More on Entrepreneurship/Creators
Aaron Dinin, PhD
3 years ago
I'll Never Forget the Day a Venture Capitalist Made Me Feel Like a Dunce
Are you an idiot at fundraising?
Humans undervalue what they don't grasp. Consider NASCAR. How is that a sport? ask uneducated observers. Circular traffic. Driving near a car's physical limits is different from daily driving. When driving at 200 mph, seemingly simple things like changing gas weight or asphalt temperature might be life-or-death.
Venture investors do something similar in entrepreneurship. Most entrepreneurs don't realize how complex venture finance is.
In my early startup days, I didn't comprehend venture capital's intricacy. I thought VCs were rich folks looking for the next Mark Zuckerberg. I was meant to be a sleek, enthusiastic young entrepreneur who could razzle-dazzle investors.
Finally, one of the VCs I was trying to woo set me straight. He insulted me.
How I learned that I was approaching the wrong investor
I was constructing a consumer-facing, pre-revenue marketplace firm. I looked for investors in my old university's alumni database. My city had one. After some research, I learned he was a partner at a growth-stage, energy-focused VC company with billions under management.
Billions? I thought. Surely he can write a million-dollar cheque. He'd hardly notice.
I emailed the VC about our shared alumni status, explaining that I was building a startup in the area and wanted advice. When he agreed to meet the next week, I prepared my pitch deck.
First error.
The meeting seemed like a funding request. Imagine the awkwardness.
His assistant walked me to the firm's conference room and told me her boss was running late. While waiting, I prepared my pitch. I connected my computer to the projector, queued up my PowerPoint slides, and waited for the VC.
He didn't say hello or apologize when he entered a few minutes later. What are you doing?
Hi! I said, Confused but confident. Dinin Aaron. My startup's pitch.
Who? Suspicious, he replied. Your email says otherwise. You wanted help.
I said, "Isn't that a euphemism for contacting investors?" Fundraising I figured I should pitch you.
As he sat down, he smiled and said, "Put away your computer." You need to study venture capital.
Recognizing the business aspects of venture capital
The VC taught me venture capital in an hour. Young entrepreneur me needed this lesson. I assume you need it, so I'm sharing it.
Most people view venture money from an entrepreneur's perspective, he said. They envision a world where venture capital serves entrepreneurs and startups.
As my VC indicated, VCs perceive their work differently. Venture investors don't serve entrepreneurs. Instead, they run businesses. Their product doesn't look like most products. Instead, the VCs you're proposing have recognized an undervalued market segment. By investing in undervalued companies, they hope to profit. It's their investment thesis.
Your company doesn't fit my investment thesis, the venture capitalist told me. Your pitch won't beat my investing theory. I invest in multimillion-dollar clean energy companies. Asking me to invest in you is like ordering a breakfast burrito at a fancy steakhouse. They could, but why? They don't do that.
Yeah, I’m not a fine steak yet, I laughed, feeling like a fool for pitching a growth-stage VC used to looking at energy businesses with millions in revenues on my pre-revenue, consumer startup.
He stressed that it's not necessary. There are investors targeting your company. Not me. Find investors and pitch them.
Remember this when fundraising. Your investors aren't philanthropists who want to help entrepreneurs realize their company goals. Venture capital is a sophisticated investment strategy, and VC firm managers are industry experts. They're looking for companies that meet their investment criteria. As a young entrepreneur, I didn't grasp this, which is why I struggled to raise money. In retrospect, I probably seemed like an idiot. Hopefully, you won't after reading this.
Grace Huang
3 years ago
I sold 100 copies of my book when I had anticipated selling none.
After a decade in large tech, I know how software engineers were interviewed. I've seen outstanding engineers fail interviews because their responses were too vague.
So I wrote Nail A Coding Interview: Six-Step Mental Framework. Give candidates a mental framework for coding questions; help organizations better prepare candidates so they can calibrate traits.
Recently, I sold more than 100 books, something I never expected.
In this essay, I'll describe my publication journey, which included self-doubt and little triumphs. I hope this helps if you want to publish.
It was originally a Medium post.
How did I know to develop a coding interview book? Years ago, I posted on Medium.
Six steps to ace a coding interview Inhale. blog.devgenius.io
This story got a lot of attention and still gets a lot of daily traffic. It indicates this domain's value.
Converted the Medium article into an ebook
The Medium post contains strong bullet points, but it is missing the “flesh”. How to use these strategies in coding interviews, for example. I filled in the blanks and made a book.
I made the book cover for free. It's tidy.
Shared the article with my close friends on my social network WeChat.
I shared the book on Wechat's Friend Circle (朋友圈) after publishing it on Gumroad. Many friends enjoyed my post. It definitely triggered endorphins.
In Friend Circle, I presented a 100% off voucher. No one downloaded the book. Endorphins made my heart sink.
Several days later, my Apple Watch received a Gumroad notification. A friend downloaded it. I majored in finance, he subsequently said. My brother-in-law can get it? He downloaded it to cheer me up.
I liked him, but was disappointed that he didn't read it.
The Tipping Point: Reddit's Free Giving
I trusted the book. It's based on years of interviewing. I felt it might help job-hunting college students. If nobody wants it, it can still have value.
I posted the book's link on /r/leetcode. I told them to DM me for a free promo code.
Momentum shifted everything. Gumroad notifications kept coming when I was out with family. Following orders.
As promised, I sent DMs a promo code. Some consumers ordered without asking for a promo code. Some readers finished the book and posted reviews.
My book was finally on track.
A 5-Star Review, plus More
A reader afterwards DMed me and inquired if I had another book on system design interviewing. I said that was a good idea, but I didn't have one. If you write one, I'll be your first reader.
Later, I asked for a book review. Yes, but how? That's when I learned readers' reviews weren't easy. I built up an email pipeline to solicit customer reviews. Since then, I've gained credibility through ratings.
Learnings
I wouldn't have gotten 100 if I gave up when none of my pals downloaded. Here are some lessons.
Your friends are your allies, but they are not your clients.
Be present where your clients are
Request ratings and testimonials
gain credibility gradually
I did it, so can you. Follow me on Twitter @imgracehuang for my publishing and entrepreneurship adventure.
Rachel Greenberg
3 years ago
The Unsettling Fact VC-Backed Entrepreneurs Don't Want You to Know
What they'll do is scarier.
My acquaintance recently joined a VC-funded startup. Money, equity, and upside possibilities were nice, but he had a nagging dread.
They just secured a $40M round and are hiring like crazy to prepare for their IPO in two years. All signals pointed to this startup's (a B2B IT business in a stable industry) success, and its equity-holding workers wouldn't pass that up.
Five months after starting the work, my friend struggled with leaving. We might overlook the awful culture and long hours at the proper price. This price plus the company's fate and survival abilities sent my friend departing in an unpleasant unplanned resignation before jumping on yet another sinking ship.
This affects founders. This affects VC-backed companies (and all businesses). This affects anyone starting, buying, or running a business.
Here's the under-the-table approach that's draining VC capital, leaving staff terrified (or jobless), founders rattled, and investors upset. How to recognize, solve, and avoid it
The unsettling reality behind door #1
You can't raise money off just your looks, right? If "looks" means your founding team's expertise, then maybe. In my friend's case, the founding team's strong qualifications and track records won over investors before talking figures.
They're hardly the only startup to raise money without a profitable customer acquisition strategy. Another firm raised money for an expensive sleep product because it's eco-friendly. They were off to the races with a few keywords and key players.
Both companies, along with numerous others, elected to invest on product development first. Company A employed all the tech, then courted half their market (they’re a tech marketplace that connects two parties). Company B spent millions on R&D to create a palatable product, then flooded the world with marketing.
My friend is on Company B's financial team, and he's seen where they've gone wrong. It's terrible.
Company A (tech market): Growing? Not quite. To achieve the ambitious expansion they (and their investors) demand, they've poured much of their little capital into salespeople: Cold-calling commission and salary salesmen. Is it working? Considering attrition and companies' dwindling capital, I don't think so.
Company B (green sleep) has been hiring, digital marketing, and opening new stores like crazy. Growing expenses should result in growing revenues and a favorable return on investment; if you grow too rapidly, you may neglect to check that ROI.
Once Company A cut headcount and Company B declared “going concerned”, my friend realized both startups had the same ailment and didn't recognize it.
I shouldn't have to ask a friend to verify a company's cash reserves and profitability to spot a financial problem. It happened anyhow.
The frightening part isn't that investors were willing to invest millions without product-market fit, CAC, or LTV estimates. That's alarming, but not as scary as the fact that startups aren't understanding the problem until VC rounds have dried up.
When they question consultants if their company will be around in 6 months. It’s a red flag. How will they stretch $20M through a 2-year recession with a $3M/month burn rate and no profitability? Alarms go off.
Who's in danger?
In a word, everyone who raised money without a profitable client acquisition strategy or enough resources to ride out dry spells.
Money mismanagement and poor priorities affect every industry (like sinking all your capital into your product, team, or tech, at the expense of probing what customer acquisition really takes and looks like).
This isn't about tech, real estate, or recession-proof luxury products. Fast, cheap, easy money flows into flashy-looking teams with buzzwords, trending industries, and attractive credentials.
If these companies can't show progress or get a profitable CAC, they can't raise more money. They die if they can't raise more money (or slash headcount and find shoestring budget solutions until they solve the real problem).
The kiss of death (and how to avoid it)
If you're running a startup and think raising VC is the answer, pause and evaluate. Do you need the money now?
I'm not saying VC is terrible or has no role. Founders have used it as a Band-Aid for larger, pervasive problems. Venture cash isn't a crutch for recruiting consumers profitably; it's rocket fuel to get you what and who you need.
Pay-to-play isn't a way to throw money at the wall and hope for a return. Pay-to-play works until you run out of money, and if you haven't mastered client acquisition, your cash will diminish quickly.
How can you avoid this bottomless pit? Tips:
Understand your burn rate
Keep an eye on your growth or profitability.
Analyze each and every marketing channel and initiative.
Make lucrative customer acquisition strategies and satisfied customers your top two priorities. not brand-new products. not stellar hires. avoid the fundraising rollercoaster to save time. If you succeed in these two tasks, investors will approach you with their thirsty offers rather than the other way around, and your cash reserves won't diminish as a result.
Not as much as your grandfather
My family friend always justified expensive, impractical expenditures by saying it was only monopoly money. In business, startups, and especially with money from investors expecting a return, that's not true.
More founders could understand that there isn't always another round if they viewed VC money as their own limited pool. When the well runs dry, you must refill it or save the day.
Venture financing isn't your grandpa's money. A discerning investor has entrusted you with dry powder in the hope that you'll use it wisely, strategically, and thoughtfully. Use it well.
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Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.
Juxtathinka
3 years ago
Why Is Blockchain So Popular?
What is Bitcoin?
The blockchain is a shared, immutable ledger that helps businesses record transactions and track assets. The blockchain can track tangible assets like cars, houses, and land. Tangible assets like intellectual property can also be tracked on the blockchain.
Imagine a blockchain as a distributed database split among computer nodes. A blockchain stores data in blocks. When a block is full, it is closed and linked to the next. As a result, all subsequent information is compiled into a new block that will be added to the chain once it is filled.
The blockchain is designed so that adding a transaction requires consensus. That means a majority of network nodes must approve a transaction. No single authority can control transactions on the blockchain. The network nodes use cryptographic keys and passwords to validate each other's transactions.
Blockchain History
The blockchain was not as popular in 1991 when Stuart Haber and W. Scott Stornetta worked on it. The blocks were designed to prevent tampering with document timestamps. Stuart Haber and W. Scott Stornetta improved their work in 1992 by using Merkle trees to increase efficiency and collect more documents on a single block.
In 2004, he developed Reusable Proof of Work. This system allows users to verify token transfers in real time. Satoshi Nakamoto invented distributed blockchains in 2008. He improved the blockchain design so that new blocks could be added to the chain without being signed by trusted parties.
Satoshi Nakomoto mined the first Bitcoin block in 2009, earning 50 Bitcoins. Then, in 2013, Vitalik Buterin stated that Bitcoin needed a scripting language for building decentralized applications. He then created Ethereum, a new blockchain-based platform for decentralized apps. Since the Ethereum launch in 2015, different blockchain platforms have been launched: from Hyperledger by Linux Foundation, EOS.IO by block.one, IOTA, NEO and Monero dash blockchain. The block chain industry is still growing, and so are the businesses built on them.
Blockchain Components
The Blockchain is made up of many parts:
1. Node: The node is split into two parts: full and partial. The full node has the authority to validate, accept, or reject any transaction. Partial nodes or lightweight nodes only keep the transaction's hash value. It doesn't keep a full copy of the blockchain, so it has limited storage and processing power.
2. Ledger: A public database of information. A ledger can be public, decentralized, or distributed. Anyone on the blockchain can access the public ledger and add data to it. It allows each node to participate in every transaction. The distributed ledger copies the database to all nodes. A group of nodes can verify transactions or add data blocks to the blockchain.
3. Wallet: A blockchain wallet allows users to send, receive, store, and exchange digital assets, as well as monitor and manage their value. Wallets come in two flavors: hardware and software. Online or offline wallets exist. Online or hot wallets are used when online. Without an internet connection, offline wallets like paper and hardware wallets can store private keys and sign transactions. Wallets generally secure transactions with a private key and wallet address.
4. Nonce: A nonce is a short term for a "number used once''. It describes a unique random number. Nonces are frequently generated to modify cryptographic results. A nonce is a number that changes over time and is used to prevent value reuse. To prevent document reproduction, it can be a timestamp. A cryptographic hash function can also use it to vary input. Nonces can be used for authentication, hashing, or even electronic signatures.
5. Hash: A hash is a mathematical function that converts inputs of arbitrary length to outputs of fixed length. That is, regardless of file size, the hash will remain unique. A hash cannot generate input from hashed output, but it can identify a file. Hashes can be used to verify message integrity and authenticate data. Cryptographic hash functions add security to standard hash functions, making it difficult to decipher message contents or track senders.
Blockchain: Pros and Cons
The blockchain provides a trustworthy, secure, and trackable platform for business transactions quickly and affordably. The blockchain reduces paperwork, documentation errors, and the need for third parties to verify transactions.
Blockchain security relies on a system of unaltered transaction records with end-to-end encryption, reducing fraud and unauthorized activity. The blockchain also helps verify the authenticity of items like farm food, medicines, and even employee certification. The ability to control data gives users a level of privacy that no other platform can match.
In the case of Bitcoin, the blockchain can only handle seven transactions per second. Unlike Hyperledger and Visa, which can handle ten thousand transactions per second. Also, each participant node must verify and approve transactions, slowing down exchanges and limiting scalability.
The blockchain requires a lot of energy to run. In addition, the blockchain is not a hugely distributable system and it is destructible. The security of the block chain can be compromised by hackers; it is not completely foolproof. Also, since blockchain entries are immutable, data cannot be removed. The blockchain's high energy consumption and limited scalability reduce its efficiency.
Why Is Blockchain So Popular?
The blockchain is a technology giant. In 2018, 90% of US and European banks began exploring blockchain's potential. In 2021, 24% of companies are expected to invest $5 million to $10 million in blockchain. By the end of 2024, it is expected that corporations will spend $20 billion annually on blockchain technical services.
Blockchain is used in cryptocurrency, medical records storage, identity verification, election voting, security, agriculture, business, and many other fields. The blockchain offers a more secure, decentralized, and less corrupt system of making global payments, which cryptocurrency enthusiasts love. Users who want to save time and energy prefer it because it is faster and less bureaucratic than banking and healthcare systems.
Most organizations have jumped on the blockchain bandwagon, and for good reason: the blockchain industry has never had more potential. The launch of IBM's Blockchain Wire, Paystack, Aza Finance and Bloom are visible proof of the wonders that the blockchain has done. The blockchain's cryptocurrency segment may not be as popular in the future as the blockchain's other segments, as evidenced by the various industries where it is used. The blockchain is here to stay, and it will be discussed for a long time, not just in tech, but in many industries.
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Joseph Mavericks
3 years ago
The world's 36th richest man uses a 5-step system to get what he wants.
Ray Dalio's super-effective roadmap
Ray Dalio's $22 billion net worth ranks him 36th globally. From 1975 to 2011, he built the world's most successful hedge fund, never losing more than 4% from 1991 to 2020. (and only doing so during 3 calendar years).
Dalio describes a 5-step process in his best-selling book Principles. It's the playbook he's used to build his hedge fund, beat the markets, and face personal challenges.
This 5-step system is so valuable and well-explained that I didn't edit or change anything; I only added my own insights in the parts I found most relevant and/or relatable as a young entrepreneur. The system's overview:
Have clear goals
Identify and don’t tolerate problems
Diagnose problems to get at their root causes
Design plans that will get you around those problems
Do what is necessary to push through the plans to get results
If you follow these 5 steps in a virtuous loop, you'll almost always see results. Repeat the process for each goal you have.
1. Have clear goals
a) Prioritize: You can have almost anything, but not everything.
I started and never launched dozens of projects for 10 years because I was scattered. I opened a t-shirt store, traded algorithms, sold art on Instagram, painted skateboards, and tinkered with electronics. I decided to try blogging for 6 months to see where it took me. Still going after 3 years.
b) Don’t confuse goals with desires.
A goal inspires you to act. Unreasonable desires prevent you from achieving your goals.
c) Reconcile your goals and desires to decide what you want.
d) Don't confuse success with its trappings.
e) Never dismiss a goal as unattainable.
Always one path is best. Your perception of what's possible depends on what you know now. I never thought I'd make money writing online so quickly, and now I see a whole new horizon of business opportunities I didn't know about before.
f) Expectations create abilities.
Don't limit your abilities. More you strive, the more you'll achieve.
g) Flexibility and self-accountability can almost guarantee success.
Flexible people accept what reality or others teach them. Self-accountability is the ability to recognize your mistakes and be more creative, flexible, and determined.
h) Handling setbacks well is as important as moving forward.
Learn when to minimize losses and when to let go and move on.
2. Don't ignore problems
a) See painful problems as improvement opportunities.
Every problem, painful situation, and challenge is an opportunity. Read The Art of Happiness for more.
b) Don't avoid problems because of harsh realities.
Recognizing your weaknesses isn't the same as giving in. It's the first step in overcoming them.
c) Specify your issues.
There is no "one-size-fits-all" solution.
d) Don’t mistake a cause of a problem with the real problem.
"I can't sleep" is a cause, not a problem. "I'm underperforming" could be a problem.
e) Separate big from small problems.
You have limited time and energy, so focus on the biggest problems.
f) Don't ignore a problem.
Identifying a problem and tolerating it is like not identifying it.
3. Identify problems' root causes
a) Decide "what to do" after assessing "what is."
"A good diagnosis takes 15 to 60 minutes, depending on its accuracy and complexity. [...] Like principles, root causes recur in different situations.
b) Separate proximate and root causes.
"You can only solve problems by removing their root causes, and to do that, you must distinguish symptoms from disease."
c) Knowing someone's (or your own) personality can help you predict their behavior.
4. Design plans that will get you around the problems
a) Retrace your steps.
Analyze your past to determine your future.
b) Consider your problem a machine's output.
Consider how to improve your machine. It's a game then.
c) There are many ways to reach your goals.
Find a solution.
d) Visualize who will do what in your plan like a movie script.
Consider your movie's actors and script's turning points, then act accordingly. The game continues.
e) Document your plan so others can judge your progress.
Accountability boosts success.
f) Know that a good plan doesn't take much time.
The execution is usually the hardest part, but most people either don't have a plan or keep changing it. Don't drive while building the car. Build it first, because it'll be bumpy.
5. Do what is necessary to push through the plans to get results
a) Great planners without execution fail.
Life is won with more than just planning. Similarly, practice without talent beats talent without practice.
b) Work ethic is undervalued.
Hyper-productivity is praised in corporate America, even if it leads nowhere. To get things done, use checklists, fewer emails, and more desk time.
c) Set clear metrics to ensure plan adherence.
I've written about the OKR strategy for organizations with multiple people here. If you're on your own, I recommend the Wheel of Life approach. Both systems start with goals and tasks to achieve them. Then start executing on a realistic timeline.
If you find solutions, weaknesses don't matter.
Everyone's weak. You, me, Gates, Dalio, even Musk. Nobody will be great at all 5 steps of the system because no one can think in all the ways required. Some are good at analyzing and diagnosing but bad at executing. Some are good planners but poor communicators. Others lack self-discipline.
Stay humble and ask for help when needed. Nobody has ever succeeded 100% on their own, without anyone else's help. That's the paradox of individual success: teamwork is the only way to get there.
Most people won't have the skills to execute even the best plan. You can get missing skills in two ways:
Self-taught (time-consuming)
Others' (requires humility) light
On knowing what to do with your life
“Some people have good mental maps and know what to do on their own. Maybe they learned them or were blessed with common sense. They have more answers than others. Others are more humble and open-minded. […] Open-mindedness and mental maps are most powerful.” — Ray Dalio
I've always known what I wanted to do, so I'm lucky. I'm almost 30 and have always had trouble executing. Good thing I never stopped experimenting, but I never committed to anything long-term. I jumped between projects. I decided 3 years ago to stick to one project for at least 6 months and haven't looked back.
Maybe you're good at staying focused and executing, but you don't know what to do. Maybe you have none of these because you haven't found your purpose. Always try new projects and talk to as many people as possible. It will give you inspiration and ideas and set you up for success.
There is almost always a way to achieve a crazy goal or idea.
Enjoy the journey, whichever path you take.