More on Personal Growth
Matthew Royse
3 years ago
Ten words and phrases to avoid in presentations
Don't say this in public!
Want to wow your audience? Want to deliver a successful presentation? Do you want practical takeaways from your presentation?
Then avoid these phrases.
Public speaking is difficult. People fear public speaking, according to research.
"Public speaking is people's biggest fear, according to studies. Number two is death. "Sounds right?" — Comedian Jerry Seinfeld
Yes, public speaking is scary. These words and phrases will make your presentation harder.
Using unnecessary words can weaken your message.
You may have prepared well for your presentation and feel confident. During your presentation, you may freeze up. You may blank or forget.
Effective delivery is even more important than skillful public speaking.
Here are 10 presentation pitfalls.
1. I or Me
Presentations are about the audience, not you. Replace "I or me" with "you, we, or us." Focus on your audience. Reward them with expertise and intriguing views about your issue.
Serve your audience actionable items during your presentation, and you'll do well. Your audience will have a harder time listening and engaging if you're self-centered.
2. Sorry if/for
Your presentation is fine. These phrases make you sound insecure and unprepared. Don't pressure the audience to tell you not to apologize. Your audience should focus on your presentation and essential messages.
3. Excuse the Eye Chart, or This slide's busy
Why add this slide if you're utilizing these phrases? If you don't like this slide, change it before presenting. After the presentation, extra data can be provided.
Don't apologize for unclear slides. Hide or delete a broken PowerPoint slide. If so, divide your message into multiple slides or remove the "business" slide.
4. Sorry I'm Nervous
Some think expressing yourself will win over the audience. Nerves are horrible. Even public speakers are nervous.
Nerves aren't noticeable. What's the point? Let the audience judge your nervousness. Please don't make this obvious.
5. I'm not a speaker or I've never done this before.
These phrases destroy credibility. People won't listen and will check their phones or computers.
Why present if you use these phrases?
Good speakers aren't necessarily public speakers. Be confident in what you say. When you're confident, many people will like your presentation.
6. Our Key Differentiators Are
Overused term. It's widely utilized. This seems "salesy," and your "important differentiators" are probably like a competitor's.
This statement has been diluted; say, "what makes us different is..."
7. Next Slide
Many slides or stories? Your presentation needs transitions. They help your viewers understand your argument.
You didn't transition well when you said "next slide." Think about organic transitions.
8. I Didn’t Have Enough Time, or I’m Running Out of Time
The phrase "I didn't have enough time" implies that you didn't care about your presentation. This shows the viewers you rushed and didn't care.
Saying "I'm out of time" shows poor time management. It means you didn't rehearse enough and plan your time well.
9. I've been asked to speak on
This phrase is used to emphasize your importance. This phrase conveys conceit.
When you say this sentence, you tell others you're intelligent, skilled, and appealing. Don't utilize this term; focus on your topic.
10. Moving On, or All I Have
These phrases don't consider your transitions or presentation's end. People recall a presentation's beginning and end.
How you end your discussion affects how people remember it. You must end your presentation strongly and use natural transitions.
Conclusion
10 phrases to avoid in a presentation. I or me, sorry if or sorry for, pardon the Eye Chart or this busy slide, forgive me if I appear worried, or I'm really nervous, and I'm not good at public speaking, I'm not a speaker, or I've never done this before.
Please don't use these phrases: next slide, I didn't have enough time, I've been asked to speak about, or that's all I have.
We shouldn't make public speaking more difficult than it is. We shouldn't exacerbate a difficult issue. Better public speakers avoid these words and phrases.
“Remember not only to say the right thing in the right place, but far more difficult still, to leave unsaid the wrong thing at the tempting moment.” — Benjamin Franklin, Founding Father
This is a summary. See the original post here.
Patryk Nawrocki
3 years ago
7 things a new UX/UI designer should know
If I could tell my younger self a few rules, they would boost my career.
1. Treat design like medicine; don't get attached.
If it doesn't help, you won't be angry, but you'll try to improve it. Designers blame others if they don't like the design, but the rule is the same: we solve users' problems. You're not your design, and neither are they. Be humble with your work because your assumptions will often be wrong and users will behave differently.
2. Consider your design flawed.
Disagree with yourself, then defend your ideas. Most designers forget to dig deeper into a pattern, screen, button, or copywriting. If someone asked, "Have you considered alternatives? How does this design stack up? Here's a functional UX checklist to help you make design decisions.
3. Codeable solutions.
If your design requires more developer time, consider whether it's worth spending more money to code something with a small UX impact. Overthinking problems and designing abstract patterns is easy. Sometimes you see something on dribbble or bechance and try to recreate it, but it's not worth it. Here's my article on it.
4. Communication changes careers
Designers often talk with users, clients, companies, developers, and other designers. How you talk and present yourself can land you a job. Like driving or swimming, practice it. Success requires being outgoing and friendly. If I hadn't said "hello" to a few people, I wouldn't be where I am now.
5. Ignorance of the law is not an excuse.
Copyright, taxation How often have you used an icon without checking its license? If you use someone else's work in your project, the owner can cause you a lot of problems — paying a lot of money isn't worth it. Spend a few hours reading about copyrights, client agreements, and taxes.
6. Always test your design
If nobody has seen or used my design, it's not finished. Ask friends about prototypes. Testing reveals how wrong your assumptions were. Steve Krug, one of the authorities on this topic will tell you more about how to do testing.
7. Run workshops
A UX designer's job involves talking to people and figuring out what they need, which is difficult because they usually don't know. Organizing teamwork sessions is a powerful skill, but you must also be a good listener. Your job is to help a quiet, introverted developer express his solution and control the group. AJ Smart has more on workshops here.
Scott Stockdale
3 years ago
A Day in the Life of Lex Fridman Can Help You Hit 6-Month Goals
The Lex Fridman podcast host has interviewed Elon Musk.
Lex is a minimalist YouTuber. His videos are sloppy. Suits are his trademark.
In a video, he shares a typical day. I've smashed my 6-month goals using its ideas.
Here's his schedule.
Morning Mantra
Not woo-woo. Lex's mantra reflects his practicality.
Four parts.
Rulebook
"I remember the game's rules," he says.
Among them:
Sleeping 6–8 hours nightly
1–3 times a day, he checks social media.
Every day, despite pain, he exercises. "I exercise uninjured body parts."
Visualize
He imagines his day. "Like Sims..."
He says three things he's grateful for and contemplates death.
"Today may be my last"
Objectives
Then he visualizes his goals. He starts big. Five-year goals.
Short-term goals follow. Lex says they're year-end goals.
Near but out of reach.
Principles
He lists his principles. Assertions. His goals.
He acknowledges his cliche beliefs. Compassion, empathy, and strength are key.
Here's my mantra routine:
Four-Hour Deep Work
Lex begins a four-hour deep work session after his mantra routine. Today's toughest.
AI is Lex's specialty. His video doesn't explain what he does.
Clearly, he works hard.
Before starting, he has water, coffee, and a bathroom break.
"During deep work sessions, I minimize breaks."
He's distraction-free. Phoneless. Silence. Nothing. Any loose ideas are typed into a Google doc for later. He wants to work.
"Just get the job done. Don’t think about it too much and feel good once it’s complete." — Lex Fridman
30-Minute Social Media & Music
After his first deep work session, Lex rewards himself.
10 minutes on social media, 20 on music. Upload content and respond to comments in 10 minutes. 20 minutes for guitar or piano.
"In the real world, I’m currently single, but in the music world, I’m in an open relationship with this beautiful guitar. Open relationship because sometimes I cheat on her with the acoustic." — Lex Fridman
Two-hour exercise
Then exercise for two hours.
Daily runs six miles. Then he chooses how far to go. Run time is an hour.
He does bodyweight exercises. Every minute for 15 minutes, do five pull-ups and ten push-ups. It's David Goggins-inspired. He aims for an hour a day.
He's hungry. Before running, he takes a salt pill for electrolytes.
He'll then take a one-minute cold shower while listening to cheesy songs. Afterward, he might eat.
Four-Hour Deep Work
Lex's second work session.
He works 8 hours a day.
Again, zero distractions.
Eating
The video's meal doesn't look appetizing, but it's healthy.
It's ground beef with vegetables. Cauliflower is his "ground-floor" veggie. "Carrots are my go-to party food."
Lex's keto diet includes 1800–2000 calories.
He drinks a "nutrient-packed" Atheltic Greens shake and takes tablets. It's:
One daily tablet of sodium.
Magnesium glycinate tablets stopped his keto headaches.
Potassium — "For electrolytes"
Fish oil: healthy joints
“So much of nutrition science is barely a science… I like to listen to my own body and do a one-person, one-subject scientific experiment to feel good.” — Lex Fridman
Four-hour shallow session
This work isn't as mentally taxing.
Lex planned to:
Finish last session's deep work (about an hour)
Adobe Premiere podcasting (about two hours).
Email-check (about an hour). Three times a day max. First, check for emergencies.
If he's sick, he may watch Netflix or YouTube documentaries or visit friends.
“The possibilities of chaos are wide open, so I can do whatever the hell I want.” — Lex Fridman
Two-hour evening reading
Nonstop work.
Lex ends the day reading academic papers for an hour. "Today I'm skimming two machine learning and neuroscience papers"
This helps him "think beyond the paper."
He reads for an hour.
“When I have a lot of energy, I just chill on the bed and read… When I’m feeling tired, I jump to the desk…” — Lex Fridman
Takeaways
Lex's day-in-the-life video is inspiring.
He has positive energy and works hard every day.
Schedule:
Mantra Routine includes rules, visualizing, goals, and principles.
Deep Work Session #1: Four hours of focus.
10 minutes social media, 20 minutes guitar or piano. "Music brings me joy"
Six-mile run, then bodyweight workout. Two hours total.
Deep Work #2: Four hours with no distractions. Google Docs stores random thoughts.
Lex supplements his keto diet.
This four-hour session is "open to chaos."
Evening reading: academic papers followed by fiction.
"I value some things in life. Work is one. The other is loving others. With those two things, life is great." — Lex Fridman
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Vitalik
4 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
Crypto Zen Monk
2 years ago
How to DYOR in the world of cryptocurrency
RESEARCH
We must create separate ideas and handle our own risks to be better investors. DYOR is crucial.
The only thing unsustainable is your cluelessness.
DYOR: Why
On social media, there is a lot of false information and divergent viewpoints. All of these facts might be accurate, but they might not be appropriate for your portfolio and investment preferences.
You become a more knowledgeable investor thanks to DYOR.
DYOR improves your portfolio's risk management.
My DYOR resources are below.
Messari: Major Blockchains' Activities
New York-based Messari provides cryptocurrency open data libraries.
Major blockchains offer 24-hour on-chain volume. https://messari.io/screener/most-active-chains-DB01F96B
What to do
Invest in stable cryptocurrencies. Sort Messari by Real Volume (24H) or Reported Market Cap.
Coingecko: Research on Ecosystems
Top 10 Ecosystems by Coingecko are good.
What to do
Invest in quality.
Leading ten Ecosystems by Market Cap
There are a lot of coins in the ecosystem (second last column of above chart)
CoinGecko's Market Cap Crypto Categories Market capitalization-based cryptocurrency categories. Ethereum Ecosystem www.coingecko.com
Fear & Greed Index for Bitcoin (FGI)
The Bitcoin market sentiment index ranges from 0 (extreme dread) to 100. (extreme greed).
How to Apply
See market sentiment:
Extreme fright = opportunity to buy
Extreme greed creates sales opportunity (market due for correction).
Glassnode
Glassnode gives facts, information, and confidence to make better Bitcoin, Ethereum, and cryptocurrency investments and trades.
Explore free and paid metrics.
Stock to Flow Ratio: Application
The popular Stock to Flow Ratio concept believes scarcity drives value. Stock to flow is the ratio of circulating Bitcoin supply to fresh production (i.e. newly mined bitcoins). The S/F Ratio has historically predicted Bitcoin prices. PlanB invented this metric.
Utilization: Ethereum Hash Rate
Ethereum miners produce an estimated number of hashes per second.
ycharts: Hash rate of the Bitcoin network
TradingView
TradingView is your go-to tool for investment analysis, watch lists, technical analysis, and recommendations from other traders/investors.
Research for a cryptocurrency project
Two key questions every successful project must ask: Q1: What is this project trying to solve? Is it a big problem or minor? Q2: How does this project make money?
Each cryptocurrency:
Check out the white paper.
check out the project's internet presence on github, twitter, and medium.
the transparency of it
Verify the team structure and founders. Verify their LinkedIn profile, academic history, and other qualifications. Search for their names with scam.
Where to purchase and use cryptocurrencies Is it traded on trustworthy exchanges?
From CoinGecko and CoinMarketCap, we may learn about market cap, circulations, and other important data.
The project must solve a problem. Solving a problem is the goal of the founders.
Avoid projects that resemble multi-level marketing or ponzi schemes.
Your use of social media
Use social media carefully or ignore it: Twitter, TradingView, and YouTube
Someone said this before and there are some truth to it. Social media bullish => short.
Your Behavior
Investigate. Spend time. You decide. Worth it!
Only you have the best interest in your financial future.
Amelia Winger-Bearskin
3 years ago
Reasons Why AI-Generated Images Remind Me of Nightmares
AI images are like funhouse mirrors.
Google's AI Blog introduced the puppy-slug in the summer of 2015.
Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.
DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.
Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!
Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.
When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.
DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.
Some of these images look scary because the models don't "know" anything, at least not in the way we do.
These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.
You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.
Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.
Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.
This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.
The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":
As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.