More on Personal Growth
Matthew Royse
1 year ago
These 10 phrases are unprofessional at work.
Successful workers don't talk this way.
"I know it's unprofessional, but I can't stop." — Author Sandy Hall
Do you realize your unprofessionalism? Do you care? Self-awareness?
Everyone can improve their unprofessionalism. Some workplace phrases and words shouldn't be said.
People often say out loud what they're thinking. They show insecurity, incompetence, and disrespect.
"Think before you speak," goes the saying.
Some of these phrases are "okay" in certain situations, but you'll lose colleagues' respect if you use them often.
Your word choice. Your tone. Your intentions. They matter.
Choose your words carefully to build work relationships and earn peer respect. You should build positive relationships with coworkers and clients.
These 10 phrases are unprofessional.
1. That Meeting Really Sucked
Wow! Were you there? You should be responsible if you attended. You can influence every conversation.
Alternatives
Improve the meeting instead of complaining afterward. Make it more meaningful and productive.
2. Not Sure if You Saw My Last Email
Referencing a previous email irritates people. Email follow-up can be difficult. Most people get tons of emails a day, so it may have been buried, forgotten, or low priority.
Alternatives
It's okay to follow up, but be direct, short, and let the recipient "save face"
3. Any Phrase About Sex, Politics, and Religion
Discussing sex, politics, and religion at work is foolish. If you discuss these topics, you could face harassment lawsuits.
Alternatives
Keep quiet about these contentious issues. Don't touch them.
4. I Know What I’m Talking About
Adding this won't persuade others. Research, facts, and topic mastery are key to persuasion. If you're knowledgeable, you don't need to say this.
Alternatives
Please don’t say it at all. Justify your knowledge.
5. Per Our Conversation
This phrase sounds like legal language. You seem to be documenting something legally. Cold, stern, and distant. "As discussed" sounds inauthentic.
Alternatives
It was great talking with you earlier; here's what I said.
6. Curse-Word Phrases
Swearing at work is unprofessional. You never know who's listening, so be careful. A child may be at work or on a Zoom or Teams call. Workplace cursing is unacceptable.
Alternatives
Avoid adult-only words.
7. I Hope This Email Finds You Well
This is a unique way to wish someone well. This phrase isn't as sincere as the traditional one. When you talk about the email, you're impersonal.
Alternatives
Genuinely care for others.
8. I Am Really Stressed
Happy, strong, stress-managing coworkers are valued. Manage your own stress. Exercise, sleep, and eat better.
Alternatives
Everyone has stress, so manage it. Don't talk about your stress.
9. I Have Too Much to Do
You seem incompetent. People think you can't say "no" or have poor time management. If you use this phrase, you're telling others you may need to change careers.
Alternatives
Don't complain about your workload; just manage it.
10. Bad Closing Salutations
"Warmly," "best," "regards," and "warm wishes" are common email closings. This conclusion sounds impersonal. Why use "warmly" for finance's payment status?
Alternatives
Personalize the closing greeting to the message and recipient. Use "see you tomorrow" or "talk soon" as closings.
Bringing It All Together
These 10 phrases are unprofessional at work. That meeting sucked, not sure if you saw my last email, and sex, politics, and religion phrases.
Also, "I know what I'm talking about" and any curse words. Also, avoid phrases like I hope this email finds you well, I'm stressed, and I have too much to do.
Successful workers communicate positively and foster professionalism. Don't waste chances to build strong work relationships by being unprofessional.
“Unprofessionalism damages the business reputation and tarnishes the trust of society.” — Pearl Zhu, an American author
This post is a summary. Read full article here
Zuzanna Sieja
1 year 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.
Darius Foroux
6 months ago
My financial life was changed by a single, straightforward mental model.
Prioritize big-ticket purchases
I've made several spending blunders. I get sick thinking about how much money I spent.
My financial mental model was poor back then.
Stoicism and mindfulness keep me from attaching to those feelings. It still hurts.
Until four or five years ago, I bought a new winter jacket every year.
Ten years ago, I spent twice as much. Now that I have a fantastic, warm winter parka, I don't even consider acquiring another one. No more spending. I'm not looking for jackets either.
Saving time and money by spending well is my thinking paradigm.
The philosophy is expressed in most languages. Cheap is expensive in the Netherlands. This applies beyond shopping.
In this essay, I will offer three examples of how this mental paradigm transformed my financial life.
Publishing books
In 2015, I presented and positioned my first book poorly.
I called the book Huge Life Success and made a funny Canva cover in 30 minutes. This:
That looks nothing like my present books. No logo or style. The book felt amateurish.
The book started bothering me a few weeks after publication. The advice was good, but it didn't appear professional. I studied the book business extensively.
I created a style for all my designs. Branding. Win Your Inner Wars was reissued a year later.
Title, cover, and description changed. Rearranging the chapters improved readability.
Seven years later, the book sells hundreds of copies a month. That taught me a lot.
Rushing to finish a project is enticing. Send it and move forward.
Avoid rushing everything. Relax. Develop your projects. Perform well. Perform the job well.
My first novel was underfunded and underworked. A bad book arrived. I then invested time and money in writing the greatest book I could.
That book still sells.
Traveling
I hate travel. Airports, flights, trains, and lines irritate me.
But, I enjoy traveling to beautiful areas.
I do it strangely. I make up travel rules. I never go to airports in summer. I hate being near airports on holidays. Unworthy.
No vacation packages for me. Those airline packages with a flight, shuttle, and hotel. I've had enough.
I try to avoid crowds and popular spots. July Paris? Nuts and bolts, please. Christmas in NYC? No, please keep me sane.
I fly business class behind. I accept upgrades upon check-in. I prefer driving. I drove from the Netherlands to southern Spain.
Thankfully, no lines. What if travel costs more? Thus? I enjoy it from the start. I start traveling then.
I rarely travel since I'm so difficult. One great excursion beats several average ones.
Personal effectiveness
New apps, tools, and strategies intrigue most productivity professionals.
No.
I researched years ago. I spent years investigating productivity in university.
I bought books, courses, applications, and tools. It was expensive and time-consuming.
Im finished. Productivity no longer costs me time or money. OK. I worked on it once and now follow my strategy.
I avoid new programs and systems. My stuff works. Why change winners?
Spending wisely saves time and money.
Spending wisely means spending once. Many people ignore productivity. It's understudied. No classes.
Some assume reading a few articles or a book is enough. Productivity is personal. You need a personal system.
Time invested is one-time. You can trust your system for life once you find it.
Concentrate on the expensive choices.
Life's short. Saving money quickly is enticing.
Spend less on groceries today. True. That won't fix your finances.
Adopt a lifestyle that makes you affluent over time. Consider major choices.
Are they causing long-term poverty? Are you richer?
Leasing cars comes to mind. The automobile costs a fortune today. The premium could accomplish a million nice things.
Focusing on important decisions makes life easier. Consider your future. You want to improve next year.
You might also like
Vitalik
1 year ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
Alex Mathers
6 months ago
How to Produce Enough for People to Not Neglect You
Internet's fantastic, right?
We've never had a better way to share our creativity.
I can now draw on my iPad and tweet or Instagram it to thousands. I may get some likes.
With such a great, free tool, you're not alone.
Millions more bright-eyed artists are sharing their work online.
The issue is getting innovative work noticed, not sharing it.
In a world where creators want attention, attention is valuable.
We build for attention.
Attention helps us establish a following, make money, get notoriety, and make a difference.
Most of us require attention to stay sane while creating wonderful things.
I know how hard it is to work hard and receive little views.
How do we receive more attention, more often, in a sea of talent?
Advertising and celebrity endorsements are options. These may work temporarily.
To attract true, organic, and long-term attention, you must create in high quality, high volume, and consistency.
Adapting Steve Martin's Be so amazing, they can't ignore you (with a mention to Dan Norris in his great book Create or Hate for the reminder)
Create a lot.
Eventually, your effort will gain traction.
Traction shows your work's influence.
Traction is when your product sells more. Traction is exponential user growth. Your work is shared more.
No matter how good your work is, it will always have minimal impact on the world.
Your work can eventually dent or puncture. Daily, people work to dent.
To achieve this tipping point, you must consistently produce exceptional work.
Expect traction after hundreds of outputs.
Dilbert creator Scott Adams says repetition persuades. If you don't stop, you can persuade practically anyone with anything.
Volume lends believability. So make more.
I worked as an illustrator for at least a year and a half without any recognition. After 150 illustrations on iStockphoto, my work started selling.
With 350 illustrations on iStock, I started getting decent client commissions.
Producing often will improve your craft and draw attention.
It's the only way to succeed. More creation means better results and greater attention.
Austin Kleon says you can improve your skill in relative anonymity before you become famous. Before obtaining traction, generate a lot and become excellent.
Most artists, even excellent ones, don't create consistently enough to get traction.
It may hurt. For makers who don't love and flow with their work, it's extremely difficult.
Your work must bring you to life.
To generate so much that others can't ignore you, decide what you'll accomplish every day (or most days).
Commit and be patient.
Prepare for zero-traction.
Anticipating this will help you persevere and create.
My online guru Grant Cardone says: Anything worth doing is worth doing every day.
Do.
Koji Mochizuki
1 year ago
How to Launch an NFT Project by Yourself
Creating 10,000 auto-generated artworks, deploying a smart contract to the Ethereum / Polygon blockchain, setting up some tools, etc.
There is so much to do from launching to running an NFT project. Creating parts for artworks, generating 10,000 unique artworks and metadata, creating a smart contract and deploying it to a blockchain network, creating a website, creating a Twitter account, setting up a Discord server, setting up an OpenSea collection. In addition, you need to have MetaMask installed in your browser and have some ETH / MATIC. Did you get tired of doing all this? Don’t worry, once you know what you need to do, all you have to do is do it one by one.
To be honest, it’s best to run an NFT project in a team of three or more, including artists, developers, and marketers. However, depending on your motivation, you can do it by yourself. Some people might come later to offer help with your project. The most important thing is to take a step as soon as possible.
Creating Parts for Artworks
There are lots of free/paid software for drawing, but after all, I think Adobe Illustrator or Photoshop is the best. The images of Skulls In Love are a composite of 48x48 pixel parts created using Photoshop.
The most important thing in creating parts for generative art is to repeatedly test what your artworks will look like after each layer has been combined. The generated artworks should not be too unnatural.
How Many Parts Should You Create?
Are you wondering how many parts you should create to avoid duplication as much as possible when generating your artworks? My friend Stephane, a developer, has created a great tool to help with that.
Generating 10,000 Unique Artworks and Metadata
I highly recommend using the HashLips Art Engine to generate your artworks and metadata. Perhaps there is no better artworks generation tool at the moment.
GitHub: https://github.com/HashLips/hashlips_art_engine
YouTube:
Storing Artworks and Metadata
Ideally, the generated artworks and metadata should be stored on-chain, but if you want to store them off-chain, you should use IPFS. Do not store in centralized storage. This is because data will be lost if the server goes down or if the company goes down. On the other hand, IPFS is a more secure way to find data because it utilizes a distributed, decentralized system.
Storing to IPFS is easy with Pinata, NFT.Storage, and so on. The Skulls In Love uses Pinata. It’s very easy to use, just upload the folder containing your artworks.
Creating and Deploying a Smart Contract
You don’t have to create a smart contract from scratch. There are many great NFT projects, many of which publish their contract source code on Etherscan / PolygonScan. You can choose the contract you like and reuse it. Of course, that requires some knowledge of Solidity, but it depends on your efforts. If you don’t know which contract to choose, use the HashLips smart contract. It’s very simple, but it has almost all the functions you need.
GitHub: https://github.com/HashLips/hashlips_nft_contract
Note: Later on, you may want to change the cost value. You can change it on Remix or Etherscan / PolygonScan. But in this case, enter the Wei value instead of the Ether value. For example, if you want to sell for 1 MATIC, you have to enter “1000000000000000000”. If you set this value to “1”, you will have a nightmare. I recommend using Simple Unit Converter as a tool to calculate the Wei value.
Creating a Website
The website here is not just a static site to showcase your project, it’s a so-called dApp that allows you to access your smart contract and mint NFTs. In fact, this level of dApp is not too difficult for anyone who has ever created a website. Because the ethers.js / web3.js libraries make it easy to interact with your smart contract. There’s also no problem connecting wallets, as MetaMask has great documentation.
The Skulls In Love uses a simple, fast, and modern dApp that I built from scratch using Next.js. It is published on GitHub, so feel free to use it.
Why do people mint NFTs on a website?
Ethereum’s gas fees are high, so if you mint all your NFTs, there will be a huge initial cost. So it makes sense to get the buyers to help with the gas fees for minting.
What about Polygon? Polygon’s gas fees are super cheap, so even if you mint 10,000 NFTs, it’s not a big deal. But we don’t do that. Since NFT projects are a kind of game, it involves the fun of not knowing what will come out after minting.
Creating a Twitter Account
I highly recommend creating a Twitter account. Twitter is an indispensable tool for announcing giveaways and reaching more people. It’s better to announce your project and your artworks little by little, 1–2 weeks before launching your project.
Creating and Setting Up a Discord Server
I highly recommend creating a Discord server as well as a Twitter account. The Discord server is a community and its home. Fans of your NFT project will want to join your community and interact with many other members. So, carefully create each channel on your Discord server to make it a cozy place for your community members.
If you are unfamiliar with Discord, you may be particularly confused by the following:
What bots should I use?
How should I set roles and permissions?
But don’t worry. There are lots of great YouTube videos and blog posts about these.
It’s also a good idea to join the Discord servers of some NFT projects and see how they’re made. Our Discord server is so simple that even beginners will find it easy to understand. Please join us and see it!
Note: First, create a test account and a test server to make sure your bots and permissions work properly. It is better to verify the behavior on the test server before setting up your production server.
UPDATED: As your Discord server grows, you cannot manage it on your own. In this case, you will be hiring several moderators, but choose carefully before hiring. And don’t give them important role permissions right after hiring. Initially, the same permissions as other members are sufficient. After a while, you can add permissions as needed, such as kicking/banning, using the “@every” tag, and adding roles. Again, don’t immediately give significant permissions to your Mod role. Your server can be messed up by fake moderators.
Setting Up Your OpenSea Collection
Before you start selling your NFTs, you need to reserve some for airdrops, giveaways, staff, and more. It’s up to you whether it’s 100, 500, or how many.
After minting some of your NFTs, your account and collection should have been created in OpenSea. Go to OpenSea, connect to your wallet, and set up your collection. Just set your logo, banner image, description, links, royalties, and more. It’s not that difficult.
Promoting Your Project
After all, promotion is the most important thing. In fact, almost every successful NFT project spends a lot of time and effort on it.
In addition to Twitter and Discord, it’s even better to use Instagram, Reddit, and Medium. Also, register your project in NFTCalendar and DISBOARD
DISBOARD is the public Discord server listing community.
About Promoters
You’ll probably get lots of contacts from promoters on your Discord, Twitter, Instagram, and more. But most of them are scams, so don’t pay right away. If you have a promoter that looks attractive to you, be sure to check the promoter’s social media accounts or website to see who he/she is. They basically charge in dollars. The amount they charge isn’t cheap, but promoters with lots of followers may have some temporary effect on your project. Some promoters accept 50% prepaid and 50% postpaid. If you can afford it, it might be worth a try. I never ask them, though.
When Should the Promotion Activities Start?
You may be worried that if you promote your project before it starts, someone will copy your project (artworks). It is true that some projects have actually suffered such damage. I don’t have a clear answer to this question right now, but:
- Do not publish all the information about your project too early
- The information should be released little by little
- Creating artworks that no one can easily copy
I think these are important.
If anyone has a good idea, please share it!
About Giveaways
When hosting giveaways, you’ll probably use multiple social media platforms. You may want to grow your Discord server faster. But if joining the Discord server is included in the giveaway requirements, some people hate it. I recommend holding giveaways for each platform. On Twitter and Reddit, you should just add the words “Discord members-only giveaway is being held now! Please join us if you like!”.
If you want to easily pick a giveaway winner in your browser, I recommend Twitter Picker.
Precautions for Distributing Free NFTs
If you want to increase your Twitter followers and Discord members, you can actually get a lot of people by holding events such as giveaways and invite contests. However, distributing many free NFTs at once can be dangerous. Some people who want free NFTs, as soon as they get a free one, sell it at a very low price on marketplaces such as OpenSea. They don’t care about your project and are only thinking about replacing their own “free” NFTs with Ethereum. The lower the floor price of your NFTs, the lower the value of your NFTs (project). Try to think of ways to get people to “buy” your NFTs as much as possible.
Ethereum vs. Polygon
Even though Ethereum has high gas fees, NFT projects on the Ethereum network are still mainstream and popular. On the other hand, Polygon has very low gas fees and fast transaction processing, but NFT projects on the Polygon network are not very popular.
Why? There are several reasons, but the biggest one is that it’s a lot of work to get MATIC (on Polygon blockchain, use MATIC instead of ETH) ready to use. Simply put, you need to bridge your tokens to the Polygon chain. So people need to do this first before minting your NFTs on your website. It may not be a big deal for those who are familiar with crypto and blockchain, but it may be complicated for those who are not. I hope that the tedious work will be simplified in the near future.
If you are confident that your NFTs will be purchased even if they are expensive, or if the total supply of your NFTs is low, you may choose Ethereum. If you just want to save money, you should choose Polygon. Keep in mind that gas fees are incurred not only when minting, but also when performing some of your smart contract functions and when transferring your NFTs.
If I were to launch a new NFT project, I would probably choose Ethereum or Solana.
Conclusion
Some people may want to start an NFT project to make money, but don’t forget to enjoy your own project. Several months ago, I was playing with creating generative art by imitating the CryptoPunks. I found out that auto-generated artworks would be more interesting than I had imagined, and since then I’ve been completely absorbed in generative art.
This is one of the Skulls In Love artworks:
This character wears a cowboy hat, black slim sunglasses, and a kimono. If anyone looks like this, I can’t help laughing!
The Skulls In Love NFTs can be minted for a small amount of MATIC on the official website. Please give it a try to see what kind of unique characters will appear 💀💖
Thank you for reading to the end. I hope this article will be helpful to those who want to launch an NFT project in the future ✨