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Enrique Dans

Enrique Dans

3 years ago

You may not know about The Merge, yet it could change society

More on Technology

Ossiana Tepfenhart

Ossiana Tepfenhart

3 years ago

Has anyone noticed what an absolute shitshow LinkedIn is?

After viewing its insanity, I had to leave this platform.

Photo by Greg Bulla on Unsplash

I joined LinkedIn recently. That's how I aim to increase my readership and gain recognition. LinkedIn's premise appealed to me: a Facebook-like platform for professional networking.

I don't use Facebook since it's full of propaganda. It seems like a professional, apolitical space, right?

I expected people to:

  • be more formal and respectful than on Facebook.

  • Talk about the inclusiveness of the workplace. Studies consistently demonstrate that inclusive, progressive workplaces outperform those that adhere to established practices.

  • Talk about business in their industry. Yep. I wanted to read articles with advice on how to write better and reach a wider audience.

Oh, sh*t. I hadn't anticipated that.

Photo by Bernard Hermant on Unsplash

After posting and reading about inclusivity and pro-choice, I was startled by how many professionals acted unprofessionally. I've seen:

  • Men have approached me in the DMs in a really aggressive manner. Yikes. huge yikes Not at all professional.

  • I've heard pro-choice women referred to as infant killers by many people. If I were the CEO of a company and I witnessed one of my employees acting that poorly, I would immediately fire them.

  • Many posts are anti-LGBTQIA+, as I've noticed. a lot, like, a lot. Some are subtly stating that the world doesn't need to know, while others are openly making fun of transgender persons like myself.

  • Several medical professionals were posting explicitly racist comments. Even if you are as white as a sheet like me, you should be alarmed by this. Who's to guarantee a patient who is black won't unintentionally die?

  • I won't even get into how many men in STEM I observed pushing for the exclusion of women from their fields. I shouldn't be surprised considering the majority of those men I've encountered have a passionate dislike for women, but goddamn, dude.

Many people appear entirely too at ease displaying their bigotry on their professional profiles.

Photo by Jon Tyson on Unsplash

As a white female, I'm always shocked by people's open hostility. Professional environments are very important.

I don't know if this is still true (people seem too politicized to care), but if I heard many of these statements in person, I'd suppose they feel ashamed. Really.

Are you not ashamed of being so mean? Are you so weak that competing with others terrifies you? Isn't this embarrassing?

LinkedIn isn't great at censoring offensive comments. These people aren't getting warnings. So they were safe while others were unsafe.

The CEO in me would want to know if I had placed a bigot on my staff.

Photo by Romain V on Unsplash

I always wondered if people's employers knew about their online behavior. If they know how horrible they appear, they don't care.

As a manager, I was picky about hiring. Obviously. In most industries, it costs $1,000 or more to hire a full-time employee, so be sure it pays off.

Companies that embrace diversity and tolerance (and are intolerant of intolerance) are more profitable, likely to recruit top personnel, and successful.

People avoid businesses that alienate them. That's why I don't eat at Chic-Fil-A and why folks avoid MyPillow. Being inclusive is good business.

CEOs are harmed by online bigots. Image is an issue. If you're a business owner, you can fire staff who don't help you.

On the one hand, I'm delighted it makes it simpler to identify those with whom not to do business.

Photo by Tim Mossholder on Unsplash

Don’t get me wrong. I'm glad I know who to avoid when hiring, getting references, or searching for a job. When people are bad, it saves me time.

What's up with professionalism?

Really. I need to know. I've crossed the boundary between acceptable and unacceptable behavior, but never on a professional platform. I got in trouble for not wearing bras even though it's not part of my gender expression.

If I behaved like that at my last two office jobs, my supervisors would have fired me immediately. Some of the behavior I've seen is so outrageous, I can't believe these people have employment. Some are even leaders.

Like…how? Is hatred now normalized?

Please pay attention whether you're seeking for a job or even simply a side gig.

Photo by Greg Bulla on Unsplash

Do not add to the tragedy that LinkedIn comments can be, or at least don't make uninformed comments. Even if you weren't banned, the site may still bite you.

Recruiters can and do look at your activity. Your writing goes on your résumé. The wrong comment might lose you a job.

Recruiters and CEOs might reject candidates whose principles contradict with their corporate culture. Bigotry will get you banned from many companies, especially if others report you.

If you want a high-paying job, avoid being a LinkedIn asshole. People care even if you think no one does. Before speaking, ponder. Is this how you want to be perceived?

Better advice:

If your politics might turn off an employer, stop posting about them online and ask yourself why you hold such objectionable ideas.

Gajus Kuizinas

Gajus Kuizinas

3 years ago

How a few lines of code were able to eliminate a few million queries from the database

I was entering tens of millions of records per hour when I first published Slonik PostgreSQL client for Node.js. The data being entered was usually flat, making it straightforward to use INSERT INTO ... SELECT * FROM unnset() pattern. I advocated the unnest approach for inserting rows in groups (that was part I).

Bulk inserting nested data into the database

However, today I’ve found a better way: jsonb_to_recordset.

jsonb_to_recordset expands the top-level JSON array of objects to a set of rows having the composite type defined by an AS clause.

jsonb_to_recordset allows us to query and insert records from arbitrary JSON, like unnest. Since we're giving JSON to PostgreSQL instead of unnest, the final format is more expressive and powerful.

SELECT *
FROM json_to_recordset('[{"name":"John","tags":["foo","bar"]},{"name":"Jane","tags":["baz"]}]')
AS t1(name text, tags text[]);
 name |   tags
------+-----------
 John | {foo,bar}
 Jane | {baz}
(2 rows)

Let’s demonstrate how you would use it to insert data.

Inserting data using json_to_recordset

Say you need to insert a list of people with attributes into the database.

const persons = [
  {
    name: 'John',
    tags: ['foo', 'bar']
  },
  {
    name: 'Jane',
    tags: ['baz']
  }
];

You may be tempted to traverse through the array and insert each record separately, e.g.

for (const person of persons) {
  await pool.query(sql`
    INSERT INTO person (name, tags)
    VALUES (
      ${person.name},
      ${sql.array(person.tags, 'text[]')}
    )
  `);
}

It's easier to read and grasp when working with a few records. If you're like me and troubleshoot a 2M+ insert query per day, batching inserts may be beneficial.

What prompted the search for better alternatives.

Inserting using unnest pattern might look like this:

await pool.query(sql`
  INSERT INTO public.person (name, tags)
  SELECT t1.name, t1.tags::text[]
  FROM unnest(
    ${sql.array(['John', 'Jane'], 'text')},
    ${sql.array(['{foo,bar}', '{baz}'], 'text')}
  ) AS t1.(name, tags);
`);

You must convert arrays into PostgreSQL array strings and provide them as text arguments, which is unsightly. Iterating the array to create slices for each column is likewise unattractive.

However, with jsonb_to_recordset, we can:

await pool.query(sql`
  INSERT INTO person (name, tags)
  SELECT *
  FROM jsonb_to_recordset(${sql.jsonb(persons)}) AS t(name text, tags text[])
`);

In contrast to the unnest approach, using jsonb_to_recordset we can easily insert complex nested data structures, and we can pass the original JSON document to the query without needing to manipulate it.

In terms of performance they are also exactly the same. As such, my current recommendation is to prefer jsonb_to_recordset whenever inserting lots of rows or nested data structures.

Farhad Malik

Farhad Malik

3 years ago

How This Python Script Makes Me Money Every Day

Starting a passive income stream with data science and programming

My website is fresh. But how do I monetize it?

Creating a passive-income website is difficult. Advertise first. But what useful are ads without traffic?

Let’s Generate Traffic And Put Our Programming Skills To Use

SEO boosts traffic (Search Engine Optimisation). Traffic generation is complex. Keywords matter more than text, URL, photos, etc.

My Python skills helped here. I wanted to find relevant, Google-trending keywords (tags) for my topic.

First The Code

I wrote the script below here.

import re
from string import punctuation

import nltk
from nltk import TreebankWordTokenizer, sent_tokenize
from nltk.corpus import stopwords


class KeywordsGenerator:
    def __init__(self, pytrends):
        self._pytrends = pytrends

    def generate_tags(self, file_path, top_words=30):
        file_text = self._get_file_contents(file_path)
        clean_text = self._remove_noise(file_text)
        top_words = self._get_top_words(clean_text, top_words)
        suggestions = []
        for top_word in top_words:
            suggestions.extend(self.get_suggestions(top_word))
        suggestions.extend(top_words)
        tags = self._clean_tokens(suggestions)
        return ",".join(list(set(tags)))

    def _remove_noise(self, text):
        #1. Convert Text To Lowercase and remove numbers
        lower_case_text = str.lower(text)
        just_text = re.sub(r'\d+', '', lower_case_text)
        #2. Tokenise Paragraphs To words
        list = sent_tokenize(just_text)
        tokenizer = TreebankWordTokenizer()
        tokens = tokenizer.tokenize(just_text)
        #3. Clean text
        clean = self._clean_tokens(tokens)
        return clean

    def _clean_tokens(self, tokens):
        clean_words = [w for w in tokens if w not in punctuation]
        stopwords_to_remove = stopwords.words('english')
        clean = [w for w in clean_words if w not in stopwords_to_remove and not w.isnumeric()]
        return clean

    def get_suggestions(self, keyword):
        print(f'Searching pytrends for {keyword}')
        result = []
        self._pytrends.build_payload([keyword], cat=0, timeframe='today 12-m')
        data = self._pytrends.related_queries()[keyword]['top']
        if data is None or data.values is None:
            return result
        result.extend([x[0] for x in data.values.tolist()][:2])
        return result

    def _get_file_contents(self, file_path):
        return open(file_path, "r", encoding='utf-8',errors='ignore').read()

    def _get_top_words(self, words, top):
        counts = dict()

        for word in words:
            if word in counts:
                counts[word] += 1
            else:
                counts[word] = 1

        return list({k: v for k, v in sorted(counts.items(), key=lambda item: item[1])}.keys())[:top]


if __name__ == "1__main__":
    from pytrends.request import TrendReq

    nltk.download('punkt')
    nltk.download('stopwords')
    pytrends = TrendReq(hl='en-GB', tz=360)
    tags = KeywordsGenerator(pytrends)\
              .generate_tags('text_file.txt')
    print(tags)

Then The Dependencies

This script requires:

nltk==3.7
pytrends==4.8.0

Analysis of the Script

I copy and paste my article into text file.txt, and the code returns the keywords as a comma-separated string.

To achieve this:

  1. A class I made is called KeywordsGenerator.

  2. This class has a function: generate_tags

  3. The function generate_tags performs the following tasks:

  • retrieves text file contents

  • uses NLP to clean the text by tokenizing sentences into words, removing punctuation, and other elements.

  • identifies the most frequent words that are relevant.

  • The pytrends API is then used to retrieve related phrases that are trending for each word from Google.

  • finally adds a comma to the end of the word list.

4. I then use the keywords and paste them into the SEO area of my website.

These terms are trending on Google and relevant to my topic. My site's rankings and traffic have improved since I added new keywords. This little script puts our knowledge to work. I shared the script in case anyone faces similar issues.

I hope it helps readers sell their work.

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Ray Dalio

Ray Dalio

3 years ago

The latest “bubble indicator” readings.

As you know, I like to turn my intuition into decision rules (principles) that can be back-tested and automated to create a portfolio of alpha bets. I use one for bubbles. Having seen many bubbles in my 50+ years of investing, I described what makes a bubble and how to identify them in markets—not just stocks.

A bubble market has a high degree of the following:

  1. High prices compared to traditional values (e.g., by taking the present value of their cash flows for the duration of the asset and comparing it with their interest rates).
  2. Conditons incompatible with long-term growth (e.g., extrapolating past revenue and earnings growth rates late in the cycle).
  3. Many new and inexperienced buyers were drawn in by the perceived hot market.
  4. Broad bullish sentiment.
  5. Debt financing a large portion of purchases.
  6. Lots of forward and speculative purchases to profit from price rises (e.g., inventories that are more than needed, contracted forward purchases, etc.).

I use these criteria to assess all markets for bubbles. I have periodically shown you these for stocks and the stock market.

What Was Shown in January Versus Now

I will first describe the picture in words, then show it in charts, and compare it to the last update in January.

As of January, the bubble indicator showed that a) the US equity market was in a moderate bubble, but not an extreme one (ie., 70 percent of way toward the highest bubble, which occurred in the late 1990s and late 1920s), and b) the emerging tech companies (ie. As well, the unprecedented flood of liquidity post-COVID financed other bubbly behavior (e.g. SPACs, IPO boom, big pickup in options activity), making things bubbly. I showed which stocks were in bubbles and created an index of those stocks, which I call “bubble stocks.”

Those bubble stocks have popped. They fell by a third last year, while the S&P 500 remained flat. In light of these and other market developments, it is not necessarily true that now is a good time to buy emerging tech stocks.

The fact that they aren't at a bubble extreme doesn't mean they are safe or that it's a good time to get long. Our metrics still show that US stocks are overvalued. Once popped, bubbles tend to overcorrect to the downside rather than settle at “normal” prices.

The following charts paint the picture. The first shows the US equity market bubble gauge/indicator going back to 1900, currently at the 40% percentile. The charts also zoom in on the gauge in recent years, as well as the late 1920s and late 1990s bubbles (during both of these cases the gauge reached 100 percent ).

The chart below depicts the average bubble gauge for the most bubbly companies in 2020. Those readings are down significantly.

The charts below compare the performance of a basket of emerging tech bubble stocks to the S&P 500. Prices have fallen noticeably, giving up most of their post-COVID gains.

The following charts show the price action of the bubble slice today and in the 1920s and 1990s. These charts show the same market dynamics and two key indicators. These are just two examples of how a lot of debt financing stock ownership coupled with a tightening typically leads to a bubble popping.

Everything driving the bubbles in this market segment is classic—the same drivers that drove the 1920s bubble and the 1990s bubble. For instance, in the last couple months, it was how tightening can act to prick the bubble. Review this case study of the 1920s stock bubble (starting on page 49) from my book Principles for Navigating Big Debt Crises to grasp these dynamics.

The following charts show the components of the US stock market bubble gauge. Since this is a proprietary indicator, I will only show you some of the sub-aggregate readings and some indicators.

Each of these six influences is measured using a number of stats. This is how I approach the stock market. These gauges are combined into aggregate indices by security and then for the market as a whole. The table below shows the current readings of these US equity market indicators. It compares current conditions for US equities to historical conditions. These readings suggest that we’re out of a bubble.

1. How High Are Prices Relatively?

This price gauge for US equities is currently around the 50th percentile.

2. Is price reduction unsustainable?

This measure calculates the earnings growth rate required to outperform bonds. This is calculated by adding up the readings of individual securities. This indicator is currently near the 60th percentile for the overall market, higher than some of our other readings. Profit growth discounted in stocks remains high.

Even more so in the US software sector. Analysts' earnings growth expectations for this sector have slowed, but remain high historically. P/Es have reversed COVID gains but remain high historical.

3. How many new buyers (i.e., non-existing buyers) entered the market?

Expansion of new entrants is often indicative of a bubble. According to historical accounts, this was true in the 1990s equity bubble and the 1929 bubble (though our data for this and other gauges doesn't go back that far). A flood of new retail investors into popular stocks, which by other measures appeared to be in a bubble, pushed this gauge above the 90% mark in 2020. The pace of retail activity in the markets has recently slowed to pre-COVID levels.

4. How Broadly Bullish Is Sentiment?

The more people who have invested, the less resources they have to keep investing, and the more likely they are to sell. Market sentiment is now significantly negative.

5. Are Purchases Being Financed by High Leverage?

Leveraged purchases weaken the buying foundation and expose it to forced selling in a downturn. The leverage gauge, which considers option positions as a form of leverage, is now around the 50% mark.

6. To What Extent Have Buyers Made Exceptionally Extended Forward Purchases?

Looking at future purchases can help assess whether expectations have become overly optimistic. This indicator is particularly useful in commodity and real estate markets, where forward purchases are most obvious. In the equity markets, I look at indicators like capital expenditure, or how much businesses (and governments) invest in infrastructure, factories, etc. It reflects whether businesses are projecting future demand growth. Like other gauges, this one is at the 40th percentile.

What one does with it is a tactical choice. While the reversal has been significant, future earnings discounting remains high historically. In either case, bubbles tend to overcorrect (sell off more than the fundamentals suggest) rather than simply deflate. But I wanted to share these updated readings with you in light of recent market activity.

Esteban

Esteban

3 years ago

The Berkus Startup Valuation Method: What Is It?

What Is That?

Berkus is a pre-revenue valuation method based exclusively on qualitative criteria, like Scorecard.

Few firms match their financial estimates, especially in the early stages, so valuation methodologies like the Berkus method are a good way to establish a valuation when the economic measures are not reliable.

How does it work?

This technique evaluates five key success factors.

  • Fundamental principle

  • Technology

  • Execution

  • Strategic alliances in its primary market

  • Production, followed by sales

The Berkus technique values the business idea and four success factors. As seen in the matrix below, each of these dimensions poses a danger to the startup's success.

It assigns $0-$500,000 to each of these beginning regions. This approach enables a maximum $2.5M pre-money valuation.

This approach relies significantly on geography and uses the US as a baseline, as it differs in every country in Europe.

A set of standards for analyzing each dimension individually

Fundamental principle (or strength of the idea)

Ideas are worthless; execution matters. Most of us can relate to seeing a new business open in our area or a startup get funded and thinking, "I had this concept years ago!" Someone did it.

The concept remains. To assess the idea's viability, we must consider several criteria.

  • The concept's exclusivity It is necessary to protect a product or service's concept using patents and copyrights. Additionally, it must be capable of generating large profits.

  • Planned growth and growth that goes in a specific direction have a lot of potential, therefore incorporating them into a business is really advantageous.

  • The ability of a concept to grow A venture's ability to generate scalable revenue is a key factor in its emergence and continuation. A startup needs a scalable idea in order to compete successfully in the market.

  • The attraction of a business idea to a broad spectrum of people is significantly influenced by the current socio-political climate. Thus, the requirement for the assumption of conformity.

  • Concept Validation Ideas must go through rigorous testing with a variety of audiences in order to lower risk during the implementation phase.

Technology (Prototype)

This aspect reduces startup's technological risk. How good is the startup prototype when facing cyber threats, GDPR compliance (in Europe), tech stack replication difficulty, etc.?

Execution

Check the management team's efficacy. A potential angel investor must verify the founders' experience and track record with previous ventures. Good leadership is needed to chart a ship's course.

Strategic alliances in its primary market

Existing and new relationships will play a vital role in the development of both B2B and B2C startups. What are the startup's synergies? potential ones?

Production, followed by sales (product rollout)

Startup success depends on its manufacturing and product rollout. It depends on the overall addressable market, the startup's ability to market and sell their product, and their capacity to provide consistent, high-quality support.

Example

We're now founders of EyeCaramba, a machine vision-assisted streaming platform. My imagination always goes to poor puns when naming a startup.

Since we're first-time founders and the Berkus technique depends exclusively on qualitative methods and the evaluator's skill, we ask our angel-investor acquaintance for a pre-money appraisal of EyeCaramba.

Our friend offers us the following table:

Because we're first-time founders, our pal lowered our Execution score. He knows the idea's value and that the gaming industry is red-hot, with worse startup ideas getting funded, therefore he gave the Basic value the highest value (idea).

EyeCaramba's pre-money valuation is $400,000 + $250,000 + $75,000 + $275,000 + $164,000 (1.16M). Good.

References

  • https://medium.com/humble-ventures/how-angel-investors-value-pre-revenue-startups-part-iii-8271405f0774#:~:text=pre%2Drevenue%20startups.-,Berkus%20Method,potential%20of%20the%20idea%20itself.%E2%80%9D

  • https://eqvista.com/berkus-valuation-method-for-startups/

  • https://www.venionaire.com/early-stage-startup-valuation-part-2-the-berkus-method/

Entreprogrammer

Entreprogrammer

3 years ago

The Steve Jobs Formula: A Guide to Everything

A must-read for everyone

Photo by AB on Unsplash

Jobs is well-known. You probably know the tall, thin guy who wore the same clothing every day. His influence is unavoidable. In fewer than 40 years, Jobs' innovations have impacted computers, movies, cellphones, music, and communication.

Steve Jobs may be more imaginative than the typical person, but if we can use some of his ingenuity, ambition, and good traits, we'll be successful. This essay explains how to follow his guidance and success secrets.

1. Repetition is necessary for success.

Be patient and diligent to master something. Practice makes perfect. This is why older workers are often more skilled.

When should you repeat a task? When you're confident and excited to share your product. It's when to stop tweaking and repeating.

Jobs stated he'd make the crowd sh** their pants with an iChat demo.

Use this in your daily life.

  • Start with the end in mind. You can put it in writing and be as detailed as you like with your plan's schedule and metrics. For instance, you have a goal of selling three coffee makers in a week.

  • Break it down, break the goal down into particular tasks you must complete, and then repeat those tasks. To sell your coffee maker, you might need to make 50 phone calls.

  • Be mindful of the amount of work necessary to produce the desired results. Continue doing this until you are happy with your product.

2. Acquire the ability to add and subtract.

How did Picasso invent cubism? Pablo Picasso was influenced by stylised, non-naturalistic African masks that depict a human figure.

Artists create. Constantly seeking inspiration. They think creatively about random objects. Jobs said creativity is linking things. Creative people feel terrible when asked how they achieved something unique because they didn't do it all. They saw innovation. They had mastered connecting and synthesizing experiences.

Use this in your daily life.

  • On your phone, there is a note-taking app. Ideas for what you desire to learn should be written down. It may be learning a new language, calligraphy, or anything else that inspires or intrigues you.

  • Note any ideas you have, quotations, or any information that strikes you as important.

  • Spend time with smart individuals, that is the most important thing. Jim Rohn, a well-known motivational speaker, has observed that we are the average of the five people with whom we spend the most time.

  • Learning alone won't get you very far. You need to put what you've learnt into practice. If you don't use your knowledge and skills, they are useless.

3. Develop the ability to refuse.

Steve Jobs deleted thousands of items when he created Apple's design ethic. Saying no to distractions meant upsetting customers and partners.

John Sculley, the former CEO of Apple, said something like this. According to Sculley, Steve’s methodology differs from others as he always believed that the most critical decisions are things you choose not to do.

Use this in your daily life.

  • Never be afraid to say "no," "I won't," or "I don't want to." Keep it simple. This method works well in some situations.

  • Give a different option. For instance, X might be interested even if I won't be able to achieve it.

  • Control your top priority. Before saying yes to anything, make sure your work schedule and priority list are up to date.

4. Follow your passion

“Follow your passion” is the worst advice people can give you. Steve Jobs didn't start Apple because he suddenly loved computers. He wanted to help others attain their maximum potential.

Great things take a lot of work, so quitting makes sense if you're not passionate. Jobs learned from history that successful people were passionate about their work and persisted through challenges.

Use this in your daily life.

  • Stay away from your passion. Allow it to develop daily. Keep working at your 9-5-hour job while carefully gauging your level of desire and endurance. Less risk exists.

  • The truth is that if you decide to work on a project by yourself rather than in a group, it will take you years to complete it instead of a week. Instead, network with others who have interests in common.

  • Prepare a fallback strategy in case things go wrong.

Success, this small two-syllable word eventually gives your life meaning, a perspective. What is success?  For most, it's achieving their ambitions. However, there's a catch. Successful people aren't always happy.

Furthermore, where do people’s goals and achievements end? It’s a never-ending process. Success is a journey, not a destination. We wish you not to lose your way on this journey.