Apple's search engine has long been rumored. Recent Google developments may confirm the rumor. Is Apple about to become Google's biggest rival?

Here's a video:

People noted Apple's changes in 2020. AppleBot, a web crawler that downloads and caches Internet content, was more active than in the last five years.

Apple hired search engine developers, including ex-Googlers, such as John Giannandrea, Google's former search chief.

Apple also changed the way iPhones search. With iOS 14, Apple's search results arrived before Google's.

These facts fueled rumors that Apple was developing a search engine.

Apple and Google Have a Contract

Many skeptics said Apple couldn't compete with Google. This didn't affect the company's competitiveness.

Apple is the only business with the resources and scale to be a Google rival, with 1.8 billion active devices and a $2 trillion market cap.

Still, people doubted that due to a license deal. Google pays Apple $8 to $12 billion annually to be the default iPhone and iPad search engine.

Apple can't build an independent search product under this arrangement.

Why would Apple enter search if it's being paid to stay out?

Ironically, this partnership has many people believing Apple is getting into search.

A New Default Search Engine May Be Needed

Google was sued for antitrust in 2020. It is accused of anticompetitive and exclusionary behavior. Justice wants to end Google's monopoly.

Authorities could restrict Apple and Google's licensing deal due to its likely effect on market competitiveness. Hence Apple needs a new default search engine.

Apple Already Has a Search Engine

The company already has a search engine, Spotlight.

Since 2004, Spotlight has aired. It was developed to help users find photos, documents, apps, music, and system preferences.

Apple's search engine could do more than organize files, texts, and apps.

Spotlight Search was updated in 2014 with iOS 8. Web, App Store, and iTunes searches became available. You could find nearby places, movie showtimes, and news.

This search engine has subsequently been updated and improved. Spotlight added rich search results last year.

If you search for a TV show, movie, or song, photos and carousels will appear at the top of the page.

This resembles Google's rich search results.

When Will the Apple Search Engine Be Available?

When will Apple's search launch? Robert Scoble says it's near.

Scoble tweeted a number of hints before this year's Worldwide Developer Conference.

Scoble bases his prediction on insider information and deductive reasoning. January 2023 is expected.

Will you use Apple's search engine?

It's cheaper, faster charging, longer lasting, safer, and better for the environment.

Batteries are the future. Next-gen and planet-saving technology, including solar power and EVs, require batteries. As these smart technologies become more popular, we find that our batteries can't keep up. Lithium-ion batteries are expensive, slow to charge, big, fast to decay, flammable, and not environmentally friendly. MIT just created a new battery that eliminates all of these problems.  So, is this the battery of the future? Or is there a catch?

When I say entirely new, I mean it. This battery employs no currently available materials. Its electrodes are constructed of aluminium and pure sulfur instead of lithium-complicated ion's metals and graphite. Its electrolyte is formed of molten chloro-aluminate salts, not an organic solution with lithium salts like lithium-ion batteries.

How does this change in materials help?

Aluminum, sulfur, and chloro-aluminate salts are abundant, easy to acquire, and cheap. This battery might be six times cheaper than a lithium-ion battery and use less hazardous mining. The world and our wallets will benefit.

But don’t go thinking this means it lacks performance.

This battery charged in under a minute in tests. At 25 degrees Celsius, the battery will charge 25 times slower than at 110 degrees Celsius. This is because the salt, which has a very low melting point, is in an ideal state at 110 degrees and can carry a charge incredibly quickly. Unlike lithium-ion, this battery self-heats when charging and discharging, therefore no external heating is needed.

Anyone who's seen a lithium-ion battery burst might be surprised. Unlike lithium-ion batteries, none of the components in this new battery can catch fire. Thus, high-temperature charging and discharging speeds pose no concern.

These batteries are long-lasting. Lithium-ion batteries don't last long, as any iPhone owner can attest. During charging, metal forms a dendrite on the electrode. This metal spike will keep growing until it reaches the other end of the battery, short-circuiting it. This is why phone batteries only last a few years and why electric car range decreases over time. This new battery's molten salt slows deposition, extending its life. This helps the environment and our wallets.

These batteries are also energy dense. Some lithium-ion batteries have 270 Wh/kg energy density (volume and mass). Aluminum-sulfur batteries could have 1392 Wh/kg, according to calculations. They'd be 5x more energy dense. Tesla's Model 3 battery would weigh 96 kg instead of 480 kg if this battery were used. This would improve the car's efficiency and handling.

These calculations were for batteries without molten salt electrolyte. Because they don't reflect the exact battery chemistry, they aren't a surefire prediction.

This battery seems great. It will take years, maybe decades, before it reaches the market and makes a difference. Right?

Nope. The project's scientists founded Avanti to develop and market this technology.

So we'll soon be driving cheap, durable, eco-friendly, lightweight, and ultra-safe EVs? Nope.

This battery must be kept hot to keep the salt molten; otherwise, it won't work and will expand and contract, causing damage. This issue could be solved by packs that can rapidly pre-heat, but that project is far off.

Rapid and constant charge-discharge cycles make these batteries ideal for solar farms, homes, and EV charging stations. The battery is constantly being charged or discharged, allowing it to self-heat and maintain an ideal temperature.

These batteries aren't as sexy as those making EVs faster, more efficient, and cheaper. Grid batteries are crucial to our net-zero transition because they allow us to use more low-carbon energy. As we move away from fossil fuels, we'll need millions of these batteries, so the fact that they're cheap, safe, long-lasting, and environmentally friendly will be huge. Who knows, maybe EVs will use this technology one day. MIT has created another world-changing technology.

The brand-new USB Rubber Ducky is much riskier than before.

With its own programming language, the well-liked hacking tool may now pwn you.

With a vengeance, the USB Rubber Ducky is back.

This year's Def Con hacking conference saw the release of a new version of the well-liked hacking tool, and its author, Darren Kitchen, was on hand to explain it. We put a few of the new features to the test and discovered that the most recent version is riskier than ever.

WHAT IS IT?

The USB Rubber Ducky seems to the untrained eye to be an ordinary USB flash drive. However, when you connect it to a computer, the computer recognizes it as a USB keyboard and will accept keystroke commands from the device exactly like a person would type them in.

Kitchen explained to me, "It takes use of the trust model built in, where computers have been taught to trust a human, in that anything it types is trusted to the same degree as the user is trusted. And a computer is aware that clicks and keystrokes are how people generally connect with it.

Over ten years ago, the first Rubber Ducky was published, quickly becoming a hacker favorite (it was even featured in a Mr. Robot scene). Since then, there have been a number of small upgrades, but the most recent Rubber Ducky takes a giant step ahead with a number of new features that significantly increase its flexibility and capability.

WHERE IS ITS USE?

The options are nearly unlimited with the proper strategy.

The Rubber Ducky has already been used to launch attacks including making a phony Windows pop-up window to collect a user's login information or tricking Chrome into sending all saved passwords to an attacker's web server. However, these attacks lacked the adaptability to operate across platforms and had to be specifically designed for particular operating systems and software versions.

The nuances of DuckyScript 3.0 are described in a new manual. 

The most recent Rubber Ducky seeks to get around these restrictions. The DuckyScript programming language, which is used to construct the commands that the Rubber Ducky will enter into a target machine, receives a significant improvement with it. DuckyScript 3.0 is a feature-rich language that allows users to write functions, store variables, and apply logic flow controls, in contrast to earlier versions that were primarily limited to scripting keystroke sequences (i.e., if this... then that).

This implies that, for instance, the new Ducky can check to see if it is hooked into a Windows or Mac computer and then conditionally run code specific to each one, or it can disable itself if it has been attached to the incorrect target. In order to provide a more human effect, it can also generate pseudorandom numbers and utilize them to add a configurable delay between keystrokes.

The ability to steal data from a target computer by encoding it in binary code and transferring it through the signals intended to instruct a keyboard when the CapsLock or NumLock LEDs should light up is perhaps its most astounding feature. By using this technique, a hacker may plug it in for a brief period of time, excuse themselves by saying, "Sorry, I think that USB drive is faulty," and then take it away with all the credentials stored on it.

HOW SERIOUS IS THE RISK?

In other words, it may be a significant one, but because physical device access is required, the majority of people aren't at risk of being a target.

The 500 or so new Rubber Duckies that Hak5 brought to Def Con, according to Kitchen, were his company's most popular item at the convention, and they were all gone on the first day. It's safe to suppose that hundreds of hackers already possess one, and demand is likely to persist for some time.

Additionally, it has an online development toolkit that can be used to create attack payloads, compile them, and then load them onto the target device. A "payload hub" part of the website makes it simple for hackers to share what they've generated, and the Hak5 Discord is also busy with conversation and helpful advice. This makes it simple for users of the product to connect with a larger community.

It's too expensive for most individuals to distribute in volume, so unless your favorite cafe is renowned for being a hangout among vulnerable targets, it's doubtful that someone will leave a few of them there. To that end, if you intend to plug in a USB device that you discovered outside in a public area, pause to consider your decision.

WOULD IT WORK FOR ME?

Although the device is quite straightforward to use, there are a few things that could cause you trouble if you have no prior expertise writing or debugging code. For a while, during testing on a Mac, I was unable to get the Ducky to press the F4 key to activate the launchpad, but after forcing it to identify itself using an alternative Apple keyboard device ID, the problem was resolved.

From there, I was able to create a script that, when the Ducky was plugged in, would instantly run Chrome, open a new browser tab, and then immediately close it once more without requiring any action from the laptop user. Not bad for only a few hours of testing, and something that could be readily changed to perform duties other than reading technology news.

40 years pass. After 14 years of retirement, you die. Am I the only one who sees the problem?

I’m the Jedi master of escaping the rat race.

Not to impress. I know this works since I've tried it. Quitting a job to make money online is worse than Kim Kardashian's internet-burning advice.

Let me help you rethink the move from a career to online income to f*ck you money.

To understand why a job is a joke, do some life math.

Without a solid why, nothing makes sense.

The retirement age is 65. Our processed food consumption could shorten our 79-year average lifespan.

You spend 40 years working.

After 14 years of retirement, you die.

Am I alone in seeing the problem?

Life is too short to work a job forever, especially since most people hate theirs. After-hours skills are vital.

Money equals unrestricted power, f*ck you.

F*ck you money is the answer.

Jack Raines said it first. He says we can do anything with the money. Jack, a young rebel straight out of college, can travel and try new foods.

F*ck you money signifies not checking your bank account before buying.

F*ck you” money is pure, unadulterated freedom with no strings attached.

Jack claims you're rich when you rarely think about money.

Avoid confusion.

This doesn't imply you can buy a Lamborghini. It indicates your costs, income, lifestyle, and bank account are balanced.

Jack established an online portfolio while working for UPS in Atlanta, Georgia. So he gained boundless power.

The portion that many erroneously believe

Yes, you need internet abilities to make money, but they're not different from 9-5 talents.

Sahil Lavingia, Gumroad's creator, explains.

A job is a way to get paid to learn.

Mistreat your boss 9-5. Drain his skills. Defuse him. Love and leave him (eventually).

Find another employment if yours is hazardous. Pick an easy job. Make sure nothing sneaks into your 6-12 time slot.

The dumb game that makes you a sheep

A 9-5 job requires many job interviews throughout life.

You email your résumé to employers and apply for jobs through advertisements. This game makes you a sheep.

You're competing globally. Work-from-home makes the competition tougher. If you're not the cheapest, employers won't hire you.

After-hours online talents (say, 6 pm-12 pm) change the game. This graphic explains it better:

Online talents boost after-hours opportunities.

You go from wanting to be picked to picking yourself. More chances equal more money. Your f*ck you fund gets the extra cash.

A novel method of learning is essential.

College costs six figures and takes a lifetime to repay.

Informal learning is distinct. 6-12pm:

• Observe the carefully controlled Twitter newsfeed.

• Make use of Teachable and Gumroad's online courses.

• Watch instructional YouTube videos

• Look through the top Substack newsletters.

Informal learning is more effective because it's not obvious. It's fun to follow your curiosity and hobbies.

The majority of people lack one attitude. It's simple to learn.

One big impediment stands in the way of f*ck you money and time independence. So often.

Too many people plan after 6-12 hours. Dreaming. Big-thinkers. Strategically. They fill their calendar with meetings.

This is after-hours masturb*tion.

Sahil Bloom reminded me that a bias towards action will determine if this approach works for you.

The key isn't knowing what to do from 6-12 a.m. Trust yourself and develop abilities as you go. It's for building the parachute after you jump.

Sounds risky. We've eliminated the risk by finishing this process after hours while you work 9-5.

With no risk, you can have an I-don't-care attitude and still be successful.

When you choose to move forward, this occurs.

Once you try 9-5/6-12, you'll tell someone.

It's bad.

Few of us hang out with problem-solvers.

It's how much of society operates. So they make reasons so they can feel better about not giving you money.

Matthew Kobach told me chasing f*ck you money is easier with like-minded folks.

Without f*ck you money friends, loneliness will take over and you'll think you've messed up when you just need to keep going.

Steal this easy guideline

Let's act. No more fluffing and caressing.

1. Learn

If you detest your 9-5 talents or don't think they'll work online, get new ones. If you're skilled enough, continue.

Easlo recommends these skills:

• Designer for Figma

• Designer Canva

• bubble creators

• editor in Photoshop

• Automation consultant for Zapier

• Designer of Webflow

• video editor Adobe

• Ghostwriter for Twitter

• Idea consultant

• Artist in Blender Studio

2. Develop the ability

Every night from 6-12, apply the skill.

Practicing ghostwriting? Write someone's tweets for free. Do someone's website copy to learn copywriting. Get a website to the top of Google for a keyword to understand SEO.

Free practice is crucial. Your 9-5 pays the money, so work for free.

3. Take off stealthily like a badass

Another mistake. Sell to few. Don't be the best. Don't claim expertise.

Sell your new expertise to others behind you.

Two ways:

• Using a digital good

• By providing a service,

Point 1 also includes digital service examples. Digital products include eBooks, communities, courses, ad-supported podcasts, and templates. It's easy. Your 9-5 job involves one of these.

Take ideas from work.

Why? They'll steal your time for profit.

4. Iterate while feeling awful

First-time launches always fail. You'll feel terrible. Okay. Remember your 9-5?

Find improvements. Ask free and paying consumers what worked.

Multiple relaunches, each 1% better.

5. Discover more

Never stop learning. Improve your skill. Add a relevant skill. Learn copywriting if you write online.

After-hours students earn the most.

6. Continue

Repetition is key.

7. Make this one small change.

Consistently. The 6-12 momentum won't make you rich in 30 days; that's success p*rn.

Consistency helps wage slaves become f*ck you money. Most people can't switch between the two.

Putting everything together

It's easy. You're probably already doing some.

This formula explains why, how, and what to do. It's a 5th-grade-friendly blueprint. Good.

Reduce financial risk with your 9-to-5. Replace Netflix with 6-12 money-making talents.

Life is short; do whatever you want. Today.

Python Volatility-Based Catapult Indicator

As a catapult, this technical indicator uses three systems: Volatility (the fulcrum), Momentum (the propeller), and a Directional Filter (Acting as the support). The goal is to get a signal that predicts volatility acceleration and direction based on historical patterns. We want to know when the market will move. and where. This indicator outperforms standard indicators.

Knowledge must be accessible to everyone. This is why my new publications Contrarian Trading Strategies in Python and Trend Following Strategies in Python now include free PDF copies of my first three books (Therefore, purchasing one of the new books gets you 4 books in total). GitHub-hosted advanced indications and techniques are in the two new books above.

The Foundation: Volatility

The Catapult predicts significant changes with the 21-period Relative Volatility Index.

The Average True Range, Mean Absolute Deviation, and Standard Deviation all assess volatility. Standard Deviation will construct the Relative Volatility Index.

Standard Deviation is the most basic volatility. It underpins descriptive statistics and technical indicators like Bollinger Bands. Before calculating Standard Deviation, let's define Variance.

Variance is the squared deviations from the mean (a dispersion measure). We take the square deviations to compel the distance from the mean to be non-negative, then we take the square root to make the measure have the same units as the mean, comparing apples to apples (mean to standard deviation standard deviation). Variance formula:

As stated, standard deviation is:

# The function to add a number of columns inside an array
def adder(Data, times):
    
    for i in range(1, times + 1):
    
        new_col = np.zeros((len(Data), 1), dtype = float)
        Data = np.append(Data, new_col, axis = 1)
        
    return Data

# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
    
    for i in range(1, times + 1):
    
        Data = np.delete(Data, index, axis = 1)
        
    return Data
    
# The function to delete a number of rows from the beginning
def jump(Data, jump):
    
    Data = Data[jump:, ]
    
    return Data

# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)

# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)

# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)

# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file

def volatility(Data, lookback, what, where):
    
  for i in range(len(Data)):

     try:

        Data[i, where] = (Data[i - lookback + 1:i + 1, what].std())
     except IndexError:
        pass
        
  return Data

The RSI is the most popular momentum indicator, and for good reason—it excels in range markets. Its 0–100 range simplifies interpretation. Fame boosts its potential.

The more traders and portfolio managers look at the RSI, the more people will react to its signals, pushing market prices. Technical Analysis is self-fulfilling, therefore this theory is obvious yet unproven.

RSI is determined simply. Start with one-period pricing discrepancies. We must remove each closing price from the previous one. We then divide the smoothed average of positive differences by the smoothed average of negative differences. The RSI algorithm converts the Relative Strength from the last calculation into a value between 0 and 100.

def ma(Data, lookback, close, where): 
    
    Data = adder(Data, 1)
    
    for i in range(len(Data)):
           
            try:
                Data[i, where] = (Data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                pass
            
    # Cleaning
    Data = jump(Data, lookback)
    
    return Data
def ema(Data, alpha, lookback, what, where):
    
    alpha = alpha / (lookback + 1.0)
    beta  = 1 - alpha
    
    # First value is a simple SMA
    Data = ma(Data, lookback, what, where)
    
    # Calculating first EMA
    Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)    
 
    # Calculating the rest of EMA
    for i in range(lookback + 2, len(Data)):
            try:
                Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta)
        
            except IndexError:
                pass
            
    return Datadef rsi(Data, lookback, close, where, width = 1, genre = 'Smoothed'):
    
    # Adding a few columns
    Data = adder(Data, 7)
    
    # Calculating Differences
    for i in range(len(Data)):
        
        Data[i, where] = Data[i, close] - Data[i - width, close]
     
    # Calculating the Up and Down absolute values
    for i in range(len(Data)):
        
        if Data[i, where] > 0:
            
            Data[i, where + 1] = Data[i, where]
            
        elif Data[i, where] < 0:
            
            Data[i, where + 2] = abs(Data[i, where])
            
    # Calculating the Smoothed Moving Average on Up and Down
    absolute values        
                             
    lookback = (lookback * 2) - 1 # From exponential to smoothed
    Data = ema(Data, 2, lookback, where + 1, where + 3)
    Data = ema(Data, 2, lookback, where + 2, where + 4)
    
    # Calculating the Relative Strength
    Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4]
    
    # Calculate the Relative Strength Index
    Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))  
    
    # Cleaning
    Data = deleter(Data, where, 6)
    Data = jump(Data, lookback)

    return Data

def relative_volatility_index(Data, lookback, close, where):

    # Calculating Volatility
    Data = volatility(Data, lookback, close, where)
    
    # Calculating the RSI on Volatility
    Data = rsi(Data, lookback, where, where + 1) 
    
    # Cleaning
    Data = deleter(Data, where, 1)
    
    return Data

The Arm Section: Speed

The Catapult predicts momentum direction using the 14-period Relative Strength Index.

As a reminder, the RSI ranges from 0 to 100. Two levels give contrarian signals:

• A positive response is anticipated when the market is deemed to have gone too far down at the oversold level 30, which is 30.

• When the market is deemed to have gone up too much, at overbought level 70, a bearish reaction is to be expected.

Comparing the RSI to 50 is another intriguing use. RSI above 50 indicates bullish momentum, while below 50 indicates negative momentum.

The direction-finding filter in the frame

The Catapult's directional filter uses the 200-period simple moving average to keep us trending. This keeps us sane and increases our odds.

Moving averages confirm and ride trends. Its simplicity and track record of delivering value to analysis make them the most popular technical indicator. They help us locate support and resistance, stops and targets, and the trend. Its versatility makes them essential trading tools.

This is the plain mean, employed in statistics and everywhere else in life. Simply divide the number of observations by their total values. Mathematically, it's:

We defined the moving average function above. Create the Catapult indication now.

Indicator of the Catapult

The indicator is a healthy mix of the three indicators:

• The first trigger will be provided by the 21-period Relative Volatility Index, which indicates that there will now be above average volatility and, as a result, it is possible for a directional shift.

• If the reading is above 50, the move is likely bullish, and if it is below 50, the move is likely bearish, according to the 14-period Relative Strength Index, which indicates the likelihood of the direction of the move.

• The likelihood of the move's direction will be strengthened by the 200-period simple moving average. When the market is above the 200-period moving average, we can infer that bullish pressure is there and that the upward trend will likely continue. Similar to this, if the market falls below the 200-period moving average, we recognize that there is negative pressure and that the downside is quite likely to continue.

lookback_rvi = 21
lookback_rsi = 14
lookback_ma  = 200
my_data = ma(my_data, lookback_ma, 3, 4)
my_data = rsi(my_data, lookback_rsi, 3, 5)
my_data = relative_volatility_index(my_data, lookback_rvi, 3, 6)

Two-handled overlay indicator Catapult. The first exhibits blue and green arrows for a buy signal, and the second shows blue and red for a sell signal.

The chart below shows recent EURUSD hourly values.

def signal(Data, rvi_col, signal):
    
    Data = adder(Data, 10)
        
    for i in range(len(Data)):
            
        if Data[i,     rvi_col] < 30 and \
           Data[i - 1, rvi_col] > 30 and \
           Data[i - 2, rvi_col] > 30 and \
           Data[i - 3, rvi_col] > 30 and \
           Data[i - 4, rvi_col] > 30 and \
           Data[i - 5, rvi_col] > 30:
               
               Data[i, signal] = 1
                           
    return Data

Signals are straightforward. The indicator can be utilized with other methods.

my_data = signal(my_data, 6, 7)

Lumiwealth shows how to develop all kinds of algorithms. I recommend their hands-on courses in algorithmic trading, blockchain, and machine learning.

Summary

To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation. Technical analysis will lose its reputation as subjective and unscientific.

After you find a trading method or approach, follow these steps:

• Put emotions aside and adopt an analytical perspective.

• Test it in the past in conditions and simulations taken from real life.

• Try improving it and performing a forward test if you notice any possibility.

• Transaction charges and any slippage simulation should always be included in your tests.

• Risk management and position sizing should always be included in your tests.

After checking the aforementioned, monitor the plan because market dynamics may change and render it unprofitable.