I'm gadget-obsessed. So I shared my top 6 desk gadgets.

These gadgets improve my workflow and are handy for working from home.

Without further ado...

Computer light bar Xiaomi Mi

I've previously recommended the Xiaomi Mi Light Bar, and I still do. It's stylish and convenient.

The Mi bar is a monitor-mounted desk lamp. The lamp's hue and brightness can be changed with a stylish wireless remote.

Changeable hue and brightness make it ideal for late-night work.

Desk Mat 2.

I wasn't planning to include a desk surface in this article, but I find it improves computer use.

The mouse feels smoother and is a better palm rest than wood or glass.

I'm currently using the overkill Razer Goliathus Extended Chroma RGB Gaming Surface, but I like RGB.

Using a desk surface or mat makes computer use more comfortable, and it's not expensive.

Third, the Logitech MX Master 3 Mouse

The Logitech MX Master 3 or any from the MX Master series is my favorite mouse.

The side scroll wheel on these mice is a feature I've never seen on another mouse.

Side scroll wheels are great for spreadsheets and video editing. It would be hard for me to switch from my Logitech MX Master 3 to another mouse. Only gaming is off-limits.

Google Nest 4.

Without a smart assistant, my desk is useless. I'm currently using the second-generation Google Nest Hub, but I've also used the Amazon Echo Dot, Echo Spot, and Apple HomePod Mini.

As a Pixel 6 Pro user, the Nest Hub works best with my phone.

My Nest Hub plays news, music, and calendar events. It also lets me control lights and switches with my smartphone. It plays YouTube videos.

Google Pixel Stand, No. 5

A wireless charger on my desk is convenient for charging my phone and other devices while I work. My desk has two wireless chargers. I have a Satechi aluminum fast charger and a second-generation Google Pixel Stand.

If I need to charge my phone and earbuds simultaneously, I use two wireless chargers. Satechi chargers are well-made and fast. Micro-USB is my only complaint.

The Pixel Stand converts compatible devices into a smart display for adjusting charging speeds and controlling other smart devices. My Pixel 6 Pro charges quickly. Here's my video review.

6. Anker Power Bank

Anker's 65W charger is my final recommendation. This online find was a must-have. This can charge my laptop and several non-wireless devices, perfect for any techie!

The charger has two USB-A ports and two USB-C ports, one with 45W and the other with 20W, so it can charge my iPad Pro and Pixel 6 Pro simultaneously.

Summary

These are some of my favorite office gadgets. My kit page has an updated list.

Links to the products mentioned in this article are in the appropriate sections. These are affiliate links.

You're up! Share the one desk gadget you can't live without and why.

Will Tesla be able to maintain its lead in the EV market for very long?

This is one of the most pressing issues in the American auto sector today. One positive aspect of Tesla is the company's devoted customer base and recognizable name recognition (similar to Apple). It also invests more in research and development per vehicle than its rivals and has a head start in EV production.

Conversely, established automakers like Volkswagen are actively plotting their strategy to surpass Tesla. As the current market leaders, they have decades of experience in the auto industry and are spending billions to catch up.

We've visualized data from the EPA's 2022 Automotive Trends Report to bring you up to speed on this developing story.

Info for the Model Year of 2021

The full production data used in this infographic is for the 2021 model year, but it comes from a report for 2022.

Combined EV and PHEV output is shown in the table below (plug-in hybrid electric vehicle).

It is important to note that Toyota and Stellantis, the two largest legacy automakers in this dataset, only produced PHEVs. Toyota's first electric vehicle, the bZ4X, won't hit the market until 2023.

Stellantis seems to be falling even further behind, despite having enormous unrealized potential in its Jeep and Ram brands. Stellantis CEO Carlos Tavares said in a recent interview that the firm has budgeted $36 billion for electrification and software.

Legacy Brands with the Most Momentum

In the race to develop electric vehicles, some long-standing manufacturers have gotten the jump on their rivals.

Volkswagen, one of these storied manufacturers, has made a significant investment in electric vehicles (EVs) in the wake of the Dieselgate scandal. The company plans to roll out multiple EV models, including the ID.3 hatchback, ID.4 SUV, and ID. Buzz, with the goal of producing 22 million EVs by 2028. (an electric revival of the classic Microbus).

Even Ford is keeping up, having just announced an EV investment of $22 billion between 2021 and 2025. In November of 2022, the company manufactured their 150,000th Mustang Mach-E, and by the end of 2023, they hoped to have 270,000 of them in circulation.

Additionally, over 200,000 F-150 Lightnings have been reserved since Ford announced the truck. The Lightning is scheduled to have a production run of 15,000 in 2022, 55,000 in 2023, and 80,000 in 2024. Ford's main competitor in the electric pickup truck segment, Rivian, is on track to sell 25,000 vehicles by 2022.

Unexpected Success

Shopify CEO and co-founder Tobias Lutke. Shopify is worth $48 billion.

World-renowned entrepreneur Tobi

Tobi never expected his first online snowboard business to become a multimillion-dollar software corporation.

Tobi founded Shopify to establish a 20-person company.

The publicly traded corporation employs over 10,000 people.

Here's Tobi Lutke's incredible story.

Elon Musk tweeted his admiration for the Shopify creator.

30-October-2019.

Musk praised Shopify founder Tobi Lutke on Twitter.

Happened:

Explore this programmer's journey.

What difficulties did Tobi experience as a young child?

Germany raised Tobi.

Tobi's parents realized he was smart but had trouble learning as a toddler.

Tobi was learning disabled.

Tobi struggled with school tests.

Tobi's learning impairments were undiagnosed.

Tobi struggled to read as a dyslexic.

Tobi also found school boring.

Germany's curriculum didn't inspire Tobi's curiosity.

“The curriculum in Germany was taught like here are all the solutions you might find useful later in life, spending very little time talking about the problem…If I don’t understand the problem I’m trying to solve, it’s very hard for me to learn about a solution to a problem.”

Studying computer programming

After tenth grade, Tobi decided school wasn't for him and joined a German apprenticeship program.

This curriculum taught Tobi software engineering.

He was an apprentice in a small Siemens subsidiary team.

Tobi worked with rebellious Siemens employees.

Team members impressed Tobi.

Tobi joined the team for this reason.

Tobi was pleased to get paid to write programming all day.

His life could not have been better.

Devoted to snowboarding

Tobi loved snowboarding.

He drove 5 hours to ski at his folks' house.

His friends traveled to the US to snowboard when he was older.

However, the cheap dollar conversion rate led them to Canada.

2000.

Tobi originally decided to snowboard instead than ski.

Snowboarding captivated him in Canada.

On the trip to Canada, Tobi encounters his wife.

Tobi meets his wife Fiona McKean on his first Canadian ski trip.

They maintained in touch after the trip.

Fiona moved to Germany after graduating.

Tobi was a startup coder.

Fiona found work in Germany.

Her work included editing, writing, and academics.

“We lived together for 10 months and then she told me that she need to go back for the master's program.”

With Fiona, Tobi immigrated to Canada.

Fiona invites Tobi.

Tobi agreed to move to Canada.

Programming helped Tobi move in with his girlfriend.

Tobi was an excellent programmer, therefore what he did in Germany could be done anywhere.

He worked remotely for his German employer in Canada.

Tobi struggled with remote work.

Due to poor communication.

No slack, so he used email.

Programmers had trouble emailing.

Tobi's startup was developing a browser.

After the dot-com crash, individuals left that startup.

It ended.

Tobi didn't intend to work for any major corporations.

Tobi left his startup.

He believed he had important skills for any huge corporation.

He refused to join a huge corporation.

Because of Siemens.

Tobi learned to write professional code and about himself while working at Siemens in Germany.

Siemens culture was odd.

Employees were distrustful.

Siemens' rigorous dress code implies that the corporation doesn't trust employees' attire.

It wasn't Tobi's place.

“There was so much bad with it that it just felt wrong…20-year-old Tobi would not have a career there.”

Focused only on snowboarding

Tobi lived in Ottawa with his girlfriend.

Canada is frigid in winter.

Ottawa's winters last.

Almost half a year.

Tobi wanted to do something worthwhile now.

So he snowboarded.

Tobi began snowboarding seriously.

He sought every snowboarding knowledge.

He researched the greatest snowboarding gear first.

He created big spreadsheets for snowboard-making technologies.

Tobi grew interested in selling snowboards while researching.

He intended to sell snowboards online.

He had no choice but to start his own company.

A small local company offered Tobi a job.

Interested.

He must sign papers to join the local company.

He needed a work permit when he signed the documents.

Tobi had no work permit.

He was allowed to stay in Canada while applying for permanent residency.

“I wasn’t illegal in the country, but my state didn’t give me a work permit. I talked to a lawyer and he told me it’s going to take a while until I get a permanent residency.”

Tobi's lawyer told him he cannot get a work visa without permanent residence.

His lawyer said something else intriguing.

Tobis lawyer advised him to start a business.

Tobi declined this local company's job offer because of this.

Tobi considered opening an internet store with his technical skills.

He sold snowboards online.

“I was thinking of setting up an online store software because I figured that would exist and use it as a way to sell snowboards…make money while snowboarding and hopefully have a good life.”

What brought Tobi and his co-founder together, and how did he support Tobi?

Tobi lived with his girlfriend's parents.

In Ottawa, Tobi encounters Scott Lake.

Scott was Tobis girlfriend's family friend and worked for Tobi's future employer.

Scott and Tobi snowboarded.

Tobi pitched Scott his snowboard sales software idea.

Scott liked the idea.

They planned a business together.

“I was looking after the technology and Scott was dealing with the business side…It was Scott who ended up developing relationships with vendors and doing all the business set-up.”

Issues they ran into when attempting to launch their business online

Neither could afford a long-term lease.

That prompted their online business idea.

They would open a store.

Tobi anticipated opening an internet store in a week.

Tobi seeks open-source software.

Most existing software was pricey.

Tobi and Scott couldn't afford pricey software.

“In 2004, I was sitting in front of my computer absolutely stunned realising that we hadn’t figured out how to create software for online stores.”

They required software to:

• to upload snowboard images to the website.

• people to look up the types of snowboards that were offered on the website. There must be a search feature in the software.

• Online users transmit payments, and the merchant must receive them.

• notifying vendors of the recently received order.

No online selling software existed at the time.

Online credit card payments were difficult.

How did they advance the software while keeping expenses down?

Tobi and Scott needed money to start selling snowboards.

Tobi and Scott funded their firm with savings.

“We both put money into the company…I think the capital we had was around CAD 20,000(Canadian Dollars).”

Despite investing their savings.

They minimized costs.

They tried to conserve.

No office rental.

They worked in several coffee shops.

Tobi lived rent-free at his girlfriend's parents.

He installed software in coffee cafes.

How were the software issues handled?

Tobi found no online snowboard sales software.

Two choices remained:

1. Change your mind and try something else.

2. Use his programming expertise to produce something that will aid in the expansion of this company.

Tobi knew he was the sole programmer working on such a project from the start.

“I had this realisation that I’m going to be the only programmer who has ever worked on this, so I don’t have to choose something that lots of people know. I can choose just the best tool for the job…There is been this programming language called Ruby which I just absolutely loved ”

Ruby was open-source and only had Japanese documentation.

Latin is the source code.

Tobi used Ruby twice.

He assumed he could pick the tool this time.

Why not build with Ruby?

How did they find their first time operating a business?

Tobi writes applications in Ruby.

He wrote the initial software version in 2.5 months.

Tobi and Scott founded Snowdevil to sell snowboards.

Tobi coded for 16 hours a day.

His lifestyle was unhealthy.

He enjoyed pizza and coke.

“I would never recommend this to anyone, but at the time there was nothing more interesting to me in the world.”

Their initial purchase and encounter with it

Tobi worked in cafes then.

“I was working in a coffee shop at this time and I remember everything about that day…At some time, while I was writing the software, I had to type the email that the software would send to tell me about the order.”

Tobi recalls everything.

He checked the order on his laptop at the coffee shop.

Pennsylvanian ordered snowboard.

Tobi walked home and called Scott. Tobi told Scott their first order.

They loved the order.

How were people made aware about Snowdevil?

2004 was very different.

Tobi and Scott attempted simple website advertising.

Google AdWords was new.

Ad clicks cost 20 cents.

Online snowboard stores were scarce at the time.

Google ads propelled the snowdevil brand.

Snowdevil prospered.

They swiftly recouped their original investment in the snowboard business because to its high profit margin.

Tobi and Scott struggled with inventories.

“Snowboards had really good profit margins…Our biggest problem was keeping inventory and getting it back…We were out of stock all the time.”

Selling snowboards returned their investment and saved them money.

They did not appoint a business manager.

They accomplished everything alone.

Sales dipped in the spring, but something magical happened.

Spring sales plummeted.

They considered stocking different boards.

They naturally wanted to add boards and grow the business.

However, magic occurred.

Tobi coded and improved software while running Snowdevil.

He modified software constantly. He wanted speedier software.

He experimented to make the software more resilient.

Tobi received emails requesting the Snowdevil license.

They intended to create something similar.

“I didn’t stop programming, I was just like Ok now let me try things, let me make it faster and try different approaches…Increasingly I got people sending me emails and asking me If I would like to licence snowdevil to them. People wanted to start something similar.”

Software or skateboards, your choice

Scott and Tobi had to choose a hobby in 2005.

They might sell alternative boards or use software.

The software was a no-brainer from demand.

Daniel Weinand is invited to join Tobi's business.

Tobis German best friend is Daniel.

Tobi and Scott chose to use the software.

Tobi and Scott kept the software service.

Tobi called Daniel to invite him to Canada to collaborate.

Scott and Tobi had quit snowboarding until then.

How was Shopify launched, and whence did the name come from?

The three chose Shopify.

Named from two words.

First:

• Shop

Final part:

• Simplify

Shopify

Shopify's crew has always had one goal:

• creating software that would make it simple and easy for people to launch online storefronts.

Launched Shopify after raising money for the first time.

Shopify began fundraising in 2005.

First, they borrowed from family and friends.

They needed roughly $200k to run the company efficiently.

$200k was a lot then.

When questioned why they require so much money. Tobi told them to trust him with their goals. The team raised seed money from family and friends.

Shopify.com has a landing page. A demo of their goal was on the landing page.

In 2006, Shopify had about 4,000 emails.

Shopify rented an Ottawa office.

“We sent a blast of emails…Some people signed up just to try it out, which was exciting.”

How things developed after Scott left the company

Shopify co-founder Scott Lake left in 2008.

Scott was CEO.

“He(Scott) realized at some point that where the software industry was going, most of the people who were the CEOs were actually the highly technical person on the founding team.”

Scott leaving the company worried Tobi.

Tobis worried about finding a new CEO.

To Tobi:

A great VC will have the network to identify the perfect CEO for your firm.

Tobi started visiting Silicon Valley to meet with venture capitalists to recruit a CEO.

Initially visiting Silicon Valley

Tobi came to Silicon Valley to start a 20-person company.

This company creates eCommerce store software.

Tobi never wanted a big corporation. He desired a fulfilling existence.

“I stayed in a hostel in the Bay Area. I had one roommate who was also a computer programmer. I bought a bicycle on Craiglist. I was there for a week, but ended up staying two and a half weeks.”

Tobi arrived unprepared.

When venture capitalists asked him business questions.

He answered few queries.

Tobi didn't comprehend VC meetings' terminology.

He wrote the terms down and looked them up.

Some were fascinated after he couldn't answer all these queries.

“I ended up getting the kind of term sheets people dream about…All the offers were conditional on moving our company to Silicon Valley.”

Canada received Tobi.

He wanted to consult his team before deciding. Shopify had five employees at the time.

2008.

A global recession greeted Tobi in Canada. The recession hurt the market.

His term sheets were useless.

The economic downturn in the world provided Shopify with a fantastic opportunity.

The global recession caused significant job losses.

Fired employees had several ideas.

They wanted online stores.

Entrepreneurship was desired. They wanted to quit work.

People took risks and tried new things during the global slump.

Shopify subscribers skyrocketed during the recession.

“In 2009, the company reached neutral cash flow for the first time…We were in a position to think about long-term investments, such as infrastructure projects.”

Then, Tobi Lutke became CEO.

How did Tobi perform as the company's CEO?

“I wasn’t good. My team was very patient with me, but I had a lot to learn…It’s a very subtle job.”

2009–2010.

Tobi limited the company's potential.

He deliberately restrained company growth.

Tobi had one costly problem:

• Whether Shopify is a venture or a lifestyle business.

The company's annual revenue approached $1 million.

Tobi battled with the firm and himself despite good revenue.

His wife was supportive, but the responsibility was crushing him.

“It’s a crushing responsibility…People had families and kids…I just couldn’t believe what was going on…My father-in-law gave me money to cover the payroll and it was his life-saving.”

Throughout this trip, everyone supported Tobi.

They believed it.

$7 million in donations received

Tobi couldn't decide if this was a lifestyle or a business.

Shopify struggled with marketing then.

Later, Tobi tried 5 marketing methods.

He told himself that if any marketing method greatly increased their growth, he would call it a venture, otherwise a lifestyle.

The Shopify crew brainstormed and voted on marketing concepts.

Tested.

“Every single idea worked…We did Adwords, published a book on the concept, sponsored a podcast and all the ones we tracked worked.”

To Silicon Valley once more

Shopify marketing concepts worked once.

Tobi returned to Silicon Valley to pitch investors.

He raised $7 million, valuing Shopify at $25 million.

All investors had board seats.

“I find it very helpful…I always had a fantastic relationship with everyone who’s invested in my company…I told them straight that I am not going to pretend I know things, I want you to help me.”

Tobi developed skills via running Shopify.

Shopify had 20 employees.

Leaving his wife's parents' home

Tobi left his wife's parents in 2014.

Tobi had a child.

Shopify has 80,000 customers and 300 staff in 2013.

Public offering in 2015

Shopify investors went public in 2015.

Shopify powers 4.1 million e-Commerce sites.

Shopify stores are 65% US-based.

It is currently valued at $48 billion.

Introduction

Markets rely on knowledge.

The internet provided practically endless knowledge and wisdom. Humanity has never seen such leverage. Technology's progress drives us to adapt to a changing world, changing our routines and behaviors.

In a digital age, people may struggle to live in the analogue world of their upbringing. Can those who can't adapt change their lives? I won't answer. We should teach those who are willing to learn, nevertheless. Unravel the modern world's riddles and give them wisdom.

Adapt or die . Accept the future or remain behind.

This essay will help you comprehend Bitcoin better than most market participants and the general public. Let's dig into Bitcoin.

Join me.

Ascension

Bitcoin.org was registered in August 2008. Bitcoin whitepaper was published on 31 October 2008. The document intrigued and motivated people around the world, including technical engineers and sovereignty seekers. Since then, Bitcoin's whitepaper has been read and researched to comprehend its essential concept.

I recommend reading the whitepaper yourself. You'll be able to say you read the Bitcoin whitepaper instead of simply Googling "what is Bitcoin" and reading the fundamental definition without knowing the revolution's scope. The article links to Bitcoin's whitepaper. To avoid being overwhelmed by the whitepaper, read the following article first.

Bitcoin isn't the first peer-to-peer digital currency. Hashcash or Bit Gold were once popular cryptocurrencies. These two Bitcoin precursors failed to gain traction and produce the network effect needed for general adoption. After many struggles, Bitcoin emerged as the most successful cryptocurrency, leading the way for others.

Satoshi Nakamoto, an active bitcointalk.org user, created Bitcoin. Satoshi's identity remains unknown. Satoshi's last bitcointalk.org login was 12 December 2010. Since then, he's officially disappeared. Thus, conspiracies and riddles surround Bitcoin's creators. I've heard many various theories, some insane and others well-thought-out.

It's not about who created it; it's about knowing its potential. Since its start, Satoshi's legacy has changed the world and will continue to.

Block-by-block blockchain

Bitcoin is a distributed ledger. What's the meaning?

Everyone can view all blockchain transactions, but no one can undo or delete them.

Imagine you and your friends routinely eat out, but only one pays. You're careful with money and what others owe you. How can everyone access the info without it being changed?

You'll keep a notebook of your evening's transactions. Everyone will take a page home. If one of you changed the page's data, the group would notice and reject it. The majority will establish consensus and offer official facts.

Miners add a new Bitcoin block to the main blockchain every 10 minutes. The appended block contains miner-verified transactions. Now that the next block has been added, the network will receive the next set of user transactions.

Bitcoin Proof of Work—prove you earned it

Any firm needs hardworking personnel to expand and serve clients. Bitcoin isn't that different.

Bitcoin's Proof of Work consensus system needs individuals to validate and create new blocks and check for malicious actors. I'll discuss Bitcoin's blockchain consensus method.

Proof of Work helps Bitcoin reach network consensus. The network is checked and safeguarded by CPU, GPU, or ASIC Bitcoin-mining machines (Application-Specific Integrated Circuit).

Every 10 minutes, miners are rewarded in Bitcoin for securing and verifying the network. It's unlikely you'll finish the block. Miners build pools to increase their chances of winning by combining their processing power.

In the early days of Bitcoin, individual mining systems were more popular due to high maintenance costs and larger earnings prospects. Over time, people created larger and larger Bitcoin mining facilities that required a lot of space and sophisticated cooling systems to keep machines from overheating.

Proof of Work is a vital part of the Bitcoin network, as network security requires the processing power of devices purchased with fiat currency. Miners must invest in mining facilities, which creates a new business branch, mining facilities ownership. Bitcoin mining is a topic for a future article.

More mining, less reward

Bitcoin is usually scarce.

Why is it rare? It all comes down to 21,000,000 Bitcoins.

Were all Bitcoins mined? Nope. Bitcoin's supply grows until it hits 21 million coins. Initially, 50BTC each block was mined, and each block took 10 minutes. Around 2140, the last Bitcoin will be mined.

But 50BTC every 10 minutes does not give me the year 2140. Indeed careful reader. So important is Bitcoin's halving process.

What is halving?

The block reward is halved every 210,000 blocks, which takes around 4 years. The initial payout was 50BTC per block and has been decreased to 25BTC after 210,000 blocks. First halving occurred on November 28, 2012, when 10,500,000 BTC (50%) had been mined. As of April 2022, the block reward is 6.25BTC and will be lowered to 3.125BTC by 19 March 2024.

The halving method is tied to Bitcoin's hashrate. Here's what "hashrate" means.

What if we increased the number of miners and hashrate they provide to produce a block every 10 minutes? Wouldn't we manufacture blocks faster?

Every 10 minutes, blocks are generated with little asymmetry. Due to the built-in adaptive difficulty algorithm, the overall hashrate does not affect block production time. With increased hashrate, it's harder to construct a block. We can estimate when the next halving will occur because 10 minutes per block is fixed.

Building with nodes and blocks

For someone new to crypto, the unusual terms and words may be overwhelming. You'll also find everyday words that are easy to guess or have a vague idea of what they mean, how they work, and what they do. Consider blockchain technology.

Nodes and blocks: Think about that for a moment. What is your first idea?

The blockchain is a chain of validated blocks added to the main chain. What's a "block"? What's inside?

The block is another page in the blockchain book that has been filled with transaction information and accepted by the majority.

We won't go into detail about what each block includes and how it's built, as long as you understand its purpose.

What about nodes?

Nodes, along with miners, verify the blockchain's state independently. But why?

To create a full blockchain node, you must download the whole Bitcoin blockchain and check every transaction against Bitcoin's consensus criteria.

What's Bitcoin's size? 

In April 2022, the Bitcoin blockchain was 389.72GB.

Bitcoin's blockchain has miners and node runners.

Let's revisit the US gold rush. Miners mine gold with their own power (physical and monetary resources) and are rewarded with gold (Bitcoin). All become richer with more gold, and so does the country.

Nodes are like sheriffs, ensuring everything is done according to consensus rules and that there are no rogue miners or network users.

Lost and held bitcoin

Does the Bitcoin exchange price match each coin's price? How many coins remain after 21,000,000? 21 million or less?

Common reason suggests a 21 million-coin supply.

What if I lost 1BTC from a cold wallet?

What if I saved 1000BTC on paper in 2010 and it was damaged?

What if I mined Bitcoin in 2010 and lost the keys?

Satoshi Nakamoto's coins? Since then, those coins haven't moved.

How many BTC are truly in circulation?

Many people are trying to answer this question, and you may discover a variety of studies and individual research on the topic. Be cautious of the findings because they can't be evaluated and the statistics are hazy guesses.

On the other hand, we have long-term investors who won't sell their Bitcoin or will sell little amounts to cover mining or living needs.

The price of Bitcoin is determined by supply and demand on exchanges using liquid BTC. How many BTC are left after subtracting lost and non-custodial BTC? 

We have significantly less Bitcoin in circulation than you think, thus the price may not reflect demand if we knew the exact quantity of coins available.

True HODLers and diamond-hand investors won't sell you their coins, no matter the market.

What's UTXO?

Unspent (U) Transaction (TX) Output (O)

Imagine taking a $100 bill to a store. After choosing a drink and munchies, you walk to the checkout to pay. The cashier takes your $100 bill and gives you $25.50 in change. It's in your wallet.

Is it simply 100$? No way.

The $25.50 in your wallet is unrelated to the $100 bill you used. Your wallet's $25.50 is just bills and coins. Your wallet may contain these coins and bills:

2x 10$ 1x 10$

1x 5$ or 3x 5$

1x 0.50$ 2x 0.25$

Any combination of coins and bills can equal $25.50. You don't care, and I'd wager you've never ever considered it.

That is UTXO. Now, I'll detail the Bitcoin blockchain and how UTXO works, as it's crucial to know what coins you have in your (hopefully) cold wallet.

You purchased 1BTC. Is it all? No. UTXOs equal 1BTC. Then send BTC to a cold wallet. Say you pay 0.001BTC and send 0.999BTC to your cold wallet. Is it the 1BTC you got before? Well, yes and no. The UTXOs are the same or comparable as before, but the blockchain address has changed. It's like if you handed someone a wallet, they removed the coins needed for a network charge, then returned the rest of the coins and notes.

UTXO is a simple concept, but it's crucial to grasp how it works to comprehend dangers like dust attacks and how coins may be tracked.

Lightning Network: fast cash

You've probably heard of "Layer 2 blockchain" projects.

What does it mean?

Layer 2 on a blockchain is an additional layer that increases the speed and quantity of transactions per minute and reduces transaction fees.

Imagine going to an obsolete bank to transfer money to another account and having to pay a charge and wait. You can transfer funds via your bank account or a mobile app without paying a fee, or the fee is low, and the cash appear nearly quickly. Layer 1 and 2 payment systems are different.

Layer 1 is not obsolete; it merely has more essential things to focus on, including providing the blockchain with new, validated blocks, whereas Layer 2 solutions strive to offer Layer 1 with previously processed and verified transactions. The primary blockchain, Bitcoin, will only receive the wallets' final state. All channel transactions until shutting and balancing are irrelevant to the main chain.

Layer 2 and the Lightning Network's goal are now clear. Most Layer 2 solutions on multiple blockchains are created as blockchains, however Lightning Network is not. Remember the following remark, as it best describes Lightning.

Lightning Network connects public and private Bitcoin wallets.

Opening a private channel with another wallet notifies just two parties. The creation and opening of a public channel tells the network that anyone can use it.

Why create a public Lightning Network channel?

Every transaction through your channel generates fees.

Money, if you don't know.

See who benefits when in doubt.

Anonymity, huh?

Bitcoin anonymity? Bitcoin's anonymity was utilized to launder money.

Well… You've heard similar stories. When you ask why or how it permits people to remain anonymous, the conversation ends as if it were just a story someone heard.

Bitcoin isn't private. Pseudonymous.

What if someone tracks your transactions and discovers your wallet address? Where is your anonymity then?

Bitcoin is like bulletproof glass storage; you can't take or change the money. If you dig and analyze the data, you can see what's inside.

Every online action leaves a trace, and traces may be tracked. People often forget this guideline.

A tool like that can help you observe what the major players, or whales, are doing with their coins when the market is uncertain. Many people spend time analyzing on-chain data. Worth it?

Ask yourself a question. What are the big players' options?  Do you think they're letting you see their wallets for a small on-chain data fee?

Instead of short-term behaviors, focus on long-term trends.

More wallet transactions leave traces. Having nothing to conceal isn't a defect. Can it lead to regulating Bitcoin so every transaction is tracked like in banks today?

But wait. How can criminals pay out Bitcoin? They're doing it, aren't they?

Mixers can anonymize your coins, letting you to utilize them freely. This is not a guide on how to make your coins anonymous; it could do more harm than good if you don't know what you're doing.

Remember, being anonymous attracts greater attention.

Bitcoin isn't the only cryptocurrency we can use to buy things. Using cryptocurrency appropriately can provide usability and anonymity. Monero (XMR), Zcash (ZEC), and Litecoin (LTC) following the Mimblewimble upgrade are examples.

Summary

Congratulations! You've reached the conclusion of the article and learned about Bitcoin and cryptocurrency. You've entered the future.

You know what Bitcoin is, how its blockchain works, and why it's not anonymous. I bet you can explain Lightning Network and UTXO to your buddies.

Markets rely on knowledge. Prepare yourself for success before taking the first step. Let your expertise be your edge.

This article is a summary of this one.

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.