Guest post by Marlon Grandy

How an Alarm App Got 25 Million Views and 100K Downloads in 30 Days

In the first 30 days after launching Wayk, a morning alarm app that makes you complete missions to turn off your alarm, we hit 25 million views on TikTok, reached #15 on the App Store, and crossed 100,000 downloads. Three 23 year olds, no VC money.

None of it happened because we were creative. It happened because we stopped trying to be. Here’s the full breakdown 👇

The Backstory

My two best friends and I could never wake up on time. Every morning was the same. Snooze, snooze, snooze, crawl out of bed late and already behind. The morning sets the tone for everything, but most people lose it before they even get up. Between snoozing and scrolling in bed, the average person wastes over 250 hours a year, almost 45 minutes every single morning, before their feet even hit the floor.

So we built Wayk. Your alarm won’t turn off until you complete a mission: photo of the sky, pushups, find a random object, say an affirmation. You start the day with a small win and by the time you’re done, you’re already up. Too late to go back to sleep.

First version took two weeks to build. We launched with a small UGC team ready to post on day one because an app with no social proof dies quietly.

The Hook That Changed Everything

About a week after launch I was scrolling TikTok studying content from apps in our space. One account kept coming up: Push-up Guy, the creator behind Pushscroll, a screen time blocker that makes you do pushups to unlock your phone.

His content was blowing up. Hundreds of thousands of likes. The format was dead simple:

• Film yourself using the app

• Show the annoying moment

• Add a relatable POV caption

No fancy editing. No production. Just the raw reaction to the product doing its thing. Here’s what was working for him:

The products were different but the energy was identical. Both apps force you into a physical action before you can do the thing you want. Both create that comedic tension of being annoyed but also impressed. The hooks practically wrote themselves. You just had to swap the context.

The Playbook: How We Ported It

Here’s exactly how we took what was working for Pushscroll and made it ours.

1. Study what’s already landing.

I wasn’t looking for inspiration in alarm clock content. I was looking at adjacent categories like screen time, productivity, and fitness apps. Anything where the product creates a visible, filmable moment. Pushscroll’s videos had a clear pattern: POV caption + real reaction + app in action + short and raw.

2. Adapt the hook, keep the energy.

Pushscroll’s hook was “POV: you have to do X pushups to unlock [app].” Ours became “My alarm won’t turn off until I do 15 pushups.” Same structure, same frustrated-but-impressed energy, different product. The caption had to sound like something you’d text a friend, not something a brand would write.

3. Test it yourself first.

We didn’t hand this to creators right away. I filmed myself first. Alarm blaring, dropping to the floor, doing pushups before it stops. No special editing, no big production. The video took off. The images below show my first viral posts (ever) that drove thousands of downloads to the app!

4. Hand it to UGC creators and run it back.

Once the hook was proven, we gave the format to our UGC team. Same hook structure, slightly different angles (see tiktok here)

5. Repeat with new hooks.

When something pops you figure out why and run it back. We didn’t stop at pushups. We tested object hunt missions, pet missions, sky photo missions. Each one a new hook ported from the same playbook.

Find content already working → Adapt the format → Test it → Scale what hits.

What Worked vs. What Didn’t

😻 What worked:

✅ In-house short-form video. Our biggest channel by far. People are always surprised when they find out we’re a 3-person team.

✅ Hook porting from adjacent apps. Studying Pushscroll and adapting their proven formats gave us a repeatable content engine.

✅ UGC creators running proven hooks. Once a hook worked for us, handing it to creators scaled it without us filming everything.

😿 What didn’t:

❌ Reddit. Total flop. Tried posting in relevant subs, got permanently banned for self-promotion 😅.

❌ X (Twitter). No traction at all.

Everything came from short-form video. Nothing else even came close. If you’re a small team launching a consumer app in 2025, TikTok is the entire game.

The Email That Says It All

We get emails from users every day now. One guy picked the “photo of grass” mission without realizing it was winter. Ended up shoveling a patch of his yard at 7am just to turn off his alarm. That email kind of says everything about why this works.

500K snooze-free mornings so far. We’re just getting started 🤝.

Written by Marlon Grandy, co-founder of Wayk

marlon@wayk.io

I’ve been sharing this journey for the last 2.5 months, so it feels good to finally say this plainly: Lazyweb is live.

This isn’t a big triumphant launch post. I just want to mark the moment, explain what Lazyweb actually is, and share a bit about what the last few months of building it were like.

What Lazyweb is

The simplest way to describe it is: Mobbin meets ChatGPT, but on a canvas.

It’s a design inspiration library supercharged with AI. You pull in references from a large library of real mobile and web screens, place them on a canvas, and quickly explore different design directions.

Instead of starting from a blank page, you work from real examples and use AI to mock variations, compare ideas, and analyze patterns and iterate quickly without getting trapped in a single thread.

That last part matters more than it sounds.

Vibe coding is too linear. You start going down a path… Claude, Codex and friends keep taking you further down that path, and before long you’re attached to it mostly because you’ve already spent time coding a lot of building blocks. That sunk-cost feeling is annoying in code in general, but it’s especially for UI/UX… the soul of the product.

What I wanted was a way to make building UI feel less like a tunnel and more like a workspace. Something that would let me look at strong patterns, compare multiple directions side by side, and most importantly quickly apply to what I am vibing on.

I wanted to move fast enough that I wouldn’t get emotionally attached to the first decent output and can leverage the thousands of A/B tests by the best in class companies to my advantage.

That’s what Lazyweb is.. it’s a fast design research and design exploration tool for vibe coders.

Under the hood, Lazyweb contains over 300,000 mobile and web screens and flows, paired with an AI agent that handles both design research and implementation. It typically begins with rough, low fidelity explorations and quickly progresses to high-fidelity variations, all grounded in real-world patterns and best practices…compressing what used to take days into minutes.

Once you land on something useful, you can keep going. You can edit with AI, extract assets, vectorize, upscale, and do the practical cleanup work that usually comes after exploration and when you are ready export these mocks and assets (and even references) to add to your favorite coding agent.

No fighting a design system, auto layout, layers, or complex tools built for designers.

Why I wanted to build it

A lot of the work I’ve done over the years has involved studying other products closely, spotting patterns, comparing flows, and turning those observations into decisions. That kind of design and product research has always been high leverage for me. It’s also always been a bit slow and messy.

Now that AI can help build things much faster, I’ve felt an even stronger need for a tool like this. AI is great at writing code, but left alone it still has pretty bad design instincts. It can make something polished-looking without making it good. It can give you output without giving you judgment.

What I wanted was a way to combine both sides: the speed of AI with the grounding of real product patterns. I wanted to be able to borrow from companies that have spent years and a lot of money refining their flows, while still leaving room for the weirder outlier ideas that make something feel distinct instead of generic.

In other words, I wanted to vibe design, not just vibe code. And the limiting factor to design vibing is inspiration followed by know how.

Lazyweb takes care of both…

The journey

Here we are 77 days later

Some of you will remember that the first version of Lazyweb was actually heading toward being a Mac analysis heavy app. At some point I had to admit that was the wrong direction, and I had to rewrite it as a web app instead. That was a painful shift, mostly because it meant throwing away work I had already spent real energy on.

One of my bigger mistakes was getting too trigger-happy with Claude and Codex.

AI makes it dangerously easy to confuse motion with progress. You can produce a lot of code very quickly and still be making the product worse. The cost of taking action drops, which is great, but the cost of making a mess drops too. I definitely felt that. I got too trigger happy and spent the better half of this month simplifying, refactoring and deleting.

The other trap was building in the wrong order. It’s easy to tell myself I need X before Y before Z, even when Z is the thing the user actually wants. That logic sounds thoughtful and responsible, but a lot of the time it just created compounding problems.

Every extra dependency slows me down, complicates the product, and makes momentum harder to hold onto because I wasn’t working on “the real thing”.

What I actually realized is building should be like an inverted pyramid… start with the biggest thing in the roadmap that would provide (which usually comes last because of dependencies), hack your dependencies and work backwards to fix. This is how I started to get a good feel of the product and help sharpen my intuitions as I went along

The roller coaster is very real

The emotional roller coaster sucks.

Mostly because it’s hard to make sense or ground why I’m feeling sad happy depressed or on top of the world. The smallest things had the biggest swings on me and a lot of times it didn’t make sense why I was feeling what I am feeling. Walks, and writing help but they’re not magic bullets. The magic bullet is listening to my body instead of fighting it. I had to constantly remind the inner voice inside my head that….

I am not machine. I am not machine. I am not machine. I am not machine.

When I need a break, I take one. When I feel momentum, I try to use it. When I feel something off, I try to write it down and get to the bottom of it instead of just pushing through blindly.

If the issue is something within my control like uncertainty, avoidance, lack of effort — then I break it down and brute force momentum. If it’s outside my control, then I try to let go for a bit and come back to it later.

Building lazyweb felt more like sharp pushes followed by real decompression. Less like discipline as people usually describe it, and more like learning how to work with my own energy instead of constantly fighting it.

That’s probably the truest thing I can say about how Lazyweb got built.

So yes, lazyweb is live

That’s the announcement.

If you try it, I’d genuinely love feedback, especially the kind that tells me where the workflow is confusing, where it breaks, or what you expected it to do that it doesn’t do yet.

And thanks for following along with the whole journey. It took longer than I thought. I’m glad I stuck with it.

Now the real fun begins 🫡,

Ali Abouelatta

For the past 3 months I’ve been building a product with AI, full send.

Agents crawling.

Agents writing copy.

Agents generating code.

Agents doing the boring stuff I used to procrastinate on.

I spent about $20,000 on AI credits.

And the craziest part is that it worked.

There are real advantages to going solo with AI:

• Speed: you can run a dozen parallel workstreams at once.

• Coherence: one decision maker, one consistent taste.

• No incentive games: no promo ladders, no résumé projects, no “stakeholder alignment” theater.

As I relied more and more on AI, what broke was not my ability to create fast. What broke was the belief that humans are optional.

I’m bullish on humans for three reasons:

1. Accountability

2. Differentiation

3. Community

1) Accountability

Accountability is the difference between shipping and spinning.

Working in a big public tech company, I saw the same pattern over and over:

• Projects with one clear owner moved fast, made decisions, and were fun to be on.

• Projects with no real owner drifted. Decisions got made by committee. Everyone had input. Nobody had responsibility. They often died slowly, even when the market signal was there.

This is not a “big company problem.” This is a human behavior problem.

When responsibility is shared, responsibility dilutes. Social psychology has documented this for decades as diffusion of responsibility. The more bystanders there are, the less any one person feels on the hook.

That’s why there are operating frameworks designed to force clarity. They exist because ambiguity is the default.

Mechanisms like:

• A DRI, one directly responsible individual, so ownership is never ambiguous.

• Single-threaded leadership, so one person can actually deliver outcomes without getting coupled to ten other teams.

Now here’s the twist.

AI does not dilute accountability. AI deletes it.

Your agent can generate code, run tests, and open PRs. It can tell you something is “correct” in the narrow sense of passing a unit test.

But it does not own the outcome. If the user hates the UX, the agent does not care. If there’s a security bug and you get exploited, the agent does not apologize. If the site goes down, the agent does not wake up at 3 a.m.

You do.

Building Lazyweb.com made this visceral.

I’m moving fast. Looking back at the last couple months, what I shipped would have taken me years pre-AI. But I also realized what I miss most is not more horsepower.

It’s shared ownership.

Because no matter how productive AI makes you, there is only so much accountability one person can hold at once.

More leverage creates more levers. More levers create more accountability, tradeoffs and tough calls more need for accountability.

2) Differentiation

When software becomes instant, customers stop rewarding effort and start rewarding difference.

Speed is an advantage. Sometimes it’s the advantage. The trap is thinking speed turns into a moat.

AI is making it easier to ship, faster than ever. That creates real first-mover opportunities. It also makes it easy to confuse “I shipped fast” with “I built something that users want.”

ChatGPT is the cleanest example.

When it first hit, expectations were low because the breakthrough was the headline. It hallucinated, it was rough, and we still loved it because it felt like the future showed up early. You could put a wrapper on an AI model, with a few adjustments to system prompts and make hundreds of thousands a month.

Fast-forward to now and “a chat UI on top of a model” is not impressive. People expect the whole bundle. Web search, tool use, deep research, strong UX, constant iteration. The floor rose, so the applause stopped.

This is not just an AI thing. This is the pattern for every category that gets easier.

When something moves from expensive to cheap, the customer stops admiring the fact that you did it. They assume you did it. Then they start judging the parts that are harder to copy: reliability, trust, distribution, taste, brand, and how well it fits into their real life.

You can see this playing out in AI video right now.

Early models got forgiven for being janky because the headline was “it works at all.”

Then the floor rises, and nobody claps for the floor.

So differentiation moves up the stack.

You need either:

A continues train of breakthroughs

Something genuinely new. Rare, and often temporary.

Or a sustaining moat

A moat is not a feature list. A moat is a benefit plus a barrier. You get value, and competitors cannot easily replicate it.

If you want the simple map, here are the seven classic moats (or “powers”) and what they look like in the real world:

1. Scale economies

   You get cheaper as you get bigger. New entrants cannot match your unit economics.

2. Network effects

   The product gets more valuable as more people use it. Users pull in more users.

3. Switching costs

   Leaving hurts. The product knows your workflow, your history, your preferences. The longer you use it, the harder it is to replace.

4. Brand

   People choose you even when alternatives are “good enough.”

5. Cornered resource

   You own something others cannot easily buy. Data, distribution, access, talent, a channel, a contract.

6. Process power

   Your operating system is the advantage. Culture, cadence, and execution that is hard to copy.

7. Counter-positioning

   You make a move incumbents cannot copy without breaking their existing business model.

Now connect this to AI coding.

People see agents generate code 10x faster and assume the job of engineering is typing.

It isn’t.

Engineering is judgment under constraints:

• what to build

• what not to build

• what tradeoff is acceptable

• what failure mode will kill trust

• what “good” even means

Agent loops can search, test, refactor, and iterate. They can be insanely helpful. They still do not automatically produce a moat, because a moat is not “more output.” A moat is a hard-to-copy advantage that usually comes from choices that feel non-obvious at the time.

So yes, an agent can generate infinite code.

But it cannot generate infinite advantage.

Because advantage comes from non-obvious calls:

• the thing you do that looks weird until it works

• the constraint you embrace that others avoid

• the wedge that breaks an incumbent’s position

• the taste to kill 90% of what is easy so you can ship the 10% that matters

AI is leverage.

Yet humans are still the differentiation engine because humans can choose to be different on purpose.

3) Community

AI will personalize everything. Humans will fight to keep something shared.

We are walking into a one-to-one world.

AI can get to know you, learn your preferences, and generate content tailored to you forever. That is genuinely useful.

The trade is that personalization deletes common ground, and shared experiences are one of the main ways humans bond.

Even if you do not talk, just experiencing something alongside someone else makes it feel more intense. People rate shared experiences as more pleasant or more unpleasant than the same experience alone.

When emotions synchronize during shared experiences, feelings of social connection increase.

This is why news matters beyond “information.” A big story becomes a shared object. Everyone reacts, argues, jokes, and forms opinions. Shared narrative creates shared dialogue. Shared dialogue creates community.

Same with movies.

A big movie is a communal object. You watch it, your friends watch it, the internet watches it, and now you have something to talk about.

Now imagine the AI future where Netflix generates a different movie for you and a different movie for your friend, each perfectly optimized to your tastes.

Cool, until you realize you cannot talk about it.

There is no “did you see that scene?” because their scene never happened.

So yes, AI will flood the world with personalized content.

And that will make the demand for shared experiences stronger, not weaker, because shared experiences are how humans create tribes, status, inside jokes, meaning, and memory.

This is also why community becomes an even bigger moat for companies.

In a world where content is infinite, trust is scarce.

Nielsen found that people trust recommendations from people they know more than any other channel.

Even inside companies, this matters.

If you “run” 100 agents but you have no camaraderie, no shared wins, no shared pain, no shared story, work becomes sterile. You lose one of the biggest motivators, the feeling that you are doing something hard together.

Maybe we do not need 80,000 people to build an e-signature company.

But we will always need humans for the part AI cannot generate.

Shared meaning.

Closing

I’m not making the case against AI.

I’m making the case for humans, precisely because AI works.

AI makes output abundant.

It makes speed cheap.

It makes average easy.

So the scarce things become more valuable:

• Ownership

• Judgment

• Trust

• Community

AI can generate infinite work.

It cannot generate someone who cares if it wins.

And by “win,” I mean the real win. Shipping something people actually want, keeping it reliable, earning trust, surviving competition, and building something that lasts.

That part is and will forever be human.

Until next time,

Ali Abouelatta

Not because we’re lazy or undisciplined, but because the next step is unclear, and our brains avoid discomfort.

When that happens, we don’t just stall. We drift. We default to whatever is easiest to do or feel. Doomscrolling. Outrage. Obsessing over others’ milestones, followers, & salaries. Motion that looks like progress, but isn’t.

While we’re going through the motions day in and day out, only a few choices truly matter: your partner, your physical and mental health, and your closest relationships. Everything else is mostly noise, even when it feels urgent.

Products work similarly. We have some latitude in what to build and how to spend our time and energy. However, only a few levers, projects, or tasks truly matter when it comes to creating value for customers and driving business outcomes. That freedom is a gift and a trap: we can choose real problems or manufacture fake ones to solve.

As I embark on building Lazyweb as a solo human collaborating with AIs, with little guardrail or expectation on how to spend my time, I had to check myself more often than I would like that I am not solving problems that don’t exist.

Work is malleable:

Parkinson’s Law is true.

I had enough work to fill 8 hours a day, 5 days a week, 50 weeks a year, sometimes more, never less, except for the one day in my career, when I had nothing to do due to a company-wide strategic shift.

What I found fascinating in my first month building Lazyweb was that the temptation to create problems to solve that should not exist never goes away. It doesn’t matter if I have 100 other things to do, 17 fires to put out, and enough of a backlog to dry out my bank accounts on Cursor, Claude, and Codex credits.

Until recently, I thought a lack of problems to solve was the main driver for us to create new unimportant problems to go after. Now I realize this is a naive interpretation of Parkinson’s law.

Here I am creating a product from scratch, with important & well-defined problems to solve. Yet every now and then, I can’t help but wonder and take a detour attempting to solve unimportant problems. My most recent being a detour into SOC2 compliance.

Who needs compliance when you have 0 users? What is there to comply with, exactly?

Some work is necessary, some is important, some is neither

I think now is as good time as ever to distinguish between necessary but unimportant work and work that shouldn’t be done in the first place.

Not everything we do needs to be important, sometimes it is just necessary.

For instance, for an app, authentication is necessary but not important: it enables sign-up, but creating an account doesn’t deliver unique value.

On the flip side, a problem that shouldn’t exist is something that isn’t needed at all, at least now. Like setting up A/B testing and feature flags when we have 17 users.

Big company

Why do people create problems that don’t exist in big companies? I don’t know all the reasons but I know there are at least two that exist in most big companies:

1) Incentive structures

Companies have promotion ladders and comp frameworks.

Sometimes, people create problems to solve to align their work perfectly with those boxes: they inflate scope of the team to hoard “more resources” in the org, spin up cross-team initiatives that go nowhere, or pitch a low-value “platform migration” to appear strategic, none of which serve users.

We’re selfish and sometimes we push our self interest at the expense of our peers, teams, and company. This sucks and is usually obvious, but is valuable to at least one person, the person creating the problems to solve.

2) Working on a problem we can’t solve

When the important thing to solve is upstream or external, it’s safer to chase tractable but not valuable work.

If we are working on the UI on the paid marketing team but the acquisition channel is broken because of poor targeting, there isn’t much we can do to fix the important problem of a lackluster performing channel.

In that case we might be tempted to solve unnecessary manufactured problems instead that we are capable of solving like investing in a new design guideline for improving the cohesion of our marketing landing pages; it is a less discomforting use of one’s time.

Unfortunately, this is value-less work.

Early-stage startup (or solopreneur as is my case):

Solving problems that don’t exist looks different if we’re working on an early pre-PMF or even pre-launch project like Lazyweb.

We lack a clear sense of what matters; at best we haven’t pinpointed where value comes from, and at worst we’re creating none.

1) High effort, high uncertainty work

Research in psychology and behavioral economics shows people prefer known mediocre outcomes over unknown risky ones (ambiguity aversion), and will go surprisingly far to avoid uncertainty (Ellsberg, 1961; overview).

I’m not doing psych research daily anymore, but my master’s was at the HCI x information systems border, with plenty of psychology. I’m familiar with this pattern in theory and in practice. When I feel lethargic, unmotivated, or just meh, it’s usually not random. It’s my brain trying to exit a high-effort, high-uncertainty moment.

Building Lazyweb made this impossible to ignore. When I hit a real uphill battle like needing to get my first MCP server up and running, debugging a crashing backend, or watching agents spin for an hour with no obvious unblock, my backlog would suddenly “explode” with urgent-sounding tasks that conveniently avoid the actual work I am stuck on.

Here are a few items from the backlog:

• Figure out pricing.

• Database redundancy

• SOC2 compliance

• Team plans

• Create the premium color brand aesthetic.

• Finish Emil’s animation course.

• Clawdbot, should I give it a try?

• Make error states fun.

• Break out my backend into smaller services.

What do they all have in common?

1. None of these move me closer to validating the core hypothesis of my product

2. They show up precisely when uncertainty spikes and I’m likely to avoid the scary, messy problem right in front of me.

   ---

2) Delusion

On the other end, the gravitational pull to create non-existent problems is strongest when things are going well...in our heads.

Delusional problem solving takes the form “If X crushes, and Y crushes, then we will have Z problem. We should solve for Z now”

“If our launch video goes viral, and everyone signs up to our service, and we have a lot of users, scammers will try to hack our system and our infrastructure is not as secure as it should be. We should invest in hardening our infrastructure today.”

We are playing three steps ahead of the game, but not in a good way.

I call these delusional because they turn hypothetical success into a present tense constraint. They seduce us into premature optimization and pull focus from the only thing that matters right now: creating something valuable and getting people to recognize its value.

None of this is new. One of YCs mantras is “Build product, talk to users,” Elon said “the most common mistake of a smart engineer is optimizing something that shouldn’t exist.”

These quotes resonate because they shed light on a human bias that we often recognize yet still fall for: resisting the temptation of spending our time solving problems that don’t matter.

When I notice myself drifting into solving problems that don’t matter, or worse, creating them, I make it a habit to treat that as a signal to take time off and reset.

A walk, cooking, journaling or a few hours of brain-rot content would just do. Sometimes I need a day or two, depending on the effort/uncertainty of the outcome of the task I am evading.

After the reset, I drop every other problem on my to do list to give myself a reasonably unlimited time to focus and break down the uncertainty of the 1 thing holding me back, one small sub-task at a time, until momentum is back on my side.

In fact, I’m taking a break right now very publicly journaling in this newsletter before doing all the work that needs to be done for the launch of Lazyweb next week.

Wish me luck,
Ali Abouelatta.

The crazy thing I dove into this weekend was creating deepfake videos using Higgsfield’s Kling 2.6 Motion Control.

So I gave it a spin. It took me less than 12 minutes to put this together. Judge the end product for yourself.

If you are curious how to do this yourself…here is what I did

1. Took a 5 sec video with my iphone

2. Used google to find an image of Sydney Sweeney

3. Signed up to Higgsfield AI and uploaded both my video and the reference image in their Motion control App

4. Got a cool video but with MY VOICE.

5. Used Elevenlabs voice changer to change the voice

6. Swapped my voice out in Capcut

7. Voila!

—
That’s it for my weekend’s detour in AI generated videos…back to building 🫡

With <3,
Ali Abouelatta

While in bed yesterday, I was thinking about the simplest, clearest way to explain Lazyweb on a landing page.

So I woke up today with a “new day, new experiment” mentality.

The experiment was simple: how fast can I take this seedling of an idea I had in the middle of the night and…

1. Translate it into a design

2. Build it

3. Publish a new website with different hosting and infrastructure

4. Write about it

The answer: 5.5–6 hours.

TL;DR of my day so far:

• 8:30: Put the idea into Figma

• 9:00: Fired up Cursor

• 10:20: Desktop UI ready

• 11:15: Mobile design ready

• 12:00: Added more examples and polish

• 12:15: Hooked up the backend

• 12:30: Published site to Render (ran into hosting + SSL certificate issues)

• 2:30: Pressed send on this post

Old vs. new landing pages

For reference, the first version of the landing page took 20 hours of work, and was much simpler, design-wise.

Until very recently, doing frontend work with AI has been the bane of my existence. I needed to learn how to reliably steer AI toward good, consistent frontend code.

Making AI great at design tasks

Share progressive references

For each prompt, share a progressive version of the UI.

Sharing the end product upfront confuses my friends: Opus 4.5 and GPT-5.2 High.

One at a time. No shortcuts.

Annotate the screens using <tags> and attach them to prompts

This helps remove ambiguity from prompting. Everything makes more sense when we can see it and feel it.
Same with the AI.

Use XML for prompts longer than a couple of sentences.

First, it adds structure to my thinking.

Second, it makes it easier to go on tangents, since I can reference a specific thing I said and expand on it. Then Opus and GPT can still track when a tangent begins and ends.

Plan Mode for every design task

Plan Mode is great for resolving ambiguity because it allows the AI to ask clarifying questions.

Since I have two sources of truth while designing the frontend (screenshot + prompt), Plan Mode helps steer the AI instead of letting it guess.

Some food for thought from this micro-experiment

What stuck with me is this: a landing page that would’ve taken me a week in 2022, and 2–3 days just a few months ago, now takes a few hours.

What I’m coming around to is that more freed-up bandwidth means more creative, weird, and out-of-the-box things will exist in the world. And generally, the bar for software will only go up from here, not down.

What is the opposite of the AI slop?

To me, AI is not making everything generic and cheap. It’s doing the exact opposite: AI empowering human greatness by allowing them to spend their valuable effort and mental bandwidth on the things that matter the most to their end users.

It’s builders with taste moving faster, shipping weirder ideas, and pushing the boundaries of what software can even feel like.

And honestly… that’s the part that’s exciting.

This is it for today.

Ciao,
Ali Abouelatta

Short version: I spent more than I needed to, on purpose.

The Non-Negotiables

Domain (Lazyweb.com): $13,000

Why spend that much on a domain?

Because descriptive names compound.

If you’ve read First1000, you already know this is a pattern for me. Descriptive names are easy to remember, easy to explain, and easy to search for. Even if someone forgets the exact name, they remember something adjacent. “First something newsletter.”

That matters a lot early on for word of mouth for nascent products that are not meant to be used daily.

Made-up names were a hard no for me for that reason.

Also, SEO is core to Lazyweb, so I wanted:

• A name that had never been used before

• A .com, because Paul Graham said so.

A descriptive, unused .com with virtually zero SEO competition is rare. It took six months of negotiation, impersonating different buyers, and $13k to make it happen.

Stripe Atlas: $500

Company setup via Stripe Atlas. I got incorporation, banking, legal boilerplate, and a few other perks without much thinking or effort. Could I have done it cheaper? Sure. Would I do it again? Yes.

Infrastructure & Backend

Render: $152

Render is hosting without the AWS anxiety.

I have personally been burned by misconfigured AWS services turning into four-figure bills overnight (that AWS generously waived after a few days of back and forth with support). Render gives me:

1. Predictable pricing

2. Great performance and monitoring

3. Enough abstraction that I can focus on the product

Big fan (:

Supabase: $0 (Startup Program)

After Render, Supabase is my second true love. Auth, database, storage, realtime, all in one place. Thanks to their startup program, it’s free for now.

I do not want to imagine building Lazyweb without it.

Twilio (SendGrid): $119

Used for welcome emails and internal alerts. I’m not thrilled with it and will give Resend a spin soon.

This is mostly inertia from First1000 days since I have used SendGrid for some email flows related to First1000.

Coding & Dev Tools

Cursor: $1,101

My daily driver for a coding agent is Cursor (Ultra $200/mo)

I tried Claude Code, Codex, and Replit agents. Cursor won.

It keeps getting better, feels native to how I think, and saves me hours every week. I still use Codex occasionally for parallel tasks, but Cursor is where real work happens.

Replit: $138

I experimented a little bit with Replit.

For instance Lazyweb’s landing page is built on Replit. It’s great for small throwaways and quick prototypes with managed hosting.

For a serious product, I found it expensive and limiting.

AI Models & Research

OpenAI (ChatGPT Team (x2 seats) + API ): $3,057

This is the biggest line item after my domain.

I pay for a two-seat ChatGPT Team plan. That gets me:

• Access to the Pro models (limited to 10 requests/month)

• Generous limits for Codex and GPT-5.2 Thinking

• Pay-as-you-go API credits for Deep research, GPT-5.2 Pro and Codex (which I occasionally but use their code review a ton)

GPT-5.2 Pro + Pay as you go is the best ROI on the money I spent so far.

When things break, and they occasionally do, I export all my code into a GPT-5.2 Pro in Deep Research inside of ChatGPT and things start working working. It’s my magic trick that I suspect not a ton of people know about.

xAI (Grok): $5

I use grok-4-fast-reasoning for runtime search queries on Lazyweb.com.

Grok 4 Fast has the best latency-to-accuracy tradeoff I’ve tested. It’s not the smartest model, but it’s fast and good enough.

It’s only $5 spend because it’s a variable usage cost and I am currently the only user/tester.

Anthropic (Claude): $20

I experimented with Claude via Anthropic.

Strong model, but I kept defaulting back to Cursor and Codex. With ChatGPT Team already paid for, Claude became a secondary option. Also reasoning over image input for Claude is subpar.

Gemini: $40

Using Nano Banana primarily for images and icons in the app.

Nano Banana is great for one-shot outputs and consistent visual styles. It’s also stubborn once it goes off track, which makes iteration painful.

Still useful in narrow cases but its not the breakthrough it is hyped to be. In fact, try editing any text inside an image and you will quickly lose your mind.

Parallel Research Agents: $165

This is a new startup from Parag (briefly the CEO of X, formerly Twitter). It uses multiple deep research agents running in parallel.

This is useful for scraping + reasoning-heavy tasks, but easy to overspend on and accuracy and evals still not there 100%.

Powerful in narrow use cases, but dangerous if you’re not careful, or let it run in the wild without supervision.

Productivity & Ops

Duckbill: $204

AI + human executive assistance via Duckbill.

I was skeptical. I’m not anymore.

It frees up 2 to 5 hours a week by handling annoying but necessary personal tasks. Filing insurance claims. Calling restaurants. Life admin.

All the stuff that steals focus without adding leverage, for almost nothing.

Wispr Flow: $0

A new favorite flow for me is using voice dictation via Wispr Flow when writing prompts or drafts for a newsletter.

I get a year free through Lenny’s subscriber bundle.

I was not a believer in voice input. I am now.

Deepgram: $0 (Startup credits)

Similar to Wispr Flow, Deepgram is Speech-to-text. Only this time it is an API.

Given my new found belief in voice dictation, I took a little detour to implement these voice features in the Lazyweb.com MVP and it puts a smile on my face every time I use it.

I got Startup credits covering the first $200.

CapCut: $192

I’m doing a decent amount of video editing in CapCut, mostly for unhinged AI-generated ads to support the Lazyweb.com’s launch on X.

CapCut has a horrendous Mac app. It is also not a great traditional editor.

But it’s the closest thing to a video editor with deeply integrated AI, which matters for launch content.

My days with CapCut are numbered but I have bigger fish to fry.

Google Workspace: $181

Email plus Gemini access via Google Workspace.

This covers business email accounts and Google’s AI features. Not exciting, but necessary.

ZeroBounce: $45

ZeroBounce is one of these obscure products that I have been relying on very heavily for years.

It’s an email validation tool to clean the Lazyweb waitlist.

It works and I moved on.

Miscellaneous: $92

Dogfooding competitor apps and random one-offs that don’t deserve their own line item.

Final Tally

I set aside $30k to get Lazyweb off the ground and so far, I’ve spent about $19,100, mostly on the domain and AI credits.

Could I have built a strong MVP for less? Absolutely. I overspent in a few places because speed, learning, and leverage mattered more to me than optimization.

The goal now is simple: make every future dollar work harder than the last one.

Ciao,
Ali Abouelatta

The cycle goes like this: I stumble upon some niche use case for a product I regularly use. I spend time looking for a niche product built for that use case. I fail to find it. I start believing I can build something there. Then, the more I think about it, the more I wish someone else would do it, because I don’t want to dedicate years of my life to a silly use case.

This cycle repeated so often that I started having different versions of the same ideas at different times, and I couldn’t even remember what the first idea was. That was the first push for me to start writing every thought down, a habit I continue to this day, not just for product ideas, but also for musings, learnings, feelings, and the back-and-forth debates I often have with the voices inside my head. I’ve added a snapshot of my 2019 ideas journal below, the earliest I could find. As you can probably tell, none of these are exactly screaming “go all in.”

2022-2023: Beginning of my Duolingo journey

In 2022, I joined Duolingo working on retention, but I soon transitioned to monetization, which is where I truly found my footing. Duolingo was a great place to start, and it helped channel the endless chaos of ideas in my brain by teaching me the ingredients that go into building a product users love.

The thing that stood out to me the most at Duolingo was how the entire product org was geared toward studying what works in larger consumer apps with similar business models, usage patterns, or problem to solve (in our case it was getting people to build a hard habit). Everyone, from our Chief Product Officer to product interns, does the following on a day to day basis: (1) study past experiments, (2) study relevant flows from other great consumer products, (3) come up with A/B tests to validate or invalidate hypotheses, and (4) double down on what works. Colloquially, we call this “bread-and-butter” product work.

As a young product manager with something to prove, I went all in on competitive analysis. I started compiling the purchase flows and subscription hooks, the core flows my team owned at the time, across the top 10 apps in each App Store category (about 220 apps total). Every six months, I revisited these apps, tracked what had changed, and did team brainstorms to use those insights, along with our knowledge of Duolingo’s historical purchase flow A/B tests, to refine (and beef up) our roadmap.

The results from these experiments were a thing of beauty. Many incremental dollars were added to Duolingo’s topline as a result and I started to develop a reputation within monetization of having a good ROI compass for monetization features.
—

2023-2024: Automating flow capture and analysis from consumer apps

These flow audits were time-consuming and I don’t think I would have kept at it for years given the effort required had GPT4-vision not come about in late 2023.

In January of 2024, a few months after GPT4-vision was released, I started automating the comparison process for purchase flows I started a 1-2 years ago. With some help from Apple’s shortcuts app and Replit, I wrote a script to have GPT4-vision compare updated screenshots to older ones I had of purchase flows from the apps I tracked. It also wrote a 3 sentence summary of the changes.

That cut the time I spent on these audits by maybe 60-80%.

While time investment went down, the end result were not amazing. GPT4-vision was good about 50% of the time and wouldn’t take much to trip it up. For instance, if I changed the screen brightness when capturing screenshots, the model would go nuts and imagine all sorts of changes that were not present.
—

2024: The First1000 Hiatus Begins

When 2024 came about I started taking on bigger and bigger projects at Duolingo, including our new years promo last year, and new ad format we called modular ads (see below)

Taking on these big project commitments were all consuming and I decided to take a year off from writing this newsletter and the 20-30 hours weekly commitment that comes with it.

I am sorry for that.

—

2025: AI Vision models are no longer fancy demos

Fast forward to 2025 and the gods of AI gave me many gifts including: GPT 5, Sonnet 4.5, Gemini 3 (particularly nano banana), Agentkits from big AI labs and even more under the radar hyper-specific vision specific models for embedding, retrieval, web search, database research and memory management.

All of a sudden I revived the lousy python script I wrote a couple of years ago to extract changelogs from purchase flows and it blossomed to a more sophisticated 100k line repo.

—

2026+: The birth of Lazyweb.com & the rebirth of First1000

With all the pieces falling into place; capable AI agents, a blossoming vision AI industry, a product that keeps pulling me in to build it… I decided to take sometime off Duolingo, starting 3 days ago, to go all in on building and launching Lazyweb.com.

Lazyweb is the culmination of everything I did manually at Duolingo to do the competitive analysis that often inspired my next big a/b test win.

I know it is a little bit hard to imagine what that would look like or how it would work. I promise it will all make sense in a couple of weeks (:

For now, the closest thing is this tweet from Ryan Hoover

What that means for first1000, is that over this sabbatical from Duolingo, I will use this newsletter to document my journey building Lazyweb.com and getting to 1000 paying subscriber (or miserably failing and learning that the true prize is the friends I made along the way).

This pivot feels right to my bones because I will be telling my story and not an edited semi-fictional PR’d version of a pre-AI company where the landscape and opportunities were completely different.

One thing I know with this pivot is that the Lazyweb.com story will zig and zag. Things will fail and things will work, and I’ll tell you all about it as it happens in near real time.

There will be zero curation and zero thread boy energy in this publication. Think of it more as public journal of a non-technical one-person company leaning heavily on AI to help design, engineer & market Lazyweb.com as well as build small internal tools to accelerate the things I’m uniquely capable of doing; the things I don’t trust the AIs to do.

Mostly expect my musings, experimentation, outcomes and guiding principles I develop (and forego) as I shape my worldview for what it means to be a non-technical person building a 0-1 product for this new AI building era.

And, of course, my tails as I try to scale Lazyweb from 0 to 1000 paying subscribers so I am not poor along this journey.

With ❤️,
Ali Abouelatta

Welcome back to #First1000, the newsletter that analyzes trends across the top 100 apps on the App Store to help you make data-informed decisions.

Today, I want to convince you that dark mode is a vitamin, not a painkiller, and you shouldn’t invest in it unless you have a ton of resources with nothing better to do.

What does the data say about dark mode support amongst the top 100 apps?

• Only about 50% of the top 100 App Store apps support both dark and light modes.

  

• Dark mode is not a significant revenue driver.

  • Transaction-based app categories have the lowest rate for supporting both light and dark modes (e-commerce, food & drink..etc).

    

• Apps known for being notorious A/B testers turn Dark mode off by default

  • This includes companies like Facebook, Doordash and Linkedin.

From analyzing the top 100 apps, we see that only half support dark and light modes, and even then, about 12% of them force you to go to settings to enable dark mode.

If it had been the incredible thing that Reddit made it out to be, companies with thousands of engineers and hundreds of millions of dollars in revenue would have invested more in it!

If you are unsure about supporting dark mode, I recommend putting it on hold for now!

Talk soon,
Ali Abouelatta

Welcome back to #First1000, the newsletter that analyzes trends from winning (and losing) experiments across the top 100 apps. Today’s topic is sign-up screens.

When it comes to sign-up screens, there is often a tradeoff between functionality and delight. Today, I want to convince you that erring on the side of functionality is the better strategy.

First, let’s take a look at the difference between functional and delightful designs:

• Functional: functional design emphasizes practicality and usability. It ensures that a product performs its intended functions efficiently and effectively.

• Delightful: delightful design goes beyond functionality to create an enjoyable and emotionally engaging user experience. It aims to evoke positive emotions and make a lasting impression.

  

Now that we understand the difference between functional and delightful designs let’s look at four recent sign-up screen redesigns in which these two extremes were tested against one another. The companies we will be looking at are Ring, Alltrails, Nextdoor, and Spotify.

As you will see, functional (and boring) designs won in each of these experiments.

In the first three experiments, a functional and plain design for the sign-up screen won against enjoyable and emotionally engaging videos.

In the next experiment from Spotify, you will see that even adding an illustration to an otherwise plain and functional sign-up screen is enough to hurt logins and registrations.

I have a few hypotheses as to why functional designs perform better on sign-up screens:

• Distracting users: adding illustrations and background videos to the sign-up screen can distract users from the CTAs, reducing the likelihood of them signing up

• Bandwidth and app size: additional elements like videos, illustrations, and animations can materially increase the app size, reducing the likelihood of users in countries with slower internet successfully downloading your app.

Try boring and functional designs for your next experiment on the sign-up flow
Or take inspiration from prior #First1000 issues on how to further improve sign-up screens:

• Disney+ Winning Message

• Registration Paywalls

• Explain the benefits on the sign-up screen

Until next time,
Ali Abouelatta

This experiment highlights the importance of keeping a copy, especially a paywall copy, short and simple, something I’ve had to learn the hard way.

In other words
Don’t get cute with words, especially when it involves money.

Don’t get cute with words, especially when it involves money.

Don’t get cute with words, especially when it involves money.

Don’t get cute with words, especially when it involves money.

Don’t get cute with words, especially when it involves money.

Talk soon,
Ali Abouelatta

Canva recently ran an experiment to eliminate “psychological pricing" or "charm pricing" from their monthly plan, and to my surprise, the experiment won!

I want to challenge you today to revisit and validate 1 or 2 things you have always assumed to be true about your product, business, or job,

Talk soon,
Ali Abouelatta

If you work on a high-growth product, there is one thing you love to see…a graph pointing up and the right. That means that the one thing you don’t want to see is a stale or declining growth chart.

Unfortunately, that has been the trend of Disney+ over the 6 quarters.

And the media & Wall Street have not been so kind to Disney+ throughout that period.

I think that sentiment is about to shift!

Why do I think so?

Disney+ ran many a/b tests over the past four months regarding messaging and differentiating its offering. Judging from these tests, it has found a winning formula for positioning and differentiating its offering from other streaming platforms. The numbers may just be lagging a little bit behind!

To better understand this point, let’s examine some of their positioning experiments over the past few months.
—

Experiment 1: Emphasize Endless story headline

Date: December 2023

Changes:

• Change the font size headline

Experiment 2: BundleHeadline

Date: March 2024

Changes:

• Changed headline

• Removed entry price from footer text

Experiment 3: Bundle Header Image

Date: April 2024

Changes:

• Changed Disney+ logo to Hulu, Disney, and ESPN+ logos

Whenever I see the scope of experiments getting smaller and the experimentation timeline getting tighter, it's usually a sign that the company is in double-down mode.

If I were a betting man, I would say the Disney+ numbers will likely surprise Wall Street analysts in the upcoming quarters.

Talk soon,
Ali Abouelatta

(Not investment advice; just the observations of a lowly PM who thinks a lot about subscriber growth, particularly when it comes to packaging said subscriptions!)

—
In both these examples, the registration paywall offers less context on what features//benefits you get with the subscription than other paywalls you see post registration.

This was very common amongst the top apps on the App Store, especially for ones with more than 1 subscription tier.

This is it for today,
Ali Abouelatta

The most surprising realization was that optimizing subject lines and send times wasn't worthwhile. It consumed too much time and energy for little long-term gain.

The one exception is brevity. Brevity is critical when it comes to writing a newsletter. Shorter subject lines and concise content can increase open rates by about 20-30%. In general, it's better to communicate your point with fewer words.

The other big realization was that for long-term subscriber growth, almost nothing mattered except writing more consistently. But I don’t think this is because consistency is a magical formula for building an audience.

What a higher publishing cadence does is that it acts as a forcing function to explore different ideas. The more variant ideas I explored, the better I understood some of the underlying principles governing them. Ultimately, these more universal principles were more interesting to explore and write about, but they took a few layers of exploration to unveil themselves.

I can’t believe how fast time goes. Here is to another four years!!

Talk soon,
Ali Abouelatta

Three things are guaranteed in life: death, taxes, and subscription products increasing prices.

About 6 months ago, I conducted my first pricing research by studying the top 100 grossing apps on the app store. The research gave me 7 pricing and discount heuristics that I shared.

Today, I am building on that initial research by examining how the top 100 grossing apps adjusted their prices over the past half-year.

Here is what I learned:

Insight #1: The average price increase was ~27%

~20% of the top 100 apps tested a price increase in the last 6 months.

Insight #2: About a third of price tests were designed to push more people to the annual plan

Most companies maintained the standard X10 ratio between monthly and yearly plan prices (2 months free if you buy yearly) when testing price increases.

However, about a third tested pushing users into the annual plans by hiking monthly prices more than the annual ones.

Insight #3: Lifetime and weekly plans were the most robust to price increases

Insight #4: Not all pricing experiments were winners
About 10% of the pricing experiments tracked were not successful.

This is it for today,
Ali Abouelatta

Yesterday, Slack announced the launch of Slack AI, almost 5 months since they first announced on September 6, 2023.

That got me curious as to what the current state of AI is. On one hand, from the news, it seems like AI is everywhere. On the other hand, in my day-to-day experience, I don’t seem to have as many meaningful interactions with AI in products I use and love regularly.

So, I did what I always do when I have an open-ended question: I researched the top 100 apps on the app store to get a sense of where things are at

Of course, app rankings change by the day, so the ones I looked at today may not be precisely the top 100 when you read this, but they should be directionally correct.

Here are my findings:

1. Excluding Meta and Google Apps, of the top 100 apps in the App Store, 12% launched AI-related features.

2. 7% of the top 100 apps announced they are testing AI features but are not broadly available. The median length of an announcement is 4 months.

3. An extra 4% of those companies announced that they are leveraging AI for backend-related work.

4. Only two of the top 100 apps on the app store had AI at the forefront of what the app offers: ChatGPT and Bytedance’s Capcut.
   (Arguably, you could also include Google’s Gemini on this list)

5. Surprisingly, the most common use case for utilizing LLMs was not AI assistants but improving the app’s internal search. AI assistants came in 3rd after ads/targeting-related AI improvement.

6. The majority of new AI features were free.

7. When charging for AI features, the lowest price was $3.99 (Snapchat), and the highest was $169 for TurboTax Intuit Assist.

8. For users charging for AI features, about a third opted to include AI in existing tiers, raising the price by 20% on average. About another third opted to create a new tier for the AI features, with the most common price being $20/month.

—
As part of this research, I have tried every AI feature available in every app on the top 100 list on the App Store. Suffice it to say that outside of a couple of notable exceptions, I have found those features to be underwhelming. The speed of adoption, however, is surprising.

More than 20% of the top 100 apps have embraced AI in one shape or another. This figure is probably an underestimate as I would expect some of these companies to be working on/testing AI features but have not made an announcement yet (or maybe they made one, which I missed in my research.)

Overlall I am optimistic.

I am excited to see what the future holds.
Ali Abouelatta

• Experience in mobile gaming, with a proven track record

• Strong In-App Purchasing(IAP) and/or subscription growth background

• Is candidate and kind

The position is at our HQ in Pittsburgh.

Full Job Description

Join us! We have fun at work <3,

Ali Abouelatta

If there’s one secret I learned at Duolingo, it is the power of positive friction.

What is positive friction? Positive friction is adding both value and effort to the user experience at the same time.

Generally, it is much easier to successfully remove friction from the user experience than to add it because, surprisingly, even the tiniest uptick in effort can deter users unless offset by substantial value.

This is why positive friction can seem counterintuitive when it really isn’t!

1) Retention
The first example of using positive friction to get users to form long-term habits comes from an experiment I shared on X.

For users starting a new streak on Duolingo, we experimented with showing them an extra screen after they did a lesson. Everyone who saw it had to press “Continue” to reach the next screen.

What may shock you is that we didn’t do anything with the “streak goal” users selected on this screen. We didn’t even store the value anywhere.

The result of this experiment was an increase in both short-term and long-term retention gains.
—
Fast forward a couple of years…

We ran a follow-up, adding more friction to the experience; we removed the preselected state. Users were 2 clicks away from exiting the flow instead of 1.

Again, the friction led to short-term and long-term retention gains.

—
2) Registration & onboarding
Nowhere do you see positive friction more than in registration and onboarding flows. Fitness and wellness apps are among the best industries in taking advantage of positive friction early in the user experience.

The first example I want to share is from the sleep app Rise.

Rise’s onboarding flow is 43 steps!

Like many other companies in the space, they realized the more screens they added to the onboarding flow, the more personalized and advanced their product appeared to the end users. People valued personalization more than the extra questions they had to answer.

Loona is another app in the wellness space that has an onboarding flow of 19 screens.

This is considered average compared to its peers at the top of the health and wellness app category.
—
Purchase Flow
Another place where positive friction repeatedly shows up is in the purchasing experience. I found that to be a little more surprising than the other flows. Purchase flows are very sensitive and high stakes, two ingredients I thought would make it a bad candidate for introducing friction.

As you will see below, it is almost a rite of passage for consumer subscription companies to start their purchasing experience with a snappy, frictionless experience, then experiment their way into offering more choice and doing so more explicitly than ever.

The first example here is from Flighty, the flight tracking app. Flighty went from a 1-click checkout experience to a clunky swiping view with all the different plans, features, and prices.

Peloton went through the same experience almost to a T when they introduced their freemium tier; they went from an animated, delightful 1, one-step purchasing carousel to an outdated plan comparison purchasing experience.

Even Uber went through a similar experience when they first introduced Uber One.

—
3) Core loop
Positive friction can also be effective in the core action loop of everyday apps, not just in high-stakes one-time flows like registration and purchasing.

For instance, not so long ago Lyft ran an experiment to auto-collapse the pickup time selection “Priority, Standard, or Wait & Save” for a more frictionless experience.

The results were loud and clear; people preferred the optionality over the frictionless experience.

Headspace
Headspace experiment introducing a primer screen in between its locked content and paywall was a winner.

Perhaps not surprisingly educating users on what they will get out of a headspace session caused more people to give a free trial a chance

—
I hope by now you are convinced that friction isn't always the enemy. It’s almost always a win-win for the users and the business when used appropriately.

This is it for today!

Talk soon,
Ali Abouelatta

In many of the founding stories I wrote for this newsletter, I noticed that there is often an external force outside of a product or feature set that was instrumental in getting the company to an inflection point.

This week, I went through the 120+ issues archive with a fresh pair to see if there were any common threads to these external non-product-related forces.

I found four themes:

1. Strategic networking and partnerships:

   • Superhuman, Onlyfans, Cameo, and Whoop: leveraged influential communities and/or figures to unlock early adopters for product or marketplace.

   • Tinder, Gopuff, Snackpass, and Bumble are more specific examples of partnering with fraternities and sororities to infiltrate and dominate the college market.

2. Building free stuff for customers:

   • Shopify: offered free business tools like logo makers and slogan creators. They expected e-commerce store owners to search for these ahead of setting up their online store.

   • Hubspot: created free tools that potential customers would find valuable, such as website grader, email signature generator, and blog ideas generator

   • Zumper: created Zumper Pro to allow realtors to post on major listing platforms like Zillow and Trulia effortlessly from their phone

3. Getting lucky by showing up every day:

   • Waze: benefited from the Apple Maps rollout debacle. Apple issues an apology and recommended Waze as one of the viable alternative

   • Morning Brew: were amongst the first to run Instagram stories ads. For the first 48 hours, their CAC went down X10+ because there weren’t as much competing ad dollars.

   • Techcrunch: gained significant recognition and credibility by breaking the story of Google acquiring YouTube.

4. Telling a great story:

   • DoNotPay: 19-year-old Stanford student builds the world's First Robot Lawyer to fight parking tickets

   • Calm: with the www.donothingfor2minutes.com pre-Calm launch gimmick

That’s it for today,

Talk soon,
Ali Abouelatta