McMurtry Spéirling outperforms Tesla in speed and efficiency.

EVs were ridiculously slow for decades. However, the 2008 Tesla Roadster revealed that EVs might go extraordinarily fast. The Tesla Model S Plaid and Rimac Nevera are the fastest-accelerating road vehicles, despite combustion-engined road cars dominating the course. A little-known firm beat Tesla and Rimac in the 0-60 race, beat F1 vehicles on a circuit, and boasts a 350-mile driving range. The McMurtry Spéirling is completely insane.

Mat Watson of CarWow, a YouTube megastar, was recently handed a Spéirling and access to Silverstone Circuit (view video above). Mat ran a quarter-mile on Silverstone straight with former F1 driver Max Chilton. The little pocket-rocket automobile touched 100 mph in 2.7 seconds, completed the quarter mile in 7.97 seconds, and hit 0-60 in 1.4 seconds. When looking at autos quickly, 0-60 times can seem near. The Tesla Model S Plaid does 0-60 in 1.99 seconds, which is comparable to the Spéirling. Despite the meager statistics, the Spéirling is nearly 30% faster than Plaid!

My vintage VW Golf 1.4s has an 8.8-second 0-60 time, whereas a BMW Z4 3.0i is 30% faster (with a 0-60 time of 6 seconds). I tried to beat a Z4 off the lights in my Golf, but the Beamer flew away. If they challenge the Spéirling in a Model S Plaid, they'll feel as I did. Fast!

Insane quarter-mile drag time. Its road car record is 7.97 seconds. A Dodge Demon, meant to run extremely fast quarter miles, finishes so in 9.65 seconds, approximately 20% slower. The Rimac Nevera's 8.582-second quarter-mile record was miles behind drag racing. This run hampered the Spéirling. Because it was employing gearing that limited its top speed to 150 mph, it reached there in a little over 5 seconds without accelerating for most of the quarter mile! McMurtry can easily change the gearing, making the Spéirling run quicker.

McMurtry did this how? First, the Spéirling is a tiny single-seater EV with a 60 kWh battery pack, making it one of the lightest EVs ever. The 1,000-hp Spéirling has more than one horsepower per kg. The Nevera has 0.84 horsepower per kg and the Plaid 0.44.

However, you cannot simply construct a car light and power it. Instead of accelerating, it would spin. This makes the Spéirling a fan car. Its huge fans create massive downforce. These fans provide the Spéirling 2 tonnes of downforce while stationary, so you could park it on the ceiling. Its fast 0-60 time comes from its downforce, which lets it deliver all that power without wheel spin.

It also possesses complete downforce at all speeds, allowing it to tackle turns faster than even race vehicles. Spéirlings overcame VW IDRs and F1 cars to set the Goodwood Hill Climb record (read more here). The Spéirling is a dragstrip winner and track dominator, unlike the Plaid and Nevera.

The Spéirling is astonishing for a single-seater. Fan-generated downforce is more efficient than wings and splitters. It also means the vehicle has very minimal drag without the fan. The Spéirling can go 350 miles per charge (WLTP) or 20-30 minutes at full speed on a track despite its 60 kWh battery pack. The G-forces would hurt your neck before the battery died if you drove around a track for longer. The Spéirling can charge at over 200 kW in about 30 minutes. Thus, driving to track days, having fun, and returning is possible. Unlike other high-performance EVs.

Tesla, Rimac, or Lucid will struggle to defeat the Spéirling. They would need to build a fan automobile because adding power to their current vehicle would make it uncontrollable. The EV and automobile industries now have a new, untouchable performance king.

Apple's latest revelation may shock iPod fans and former owners.

Apple discontinued the iPod touch on May 11, 2022. After 21 years, Apple killed the last surviving iPod, a device Steve Jobs believed would revolutionize the music industry.

Jobs was used to making bold predictions, but few expected Apple's digital music player to change the music industry. It did.

This chaos created new business opportunities. Spotify, YouTube, and Amazon are products of that chaotic era.

As the digital landscape changes, so do consumers, and the iPod has lost favor. I'm sure Apple realizes the importance of removing an icon. The iPod was Apple like the Mac and iPhone. I think it's bold to retire such a key Apple cornerstone. What would Jobs do?

iPod evolution across the ages

Here's an iPod family tree for all you enthusiasts.

iPod vintage (Oct 2001 to Sep 2014, 6 generations)

The original iPod had six significant upgrades since 2001. Apple announced an 80 GB ($249) and 160 GB ($349) iPod classic in 2007.

Apple updated the 80 GB model with a 120 GB device in September 2008. Apple upgraded the 120 GB model with a 160 GB variant a year later (2009). This was the last iteration, and Apple discontinued the classic in September 2014.

iPod nano (Jan 2004 to Sep 2005, 2 generations)

Apple debuted a smaller, brightly-colored iPod in 2004. The first model featured 4 GB, enough for 1,000 songs.

Apple produced a new 4 GB or 6 GB iPod mini in February 2005 and discontinued it in September when they released a better-looking iPod nano.

iTouch nano (Sep 2005 to July 2017, 7 generations)

I loved the iPod nano. It was tiny and elegant with enough tech to please most music aficionados, unless you carry around your complete music collection.

Apple owed much of the iPod nano's small form and success to solid-state flash memory. Flash memory doesn't need power because it has no moving parts. This makes the iPod nano more durable than the iPod classic and mini, which employ hard drives.

Apple manufactured seven generations of the iPod nano, improving its design, display screen, memory, battery, and software, but abandoned it in July 2017 due to dwindling demand.

Shuffle iPod (Jan 2005 to Jul 2017, 4 generations)

The iPod shuffle was entry-level. It was a simple, lightweight, tiny music player. The iPod shuffle was perfect for lengthy bike trips, runs, and hikes.

Apple sold 10 million iPod shuffles in the first year and kept making them for 12 years, through four significant modifications.

iOS device (Sep 2007 to May 2022, 7 generations)

The iPod touch's bigger touchscreen interface made it a curious addition to the iPod family. The iPod touch resembled an iPhone more than the other iPods, making them hard to tell apart.

Many were dissatisfied that Apple removed functionality from the iPod touch to avoid making it too similar to the iPhone. Seven design improvements over 15 years brought the iPod touch closer to the iPhone, but not completely.

The iPod touch uses the same iOS operating system as the iPhone, giving it access to many apps, including handheld games.

The iPod touch's long production run is due to the next generation of music-loving gamers.

What made the iPod cool

iPod revolutionized music listening. It was the first device to store and play MP3 music, allowing you to carry over 1,000 songs anywhere.

The iPod changed consumer electronics with its scroll wheel and touchscreen. Jobs valued form and function equally. He showed people that a product must look good to inspire an emotional response and ignite passion.

The elegant, tiny iPod was a tremendous sensation when it arrived for $399 in October 2001. Even at this price, it became a must-have for teens to CEOs.

It's hard to identify any technology that changed how music was downloaded and played like the iPod. Apple iPod and iTunes had 63% of the paid music download market in the fourth quarter of 2012.

The demise of the iPod was inevitable

Apple discontinuing the iPod touch after 21 years is sad. This ends a 00s music icon.

Jobs was a genius at anticipating market needs and opportunities, and Apple launched the iPod at the correct time.

Few consumer electronics items have had such a lasting impact on music lovers and the music industry as the iPod.

Smartphones and social media have contributed to the iPod's decline. Instead of moving to the music, the new generation of consumers is focused on social media. They're no longer passive content consumers; they're active content creators seeking likes and followers. Here, the smartphone has replaced the iPod.

It's hard not to feel a feeling of loss, another part of my adolescence now forgotten by the following generation.

So, if you’re lucky enough to have a working iPod, hang on to that relic and enjoy the music and the nostalgia.

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.

Believe what you see, what people say

It’s sad that we never get trained to leave assumptions behind. - Sebastian Thrun

Many problems in software development are not because of code but because developers create the wrong software. This isn't rare because software is emergent and most individuals only realize what they want after it's built.

Inquisitive developers who pass the yellow cat test can improve the process.

Carpenters measure twice and cut the wood once. Developers are rarely so careful.

The Yellow Cat Test

Game of Thrones made dragons cool again, so I am reading The Game of Thrones book.

The yellow cat exam is from Syrio Forel, Arya Stark's fencing instructor.

Syrio tells Arya he'll strike left when fencing. He hits her after she dodges left. Arya says “you lied”. Syrio says his words lied, but his eyes and arm told the truth.

Arya learns how Syrio became Bravos' first sword.

“On the day I am speaking of, the first sword was newly dead, and the Sealord sent for me. Many bravos had come to him, and as many had been sent away, none could say why. When I came into his presence, he was seated, and in his lap was a fat yellow cat. He told me that one of his captains had brought the beast to him, from an island beyond the sunrise. ‘Have you ever seen her like?’ he asked of me.

“And to him I said, ‘Each night in the alleys of Braavos I see a thousand like him,’ and the Sealord laughed, and that day I was named the first sword.”

Arya screwed up her face. “I don’t understand.”

Syrio clicked his teeth together. “The cat was an ordinary cat, no more. The others expected a fabulous beast, so that is what they saw. How large it was, they said. It was no larger than any other cat, only fat from indolence, for the Sealord fed it from his own table. What curious small ears, they said. Its ears had been chewed away in kitten fights. And it was plainly a tomcat, yet the Sealord said ‘her,’ and that is what the others saw. Are you hearing?” Reddit discussion.

Development teams should not believe what they are told.

We created an appointment booking system. We thought it was an appointment-booking system. Later, we realized the software's purpose was to book the right people for appointments and discourage the unneeded ones.

The first 3 months of the project had half-correct requirements and software understanding.

Open your eyes

“Open your eyes is all that is needed. The heart lies and the head plays tricks with us, but the eyes see true. Look with your eyes, hear with your ears. Taste with your mouth. Smell with your nose. Feel with your skin. Then comes the thinking afterwards, and in that way, knowing the truth” Syrio Ferel

We must see what exists, not what individuals tell the development team or how developers think the software should work. Initial criteria cover 50/70% and change.

Developers build assumptions problems by assuming how software should work. Developers must quickly explain assumptions.

When a development team's assumptions are inaccurate, they must alter the code, DevOps, documentation, and tests.

It’s always faster and easier to fix requirements before code is written.

First-draft requirements can be based on old software. Development teams must grasp corporate goals and consider needs from many angles.

Testers help rethink requirements. They look at how software requirements shouldn't operate.

Technical features and benefits might misdirect software projects.

The initiatives that focused on technological possibilities developed hard-to-use software that needed extensive rewriting following user testing.

Software development

High-level criteria are different from detailed ones.

• The interpretation of words determines their meaning.

• Presentations are lofty, upbeat, and prejudiced.

• People's perceptions may be unclear, incorrect, or just based on one perspective (half the story)

• Developers can be misled by requirements, circumstances, people, plans, diagrams, designs, documentation, and many other things.

Developers receive misinformation, misunderstandings, and wrong assumptions. The development team must avoid building software with erroneous specifications.

Once code and software are written, the development team changes and fixes them.

Developers create software with incomplete information, they need to fill in the blanks to create the complete picture.

Conclusion

Yellow cats are often inaccurate when communicating requirements.

Before writing code, clarify requirements, assumptions, etc.

Everyone will pressure the development team to generate code rapidly, but this will slow down development.

Code changes are harder than requirements.