Genius or lucky?

From the title, you might think I'm selling advertising for a financial influencer, a dubious trading site, or a training organization to attract clients. I'm suspicious. Better safe than sorry.

But not here.

Jake Freeman, 20, made $110 million in a month, according to the Financial Times. At 18, he ran for president. He made his name in markets, not politics. Two years later, he's Wall Street's prince. Interview requests flood the prodigy.

Jake Freeman bought 5 million Bed Bath & Beyond Group shares for $5.5 in July 2022 and sold them for $27 a month later. He thought the stock might double. Since speculation died down, he sold well. The stock fell 40.5% to 11 dollars on Friday, 19 August 2022. On August 22, 2022, it fell 16% to $9.

Smallholders have been buying the stock for weeks and will lose heavily if it falls further. Bed Bath & Beyond is the second most popular stock after Foot Locker, ahead of GameStop and Apple.

Jake Freeman earned $110 million thanks to a significant stock market flurry.

Online broker customers aren't the only ones with jitters. By June 2022, Ken Griffin's Citadel and Stephen Mandel's Lone Pine Capital held nearly a third of the company's capital. Did big managers sell before the stock plummeted?

Recent stock movements (derivatives) and rumors could prompt a SEC investigation.

Jake Freeman wrote to the board of directors after his investment to call for a turnaround, given the company's persistent problems and short sellers. The bathroom and kitchen products distribution group's stock soared in July 2022 due to renewed buying by private speculators, who made it one of their meme stocks with AMC and GameStop.

Second-quarter 2022 results and financial health worsened. He didn't celebrate his miraculous operation in a nightclub. He told a British newspaper, "I'm shocked." His parents dined in New York. He returned to Los Angeles to study math and economics.

Jake Freeman founded Freeman Capital Management with his savings and $25 million from family, friends, and acquaintances. They are the ones who are entitled to the $110 million he raised in one month. Will his investors pocket and withdraw all or part of their profits or will they trust the young prodigy for new stunts on Wall Street?

His operation should attract new clients. Well-known hedge funds may hire him.

Jake Freeman didn't listen to gurus or former traders. At 17, he interned at a quantitative finance and derivatives hedge fund, Volaris. At 13, he began investing with his pharmaceutical executive uncle. All countries have increased their Google searches for the young trader in the last week.

Naturally, his success has inspired resentment.

His success stirs jealousy, and he's attacked on social media. On Reddit, people who lost money on Bed Bath & Beyond, Jake Freeman's fortune, are mourning.

Several conspiracy theories circulate about him, including that he doesn't exist or is working for a Taiwanese amusement park.

If all 20 million American students had the same trading skills, they would have generated $1.46 trillion. Jake Freeman is unique. Apprentice traders' careers are often short, disillusioning, and tragic.

Two years ago, 20-year-old Robinhood client Alexander Kearns committed suicide after losing $750,000 trading options. Great traders start young. Michael Platt of BlueCrest invested in British stocks at age 12 under his grandmother's supervision and made a £30,000 fortune. Paul Tudor Jones started trading before he turned 18 with his uncle. Warren Buffett, at age 10, was discussing investments with Goldman Sachs' head. Oracle of Omaha tells all.

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.

Y Combinators Winter 2022 Demo Day continues the trend of more startups engaging in accelerator Demo Days. Our team evaluated almost 400 projects in Y Combinator's ninth year.

After Winter 2021 Demo Day, we noticed a hurry pushing shorter rounds, inflated valuations, and larger batches.

Despite the batch size, this event's behavior showed a return to normalcy. Our observations show that investors evaluate and fund businesses more carefully. Unlike previous years, more YC businesses gave investors with data rooms and thorough pitch decks in addition to valuation data before Demo Day.

Demo Day pitches were virtual and fast-paced, limiting unplanned meetings. Investors had more time and information to do their due research before meeting founders. Our staff has more time to study diverse areas and engage with interesting entrepreneurs and founders.

This was one of the most regionally diversified YC cohorts to date. This year's Winter Demo Day startups showed some interesting tendencies.

Trends and Industries to Watch Before Demo Day

Demo day events at any accelerator show how investment competition is influencing startups. As startups swiftly become scale-ups and big success stories in fintech, e-commerce, healthcare, and other competitive industries, entrepreneurs and early-stage investors feel pressure to scale quickly and turn a notion into actual innovation.

Too much eagerness can lead founders to focus on market growth and team experience instead of solid concepts, technical expertise, and market validation. Last year, YC Winter Demo Day funding cycles ended too quickly and valuations were unrealistically high.

Scrum Ventures observed a longer funding cycle this year compared to last year's Demo Day. While that seems promising, many factors could be contributing to change, including:

• Market patterns are changing and the economy is becoming worse.

• the industries that investors are thinking about.

• Individual differences between each event batch and the particular businesses and entrepreneurs taking part

The Winter 2022 Batch's Trends

Each year, we also wish to examine trends among early-stage firms and YC event participants. More international startups than ever were anticipated to present at Demo Day.

Less than 50% of demo day startups were from the U.S. For the S21 batch, firms from outside the US were most likely in Latin America or Europe, however this year's batch saw a large surge in startups situated in Asia and Africa.

YC Startup Directory

163 out of 399 startups were B2B software and services companies. Financial, healthcare, and consumer startups were common.

Our team doesn't plan to attend every pitch or speak with every startup's founders or team members. Let's look at cleantech, Web3, and health and wellness startup trends.

Our Opinions Following Conversations with 87 Startups at Demo Day

In the lead-up to Demo Day, we spoke with 87 of the 125 startups going. Compared to B2C enterprises, B2B startups had higher average valuations. A few outliers with high valuations pushed B2B and B2C means above the YC-wide mean and median.

Many of these startups develop business and technology solutions we've previously covered. We've seen API, EdTech, creative platforms, and cybersecurity remain strong and increase each year.

While these persistent tendencies influenced the startups Scrum Ventures looked at and the founders we interacted with on Demo Day, new trends required more research and preparation. Let's examine cleantech, Web3, and health and wellness startups.

Hardware and software that is green

Cleantech enterprises demand varying amounts of funding for hardware and software. Although the same overarching trend is fueling the growth of firms in this category, each subgroup has its own strategy and technique for investigation and identifying successful investments.

Many cleantech startups we spoke to during the YC event are focused on helping industrial operations decrease or recycle carbon emissions.

• Carbon Crusher: Creating carbon negative roads

• Phase Biolabs: Turning carbon emissions into carbon negative products and carbon neutral e-fuels

• Seabound: Capturing carbon dioxide emissions from ships

• Fleetzero: Creating electric cargo ships

• Impossible Mining: Sustainable seabed mining

• Beyond Aero: Creating zero-emission private aircraft

• Verdn: Helping businesses automatically embed environmental pledges for product and service offerings, boost customer engagement

• AeonCharge: Allowing electric vehicle (EV) drivers to more easily locate and pay for EV charging stations

• Phoenix Hydrogen: Offering a hydrogen marketplace and a connected hydrogen hub platform to connect supply and demand for hydrogen fuel and simplify hub planning and partner program expansion

• Aklimate: Allowing businesses to measure and reduce their supply chain’s environmental impact

• Pina Earth: Certifying and tracking the progress of businesses’ forestry projects

• AirMyne: Developing machines that can reverse emissions by removing carbon dioxide from the air

• Unravel Carbon: Software for enterprises to track and reduce their carbon emissions

Web3: NFTs, the metaverse, and cryptocurrency

Web3 technologies handle a wide range of business issues. This category includes companies employing blockchain technology to disrupt entertainment, finance, cybersecurity, and software development.

Many of these startups overlap with YC's FinTech trend. Despite this, B2C and B2B enterprises were evenly represented in Web3. We examined:

• Stablegains: Offering consistent interest on cash balance from the decentralized finance (DeFi) market

• LiquiFi: Simplifying token management with automated vesting contracts, tax reporting, and scheduling. For companies, investors, and finance & accounting

• NFTScoring: An NFT trading platform

• CypherD Wallet: A multichain wallet for crypto and NFTs with a non-custodial crypto debit card that instantly converts coins to USD

• Remi Labs: Allowing businesses to more easily create NFT collections that serve as access to products, memberships, events, and more

• Cashmere: A crypto wallet for Web3 startups to collaboratively manage funds

• Chaingrep: An API that makes blockchain data human-readable and tokens searchable

• Courtyard: A platform for securely storing physical assets and creating 3D representations as NFTs

• Arda: “Banking as a Service for DeFi,” an API that FinTech companies can use to embed DeFi products into their platforms

• earnJARVIS: A premium cryptocurrency management platform, allowing users to create long-term portfolios

• Mysterious: Creating community-specific experiences for Web3 Discords

• Winter: An embeddable widget that allows businesses to sell NFTs to users purchasing with a credit card or bank transaction

• SimpleHash: An API for NFT data that provides compatibility across blockchains, standardized metadata, accurate transaction info, and simple integration

• Lifecast: Tools that address motion sickness issues for 3D VR video

• Gym Class: Virtual reality (VR) multiplayer basketball video game

• WorldQL: An asset API that allows NFT creators to specify multiple in-game interpretations of their assets, increasing their value

• Bonsai Desk: A software development kit (SDK) for 3D analytics

• Campfire: Supporting virtual social experiences for remote teams

• Unai: A virtual headset and Visual World experience

• Vimmerse: Allowing creators to more easily create immersive 3D experiences

Fitness and health

Scrum Ventures encountered fewer health and wellness startup founders than Web3 and Cleantech. The types of challenges these organizations solve are still diverse. Several of these companies are part of a push toward customization in healthcare, an area of biotech set for growth for companies with strong portfolios and experienced leadership.

Here are several startups we considered:

• Syrona Health: Personalized healthcare for women in the workplace

• Anja Health: Personalized umbilical cord blood banking and stem cell preservation

• Alfie: A weight loss program focused on men’s health that coordinates medical care, coaching, and “community-based competition” to help users lose an average of 15% body weight

• Ankr Health: An artificial intelligence (AI)-enabled telehealth platform that provides personalized side effect education for cancer patients and data collection for their care teams

• Koko — A personalized sleep program to improve at-home sleep analysis and training

• Condition-specific telehealth platforms and programs:

• Reviving Mind: Chronic care management covered by insurance and supporting holistic, community-oriented health care

• Equipt Health: At-home delivery of prescription medical equipment to help manage chronic conditions like obstructive sleep apnea

• LunaJoy: Holistic women’s healthcare management for mental health therapy, counseling, and medication

12 Startups from YC's Winter 2022 Demo Day to Watch

Bobidi: 10x faster AI model improvement

Artificial intelligence (AI) models have become a significant tool for firms to improve how well and rapidly they process data. Bobidi helps AI-reliant firms evaluate their models, boosting data insights in less time and reducing data analysis expenditures. The business has created a gamified community that offers a bug bounty for AI, incentivizing community members to test and find weaknesses in clients' AI models.

Magna: DeFi investment management and token vesting

Magna delivers rapid, secure token vesting so consumers may turn DeFi investments into primitives. Carta for Web3 allows enterprises to effortlessly distribute tokens to staff or investors. The Magna team hopes to allow corporations use locked tokens as collateral for loans, facilitate secondary liquidity so investors can sell shares on a public exchange, and power additional DeFi applications.

Perl Street: Funding for infrastructure

This Fintech firm intends to help hardware entrepreneurs get financing by [democratizing] structured finance, unleashing billions for sustainable infrastructure and next-generation hardware solutions. This network has helped hardware entrepreneurs achieve more than $140 million in finance, helping companies working on energy storage devices, EVs, and creating power infrastructure.

CypherD: Multichain cryptocurrency wallet

CypherD seeks to provide a multichain crypto wallet so general customers can explore Web3 products without knowledge hurdles. The startup's beta app lets consumers access crypto from EVM blockchains. The founders have crypto, financial, and startup experience.

Unravel Carbon: Enterprise carbon tracking and offsetting

Unravel Carbon's AI-powered decarbonization technology tracks companies' carbon emissions. Singapore-based startup focuses on Asia. The software can use any company's financial data to trace the supply chain and calculate carbon tracking, which is used to make regulatory disclosures and suggest carbon offsets.

LunaJoy: Precision mental health for women

LunaJoy helped women obtain mental health support throughout life. The platform combines data science to create a tailored experience, allowing women to access psychotherapy, medication management, genetic testing, and health coaching.

Posh: Automated EV battery recycling

Posh attempts to solve one of the EV industry's largest logistical difficulties. Millions of EV batteries will need to be decommissioned in the next decade, and their precious metals and residual capacity will go unused for some time. Posh offers automated, scalable lithium battery disassembly, making EV battery recycling more viable.

Unai: VR headset with 5x higher resolution

Unai stands apart from metaverse companies. Its VR headgear has five times the resolution of existing options and emphasizes human expression and interaction in a remote world. Maxim Perumal's method of latency reduction powers current VR headsets.

Palitronica: Physical infrastructure cybersecurity

Palitronica blends cutting-edge hardware and software to produce networked electronic systems that support crucial physical and supply chain infrastructure. The startup's objective is to build solutions that defend national security and key infrastructure from cybersecurity threats.

Reality Defender: Deepfake detection

Reality Defender alerts firms to bogus users and changed audio, video, and image files. Reality Deference's API and web app score material in real time to prevent fraud, improve content moderation, and detect deception.

Micro Meat: Infrastructure for the manufacture of cell-cultured meat

MicroMeat promotes sustainable meat production. The company has created technologies to scale up bioreactor-grown meat muscle tissue from animal cells. Their goal is to scale up cultured meat manufacturing so cultivated meat products can be brought to market feasibly and swiftly, boosting worldwide meat consumption.

Fleetzero: Electric cargo ships

This startup's battery technology will make cargo ships more sustainable and profitable. Fleetzero's electric cargo ships have five times larger profit margins than fossil fuel ships. Fleetzeros' founder has marine engineering, ship operations, and enterprise sales and business experience.

Yes, client meetings are difficult. Especially when I'm alone.

Clients must tell us their problems so we can help.

In-meeting challenges contribute nothing to our work. Consider this:

• Clients are unprepared.

• Clients are distracted.

• Clients are confused.

Introducing Jack Dorsey's Google Doc approach

I endorse his approach to meetings.

Not Google Doc-related. Jack uses it for meetings.

This is what his meetings look like.

• Prior to the meeting, the Chair creates the agenda, structure, and information using Google Doc.

• Participants in the meeting would have 5-10 minutes to read the Google Doc.

• They have 5-10 minutes to type their comments on the document.

• In-depth discussion begins

There is elegance in simplicity. Here's how Jack's approach is fantastic.

Unprepared clients are given time to read.

During the meeting, they think and work on it.

They can see real-time remarks from others.

Discussion ensues.

Three months ago, I fell for this strategy. After trying it with a client, I got good results.

I conducted social control experiments in a few client workshops.

Context matters.

I am sure Jack Dorsey’s method works well in meetings. What about client workshops?

So, I tested Enterprise of the Future with a consulting client.

I sent multiple emails to client stakeholders describing the new approach.

No PowerPoints that day. I spent the night setting up the Google Doc with conversation topics, critical thinking questions, and a Before and After section.

The client was shocked. First, a Google Doc was projected. Second surprise was a verbal feedback.

“No pre-meeting materials?”

“Don’t worry. I know you are not reading it before our meeting, anyway.”

We laughed. The experiment started.

Observations throughout a 90-minute engagement workshop from beginning to end

For 10 minutes, the workshop was silent.

People read the Google Doc. For some, the silence was unnerving.

“Are you not going to present anything to us?”

I said everything's in Google Doc. I asked them to read, remark, and add relevant paragraphs.

As they unlocked their laptops, they were annoyed.

Ten client stakeholders are typing on the Google Doc. My laptop displays comment bubbles, red lines, new paragraphs, and strikethroughs.

The first 10 minutes were productive. Everyone has seen and contributed to the document.

I was silent.

The move to a classical workshop was smooth. I didn't stimulate dialogue. They did.

Stephanie asked Joe why a blended workforce hinders company productivity. She questioned his comments and additional paragraphs.

That is when a light bulb hit my head. Yes, you want to speak to the right person to resolve issues!

Not only that was discussed. Others discussed their remark bubbles with neighbors. Debate circles sprung up one after the other.

The best part? I asked everyone to add their post-discussion thoughts on a Google Doc.

After the workshop, I have:

• An agreement-based working document

• A post-discussion minutes that are prepared for publication

• A record of the discussion points that were brought up, argued, and evaluated critically

It showed me how stakeholders viewed their Enterprise of the Future. It allowed me to align with them.

Finale Keynotes

Client meetings are a hit-or-miss. I know that.

Jack Dorsey's meeting strategy works for consulting. It promotes session alignment.

It relieves clients of preparation.

I get the necessary information to advance this consulting engagement.

It is brilliant.