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Hi All! Here is my weekly wrap up email. If you love this content (please share it), but also… Check out my , connect with me on / , and Podcast YouTube Twitter Subscribe to my weekly newsletter Join our free slack community What’s in today’s newsletter? Bikeshedding Idea: Paralysis Triggers Question: Latticework Thinking Quote: Podcast: Renée Mauborgne, Researcher & Author of the Blue Ocean Strategy Jairek Robbins, Author, Coach & Speaker 7 Steps To Become A Millionaire Article: Idea: Bikeshedding Have you ever been in a meeting where the for the break room sparked a passionate hour-long debate? Meanwhile, the – the real reason you're all there – barely got a nod. choice of coffee business strategy for the next quarter That's Parkinson's Law of Triviality, or . bikeshedding Cyril Northcote Parkinson showed us this with an example of a committee assigned to build a nuclear plant. On the grounds of the proposed nuclear power plant was a proposed bike shed for employees of the plant to store their bikes while they worked. Meeting after meeting, the employees spent more time discussing details of the simple bike shed issue and at the complex nuclear reactor plans. barely glanced Why? Maybe fear, maybe confusion. Here’s a possible explanation, complexity is like . deep water We stick to the small things because they're . It's like floating in the shallow end. easy In the boardroom, on something like logo fonts. It's simple, it's subjective, it’s the safe zone. everyone’s a critic But ask about the ? You could hear a pin drop. ROI on a big project We lose ourselves in minor details and miss the . Like obsessing over a font when you should be crafting a winning strategy. major issues But you can conquer bikeshedding. Try this: . Stick to it like glue. Each topic gets just enough time, no more. Set a laser-focused agenda . Address the big stuff while brains are fresh. Dive into the deep end first . Less people, less noise, more action. Small team, big thinking . Ideas are team sport, decisions are not. Appoint a decider. One voice to rule them all is just step one. on it, that's the leap. Knowing about bikeshedding Acting So, at your next meeting, . prioritize Are you talking about the shed's color while your project's own nuclear reactor gets sidelined? It’s not about the shed. It's about the . journey ahead , not the distractions. Focus on the destination That's the key. That's what pushes you . forward This idea is from my short, daily newsletter. Read it here. Question: Paralysis Triggers Here’s a question on what’s stopping you from taking action: What are the that keep you from moving forward in your life? “paralysis triggers” As you pursue your goals and dreams in your life, there will be situations, events, thoughts, and emotions that trigger a sense of paralysis in you. These are your “paralysis triggers” in your life. You have a clear vision of what you want to achieve, but these “paralysis triggers” make you , , or . freeze procrastinate give up They prevent you from taking the necessary actions to make your vision a reality. Some examples of “paralysis triggers” are: that overwhelm you with too many choices, options, or possibilities. You feel confused, indecisive, or stuck. You don’t know where to start or what to do next. Situations that challenge your beliefs, assumptions, or expectations. You feel threatened, insecure, or doubtful. You question your abilities, your worth, or your purpose. Events that fill your mind with negative, limiting, or self-defeating messages. You feel anxious, fearful, or hopeless. You focus on the worst-case scenarios, the potential failures, or the reasons why you can’t. Thoughts that consume your energy, motivation, or enthusiasm. You feel angry, sad, or depressed. You lose interest, passion, or joy in what you do. Emotions It's easy to get stuck in paralysis if you don't take time to: your personal triggers Recognize where they come from Understand with them effectively Deal But this paralysis doesn't have to be permanent - you have the power to overcome it. This week, carve out some quiet time for self-reflection. Ask yourself: What specific situations, thoughts, or emotions tend to paralyze me? Trace these back to their roots. Did something happen that led you to develop these patterns? What beliefs or assumptions fuel the paralysis? Once you have more clarity, make an action plan. How can you respond differently to defuse your triggers when they arise? What affirmations or mantras can you use to stay motivated? Who can you turn to for support? Small steps add up. Regaining momentum is a process of patient persistence and with some critical thought, compassion for yourself and commitment to moving forward, you can break through those paralysis triggers so they don’t freeze you in your tracks. Quote: Latticework Thinking “The first rule is that you can’t really know anything if you just remember isolated facts... If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form." - Charlie Munger It's about the links between the facts. Business giants don’t just amass information. They construct frameworks to use it. Isolated facts? Useless. A single bee isn't a problem. A swarm? That’s a different story. Consider Elon Musk. His companies might look unrelated at first glance. But look closer. Energy. Efficiency. Innovation. These aren't just sectors. They're principles. They're the framework. Mental models are the new currency in the knowledge economy. Education rewards memorization. Real-world rewards those who synthesize. Are you just collecting facts? Or are you crafting a lens to view them through? It’s not about knowing more. It's about understanding better. . Look for Step back patterns, systems, connections. A fact is a single point. Knowledge is seeing the grid. Wisdom is navigating the map. When you wake up today, think: Are you simply gathering? Or are you truly linking? Because after all, Knowledge is only potential power. The true power is in the connections you make. Podcast: 1. Renée Mauborgne - Co-Director of the INSEAD Blue Ocean Strategy Institute | | YouTube Spotify Apple Renée Mauborgne is a prominent figure in the world of business and strategy, known for her groundbreaking work in the field. She is an INSEAD Distinguished Fellow and a professor of strategy at INSEAD, one of the world's leading business schools. Renée is also the Co-Director of the INSEAD Blue Ocean Strategy Institute, a center dedicated to exploring and promoting innovative strategies that create uncontested market spaces. Her most notable work, "Blue Ocean Strategy," published in 2005, has become an international sensation, selling over two million copies and translated into 42 languages. 2. Jairek Robbins - Speaker, Author & Business Performance Coach | | YouTube Spotify Apple Jairek Robbins is a celebrated speaker, author, and business coach dedicated to aiding entrepreneurs, executives, and business owners in achieving both personal and professional success while enjoying a balanced life. He promotes the philosophy of owning your business instead of being owned by it, empowering individuals to unleash their full potential. Jairek insists that true success is incomplete without fulfillment and values meaningful relationships as its bedrock. Article: 7 Steps To Become A Millionaire In this article, the Benjamin Hardy shares — what works. the core fundamentals of money really Along the way, you will also learn: How to change your mind to become compatible with far more money How to implement the systems that will make you rich How to begin thinking and wealthy, in the moment, today feeling Read it here. Sponsors (Most Have Special Offers) Hubspot — https://hubspot.com/ Greenlight — https://greenlight.com/success Kajabi — kajabi.com/success ButcherBox — (Code: success) https://butcherbox.com/success Justin Wine — (Code: success) https://justinwine.com/ Indeed — https://indeed.com/clary The Goal Digger Podcast — https://www.youtube.com/@jenna.kutcher/ DeleteMe — (Code: success) https://joindeleteme.com/success NetSuite — https://netsuite.com/scottclary/ Factor — (Code: successpod50) https://factormeals.com/successpod50 HelloFresh — (Code: 50succespod) https://hellofresh.com/50successpod Munch — (Code: success) https://www.getmunch.com/ ZBiotics — (Code: success) https://zbiotics.com/success My Links Success Story (Top 10 Business Podcast) Newsletter (310k Subs) Social Media Also published . here

Are You Spending Time on Trivial Issues?

The Wonderful Visit by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . A Trivial Incident A Trivial Incident. XXXII. The Angel came thoughtfully by the hedge across the field towards the Vicarage. The rays of the setting sun shone on his shoulders, and touched the Vicarage with gold, and blazed like fire in all the windows. By the gate, bathed in the sunlight, stood little Delia, the waiting maid. She stood watching him under her hand. It suddenly came into the Angel's mind that she, at least, was beautiful, and not only beautiful but alive and warm. She opened the gate for him and stood aside. She was sorry for him, for her elder sister was a cripple. He bowed to her, as he would have done to any woman, and for just one moment looked into her face. She looked back at him and something leapt within her. The Angel made an irresolute movement. "Your eyes are very beautiful," he said quietly, with a remote wonder in his voice. "Oh, sir!" she said, starting back. The Angel's expression changed to perplexity. He went on up the pathway between the Vicar's flower-beds, and she stood with the gate held open in her hand, staring after him. Just under the rose-twined verandah he turned and looked at her. She still stared at him for a moment, and then with a queer gesture turned round with her back to him, shutting the gate as she did so, and seemed to be looking down the valley towards the church tower. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2010). The Wonderful Visit. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/33913/33913-h/33913-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

A Trivial Incident

Choice Is Not the Enemy: Why Users Need More Options for Better Decision-Making

Get Rich Building Verticalized AI Wrappers (Even If You Don’t Code)

রিচ বিল্ডিং ভার্টিক্যালাইজড এআই র‍্যাপার পান (এমনকি যদি আপনি কোড না করেন)

Werden Sie reich durch die Entwicklung vertikaler KI-Wrapper (auch wenn Sie nicht programmieren können)

Hazte rico creando contenedores de IA verticalizados (aunque no sepas programar)

Devenez riche en créant des wrappers d’IA verticalisés (même si vous ne codez pas)

वर्टिकलाइज़्ड AI रैपर बनाकर अमीर बनें (भले ही आप कोड न जानते हों)

Menjadi Kaya dengan Membangun Pembungkus AI Vertikal (Bahkan Jika Anda Tidak Membuat Kode)

垂直統合型 AI ラッパーを構築して金持ちになる (コードを書かなくても)

Rich Building вертикалдандырылған AI орауыштарын алыңыз (тіпті кодтамасаңыз да)

수직화된 AI 래퍼를 구축하여 부자가 되세요(코딩하지 않아도)

Баян барилгын босоо AI боодолтой бол (та код бичээгүй байсан ч)

Fique rico construindo wrappers de IA verticalizados (mesmo que você não codifique)

Разбогатейте, создавая вертикальные оболочки ИИ (даже если вы не пишете код)

Hel dhismo qani ah oo toosan AI ku duub (Xitaa Haddi aadan Codayn)

Get Rich Building Verticalized AI Wrappers (Kahit Hindi Ka Mag-code)

Dikey AI Sarmalayıcıları Oluşturarak Zengin Olun (Kodlamasanız Bile)

رچ بلڈنگ ورٹیکلائزڈ AI ریپر حاصل کریں (چاہے آپ کوڈ نہ بھی کریں)

Làm giàu bằng cách xây dựng các trình bao bọc AI theo chiều dọc (ngay cả khi bạn không viết mã)

通过构建垂直化 AI 包装器致富（即使你不会编码）

If you check popular benchmarks used for measuring LLM performance, then you probably feel like AI is oh sooo clever. This is a fair surface-level impression; however, is AI truly better than an average human for cognitive tasks? Frontier models like o1 from OpenAI and Claude 3.5 Sonnet from Anthropic do perform better than expert humans in a number of fields, including law, coding, and math. Why, then, can’t ChatGPT solve some simple spatial reasoning tasks or some silly trick questions? Well, we are still talking about “large language models"—they take in a ton of letters and try to predict what letters to spit out for a given query. Notice, that nowhere in this equation the actual “thinking” is mentioned. The models are stochastic parrots of a sort, as they try to retrieve the right information from their training dataset instead of actually considering your question. At least, this was the case until OpenAI released o1-preview, but more on this later. Among those who started questioning the existing LLM benchmarks for relevance is the author of “AI Explained” a popular YouTube channel of which I’m a big fan. Phillip (the YouTuber’s name) noticed that the industry standard benchmarks have a clear style of questions that are also mostly publicly available. This means that not only can those exact questions be part of the training dataset, but due to standardization, it is easier for models to spot & apply patterns from the overall training data. Put simply, AI researchers who create groundbreaking complex tech can surely find a way to give their model the relevant questions and answers to “remember” before benchmarking. Looking at the results of the top model out there, o1 from OpenAI, one can imply that it scores above average in a lot of professional domains. And this is true, but this result relies on the availability of relevant training data and past examples from those specific fields. Don’t get me wrong, the models are now amazing at giving textbook answers to textbook questions, and this in itself is insanely impressive. The term “artificial intelligence," though, implies a bit more than just information retrieval; there should be some actual thinking involved. So a logical follow-up to all of the impressive numbers above is whether such “AI” can answer a tricky reasoning question. Does it have any spatial intelligence? Or can it navigate well in common social scenarios? The answer is - sometimes. Unlike field-specific questions with predefined answers, problems that humans solve on a daily basis often require understanding the context beyond natural language (which is the only thing LLMs have). Above are the top scorers on the SIMPLE benchmark, which gives LLMs questions that an average person would deem trivial but the models can’t necessarily answer yet. We are used to seeing AI doing so much better than an average human in exams or specialized benchmarks, but here, the top model performance is actually just 41.7% (o1-preview) vs. 83.7% of an average human. This benchmark uses 200 multiple-choice text questions focused on spatio-temporal reasoning, social intelligence, and trick questions. The most important feature of the benchmark is that those questions are not publicly available, so AI labs cannot just add them to their training data. You can learn more about this benchmark here. This new approach to measuring LLM performance shows just how far all the models still are from an average human reasoning capability. The quicker this gap closes in the coming months, the more definitive the answer “yes” to our headline becomes. An interesting new metric to look out for if you are enthusiastic but cautious about AI. If you check popular benchmarks used for measuring LLM performance, then you probably feel like AI is oh sooo clever. This is a fair surface-level impression; however, is AI truly better than an average human for cognitive tasks? Frontier models like o1 from OpenAI and Claude 3.5 Sonnet from Anthropic do perform better than expert humans in a number of fields, including law, coding, and math. Why, then, can’t ChatGPT solve some simple spatial reasoning tasks or some silly trick questions? Well, we are still talking about “large language models"—they take in a ton of letters and try to predict what letters to spit out for a given query. Notice, that nowhere in this equation the actual “thinking” is mentioned. The models are stochastic parrots of a sort, as they try to retrieve the right information from their training dataset instead of actually considering your question. At least, this was the case until OpenAI released o1-preview, but more on this later. stochastic parrots Among those who started questioning the existing LLM benchmarks for relevance is the author of “AI Explained” a popular YouTube channel of which I’m a big fan. Phillip (the YouTuber’s name) noticed that the industry standard benchmarks have a clear style of questions that are also mostly publicly available. This means that not only can those exact questions be part of the training dataset, but due to standardization, it is easier for models to spot & apply patterns from the overall training data. Put simply, AI researchers who create groundbreaking complex tech can surely find a way to give their model the relevant questions and answers to “remember” before benchmarking. Looking at the results of the top model out there, o1 from OpenAI, one can imply that it scores above average in a lot of professional domains. And this is true, but this result relies on the availability of relevant training data and past examples from those specific fields. Don’t get me wrong, the models are now amazing at giving textbook answers to textbook questions, and this in itself is insanely impressive. The term “artificial intelligence," though, implies a bit more than just information retrieval; there should be some actual thinking involved. So a logical follow-up to all of the impressive numbers above is whether such “AI” can answer a tricky reasoning question. Does it have any spatial intelligence? Or can it navigate well in common social scenarios? The answer is - sometimes. Unlike field-specific questions with predefined answers, problems that humans solve on a daily basis often require understanding the context beyond natural language (which is the only thing LLMs have). Above are the top scorers on the SIMPLE benchmark, which gives LLMs questions that an average person would deem trivial but the models can’t necessarily answer yet. We are used to seeing AI doing so much better than an average human in exams or specialized benchmarks, but here, the top model performance is actually just 41.7% (o1-preview) vs. 83.7% of an average human. This benchmark uses 200 multiple-choice text questions focused on spatio-temporal reasoning, social intelligence, and trick questions. The most important feature of the benchmark is that those questions are not publicly available, so AI labs cannot just add them to their training data. You can learn more about this benchmark here . here This new approach to measuring LLM performance shows just how far all the models still are from an average human reasoning capability. The quicker this gap closes in the coming months, the more definitive the answer “yes” to our headline becomes. An interesting new metric to look out for if you are enthusiastic but cautious about AI.

AI vs Human - Is the Machine Already Superior? 

الذكاء الاصطناعي مقابل الإنسان - هل الآلة متفوقة بالفعل؟

KI vs Mensch – Ist die Maschine schon überlegen?

IA vs. humanos: ¿la máquina ya es superior?

AI protiv čovjeka - je li stroj već superioran? 

Intelligenza artificiale contro uomo: la macchina è già superiore?

AI vs 人間 - 機械はすでに優れているのか?

AI prieš žmogų – ar mašina jau pranašesnė? 

Sztuczna inteligencja kontra człowiek – czy maszyna jest już lepsza?

AI بمقابله انسان - ایا ماشین دمخه غوره دی؟ 

AI vs Aadanaha - Mashiinku mar hore ma ka sarreeyaa? 

AI vs மனித - இயந்திரம் ஏற்கனவே உயர்ந்ததா? 

AI กับ มนุษย์ - เครื่องจักรเหนือกว่าแล้วจริงหรือ?

The Power of Market Disruption: How to Detect Fraud With Graph Data

Trust-based Currencies on Blockchain: Trustlines Network, Circles UBI, and Karma

Blockchain-də etimad əsaslı valyutalar: Trustlines Network, Circles UBI və Karma

Валюты на аснове даверу на блокчейне: сетка Trustlines, Circles UBI і Karma

ব্লকচেইনে বিশ্বাস-ভিত্তিক মুদ্রা: ট্রাস্টলাইন নেটওয়ার্ক, সার্কেল ইউবিআই, এবং কর্ম

Monedas basadas en la confianza en la cadena de bloques: Trustlines Network, Circles UBI y Karma

Luottamukseen perustuvat valuutat Blockchainissa: Trustlines Network, Circles UBI ja Karma

מטבעות מבוססי אמון בבלוקצ'יין: Trustlines Network, Circles UBI ו-Karma

ブロックチェーン上の信頼に基づく通貨: Trustlines Network、Circles UBI、Karma

Waluty oparte na zaufaniu w Blockchain: Trustlines Network, Circles UBI i Karma

Meny založené na dôvere na blockchaine: Trustlines Network, Circles UBI a Karma

Trust-based Currencies paBlockchain: Trustlines Network, Circles UBI, uye Karma

Асъорҳои боваринок дар Blockchain: Шабакаи Trustlines, Circles UBI ва Karma

Tiền tệ dựa trên sự tin cậy trên Blockchain: Trustlines Network, Circles UBI và Karma

Here's Why You Should Stop Over-Apologizing at Work

Kunatsa irnaqäwin jan sinti perdón mayisiñamaxa, akax akawa

Вось чаму вы павінны спыніць празмерныя прабачэнні на працы

Esta es la razón por la que deberías dejar de disculparte demasiado en el trabajo

Tässä on miksi sinun pitäisi lopettaa liiallinen anteeksipyyntö töissä

הנה למה אתה צריך להפסיק להתנצל יותר מדי בעבודה

職場で過剰な謝罪をやめるべき理由

Tala Pourquoi Osengeli Kotika Kosenga Pardon Koleka Na Mosala

Та яагаад ажил дээрээ хэт уучлалт гуйхаа болих хэрэгтэйг эндээс харж болно

Dit is waarom u moet stoppen met u te vaak te verontschuldigen op het werk

Tu je dôvod, prečo by ste sa v práci mali prestať príliš ospravedlňovať

Waa tan sababta ay tahay inaad u joojiso raaligelinta xad dhaafka ah ee shaqada

Ево зашто би требало да престанете да се превише извињавате на послу

Last week, Gotbit's CEO was arrested in Portugal at the request of the U.S. The company was charged with market manipulation and wash trading. Wash trading occurs when one person (or company) acts as both the buyer and seller in the same transaction. At first glance, it makes no sense. However, the goal of wash trading is to either boost the price of an instrument (by constantly buying at higher prices) or to boost trading volumes in an illiquid asset. Many traders and algorithms use price and volume patterns to find attractive investments. If an instrument goes up in price at a high trading volume, it will attract real traders and investors, providing the illicit actor with an opportunity to cash out by unloading his initial position into new money at high prices. That’s why wash trading is prohibited. At first glance, the Gotbit case looks like a trivial fraud case that happened in crypto. However, several days after Gotbit CEO was arrested, the SEC filed a lawsuit against a major crypto market maker, Cumberland. The SEC charges Cumberland for “operating as an unregistered dealer in the crypto asset markets.” According to the SEC, Cumberland bought and sold crypto assets offered and sold as securities for its own accounts as part of its regular business. As usual, the SEC treats most cryptos as securities and denies the industry’s view, which implies that the sales of crypto assets are similar to the sales of commodities. The SEC has been long trying to increase its control over the crypto markets. This time, the SEC has decided to focus on market makers. The market maker function is extremely important in the markets. Market makers serve as liquidity providers of last resort and smoothen price fluctuations. The cases of Gotbit and Cumberland have just one similarity — both companies were market makers. While Gotbit was accused of outright criminal activity, the claims against Cumberland are centered around the fact that the company did not register as a securities dealer with the regulator. Given the role market makers play in financial markets, it’s safe to say that the SEC continues to boost its control over crypto and tries to limit the pace of crypto market development. Any restrictions on market makers’ activities will hurt crypto market liquidity. Should we worry about this scenario? In Gotbit’s case, we are talking about scam practices in illiquid coins. The elimination of such actors will improve the marketplace for all. In Cumberland’s case, we are looking at a legitimate market maker. At first glance, the company’s business is not at risk, since the SEC accuses the company of violating registration rules rather than fraud. Apparently, the case of SEC vs Cumberland could drag on for a long time, similar to the seemingly endless SEC vs Ripple. It should be noted that the market has barely noticed the SEC’s latest attempt to put pressure on crypto, which indicates that investors have already developed immunity to the SEC’s FUD. Last week, Gotbit's CEO was arrested in Portugal at the request of the U.S. The company was charged with market manipulation and wash trading. Wash trading occurs when one person (or company) acts as both the buyer and seller in the same transaction. At first glance, it makes no sense. However, the goal of wash trading is to either boost the price of an instrument (by constantly buying at higher prices) or to boost trading volumes in an illiquid asset. Many traders and algorithms use price and volume patterns to find attractive investments. If an instrument goes up in price at a high trading volume, it will attract real traders and investors, providing the illicit actor with an opportunity to cash out by unloading his initial position into new money at high prices. That’s why wash trading is prohibited. At first glance, the Gotbit case looks like a trivial fraud case that happened in crypto. However, several days after Gotbit CEO was arrested, the SEC filed a lawsuit against a major crypto market maker, Cumberland. lawsuit lawsuit The SEC charges Cumberland for “operating as an unregistered dealer in the crypto asset markets.” According to the SEC, Cumberland bought and sold crypto assets offered and sold as securities for its own accounts as part of its regular business. As usual, the SEC treats most cryptos as securities and denies the industry’s view, which implies that the sales of crypto assets are similar to the sales of commodities. The SEC has been long trying to increase its control over the crypto markets. This time, the SEC has decided to focus on market makers. The market maker function is extremely important in the markets. Market makers serve as liquidity providers of last resort and smoothen price fluctuations. The cases of Gotbit and Cumberland have just one similarity — both companies were market makers. While Gotbit was accused of outright criminal activity, the claims against Cumberland are centered around the fact that the company did not register as a securities dealer with the regulator. Given the role market makers play in financial markets, it’s safe to say that the SEC continues to boost its control over crypto and tries to limit the pace of crypto market development. Any restrictions on market makers’ activities will hurt crypto market liquidity. Should we worry about this scenario? In Gotbit’s case, we are talking about scam practices in illiquid coins. The elimination of such actors will improve the marketplace for all. In Cumberland’s case, we are looking at a legitimate market maker. At first glance, the company’s business is not at risk, since the SEC accuses the company of violating registration rules rather than fraud. Apparently, the case of SEC vs Cumberland could drag on for a long time, similar to the seemingly endless SEC vs Ripple. It should be noted that the market has barely noticed the SEC’s latest attempt to put pressure on crypto, which indicates that investors have already developed immunity to the SEC’s FUD.

Why Does the SEC Attack Crypto Market Makers?

It's Not A Motivation Problem That You Have (It's a Clarity Problem)

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