to publish one vibe-coded project per week. 52 things in a year. Some will be dumb, some might become useful, but all of them will exist in a way that the ideas sitting in my notes app do not.

The distance between an idea and a real thing on the internet isn’t years. Sometimes it’s just 60 minutes and a willingness to treat the whole process like arts and crafts.

P.S. The app is live for those of you who want to play with it: TaMaGoTchi inspires

(3/8/26 11:56pm) Built a v2 w/ new features: https://tomagotchiinspires.vercel.app/

— Akash

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Earlier this week, I had my first call with a student through NYU’s mentorship program. Right away she said: “I have no idea what I want to do, but I’d love your help figuring that out.”

That alone caught me, but then she kept going - I’m interested in philosophy, creativity, art, technology. I don’t know how to pick one.

I almost laughed. Not at her, but because I’d heard that voice before. Mine at 22 (and sometimes today).

When you’re a multi-passionate individual, uncertainty hits different. It’s not just “what do I want to do?” It’s “how do I choose when everything interests me and none of it fits neatly into one job title or industry?” That was my exact problem eight years ago when I moved to the US for my degree. It was my problem when I finished undergrad, my masters, and it’s still my problem now. It also turns out I’m not alone.

After the call, I went back through my LinkedIn looking for something I knew I’d written. I found it - a post from four years ago, right before I graduated:

“It’s okay not to know. I didn’t know what university I would get into, I didn’t know where I would find a job... All I know is that I have a better understanding of what I don’t want - which means I am a step closer to understanding what I deserve.”

146 people reacted to that, I think because it was honest. I also think I needed to hear it again more than my mentee did.

Four years later, I’ve done things I’m proud of. I shipped Gen-AI search at Walmart. I’m building AI agents at Adobe. I’m speaking at a conference next month. I run a newsletter. I coach aspiring PMs. By any external measure, the uncertainty should be gone by now.

It’s not. If anything, it's expanded.

The more I do things I actually want to do and say no to the things I don’t, the more doors appear - and they keep getting more exciting. There’s growing certainty about the broader direction, but still total uncertainty about which specific door to walk through. None of them have signs on them.

AI is also redrawing the boundaries of every role I know. I could lean deeper into engineering or move toward design. I could go all in on creative work, on writing or content. I could build Product Rookies into a real business. The more experience I accumulate, the more doors appear.

The difference between me at 22 and me now isn’t that I have answers. It’s that I have better instruments. Every role, every project, every environment I’ve walked into and walked out of added signal. A feel for what gives me energy vs what drains it, what I’ll fight for vs what I’ll tolerate, or what I’m building toward vs what I’m just doing.

I think that’s what intuition actually is. It doesn’t feel like a flash of insight. It’s accumulated evidence from lived experience, forming so slowly you don’t notice until someone asks you a question and you realize you already know your answer.

Here's what I've come to believe about uncertainty: you don't solve it by looking forward. Not by mapping every possible career path, or comparing yourself to people who seem like they figured it out, or optimizing for what looks right from the outside. The answer isn't ahead of you. It's behind you - in the pattern of what you've said yes to, what you've said no to, and what that trail is telling you about who you're becoming.

My mentee doesn’t know what she wants to do and neither did I. Neither do I, fully, right now. But I’m closer than I was four years ago.

I told her the same thing I told myself at 22: choose intentionally and pay attention to what you eliminate. That’s your compass building itself.

— Akash

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I wasn’t expecting to feel anything. I was just organizing old files.

Then I opened a document from 2014. I was 15. It was a speech I’d written to run for VP of my school student council. Short, confident, slightly ridiculous. I wrote: “I’m annoying - which means I won’t give up on your demands no matter how many times we are rejected.”

I laughed. Then I sat with it a moment longer than I expected to, because that sentence felt more like me than a lot of things I’ve published in the last year.

I’ve spent the last week going back through everything I wrote before AI existed in my life. School debate speeches, university applications, cover letters from my first job hunt, a research paper I co-authored with my professor at Chapman on the symbolic nature of price, and essays from a course I took called Humanomics that I never stopped thinking about.

The oldest piece is from 2014 and the most recent is from late 2022, just before AI changed things.

What I noticed is that my voice got clearer over those eight years. I was writing constantly and paying attention to what felt true versus what felt performed. Sentence by sentence, year by year, I was becoming a writer… I just didn’t know it.

This is the speech (from 15 year old Akash):

In 2017, I made a short film about construction workers in Abu Dhabi. I wanted to capture what the city’s skyline doesn’t show - the labor underneath it. In my university application essay, I wrote about the experience: “Making this video was not easy. The exposure was off and there was no way to drown out the unglamorous truth of our home.”

I couldn’t solve the technical problem so I made something that lived inside it instead. That was a writing instinct, and it was mine before anyone taught me to have it.

A year later, I took a course at Chapman called Humanomics - the intersection of economics, humanities, and human behavior. The opening question of the course: What makes a rich nation rich and a good person good? I thought I knew walking in. I was wrong. By the end, I had written something I still believe: “People make a nation rich and a united nation makes its people good. I must continue to strive to be atypical.”

Clunky sentence, but a true idea - shoutout early Akash.

Then came the research fellowship at Chapman’s Economic Science Institute - a deep dive into a question that had been forming for years: are prices like words? Both carry symbolic meaning. Both are interpretations of value. Both require someone to bring something to them in order to mean anything at all. I wrote: “To buy a good for the right price is like choosing the right word; it is a personal process of aligning theory with reality, of creating meaning out of perception.”

That project became a published paper. But more than that, it became a way of seeing. The way of seeing became a newsletter. And the newsletter became the question I’m still sitting with:

How do humans make meaning inside systems that increasingly make meaning for them?

I didn’t plan that arc, I only see it now by going back.

Here’s the thing nobody tells you about writing with AI: you don’t lose your voice all at once. You lose it the way you lose a tan, so gradually that you don’t notice until you see a photo from before.

First you use AI to polish a draft, then to structure one, then to start one. One day you’re reading something that sounds like you — same topics, similar rhythm — but something is off. Like a cover version where the notes are right but the phrasing is someone else’s. Slowly, you stop discovering your voice and start supervising it.

I didn’t realize this was happening to me until I read what I used to write. The VP speech from 2014 didn’t have any signposting. No “here’s what I’ll argue” or “the key insight is.” It just said what it had to say and trusted you to follow. The Humanomics essay didn’t use a single bullet point. It moved through ideas the way a conversation does → associatively. Chasing what was interesting rather than what was efficient. The Language of Price paper opened with a rabbit hole and ended with a Lewis Carroll quote about a new idea being “a pleasure very near to sadness, bringing tears to one’s eyes like a beautiful picture or poem.”

None of that is optimized for clarity alone. All of it is unmistakably mine.

There’s a kind of AI hallucination nobody’s building safeguards for, the one where the model learns your patterns and starts generating a flattened version of you. It brings your voice to baseline, an average. Same vocabulary, similar structure, but the rough edges that made it interesting are sanded off. You don’t notice because the output is good. It’s just not yours anymore.

The only defense I’ve found is going back to the source material. Not to replicate it… I’m not trying to write like a 15-year-old again (though that kid had better instincts than he knew). But to remember what it felt like to write before I started asking "is this clear enough?" Clear to who?

Your old writing is your golden training data - the unaugmented version of you. The proof that a voice existed before anyone offered to improve it.

If AI is becoming a creative collaborator, then identity is becoming an interface problem. We have tools that remember everything we’ve ever asked a model to generate, but almost nothing that preserves who we were before optimization entered the loop.

I’ve been thinking about this phrase, golden training data, because it captures something I don’t hear people talking about yet. We spend so much energy curating our inputs: the right prompts, the right tools, the right workflows. But the most valuable dataset I’ve found isn’t something I downloaded or engineered. It’s a folder of Word documents from 2014 to 2022, written by someone who didn’t know he was building a voice, one draft at a time.

That folder is now the foundation of everything I’m building with Product Rookies, a newsletter about how humans make meaning inside the systems we create. The through-line from that 15-year-old’s student council speech to this essay you’re reading is not strategy. It’s just a person who kept writing, kept paying attention, and got lucky enough to look back before he forgot what his own voice sounded like.

I’d recommend the exercise. Go find yours before optimization convinces you it never existed.

— Akash

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Context

I hadn’t organized my Documents folder since 2022 and it was built for a version of me that was still in school, still early-career, still treating “save everything just in case” as a system. I’m a PM at Adobe now. I publish a newsletter. I have side hustles. I vibe-code projects. I invest. I create content. My file system was still organized for a grad student who might need those folders someday.

Someday had long passed.

So when Anthropic launched Claude Cowork: an AI tool that can actually touch your files and execute tasks on your computer, I figured, perfect use case. Just a little reorganizing right? Not a massive overhaul… just reframe things, move some folders, and optimize the structure for my life today.

I build AI-powered product experiences as a PM. I figured if anyone could get a good result out of this, it’d be me.

What Happened

I gave Claude clear instructions: no deletions, move files don’t copy them, dump anything uncertain into a holding folder. It scanned my 66GB Documents folder and, to its credit, identified real problems. Education files duplicated across two locations eating 23GB each. Immigration docs scattered across 14 different folders. Work files sitting in “Archives” even though I currently use them, and ultimately, lacked a structure that reflected major pillars of my life. It proposed a cleaner structure, and I said go.

Then my 66GB became 102GB.

I watched the terminal scroll for ten minutes, creating folders I never asked for, duplicating files into structures that made sense to no one. By the time it finished, my Documents folder looked like someone had shaken it like a snow globe.

Somewhere between my instruction to “move” and Claude’s execution, copy commands replaced move commands. When sessions timed out mid-operation (which happened constantly with larger folders), copies landed in the new location while originals stayed put. Duplicates that already existed got tripled. Watching your storage bar climb while knowing all your important paperwork lives somewhere in that expanding mess is a uniquely modern kind of anxiety. The files aren’t gone, probably.

The session got long and Claude started losing context. Forgetting constraints I’d set earlier. Re-proposing solutions we’d already tried. Losing track of what had moved and what hadn’t. I opened a new chat hoping fresh context would help. It didn’t.

The Undo

The one thing I did do before any of this was take a full backup. So I restored it. Wiped the mess. Back to zero.

I sat there for a second, because here’s the thing about restoring from backup: you’re right back where you started, except now you’ve also wasted three hours and have a much clearer picture of what not to do. Which, if you squint, is a kind of progress.

So I did the whole thing myself.

The Part That Actually Mattered

Manually sorting through 66GB sounds tedious, and it was. But it forced something Claude never could have: actual decisions.

Every folder was a question I’d been avoiding. What’s the boundary between “active work” and “archive” when you’re mid-career and your identity is still shifting? Where does Product Rookies end and personal writing begin… or does it? Which immigration documents need to be two clicks away versus buried deep? Why was I still holding onto every assignment from college like I might need to prove I graduated?

The system I used, PARA, calls one of its categories (the last ‘A’) “Archives.” That word had stopped feeling right to me. Archive implies done. But my Walmart experience isn’t archived, it’s foundational. My Economics education isn’t stored away, it shows up in how I think every day. Calling something an “archive” is an identity claim disguised as a folder name. And I’d been making that claim on autopilot for years.

These aren’t file system decisions. They’re identity questions wearing folder names as costumes. And you can’t outsource identity to an AI, no matter how good the model is, because the model doesn’t know who you’re becoming and what you need. Only you do.

By the time I finished, the structure wasn’t just clean, it was mine. It reflected how I actually think and work right now, with room for where I’m headed - not how a framework said I should organize things three years ago.

The Flip

Here’s where it gets interesting.

I went back to Claude. Same tool that had just spent three hours creating a 36GB mess. But this time I didn’t ask it to figure out my system. I asked it to read the structure I’d built, understand the logic behind it, and recreate it in Notion - extending it into a connected workspace across content, projects, finances, life admin.

After learning from my mistakes, I wanted to make sure we properly visualized what it was going to create ahead of time. So I asked Cowork to create an interactive dashboard to show me the new structure, here’s the prompt I used:

Read my existing folder structure. Don’t reorganize anything. Map what’s there, understand the logic behind it, and give me an interactive snapshot I can use to build a Notion second brain.

It did a great job:

After playing around a bit with the dashboard and the written summary to go with it, I approved and asked it to proceed with the Notion creation. It nailed it, close to first try. Same tool, same user, but completely different result.

The difference was what I asked it to do.

Round one was: here’s an ambiguous mess, make it better. Claude had to interpret my intent, infer my priorities, make judgment calls about categorization, and execute complex file operations simultaneously, with too many degrees of freedom and too little clarity about what actually mattered to me.

Round two was: here’s a clear, opinionated structure that a human already built based on some logic. Understand it, replicate it, and extend it.

Round one was construction without a blueprint, round two had a blueprint as a starting point.

The Bigger Pattern

I build AI agents at Adobe. Enterprise-grade systems that help Fortune 500 brands make real-time decisions about audiences, personalization, targeting. The pattern I saw on my laptop is the same one I see at work:

When someone says “let’s use AI to figure it out,” what they usually mean is “we haven’t decided yet.”

AI is extraordinary at extending, optimizing, and scaling clear decisions across contexts. It’s significantly weaker at making those decisions in the first place - especially when they’re entangled with identity, priority, and judgment. The tool doesn’t know what matters to you. It can only observe the patterns you’ve already established and multiply them.

Which means the most important input to any AI isn’t the prompt. It’s the clarity of thought that exists before the prompt. Structure isn’t just organization, it’s crystallized decision-making. AI amplifies decision clarity.

I went into this thinking Claude would just handle it. What I learned is that “handle it” only works when “it” is well-defined. When it’s not, when you’re asking AI to navigate ambiguity that’s actually yours to resolve, you get 102GB and a panic attack about your important paperwork.

The Takeaway

I’ll use Cowork again, I’ll probably use it often. But I’ll come to it differently.

Not with a vague ask and hope. With decisions clearly called out, structure already built, and a bounded task that plays to what AI is genuinely great at - replication, extension, pattern-matching across systems.

The move that worked wasn’t asking Claude to organize my life. It was organizing my life myself, then asking Claude to scale it.

And maybe that’s the real lesson: sometimes the most productive thing an AI tool can do is fail badly enough to force you into the thinking you were trying to skip. That messy, manual, surprisingly satisfying work of deciding what matters, what’s active, what’s done, and who you’re solving for - that’s not the part before the AI. That is the product. Everything after is execution.

— Akash

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Growing up, the kaleidoscope was one of my favorite toys.

I’d point it at the most ordinary things - a wall, a table, my hand, my mom’s face - and suddenly I was looking at something else entirely. Crazy patterns and colors bouncing and folding into each other. The same thing I’d just been staring at, but transformed in ways that felt both chaotic and somehow universal.

What mesmerized me wasn’t that it transported me somewhere else. It wasn’t like playing a video game or watching a movie. I was exactly where I’d been, looking at exactly what I’d been looking at. I would just see it completely differently.

As a kid, I had no idea there were angled mirrors inside creating all those reflections. I just knew I could see pieces of the thing I was pointing at in a hundred different ways. Each view revealed something my ‘direct’ view had missed. I could sit with it for hours and discover endless patterns. That sense of wonder, knowing I could look at the same object and find endless patterns, has stayed with me.

I’ve been thinking about this lately and call it Kaleidoscope Vision: one’s ability to rotate a problem and see it from angles others can’t conceive.

When I’m dealt a problem, the first thing I do - before running with a solution, before treating anything as settled - is put it under my kaleidoscope. I want to see all the different ways this thing can be solved, and can’t be solved. The patterns that emerge when you change the angle of view.

The kaleidoscope gives the kind of treatment no microscope ever could. A microscope shows you one thing in greater detail. The kaleidoscope shows you the same thing transformed by perspective.

I’ve noticed some people have this built in. They see the world - people, products, decisions, data - through multiple lenses at once. You hand them the same customer feedback, the same product spec, the same dataset as everyone else, and they see something different. This is not about smarts. It’s because their kaleidoscope has been shaped by the patterns they’ve learned across different disciplines, different experiences, and different things they genuinely care about.

Everyone’s kaleidoscope looks different. That’s what makes it useful.

When you’re building products, especially in a big company where everyone operates in their own silo, being able to see how things connect becomes a real big differentiator. Meanwhile, in a start-up, a single product decision doesn’t just impact the product. It ripples into sales, marketing, growth, the content you create, the subsequent decisions you’ll make, the tech stack you’ll need six months from now.

Being able to catch those ripples early - to see the living, breathing, interconnected system instead of isolated pieces - that’s kaleidoscope vision. And in my experience, the people who flourish are the ones who can hold multiple views at once and make calls others can’t see yet.

But I don’t think everyone on a great team needs to be a kaleidoscope thinker.

You also need people who go deep on one thing. Really deep. Once you’ve looked at a problem through the kaleidoscope and seen the patterns, you might want to zoom in. And when you do, you need someone already there who understands that domain better than anyone else could.

Not everyone’s job is to be the kaleidoscope. Not everyone’s job is to be the specialist. We gravitate toward what we’re strong at, what we enjoy, and what the work demands of us. The best teams have both kinds of people, and they know when to deploy which kind of seeing.

But if I’m being honest about what I think matters most for someone trying to build something that lasts - whether that’s a product, a company, or just a clearer way of thinking - the kaleidoscope vision is where real perspective lives.

Because the problems worth solving aren’t hiding in the details of one domain. They’re in how five domains intersect in ways no one else is tracking, and you can’t see that if you’re only looking straight ahead.

You need to rotate the view. See the patterns. Watch how the same thing transforms when the light hits it differently.

That’s what the kaleidoscope taught me when I was eight years old, pointing it at my mom’s face and watching her become a constellation. Today, it’s still one of my favorite ways to see.

— Akash

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Last year, I spent a lot of time thinking about creating content. Film, photography, visual storytelling - all the things I used to do as a kid. I told myself I’d get back to it because I was ‘gifted’ and ‘had done it before.’

Then I actually started this year. New year’s resolution: post daily - one piece of writing, one visual. An uncomfortable realization hit fast.

Writing felt natural. I’d been practicing that through product work, writing on LinkedIn, and this newsletter. But the visual side? The world had moved on. Tools changed, platforms evolved, and my references felt dated. I knew how things had evolved as a consumer, but for a long time, I didn’t play the creator role.

I didn’t have the right to win in that space anymore - even though a younger version of me absolutely did. That 9th grader making videos was probably creating some of the best content for his age. But I’d stopped practicing, and those skills didn’t wait around for me to come back.

Potential expired.

What I did have was writing. I’d been practicing storytelling the whole time, just in different forms. Through product work. Through explaining complex systems. And eventually, through LinkedIn.

I started writing on LinkedIn about a year into my PM journey. Expanded to Substack a couple years after that. I was inconsistent at times, but I kept showing up. I was practicing publicly without even realizing it. Looking back now, I can see the breadcrumbs. And today, I have something I didn't have in film or photography anymore.

I call it the right to win.

What it actually means

The right to win is the strategic convergence of experience, consistent practice, and positioning. Not pure talent or passion - just sustained practice in a strategically chosen space.

I have it as a PM voice on LinkedIn and Product Rookies because I’ve worked at scale (Walmart, Adobe), practiced publicly for years, and positioned myself at the intersection of PM, storytelling, and AI. Those things compound.

I don’t have it as a filmmaker because I stopped that practice. But I’m rebuilding it now.

How companies think about it

When Google launched UCP (Universal Commerce Protocol) - a way for agentic shopping to connect directly to retailer inventory and supply chain - a lot of people panicked about the future of retail. ‘What if all shopping becomes Google Shopping?’

Seeing the news, I didn’t panic about Walmart - they still had the right to win. Google could handle discovery and checkout, but atoms still need to move. Walmart’s supply chain infrastructure, built over decades of practice, doesn’t disappear because software gets smarter. That practice was the moat.

Harvey AI has the right to win in legal AI because they’ve been building exclusively in that vertical, understanding the workflows, earning trust with law firms. That focus and repetition creates advantage.

The right to win shows up when experience, practice, and timing converge - not because you planned it perfectly, but because you stayed in the arena long enough for the pieces to align.

The strategic question(s)

So where do you have the right to win?

Not where you’re interested. Not where you have potential. Where have you actually been practicing? Where does your experience create leverage? Where are you positioned in a way that’s hard to replicate? And just as importantly: where do you want to win?

This is about choosing where to compete and doing the work to make that choice matter.

Potential doesn't give you the right to win, practice does.

— Akash

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Someone I was coaching recently asked me how to answer a question they kept getting stuck on: “How do you work cross-functionally?”

They’d been asked variations of it multiple times. How do you collaborate? How do you handle disagreements with engineering? How do you influence stakeholders? Each time, they tried to come up with a different answer because the questions sounded different. Each time, they left feeling unsure.

I’m sitting there thinking: that’s such a simple question. But I let them explain. And in listening, I realized where the real issue was.

We spend so much energy trying to make things sound complex that we miss what’s actually being asked.

I walked them through it. Cross-functional collaboration? They want to know if you can bring people along, resolve conflicts, and keep things moving when there are disagreements. That’s it. You’ve done all of this - I see it on your resume and when we talk.

They paused and said: “You’re right. I have everything. I don’t know why I overcomplicate it.”

This isn’t just one person. I’ve noticed it with almost everyone I coach.

Earlier in our sessions, before any mock interviews, we just talk through their real experience and examples. Most people are clear, articulate, and their stories are all there. Their examples make sense and the judgment comes through naturally.

The moment it becomes a real or mock interview, things change. They start adding complexity, layering detail, and explaining things in elaborate ways when the real value is explaining simply.

Here’s the thing: if you’re explaining complicated things in a complicated manner, you probably don’t understand them simply yourself. Real understanding looks like simplification.

I get why people do it… they fear sounding dumb. They think they’ll leave out important details or assume the senior person interviewing them cares about all the nuances. We think showing our work proves we’re thorough.

But it’s the opposite. As you grow in your career, the simpler your communication needs to be. Senior leaders don’t have time for the weeds, the engineering details, the edge cases. They need the principle understood, the action taken, the outcome delivered.

Would you rather hear overwhelming details you can’t parse, or a clear story? A concept you can remember?

Think about it like a book. If a story is so detailed and convoluted that you can’t follow it, you put it down. Communication works the same way. Details matter, but at the right time and place where they add value - not extra pages of text or extra minutes of talking.

What actually works - in interviews, in stakeholder conversations, in product strategy - is finding the simple truth underneath all the complexity and leading with that.

Simplicity leaves room for intrigue. It invites follow-up questions instead of overwhelming people upfront.

Children simplify because they’re trying to understand. Rookies simplify because they’re trying to learn. Adults complicate because they’re trying to sound credible.

The best PMs I know protect that rookie instinct. They ask the obvious questions and push for clarity when everyone else is adding nuance. Not because they can’t handle complexity, but because simplicity is what moves people.

In my own work, whether it’s coaching someone through their career or aligning teams around a product vision, the moments that create breakthrough aren’t when I explain all the details. They’re when I find the few words that make things click.

Because simplicity is what actually moves people forward.

— Akash

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About four years ago, someone on the product team asked our VP of Product for e-commerce at Walmart a question during all-hands: “How should I think about my roadmap this year? What does good actually look like?”

The answer came back as an analogy: a few boulders, a handful of pebbles, and a bit of sand.

It sounded interesting, but abstract. Over time, it became one of the most practical ways I’ve learned to think about product work. A portfolio isn’t about doing more, it’s about carrying the right weight.

You can’t carry many boulders, one or two is usually enough. These are the big bets that drive step change in a product or how users interact with it. For me, those boulders looked like building a generative AI search experience, an algo-driven new arrivals page, complete-the-look features in fashion, and now hybrid agentic AI workflows in enterprise software.

Pebbles show up differently. They aren’t breaking changes, but they still matter. They improve the product in ways users feel every day. They rarely headline a roadmap or keynote, but they reduce friction, sharpen experiences, and still move metrics and revenue in meaningful ways.

Sand is the foundation. Quality work, tech debt, fixing small bugs before they turn into large ones. This work doesn’t always map cleanly to business impact, but without it, nothing else holds. It keeps systems healthy enough for bigger bets to exist at all.

How I started seeing it

On search and discovery, the boulder was a new generative AI search experience. Around it were things like intent classifiers that needed to work correctly. Beneath that, we had to improve how the system handled synonyms and units of measurement like “oz” versus “ounces,” while also maintaining the core search experience and retiring old ranking signals so everything actually felt better to use.

On personalization, the boulder was a fashion shopping experience that understood size and fit, a multi-quarter, multi-team effort unique to Walmart. Alongside it were complete-the-look features and other enhancements on item pages. Then came dozens of smaller UI improvements that made the experience feel more premium. Together, it all compounded. Even when one piece slipped, the overall momentum stayed intact.

Now at Adobe, I’m working on enterprise agents and hybrid AI experiences. At the same time, I still care about and work on core platform problems, migrations, quality issues, and the smaller work that keeps a large system usable and extensible.

Why this matters

Different people care about different rocks.

Executives want to understand and evangelize the boulders. Managers care about how everything connects. Meanwhile, teams feel the sand most acutely because they live with quality issues and technical debt every day.

If you only have boulders, you’re fragile. If you only have pebbles, you’re forgettable. If you’re drowning in sand, you’re busy but invisible.

The job is learning how to balance all three, knowing which one needs the most attention when, and telling the story at the right altitude, at the right time.

This feels especially relevant now. A lot of attention is on flashy boulders, often big AI bets. There’s real demand and value there, but it doesn’t mean the other work stops mattering. Pebbles and sand are still where users feel consistency, trust, and everyday delight (especially in large product spaces like enterprise platforms or e-commerce), while the bigger vision takes shape.

I’ve seen boulders fall off, and when all you have are boulders, you neither delight users nor change their experience in a lasting way.

Every PM ends up carrying weight, that’s part of the job. The ones who grow are intentional about what they carry, when they carry it, and why.

— Akash

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One of my managers introduced me to an idea years ago when I was at Walmart. At the time, it was meant as a way to think about product scope and team impact. I didn’t realize then how often I’d come back to it, not just in work, but in how I think about building, careers, and even life.

We had just gone through a re-org and formed a new pod focused on new arrivals, seasonality, and trends. On paper, we were part of the search product team. In reality, the problem we were trying to solve didn't live neatly inside search at all.

When you think about newness or trends, you quickly realize you're dealing with the real world: culture, timing, momentum, and signals that move much faster than most retail systems or algorithms are designed to track, unless you’re TikTok.

Speaking of TikTok, that was where I first came across #walmartfinds. We started noticing that certain items would sell out almost immediately after an influencer posted them, yet they wouldn’t surface naturally in search results. You might think the algorithm was broken, but it was actually behaving exactly as designed. The issue was that trends now form and disappear in hours or days, sometimes even minutes, while our systems were optimized for longer feedback cycles.

So we looked outward, comparing trending videos to inventory, asking why certain items took off in the real world but stayed invisible on the website. That pushed us to collaborate with social commerce, merchandising, and trend analytics teams outside of the company, and eventually rethink parts of Walmart’s broader strategy. Just as importantly, those learnings flowed back into how we thought about search itself.

That’s when my manager framed it in a way that gave me language for what we were doing:

Some teams farm, and some teams hunt.

Hunting

Hunting is about venturing out beyond your immediate scope to find new problems worth solving. It’s exploratory, messy, risky. It often looks like doing more than your job description would suggest, and it often requires stepping outside the boundaries of your team, your surface area, or even your org.

Looking back, I realized that instinct showed up in me earlier than that moment. I just didn’t have a name for it. When I was asked to work on cold start problems in search, I didn’t limit myself to search real estate, I started asking a different question: why didn’t we have a meaningful new arrivals experience at all?

That question led to an algorithm-driven page that defined “new” at scale using data, ranked items by predicted engagement, and surfaced them without manual curation. It won Walmart’s internal Shark Tank and eventually shipped.

At the time, I wasn’t trying to expand scope for its own sake. I was following the problem, and it naturally pulled me beyond the system boundary I had been handed. In hindsight, that was hunting. I was challenging Conway's Law without realizing it.

Farming

Farming is different. It is not about expanding the land you work on, but committing to it and tending it well. In product terms, farming is where teams refine, stabilize, and compound value on an existing surface area. It is harder than it looks. There is less novelty to hide behind, progress shows up in small increments, and the work demands patience and consistency more than ambition.

For a long time, I didn’t fully appreciate farming. Hunting felt more exciting, more visible, and frankly more rewarded. We are often taught, implicitly, that progress comes from bold moves and constant expansion. But over time, I’ve noticed something else.

Hunters may bring back the big win, but farmers keep everyone fed, at all times.

In nature, in history, and in business, civilizations don’t survive on hunting alone. They survive because someone stayed, tended the land, and kept things going when exploration slowed.

The same pattern shows up in the business world: someone raises a large round, launches something new, or has a breakout moment, and then the real work begins. Telling the story well, compounding momentum, extracting value from what already exists instead of immediately chasing the next risky bet.

A balance between Hunting and Farming

What I’ve come to believe is that most successful teams, people, and careers are rarely purely hunters or purely farmers. They’re a portfolio of both. Not every team should be hunting all the time. Not every person should be farming forever. Different moments call for different modes.

You see this in life as much as in work: when you feel stuck, restless, or misaligned, that is often a signal to hunt. You can change environments, explore something new, or take a risk that creates step change. And when things are working, when there is momentum and alignment, that is not the moment to burn everything down or put your team at risk. That is when farming matters: staying consistent, deepening the work, and letting what is already growing mature.

I was introduced to this idea as a product framework, but I've come to see it as something broader. It goes beyond prescribing what to do and helps me notice where I am. It gives language to phases that exist whether we name them or not. And once you see it, you have a way of understanding teams, businesses, culture, and even yourself a little better.

Some frameworks are useful because you apply them deliberately. Others are useful because they quietly change how you see things long after you first learn them.

— Akash

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Happy New Year. I hope 2026 brings you some peace, some growth, and fewer moments where you're fighting yourself.

To start the year, I wanted to share something I’ve been sitting with lately, a way of seeing that turned out to be more useful than I expected.

Looking Back

I ended the year on a solo trip to Puerto Rico. I went in wanting it to be reflective, exploratory, and fun. Instead, I caught myself doing what I usually do first: planning every hour, mapping every route, trying to make sure the experience would turn out “right.”

A few hours into that, it became obvious what I was doing. I was turning the trip into the exact thing I didn’t want it to be. Tight. Overthought. An itinerary pretending to be freedom. So I scrapped it. No schedule, no hourly plans, just me, a place I didn’t know well, and whatever showed up next.

That decision came with some anxiety at first. Open space tends to do that to me. When there isn’t a plan, my brain tries to create one immediately. The first night, I found myself sitting alone at a bar and could feel that familiar pressure to justify what I was doing, even if no one was asking.

So I paused and asked myself one simple question:

Who am I when no one here knows my job, my story, or what I’m working toward?

The answer was simple. I was just noticing things. Talking when I wanted. Relaxed. Not trying to impress or perform. I fell asleep that night feeling something I hadn’t felt consistently in a while: content, without the urge to extract meaning or turn the moment into something else.

From there, the trip started to unfold naturally. Whenever I felt drawn somewhere, I went. Wandering Old San Juan without a plan. Finding coffee shops tucked into corners like Cuatro Sombras. Sitting in a church on Christmas Eve because it felt right to be in a place that had held other people’s uncertainty for a long time. Salsa in the streets. Birds moving in these strangely coordinated patterns that made me smile. Local dishes like mofongo recommended by bartenders and strangers who quickly became friends.

What I kept noticing was that things worked out when I stopped forcing them into shape. The days felt better when I let myself respond to what was actually in front of me instead of what I thought should happen next. Over time, something familiar came back. I trusted my instincts more. Decisions felt lighter. Choosing became easier because I wasn’t trying to optimize every step.

I'm not anti-planning, planning has its place. But I also saw how easily it becomes a shield for me, a way to stay in my head when the moment is asking for something simpler.

Looking Ahead

Somewhere in the middle of the trip, I found a phrase that helped bring me back whenever my mind started jumping ahead. I started calling it NBA: Next Best Action.

My brain loves to extrapolate. It will build a five-year plan while I’m sitting in front of a plate of food. NBA was a way to bring the decision back down to the size of the moment. I’d ask myself, right now, from where I actually am, what is the next best action?

If I was in bed, the next best action was sleep. If I was at dinner, the next best action was to eat and enjoy it, not plan the rest of the night. If I was watching the sunset, the next best action was to stay and watch.

It sounds almost too simple, but that simplicity is what made it work. NBA turned a huge, abstract question — what should I do with my life — into a smaller, honest one: what should I do next, right now?

A lot of my stress, I realized, comes from trying to live a few steps ahead of myself. NBA brought me back to the only place where agency actually exists.

The trip also clarified something else I’d been carrying without fully naming. A lot of what drains me isn’t effort, it’s the feeling of being evaluated. In big systems, it’s easy to learn to associate screens and refinement with judgment and stakes. Sitting down to edit can start to feel like a review instead of an act of expression.

Puerto Rico stripped that away. No one knew my job. No one was watching my output. There was nothing to prove. I could just be a person moving through the world. NBA helped with that too, because it kept me focused on honest movement instead of impressive movement.

I’m sharing this because I suspect I’m not the only one who spends too much time ahead of himself. If you want to try this idea, don’t apply it to a big life decision. Try it for the next ten minutes. Ask what the next best action is from where you are, and choose the one that keeps you present rather than stuck.

The sentence I’m carrying into 2026 is simple: My job is not to know the whole path. My job is to take the next step that keeps me engaged with my life. That’s it. Just a way of moving that feels sustainable and honest.

Here’s to the year ahead and the next best action.

— Akash

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I want to share something that came up when I spoke at NYU’s PM club a few weeks back.

It was a fireside chat with PMs from Meta and Adobe Photoshop, and about 30–40 students showed up with the questions you’d expect: how to break into PM, what skills actually matter, how AI is changing everything. All fair questions, and ones I’ve asked myself at different points.

But the question that stayed with me came from a student who asked what creativity actually means in our work.

That question goes deeper than it sounds, especially in product management. A lot of the job is framed around execution, optimization, and shipping against roadmaps that already exist. Creativity can start to feel like something adjacent to the work instead of central to it.

What I told them is that creativity isn’t a job title or a role you graduate into. It’s an approach. It’s the way you bring your own set of interests, instincts, and experiences into problems that already exist and shape them differently because you are the one doing the shaping.

You can see it when someone connects things that don’t obviously belong together. For me, it was Economics informing personalization at scale. Filmmaking instincts influencing how a product story gets told. A sensitivity to human behavior showing up in how systems are designed. None of that shows up in a skills matrix, but it changes the quality of the work.

This is also why I think people underestimate how much agency they actually have, even inside rigid systems. You can be creative in a boring job if you decide to be. You can find room to steer, not just execute. You can choose to be a driver rather than a passenger, even when the road looks predetermined.

We also talked about AI, and the anxiety that sits underneath a lot of conversations right now. The question people ask is whether AI will replace them. The more useful question is what remains distinctly human in the work.

It isn’t raw technical skill. It’s the ability to make judgments, to synthesize ideas across domains, and to decide what matters in context. It’s knowing when something is good enough to ship and when it needs more time, and being able to explain that call to other humans. That kind of discernment is closer to creativity than people realize.

At the end of the night, one student came up to me and said that my journey was one of the most inspiring they’d heard at one of these events. That stopped me, mostly because I don’t think of my path as particularly clean or impressive. I didn’t follow a traditional PM track. I studied economics, not computer science. I worked at Walmart before Adobe. There was no linear roadmap.

But maybe that messiness is the point. Maybe the non-linear parts are what create perspective. And maybe being open about the uncertainty, the detours, and the unfinished thinking is what actually helps people see what’s possible.

I do these talks because I love the energy younger builders bring, especially before they’ve been fully trained on what the “rules” are. But they’re also a reminder that sharing your own story, without sanding off the rough edges, can matter more than you think.

To the rookies: keep connecting strange dots, keep choosing agency, and don’t assume your non-traditional path is something you need to fix. It might be the most valuable thing you’re carrying.

— Akash

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The job search today isn’t a numbers game, it’s a clarity game. A test of how clearly you can name your value and match it to someone else’s need.

You're not just competing with other candidates. You're competing with confusion (both yours and theirs), about what you solve, how you operate, and where you belong.

This is my story about not only getting hired, but about getting clear. And treating a job search like the most important product you’ll ever work on: you.

The Insight: You're Solving a Matching Problem

At first, I treated the job search like a performance. Tweak the bullet, polish the intro, say the right thing, and hope to get picked.

Then I heard something that changed my approach. Peter Trinh (P.T. throughout) said:

P.T. - “Every open role is a symptom. Something’s broken, blocked, or under-resourced. Someone’s betting on growth, but the path isn’t clear yet. Your job isn’t to be hireable everywhere, it’s to be impossible not to hire, somewhere.”

So I stopped performing and started reflecting. It became a 3-persona process:

1. Historian: asked myself, “what have I done so far?” Tiny wins, big milestones, hacky solutions, messy drafts that led to something real. I pulled everything: prototypes, launch notes, write-ups, slack threads, performance reviews, and more. I hunted for moments I created value.

2. Writer: pieced a story together. Not just a list of jobs, but a reflection of impact, growth, and strengths over time. This became part-essay, part-resume, and part-cover-letter. Long, honest, and unedited.

3. Editor: edited relentlessly. I iterated it down a 1–2 page version that felt holistic. Something that told my story and communicated my value, without fluff.

Kept this a living document, not something I wrote once and forgot. My story evolved as I did.

With this foundation, I looked outward (this time to find the right fit):

1. Understand Pain: studied the job posting. What’s not working for them? Why does this role exist? What’s the hidden pain?

2. Match Patterns: matched it against my story. Have I seen or solved something similar? Can I explain how and what changed?

3. Align Values: asked the basic questions. What does this company do? What do they care about? How do we overlap?

You only need one yes. But you need enough clarity to earn the right one.

The Game: Two Parts to the Process

Once I had clarity on my story, I started studying the process. Turns out the job search isn't one funnel, but two. Each needs a different strategy but one should inform the other.

Clarity gets you started, but context gets you through.

Part 1: Don’t Get Filtered Out (Applications → Screens)

This stage is about being the needle in the haystack. Your job now is to translate your value into their language. It’s not about exaggeration. It’s about believable relevance.

A great application is just a believable match story.

Tactics:

• Use a 1–2 line fit statement at the top of your resume to frame your alignment.

  • Fit statement: 1-2 lines on exactly why you and your experience are a good fit for this problem/ role and what relevant value you bring to the company.

• Apply fast. Speed > referrals. You can always get referred post-submission.

  • The dreaded “100+ applied” within 5 mins of a job posted is common now. If you don’t have a reference already, prioritize getting a quality application in first, hunt for the referral after - the system still bumps your profile.

• Update your LinkedIn headline, summary, and engage where recruiters hang out. Write stories of your wins in public, this also helps with signal.

  • Fit also works in reverse: it helps the right people find you (majority of my interviews were outreach once I started writing my value in public.)

Once you’re past the ATS filter and your application is through, the next filter is human: recruiter screens and early rounds.

Tactic:

• Start every interview like a conversation. Talk human first, and shop second.

  • Begin like you’re talking to a friend, not a hiring panel. Interviews are easier when they feel like conversations instead of auditions

• Anchor your “tell me about yourself” with intention and lead with it. Your story makes you memorable.

  • If you ramble, you raise doubt. If you show clear intent, you buy trust. Use my 3-part structure: How I started → What I learned to love solving → Why I’m excited to solve for this role.

• Don’t try too hard. You’ve done the initial work, just practice bringing your clarity with you.

  • No need to dazzle. Just be coherent, confident, and clear.

P.T. - "Filtering is an elimination game. They’re scanning for risk. Your job is to make the yes easy."

Part 2: Get Filtered In (Mid → Final Round)

Now, the game shifts. You’ve already shown you’re a fit - so have others. Now you need to make them believe you’re the best one. This stage isn’t about standing out, it’s about locking in.

Interviewers aren’t just listening for what you know. They’re watching how you think, how you adapt, and how you show up under pressure.

Being great here isn’t about sounding smart. It’s about being present, prepared, and genuinely invested. It’s caring out loud.

Tactics:

• Study like you’re already on the team. Go beyond just browsing the product, dissect it.

  • What’s working? What’s broken? What’s next? Come in like someone who’s already thinking about their problems, not just your resume.

• Anchor in lived thinking. Approach everything with a real world angle, not theory.

  • Product sense? Analytics? Leadership? Share how you think in your world, and map it to theirs. What tradeoffs did you navigate before? What metrics did you move? What tension do you look for?

• Connect like a future teammate. What would you look for if you were hiring for that team?

  • A final round isn’t a test, it’s a two-way fit exercise. Bring curiosity, not choreography. Show you’re already thinking like one of them.

P.T. - Hiring managers aren’t trying to eliminate. They’re trying to choose. When they ask, "Tell me about yourself," they’re not looking for a perfect answer. They’re listening for who you are, how you think about their problem, how you adapt when things shift — and whether you might be the teammate who helps solve what they’re holding. This is your moment to connect your story to their need. Not by reciting your history, but by showing that you’ve understood their challenge and can speak to it with clarity.

How I Played (And what actually made a difference)

I didn’t crack this immediately. But once I stopped guessing and started treating it like a strategy game, things started to click.

I still applied broadly, but never applied blind.

I didn’t limit myself to roles, I just scanned for the real pain behind them - if I couldn’t clearly see a 50%+ match between their pain and my story, I skipped it. No vibe-based applications.

I stopped asking “how do I sound impressive?” and started asking “how do I sound like me?”

That didn’t mean unpolished. It meant rooted. Clear. Like someone who knows what they’re talking about, because they’ve lived it.

I practiced in voice mode.

Literally. I’d share out loud to ChatGPT, or record voice notes. Not to memorize, but to hear myself. You’d be surprised how often we sell a version of ourselves that doesn’t even sound like us.

I shared my value until it felt obvious.

Not rambling, but being thoughtful. At the top of every resume, or the first 60 seconds of any call, I made it undeniably clear what I solve, how I think, and why that matters for them.

I linked everything back to them.

What I knew, what I’d solved, what I still didn’t, I mapped it to their world. If I had an insight, I tied it to their product. Even when our spaces were different, I found the overlap.

I treated rejection like radar.

Every "no" gave me intel: Was my story unclear? Did I miss their real need? Was it just not a match? I'd adjust and keep going.

Here's the thing about rejection when you're not performing - it doesn't sting the same. You're looking for fit, not validation. Sometimes you find it, sometimes you don't. Both outcomes are useful.

Eventually, one hiring manager said, “It feels like you’re already on the team.” That’s when I knew I wasn’t performing anymore. I was contributing.

In Game 1, I optimized for precision.

In Game 2, I optimized for presence.

You need enough precision to earn the call. But it’s your presence that earns the offer.

The Outcome: Yes, It Was About the Job

Let's not romanticize this. I wanted an offer, ideally the right one. That’s never easy. And ultimately, I did get multiple offers. What helped me most? Treating it like a product loop:

• User research: What are they really hiring for?

• Positioning: What do I solve best? Why me?

• Execution: Can I show up like I already belong?

• Go-to-market: How do I signal clarity at scale?

• Iteration: What did I learn? What would I do differently?

Asking these questions showed me where I create real value, what I won't compromise on, and how to tell my story with depth.

P.T. - The process isn’t about proving your worth. It’s about discovering it — and standing in it. Confidence doesn’t begin in the interview. It begins when you name what you solve. And when you trust it has something to offer — that’s when it starts to carry you.

The job was the outcome, the clarity was the upgrade

When the offers came, I wasn’t choosing blindly, I was choosing with alignment. The work I did before made the decision feel obvious - like I’d already met the version of me that belonged there.

We treat the job search like a transaction. But it can actually be a transformation.
You don't just land a job, you build your compass.

The market doesn’t hand you clarity, but when you bring it, when you trust your value and speak to it, people feel it. That’s when matching becomes inevitable.

We're all rookies now. The rules keep changing. But clarity compounds.

Share this with a friend in the search →

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See you out there.

— Akash

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Your family members are posting reels. Your coworkers are LinkedIn poets. Your friends are sharing fitness diaries, travel guides, and outfit inspiration. Everywhere you scroll: posts, threads, reels, stories, articles. It feels like everyone is creating.

It’s easy to feel like you missed the memo. But in reality? Most people are still silent. Maybe even you. Here's an analogy that brings this into focus: The internet is the world's longest Zoom call and most of us are sitting with our cameras off and microphones muted.

In real life, you wouldn’t sit through a meeting or show up to a party without speaking up when it mattered. You’d share a sharp take, tell a story, ask a better question, something to shape the room with your perspective.

But online? We stay silent. We lurk. We scroll. And we let the feed thinks for us. The cost of this silence has never been higher because in the age of AI, where output is infinite, your lens is your edge. And the less you share it, the more the algorithm fills in the gaps.

So ask yourself: How am I showing up to the most important Zoom conversation of our time?

Content Is the New Currency

We’ve always shared ideas: to be seen, understood, and remembered. What’s changed isn’t that instinct, but the economy around it. Content isn't just how you communicate anymore, it's how you compete. It's currency.

Content is no longer a vanity project, it’s a visibility system. It’s how you think in public and leave breadcrumbs for future collaborators, customers, or your next self. It’s not just for followers, it’s for leverage.

We’ve moved beyond the information age into the identity age:

• Where your perspective is your product

• Where your voice compounds in value over time

• Where attention follows authenticity

The old game: “Establish credentials, gain position → then speak with authority.”

The new game: “Speak with clarity → and the right opportunities and people will find you.”

The creator economy gave everyone a mic. AI made it easy to generate noise. And suddenly, it’s not about who can speak, it’s about who people want to listen to. Most people default to extremes:

• "Stay private. Keep your head down. Focus on 'real work.' Don't feed the algorithm."

• "Build in public! Document everything! Content is king! Grow your audience!"

The real challenge isn't whether to create content, it's how to share your perspective without feeling like you've compromised your integrity or wasted your time. We need a framework for thinking about our online presence that feels aligned with who we actually are and what we actually value. It starts with a reframe:

Content as Product, Not Performance

When you treat content like performance, you chase validation. You count likes, tweak headlines, and play the algorithm’s game. But when you treat it like a product, the posture shifts. Products solve problems. They have users, feedback loops, a purpose. They’re built to work, not just to impress. Your content should do the same.

The Three Jobs Your Content Should Do

Like any good product, your content does specific jobs for its users (both you and your audience):

• Thinking Tool: Helps you clarify what you know. Your public notebook. A forcing function for fuzzy ideas.

• Distribution Layer: Your best insights travel ahead of you. The right people find them before they find you.

• Trust Signal: It shows how you think over time, not just what you say. That’s draws the right people in.

Case Study: How This Works in Practice

Meet Nicole, a product manager at a mid-sized tech company: For years, she stayed quiet online. Scrolling, consuming, never posting. Then came the pressure: “Everyone’s posting about AI.” So she caved. Posted once. Overthought every word, every like. Deleted it. Later, after launching a new feature she was genuinely excited about, she decided to write a short post (not for clout, just to unpack her thinking.)

That single post:

• Helped her clarify her decision-making framework (thinking tool)

• Got shared by her VP to the exec team (distribution layer)

• Led to 3 other PMs reaching out for mentorship (trust signal)

She wasn't performing, she was building a product that served both her thinking and her reach.

The Content Framework

NYT research shows we share content for two big reasons:

• To express who we are (68%), Mirror

• To connect with others (94%), Magnet

Every piece of meaningful content does one (or both) of these:

• Mirror, It reflects how you uniquely process the world.

• Magnet, It attracts people who process the world in compatible ways.

Sometimes your content serves as self-therapy, clarifying your own thinking. Sometimes it's a clear signal to find your people. Both matter, both compound. In a noisy market, clarity compounds. So don’t perform or pretend, just ship YOUR product.

The 3C Content Filter

When deciding if something's worth sharing, I run it through this filter:

• Curiosity: What am I genuinely exploring or questioning?

• Credibility: What have I actually lived, built, or studied?

• Clarity: Can both a 21-year-old new grad and a 60-year-old exec understand it?

If it fails more than 1 out of 3, don't share it.

This isn't about setting a perfect bar for quality. It's about alignment: making sure your content reflects how you think, and why it matters.

The Content Practice: Start Small, Think Long

You don’t need a content strategy. You need a practice - one that compounds, sharpens, and reflects you over time. Just like a product gets better with every version, your content gets clearer every time you show up.

Treat every scroll like a Zoom call

• Would you speak up in this conversation?

• Would you drop a thoughtful comment on that post?

• Would you share your take — not to impress, but to move the dialogue forward?

• Why join the room just to watch or listen?

  • You didn’t sign up for LinkedIn to scroll. You signed up to be seen.

• If your work is strong, doesn’t your voice deserve to be heard?

This isn’t about constant broadcasting. It’s about showing up with care, in rooms that matter to you. Don't post to perform. Post to participate. This is how trust is built in the new world: Not by shouting. Not by hiding. But by thinking out loud → consistently, and with care.

Just like in a real Zoom room, the person we trust isn’t the one who speaks the most in one meeting, it’s the one who keeps showing up, sharpens the conversation, and helps the room move forward, every time.

Don’t wait to be discovered, be understood from the start. Turn on your camera and unmute yourself, because the conversation is better with you in it.

— Akash

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You can't out-expert this moment. But you can out-question it.

We’re taught that expertise is the goal. That if you collect enough frameworks, wins, and proof points, you’ll eventually graduate from not knowing. But in my experience, the opposite is true.

We grow through the process of figuring things out, not by having it figured out. That’s the rookie mindset.

And now, in a world where AI makes it easier than ever to skip that process, staying rookie-minded isn’t just helpful, it might be your only edge.

The Default Thinking Crisis

Everyone’s rushing to use AI, but nobody’s asking how it’s shaping them in return.

We're optimizing workflows, stacking tools, fast-tracking decisions, and calling it productivity. But the faster we go, the more we forget what made the work meaningful in the first place: curiosity, originality, and intent.

AI isn't just a tool. It's a mirror. And people are too busy automating to notice what it's reflecting back.

We've entered an era of default thinking—where convenience wins, templates replace thought, and speed is confused for clarity.

Default is the path of least resistance. It’s what happens when we outsource the hard part—not the typing, the thinking.

Default thinking robs you of ownership. Your ideas sound right, but they don’t sound like you. The pattern is everywhere:

• 🧠 Stanford outlines how struggle is essential for deep learning, but AI is training us to skip it.

• 🏢 Microsoft reports that higher confidence in AI is associated with less critical thinking, while higher self-confidence is associated with more critical thinking.

• 👉 Forbes explains that AI is steering us toward a more monolithic and less diverse world.

The best work comes from unique perspectives, challenging assumptions, and noticing what others miss.

You can’t automate your way to originality, you have to think your way there. If you don’t protect your perspective, AI will flatten it.

So the question isn't whether to use AI, it’s how to use it without losing your mind—literally.

The Rookie Mindset

My antidote isn't about rejecting AI, it's about rebuilding our relationship with the learning process itself.

Embracing that process, running towards it instead of away, is what I call the rookie mindset. It's not about being new, but staying new: in how you think, question, build, and adapt. It's remaining open when the room wants to sound certain.

So what does that actually look like in practice? These aren't just traits, they're deliberate choices:

1. Confident Doubt

WHAT: Confident Doubt is the the characteristic of moving forward with conviction but staying open to being challenged. The best thinkers treat beliefs like prototypes: testable, flexible, and never too precious to revise.

WHY: In a culture obsessed with confidence, we reward people for having fast answers. But in a world shaped by AI, speed is cheap. The real value lies in the ability to shift your thinking as the context changes,

→ When the room wants to sound sure, be the one still asking better questions.

2. Beginner's Curiosity

WHAT: Beginner’s Curiosity is the mindset of approaching familiar problems like you’re seeing them for the first time. It helps you stay alert to what others miss, simply because they’ve stopped noticing.

WHY: In a world of best practices, curiosity gets flattened. We assume because something exists, it must be optimal. But progress doesn’t come from what’s accepted, it comes from questioning what’s taken for granted.

→ When the room is building on assumptions, be the one breaking them.

3. Effortful Thinking

WHAT: Effortful Thinking is the choice to slow down and go deeper, even when tools offer you the fast-forward button. It’s willingness to struggle with a problem before the answer.

WHY: AI promises instant answers without the messy middle. But the middle is where insight lives - in the confusions, dead ends, and reformulations. Skip the struggle and you'll produce what everyone else produces: ideas that are good but common.

→ When the room automates the thinking, be the one still doing the manual reps.

4. Pattern Sense

WHAT: Pattern Sense is the intuition of seeing beyond individual problems to the systems that generate them in the first place. It’s systems thinking applied to daily decisions—the discipline of zooming out before zooming in. Instead of asking why this user dropped off, ask what journey brought them here.

WHY: We default to addressing symptoms rather than causes. We fix the same problems repeatedly without addressing their shared source. Pattern Sense shifts you from firefighting to preventing fires altogether.

→ When the room fixes what’s broken, be the one redesigning the system that broke it.

The Rookie in the Wild

The rookie mindset isn’t just a theory, it’s alive in the daily choice to question the default. Here’s mine:

[Akash’s rookie process note]

I used ChatGPT, Claude, Perplexity, Gemini, and more late-night queries than I care to admit.

I started with a simple story I wanted to share. Then paused. Asked: What are people actually wrestling with right now?

AI helped me dig through Reddit threads, Google Trends, and other newsletters, but it couldn’t name the tension I was circling: staying original in an automated world.

I didn’t just listen to AI. I broke it, rewrote it, disagreed with it, and dropped in my favorite prompt many times:

Pretend you completely disagree with this post. Break down the flaws in my argument, challenge my assumptions, and identify any gaps in logic. If something is vague or unconvincing, call it out. Then suggest stronger ways to frame my point.

The goal wasn’t to get the right answer. It was to clarify through the iterations. I didn’t use AI to skip the work, I used it to stretch it. To catch my blind spots, challenge soft logic, sharpen structure, and kept on editing until I made every word count.

This isn’t draft 1 or draft 10, it’s probably draft 25… and that’s exactly the point.

The Rookie Practice

You made it this far so let’s get practical. This isn’t a mindset you adopt once, it’s a practice: a series of small choices that compound over time.

These are the rookie rituals. Small shifts that compound. Practice one, and the rest get easier.

1. Start with a blank page – Before reaching for a template or prompt, open a blank doc and invite your brain to make the first move. Let your own thinking lead. Clarity loves a blank canvas.

2. Zoom out until the REAL question reveals itself – Instead of debating tiny answers, ask the bigger question that makes the rest irrelevant—the one that shifts the frame, not just the tactic.

3. Reverse your strongest opinion – Take your most confident belief and spend 15 minutes arguing the opposite. What would make you wrong? What might someone with a different background say?

4. Designate a rookie in every meeting – Rotate someone to ask the naive, disruptive questions no one else will. Bonus: it turns critique into a creative role, not a threat (you can also ask AI to do this for you).

5. Build something with no outcome in mind – Run a 30-minute creation sprint with zero stakes. Make a landing page, a poem, a visual, a write-up. Explore by doing, not performing.

6. Relearn the basics – Return to a beginner resource in your field and notice what hits different now. The fundamentals evolve as you do and the basics reveal different insights at different stages of expertise.

7. Audit your defaults – Once a week, write down one default decision you made on autopilot and question if it still serves you. Also question the assumptions that led you to that decision in the first place.

8. Stretch the argument – Ask AI (or friends and colleagues) to disagree with you. Not to win but to stretch, spot the gaps in your logic, and refine your thinking. Treat it like a mental sparring partner.

9. Change your inputs to change your thinking – Step out of your usual context, read outside your domain, rearrange your workspace, take the longer but more scenic route. Creativity follows contrast.

10. Think promptly – Don’t ask “How do I get this done?”, think “How do I write the perfect prompt to get this done?” What are the constraints? The goals? The assumptions baked in? Prompt like a product thinker.

Some rituals are solo, some collaborative, but all repeatable. Pick one, try this week, and see what happens.

This first issue took 27+ drafts, 4 AI models, countless rewrites, moments of doubt, and 100% effort.

The Rookie Mindset isn’t a theory I’m sharing, it’s how this was made.

— Akash

📩 What’s one part of your life running on “autopilot” and how would the rookie version of you approach it differently?

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Once upon a time, in a world not so far from ours, Alice found herself wandering through a peculiar landscape. This was no ordinary place; it was a realm that blended the enchanting chaos of Wonderland with the intricate world of Product Management. Here, user stories were as common as white rabbits, and roadmaps were as winding as the rabbit hole. She had ventured into a place where ideas were born, nurtured, and sometimes, tragically abandoned.

As she wandered aimlessly through a strange and unfamiliar world, Alice stumbled upon a curious sight: a tea party hosted by none other than the Mad Hatter. A product leader of legendary proportions. His table was a chaotic masterpiece of sticky notes, wireframes, and half-eaten pastries.

The Hatter was a whirlwind of ideas, each more outlandish than the last. He seemed to be leading his team without any clear direction or structure. Alice was curious, and she couldn't resist asking the Hatter about his strategy...

"Strategy?" the Hatter replied, "Who needs strategy when we have so many ideas brewing and know how to tell good stories?" as he scribbled down a feature no one had ever conceived.

Alice watched in awe as the Hatter's team brought these seemingly mad ideas to life. It was a chaotic, yet exhilarating process, filled with equal parts genius and madness.

In her experience, a product team needed a clear strategy and structure in order to succeed. But the Mad Hatter seemed to be leading his team down a different path, one filled with endless possibilities and unbridled creativity. She began to see value in this approach and thought to herself, "perhaps creativity and madness are the ultimate currency of this land."

Next, Alice stumbled upon a grand product kitchen run by the Cheshire Cat. The Cat, with his enigmatic grin, presided over a bustling team of chefs. Each chef specialized in a different product, creating a symphony of features and requirements that delighted every experience.

“Welcome, Alice,” purred the Cheshire Cat. “Here, product management is like a Michelin-starred kitchen. Each team member has a specific role, contributing their expertise to craft a masterpiece."

Alice, intrigued, watched as the chefs worked in harmony. The Cat explained, “Just like a kitchen needs precise roles and coordination, so does a product team. But, much like a kitchen, a product team must be agile and adaptable. We pivot, we adjust, we innovate based on feedback from the creatures who eat our food and the changing trends in our world.”

The Cheshire Cat, who seemed to have an uncanny ability to keep the team focused and on track, had a very different approach from the Mad Hatter. Alice marveled at this perspective. But she still had one burning question: "How does the Mad Hatter's approach fit into all of this?"

The Cheshire Cat chuckled. "Ah, the Mad Hatter," he said. "He's like a chef who throws all kinds of ingredients into a pot, stirs them around, and somehow creates a masterpiece."

The product management world was indeed as varied as Wonderland itself. The Hatter’s chaotic creativity and the Cat’s structured kitchen approach were just different slices of a larger, more complex pie.

But the world of product was not all tea parties and well-run kitchens. There was also the Queen of Hearts, the embodiment of the most demanding stakeholders. Her reign was marked by constant changes, impossible deadlines, and a penchant for beheadings (metaphorical, of course). She was busily commanding her card soldiers. “Everything I ask for must be done and it must be done my way!” the Queen declared with a booming voice.

Alice, recalling her experiences, addressed the Queen, “Managing stakeholders in product management often feels like dealing with you, Your Majesty. It’s essential to align everyone’s expectations and vision to avoid chaos.”

The Queen’s temper flared momentarily, but Alice’s point was well taken. The Queen’s demands reflected the challenge of balancing conflicting stakeholder interests and driving towards a unified product vision. Alice quickly learned the art of diplomacy, juggling the desires of the Queen with the realities of her development.

Continuing her journey, Alice came across a Caterpillar, a wise old creature who smoked a hookah and dispensed sage advice. He asked her the famous question, "Who are you?"

Alice replied, "I'm a product manager, I think. I'm trying to navigate this strange world."

The Caterpillar responded, "Ah, a product manager. You must be very good at managing expectations and driving outcomes."

"As a product manager, you need to be constantly collecting and analyzing data to make informed decisions," he told her, "just like I use my hookah smoke to see patterns in the air."

She looked up, and upon closer inspection, realized the smoke floating above was not simply smoke, but it also created patterns that told stories. The Caterpillar taught Alice the importance of data-informed decision making, of looking beyond the surface to uncover hidden patterns.

As Alice delved deeper into this world, she encountered a variety of peculiar characters: the Dodo, a project manager obsessed with timelines; the King of Hearts, a frantic scrum master always counting down to the next sprint; and the Tweedledee and Tweedledum, a pair of designers with opposing views on every design decision.

She learned that product management was a delicate balancing act, a world of constant negotiation.

Alice's journey wasn't without its challenges. She encountered product teams that were stuck in a rut, following outdated methodologies and resisting change. She witnessed the downfall of products due to a lack of creature-focus and the rise of those that were built on a deep understanding of creature-needs and first principals thinking.

As she spent more time here, Alice began to realize that successful product managers should embrace the chaos, the uncertainty, and the constant change that defined this world. Those are the ones that can see the big picture while also paying attention to the smallest details. Ones who might blend the art and science of wonderful development.

They are visionaries who could inspire their teams to cook the best features, analysts who could make sense of complex data-clouds, and diplomats who could navigate the political landscape of conversations with the Queen (of Hearts).

And so, with her newfound insight, Alice now tackles challenges with her strategic mind, incorporating the creativity of the Mad Hatter, agility of the Cheshire Cat, and the diplomacy she built with the Queen of Hearts, upon all the other things she learned from the creatures she encountered.

She knows she is in a world as enchanting and perplexing as any fairytale. As as she looks back on her past adventures, she seems certain that she has found her true calling: to be a product manager, a master of the chaos and a weaver of digital magic, adding the wonder to Wonderland.