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2 years ago
Using Synthesia, DALL-E 2, and Chat GPT-3, create AI news videos
Combining AIs creates realistic AI News Videos.
Powerful AI tools like Chat GPT-3 are trending. Have you combined AIs?
The 1-minute fake news video below is startlingly realistic. Artificial Intelligence developed NASA's Mars exploration breakthrough video (AI). However, integrating the aforementioned AIs generated it.
AI-generated text for the Chat GPT-3 based on a succinct tagline
DALL-E-2 AI generates an image from a brief slogan.
Artificial intelligence-generated avatar and speech
This article shows how to use and mix the three AIs to make a realistic news video. First, watch the video (1 minute).
Talk GPT-3
Chat GPT-3 is an OpenAI NLP model. It can auto-complete text and produce conversational responses.
Try it at the playground. The AI will write a comprehensive text from a brief tagline. Let's see what the AI generates with "Breakthrough in Mars Project" as the headline.
Amazing. Our tagline matches our complete and realistic text. Fake news can start here.
DALL-E-2
OpenAI's huge transformer-based language model DALL-E-2. Its GPT-3 basis is geared for image generation. It can generate high-quality photos from a brief phrase and create artwork and images of non-existent objects.
DALL-E-2 can create a news video background. We'll use "Breakthrough in Mars project" again. Our AI creates four striking visuals. Last.
Synthesia
Synthesia lets you quickly produce videos with AI avatars and synthetic vocals.
Avatars are first. Rosie it is.
Upload and select DALL-backdrop. E-2's
Copy the Chat GPT-3 content and choose a synthetic voice.
Voice: English (US) Professional.
Finally, we generate and watch or download our video.
Synthesia AI completes the AI video.
Overview & Resources
We used three AIs to make surprisingly realistic NASA Mars breakthrough fake news in this post. Synthesia generates an avatar and a synthetic voice, therefore it may be four AIs.
These AIs created our fake news.
AI-generated text for the Chat GPT-3 based on a succinct tagline
DALL-E-2 AI generates an image from a brief slogan.
Artificial intelligence-generated avatar and speech
Christianlauer
3 years ago
Looker Studio Pro is now generally available, according to Google.
Great News about the new Google Business Intelligence Solution
Google has renamed Data Studio to Looker Studio and Looker Studio Pro.
Now, Google releases Looker Studio Pro. Similar to the move from Data Studio to Looker Studio, Looker Studio Pro is basically what Looker was previously, but both solutions will merge. Google says the Pro edition will acquire new enterprise management features, team collaboration capabilities, and SLAs.
![Dashboard Example in Looker Studio Pro — Image Source: Google[2]](https://storage.googleapis.com/int3grity/posts/m9yb4IqJCm7D/images/ZuJudlWT6GUeTKKNVduA5)
In addition to Google's announcements and sales methods, additional features include:
Looker Studio assets can now have organizational ownership. Customers can link Looker Studio to a Google Cloud project and migrate existing assets once. This provides:
Your users' created Looker Studio assets are all kept in a Google Cloud project.
When the users who own assets leave your organization, the assets won't be removed.
Using IAM, you may provide each Looker Studio asset in your company project-level permissions.
Other Cloud services can access Looker Studio assets that are owned by a Google Cloud project.
Looker Studio Pro clients may now manage report and data source access at scale using team workspaces.
Google announcing these features for the pro version is fascinating. Both products will likely converge, but Google may only release many features in the premium version in the future. Microsoft with Power BI and its free and premium variants already achieves this.
Sources and Further Readings
Google, Release Notes (2022)
Google, Looker (2022)
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.
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Adam Hayes
3 years ago
Bernard Lawrence "Bernie" Madoff, the largest Ponzi scheme in history
Madoff who?
Bernie Madoff ran the largest Ponzi scheme in history, defrauding thousands of investors over at least 17 years, and possibly longer. He pioneered electronic trading and chaired Nasdaq in the 1990s. On April 14, 2021, he died while serving a 150-year sentence for money laundering, securities fraud, and other crimes.
Understanding Madoff
Madoff claimed to generate large, steady returns through a trading strategy called split-strike conversion, but he simply deposited client funds into a single bank account and paid out existing clients. He funded redemptions by attracting new investors and their capital, but the market crashed in late 2008. He confessed to his sons, who worked at his firm, on Dec. 10, 2008. Next day, they turned him in. The fund reported $64.8 billion in client assets.
Madoff pleaded guilty to 11 federal felony counts, including securities fraud, wire fraud, mail fraud, perjury, and money laundering. Ponzi scheme became a symbol of Wall Street's greed and dishonesty before the financial crisis. Madoff was sentenced to 150 years in prison and ordered to forfeit $170 billion, but no other Wall Street figures faced legal ramifications.
Bernie Madoff's Brief Biography
Bernie Madoff was born in Queens, New York, on April 29, 1938. He began dating Ruth (née Alpern) when they were teenagers. Madoff told a journalist by phone from prison that his father's sporting goods store went bankrupt during the Korean War: "You watch your father, who you idolize, build a big business and then lose everything." Madoff was determined to achieve "lasting success" like his father "whatever it took," but his career had ups and downs.
Early Madoff investments
At 22, he started Bernard L. Madoff Investment Securities LLC. First, he traded penny stocks with $5,000 he earned installing sprinklers and as a lifeguard. Family and friends soon invested with him. Madoff's bets soured after the "Kennedy Slide" in 1962, and his father-in-law had to bail him out.
Madoff felt he wasn't part of the Wall Street in-crowd. "We weren't NYSE members," he told Fishman. "It's obvious." According to Madoff, he was a scrappy market maker. "I was happy to take the crumbs," he told Fishman, citing a client who wanted to sell eight bonds; a bigger firm would turn it down.
Recognition
Success came when he and his brother Peter built electronic trading capabilities, or "artificial intelligence," that attracted massive order flow and provided market insights. "I had all these major banks coming down, entertaining me," Madoff told Fishman. "It was mind-bending."
By the late 1980s, he and four other Wall Street mainstays processed half of the NYSE's order flow. Controversially, he paid for much of it, and by the late 1980s, Madoff was making in the vicinity of $100 million a year. He was Nasdaq chairman from 1990 to 1993.
Madoff's Ponzi scheme
It is not certain exactly when Madoff's Ponzi scheme began. He testified in court that it began in 1991, but his account manager, Frank DiPascali, had been at the firm since 1975.
Why Madoff did the scheme is unclear. "I had enough money to support my family's lifestyle. "I don't know why," he told Fishman." Madoff could have won Wall Street's respect as a market maker and electronic trading pioneer.
Madoff told Fishman he wasn't solely responsible for the fraud. "I let myself be talked into something, and that's my fault," he said, without saying who convinced him. "I thought I could escape eventually. I thought it'd be quick, but I couldn't."
Carl Shapiro, Jeffry Picower, Stanley Chais, and Norm Levy have been linked to Bernard L. Madoff Investment Securities LLC for years. Madoff's scheme made these men hundreds of millions of dollars in the 1960s and 1970s.
Madoff told Fishman, "Everyone was greedy, everyone wanted to go on." He says the Big Four and others who pumped client funds to him, outsourcing their asset management, must have suspected his returns or should have. "How can you make 15%-18% when everyone else is making less?" said Madoff.
How Madoff Got Away with It for So Long
Madoff's high returns made clients look the other way. He deposited their money in a Chase Manhattan Bank account, which merged to become JPMorgan Chase & Co. in 2000. The bank may have made $483 million from those deposits, so it didn't investigate.
When clients redeemed their investments, Madoff funded the payouts with new capital he attracted by promising unbelievable returns and earning his victims' trust. Madoff created an image of exclusivity by turning away clients. This model let half of Madoff's investors profit. These investors must pay into a victims' fund for defrauded investors.
Madoff wooed investors with his philanthropy. He defrauded nonprofits, including the Elie Wiesel Foundation for Peace and Hadassah. He approached congregants through his friendship with J. Ezra Merkin, a synagogue officer. Madoff allegedly stole $1 billion to $2 billion from his investors.
Investors believed Madoff for several reasons:
- His public portfolio seemed to be blue-chip stocks.
- His returns were high (10-20%) but consistent and not outlandish. In a 1992 interview with Madoff, the Wall Street Journal reported: "[Madoff] insists the returns were nothing special, given that the S&P 500-stock index returned 16.3% annually from 1982 to 1992. 'I'd be surprised if anyone thought matching the S&P over 10 years was remarkable,' he says.
- "He said he was using a split-strike collar strategy. A collar protects underlying shares by purchasing an out-of-the-money put option.
SEC inquiry
The Securities and Exchange Commission had been investigating Madoff and his securities firm since 1999, which frustrated many after he was prosecuted because they felt the biggest damage could have been prevented if the initial investigations had been rigorous enough.
Harry Markopolos was a whistleblower. In 1999, he figured Madoff must be lying in an afternoon. The SEC ignored his first Madoff complaint in 2000.
Markopolos wrote to the SEC in 2005: "The largest Ponzi scheme is Madoff Securities. This case has no SEC reward, so I'm turning it in because it's the right thing to do."
Many believed the SEC's initial investigations could have prevented Madoff's worst damage.
Markopolos found irregularities using a "Mosaic Method." Madoff's firm claimed to be profitable even when the S&P fell, which made no mathematical sense given what he was investing in. Markopolos said Madoff Securities' "undisclosed commissions" were the biggest red flag (1 percent of the total plus 20 percent of the profits).
Markopolos concluded that "investors don't know Bernie Madoff manages their money." Markopolos learned Madoff was applying for large loans from European banks (seemingly unnecessary if Madoff's returns were high).
The regulator asked Madoff for trading account documentation in 2005, after he nearly went bankrupt due to redemptions. The SEC drafted letters to two of the firms on his six-page list but didn't send them. Diana Henriques, author of "The Wizard of Lies: Bernie Madoff and the Death of Trust," documents the episode.
In 2008, the SEC was criticized for its slow response to Madoff's fraud.
Confession, sentencing of Bernie Madoff
Bernard L. Madoff Investment Securities LLC reported 5.6% year-to-date returns in November 2008; the S&P 500 fell 39%. As the selling continued, Madoff couldn't keep up with redemption requests, and on Dec. 10, he confessed to his sons Mark and Andy, who worked at his firm. "After I told them, they left, went to a lawyer, who told them to turn in their father, and I never saw them again. 2008-12-11: Bernie Madoff arrested.
Madoff insists he acted alone, but several of his colleagues were jailed. Mark Madoff died two years after his father's fraud was exposed. Madoff's investors committed suicide. Andy Madoff died of cancer in 2014.
2009 saw Madoff's 150-year prison sentence and $170 billion forfeiture. Marshals sold his three homes and yacht. Prisoner 61727-054 at Butner Federal Correctional Institution in North Carolina.
Madoff's lawyers requested early release on February 5, 2020, claiming he has a terminal kidney disease that may kill him in 18 months. Ten years have passed since Madoff's sentencing.
Bernie Madoff's Ponzi scheme aftermath
The paper trail of victims' claims shows Madoff's complexity and size. Documents show Madoff's scam began in the 1960s. His final account statements show $47 billion in "profit" from fake trades and shady accounting.
Thousands of investors lost their life savings, and multiple stories detail their harrowing loss.
Irving Picard, a New York lawyer overseeing Madoff's bankruptcy, has helped investors. By December 2018, Picard had recovered $13.3 billion from Ponzi scheme profiteers.
A Madoff Victim Fund (MVF) was created in 2013 to help compensate Madoff's victims, but the DOJ didn't start paying out the $4 billion until late 2017. Richard Breeden, a former SEC chair who oversees the fund, said thousands of claims were from "indirect investors"
Breeden and his team had to reject many claims because they weren't direct victims. Breeden said he based most of his decisions on one simple rule: Did the person invest more than they withdrew? Breeden estimated 11,000 "feeder" investors.
Breeden wrote in a November 2018 update for the Madoff Victim Fund, "We've paid over 27,300 victims 56.65% of their losses, with thousands more to come." In December 2018, 37,011 Madoff victims in the U.S. and around the world received over $2.7 billion. Breeden said the fund expected to make "at least one more significant distribution in 2019"
This post is a summary. Read full article here
Michael Salim
3 years ago
300 Signups, 1 Landing Page, 0 Products
I placed a link on HackerNews and got 300 signups in a week. This post explains what happened.
Product Concept
The product is DbSchemaLibrary. A library of Database Schema.
I'm not sure where this idea originated from. Very fast. Build fast, fail fast, test many ideas, and one will be a hit. I tried it. Let's try it anyway, even though it'll probably fail. I finished The Lean Startup book and wanted to use it.
Database job bores me. Important! I get drowsy working on it. Someone must do it. I remember this happening once. I needed examples at the time. Something similar to Recall (my other project) that I can copy — or at least use as a reference.
Frequently googled. Many tabs open. The results were useless. I raised my hand and agreed to construct the database myself.
It resurfaced. I decided to do something.
Due Diligence
Lean Startup emphasizes validated learning. Everything the startup does should result in learning. I may build something nobody wants otherwise. That's what happened to Recall.
So, I wrote a business plan document. This happens before I code. What am I solving? What is my proposed solution? What is the leap of faith between the problem and solution? Who would be my target audience?
My note:
In my previous project, I did the opposite!
I wrote my expectations after reading the book's advice.
“Failure is a prerequisite to learning. The problem with the notion of shipping a product and then seeing what happens is that you are guaranteed to succeed — at seeing what happens.” — The Lean Startup book
These are successful metrics. If I don't reach them, I'll drop the idea and try another. I didn't understand numbers then. Below are guesses. But it’s a start!
I then wrote the project's What and Why. I'll use this everywhere. Before, I wrote a different pitch each time. I thought certain words would be better. I felt the audience might want something unusual.
Occasionally, this works. I'm unsure if it's a good idea. No stats, just my writing-time opinion. Writing every time is time-consuming and sometimes hazardous. Having a copy saved me duplication.
I can measure and learn from performance.
Last, I identified communities that might demand the product. This became an exercise in creativity.
The MVP
So now it’s time to build.
A MVP can test my assumptions. Business may learn from it. Not low-quality. We should learn from the tiniest thing.
I like the example of how Dropbox did theirs. They assumed that if the product works, people will utilize it. How can this be tested without a quality product? They made a movie demonstrating the software's functionality. Who knows how much functionality existed?
So I tested my biggest assumption. Users want schema references. How can I test if users want to reference another schema? I'd love this. Recall taught me that wanting something doesn't mean others do.
I made an email-collection landing page. Describe it briefly. Reference library. Each email sender wants a reference. They're interested in the product. Few other reasons exist.
Header and footer were skipped. No name or logo. DbSchemaLibrary is a name I thought of after the fact. 5-minute logo. I expected a flop. Recall has no users after months of labor. What could happen to a 2-day project?
I didn't compromise learning validation. How many visitors sign up? To draw a conclusion, I must track these results.
Posting Time
Now that the job is done, gauge interest. The next morning, I posted on all my channels. I didn't want to be spammy, therefore it required more time.
I made sure each channel had at least one fan of this product. I also answer people's inquiries in the channel.
My list stinks. Several channels wouldn't work. The product's target market isn't there. Posting there would waste our time. This taught me to create marketing channels depending on my persona.
Statistics! What actually happened
My favorite part! 23 channels received the link.
I stopped posting to Discord despite its high conversion rate. I eliminated some channels because they didn't fit. According to the numbers, some users like it. Most users think it's spam.
I was skeptical. And 12 people viewed it.
I didn't expect much attention on a startup subreddit. I'll likely examine Reddit further in the future. As I have enough info, I didn't post much. Time for the next validated learning
No comment. The post had few views, therefore the numbers are low.
The targeted people come next.
I'm a Toptal freelancer. There's a member-only Slack channel. Most people can't use this marketing channel, but you should! It's not as spectacular as discord's 27% conversion rate. But I think the users here are better.
I don’t really have a following anywhere so this isn’t something I can leverage.
The best yet. 10% is converted. With more data, I expect to attain a 10% conversion rate from other channels. Stable number.
This number required some work. Did you know that people use many different clients to read HN?
Unknowns
Untrackable views and signups abound. 1136 views and 135 signups are untraceable. It's 11%. I bet much of that came from Hackernews.
Overall Statistics
The 7-day signup-to-visit ratio was 17%. (Hourly data points)
First-day percentages were lower, which is noteworthy. Initially, it was little above 10%. The HN post started getting views then.
When traffic drops, the number reaches just around 20%. More individuals are interested in the connection. hn.algolia.com sent 2 visitors. This means people are searching and finding my post.
Interesting discoveries
1. HN post struggled till the US woke up.
11am UTC. After an hour, it lost popularity. It seemed over. 7 signups converted 13%. Not amazing, but I would've thought ahead.
After 4pm UTC, traffic grew again. 4pm UTC is 9am PDT. US awakened. 10am PDT saw 512 views.
2. The product was highlighted in a newsletter.
I found Revue references when gathering data. Newsletter platform. Someone posted the newsletter link. 37 views and 3 registrations.
3. HN numbers are extremely reliable
I don't have a time-lapse graph (yet). The statistics were constant all day.
2717 views later 272 new users, or 10.1%
With 293 signups at 2856 views, 10.25%
At 306 signups at 2965 views, 10.32%
Learnings
1. My initial estimations were wildly inaccurate
I wrote 30% conversion. Reading some articles, looks like 10% is a good number to aim for.
2. Paying attention to what matters rather than vain metrics
The Lean Startup discourages vanity metrics. Feel-good metrics that don't measure growth or traction. Considering the proportion instead of the total visitors made me realize there was something here.
What’s next?
There are lots of work to do. Data aggregation, display, website development, marketing, legal issues. Fun! It's satisfying to solve an issue rather than investigate its cause.
In the meantime, I’ve already written the first project update in another post. Continue reading it if you’d like to know more about the project itself! Shifting from Quantity to Quality — DbSchemaLibrary
ANTHONY P.
3 years ago
Startups are difficult. Streamlining the procedure for creating the following unicorn.
New ventures are exciting. It's fun to imagine yourself rich, successful, and famous (if that's your thing). How you'll help others and make your family proud. This excitement can pull you forward for years, even when you intuitively realize that the path you're on may not lead to your desired success.
Know when to change course. Switching course can mean pivoting or changing direction.
In this not-so-short blog, I'll describe the journey of building your dream. And how the journey might look when you think you're building your dream, but fall short of that vision. Both can feel similar in the beginning, but there are subtle differences.
Let’s dive in.
How an exciting journey to a dead end looks and feels.
You want to help many people. You're business-minded, creative, and ambitious. You jump into entrepreneurship. You're excited, free, and in control.
I'll use tech as an example because that's what I know best, but this applies to any entrepreneurial endeavor.
So you start learning the basics of your field, say coding/software development. You read books, take courses, and may even join a bootcamp. You start practicing, and the journey begins. Once you reach a certain level of skill (which can take months, usually 12-24), you gain the confidence to speak with others in the field and find common ground. You might attract a co-founder this way with time. You and this person embark on a journey (Tip: the idea you start with is rarely the idea you end with).
Amateur mistake #1: You spend months building a product before speaking to customers.
Building something pulls you forward blindly. You make mistakes, avoid customers, and build with your co-founder or small team in the dark for months, usually 6-12 months.
You're excited when the product launches. We'll be billionaires! The market won't believe it. This excites you and the team. Launch.
….
Nothing happens.
Some people may sign up out of pity, only to never use the product or service again.
You and the team are confused, discouraged and in denial. They don't get what we've built yet. We need to market it better, we need to talk to more investors, someone will understand our vision.
This is a hopeless path, and your denial could last another 6 months. If you're lucky, while talking to consumers and investors (which you should have done from the start), someone who has been there before would pity you and give you an idea to pivot into that can create income.
Suppose you get this idea and pivot your business. Again, you've just pivoted into something limited by what you've already built. It may be a revenue-generating idea, but it's rarely new. Now you're playing catch-up, doing something others are doing but you can do better. (Tip #2: Don't be late.) Your chances of winning are slim, and you'll likely never catch up.
You're finally seeing revenue and feel successful. You can compete, but if you're not a first mover, you won't earn enough over time. You'll get by or work harder than ever to earn what a skilled trade could provide. You didn't go into business to stress out and make $100,000 or $200,000 a year. When you can make the same amount by becoming a great software developer, electrician, etc.
You become stuck. Either your firm continues this way for years until you realize there isn't enough growth to recruit a strong team and remove yourself from day-to-day operations due to competition. Or a catastrophic economic event forces you to admit that what you were building wasn't new and unique and wouldn't get you where you wanted to be.
This realization could take 6-10 years. No kidding.
The good news is, you’ve learned a lot along the way and this information can be used towards your next venture (if you have the energy).
Key Lesson: Don’t build something if you aren’t one of the first in the space building it just for the sake of building something.
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Let's discuss what it's like to build something that can make your dream come true.
Case 2: Building something the market loves is difficult but rewarding.
It starts with a problem that hasn't been adequately solved for a long time but is now solvable due to technology. Or a new problem due to a change in how things are done.
Let's examine each example.
Example #1: Mass communication. The problem is now solvable due to some technological breakthrough.
Twitter — One of the first web 2 companies that became successful with the rise of smart mobile computing.
People can share their real-time activities via mobile device with friends, family, and strangers. Web 2 and smartphones made it easy and fun.
Example #2: A new problem has emerged due to some change in the way things are conducted.
Zoom- A web-conferencing company that reached massive success due to the movement towards “work from home”, remote/hybrid work forces.
Online web conferencing allows for face-to-face communication.
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These two examples show how to build a unicorn-type company. It's a mix of solving the right problem at the right time, either through a technological breakthrough that opens up new opportunities or by fundamentally changing how people do things.
Let's find these opportunities.
Start by examining problems, such as how the world has changed and how we can help it adapt. It can also be both. Start team brainstorming. Research technologies, current world-trends, use common sense, and make a list. Then, choose the top 3 that you're most excited about and seem most workable based on your skillsets, values, and passion.
Once you have this list, create the simplest MVP you can and test it with customers. The prototype can be as simple as a picture or diagram of user flow and end-user value. No coding required. Market-test. Twitter's version 1 was simple. It was a web form that asked, "What are you doing?" Then publish it from your phone. A global status update, wherever you are. Currently, this company has a $50 billion market cap.
Here's their MVP screenshot.
Small things grow. Tiny. Simplify.
Remember Frequency and Value when brainstorming. Your product is high frequency (Twitter, Instagram, Snapchat, TikTok) or high value (Airbnb for renting travel accommodations), or both (Gmail).
Once you've identified product ideas that meet the above criteria, they're simple, have a high frequency of use, or provide deep value. You then bring it to market in the simplest, most cost-effective way. You can sell a half-working prototype with imagination and sales skills. You need just enough of a prototype to convey your vision to a user or customer.
With this, you can approach real people. This will do one of three things: give you a green light to continue on your vision as is, show you that there is no opportunity and people won't use it, or point you in a direction that is a blend of what you've come up with and what the customer / user really wants, and you update the prototype and go back to the maze. Repeat until you have enough yeses and conviction to build an MVP.