After a decade in large tech, I know how software engineers were interviewed. I've seen outstanding engineers fail interviews because their responses were too vague.

So I wrote Nail A Coding Interview: Six-Step Mental Framework. Give candidates a mental framework for coding questions; help organizations better prepare candidates so they can calibrate traits.

Recently, I sold more than 100 books, something I never expected.

In this essay, I'll describe my publication journey, which included self-doubt and little triumphs. I hope this helps if you want to publish.

It was originally a Medium post.

How did I know to develop a coding interview book? Years ago, I posted on Medium.

Six steps to ace a coding interview Inhale. blog.devgenius.io

This story got a lot of attention and still gets a lot of daily traffic. It indicates this domain's value.

Converted the Medium article into an ebook

The Medium post contains strong bullet points, but it is missing the “flesh”. How to use these strategies in coding interviews, for example. I filled in the blanks and made a book.

I made the book cover for free. It's tidy.

Shared the article with my close friends on my social network WeChat.

I shared the book on Wechat's Friend Circle (朋友圈) after publishing it on Gumroad. Many friends enjoyed my post. It definitely triggered endorphins.

In Friend Circle, I presented a 100% off voucher. No one downloaded the book. Endorphins made my heart sink.

Several days later, my Apple Watch received a Gumroad notification. A friend downloaded it. I majored in finance, he subsequently said. My brother-in-law can get it? He downloaded it to cheer me up.

I liked him, but was disappointed that he didn't read it.

The Tipping Point: Reddit's Free Giving

I trusted the book. It's based on years of interviewing. I felt it might help job-hunting college students. If nobody wants it, it can still have value.

I posted the book's link on /r/leetcode. I told them to DM me for a free promo code.

Momentum shifted everything. Gumroad notifications kept coming when I was out with family. Following orders.

As promised, I sent DMs a promo code. Some consumers ordered without asking for a promo code. Some readers finished the book and posted reviews.

My book was finally on track.

A 5-Star Review, plus More

A reader afterwards DMed me and inquired if I had another book on system design interviewing. I said that was a good idea, but I didn't have one. If you write one, I'll be your first reader.

Later, I asked for a book review. Yes, but how? That's when I learned readers' reviews weren't easy. I built up an email pipeline to solicit customer reviews. Since then, I've gained credibility through ratings.

Learnings

I wouldn't have gotten 100 if I gave up when none of my pals downloaded. Here are some lessons.

• Your friends are your allies, but they are not your clients.

• Be present where your clients are

• Request ratings and testimonials

• gain credibility gradually

I did it, so can you. Follow me on Twitter @imgracehuang for my publishing and entrepreneurship adventure.

If asked, run.

An entrepreneur asked me about her pitch deck. A Platform as a Service (PaaS).

She told me she hadn't done her 5-year forecasts but would soon.

I said, Don't bother. I added "time-wasting."

“I've been asked”, she said.

“Who asked?”

“a VC”

“5-year forecast?”

“Yes”

“Get another VC. If he asks, it's because he doesn't understand your solution or to waste your time.”

Some VCs are lagging. They're still using steam engines.

10-years ago, 5-year forecasts were requested.

Since then, we've adopted a 3-year plan.

But It's outdated.

Max one year.

What has happened?

Revolutionary technology. NO-CODE.

Revolution's consequences?

Product viability tests are shorter. Hugely. SaaS and PaaS.

Let me explain:

• Building a minimum viable product (MVP) that works only takes a few months.

• 1 to 2 months for practical testing.

• Your company plan can be validated or rejected in 4 months as a consequence.

After validation, you can ask for VC money. Even while a prototype can generate revenue, you may not require any.

Good VCs won't ask for a 3-year business plan in that instance.

One-year, though.

If you want, establish a three-year plan, but realize that the second year will be different.

You may have changed your business model by then.

A VC isn't interested in a three-year business plan because your solution may change.

Your ability to create revenue will be key.

• But also, to pivot.

• They will be interested in your value proposition.

• They will want to know what differentiates you from other competitors and why people will buy your product over another.

• What will interest them is your resilience, your ability to bounce back.

• Not to mention your mindset. The fact that you won’t get discouraged at the slightest setback.

• The grit you have when facing adversity, as challenges will surely mark your journey.

• The authenticity of your approach. They’ll want to know that you’re not just in it for the money, let alone to show off.

• The fact that you put your guts into it and that you are passionate about it. Because entrepreneurship is a leap of faith, a leap into the void.

• They’ll want to make sure you are prepared for it because it’s not going to be a walk in the park.

• They’ll want to know your background and why you got into it.

• They’ll also want to know your family history.

• And what you’re like in real life.

So a 5-year plan…. You can bet they won’t give a damn. Like their first pair of shoes.

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.

Wishing for Lasting Companies, Not Penny Stocks or Goodwill Write-Downs

Get me a NASCAR-style company-logoed cremation urn (notice to the executor of my will, theres gonna be a lot of weird requests). I believe in working on projects that would be on your tombstone. As the Homebrew logo is tattooed on my shoulder, expanding the portfolio to my posthumous commemoration is easy. But this isn't an IRR victory lap; it's a hope that the firms we worked for would last beyond my lifetime.

Venture investors too often take credit or distance themselves from startups based on circumstances. Successful companies tell stories of crucial introductions, strategy conversations, and other value. Defeats Even whether our term involves Board service or systematic ethical violations, I'm just a little investment, so there's not much I can do. Since I'm guilty, I'm tossing stones from within the glass home (although we try to own our decisions through the lifecycle).

Post-exit company trajectories are usually unconfounded. Off the cap table, no longer a shareholder (or a diminishing one as you sell off/distribute), eventually leaving the Board. You can cheer for the squad or forget about it, but you've freed the corporation and it's back to portfolio work.

As I look at the downward track of most SPACs and other tarnished IPOs from the last few years, I wonder how I would feel if those were my legacy. Is my job done? Yes. When investing in a business, the odds are against it surviving, let alone thriving and being able to find sunlight. SPAC sponsors, institutional buyers, retail investments. Free trade in an open market is their right. Risking and losing capital is the system working! But

We were lead or co-lead investors in our first three funds, but as additional VCs joined the company, we were pushed down the cap table. Voting your shares rarely matters; supporting the firm when they need it does. Being valuable, consistent, and helping the company improve builds trust with the founders.

I hope every startup we sponsor becomes a successful public company before, during, and after we benefit. My perspective of American capitalism. Well, a stock ticker has a lot of garbage, and I support all types of regulation simplification (in addition to being a person investor in the Long-Term Stock Exchange). Yet being owned by a large group of investors and making actual gains for them is great. Likewise does seeing someone you met when they were just starting out become a public company CEO without losing their voice, leadership, or beliefs.

I'm just thinking about what we can do from the start to realize value from our investments and build companies with bright futures. Maybe seed venture financing shouldn't impact those outcomes, but I'm not comfortable giving up that obligation.

Recent investigations have shown that generative AI can boost hackers and misinformation spreaders.

Since its inception in late November 2022, OpenAI's ChatGPT has entertained and assisted many online users in writing, coding, task automation, and linguistic translation. Given this versatility, it is maybe unsurprising but nonetheless regrettable that fraudsters and mis-, dis-, and malinformation (MDM) spreaders are also considering ChatGPT and related AI models to streamline and improve their operations.

Malign actors may benefit from ChatGPT, according to a WithSecure research. ChatGPT promises to elevate unlawful operations across many attack channels. ChatGPT can automate spear phishing attacks that deceive corporate victims into reading emails from trusted parties. Malware, extortion, and illicit fund transfers can result from such access.

ChatGPT's ability to simulate a desired writing style makes spear phishing emails look more genuine, especially for international actors who don't speak English (or other languages like Spanish and French).

This technique could let Russian, North Korean, and Iranian state-backed hackers conduct more convincing social engineering and election intervention in the US. ChatGPT can also create several campaigns and various phony online personas to promote them, making such attacks successful through volume or variation. Additionally, image-generating AI algorithms and other developing techniques can help these efforts deceive potential victims.

Hackers are discussing using ChatGPT to install malware and steal data, according to a Check Point research. Though ChatGPT's scripts are well-known in the cyber security business, they can assist amateur actors with little technical understanding into the field and possibly develop their hacking and social engineering skills through repeated use.

Additionally, ChatGPT's hacking suggestions may change. As a writer recently indicated, ChatGPT's ability to blend textual and code-based writing might be a game-changer, allowing the injection of innocent content that would subsequently turn out to be a malicious script into targeted systems. These new AI-powered writing- and code-generation abilities allow for unique cyber attacks, regardless of viability.

OpenAI fears ChatGPT usage. OpenAI, Georgetown University's Center for Security and Emerging Technology, and Stanford's Internet Observatory wrote a paper on how AI language models could enhance nation state-backed influence operations. As a last resort, the authors consider polluting the internet with radioactive or misleading data to ensure that AI language models produce outputs that other language models can identify as AI-generated. However, the authors of this paper seem unaware that their "solution" might cause much worse MDM difficulties.

Literally False News

The public argument about ChatGPTs content-generation has focused on originality, bias, and academic honesty, but broader global issues are at stake. ChatGPT can influence public opinion, troll individuals, and interfere in local and national elections by creating and automating enormous amounts of social media material for specified audiences.

ChatGPT's capacity to generate textual and code output is crucial. ChatGPT can write Python scripts for social media bots and give diverse content for repeated posts. The tool's sophistication makes it irrelevant to one's language skills, especially English, when writing MDM propaganda.

I ordered ChatGPT to write a news piece in the style of big US publications declaring that Ukraine is on the verge of defeat in its fight against Russia due to corruption, desertion, and exhaustion in its army. I also gave it a fake reporter's byline and an unidentified NATO source's remark. The outcome appears convincing:

Worse, terrible performers can modify this piece to make it more credible. They can edit the general's name or add facts about current wars. Furthermore, such actors can create many versions of this report in different forms and distribute them separately, boosting its impact.

In this example, ChatGPT produced a news story regarding (fictional) greater moviegoer fatality rates:

Editing this example makes it more plausible. Dr. Jane Smith, the putative author of the medical report, might be replaced with a real-life medical person or a real victim of this supposed medical hazard.

Can deceptive texts be found? Detecting AI text is behind AI advancements. Minor AI-generated text alterations can upset these technologies.

Some OpenAI individuals have proposed covert methods to watermark AI-generated literature to prevent its abuse. AI models would create information that appears normal to humans but would follow a cryptographic formula that would warn other machines that it was AI-made. However, security experts are cautious since manually altering the content interrupts machine and human detection of AI-generated material.

How to Prepare

Cyber security and IT workers can research and use generative AI models to fight spear fishing and extortion. Governments may also launch MDM-defence projects.

In election cycles and global crises, regular people may be the most vulnerable to AI-produced deceit. Until regulation or subsequent technical advances, individuals must recognize exposure to AI-generated fraud, dating scams, other MDM activities.

A three-step verification method of new material in suspicious emails or social media posts can help identify AI content and manipulation. This three-step approach asks about the information's distribution platform (is it reliable? ), author (is the reader familiar with them? ), and plausibility given one's prior knowledge of the topic.

Consider a report by a trusted journalist that makes shocking statements in their typical manner. AI-powered fake news may be released on an unexpected platform, such as a newly created Facebook profile. However, if it links to a known media source, it is more likely to be real.

Though hard and subjective, this verification method may be the only barrier against manipulation for now.

AI language models:

How to Recognize an AI-Generated Article ChatGPT, the popular AI-powered chatbot, can and likely does generate medium.com-style articles.

AI-Generated Text Detectors Fail. Do This. Online tools claim to detect ChatGPT output. Even with superior programming, I tested some of these tools. pub

Why Original Writers Matter Despite AI Language Models Creative writers may never be threatened by AI language models.