More on Personal Growth

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.

Khyati Jain
3 years ago
By Engaging in these 5 Duplicitous Daily Activities, You Rapidly Kill Your Brain Cells
No, it’s not smartphones, overeating, or sugar.
Everyday practices affect brain health. Good brain practices increase memory and cognition.
Bad behaviors increase stress, which destroys brain cells.
Bad behaviors can reverse evolution and diminish the brain. So, avoid these practices for brain health.
1. The silent assassin
Introverts appreciated quarantine.
Before the pandemic, they needed excuses to remain home; thereafter, they had enough.
I am an introvert, and I didn’t hate quarantine. There are billions of people like me who avoid people.
Social relationships are important for brain health. Social anxiety harms your brain.
Antisocial behavior changes brains. It lowers IQ and increases drug abuse risk.
What you can do is as follows:
Make a daily commitment to engage in conversation with a stranger. Who knows, you might turn out to be your lone mate.
Get outside for at least 30 minutes each day.
Shop for food locally rather than online.
Make a call to a friend you haven't spoken to in a while.
2. Try not to rush things.
People love hustle culture. This economy requires a side gig to save money.
Long hours reduce brain health. A side gig is great until you burn out.
Work ages your wallet and intellect. Overworked brains age faster and lose cognitive function.
Working longer hours can help you make extra money, but it can harm your brain.
Side hustle but don't overwork.
What you can do is as follows:
Decide what hour you are not permitted to work after.
Three hours prior to night, turn off your laptop.
Put down your phone and work.
Assign due dates to each task.
3. Location is everything!
The environment may cause brain fog. High pollution can cause brain damage.
Air pollution raises Alzheimer's risk. Air pollution causes cognitive and behavioral abnormalities.
Polluted air can trigger early development of incurable brain illnesses, not simply lung harm.
Your city's air quality is uncontrollable. You may take steps to improve air quality.
In Delhi, schools and colleges are closed to protect pupils from polluted air. So I've adapted.
What you can do is as follows:
To keep your mind healthy and young, make an investment in a high-quality air purifier.
Enclose your windows during the day.
Use a N95 mask every day.
4. Don't skip this meal.
Fasting intermittently is trendy. Delaying breakfast to finish fasting is frequent.
Some skip breakfast and have a hefty lunch instead.
Skipping breakfast might affect memory and focus. Skipping breakfast causes low cognition, delayed responsiveness, and irritation.
Breakfast affects mood and productivity.
Intermittent fasting doesn't prevent healthy breakfasts.
What you can do is as follows:
Try to fast for 14 hours, then break it with a nutritious breakfast.
So that you can have breakfast in the morning, eat dinner early.
Make sure your breakfast is heavy in fiber and protein.
5. The quickest way to damage the health of your brain
Brain health requires water. 1% dehydration can reduce cognitive ability by 5%.
Cerebral fog and mental clarity might result from 2% brain dehydration. Dehydration shrinks brain cells.
Dehydration causes midday slumps and unproductivity. Water improves work performance.
Dehydration can harm your brain, so drink water throughout the day.
What you can do is as follows:
Always keep a water bottle at your desk.
Enjoy some tasty herbal teas.
With a big glass of water, begin your day.
Bring your own water bottle when you travel.
Conclusion
Bad habits can harm brain health. Low cognition reduces focus and productivity.
Unproductive work leads to procrastination, failure, and low self-esteem.
Avoid these harmful habits to optimize brain health and function.

Tim Denning
2 years ago
In this recession, according to Mark Cuban, you need to outwork everyone
Here’s why that’s baloney
Mark Cuban popularized entrepreneurship.
Shark Tank (which made Mark famous) made starting a business glamorous to attract more entrepreneurs. First off
This isn't an anti-billionaire rant.
Mark Cuban has done excellent. He's a smart, principled businessman. I enjoy his Web3 work. But Mark's work and productivity theories are absurd.
You don't need to outwork everyone in this recession to live well.
You won't be able to outwork me.
Yuck! Mark's words made me gag.
Why do boys think working is a football game where the winner wins a Super Bowl trophy? To outwork you.
Hard work doesn't equal intelligence.
Highly clever professionals spend 4 hours a day in a flow state, then go home to relax with family.
If you don't put forth the effort, someone else will.
- Mark.
He'll burn out. He's delusional and doesn't understand productivity. Boredom or disconnection spark our best thoughts.
TikTok outlaws boredom.
In a spare minute, we check our phones because we can't stand stillness.
All this work p*rn makes things worse. When is it okay to feel again? Because I can’t feel anything when I’m drowning in work and haven’t had a holiday in 2 years.
Your rivals are actively attempting to undermine you.
Ohhh please Mark…seriously.
This isn't a Tom Hanks war film. Relax. Not everyone is a rival. Only yourself is your competitor. To survive the recession, be better than a year ago.
If you get rich, great. If not, there's more to life than Lambos and angel investments.
Some want to relax and enjoy life. No competition. We witness people with lives trying to endure the recession and record-high prices.
This fictitious rival worsens life and work.
If you are truly talented, you will motivate others to work more diligently and effectively.
No Mark. Soz.
If you're a good leader, you won't brag about working hard and treating others like cogs. Treat them like humans. You'll have EQ.
Silly statements like this are caused by an out-of-control ego. No longer watch Shark Tank.
Ego over humanity.
Good leaders will urge people to keep together during the recession. Good leaders support those who are laid off and need a reference.
Not harder, quicker, better. That created my mental health problems 10 years ago.
Truth: we want to work less.
The promotion of entrepreneurship is ludicrous.
Marvel superheroes. Seriously, relax Max.
I used to write about entrepreneurship, then I quit. Many WeWork Adam Neumanns. Carelessness.
I now utilize the side hustle title when writing about online company or entrepreneurship. Humanizes.
Stop glorifying. Thinking we'll all be Elon Musks who send rockets to Mars is delusional. Most of us won't create companies employing hundreds.
OK.
The true epidemic is glorification. fewer selfies Little birdy needs less bank account screenshots. Less Uber talk.
We're exhausted.
Fun, ego-free business can transform the world. Take a relax pill.
Work as if someone were attempting to take everything from you.
I've seen people lose everything.
Myself included. My 20s startup failed. I was almost bankrupt. I thought I'd never recover. Nope.
Best thing ever.
Losing everything reveals your true self. Unintelligent entrepreneur egos perish instantly. Regaining humility revitalizes relationships.
Money's significance shifts. Stop chasing it like a puppy with a bone.
Fearing loss is unfounded.
Here is a more effective approach than outworking nobody.
(You'll thrive in the recession and become wealthy.)
Smarter work
Overworking is donkey work.
You don't want to be a career-long overworker. Instead than wasting time, write down what you do. List tasks and processes.
Keep doing/outsource the list. Step-by-step each task. Continuously systematize.
Then recruit a digital employee like Zapier or a virtual assistant in the same country.
Intelligent, not difficult.
If your big break could burn in hell, diversify like it will.
People err by focusing on one chance.
Chances can vanish. All-in risky. Instead of working like a Mark Cuban groupie, diversify your income.
If you're employed, your customer is your employer.
Sell the same abilities twice and add 2-3 contract clients. Reduce your hours at your main job and take on more clients.
Leave brand loyalty behind
Mark desires his employees' worship.
That's stupid. When times are bad, layoffs multiply. The problem is the false belief that companies care. No. A business maximizes profit and pays you the least.
To care or overpay is anti-capitalist (that run the world). Be honest.
I was a banker. Then the bat virus hit and jobs disappeared faster than I urinate after a night of drinking.
Start being disloyal now since your company will cheerfully replace you with a better applicant. Meet recruiters and hiring managers on LinkedIn. Whenever something goes wrong at work, act.
Loyalty to self and family. Nobody.
Outwork this instead
Mark doesn't suggest outworking inflation instead of people.
Inflation erodes your time on earth. If you ignore inflation, you'll work harder for less pay every minute.
Financial literacy beats inflation.
Get a side job and earn money online
So you can stop outworking everyone.
Internet leverages time. Same effort today yields exponential results later. There are still whole places not online.
Instead of working forever, generate money online.
Final Words
Overworking is stupid. Don't listen to wealthy football jocks.
Work isn't everything. Prioritize diversification, internet income streams, boredom, and financial knowledge throughout the recession.
That’s how to get wealthy rather than burnout-rich.
You might also like

Jano le Roux
3 years ago
Apple Quietly Introduces A Revolutionary Savings Account That Kills Banks
Would you abandon your bank for Apple?
Banks are struggling.
not as a result of inflation
not due to the economic downturn.
not due to the conflict in Ukraine.
But because they’re underestimating Apple.
Slowly but surely, Apple is looking more like a bank.
An easy new savings account like Apple
Apple has a new savings account.
Apple says Apple Card users may set up and manage savings straight in Wallet.
No more charges
Colorfully high yields
With no minimum balance
No minimal down payments
Most consumer-facing banks will have to match Apple's offer or suffer disruption.
Users may set it up from their iPhones without traveling to a bank or filling out paperwork.
It’s built into the iPhone in your pocket.
So now more waiting for slow approval processes.
Once the savings account is set up, Apple will automatically transfer all future Daily Cash into it. Users may also add these cash to an Apple Cash card in their Apple Wallet app and adjust where Daily Cash is paid at any time.
Apple Pay and Apple Wallet VP Jennifer Bailey:
Savings enables Apple Card users to grow their Daily Cash rewards over time, while also saving for the future.
Bailey says Savings adds value to Apple Card's Daily Cash benefit and offers another easy-to-use tool to help people lead healthier financial lives.
Transfer money from a linked bank account or Apple Cash to a Savings account. Users can withdraw monies to a connected bank account or Apple Cash card without costs.
Once set up, Apple Card customers can track their earnings via Wallet's Savings dashboard. This dashboard shows their account balance and interest.
This product targets younger people as the easiest way to start a savings account on the iPhone.
Why would a Gen Z account holder travel to the bank if their iPhone could be their bank?
Using this concept, Apple will transform the way we think about banking by 2030.
Two other nightmares keep bankers awake at night
Apple revealed two new features in early 2022 that banks and payment gateways hated.
Tap to Pay with Apple
Late Apple Pay
They startled the industry.
Tap To Pay converts iPhones into mobile POS card readers. Apple Pay Later is pushing the BNPL business in a consumer-friendly direction, hopefully ending dodgy lending practices.
Tap to Pay with Apple
iPhone POS
Millions of US merchants, from tiny shops to huge establishments, will be able to accept Apple Pay, contactless credit and debit cards, and other digital wallets with a tap.
No hardware or payment terminal is needed.
Revolutionary!
Stripe has previously launched this feature.
Tap to Pay on iPhone will provide companies with a secure, private, and quick option to take contactless payments and unleash new checkout experiences, said Bailey.
Apple's solution is ingenious. Brilliant!
Bailey says that payment platforms, app developers, and payment networks are making it easier than ever for businesses of all sizes to accept contactless payments and thrive.
I admire that Apple is offering this up to third-party services instead of closing off other functionalities.
Slow POS terminals, farewell.
Late Apple Pay
Pay Apple later.
Apple Pay Later enables US consumers split Apple Pay purchases into four equal payments over six weeks with no interest or fees.
The Apple ecosystem integration makes this BNPL scheme unique. Nonstick. No dumb forms.
Frictionless.
Just double-tap the button.
Apple Pay Later was designed with users' financial well-being in mind. Apple makes it easy to use, track, and pay back Apple Pay Later from Wallet.
Apple Pay Later can be signed up in Wallet or when using Apple Pay. Apple Pay Later can be used online or in an app that takes Apple Pay and leverages the Mastercard network.
Apple Pay Order Tracking helps consumers access detailed receipts and order tracking in Wallet for Apple Pay purchases at participating stores.
Bad BNPL suppliers, goodbye.
Most bankers will be caught in Apple's eye playing mini golf in high-rise offices.
The big problem:
Banks still think about features and big numbers just like other smartphone makers did not too long ago.
Apple thinks about effortlessness, seamlessness, and frictionlessness that just work through integrated hardware and software.
Let me know what you think Apple’s next power moves in the banking industry could be.

TheRedKnight
3 years ago
Say goodbye to Ponzi yields - A new era of decentralized perpetual
Decentralized perpetual may be the next crypto market boom; with tons of perpetual popping up, let's look at two protocols that offer organic, non-inflationary yields.
Decentralized derivatives exchanges' market share has increased tenfold in a year, but it's still 2% of CEXs'. DEXs have a long way to go before they can compete with centralized exchanges in speed, liquidity, user experience, and composability.
I'll cover gains.trade and GMX protocol in Polygon, Avalanche, and Arbitrum. Both protocols support leveraged perpetual crypto, stock, and Forex trading.
Why these protocols?
Decentralized GMX Gains protocol
Organic yield: path to sustainability
I've never trusted Defi's non-organic yields. Example: XYZ protocol. 20–75% of tokens may be set aside as farming rewards to provide liquidity, according to tokenomics.
Say you provide ETH-USDC liquidity. They advertise a 50% APR reward for this pair, 10% from trading fees and 40% from farming rewards. Only 10% is real, the rest is "Ponzi." The "real" reward is in protocol tokens.
Why keep this token? Governance voting or staking rewards are promoted services.
Most liquidity providers expect compensation for unused tokens. Basic psychological principles then? — Profit.
Nobody wants governance tokens. How many out of 100 care about the protocol's direction and will vote?
Staking increases your token's value. Currently, they're mostly non-liquid. If the protocol is compromised, you can't withdraw funds. Most people are sceptical of staking because of this.
"Free tokens," lack of use cases, and skepticism lead to tokens moving south. No farming reward protocols have lasted.
It may have shown strength in a bull market, but what about a bear market?
What is decentralized perpetual?
A perpetual contract is a type of futures contract that doesn't expire. So one can hold a position forever.
You can buy/sell any leveraged instruments (Long-Short) without expiration.
In centralized exchanges like Binance and coinbase, fees and revenue (liquidation) go to the exchanges, not users.
Users can provide liquidity that traders can use to leverage trade, and the revenue goes to liquidity providers.
Gains.trade and GMX protocol are perpetual trading platforms with a non-inflationary organic yield for liquidity providers.
GMX protocol
GMX is an Arbitrum and Avax protocol that rewards in ETH and Avax. GLP uses a fast oracle to borrow the "true price" from other trading venues, unlike a traditional AMM.
GLP and GMX are protocol tokens. GLP is used for leveraged trading, swapping, etc.
GLP is a basket of tokens, including ETH, BTC, AVAX, stablecoins, and UNI, LINK, and Stablecoins.
GLP composition on arbitrum
GLP composition on Avalanche
GLP token rebalances based on usage, providing liquidity without loss.
Protocol "runs" on Staking GLP. Depending on their chain, the protocol will reward users with ETH or AVAX. Current rewards are 22 percent (15.71 percent in ETH and the rest in escrowed GMX) and 21 percent (15.72 percent in AVAX and the rest in escrowed GMX). escGMX and ETH/AVAX percentages fluctuate.
Where is the yield coming from?
Swap fees, perpetual interest, and liquidations generate yield. 70% of fees go to GLP stakers, 30% to GMX. Organic yields aren't paid in inflationary farm tokens.
Escrowed GMX is vested GMX that unlocks in 365 days. To fully unlock GMX, you must farm the Escrowed GMX token for 365 days. That means less selling pressure for the GMX token.
GMX's status
These are the fees in Arbitrum in the past 11 months by GMX.
GMX works like a casino, which increases fees. Most fees come from Margin trading, which means most traders lose money; this money goes to the casino, or GLP stakers.
Strategies
My personal strategy is to DCA into GLP when markets hit bottom and stake it; GLP will be less volatile with extra staking rewards.
GLP YoY return vs. naked buying
Let's say I invested $10,000 in BTC, AVAX, and ETH in January.
BTC price: 47665$
ETH price: 3760$
AVAX price: $145
Current prices
BTC $21,000 (Down 56 percent )
ETH $1233 (Down 67.2 percent )
AVAX $20.36 (Down 85.95 percent )
Your $10,000 investment is now worth around $3,000.
How about GLP? My initial investment is 50% stables and 50% other assets ( Assuming the coverage ratio for stables is 50 percent at that time)
Without GLP staking yield, your value is $6500.
Let's assume the average APR for GLP staking is 23%, or $1500. So 8000$ total. It's 50% safer than holding naked assets in a bear market.
In a bull market, naked assets are preferable to GLP.
Short farming using GLP
Simple GLP short farming.
You use a stable asset as collateral to borrow AVAX. Sell it and buy GLP. Even if GLP rises, it won't rise as fast as AVAX, so we can get yields.
Let's do the maths
You deposit $10,000 USDT in Aave and borrow Avax. Say you borrow $8,000; you sell it, buy GLP, and risk 20%.
After a year, ETH, AVAX, and BTC rise 20%. GLP is $8800. $800 vanishes. 20% yields $1600. You're profitable. Shorting Avax costs $1600. (Assumptions-ETH, AVAX, BTC move the same, GLP yield is 20%. GLP has a 50:50 stablecoin/others ratio. Aave won't liquidate
In naked Avax shorting, Avax falls 20% in a year. You'll make $1600. If you buy GLP and stake it using the sold Avax and BTC, ETH and Avax go down by 20% - your profit is 20%, but with the yield, your total gain is $2400.
Issues with GMX
GMX's historical funding rates are always net positive, so long always pays short. This makes long-term shorts less appealing.
Oracle price discovery isn't enough. This limitation doesn't affect Bitcoin and ETH, but it affects less liquid assets. Traders can buy and sell less liquid assets at a lower price than their actual cost as long as GMX exists.
As users must provide GLP liquidity, adding more assets to GMX will be difficult. Next iteration will have synthetic assets.
Gains Protocol
Best leveraged trading platform. Smart contract-based decentralized protocol. 46 crypto pairs can be leveraged 5–150x and 10 Forex pairs 5–1000x. $10 DAI @ 150x (min collateral x leverage pos size is $1500 DAI). No funding fees, no KYC, trade DAI from your wallet, keep funds.
DAI single-sided staking and the GNS-DAI pool are important parts of Gains trading. GNS-DAI stakers get 90% of trading fees and 100% swap fees. 10 percent of trading fees go to DAI stakers, which is currently 14 percent!
Trade volume
When a trader opens a trade, the leverage and profit are pulled from the DAI pool. If he loses, the protocol yield goes to the stakers.
If the trader's win rate is high and the DAI pool slowly depletes, the GNS token is minted and sold to refill DAI. Trader losses are used to burn GNS tokens. 25%+ of GNS is burned, making it deflationary.
Due to high leverage and volatility of crypto assets, most traders lose money and the protocol always wins, keeping GNS deflationary.
Gains uses a unique decentralized oracle for price feeds, which is better for leverage trading platforms. Let me explain.
Gains uses chainlink price oracles, not its own price feeds. Chainlink oracles only query centralized exchanges for price feeds every minute, which is unsuitable for high-precision trading.
Gains created a custom oracle that queries the eight chainlink nodes for the current price and, on average, for trade confirmation. This model eliminates every-second inquiries, which waste gas but are more efficient than chainlink's per-minute price.
This price oracle helps Gains open and close trades instantly, eliminate scam wicks, etc.
Other benefits include:
Stop-loss guarantee (open positions updated)
No scam wicks
Spot-pricing
Highest possible leverage
Fixed-spreads. During high volatility, a broker can increase the spread, which can hit your stop loss without the price moving.
Trade directly from your wallet and keep your funds.
>90% loss before liquidation (Some platforms liquidate as little as -50 percent)
KYC-free
Directly trade from wallet; keep funds safe
Further improvements
GNS-DAI liquidity providers fear the impermanent loss, so the protocol is migrating to its own liquidity and single staking GNS vaults. This allows users to stake GNS without permanent loss and obtain 90% DAI trading fees by staking. This starts in August.
Their upcoming improvements can be found here.
Gains constantly add new features and change pairs. It's an interesting protocol.
Conclusion
Next bull run, watch decentralized perpetual protocols. Effective tokenomics and non-inflationary yields may attract traders and liquidity providers. But still, there is a long way for them to develop, and I don't see them tackling the centralized exchanges any time soon until they fix their inherent problems and improve fast enough.
Read the full post here.

Vitalik
3 years 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
