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Mark Schaefer
2 years ago
20 Fun Uses for ChatGPT
Our RISE community is stoked on ChatGPT. ChatGPT has countless uses.
Early on. Companies are figuring out the legal and ethical implications of AI's content revolution. Using AI for everyday tasks is cool.
So I challenged RISE friends... Let's have fun and share non-obvious uses.
Onward!
1. Tweet
Jim MacLeod requested a design-themed Twitter thread from ChatGPT.
2. Novelize
Giuseppe Fratoni writes novels.
"This is amazing!" Impotent. I asked about writing, mystery novels, and jazz.
"Develop a mystery novel plot in which a disillusioned homicide detective hunts a serial killer who targets jazz musicians who play Autumn Leaves."
"I have:
Detective James Thompson is a seasoned veteran of the Homicide department, but lately he’s been feeling burnt out and disillusioned. His latest case, the murder of a young jazz pianist who was found brutally killed in his home, only serves to deepen his sense of despair … (much more provided)
3. Question-and-answer sessions
Bliss recommends:
You have a guest speaker and want to ask audience questions.
Pre-generate questions with ChatGPT. "And here's a question from our AI?"
The conference is about Western water scarcity. Water management expert. My AI question was...
Which desalination-related questions should I ask an expert?
ChatGPT said...
How is desalination currently being used around the world, and in what types of locations is it most effective?
What are the potential advantages and disadvantages of using desalination to provide fresh water?
What are some of the key challenges and limitations of desalination technology, and how are they being addressed?
4. Game-making
Richard's a board game expert. He suggests using ChatGPT to plan games. Note: Some people have started getting help solving crossword puzzle clues.
5. Matchmaking
Tanya Thompson thinks it helps dating. ChatGPT:
Here are some flirty first-message ideas for dating apps:
I noticed you from afar. I had to say hi and see if we clicked.
Your profile picture is gorgeous. I'm obsessed with your [Feature] and want to know you better.
I don't usually message first, but I had to. You're [Attractive Quality] and I can't wait to talk.”
Hello. Your profile intimidates me. I'm starstruck by your [Attractiveness]. (Many instances)
5. Solitude/anxiety
ChatGPT excels at one-on-one conversations.
PepperBrooks: "I'm sad and lonely."
ChatGPT: I’m sorry to hear that you’re feeling sad and lonely. It’s natural to feel down sometimes, but it’s important to remember that you’re not alone. There are many people who care about you and want to help you. It can be helpful to talk to someone about your feelings, whether it’s a close friend, family member, or mental health professional.
Sometimes, just expressing your thoughts and emotions to someone else can help to alleviate your sadness and loneliness. It’s also important to take care of yourself and do things that make you happy, such as spending time with loved ones, pursuing a hobby, or exercising. These things can help to improve your mood and make you feel more connected to others. Remember that you are strong and capable, and that you can get through this difficult time.
Daniel Nestle asked ChatGPT to generate journal prompts to help him deal with anxiety.
Write down the things that are causing you anxiety at the moment.
Describe a situation in which you felt anxious and how you coped with it.
Reflect on how your thoughts and beliefs contribute to your anxiety.
Write a letter to your anxiety, expressing your feelings and concerns
Imagine yourself in a calm and peaceful place, and describe it in detail.
(many more items followed)
6. Names
Continued Daniel:
Also, naming ideas.
Me: Name this drink. Japanese gin, unsweetened tonic, lime, Szechuan pepper, grapefruit.
ChatGPT: How about “Sakura Sunrise?” The name incorporates the Japanese gin and the citrus flavors of grapefruit and lime, and the word “sunrise” suggests a fresh and invigorating start to the day. Alternatively, you could go with something like “Peppered Grapefruit Gin Fizz” to emphasize the unique flavor combination of Szechuan pepper and grapefruit.
7. Translation on the go
Monks recommends: ChatGPT is a perfect mobile translation tool. Say you need help in Italy.
ME: Translate to Italian
Where is the nearest gas station?
ChatGPT: “La mia auto sta per esaurire il carburante, mi puoi dire dove si trova la stazione di servizio più vicina?”
8. Fitness
Monks added:
ME: In 3 months, I want to run 5km. Plan out how I can do this.
ChatGPT: Here's a 3-month plan to run 5km.
Start by walking for 15–20 minutes each day to build up your endurance and get your body used to regular exercise.
After a week of walking, begin incorporating short jogs into your routine. Start by jogging for 1–2 minutes at a time, with walking breaks in between. Gradually increase the length of your jogs and reduce the amount of time you spend walking.
Once you can jog for 10 minutes without stopping, start increasing the distance of your runs. Aim to run 1km without stopping and then gradually increase the distance over time.
In addition to your regular runs, incorporate strength training and stretching into your routine to improve your overall fitness and reduce the risk of injury.
(more details continued on)
9. Integrations and coding
10. Teacher lesson plans
Spencer Crandall saved time.
This morning, I wondered who has the hardest job. Teachers.
Many students, little time. ChatGPT could handle lesson planning. I tried it and got this.
Lesson Title: The History and Impact of Artificial Intelligence
Students will understand the history and development of AI and the potential positive and negative impacts on society.
AI timeline AI technologies and applications
Overview:
Introduce the topic of artificial intelligence, and explain that it refers to the development of computer systems and algorithms that can simulate human intelligence and behavior.
Show the timeline of significant events in the history of AI, and discuss the key milestones and developments in the field.
Show examples of AI technologies and applications, and explain how they are used in various industries and fields.
Discuss the potential positive and negative impacts of AI on society, including its potential to improve productivity, efficiency, and safety, as well as its potential to create new challenges and concerns, such as job displacement and ethical issues.
(many more details followed)
11. Find podcast guests.
Joeri Billast suggested:
Who should be on my Web3 podcast?
ChatGPT: Web3 marketing experts, influencers, and thought leaders make great podcast guests. These include Web3 marketers, content creators, business owners, entrepreneurs, and technologists. You could invite Web3 startups and organizations to your podcast.
Please give examples.
ChatGPT: Ideal Web3 marketing podcast guests include:
Tim Berner-Lee, inventor of the World Wide Web
Chris Dixon, general partner at Andreessen Horowitz
Vitalik Buterin, founder of Ethereum
Joe Lubin, founder of ConsenSys
Susan Fowler, author of Whistleblower
(many other suggestions)
12. Comments and reviews on social media
Lisa Monk thinks ChatGPT can help retail staff with social media.
She asked ChatGPT to "review this restaurant"
POSITIVE CUSTOMER REVIEW: I absolutely love this place. We eat out a lot however this place has topped it all. Perfect meals, amazing staff and the timing for it all was great! Special thanks to Sophie B (you rock) From your guests all the way from the Gold Coast, THANK YOU
ChatGPT REPLY: Thank you so much for your kind words and for taking the time to write a review. We are delighted to hear that you had such a great experience at our restaurant. We are thrilled that you enjoyed your meal and the service provided by Sophie. It’s always a pleasure to serve guests visiting from the Gold Coast and we are grateful for your support. We look forward to welcoming you back again soon. Thank you again for your positive review.
CUSTOMER REVIEW: Most average food, there is a nice view, But that’s about it
ChatGPT: Thank you for your review. We apologize if your dining experience was not up to your expectations. We strive to provide top-quality food and service to all of our customers, and we will take your feedback into consideration as we continue to improve. Thank you for letting us know about your experience.
13. Crime-fighting
Zack Seipert said, "I researched some famous unsolved murders, and ChatGPT laid out the crime, suspects, clues, and more." I bet experienced detectives could solve some of these cold cases.
14. Reduce bias
Joanne Taylor struggles to remove biases from her writing. She asked ChatGPT for ideas.
I asked ChatGPT to check a text for racial bias, and it did well.
15. Furnishings
I designed a small, unique table using ChatGPT and MidJourney. I edited ChatGPT's idea for clarity and brevity before entering it in MidJourney.
Results:
16. Serialize a novel
Scott Scowcoft led ChatCPT through iterations of a story text to create serialized novel frames. You can view this here.
17. Interview preparation
I was on a sales podcast. Although this is marketing-related, it's not my forte. I asked ChatGPT for help with a sales interview. Response:
Here are some interview questions about the future of sales and how to answer them.
How will technology affect sales in the future?
Technology will impact sales in the coming years. Automation and AI could be used to handle routine tasks, and data analysis and CRM could become more important. E-commerce and online sales channels are also reshaping the sales landscape.
How will the salesperson's role change?
Future sales roles may change in response to changing customer needs and expectations. Salespeople must be knowledgeable and consultative to help customers navigate complex products and services. Increasing importance of data analysis and CRM tools will require salespeople to understand and use technology effectively.
Future sales professionals will need what skills?
Future salespeople will need many skills. Sales professionals must have strong communication and interpersonal skills, as well as product and service knowledge and the ability to use data analysis and CRM tools effectively. Adapting to changing customer needs and staying current on industry trends will also be important for sales success.
18. Gift ideas
I need Christmas gift ideas for my wife. I asked:
Suggest a gift idea under $200 for a middle-aged woman who loves The Eagles.
A middle-aged woman who loves The Eagles might like a vinyl record of "Hotel California" or "The Long Run." Many music fans enjoy the unique sound and experience of vinyl records.
Pair the record with a high-quality record player for a complete gift. The vinyl record and record player should fit within your $200 budget for an Eagles fan.
19. Explaining complex concepts
I entered: Explain quantum physics to me as if I were 10 years old.
Quantum physics is a branch of science that studies how tiny particles, like atoms and subatomic particles, behave. These particles are so small that they don’t always follow the same rules as the things we can see and touch every day.
For example, sometimes they can be in two places at the same time, or they can be friends with each other even if they are far apart. This might sound strange, but it’s what scientists have learned from studying these tiny particles. Quantum physics helps us understand how the world works at a very small scale, and it can also help us make new technologies, like computers that are much faster than the ones we have today.
20. AP-style editing
Blogs, journals, and books follow AP Style. I asked ChatGPT to "edit using AP Style" and it did a great job.
And then... "Write a catchy headline for a blog post about 16 ChatGPT uses"
Here are some possible blog post titles for 20 creative ChatGPT uses:
“20 Fun and Creative Ways to Use ChatGPT: From Games to Quizzes and More”
“The Ultimate Guide to Using ChatGPT for Entertainment and Creativity”
“20 Reasons to Use ChatGPT: How it Can Enhance Your Gaming, Storytelling, and More”
20 Entertaining Uses for ChatGPT You Never Knew Were Possible”
“Unleash Your Imagination with ChatGPT: 16 Creative and Fun Ways to Use it”
In a previous post, I said ChatGPT will change marketing careers forever. I have never made a statement like that in my life. Even in the early days of the internet, people were cautious.
This technology was just released two weeks ago, and over a million people are already using it. This is the fastest technology adoption in history.
Today's post offers inventive and entertaining ideas, but it's just the beginning. ChatGPT writes code, music, and papers.

Dmitrii Eliuseev
2 years ago
Creating Images on Your Local PC Using Stable Diffusion AI
Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.
Let’s get started.
What It Does
Stable Diffusion uses numerous components:
A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).
An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).
A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).
This figure shows all data flow:
The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.
Install
Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):
wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults condaInstall the source and prepare the environment:
git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgradeDownload the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:
python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.
Running the optimized version
Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:
python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).
Running Stable Diffusion without GPU
If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:
Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().
Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().
Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.
Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().
Run the script again.
Testing
Test the model. Text-to-image is the first choice. Test the command line example again:
python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:
Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:
Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):
I can create an image from this drawing:
python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8It was far better than my initial drawing:
I hope readers understand and experiment.
Stable Diffusion UI
Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:
Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).
Start the script.
Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:
V2.1 of Stable Diffusion
I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:
alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.
a new depth model that may be used to the output of image-to-image generation.
a revolutionary upscaling technique that can quadruple the resolution of an image.
Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.
The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:
conda deactivate
conda env remove -n ldm # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldmHugging Face offers a new weights ckpt file.
The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:
It looks different from v1, but it functions and has a higher resolution.
The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):
python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckptThis code allows the web browser UI to select the image to upscale:
The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:
Stable Diffusion Limitations
When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:
V1:
V2.1:
The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.
I can also ask the model to draw a gorgeous woman:
V1:
V2.1:
The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.
If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:
V1:
V2.1:
Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:
V1:
V2.1: improved but not perfect.
V1 produces a fun cartoon flying mouse if I want something more abstract:
I tried multiple times with V2.1 but only received this:
The image is OK, but the first version is closer to the request.
Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:
V1:
V2.1:
Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:
I typed "abstract oil painting of people dancing" and got this:
V1:
V2.1:
It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.
The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:
This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.
I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).
Conclusion
The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).
Is Generative AI a game-changer? My humble experience tells me:
I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.
Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.
It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).
When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.
Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

Shawn Mordecai
3 years ago
The Apple iPhone 14 Pill is Easier to Swallow
Is iPhone's Dynamic Island invention or a marketing ploy?
First of all, why the notch?
When Apple debuted the iPhone X with the notch, some were surprised, confused, and amused by the goof. Let the Brits keep the new meaning of top-notch.
Apple removed the bottom home button to enhance screen space. The tides couldn't overtake part of the top. This section contained sensors, a speaker, a microphone, and cameras for facial recognition. A town resisted Apple's new iPhone design.
From iPhone X to 13, the notch has gotten smaller. We expected this as technology and engineering progressed, but we hated the notch. Apple approved. They attached it to their other gadgets.
Apple accepted, owned, and ran with the iPhone notch, it has become iconic (or infamous); and that’s intentional.
The Island Where Apple Is
Apple needs to separate itself, but they know how to do it well. The iPhone 14 Pro finally has us oohing and aahing. Life-changing, not just higher pixel density or longer battery.
Dynamic Island turned a visual differentiation into great usefulness, which may not be life-changing. Apple always welcomes the controversy, whether it's $700 for iMac wheels, no charging block with a new phone, or removing the headphone jack.
Apple knows its customers will be loyal, even if they're irritated. Their odd design choices often cause controversy. It's calculated that people blog, review, and criticize Apple's products. We accept what works for them.
While the competition zigs, Apple zags. Sometimes they zag too hard and smash into a wall, but we talk about it anyways, and that’s great publicity for them.
Getting Dependent on the drug
The notch became a crop. Dynamic Island's design is helpful, intuitive, elegant, and useful. It increases iPhone usability, productivity (slightly), and joy. No longer unsightly.
The medication helps with multitasking. It's a compact version of the iPhone's Live Activities lock screen function. Dynamic Island enhances apps and activities with visual effects and animations whether you engage with it or not. As you use the pill, its usefulness lessens. It lowers user notifications and consolidates them with live and permanent feeds, delivering quick app statuses. It uses the black pixels on the iPhone 14's display, which looked like a poor haircut.
The pill may be a gimmick to entice customers to use more Apple products and services. Apps may promote to their users like a live billboard.
Be prepared to get a huge dose of Dynamic Island’s “pill” like you never had before with the notch. It might become so satisfying and addicting to use, that every interaction with it will become habit-forming, and you’re going to forget that it ever existed.
WARNING: A Few Potential Side Effects
Vision blurred Dynamic Island's proximity to the front-facing camera may leave behind grease that blurs photos. Before taking a selfie, wipe the camera clean.
Strained thumb To fully use Dynamic Island, extend your thumb's reach 6.7 inches beyond your typical, comfortable range.
Happiness, contentment The Dynamic Island may enhance Endorphins and Dopamine. Multitasking, interactions, animations, and haptic feedback make you want to use this function again and again.
Motion-sickness Dynamic Island's motions and effects may make some people dizzy. If you can disable animations, you can avoid motion sickness.
I'm not a doctor, therefore they aren't established adverse effects.
Does Dynamic Island Include Multiple Tasks?
Dynamic Islands is a placebo for multitasking. Apple might have compromised on iPhone multitasking. It won't make you super productive, but it's a step up.
iPhone is primarily for personal use, like watching videos, messaging friends, sending money to friends, calling friends about the money you were supposed to send them, taking 50 photos of the same leaf, investing in crypto, driving for Uber because you lost all your money investing in crypto, listening to music and hailing an Uber from a deserted crop field because while you were driving for Uber your passenger stole your car and left you stranded, so you used Apple’s new SOS satellite feature to message your friend, who still didn’t receive their money, to hail you an Uber; now you owe them more money… karma?
We won't be watching videos on iPhones while perusing 10,000-row spreadsheets anytime soon. True multitasking and productivity aren't priorities for Apple's iPhone. Apple doesn't to preserve the iPhone's experience. Like why there's no iPad calculator. Apple doesn't want iPad users to do math, but isn't essential for productivity?
Digressing.
Apple will block certain functions so you must buy and use their gadgets and services, immersing yourself in their ecosystem and dictating how to use their goods.
Dynamic Island is a poor man’s multi-task for iPhone, and that’s fine it works for most iPhone users. For substantial productivity Apple prefers you to get an iPad or a MacBook. That’s part of the reason for restrictive features on certain Apple devices, but sometimes it’s based on principles to preserve the integrity of the product, according to Apple’s definition.
Is Apple using deception?
Dynamic Island may be distracting you from a design decision. The answer is kind of. Elegant distraction
When you pull down a smartphone webpage to refresh it or minimize an app, you get seamless animations. It's not simply because it appears better; it's due to iPhone and smartphone processing speeds. Such limits reduce the system's response to your activity, slowing the experience. Designers and developers use animations and effects to distract us from the time lag (most of the time) and sometimes because it looks cooler and smoother.
Dynamic Island makes apps more useable and interactive. It shows system states visually. Turn signal audio and visual cues, voice assistance, physical and digital haptic feedbacks, heads-up displays, fuel and battery level gauges, and gear shift indicators helped us overcome vehicle design problems.
Dynamic Island is a wonderfully delightful (and temporary) solution to a design “problem” until Apple or other companies can figure out a way to sink the cameras under the smartphone screen.
Apple Has Returned to Being an Innovative & Exciting Company
Now Apple's products are exciting. Next, bring back real Apple events, not pre-recorded demos.
Dynamic Island integrates hardware and software. What will this new tech do? How would this affect device use? Or is it just hype?
Dynamic Island may be an insignificant improvement to the iPhone, but it sure is promising for the future of bridging the human and computer interaction gap.
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Vitalik
3 years ago
Fairness alternatives to selling below market clearing prices (or community sentiment, or fun)
When a seller has a limited supply of an item in high (or uncertain and possibly high) demand, they frequently set a price far below what "the market will bear." As a result, the item sells out quickly, with lucky buyers being those who tried to buy first. This has happened in the Ethereum ecosystem, particularly with NFT sales and token sales/ICOs. But this phenomenon is much older; concerts and restaurants frequently make similar choices, resulting in fast sell-outs or long lines.
Why do sellers do this? Economists have long wondered. A seller should sell at the market-clearing price if the amount buyers are willing to buy exactly equals the amount the seller has to sell. If the seller is unsure of the market-clearing price, they should sell at auction and let the market decide. So, if you want to sell something below market value, don't do it. It will hurt your sales and it will hurt your customers. The competitions created by non-price-based allocation mechanisms can sometimes have negative externalities that harm third parties, as we will see.
However, the prevalence of below-market-clearing pricing suggests that sellers do it for good reason. And indeed, as decades of research into this topic has shown, there often are. So, is it possible to achieve the same goals with less unfairness, inefficiency, and harm?
Selling at below market-clearing prices has large inefficiencies and negative externalities
An item that is sold at market value or at an auction allows someone who really wants it to pay the high price or bid high in the auction. So, if a seller sells an item below market value, some people will get it and others won't. But the mechanism deciding who gets the item isn't random, and it's not always well correlated with participant desire. It's not always about being the fastest at clicking buttons. Sometimes it means waking up at 2 a.m. (but 11 p.m. or even 2 p.m. elsewhere). Sometimes it's just a "auction by other means" that's more chaotic, less efficient, and has far more negative externalities.
There are many examples of this in the Ethereum ecosystem. Let's start with the 2017 ICO craze. For example, an ICO project would set the price of the token and a hard maximum for how many tokens they are willing to sell, and the sale would start automatically at some point in time. The sale ends when the cap is reached.
So what? In practice, these sales often ended in 30 seconds or less. Everyone would start sending transactions in as soon as (or just before) the sale started, offering higher and higher fees to encourage miners to include their transaction first. Instead of the token seller receiving revenue, miners receive it, and the sale prices out all other applications on-chain.
The most expensive transaction in the BAT sale set a fee of 580,000 gwei, paying a fee of $6,600 to get included in the sale.
Many ICOs after that tried various strategies to avoid these gas price auctions; one ICO notably had a smart contract that checked the transaction's gasprice and rejected it if it exceeded 50 gwei. But that didn't solve the issue. Buyers hoping to game the system sent many transactions hoping one would get through. An auction by another name, clogging the chain even more.
ICOs have recently lost popularity, but NFTs and NFT sales have risen in popularity. But the NFT space didn't learn from 2017; they do fixed-quantity sales just like ICOs (eg. see the mint function on lines 97-108 of this contract here). So what?
That's not the worst; some NFT sales have caused gas price spikes of up to 2000 gwei.
High gas prices from users fighting to get in first by sending higher and higher transaction fees. An auction renamed, pricing out all other applications on-chain for 15 minutes.
So why do sellers sometimes sell below market price?
Selling below market value is nothing new, and many articles, papers, and podcasts have written (and sometimes bitterly complained) about the unwillingness to use auctions or set prices to market-clearing levels.
Many of the arguments are the same for both blockchain (NFTs and ICOs) and non-blockchain examples (popular restaurants and concerts). Fairness and the desire not to exclude the poor, lose fans or create tension by being perceived as greedy are major concerns. The 1986 paper by Kahneman, Knetsch, and Thaler explains how fairness and greed can influence these decisions. I recall that the desire to avoid perceptions of greed was also a major factor in discouraging the use of auction-like mechanisms in 2017.
Aside from fairness concerns, there is the argument that selling out and long lines create a sense of popularity and prestige, making the product more appealing to others. Long lines should have the same effect as high prices in a rational actor model, but this is not the case in reality. This applies to ICOs and NFTs as well as restaurants. Aside from increasing marketing value, some people find the game of grabbing a limited set of opportunities first before everyone else is quite entertaining.
But there are some blockchain-specific factors. One argument for selling ICO tokens below market value (and one that persuaded the OmiseGo team to adopt their capped sale strategy) is community dynamics. The first rule of community sentiment management is to encourage price increases. People are happy if they are "in the green." If the price drops below what the community members paid, they are unhappy and start calling you a scammer, possibly causing a social media cascade where everyone calls you a scammer.
This effect can only be avoided by pricing low enough that post-launch market prices will almost certainly be higher. But how do you do this without creating a rush for the gates that leads to an auction?
Interesting solutions
It's 2021. We have a blockchain. The blockchain is home to a powerful decentralized finance ecosystem, as well as a rapidly expanding set of non-financial tools. The blockchain also allows us to reset social norms. Where decades of economists yelling about "efficiency" failed, blockchains may be able to legitimize new uses of mechanism design. If we could use our more advanced tools to create an approach that more directly solves the problems, with fewer side effects, wouldn't that be better than fiddling with a coarse-grained one-dimensional strategy space of selling at market price versus below market price?
Begin with the goals. We'll try to cover ICOs, NFTs, and conference tickets (really a type of NFT) all at the same time.
1. Fairness: don't completely exclude low-income people from participation; give them a chance. The goal of token sales is to avoid high initial wealth concentration and have a larger and more diverse initial token holder community.
2. Don’t create races: Avoid situations where many people rush to do the same thing and only a few get in (this is the type of situation that leads to the horrible auctions-by-another-name that we saw above).
3. Don't require precise market knowledge: the mechanism should work even if the seller has no idea how much demand exists.
4. Fun: The process of participating in the sale should be fun and game-like, but not frustrating.
5. Give buyers positive expected returns: in the case of a token (or an NFT), buyers should expect price increases rather than decreases. This requires selling below market value.
Let's start with (1). From Ethereum's perspective, there is a simple solution. Use a tool designed for the job: proof of personhood protocols! Here's one quick idea:
Mechanism 1 Each participant (verified by ID) can buy up to ‘’X’’ tokens at price P, with the option to buy more at an auction.
With the per-person mechanism, buyers can get positive expected returns for the portion sold through the per-person mechanism, and the auction part does not require sellers to understand demand levels. Is it race-free? The number of participants buying through the per-person pool appears to be high. But what if the per-person pool isn't big enough to accommodate everyone?
Make the per-person allocation amount dynamic.
Mechanism 2 Each participant can deposit up to X tokens into a smart contract to declare interest. Last but not least, each buyer receives min(X, N / buyers) tokens, where N is the total sold through the per-person pool (some other amount can also be sold by auction). The buyer gets their deposit back if it exceeds the amount needed to buy their allocation.
No longer is there a race condition based on the number of buyers per person. No matter how high the demand, it's always better to join sooner rather than later.
Here's another idea if you like clever game mechanics with fancy quadratic formulas.
Mechanism 3 Each participant can buy X units at a price P X 2 up to a maximum of C tokens per buyer. C starts low and gradually increases until enough units are sold.
The quantity allocated to each buyer is theoretically optimal, though post-sale transfers will degrade this optimality over time. Mechanisms 2 and 3 appear to meet all of the above objectives. They're not perfect, but they're good starting points.
One more issue. For fixed and limited supply NFTs, the equilibrium purchased quantity per participant may be fractional (in mechanism 2, number of buyers > N, and in mechanism 3, setting C = 1 may already lead to over-subscription). With fractional sales, you can offer lottery tickets: if there are N items available, you have a chance of N/number of buyers of getting the item, otherwise you get a refund. For a conference, groups could bundle their lottery tickets to guarantee a win or a loss. The certainty of getting the item can be auctioned.
The bottom tier of "sponsorships" can be used to sell conference tickets at market rate. You may end up with a sponsor board full of people's faces, but is that okay? After all, John Lilic was on EthCC's sponsor board!
Simply put, if you want to be reliably fair to people, you need an input that explicitly measures people. Authentication protocols do this (and if desired can be combined with zero knowledge proofs to ensure privacy). So we should combine the efficiency of market and auction-based pricing with the equality of proof of personhood mechanics.
Answers to possible questions
Q: Won't people who don't care about your project buy the item and immediately resell it?
A: Not at first. Meta-games take time to appear in practice. If they do, making them untradeable for a while may help mitigate the damage. Using your face to claim that your previous account was hacked and that your identity, including everything in it, should be moved to another account works because proof-of-personhood identities are untradeable.
Q: What if I want to make my item available to a specific community?
A: Instead of ID, use proof of participation tokens linked to community events. Another option, also serving egalitarian and gamification purposes, is to encrypt items within publicly available puzzle solutions.
Q: How do we know they'll accept? Strange new mechanisms have previously been resisted.
A: Having economists write screeds about how they "should" accept a new mechanism that they find strange is difficult (or even "equity"). However, abrupt changes in context effectively reset people's expectations. So the blockchain space is the best place to try this. You could wait for the "metaverse", but it's possible that the best version will run on Ethereum anyway, so start now.

Tora Northman
3 years ago
Pixelmon NFTs are so bad, they are almost good!
Bored Apes prices continue to rise, HAPEBEAST launches, Invisible Friends hype continues to grow. Sadly, not all projects are as successful.
Of course, there are many factors to consider when buying an NFT. Is the project a scam? Will the reveal derail the project? Possibly, but when Pixelmon first teased its launch, it generated a lot of buzz.
With a primary sale mint price of 3 ETH ($8,100 USD), it started as an expensive project, with plenty of fans willing to invest in what was sold as a game. After it was revealed, it fell rapidly.
Why? It was overpromised and under delivered.
According to the project's creator[^1], the funds generated will be used to develop the artwork. "The Pixelmon reveal was wrong. This is what our Pixelmon look like in-game. "Despite the fud, I will not go anywhere," he wrote on Twitter. The goal remains. The funds will still be used to build our game. I will finish this project."
The project raised $70 million USD, but the NFTs buyers received were not the project's original teasers. Some call it "the worst NFT project ever," while others call it a complete scam.
But there's hope for some buyers. Kevin emerged from the ashes as the project was roasted over the fire.
A Minecraft character meets Salad Fingers - that's Kevin. He's a frog-like creature whose reveal was such a terrible NFT that it became part of history – and a meme.
If you're laughing at people paying $8K for a silly pixelated image, you might need to take it back. Precisely because of this, lucky holders who minted Kevin have been able to sell the now-memed NFT for over 8 ETH (around $24,000 USD), with some currently listed for 100 ETH.
Of course, Twitter has been awash in memes mocking those who invested in the project, because what else can you do when so many people lose money?
It's still unclear if the NFT project is a scam, but the team behind it was hired on Upwork. There's still hope for redemption, but Kevin's rise to fame appears to be the only positive outcome so far.
[^1] This is not the first time the creator (A 20-yo New Zealanders) has sought money via an online platform and had people claiming he under-delivered. He raised $74,000 on Kickstarter for a card game called Psycho Chicken. There are hundreds of comments on the Kickstarter project saying they haven't received the product and pleading for a refund or an update.

Pat Vieljeux
3 years ago
In 5 minutes, you can tell if a startup will succeed.
Or the “lie to me” method.

I can predict a startup's success in minutes.
Just interview its founder.
Ask "why?"
I question "why" till I sense him.
I need to feel the person I have in front of me. I need to know if he or she can deliver. Startups aren't easy. Without abilities, a brilliant idea will fail.
Good entrepreneurs have these qualities: He's a leader, determined, and resilient.
For me, they can be split in two categories.
The first entrepreneur aspires to live meaningfully. The second wants to get rich. The second is communicative. He wants to wow the crowd. He's motivated by the thought of one day sailing a boat past palm trees and sunny beaches.
What drives the first entrepreneur is evident in his speech, face, and voice. He will not speak about his product. He's (nearly) uninterested. He's not selling anything. He's not a salesman. He wants to succeed. The product is his fuel.
He'll explain his decision. He'll share his motivations. His desire. And he'll use meaningful words.
Paul Ekman has shown that face expressions aren't cultural. His study influenced the American TV series "lie to me" about body language and speech.
Passionate entrepreneurs are obvious. It's palpable. Faking passion is tough. Someone who wants your favor and money will expose his actual motives through his expressions and language.
The good liar will be able to fool you for a while, but not for long if you pay attention to his body language and how he expresses himself.
And also, if you look at his business plan.
His business plan reveals his goals. Read between the lines.
Entrepreneur 1 will focus on his "why", whereas Entrepreneur 2 will focus on the "how".
Entrepreneur 1 will develop a vision-driven culture.
The second, on the other hand, will focus on his EBITDA.
Why is the culture so critical? Because it will allow entrepreneur 1 to develop a solid team that can tackle his problems and trials. His team's "why" will keep them together in tough times.
"Give me a terrific start-up team with a mediocre idea over a weak one any day." Because a great team knows when to pivot and trusts each other. Weak teams fail.” — Bernhard Schroeder
Closings thoughts
Every VC must ask Why. Entrepreneur's motivations. This "why" will create the team's culture. This culture will help the team adjust to any setback.
