The holy grail of plain text task management is finally within reach

Plain text task management? Are you serious?? Dedicated task managers exist for a reason, you know. Sheesh.

—Oh, I know. Believe me, I know! But hear me out.

I've managed projects and tasks in plain text for more than four years. Since reorganizing my to-do list, plain text task management is within reach.

Data completely yours? One billion percent. Beef it up with coding? Be my guest.

Enter: The List

The answer? A list. That’s it!

Write down tasks. Obsidian, Notenik, Drafts, or iA Writer are good plain text note-taking apps.

List too long? Of course, it is! A large list tells you what to do. Feel the itch and friction. Then fix it.

• But I want to be able to distinguish between work and personal life! List two things.

• However, I need to know what should be completed first. Put those items at the top.

• However, some things keep coming up, and I need to be reminded of them! Put those in your calendar and make an alarm for them.

• But since individual X hasn't completed task Y, I can't proceed with this. Create a Waiting section on your list by dividing it.

• But I must know what I'm supposed to be doing right now! Read your list(s). Check your calendar. Think critically.

Before I begin a new one, I remind myself that "Listory Never Repeats."

There’s no such thing as too many lists if all are needed. There is such a thing as too many lists if you make them before they’re needed. Before they complain that their previous room was small or too crowded or needed a new light.

A list that feels too long has a voice; it’s telling you what to do next.

I use one Master List. It's a control panel that tells me what to focus on short-term. If something doesn't need semi-immediate attention, it goes on my Backlog list.

Todd Lewandowski's DWTS (Done, Waiting, Top 3, Soon) performance deserves praise. His DWTS to-do list structure has transformed my plain-text task management. I didn't realize it was upside down.

This is my take on it:

D = Done

Move finished items here. If they pile up, clear them out every week or month. I have a Done Archive folder.

W = Waiting

Things seething in the background, awaiting action. Stir them occasionally so they don't burn.

T = Top 3

Three priorities. Personal comes first, then work. There will always be a top 3 (no more than 5) in every category. Projects, not chores, usually.

S = Soon

This part is action-oriented. It's for anything you can accomplish to finish one of the Top 3. This collection includes thoughts and project lists. The sole requirement is that they should be short-term goals.

Some of you have probably concluded this isn't for you. Please read Todd's piece before throwing out the baby. Often. You shouldn't miss a newborn.

As much as Dancing With The Stars helps me recall this method, I may try switching their order. TSWD; Drilling Tunnel Seismic? Serenity After Task?

Master List Showcase

My Master List lives alone in its own file, but sometimes appears in other places.  It's included in my Weekly List template. Here's a (soon-to-be-updated) demo vault of my Obsidian planning setup to download for free.

Here's the code behind my weekly screenshot:

## [[Master List - 2022|✓]]  TO DO

![[Master List - 2022]]

FYI, I use the Minimal Theme in Obsidian, with a few tweaks.

You may note I'm utilizing a checkmark as a link. For me, that's easier than locating the proper spot to click on the embed.

Blue headings for Done and Waiting are links. Done links to the Done Archive page and Waiting to a general waiting page.

Read my full article here.

Yes, client meetings are difficult. Especially when I'm alone.

Clients must tell us their problems so we can help.

In-meeting challenges contribute nothing to our work. Consider this:

• Clients are unprepared.

• Clients are distracted.

• Clients are confused.

Introducing Jack Dorsey's Google Doc approach

I endorse his approach to meetings.

Not Google Doc-related. Jack uses it for meetings.

This is what his meetings look like.

• Prior to the meeting, the Chair creates the agenda, structure, and information using Google Doc.

• Participants in the meeting would have 5-10 minutes to read the Google Doc.

• They have 5-10 minutes to type their comments on the document.

• In-depth discussion begins

There is elegance in simplicity. Here's how Jack's approach is fantastic.

Unprepared clients are given time to read.

During the meeting, they think and work on it.

They can see real-time remarks from others.

Discussion ensues.

Three months ago, I fell for this strategy. After trying it with a client, I got good results.

I conducted social control experiments in a few client workshops.

Context matters.

I am sure Jack Dorsey’s method works well in meetings. What about client workshops?

So, I tested Enterprise of the Future with a consulting client.

I sent multiple emails to client stakeholders describing the new approach.

No PowerPoints that day. I spent the night setting up the Google Doc with conversation topics, critical thinking questions, and a Before and After section.

The client was shocked. First, a Google Doc was projected. Second surprise was a verbal feedback.

“No pre-meeting materials?”

“Don’t worry. I know you are not reading it before our meeting, anyway.”

We laughed. The experiment started.

Observations throughout a 90-minute engagement workshop from beginning to end

For 10 minutes, the workshop was silent.

People read the Google Doc. For some, the silence was unnerving.

“Are you not going to present anything to us?”

I said everything's in Google Doc. I asked them to read, remark, and add relevant paragraphs.

As they unlocked their laptops, they were annoyed.

Ten client stakeholders are typing on the Google Doc. My laptop displays comment bubbles, red lines, new paragraphs, and strikethroughs.

The first 10 minutes were productive. Everyone has seen and contributed to the document.

I was silent.

The move to a classical workshop was smooth. I didn't stimulate dialogue. They did.

Stephanie asked Joe why a blended workforce hinders company productivity. She questioned his comments and additional paragraphs.

That is when a light bulb hit my head. Yes, you want to speak to the right person to resolve issues!

Not only that was discussed. Others discussed their remark bubbles with neighbors. Debate circles sprung up one after the other.

The best part? I asked everyone to add their post-discussion thoughts on a Google Doc.

After the workshop, I have:

• An agreement-based working document

• A post-discussion minutes that are prepared for publication

• A record of the discussion points that were brought up, argued, and evaluated critically

It showed me how stakeholders viewed their Enterprise of the Future. It allowed me to align with them.

Finale Keynotes

Client meetings are a hit-or-miss. I know that.

Jack Dorsey's meeting strategy works for consulting. It promotes session alignment.

It relieves clients of preparation.

I get the necessary information to advance this consulting engagement.

It is brilliant.

Sundar Pichai unveiled Simplicity Sprint at Google's latest all-hands conference.

To boost employee efficiency.

Not surprising. Few envisioned Google declaring a productivity drive.

Sunder Pichai's speech:

“There are real concerns that our productivity as a whole is not where it needs to be for the head count we have. Help me create a culture that is more mission-focused, more focused on our products, more customer focused. We should think about how we can minimize distractions and really raise the bar on both product excellence and productivity.”

The primary driver driving Google's efficiency push is:

Google's efficiency push follows 13% quarterly revenue increase. Last year in the same quarter, it was 62%.

Market newcomers may argue that the previous year's figure was fuelled by post-Covid reopening and growing consumer spending. Investors aren't convinced. A promising company like Google can't afford to drop so quickly.

Google’s quarterly revenue growth stood at 13%, against 62% in last year same quarter.

Google isn't alone. In my recent essay regarding 2025 programmers, I warned about the economic downturn's effects on FAAMG's workforce. Facebook had suspended hiring, and Microsoft had promised hefty bonuses for loyal staff.

In the same article, I predicted Google's troubles. Online advertising, especially the way Google and Facebook sell it using user data, is over.

FAAMG and 2nd rung IT companies could be the first to fall without Post-COVID revival and uncertain global geopolitics.

Google has hardly ever discussed effectiveness:

Apparently openly.

Amazon treats its employees like robots, even in software positions. It has significant turnover and a terrible reputation as a result. Because of this, it rarely loses money due to staff productivity.

Amazon trumps Google. In reality, it treats its employees poorly.

Google was the founding father of the modern-day open culture.

Larry and Sergey Google founded the IT industry's Open Culture. Silicon Valley called Google's internal democracy and transparency near anarchy. Management rarely slammed decisions on employees. Surveys and internal polls ensured everyone knew the company's direction and had a vote.

20% project allotment (weekly free time to build own project) was Google's open-secret innovation component.

After Larry and Sergey's exit in 2019, this is Google's first profitability hurdle. Only Google insiders can answer these questions.

• Would Google's investors compel the company's management to adopt an Amazon-style culture where the developers are treated like circus performers?

• If so, would Google follow suit?

• If so, how does Google go about doing it?

Before discussing Google's likely plan, let's examine programming productivity.

What determines a programmer's productivity is simple:

How would we answer Google's questions?

As a programmer, I'm more concerned about Simplicity Sprint's aftermath than its economic catalysts.

Large organizations don't care much about quarterly and annual productivity metrics. They have 10-year product-launch plans. If something seems horrible today, it's likely due to someone's lousy judgment 5 years ago who is no longer in the blame game.

Deconstruct our main question.

• How exactly do you change the culture of the firm so that productivity increases?

• How can you accomplish that without affecting your capacity to profit? There are countless ways to increase output without decreasing profit.

• How can you accomplish this with little to no effect on employee motivation? (While not all employers care about it, in this case we are discussing the father of the open company culture.)

• How do you do it for a 10-developer IT firm that is losing money versus a 1,70,000-developer organization with a trillion-dollar valuation?

When implementing a large-scale organizational change, success must be carefully measured.

The fastest way to do something is to do it right, no matter how long it takes.

You require clearly-defined group/team/role segregation and solid pass/fail matrices to:

• You can give performers rewards.

• Ones that are average can be inspired to improve

• Underachievers may receive assistance or, in the worst-case scenario, rehabilitation

As a 20-year programmer, I associate productivity with greatness.

Doing something well, no matter how long it takes, is the fastest way to do it.

Let's discuss a programmer's productivity.

Why productivity is a strange term in programming:

Productivity is work per unit of time.

Money=time This is an economic proverb. More hours worked, more pay. Longer projects cost more.

As a buyer, you desire a quick supply. As a business owner, you want employees who perform at full capacity, creating more products to transport and boosting your profits.

All economic matrices encourage production because of our obsession with it. Productivity is the only organic way a nation may increase its GDP.

Time is money — is not just a proverb, but an economical fact.

Applying the same productivity theory to programming gets problematic. An automating computer. Its capacity depends on the software its master writes.

Today, a sophisticated program can process a billion records in a few hours. Creating one takes a competent coder and the necessary infrastructure. Learning, designing, coding, testing, and iterations take time.

Programming productivity isn't linear, unlike manufacturing and maintenance.

Average programmers produce code every day yet miss deadlines. Expert programmers go days without coding. End of sprint, they often surprise themselves by delivering fully working solutions.

Reversing the programming duties has no effect. Experts aren't needed for productivity.

These patterns remind me of an XKCD comic.

Programming productivity depends on two factors:

• The capacity of the programmer and his or her command of the principles of computer science

• His or her productive bursts, how often they occur, and how long they last as they engineer the answer

At some point, productivity measurement becomes Schrödinger’s cat.

Product companies measure productivity using use cases, classes, functions, or LOCs (lines of code). In days of data-rich source control systems, programmers' merge requests and/or commits are the most preferred yardstick. Companies assess productivity by tickets closed.

Every organization eventually has trouble measuring productivity. Finer measurements create more chaos. Every measure compares apples to oranges (or worse, apples with aircraft.) On top of the measuring overhead, the endeavor causes tremendous and unnecessary stress on teams, lowering their productivity and defeating its purpose.

Macro productivity measurements make sense. Amazon's factory-era management has done it, but at great cost.

Google can pull it off if it wants to.

What Google meant in reality when it said that employee productivity has decreased:

When Google considers its employees unproductive, it doesn't mean they don't complete enough work in the allotted period.

They can't multiply their work's influence over time.

• Programmers who produce excellent modules or products are unsure on how to use them.

• The best data scientists are unable to add the proper parameters in their models.

• Despite having a great product backlog, managers struggle to recruit resources with the necessary skills.

• Product designers who frequently develop and A/B test newer designs are unaware of why measures are inaccurate or whether they have already reached the saturation point.

• Most ignorant: All of the aforementioned positions are aware of what to do with their deliverables, but neither their supervisors nor Google itself have given them sufficient authority.

So, Google employees aren't productive.

How to fix it?

• Business analysis: White suits introducing novel items can interact with customers from all regions. Track analytics events proactively, especially the infrequent ones.

• SOLID, DRY, TEST, and AUTOMATION: Do less + reuse. Use boilerplate code creation. If something already exists, don't implement it yourself.

• Build features-building capabilities: N features are created by average programmers in N hours. An endless number of features can be built by average programmers thanks to the fact that expert programmers can produce 1 capability in N hours.

• Work on projects that will have a positive impact: Use the same algorithm to search for images on YouTube rather than the Mars surface.

• Avoid tasks that can only be measured in terms of time linearity at all costs (if a task can be completed in N minutes, then M copies of the same task would cost M*N minutes).

In conclusion:

Software development isn't linear. Why should the makers be measured?

Notation for The Big O

I'm discussing a new way to quantify programmer productivity. (It applies to other professions, but that's another subject)

The Big O notation expresses the paradigm (the algorithmic performance concept programmers rot to ace their Google interview)

Google (or any large corporation) can do this.

1. Sort organizational roles into categories and specify their impact vs. time objectives. A CXO role's time vs. effect function, for instance, has a complexity of O(log N), meaning that if a CEO raises his or her work time by 8x, the result only increases by 3x.

2. Plot the influence of each employee over time using the X and Y axes, respectively.

3. Add a multiplier for Y-axis values to the productivity equation to make business objectives matter. (Example values: Support = 5, Utility = 7, and Innovation = 10).

4. Compare employee scores in comparable categories (developers vs. devs, CXOs vs. CXOs, etc.) and reward or help employees based on whether they are ahead of or behind the pack.

After measuring every employee's inventiveness, it's straightforward to help underachievers and praise achievers.

Example of a Big(O) Category:

If I ran Google (God forbid, its worst days are far off), here's how I'd classify it. You can categorize Google employees whichever you choose.

The Google interview truth:

O(1) < O(log n) < O(n) < O(n log n) < O(n^x) where all logarithmic bases are < n.

O(1): Customer service workers' hours have no impact on firm profitability or customer pleasure.

CXOs Most of their time is spent on travel, strategic meetings, parties, and/or meetings with minimal floor-level influence. They're good at launching new products but bad at pivoting without disaster. Their directions are being followed.

Devops, UX designers, testers Agile projects revolve around deployment. DevOps controls the levers. Their automation secures results in subsequent cycles.

UX/UI Designers must still prototype UI elements despite improved design tools.

All test cases are proportional to use cases/functional units, hence testers' work is O(N).

Architects Their effort improves code quality. Their right/wrong interference affects product quality and rollout decisions even after the design is set.

Core Developers Only core developers can write code and own requirements. When people understand and own their labor, the output improves dramatically. A single character error can spread undetected throughout the SDLC and cost millions.

Core devs introduce/eliminate 1000x bugs, refactoring attempts, and regression. Following our earlier hypothesis.

The fastest way to do something is to do it right, no matter how long it takes.

Conclusion:

Google is at the liberal extreme of the employee-handling spectrum

Microsoft faced an existential crisis after 2000. It didn't choose Amazon's data-driven people management to revitalize itself.

Instead, it entrusted developers. It welcomed emerging technologies and opened up to open source, something it previously opposed.

Google is too lax in its employee-handling practices. With that foundation, it can only follow Amazon, no matter how carefully.

Any attempt to redefine people's measurements will affect the organization emotionally.

The more Google compares apples to apples, the higher its chances for future rebirth.

UST depegs and LUNA crashes 99.999% in a fraction of the time it takes the Moon to orbit the Earth.

Fat Man, a Terra whistle-blower, promises to expose Do Kwon's dirty secrets and shady deals.

The Terra community has voted to relaunch Terra LUNA on a new blockchain. The Terra 2.0 Pheonix-1 blockchain went live on May 28, 2022, and people were airdropped the new LUNA, now called LUNA, while the old LUNA became LUNA Classic.

Does LUNA deserve another chance? To answer this, or at least start a conversation about the Terra 2.0 chain's advantages and limitations, we must assess its fundamentals, ideology, and long-term vision.

Whatever the result, our analysis must be thorough and ruthless. A failure of this magnitude cannot happen again, so we must magnify every potential breaking point by 10.

Will UST and LUNA holders be compensated in full?

The obvious. First, and arguably most important, is to restore previous UST and LUNA holders' bags.

Terra 2.0 has 1,000,000,000,000 tokens to distribute.

• 25% of a community pool

• Holders of pre-attack LUNA: 35%

• 10% of aUST holders prior to attack

• Holders of LUNA after an attack: 10%

• UST holders as of the attack: 20%

Every LUNA and UST holder has been compensated according to the above proposal.

According to self-reported data, the new chain has 210.000.000 tokens and a $1.3bn marketcap. LUNC and UST alone lost $40bn. The new token must fill this gap. Since launch:

LUNA holders collectively own $1b worth of LUNA if we subtract the 25% community pool airdrop from the current market cap and assume airdropped LUNA was never sold.

At the current supply, the chain must grow 40 times to compensate holders. At the current supply, LUNA must reach $240.

LUNA needs a full-on Bull Market to make LUNC and UST holders whole.

Who knows if you'll be whole? From the time you bought to the amount and price, there are too many variables to determine if Terra can cover individual losses.

The above distribution doesn't consider individual cases. Terra didn't solve individual cases. It would have been huge.

What does LUNA offer in terms of value?

UST's marketcap peaked at $18bn, while LUNC's was $41bn. LUNC and UST drove the Terra chain's value.

After it was confirmed (again) that algorithmic stablecoins are bad, Terra 2.0 will no longer support them.

Algorithmic stablecoins contributed greatly to Terra's growth and value proposition. Terra 2.0 has no product without algorithmic stablecoins.

Terra 2.0 has an identity crisis because it has no actual product. It's like Volkswagen faking carbon emission results and then stopping car production.

A project that has already lost the trust of its users and nearly all of its value cannot survive without a clear and in-demand use case.

Do Kwon, how about him?

Oh, the Twitter-caller-poor? Who challenges crypto billionaires to break his LUNA chain? Who dissolved Terra Labs South Korea before depeg? Arrogant guy?

That's not a good image for LUNA, especially when making amends. I think he should step down and let a nicer person be Terra 2.0's frontman.

The verdict

Terra has a terrific community with an arrogant, unlikeable leader. The new LUNA chain must grow 40 times before it can start making up its losses, and even then, not everyone's losses will be covered.

I won't invest in Terra 2.0 or other algorithmic stablecoins in the near future. I won't be near any Do Kwon-related project within 100 miles. My opinion.

Can Terra 2.0 be saved? Comment below.

Follower count, earnings, and more

I created my 7th TikTok account six months ago. TikTok's great. I've developed accounts for Amazon products, content creators/brand deals education, website flipping, and more.

Introverted or shy people use faceless TikTok accounts.

Maybe they don't want millions of people to see their face online, or they want to remain anonymous so relatives and friends can't locate them.

Going faceless on TikTok can help you grow a following, communicate your message, and make money online.

Here are 6 steps I took to turn my Tik Tok account into a $60,000/year side gig.

From nothing to $60K in 6 months

It's clickbait, but it’s true. Here’s what I did to get here.

Quick context:

I've used social media before. I've spent years as a social creator and brand.

I've built Instagram, TikTok, and YouTube accounts to nearly 100K.

How I did it

First, select a niche.

If you can focus on one genre on TikTok, you'll have a better chance of success, however lifestyle creators do well too.

Niching down is easier, in my opinion.

Examples:

• Travel

• Food

• Kids

• Earning cash

• Finance

You can narrow these niches if you like.

During the pandemic, a travel blogger focused on Texas-only tourism and gained 1 million subscribers.

Couponing might be a finance specialization.

One of my finance TikTok accounts gives credit tips and grants and has 23K followers.

Tons of ways you can get more specific.

Consider how you'll monetize your TikTok account. I saw many enormous TikTok accounts that lose money.

Why?

They can't monetize their niche. Not impossible to commercialize, but tough enough to inhibit action.

First, determine your goal.

In this first step, consider what your end goal is.

Are you trying to promote your digital products or social media management services?

You want brand deals or e-commerce sales.

This will affect your TikTok specialty.

This is the first step to a TikTok side gig.

Step 2: Pick a content style

Next, you want to decide on your content style.

Do you do voiceover and screenshots?

You'll demonstrate a product?

Will you faceless vlog?

Step 3: Look at the competition

Find anonymous accounts and analyze what content works, where they thrive, what their audience wants, etc.

This can help you make better content.

Like the skyscraper method for TikTok.

Step 4: Create a content strategy.

Your content plan is where you sit down and decide:

• How many videos will you produce each day or each week?

• Which links will you highlight in your biography?

• What amount of time can you commit to this project?

You may schedule when to post videos on a calendar. Make videos.

5. Create videos.

No video gear needed.

Using a phone is OK, and I think it's preferable than posting drafts from a computer or phone.

TikTok prefers genuine material.

Use their app, tools, filters, and music to make videos.

And imperfection is preferable. Tik okers like to see videos made in a bedroom, not a film studio.

Make sense?

When making videos, remember this.

I personally use my phone and tablet.

Step 6: Monetize

Lastly, it’s time to monetize How will you make money? You decided this in step 1.

Time to act!

For brand agreements

• Include your email in the bio.

• Share several sites and use a beacons link in your bio.

• Make cold calls to your favorite companies to get them to join you in a TikTok campaign.

For e-commerce

• Include a link to your store's or a product's page in your bio.

For client work

• Include your email in the bio.

• Use a beacons link to showcase your personal website, portfolio, and other resources.

For affiliate marketing

• Include affiliate product links in your bio.

• Join the Amazon Influencer program and provide a link to your storefront in your bio.

$60,000 per year from Tik Tok?

Yes, and some creators make much more.

Tori Dunlap (herfirst100K) makes $100,000/month on TikTok.

My TikTok adventure took 6 months, but by month 2 I was making $1,000/month (or $12K/year).

By year's end, I want this account to earn $100K/year.

Imagine if my 7 TikTok accounts made $100K/year.

7 Tik Tok accounts X $100K/yr = $700,000/year

Discover How We Used Facebook Ads to Grow a New Mobile App from $0 to $15K MRR in Just 6 Months and Our Strategy to Hit $100K a Month.

Our client introduced a mobile app for Poshmark resellers in December and wanted as many to experience it and subscribe to the monthly plan.

An Error We Committed

We initiated a Facebook ad campaign with a "awareness" goal, not "installs." This sent them to a landing page that linked to the iPhone App Store and Android Play Store. Smart, right?

We got some installs, but we couldn't tell how many came from the ad versus organic/other channels because the objective we chose only reported landing page clicks, not app installs.

We didn't know which interest groups/audiences had the best cost per install (CPI) to optimize and scale our budget.

After spending $700 without adequate data (installs and trials report), we stopped the campaign and worked with our client's app developer to set up app events tracking.

This allowed us to create an installs campaign and track installs, trials, and purchases (in some cases).

Finding a Successful Audience

Once we knew what ad sets brought in what installs at what cost, we began optimizing and testing other interest groups and audiences, growing the profitable low CPI ones and eliminating the high CPI ones.

We did all our audience testing using an ABO campaign (Ad Set Budget Optimization), spending $10 to $30 on each ad set for three days and optimizing afterward. All ad sets under $30 were moved to a CBO campaign (Campaign Budget Optimization).

We let Facebook's AI decide how much to spend on each ad set, usually the one most likely to convert at the lowest cost.

If the CBO campaign maintains a nice CPI, we keep increasing the budget by $50 every few days or duplicating it sometimes in order to double the budget. This is how we've scaled to $400/day profitably.

Finding Successful Creatives

Per campaign, we tested 2-6 images/videos. Same ad copy and CTA. There was no clear winner because some images did better with some interest groups.

The image above with mail packages, for example, got us a cheap CPI of $9.71 from our Goodwill Stores interest group but, a high $48 CPI from our lookalike audience. Once we had statistically significant data, we turned off the high-cost ad.

New marketers who are just discovering A/B testing may assume it's black and white — winner and loser. However, Facebook ads' machine learning and reporting has gotten so sophisticated that it's hard to call a creative a flat-out loser, but rather a 'bad fit' for some audiences, and perfect for others.

You can see how each creative performs across age groups and optimize.

How Many Installs Did It Take Us to Earn $15K Per Month?

Six months after paying $25K, we got 1,940 app installs, 681 free trials, and 522 $30 monthly subscriptions. 522 * $30 gives us $15,660 in monthly recurring revenue (MRR).

Next, what? $100K per month

The conversation above is with the app's owner. We got on a 30-minute call where I shared how I plan to get the app to be making $100K a month like I’ve done for other businesses.

Reverse Engineering $100K

Formula:

For $100K/month, we need 3,334 people to pay $30/month. 522 people pay that. We need 2,812 more paid users.

522 paid users from 1,940 installs is a 27% conversion rate. To hit $100K/month, we need 10,415 more installs. Assuming...

With a $400 daily ad spend, we average 40 installs per day. This means that if everything stays the same, it would take us 260 days (around 9 months) to get to $100K a month (MRR).

Conclusion

You must market your goods to reach your income objective (without waiting forever). Paid ads is the way to go if you hate knocking on doors or irritating friends and family (who aren’t scalable anyways).

You must also test and optimize different angles, audiences, interest groups, and creatives.