More on Web3 & Crypto
The Verge
3 years ago
Bored Ape Yacht Club creator raises $450 million at a $4 billion valuation.
Yuga Labs, owner of three of the biggest NFT brands on the market, announced today a $450 million funding round. The money will be used to create a media empire based on NFTs, starting with games and a metaverse project.
The team's Otherside metaverse project is an MMORPG meant to connect the larger NFT universe. They want to create “an interoperable world” that is “gamified” and “completely decentralized,” says Wylie Aronow, aka Gordon Goner, co-founder of Bored Ape Yacht Club. “We think the real Ready Player One experience will be player run.”
Just a few weeks ago, Yuga Labs announced the acquisition of CryptoPunks and Meebits from Larva Labs. The deal brought together three of the most valuable NFT collections, giving Yuga Labs more IP to work with when developing games and metaverses. Last week, ApeCoin was launched as a cryptocurrency that will be governed independently and used in Yuga Labs properties.
Otherside will be developed by “a few different game studios,” says Yuga Labs CEO Nicole Muniz. The company plans to create development tools that allow NFTs from other projects to work inside their world. “We're welcoming everyone into a walled garden.”
However, Yuga Labs believes that other companies are approaching metaverse projects incorrectly, allowing the startup to stand out. People won't bond spending time in a virtual space with nothing going on, says Yuga Labs co-founder Greg Solano, aka Gargamel. Instead, he says, people bond when forced to work together.
In order to avoid getting smacked, Solano advises making friends. “We don't think a Zoom chat and walking around saying ‘hi' creates a deep social experience.” Yuga Labs refused to provide a release date for Otherside. Later this year, a play-to-win game is planned.
The funding round was led by Andreessen Horowitz, a major investor in the Web3 space. It previously backed OpenSea and Coinbase. Animoca Brands, Coinbase, and MoonPay are among those who have invested. Andreessen Horowitz general partner Chris Lyons will join Yuga Labs' board. The Financial Times broke the story last month.
"META IS A DOMINANT DIGITAL EXPERIENCE PROVIDER IN A DYSTOPIAN FUTURE."
This emerging [Web3] ecosystem is important to me, as it is to companies like Meta,” Chris Dixon, head of Andreessen Horowitz's crypto arm, tells The Verge. “In a dystopian future, Meta is the dominant digital experience provider, and it controls all the money and power.” (Andreessen Horowitz co-founder Marc Andreessen sits on Meta's board and invested early in Facebook.)
Yuga Labs has been profitable so far. According to a leaked pitch deck, the company made $137 million last year, primarily from its NFT brands, with a 95% profit margin. (Yuga Labs declined to comment on deck figures.)
But the company has built little so far. According to OpenSea data, it has only released one game for a limited time. That means Yuga Labs gets hundreds of millions of dollars to build a gaming company from scratch, based on a hugely lucrative art project.
Investors fund Yuga Labs based on its success. That's what they did, says Dixon, “they created a culture phenomenon”. But ultimately, the company is betting on the same thing that so many others are: that a metaverse project will be the next big thing. Now they must construct it.
Sam Hickmann
3 years ago
A quick guide to formatting your text on INTΞGRITY
[06/20/2022 update] We have now implemented a powerful text editor, but you can still use markdown.
Markdown:
Headers
SYNTAX:
# This is a heading 1
## This is a heading 2
### This is a heading 3
#### This is a heading 4
RESULT:
This is a heading 1
This is a heading 2
This is a heading 3
This is a heading 4
Emphasis
SYNTAX:
**This text will be bold**
~~Strikethrough~~
*You **can** combine them*
RESULT:
This text will be italic
This text will be bold
You can combine them
Images
SYNTAX:
RESULT:
Videos
SYNTAX:
https://www.youtube.com/watch?v=7KXGZAEWzn0
RESULT:
Links
SYNTAX:
[Int3grity website](https://www.int3grity.com)
RESULT:
Tweets
SYNTAX:
https://twitter.com/samhickmann/status/1503800505864130561
RESULT:
Blockquotes
SYNTAX:
> Human beings face ever more complex and urgent problems, and their effectiveness in dealing with these problems is a matter that is critical to the stability and continued progress of society. \- Doug Engelbart, 1961
RESULT:
Human beings face ever more complex and urgent problems, and their effectiveness in dealing with these problems is a matter that is critical to the stability and continued progress of society. - Doug Engelbart, 1961
Inline code
SYNTAX:
Text inside `backticks` on a line will be formatted like code.
RESULT:
Text inside backticks on a line will be formatted like code.
Code blocks
SYNTAX:
'''js
function fancyAlert(arg) {
if(arg) {
$.facebox({div:'#foo'})
}
}
'''
RESULT:
function fancyAlert(arg) {
if(arg) {
$.facebox({div:'#foo'})
}
}
Maths
We support LaTex to typeset math. We recommend reading the full documentation on the official website
SYNTAX:
$$[x^n+y^n=z^n]$$
RESULT:
[x^n+y^n=z^n]
Tables
SYNTAX:
| header a | header b |
| ---- | ---- |
| row 1 col 1 | row 1 col 2 |
RESULT:
| header a | header b | header c |
|---|---|---|
| row 1 col 1 | row 1 col 2 | row 1 col 3 |
Sam Hickmann
3 years ago
Nomad.xyz got exploited for $190M
Key Takeaways:
Another hack. This time was different. This is a doozy.
Why? Nomad got exploited for $190m. It was crypto's 5th-biggest hack. Ouch.
It wasn't hackers, but random folks. What happened:
A Nomad smart contract flaw was discovered. They couldn't drain the funds at once, so they tried numerous transactions. Rookie!
People noticed and copied the attack.
They just needed to discover a working transaction, substitute the other person's address with theirs, and run it.
In a two-and-a-half-hour attack, $190M was siphoned from Nomad Bridge.
Nomad is a novel approach to blockchain interoperability that leverages an optimistic mechanism to increase the security of cross-chain communication. — nomad.xyz
This hack was permissionless, therefore anyone could participate.
After the fatal blow, people fought over the scraps.
Cross-chain bridges remain a DeFi weakness and exploit target. When they collapse, it's typically total.
$190M...gobbled.
Unbacked assets are hurting Nomad-dependent chains. Moonbeam, EVMOS, and Milkomeda's TVLs dropped.
This incident is every-man-for-himself, although numerous whitehats exploited the issue...
But what triggered the feeding frenzy?
How did so many pick the bones?
After a normal upgrade in June, the bridge's Replica contract was initialized with a severe security issue. The 0x00 address was a trusted root, therefore all messages were valid by default.
After a botched first attempt (costing $350k in gas), the original attacker's exploit tx called process() without first 'proving' its validity.
The process() function executes all cross-chain messages and checks the merkle root of all messages (line 185).
The upgrade caused transactions with a'messages' value of 0 (invalid, according to old logic) to be read by default as 0x00, a trusted root, passing validation as 'proven'
Any process() calls were valid. In reality, a more sophisticated exploiter may have designed a contract to drain the whole bridge.
Copycat attackers simply copied/pasted the same process() function call using Etherscan, substituting their address.
The incident was a wild combination of crowdhacking, whitehat activities, and MEV-bot (Maximal Extractable Value) mayhem.
For example, 🍉🍉🍉. eth stole $4M from the bridge, but claims to be whitehat.
Others stood out for the wrong reasons. Repeat criminal Rari Capital (Artibrum) exploited over $3M in stablecoins, which moved to Tornado Cash.
The top three exploiters (with 95M between them) are:
$47M: 0x56D8B635A7C88Fd1104D23d632AF40c1C3Aac4e3
$40M: 0xBF293D5138a2a1BA407B43672643434C43827179
$8M: 0xB5C55f76f90Cc528B2609109Ca14d8d84593590E
Here's a list of all the exploiters:
The project conducted a Quantstamp audit in June; QSP-19 foreshadowed a similar problem.
The auditor's comments that "We feel the Nomad team misinterpreted the issue" speak to a troubling attitude towards security that the project's "Long-Term Security" plan appears to confirm:
Concerns were raised about the team's response time to a live, public exploit; the team's official acknowledgement came three hours later.
"Removing the Replica contract as owner" stopped the exploit, but it was too late to preserve the cash.
Closed blockchain systems are only as strong as their weakest link.
The Harmony network is in turmoil after its bridge was attacked and lost $100M in late June.
What's next for Nomad's ecosystems?
Moonbeam's TVL is now $135M, EVMOS's is $3M, and Milkomeda's is $20M.
Loss of confidence may do more damage than $190M.
Cross-chain infrastructure is difficult to secure in a new, experimental sector. Bridge attacks can pollute an entire ecosystem or more.
Nomadic liquidity has no permanent home, so consumers will always migrate in pursuit of the "next big thing" and get stung when attentiveness wanes.
DeFi still has easy prey...
Sources: rekt.news & The Milk Road.
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Alexander Nguyen
3 years ago
How can you bargain for $300,000 at Google?
Don’t give a number
Google pays its software engineers generously. While many of their employees are competent, they disregard a critical skill to maximize their pay.
Negotiation.
If Google employees have never negotiated, they're as helpless as anyone else.
In this piece, I'll reveal a compensation negotiation tip that will set you apart.
The Fallacy of Negotiating
How do you negotiate your salary? “Just give them a number twice the amount you really want”. - Someplace on the internet
Above is typical negotiation advice. If you ask for more than you want, the recruiter may meet you halfway.
It seems logical and great, but here's why you shouldn't follow that advice.
Haitian hostage rescue
In 1977, an official's aunt was kidnapped in Haiti. The kidnappers demanded $150,000 for the aunt's life. It seems reasonable until you realize why kidnappers want $150,000.
FBI detective and negotiator Chris Voss researched why they demanded so much.
“So they could party through the weekend”
When he realized their ransom was for partying, he offered $4,751 and a CD stereo. Criminals freed the aunt.
These thieves gave 31.57x their estimated amount and got a fraction. You shouldn't trust these thieves to negotiate your compensation.
What happened?
Negotiating your offer and Haiti
This narrative teaches you how to negotiate with a large number.
You can and will be talked down.
If a recruiter asks your wage expectation and you offer double, be ready to explain why.
If you can't justify your request, you may be offered less. The recruiter will notice and talk you down.
Reasonably,
a tiny bit more than the present amount you earn
a small premium over an alternative offer
a little less than the role's allotted amount
Real-World Illustration
Recruiter: What’s your expected salary? Candidate: (I know the role is usually $100,000) $200,000 Recruiter: How much are you compensated in your current role? Candidate: $90,000 Recruiter: We’d be excited to offer you $95,000 for your experiences for the role.
So Why Do They Even Ask?
Recruiters ask for a number to negotiate a lower one. Asking yourself limits you.
You'll rarely get more than you asked for, and your request can be lowered.
The takeaway from all of this is to never give an expected compensation.
Tell them you haven't thought about it when you applied.
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.
Aure's Notes
3 years ago
I met a man who in just 18 months scaled his startup to $100 million.
A fascinating business conversation.
This week at Web Summit, I had mentor hour.
Mentor hour connects startups with experienced entrepreneurs.
The YC-selected founder who mentored me had grown his company to $100 million in 18 months.
I had 45 minutes to question him.
I've compiled this.
Context
Founder's name is Zack.
After working in private equity, Zack opted to acquire an MBA.
Surrounded by entrepreneurs at a prominent school, he decided to become one himself.
Unsure how to proceed, he bet on two horses.
On one side, he received an offer from folks who needed help running their startup owing to lack of time. On the other hand, he had an idea for a SaaS to start himself.
He just needed to validate it.
Validating
Since Zack's proposal helped companies, he contacted university entrepreneurs for comments.
He contacted university founders.
Once he knew he'd correctly identified the problem and that people were willing to pay to address it, he started developing.
He earned $100k in a university entrepreneurship competition.
His plan was evident by then.
The other startup's founders saw his potential and granted him $400k to launch his own SaaS.
Hiring
He started looking for a tech co-founder because he lacked IT skills.
He interviewed dozens and picked the finest.
As he didn't want to wait for his program to be ready, he contacted hundreds of potential clients and got 15 letters of intent promising they'd join up when it was available.
YC accepted him by then.
He had enough positive signals to raise.
Raising
He didn't say how many VCs he called, but he indicated 50 were interested.
He jammed meetings into two weeks to generate pressure and encourage them to invest.
Seed raise: $11 million.
Selling
His objective was to contact as many entrepreneurs as possible to promote his product.
He first contacted startups by scraping CrunchBase data.
Once he had more money, he started targeting companies with ZoomInfo.
His VC urged him not to hire salespeople until he closed 50 clients himself.
He closed 100 and hired a CRO through a headhunter.
Scaling
Three persons started the business.
He primarily works in sales.
Coding the product was done by his co-founder.
Another person performing operational duties.
He regretted recruiting the third co-founder, who was ineffective (could have hired an employee instead).
He wanted his company to be big, so he hired two young marketing people from a competing company.
After validating several marketing channels, he chose PR.
$100 Million and under
He developed a sales team and now employs 30 individuals.
He raised a $100 million Series A.
Additionally, he stated
He’s been rejected a lot. Like, a lot.
Two great books to read: Steve Jobs by Isaacson, and Why Startups Fail by Tom Eisenmann.
The best skill to learn for non-tech founders is “telling stories”, which means sales. A founder’s main job is to convince: co-founders, employees, investors, and customers. Learn code, or learn sales.
Conclusion
I often read about these stories but hardly take them seriously.
Zack was amazing.
Three things about him stand out:
His vision. He possessed a certain amount of fire.
His vitality. The man had a lot of enthusiasm and spoke quickly and decisively. He takes no chances and pushes the envelope in all he does.
His Rolex.
He didn't do all this in 18 months.
Not really.
He couldn't launch his company without private equity experience.
These accounts disregard entrepreneurs' original knowledge.
Hormozi will tell you how he founded Gym Launch, but he won't tell you how he had a gym first, how he worked at uni to pay for his gym, or how he went to the gym and learnt about fitness, which gave him the idea to open his own.
Nobody knows nothing. If you scale quickly, it's probable because you gained information early.
Lincoln said, "Give me six hours to chop down a tree, and I'll spend four sharpening the axe."
Sharper axes cut trees faster.