Open to ideas or acquisitions

Failure is an unavoidable aspect of life, yet many recoil at the word.

I've worked on unrelated startup projects. This is a list of products I developed (often as the tech lead or co-founder) and why they failed to launch.

Chess Bet (Betting)

As a chess player who plays 5 games a day and has an ELO rating of 2100, I tried to design a chess engine to rival stockfish and Houdini.

While constructing my chess engine, my cofounder asked me about building a p2p chess betting app. Chess Bet. There couldn't be a better time.

Two people in different locations could play a staked game. The winner got 90% of the bet and we got 10%. The business strategy was clear, but our mini-launch was unusual.

People started employing the same cheat engines I mentioned, causing user churn and defaming our product.

It was the first programming problem I couldn't solve after building a cheat detection system based on player move strengths and prior games. Chess.com, the most famous online chess software, still suffers from this.

We decided to pivot because we needed an expensive betting license.

We relaunched as Chess MVP after deciding to focus on chess learning. A platform for teachers to create chess puzzles and teach content. Several chess students used our product, but the target market was too tiny.

We chose to quit rather than persevere or pivot.

BodaCare (Insure Tech)

‘BodaBoda’ in Swahili means Motorcycle. My Dad approached me in 2019 (when I was working for a health tech business) about establishing an Insurtech/fintech solution for motorbike riders to pay for insurance using SNPL.

We teamed up with an underwriter to market motorcycle insurance. Once they had enough premiums, they'd get an insurance sticker in the mail. We made it better by splitting the cover in two, making it more reasonable for motorcyclists struggling with lump-sum premiums.

Lack of capital and changing customer behavior forced us to close, with 100 motorcyclists paying 0.5 USD every day. Our unit econ didn't make sense, and CAC and retention capital only dug us deeper.

Circle (Social Networking)

Having learned from both product failures, I began to understand what worked and what didn't. While reading through Instagram, an idea struck me.

Suppose social media weren't virtual.

Imagine meeting someone on your way home. Like-minded person

People were excited about social occasions after covid restrictions were eased. Anything to escape. I just built a university student-popular experiences startup. Again, there couldn't be a better time.

I started the Android app. I launched it on Google Beta and oh my! 200 people joined in two days.

It works by signaling if people are in a given place and allowing users to IM in hopes of meeting up in near real-time. Playstore couldn't deploy the app despite its success in beta for unknown reasons. I appealed unsuccessfully.

My infrastructure quickly lost users because I lacked funding.

In conclusion

This essay contains many failures, some of which might have been avoided and others not, but they were crucial learning points in my startup path.

If you liked any idea, I have the source code on Github.

Happy reading until then!

If you're not genuine, you'll be revealed.

Sam Bankman-Fried (SBF) was called the Cryptocurrency Warren Buffet.

No wonder.

SBF's trading expertise, Blockchain knowledge, and ability to construct FTX attracted mainstream investors.

He had a fantastic worldview, donating much of his riches to charity.

As the onion layers peel back, it's clear he wasn't the altruistic media figure he portrayed.

SBF's mistakes were disastrous.

• Customer deposits were traded and borrowed by him.

• With ten other employees, he shared a $40 million mansion where they all had polyamorous relationships.

• Tone-deaf and wasteful marketing expenditures, such as the $200 million spent to change the name of the Miami Heat stadium to the FTX Arena

• Democrats received a $40 million campaign gift.

• And now there seems to be no regret.

FTX was a 32-billion-dollar cryptocurrency exchange.

It went bankrupt practically overnight.

SBF, FTX's creator, exploited client funds to leverage trade.

FTX had $1 billion in customer withdrawal reserves against $9 billion in liabilities in sister business Alameda Research.

Bloomberg Billionaire Index says it's the largest and fastest net worth loss in history.

It gets worse.

SBF's net worth is $900 Million, however he must still finalize FTX's bankruptcy.

SBF's arrest in the Bahamas and SEC inquiry followed news that his cryptocurrency exchange had crashed, losing billions in customer deposits.

A journalist contacted him on Twitter D.M., and their exchange is telling.

His ideas are revealed.

Kelsey Piper says they didn't expect him to answer because people under investigation don't comment.

Bankman-Fried wanted to communicate, and the interaction shows he has little remorse.

SBF talks honestly about FTX gaming customers' money and insults his competition.

Reporter Kelsey Piper was outraged by what he said and felt the mistakes SBF says plague him didn't evident in the messages.

Before FTX's crash, SBF was a poster child for Cryptocurrency regulation and avoided criticizing U.S. regulators.

He tells Piper that his lobbying is just excellent PR.

It shows his genuine views and supports cynics' opinions that his attempts to win over U.S. authorities were good for his image rather than Crypto.

SBF’s responses are in Grey, and Pipers are in Blue.

It's unclear if SBF cut corners for his gain. In their Twitter exchange, Piper revisits an interview question about ethics.

SBF says, "All the foolish sh*t I said"

SBF claims FTX has never invested customer monies.

Piper challenged him on Twitter.

While he insisted FTX didn't use customer deposits, he said sibling business Alameda borrowed too much from FTX's balance sheet.

He did, basically.

When consumers tried to withdraw money, FTX was short.

SBF thought Alameda had enough money to cover FTX customers' withdrawals, but life sneaks up on you.

SBF believes most exchanges have done something similar to FTX, but they haven't had a bank run (a bunch of people all wanting to get their deposits out at the same time).

SBF believes he shouldn't have consented to the bankruptcy and kept attempting to raise more money because withdrawals would be open in a month with clients whole.

If additional money came in, he needed $8 billion to bridge the creditors' deficit, and there aren't many corporations with $8 billion to spare.

Once clients feel protected, they will continue to leave their assets on the exchange, according to one idea.

Kevin OLeary, a world-renowned hedge fund manager, says not all investors will walk through the open gate once the company is safe, therefore the $8 Billion wasn't needed immediately.

SBF claims the bankruptcy was his biggest error because he could have accumulated more capital.

Final Reflections

Sam Bankman-Fried, 30, became the world's youngest billionaire in four years.

Never listen to what people say about investing; watch what they do.

SBF is a trader who gets wrecked occasionally.

Ten first-time entrepreneurs ran FTX, screwing each other with no risk management.

It prevents opposing or challenging perspectives and echo chamber highs.

Twitter D.M. conversation with a journalist is the final nail.

He lacks an experienced crew.

This event will surely speed up much-needed regulation.

It's also prompted cryptocurrency exchanges to offer proof of reserves to calm customers.

Reflections on the closed company Hola! Dating app

I'll discuss my fuckups as an entrepreneur and CEO. All of them refer to the dating app Hola!, which I co-founded and starred in.

Spring 2021 was when we started. Two techies and two non-techies created a dating app. Pokemon Go and Tinder were combined.

Online dating is a business, and it takes two weeks from a like to a date. We questioned online dating app users if they met anyone offline last year.

75% replied yes, 50% sometimes, 25% usually.

Offline dating is popular, yet people have concerns.

• Men are reluctant to make mistakes in front of others.

• Women are curious about the background of everyone who approaches them.

We designed unique mechanics that let people date after a match. No endless chitchat. Women would be safe while men felt like cowboys.

I wish to emphasize three faults that lead to founders' estrangement.

This detachment ultimately led to us shutting down the company.

The wrong technology stack

Situation

Instead of generating a faster MVP and designing an app in a universal stack for iOS and Android, I argued we should pilot the app separately for iOS and Android. Technical founders' expertise made this possible.

Self-reflection

Mistaken strategy. We lost time and resources developing two apps at once. We chose iOS since it's more profitable. Apple took us out after the release, citing Guideline 4.3 Spam. After 4 months, we had nothing. We had a long way to go to get the app on Android and the Store.

I suggested creating a uniform platform for the company's growth. This makes parallel product development easier. The strategist's lack of experience and knowledge made it a piece of crap.

What would I have changed if I could?

We should have designed an Android universal stack. I expected Apple to have issues with a dating app.

Our approach should have been to launch something and subsequently improve it, but prejudice won.

The lesson

Discuss the IT stack with your CTO. It saves time and money. Choose the easiest MVP method.

2. A tardy search for investments

Situation

Though the universe and other founders encouraged me to locate investors first, I started pitching when we almost had an app.

When angels arrived, it was time to close. The app was banned, war broke out, I left the country, and the other co-founders stayed. We had no savings.

Self-reflection

I loved interviewing users. I'm proud of having done 1,000 interviews. I wanted to understand people's pain points and improve the product.

Interview results no longer affected the product. I was terrified to start pitching. I filled out accelerator applications and redid my presentation. You must go through that so you won't be terrified later.

What would I have changed if I could?

Get an external or internal mentor to help me with my first pitch as soon as possible. I'd be supported if criticized. He'd cheer with me if there was enthusiasm.

In 99% of cases, I'm comfortable jumping into the unknown, but there are exceptions. The mentor's encouragement would have prompted me to act sooner.

The lesson

Begin fundraising immediately. Months may pass. Show investors your pre-MVP project. Draw inferences from feedback.

3. Role ambiguity

Situation

My technical co-founders were also part-time lead developers, which produced communication issues. As co-founders, we communicated well and recognized the problems. Stakes, vesting, target markets, and approach were agreed upon.

We were behind schedule. Technical debt and strategic gap grew.

Bi-daily and weekly reviews didn't help. Each time, there were explanations. Inside, I was freaking out.

Self-reflection

I am a fairly easy person to talk to. I always try to stick to agreements; otherwise, my head gets stuffed with unnecessary information, interpretations, and emotions.

Sit down -> talk -> decide -> do -> evaluate the results. Repeat it.

If I don't get detailed comments, I start ruining everyone's mood. If there's a systematic violation of agreements without a good justification, I won't join the project or I'll end the collaboration.

What would I have done otherwise?

This is where it’s scariest to draw conclusions. Probably the most logical thing would have been not to start the project as we started it. But that was already a completely different project. So I would not have done anything differently and would have failed again.

But I drew conclusions for the future.

The lesson

First-time founders should find an adviser or team coach for a strategic session. It helps split the roles and responsibilities.

Ethereum is the second-largest cryptocurrency. The Merge, a mid-September event that will convert Ethereum's consensus process from proof-of-work to proof-of-stake if all goes according to plan, will be a game changer.

Why is Ethereum ditching proof-of-work? Because it can. We're talking about a fully functioning, open-source ecosystem with a capacity for evolution that other cryptocurrencies lack, a change that would allow it to scale up its performance from 15 transactions per second to 100,000 as its blockchain is used for more and more things. It would reduce its energy consumption by 99.95%. Vitalik Buterin, the system's founder, would play a less active role due to decentralization, and miners, who validated transactions through proof of work, would be far less important.

Why has this conversion taken so long and been so cautious? Because it involves modifying a core process while it's running to boost its performance. It requires running the new mechanism in test chains on an ever-increasing scale, assessing participant reactions, and checking for issues or restrictions. The last big test was in early June and was successful. All that's left is to converge the mechanism with the Ethereum blockchain to conclude the switch.

What's stopping Bitcoin, the leader in market capitalization and the cryptocurrency that began blockchain's appeal, from doing the same? Satoshi Nakamoto, whoever he or she is, departed from public life long ago, therefore there's no community leadership. Changing it takes a level of consensus that is impossible to achieve without strong leadership, which is why Bitcoin's evolution has been sluggish and conservative, with few modifications.

Secondly, The Merge will balance the consensus mechanism (proof-of-work or proof-of-stake) and the system decentralization or centralization. Proof-of-work prevents double-spending, thus validators must buy hardware. The system works, but it requires a lot of electricity and, as it scales up, tends to re-centralize as validators acquire more hardware and the entire network activity gets focused in a few nodes. Larger operations save more money, which increases profitability and market share. This evolution runs opposed to the concept of decentralization, and some anticipate that any system that uses proof of work as a consensus mechanism will evolve towards centralization, with fewer large firms able to invest in efficient network nodes.

Yet radical bitcoin enthusiasts share an opposite argument. In proof-of-stake, transaction validators put their funds at stake to attest that transactions are valid. The algorithm chooses who validates each transaction, giving more possibilities to nodes that put more coins at stake, which could open the door to centralization and government control.

In both cases, we're talking about long-term changes, but Bitcoin's proof-of-work has been evolving longer and seems to confirm those fears, while proof-of-stake is only employed in coins with a minuscule volume compared to Ethereum and has no predictive value.

As of mid-September, we will have two significant cryptocurrencies, each with a different consensus mechanisms and equally different characteristics: one is intrinsically conservative and used only for economic transactions, while the other has been evolving in open source mode, and can be used for other types of assets, smart contracts, or decentralized finance systems. Some even see it as the foundation of Web3.

Many things could change before September 15, but The Merge is likely to be a turning point. We'll have to follow this closely.

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.

What does Ukraine's murderous craziness mean?

Vladimir Putin expressed his patience to Israeli Prime Minister Naftali Bennet. Thousands, even hundreds of thousands of young and middle-aged males in his country have no meaning to him.

During a meeting in March with Prime Minister Naftali Bennett of Israel, Mr. Putin admitted that the Ukrainians were tougher “than I was told,” according to two people familiar with the exchange. “This will probably be much more difficult than we thought. But the war is on their territory, not ours. We are a big country and we have patience (The Inside Story of a Catastrophe).”

Putin should explain to Russian mothers how patient he is with his invasion of Ukraine.

Putin is rich. Even while sanctions have certainly limited Putin's access to his fortune, he has access to everything in Russia. Unlimited wealth.

The Russian leader's infrastructure was designed with his whims in mind. Vladimir Putin is one of the wealthiest and most catered-to people alive. He's also all-powerful, as his lack of opposition shows. His incredible wealth and power have isolated him from average people so much that he doesn't mind turning lives upside down to prove a point.

For many, losing a Russian spouse or son is painful. Whether the soldier was a big breadwinner or unemployed, the loss of a male figure leaves many families bewildered and anxious. Putin, Russia's revered president, seems unfazed.

People who know Mr. Putin say he is ready to sacrifice untold lives and treasure for as long as it takes, and in a rare face-to-face meeting with the Americans last month the Russians wanted to deliver a stark message to President Biden: No matter how many Russian soldiers are killed or wounded on the battlefield, Russia will not give up (The Inside Story of a Catastrophe).

Imagine a country's leader publicly admitting a mistake he's made. Imagine getting Putin's undivided attention.

So, I underestimated Ukrainians. I can't allow them make me appear terrible, so I'll utilize as many drunken dopes as possible to cover up my error. They'll die fulfilled and heroic.

Russia's human resources are limited, but its willingness to cause suffering is not. How many Russian families must die before the curse is broken? If mass protests started tomorrow, Russia's authorities couldn't stop them.

When Moscovites faced down tanks in August 1991, the Gorbachev coup ended in three days. Even though few city residents showed up, everything collapsed. This wicked disaster won't require many Russians.

One NATO member is warning allies that Mr. Putin is ready to accept the deaths or injuries of as many as 300,000 Russian troops — roughly three times his estimated losses so far.

If 100,000 Russians have died in Ukraine and Putin doesn't mind another 200,000 dying, why don't these 200,000 ghosts stand up and save themselves? Putin plays the role of concerned and benevolent leader effectively, but things aren't going well for Russia.

What would 300,000 or more missing men signify for Russia's future? How many kids will have broken homes? How many families won't form, and what will the economy do?

Putin reportedly cared about his legacy. His place in Russian history Putin's invasion of Ukraine settled his legacy. He has single-handedly weakened and despaired Russia since the 1980s.

Putin will be viewed by sensible people as one of Russia's worst adversaries, but Russians will think he was fantastic despite Ukraine.

The more setbacks Mr. Putin endures on the battlefield, the more fears grow over how far he is willing to go. He has killed tens of thousands in Ukraine, leveled cities, and targeted civilians for maximum pain — obliterating hospitals, schools, and apartment buildings while cutting off power and water to millions before winter. Each time Ukrainian forces score a major blow against Russia, the bombing of their country intensifies. And Mr. Putin has repeatedly reminded the world that he can use anything at his disposal, including nuclear arms, to pursue his notion of victory.

How much death and damage will there be in Ukraine if Putin sends 200,000 more Russians to the front? It's scary, sad, and sick.

Monster.