And tips for anyone trying.

We see the same story every few years.

BREAKING NEWS: [Platform X] launched a social network. With Facebook's reputation down, the new startup bets millions will switch.

Despite the excitement surrounding each new platform (Diaspora, Ello, Path, MeWe, Minds, Vero, etc.), no major exodus occurred.

Snapchat and TikTok attracted teens with fresh experiences (ephemeral messaging and rapid-fire videos). These features aren't Facebook, even if Facebook replicated them.

Facebook's core is simple: you publish items (typically text/images) and your friends (generally people you know IRL) can discuss them.

It's cool. Sometimes I don't want to, but sh*t. I like it.

Because, well, I like many folks I've met. I enjoy keeping in touch with them and their banter.

I dislike Facebook's corporation. I've been cautiously optimistic whenever a Facebook-killer surfaced.

None succeeded.

Why? Two causes, I think:

People couldn't switch quickly enough, which is reason #1

Your buddies make a social network social.

Facebook started in self-contained communities (college campuses) then grew outward. But a new platform can't.

If we're expected to leave Facebook, we want to know that most of our friends will too.

Most Facebook-killers had bottlenecks. You have to waitlist or jump through hoops (e.g. setting up a server).

Same outcome. Upload. Chirp.

After a week or two of silence, individuals returned to Facebook.

Reason #2: The fundamental experience was different.

Even when many of our friends joined in the first few weeks, it wasn't the same.

There were missing features or a different UX.

Want to reply with a meme? No photos in comments yet. (Trying!)

Want to tag a friend? Nope, sorry. 2019!

Want your friends to see your post? You must post to all your friends' servers. Good luck!

It's difficult to introduce a platform with 100% of the same features as one that's been there for 20 years, yet customers want a core experience.

If you can't, they'll depart.

The causes that led to the causes

Having worked on software teams for 14+ years, I'm not surprised by these challenges. They are a natural development of a few tech sector meta-problems:

Lean startup methodology

Silicon Valley worships lean startup. It's a way of developing software that involves testing a stripped-down version with a limited number of people before selecting what to build.

Billion people use Facebook's functions. They aren't tested. It must work right away*

*This may seem weird to software people, but it's how non-software works! You can't sell a car without wheels.

2. Creativity

Startup entrepreneurs build new things, not copies. I understand. Reinventing the wheel is boring.

We know what works. Different experiences raise adoption friction. Once millions have transferred, more features (and a friendlier UX) can be implemented.

3. Cost scaling

True. Building a product that can sustain hundreds of millions of users in weeks is expensive and complex.

Your lifeboats must have the same capacity as the ship you're evacuating. It's required.

4. Pure ideologies

People who work on Facebook-alternatives are (understandably) critical of Facebook.

They build an open-source, fully-distributed, data-portable, interface-customizable, offline-capable, censorship-proof platform.

Prioritizing these aims can prevent replicating the straightforward experience users expect. Github, not Facebook, is for techies only.

What about the business plan, though?

Facebook-killer attempts have followed three models.

1. Utilize VC funding to increase your user base, then monetize them later. (If you do this, you won't kill Facebook; instead, Facebook will become you.)

2. Users must pay to utilize it. (This causes a huge bottleneck and slows the required quick expansion, preventing it from seeming like a true social network.)

3. Make it a volunteer-run, open-source endeavor that is free. (This typically denotes that something is cumbersome, difficult to operate, and is only for techies.)

Wikipedia is a fourth way.

Wikipedia is one of the most popular websites and a charity. No ads. Donations support them.

A Facebook-killer managed by a good team may gather millions (from affluent contributors and the crowd) for their initial phase of development. Then it might sustain on regular donations, ethical transactions (e.g. fees on commerce, business sites, etc.), and government grants/subsidies (since it would essentially be a public utility).

When you're not aiming to make investors rich, it's remarkable how little money you need.

If you want to build a Facebook competitor, follow these tips:

1. Drop the lean startup philosophy. Wait until you have a finished product before launching. Build it, thoroughly test it for bugs, and then release it.

2. Delay innovating. Wait till millions of people have switched before introducing your great new features. Make it nearly identical for now.

3. Spend money climbing. Make sure that guests can arrive as soon as they are invited. Never keep them waiting. Make things easy for them.

4. Make it accessible to all. Even if doing so renders it less philosophically pure, it shouldn't require technical expertise to utilize.

5. Constitute a nonprofit. Additionally, develop community ownership structures. Profit maximization is not the only strategy for preserving valued assets.

Last thoughts

Nobody has killed Facebook, but Facebook is killing itself.

The startup is burying the newsfeed to become a TikTok clone. Meta itself seems to be ditching the platform for the metaverse.

I wish I was happy, but I'm not. I miss (understandably) removed friends' postings and remarks. It could be a ghost town in a few years. My dance moves aren't TikTok-worthy.

Who will lead? It's time to develop a social network for the people.

Greetings if you're working on it. I'm not a company founder, but I like to help hard-working folks.

I've always bucked social media norms. YouTube doesn't compare. Traditional video made me zig when everyone zagged. Audio, picture personality animation, thought movies, and slide show videos are most popular and profitable.

YouTube's business is shifting. While most video experts swear by the idea that YouTube success is all about making personal and professional Face-Share-Videos, those who use YouTube for business know things are different.

In this article, I will share concepts from my mini master class Figures to Followers: Prioritizing Purposeful Profits Over Popularity on YouTube to Create the Win-Win for You, Your Audience & More and my forthcoming publication The WOWTUBE-PRENEUR FACTOR EVOLUTION: The Basics of Powerfully & Profitably Positioning Yourself as a Video Communications Authority to Broadcast Your WOW Effect as a Video Entrepreneur.

I've researched the psychology, anthropology, and anatomy of significant social media platforms as an entrepreneur and social media marketing expert. While building my YouTube empire, I've paid particular attention to what works for short, mid, and long-term success, whether it's a niche-focused, lifestyle, or multi-interest channel.

Most new, semi-new, and seasoned YouTubers feel vlog-style or live-on-camera videos are popular. Faceless, animated, music-text-based, and slideshow videos do well for businesses.

Buyer-consumer vs. content-consumer thinking is totally different when absorbing content. Profitability and popularity are closely related, however most people become popular with traditional means but not profitable.

In my experience, Faceless videos are more profitable, although it depends on the channel's style. Several professionals are now teaching in their courses that non-traditional films are making the difference in their business success and popularity.

Face-Share-Personal-Touch videos make audiences feel like they know the personality, but they're not profitable.

Most spend hours creating articles, videos, and thumbnails to seem good. That's how most YouTubers gained their success in the past, but not anymore.

Looking the part and performing a typical role in videos doesn't convert well, especially for newbie channels.

Working with video marketers and YouTubers for years, I've noticed that most struggle to be consistent with content publishing since they exclusively use formats that need extensive development. Camera and green screen set ups, shooting/filming, and editing for post productions require their time, making it less appealing to post consistently, especially if they're doing all the work themselves.

Because they won't make simple format videos or audio videos with an overlay image, they overcomplicate the procedure (even with YouTube Shorts), and they leave their channels for weeks or months. Again, they believe YouTube only allows specific types of videos. Even though this procedure isn't working, they plan to keep at it.

A successful YouTube channel needs multiple video formats to suit viewer needs, I teach. Face-Share-Personal Touch and Faceless videos are both useful.

How people engage with YouTube content has changed over the years, and the average customer is no longer interested in an all-video channel.

Face-Share-Personal-Touch videos are great

• Google Live

• Online training

• Giving listeners a different way to access your podcast that is being broadcast on sites like Anchor, BlogTalkRadio, Spreaker, Google, Apple Store, and others Many people enjoy using a video camera to record themselves while performing the internet radio, Facebook, or Instagram Live versions of their podcasts.

• Video Blog Updates

• even more

Faceless videos are popular for business and benefit both entrepreneurs and audiences.

For the business owner/entrepreneur…

• Less production time results in time dollar savings.

• enables the business owner to demonstrate the diversity of content development

For the Audience…

• The channel offers a variety of appealing content options.

• The same format is not monotonous or overly repetitive for the viewers.

Below are a couple videos from YouTube guru Make Money Matt's channel, which has over 347K subscribers.

Enjoy

24 Best Niches to Make Money on YouTube Without Showing Your Face

Make Money on YouTube Without Making Videos (Free Course)

In conclusion, you have everything it takes to build your own YouTube brand and empire. Learn the rules, then adapt them to succeed.

Please reread this and the other suggested articles for optimal benefit.

I hope this helped. How has this article helped you? Follow me for more articles like this and more multi-mission expressions.

Many developers do it, but I don't approve.

Aren't many programmers part-time? Many work full-time but also freelance. If the job agreement allows it, I see no problem.

Tech businesses' policies vary. I have a friend in Google, Germany. According to his contract, he couldn't do an outside job. Google owns any code he writes while employed.

I was shocked. Later, I found that different Google regions have different policies.

A corporation can normally establish any agreement before hiring you. They're negotiable. When there's no agreement, state law may apply. In court, law isn't so simple.

I won't delve into legal details. Instead, let’s talk about the incident.

How he was discovered

In one month, he missed two deadlines. His boss was frustrated because the assignment wasn't difficult to miss twice. When a team can't finish work on time, they all earn bad grades.

He annoyed the whole team. One team member (anonymous) told the project manager he worked on side projects during office hours. He may have missed deadlines because of this.

The project manager was furious. He needed evidence. The manager caught him within a week. The manager told higher-ups immediately.

The company wanted to set an example

Management could terminate him and settle the problem. But the company wanted to set an example for those developers who breached the regulation.

Because dismissal isn't enough. Every organization invests heavily in developer hiring. If developers depart or are fired after a few months, the company suffers.

The developer spent 10 months there. The employer sacked him and demanded ten months' pay. Or they'd sue him.

It was illegal and unethical. The youngster paid the fine and left the company quietly to protect his career.

Right or wrong?

Is the developer's behavior acceptable? Let's discuss developer malpractice.

During office hours, may developers work on other projects? If they're bored during office hours, they might not. Check the employment contract or state law.

If there's no employment clause, check country/state law. Because you can't justify breaking the law. Always. Most employers own their employees' work hours unless it's a contractual position.

If the company agrees, it's fine.

I also oppose companies that force developers to work overtime without pay.

Most states and countries have laws that help companies and workers. Law supports employers in this case. If any of the following are true, the company/employer owns the IP under California law.

• using the business's resources

• any equipment, including a laptop used for business.

• company's mobile device.

• offices of the company.

• business time as well. This is crucial. Because this occurred in the instance of my junior.

Company resources are dangerous. Because your company may own the product's IP.  If you have seen the TV show Silicon Valley, you have seen a similar situation there, right?

Conclusion

Simple rule. I avoid big side projects. I work on my laptop on weekends for side projects. I'm safe. But I also know that my company might not be happy with that.

As an employee, I suppose I can. I can make side money. I won't promote it, but I'll respect their time, resources, and task. I also sometimes work extra time to finish my company’s deadlines.

Fox executives will invest $100 million in NFT-based TV shows. Fox brought in "Rick and Morty" co-creator Dan Harmon to create "Krapopolis"

Fox's Blockchain Creative Labs (BCL) will develop these NFT TV shows with Bento Box Entertainment. BCL markets Fox's WWE "Moonsault" NFT.

Fox said it would use the $100 million to build a "creative community" and "brand ecosystem." The media giant mentioned using these funds for NFT "benefits."

"Krapopolis" will be a Greek-themed animated comedy, per Rarity Sniper. Initial reports said NFT buyers could collaborate on "character development" and get exclusive perks.

Fox Entertainment may drop "Krapopolis" NFTs on Ethereum, according to new reports. Fox says it will soon release more details on its NFT plans for "Krapopolis."

Media Giants Favor "NFT Storytelling"

"Krapopolis" is one of the largest "NFT storytelling" experiments due to Dan Harmon's popularity and Fox Entertainment's reach. Many celebrities have begun exploring Web3 for TV shows.

Mila Kunis' animated sitcom "The Gimmicks" lets fans direct the show. Any "Gimmick" NFT holder could contribute to episode plots.

"The Gimmicks" lets NFT holders write fan fiction about their avatars. If show producers like what they read, their NFT may appear in an episode.

Rob McElhenney recently launched "Adimverse," a Web3 writers' community. Anyone with a "Adimverse" NFT can collaborate on creative projects and share royalties.

Many blue-chip NFTs are appearing in movies and TV shows. Coinbase will release Bored Ape Yacht Club shorts at NFT. NYC. Reese Witherspoon is working on a World of Women NFT series.

PFP NFT collections have Hollywood media partners. Guy Oseary manages Madonna's World of Women and Bored Ape Yacht Club collections. The Doodles signed with Billboard's Julian Holguin and the Cool Cats with CAA.

Web3 and NFTs are changing how many filmmakers tell stories.

The Traffic Light Approach to Reviewing Personal, Team and Project Development

What needs improvement? If you want to improve, you need to practice your sport, musical instrument, habit, or work project. You need to assess your progress.

Continuous improvement is the foundation of focused practice and a growth mentality. Not just individually. High-performing teams pursue improvement. Right? Why is it hard?

As a leadership coach, senior manager, and high-level athlete, I've found three key questions that may unlock high performance in individuals and teams.

Problems with Reviews

Reviewing and improving performance is crucial, however I hate seeing review sessions in my diary. I rarely respond to questionnaire pop-ups or emails. Why?

Time constrains. Requests to fill out questionnaires often state they will take 10–15 minutes, but I can think of a million other things to do with that time. Next, review overload. Businesses can easily request comments online. No matter what you buy, someone will ask for your opinion. This bombardment might make feedback seem bad, which is bad.

The problem is that we might feel that way about important things like personal growth and work performance. Managers and team leaders face a greater challenge.

When to Conduct a Review

We must be wise about reviewing things that matter to us. Timing and duration matter. Reviewing the experience as quickly as possible preserves information and sentiments. Time must be brief. The review's importance and size will determine its length. We might only take a few seconds to review our morning coffee, but we might require more time for that six-month work project.

These post-event reviews should be supplemented by periodic reflection. Journaling can help with daily reflections, but I also like to undertake personal reviews every six months on vacation or at a retreat.

As an employee or line manager, you don't want to wait a year for a performance assessment. Little and frequently is best, with a more formal and in-depth assessment (typically with a written report) in 6 and 12 months.

The Easiest Method to Conduct a Review Session

I follow Einstein's review process:

“Make things as simple as possible but no simpler.”

Thus, it should be brief but deliver the necessary feedback. Quality critique is hard to receive if the process is overly complicated or long.

I have led or participated in many review processes, from strategic overhauls of big organizations to personal goal coaching. Three key questions guide the process at either end:

• What ought to stop being done?

• What should we do going forward?

• What should we do first?

Following the Rule of 3, I compare it to traffic lights. Red, amber, and green lights:

• Red What ought should we stop?

• Amber What ought to we keep up?

• Green Where should we begin?

This approach is easy to understand and self-explanatory, however below are some examples under each area.

Red What ought should we stop?

As a team or individually, we must stop doing things to improve.

Sometimes they're bad. If we want to lose weight, we should avoid sweets. If a team culture is bad, we may need to stop unpleasant behavior like gossiping instead of having difficult conversations.

Not all things we should stop are wrong. Time matters. Since it is finite, we sometimes have to stop nice things to focus on the most important. Good to Great author Jim Collins famously said:

“Don’t let the good be the enemy of the great.”

Prioritizing requires this idea. Thus, decide what to stop to prioritize.

Amber What ought to we keep up?

Should we continue with the amber light? It helps us decide what to keep doing during review. Many items fall into this category, so focus on those that make the most progress.

Which activities have the most impact? Which behaviors create the best culture? Success-building habits?

Use these questions to find positive momentum. These are the fly-wheel motions, according to Jim Collins. The Compound Effect author Darren Hardy says:

“Consistency is the key to achieving and maintaining momentum.”

What can you do consistently to reach your goal?

Green Where should we begin?

Finally, green lights indicate new beginnings. Red/amber difficulties may be involved. Stopping a red issue may give you more time to do something helpful (in the amber).

This green space inspires creativity. Kolbs learning cycle requires active exploration to progress. Thus, it's crucial to think of new approaches, try them out, and fail if required.

This notion underpins lean start-build, up's measure, learn approach and agile's trying, testing, and reviewing. Try new things until you find what works. Thomas Edison, the lighting legend, exclaimed:

“There is a way to do it better — find it!”

Failure is acceptable, but if you want to fail forward, look back on what you've done.

John Maxwell concurred with Edison:

“Fail early, fail often, but always fail forward”

A good review procedure lets us accomplish that. To avoid failure, we must act, experiment, and reflect.

Use the traffic light system to prioritize queries. Ask:

• Red What needs to stop?

• Amber What should continue to occur?

• Green What might be initiated?

Take a moment to reflect on your day. Check your priorities with these three questions. Even if merely to confirm your direction, it's a terrific exercise!

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:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. 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.