More on Entrepreneurship/Creators
Jenn Leach
3 years ago
What TikTok Paid Me in 2021 with 100,000 Followers
I thought it would be interesting to share how much TikTok paid me in 2021.
Onward!
Oh, you get paid by TikTok?
Yes.
They compensate thousands of creators. My Tik Tok account
I launched my account in March 2020 and generally post about money, finance, and side hustles.
TikTok creators are paid in several ways.
Fund for TikTok creators
Sponsorships (aka brand deals)
Affiliate promotion
My own creations
Only one, the TikTok Creator Fund, pays me.
The TikTok Creator Fund: What Is It?
TikTok's initiative pays creators.
YouTube's Shorts Fund, Snapchat Spotlight, and other platforms have similar programs.
Creator Fund doesn't pay everyone. Some prerequisites are:
age requirement of at least 18 years
In the past 30 days, there must have been 100,000 views.
a minimum of 10,000 followers
If you qualify, you can apply using your TikTok account, and once accepted, your videos can earn money.
My earnings from the TikTok Creator Fund
Since 2020, I've made $273.65. My 2021 payment is $77.36.
Yikes!
I made between $4.91 to around $13 payout each time I got paid.
TikTok reportedly pays 3 to 5 cents per thousand views.
To live off the Creator Fund, you'd need billions of monthly views.
Top personal finance creator Sara Finance has millions (if not billions) of views and over 700,000 followers yet only received $3,000 from the TikTok Creator Fund.
Goals for 2022
TikTok pays me in different ways, as listed above.
My largest TikTok account isn't my only one.
In 2022, I'll revamp my channel.
It's been a tumultuous year on TikTok for my account, from getting shadow-banned to being banned from the Creator Fund to being accepted back (not at my wish).
What I've experienced isn't rare. I've read about other creators' experiences.
So, some quick goals for this account…
200,000 fans by the year 2023
Consistent monthly income of $5,000
two brand deals each month
For now, that's all.
Greg Lim
3 years ago
How I made $160,000 from non-fiction books
I've sold over 40,000 non-fiction books on Amazon and made over $160,000 in six years while writing on the side.
I have a full-time job and three young sons; I can't spend 40 hours a week writing. This article describes my journey.
I write mainly tech books:
Thanks to my readers, many wrote positive evaluations. Several are bestsellers.
A few have been adopted by universities as textbooks:
My books' passive income allows me more time with my family.
Knowing I could quit my job and write full time gave me more confidence. And I find purpose in my work (i am in christian ministry).
I'm always eager to write. When work is a dread or something bad happens, writing gives me energy. Writing isn't scary. In fact, I can’t stop myself from writing!
Writing has also established my tech authority. Universities use my books, as I've said. Traditional publishers have asked me to write books.
These mindsets helped me become a successful nonfiction author:
1. You don’t have to be an Authority
Yes, I have computer science experience. But I'm no expert on my topics. Before authoring "Beginning Node.js, Express & MongoDB," my most profitable book, I had no experience with those topics. Node was a new server-side technology for me. Would that stop me from writing a book? It can. I liked learning a new technology. So I read the top three Node books, took the top online courses, and put them into my own book (which makes me know more than 90 percent of people already).
I didn't have to worry about using too much jargon because I was learning as I wrote. An expert forgets a beginner's hardship.
"The fellow learner can aid more than the master since he knows less," says C.S. Lewis. The problem he must explain is recent. The expert has forgotten.”
2. Solve a micro-problem (Niching down)
I didn't set out to write a definitive handbook. I found a market with several challenges and wrote one book. Ex:
- Instead of web development, what about web development using Angular?
- Instead of Blockchain, what about Blockchain using Solidity and React?
- Instead of cooking recipes, how about a recipe for a specific kind of diet?
- Instead of Learning math, what about Learning Singapore Math?
3. Piggy Backing Trends
The above topics may still be a competitive market. E.g. Angular, React. To stand out, include the latest technologies or trends in your book. Learn iOS 15 instead of iOS programming. Instead of personal finance, what about personal finance with NFTs.
Even though you're a newbie author, your topic is well-known.
4. Publish short books
My books are known for being direct. Many people like this:
Your reader will appreciate you cutting out the fluff and getting to the good stuff. A reader can finish and review your book.
Second, short books are easier to write. Instead of creating a 500-page book for $50 (which few will buy), write a 100-page book that answers a subset of the problem and sell it for less. (You make less, but that's another subject). At least it got published instead of languishing. Less time spent creating a book means less time wasted if it fails. Write a small-bets book portfolio like Daniel Vassallo!
Third, it's $2.99-$9.99 on Amazon (gets 70 percent royalties for ebooks). Anything less receives 35% royalties. $9.99 books have 20,000–30,000 words. If you write more and charge more over $9.99, you get 35% royalties. Why not make it a $9.99 book?
(This is the ebook version.) Paperbacks cost more. Higher royalties allow for higher prices.
5. Validate book idea
Amazon will tell you if your book concept, title, and related phrases are popular. See? Check its best-sellers list.
150,000 is preferable. It sells 2–3 copies daily. Consider your rivals. Profitable niches have high demand and low competition.
Don't be afraid of competitive niches. First, it shows high demand. Secondly, what are the ways you can undercut the completion? Better book? Or cheaper option? There was lots of competition in my NodeJS book's area. None received 4.5 stars or more. I wrote a NodeJS book. Today, it's a best-selling Node book.
What’s Next
So long. Part II follows. Meanwhile, I will continue to write more books!
Follow my journey on Twitter.
This post is a summary. Read full article here
DC Palter
2 years ago
Is Venture Capital a Good Fit for Your Startup?
5 VC investment criteria
I reviewed 200 startup business concepts last week. Brainache.
The enterprises sold various goods and services. The concepts were achingly similar: give us money, we'll produce a product, then get more to expand. No different from daily plans and pitches.
Most of those 200 plans sounded plausible. But 10% looked venture-worthy. 90% of startups need alternatives to venture finance.
With the success of VC-backed businesses and the growth of venture funds, a common misperception is that investors would fund any decent company idea. Finding investors that believe in the firm and founders is the key to funding.
Incorrect. Venture capital needs investing in certain enterprises. If your startup doesn't match the model, as most early-stage startups don't, you can revise your business plan or locate another source of capital.
Before spending six months pitching angels and VCs, make sure your startup fits these criteria.
Likely to generate $100 million in sales
First, I check the income predictions in a pitch deck. If it doesn't display $100M, don't bother.
The math doesn't work for venture financing in smaller businesses.
Say a fund invests $1 million in a startup valued at $5 million that is later acquired for $20 million. That's a win everyone should celebrate. Most VCs don't care.
Consider a $100M fund. The fund must reach $360M in 7 years with a 20% return. Only 20-30 investments are possible. 90% of the investments will fail, hence the 23 winners must return $100M-$200M apiece. $15M isn't worth the work.
Angel investors and tiny funds use the same ideas as venture funds, but their smaller scale affects the calculations. If a company can support its growth through exit on less than $2M in angel financing, it must have $25M in revenues before large companies will consider acquiring it.
Aiming for Hypergrowth
A startup's size isn't enough. It must expand fast.
Developing a great business takes time. Complex technology must be constructed and tested, a nationwide expansion must be built, or production procedures must go from lab to pilot to factories. These can be enormous, world-changing corporations, but venture investment is difficult.
The normal 10-year venture fund life. Investments are made during first 3–4 years.. 610 years pass between investment and fund dissolution. Funds need their investments to exit within 5 years, 7 at the most, therefore add a safety margin.
Longer exit times reduce ROI. A 2-fold return in a year is excellent. Loss at 2x in 7 years.
Lastly, VCs must prove success to raise their next capital. The 2nd fund is raised from 1st fund portfolio increases. Third fund is raised using 1st fund's cash return. Fund managers must raise new money quickly to keep their jobs.
Branding or technology that is protected
No big firm will buy a startup at a high price if they can produce a competing product for less. Their development teams, consumer base, and sales and marketing channels are large. Who needs you?
Patents, specialist knowledge, or brand name are the only answers. The acquirer buys this, not the thing.
I've heard of several promising startups. It's not a decent investment if there's no exit strategy.
A company that installs EV charging stations in apartments and shopping areas is an example. It's profitable, repeatable, and big. A terrific company. Not a startup.
This building company's operations aren't secret. No technology to protect, no special information competitors can't figure out, no go-to brand name. Despite the immense possibilities, a large construction company would be better off starting their own.
Most venture businesses build products, not services. Services can be profitable but hard to safeguard.
Probable purchase at high multiple
Once a software business proves its value, acquiring it is easy. Pharma and medtech firms have given up on their own research and instead acquire startups after regulatory permission. Many startups, especially in specialized areas, have this weakness.
That doesn't mean any lucrative $25M-plus business won't be acquired. In many businesses, the venture model requires a high exit premium.
A startup invents a new glue. 3M, BASF, Henkel, and others may buy them. Adding more adhesive to their catalogs won't boost commerce. They won't compete to buy the business. They'll only buy a startup at a profitable price. The acquisition price represents a moderate EBITDA multiple.
The company's $100M revenue presumably yields $10m in profits (assuming they’ve reached profitability at all). A $30M-$50M transaction is likely. Not terrible, but not what venture investors want after investing $25M to create a plant and develop the business.
Private equity buys profitable companies for a moderate profit multiple. It's a good exit for entrepreneurs, but not for investors seeking 10x or more what PE firms pay. If a startup offers private equity as an exit, the conversation is over.
Constructed for purchase
The startup wants a high-multiple exit. Unless the company targets $1B in revenue and does an IPO, exit means acquisition.
If they're constructing the business for acquisition or themselves, founders must decide.
If you want an indefinitely-running business, I applaud you. We need more long-term founders. Most successful organizations are founded around consumer demands, not venture capital's urge to grow fast and exit. Not venture funding.
if you don't match the venture model, what to do
VC funds moonshots. The 10% that succeed are extraordinary. Not every firm is a rocketship, and launching the wrong startup into space, even with money, will explode.
But just because your startup won't make $100M in 5 years doesn't mean it's a bad business. Most successful companies don't follow this model. It's not venture capital-friendly.
Although venture capital gets the most attention due to a few spectacular triumphs (and disasters), it's not the only or even most typical option to fund a firm.
Other ways to support your startup:
Personal and family resources, such as credit cards, second mortgages, and lines of credit
bootstrapping off of sales
government funding and honors
Private equity & project financing
collaborating with a big business
Including a business partner
Before pitching angels and VCs, be sure your startup qualifies. If so, include them in your pitch.
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Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
rekt
3 years ago
LCX is the latest CEX to have suffered a private key exploit.
The attack began around 10:30 PM +UTC on January 8th.
Peckshield spotted it first, then an official announcement came shortly after.
We’ve said it before; if established companies holding millions of dollars of users’ funds can’t manage their own hot wallet security, what purpose do they serve?
The Unique Selling Proposition (USP) of centralised finance grows smaller by the day.
The official incident report states that 7.94M USD were stolen in total, and that deposits and withdrawals to the platform have been paused.
LCX hot wallet: 0x4631018f63d5e31680fb53c11c9e1b11f1503e6f
Hacker’s wallet: 0x165402279f2c081c54b00f0e08812f3fd4560a05
Stolen funds:
- 162.68 ETH (502,671 USD)
- 3,437,783.23 USDC (3,437,783 USD)
- 761,236.94 EURe (864,840 USD)
- 101,249.71 SAND Token (485,995 USD)
- 1,847.65 LINK (48,557 USD)
- 17,251,192.30 LCX Token (2,466,558 USD)
- 669.00 QNT (115,609 USD)
- 4,819.74 ENJ (10,890 USD)
- 4.76 MKR (9,885 USD)
**~$1M worth of $LCX remains in the address, along with 611k EURe which has been frozen by Monerium.
The rest, a total of 1891 ETH (~$6M) was sent to Tornado Cash.**
Why can’t they keep private keys private?
Is it really that difficult for a traditional corporate structure to maintain good practice?
CeFi hacks leave us with little to say - we can only go on what the team chooses to tell us.
Next time, they can write this article themselves.
See below for a template.
VIP Graphics
3 years ago
Leaked pitch deck for Metas' new influencer-focused live-streaming service
As part of Meta's endeavor to establish an interactive live-streaming platform, the company is testing with influencers.
The NPE (new product experimentation team) has been testing Super since late 2020.
Bloomberg defined Super as a Cameo-inspired FaceTime-like gadget in 2020. The tool has evolved into a Twitch-like live streaming application.
Less than 100 creators have utilized Super: Creators can request access on Meta's website. Super isn't an Instagram, Facebook, or Meta extension.
“It’s a standalone project,” the spokesperson said about Super. “Right now, it’s web only. They have been testing it very quietly for about two years. The end goal [of NPE projects] is ultimately creating the next standalone project that could be part of the Meta family of products.” The spokesperson said the outreach this week was part of a drive to get more creators to test Super.
A 2021 pitch deck from Super reveals the inner workings of Meta.
The deck gathered feedback on possible sponsorship models, with mockups of brand deals & features. Meta reportedly paid creators $200 to $3,000 to test Super for 30 minutes.
Meta's pitch deck for Super live streaming was leaked.
What were the slides in the pitch deck for Metas Super?
Embed not supported: see full deck & article here →
View examples of Meta's pitch deck for Super:
Product Slides, first
The pitch deck begins with Super's mission:
Super is a Facebook-incubated platform which helps content creators connect with their fans digitally, and for super fans to meet and support their favorite creators. In the spirit of Late Night talk shows, we feature creators (“Superstars”), who are guests at a live, hosted conversation moderated by a Host.
This slide (and most of the deck) is text-heavy, with few icons, bullets, and illustrations to break up the content. Super's online app status (which requires no download or installation) might be used as a callout (rather than paragraph-form).
Meta's Super platform focuses on brand sponsorships and native placements, as shown in the slide above.
One of our theses is the idea that creators should benefit monetarily from their Super experiences, and we believe that offering a menu of different monetization strategies will enable the right experience for each creator. Our current focus is exploring sponsorship opportunities for creators, to better understand what types of sponsor placements will facilitate the best experience for all Super customers (viewers, creators, and advertisers).
Colorful mockups help bring Metas vision for Super to life.
2. Slide Features
Super's pitch deck focuses on the platform's features. The deck covers pre-show, pre-roll, and post-event for a Sponsored Experience.
Pre-show: active 30 minutes before the show's start
Pre-roll: Play a 15-minute commercial for the sponsor before the event (auto-plays once)
Meet and Greet: This event can have a branding, such as Meet & Greet presented by [Snickers]
Super Selfies: Makers and followers get a digital souvenir to post on social media.
Post-Event: Possibility to draw viewers' attention to sponsored content/links during the after-show
Almost every screen displays the Sponsor logo, link, and/or branded background. Viewers can watch sponsor video while waiting for the event to start.
Slide 3: Business Model
Meta's presentation for Super is incomplete without numbers. Super's first slide outlines the creator, sponsor, and Super's obligations. Super does not charge creators any fees or commissions on sponsorship earnings.
How to make a great pitch deck
We hope you can use the Super pitch deck to improve your business. Bestpitchdeck.com/super-meta is a bookmarkable link.
You can also use one of our expert-designed templates to generate a pitch deck.
Our team has helped close $100M+ in agreements and funding for premier companies and VC firms. Use our presentation templates, one-pagers, or financial models to launch your pitch.
Every pitch must be audience-specific. Our team has prepared pitch decks for various sectors and fundraising phases.
Pitch Deck Software VIP.graphics produced a popular SaaS & Software Pitch Deck based on decks that closed millions in transactions & investments for orgs of all sizes, from high-growth startups to Fortune 100 enterprises. This easy-to-customize PowerPoint template includes ready-made features and key slides for your software firm.
Accelerator Pitch Deck The Accelerator Pitch Deck template is for early-stage founders seeking funding from pitch contests, accelerators, incubators, angels, or VC companies. Winning a pitch contest or getting into a top accelerator demands a strategic investor pitch.
Pitch Deck Template Series Startup and founder pitch deck template: Workable, smart slides. This pitch deck template is for companies, entrepreneurs, and founders raising seed or Series A finance.
M&A Pitch Deck Perfect Pitch Deck is a template for later-stage enterprises engaging more sophisticated conversations like M&A, late-stage investment (Series C+), or partnerships & funding. Our team prepared this presentation to help creators confidently pitch to investment banks, PE firms, and hedge funds (and vice versa).
Browse our growing variety of industry-specific pitch decks.