More on INTΞGRITY

The Secret Developer
3 years ago
What Elon Musk's Take on Bitcoin Teaches Us
Tesla Q2 earnings revealed unethical dealings.
As of end of Q2, we have converted approximately 75% of our Bitcoin purchases into fiat currency
That’s OK then, isn’t it?
Elon Musk, Tesla's CEO, is now untrustworthy.
It’s not about infidelity, it’s about doing the right thing
And what can we learn?
The Opening Remark
Musk tweets on his (and Tesla's) future goals.
Don’t worry, I’m not expecting you to read it.
What's crucial?
Tesla will not be selling any Bitcoin
The Situation as It Develops
2021 Tesla spent $1.5 billion on Bitcoin. In 2022, they sold 75% of the ownership for $946 million.
That’s a little bit of a waste of money, right?
Musk predicted the reverse would happen.
What gives? Why would someone say one thing, then do the polar opposite?
The Justification For Change
Tesla's public. They must follow regulations. When a corporation trades, they must record what happens.
At least this keeps Musk some way in line.
We now understand Musk and Tesla's actions.
Musk claimed that Tesla sold bitcoins to maximize cash given the unpredictability of COVID lockdowns in China.
Tesla may buy Bitcoin in the future, he said.
That’s fine then. He’s not knocking the NFT at least.
Tesla has moved investments into cash due to China lockdowns.
That doesn’t explain the 180° though
Musk's Tweet isn't company policy. Therefore, the CEO's change of heart reflects the organization. Look.
That's okay, since
Leaders alter their positions when circumstances change.
Leaders must adapt to their surroundings. This isn't embarrassing; it's a leadership prerequisite.
Yet
The Man
Someone stated if you're not in the office full-time, you need to explain yourself. He doesn't treat his employees like adults.
This is the individual mentioned in the quote.
If Elon was not happy, you knew it. Things could get nasty
also, He fired his helper for requesting a raise.
This public persona isn't good. Without mentioning his disastrous performances on Twitter (pedo dude) or Joe Rogan. This image sums up the odd Podcast appearance:
Which describes the man.
I wouldn’t trust this guy to feed a cat
What we can discover
When Musk's company bet on Bitcoin, what happened?
Exactly what we would expect
The company's position altered without the CEO's awareness. He seems uncaring.
This article is about how something happened, not what happened. Change of thinking requires contrition.
This situation is about a lack of respect- although you might argue that followers on Twitter don’t deserve any
Tesla fans call the sale a great move.
It's absurd.
As you were, then.
Conclusion
Good luck if you gamble.
When they pay off, congrats!
When wrong, admit it.
You must take chances if you want to succeed.
Risks don't always pay off.
Mr. Musk lacks insight and charisma to combine these two attributes.
I don’t like him, if you hadn’t figured.
It’s probably all of the cheating.
INTΞGRITY team
3 years ago
Privacy Policy
Effective date: August 31, 2022
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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

Tim Smedley
2 years ago
When Investment in New Energy Surpassed That in Fossil Fuels (Forever)
A worldwide energy crisis might have hampered renewable energy and clean tech investment. Nope.
BNEF's 2023 Energy Transition Investment Trends study surprised and encouraged. Global energy transition investment reached $1 trillion for the first time ($1.11t), up 31% from 2021. From 2013, the clean energy transition has come and cannot be reversed.
BNEF Head of Global Analysis Albert Cheung said our findings ended the energy crisis's influence on renewable energy deployment. Energy transition investment has reached a record as countries and corporations implement transition strategies. Clean energy investments will soon surpass fossil fuel investments.
The table below indicates the tripping point, which means the energy shift is occuring today.
BNEF calls money invested on clean technology including electric vehicles, heat pumps, hydrogen, and carbon capture energy transition investment. In 2022, electrified heat received $64b and energy storage $15.7b.
Nonetheless, $495b in renewables (up 17%) and $466b in electrified transport (up 54%) account for most of the investment. Hydrogen and carbon capture are tiny despite the fanfare. Hydrogen received the least funding in 2022 at $1.1 billion (0.1%).
China dominates investment. China spends $546 billion on energy transition, half the global amount. Second, the US total of $141 billion in 2022 was up 11% from 2021. With $180 billion, the EU is unofficially second. China invested 91% in battery technologies.
The 2022 transition tipping point is encouraging, but the BNEF research shows how far we must go to get Net Zero. Energy transition investment must average $4.55 trillion between 2023 and 2030—three times the amount spent in 2022—to reach global Net Zero. Investment must be seven times today's record to reach Net Zero by 2050.
BNEF 2023 Energy Transition Investment Trends.
As shown in the graph above, BNEF experts have been using their crystal balls to determine where that investment should go. CCS and hydrogen are still modest components of the picture. Interestingly, they see nuclear almost fading. Active transport advocates like me may have something to say about the massive $4b in electrified transport. If we focus on walkable 15-minute cities, we may need fewer electric automobiles. Though we need more electric trains and buses.
Albert Cheung of BNEF emphasizes the challenge. This week's figures promise short-term job creation and medium-term energy security, but more investment is needed to reach net zero in the long run.
I expect the BNEF Energy Transition Investment Trends report to show clean tech investment outpacing fossil fuels investment every year. Finally saying that is amazing. It's insufficient. The planet must maintain its electric (not gas) pedal. In response to the research, Christina Karapataki, VC at Breakthrough Energy Ventures, a clean tech investment firm, tweeted: Clean energy investment needs to average more than 3x this level, for the remainder of this decade, to get on track for BNEFs Net Zero Scenario. Go!

Techletters
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