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Independence, Impact, Money and Mission… As I sit here writing, I am faced with dilemma — a question, one many founders are forced to confront. What matters more: the money or the mission, the impact or the independence? The answer is complicated, and reveals not your character but something more. Let me explain. Start with Why Simon Sinek is a smart guy. If you haven’t watched his TED Talk on the power of “why,” ie motivation, I suggest you do. But first, let me provide some context. My mission is to help startups succeed, as many as humanly possible. I’m a advisor, angel investor and serial entrepreneur and have built solo businesses for as long as I can remember ( ). startup more on my background here Today I am working to grow , our accredited investor network which invests promising pre-Series A startups ( ). The Syndicate more on our investment thesis here The biggest challenge I face is one of resources. I’m a one-man show and our syndicate is not yet large enough to write large checks. This makes securing allocations in the best companies challenging. VCs can cut bigger checks and have more resources at their disposal to help portfolio companies succeed. Which brings up a question… What is the mission? I am currently advising several pretty awesome startups and making occasional investments, but I find myself wondering if my impact could be amplified at a fund. As a serial entrepreneur I find myself asking: “Should I join someone else’s firm?” This is a foreign concept for me, and one I am not fully comfortable with. But again, do the ends justify the means? If my mission is really to exponentially impact startups, isn’t this a no-brainer? Is it selfish if I don’t? Is it selling out if I do? These are the questions bouncing around my head — and they mirror those of founders everywhere, hence the need for the article. Compromise Every great founder starts with a WHY. What is the driving force that motivates you to sacrifice security and peace of mind to put in 80+ hour grueling work weeks? Why would anyone put themselves through that kind of emotional rollercoaster? Is it the money? I hope not. Money is NEVER a strong enough motivator (more on this below). _5 Steps to a Perfect Elevator Pitch and How Startups can Use the Pitch to Inspire Investors and Employees…_thinkgrowth.org The Memorable Elevator Pitch that VCs Can’t Ignore Founders make it their mission to change the world. But when is compromise acceptable? When are you sacrificing the mission for money? The idea of the acquisition There are three ways to view any acquisition: 1) a payday 2) a failure or 3) an amplifier. Source: SimplyBusiness.co.uk 1. Paydays Paydays are obvious. You build something great, another company wants the , the team, the customers or to kill competition and they offer a ton of money to acquire you. tech 2. Failures The second type of acquisition are the firesales. Your startup raised money and hit its goals. It is obvious you won’t make it and investors want to recoup their investment. The company gets sold for relatively small amount, no one is particularly happy and everyone ultimately is stuck accepting. 3. Amplifiers The most interesting acquisitions are amplifiers — acquisitions where the acquirer brings assets to the table to help the startup scale and succeed. This is often true with pharma companies but can apply to any industry ( ). often related to business line expansion or distribution deals Amplifiers are the hardest acquisitions to evaluate. Basically an acquisition is like a partnership that could accelerate the startup’s growth and help them achieve their mission but likely means leaving money on the table (by not selling later). Evaluating acquisitions The easiest way to evaluate acquisition offers is to understand what drives your decision? When the offer 1st comes in, what is your initial response? It is impossible to fool your instincts and gut reactions — remember those. Write down in detail how you felt and what your first thoughts were. That is the best way to evaluate any offer. The unspoken stress Founder’s rarely talk about this. Few want to own up to the moral dilemma of deciding between mission and money — either choice can alienate investors, employees or other co-founders. Entrepreneur’s don’t want to face their own scorn either. “Is money really more important for me?” The good news is, there is no right answer. Your gut will usually tell you what to do. The reptilian brain in our stomachs is designed to process information and create subconscious responses to stimuli and past experiences — our own little black box ( ) for making decisions. neural net I once heard a podcast interview where the interviewee said something to the effect of “every time I make a pro and con list, I am either trying to talk myself into a bad opportunity or out of a good one…” I believe this applies to the majority of decisions. Weighing your options Numbers alone won’t help you make the right decision — intangibles are often the most important factors. For founders, this means meeting prospective acquirers and hearing what they have to say. It means taking offers seriously and interviewing employees or past acquirees of the company to understand the culture, red flags and how the acquisition could affect your company. Get a feel for the guys and gals on the other side of the table — assholes are not the best business partners, regardless of the check size. For myself, I’m weighing my options carefully and headed to Toronto to meet with a few firms. I haven’t decided anything yet and am going into the experience with an open mind. Know your weaknesses and yourself I am ADD. I get excited about and often agree to things without really thinking through the details. This can create problems. I know from experience that I need time away from the situation to make a rational decision. And talking things over ( ) helps me to clarify my thoughts ( ). and/or writing hence part of the purpose of this post Before you go into any acquisition or big commitment conversations, make sure you consider these things carefully. What are your weaknesses, what are your patterns? Have you made mistakes or done things you regret in the past? Simply knowing and acknowledging these helps you control the situation. — Pythagoras “Know thyself, and thou shalt know the Universe and God.” (I would modify this slightly ) “Know thyself, and thou shalt [own] the Universe and [be thy own] god.” Selling a business Three times I have decided to sell a business I had built. Each time there came a day where I could not stand running it anymore. I was sick and tired of the day-to-day and excited about exploring new opportunities. While I lacked the mission motivation which made the decision easy, I still went through the same decision making process. Every time I felt a weight lift when the process was finally complete. My internal and emotional responses validated my decision. Yours will do the same. When you make a decision, pay careful attention to how you feel (and how your body reacts). These simple reactions often tell you if you made the right choice. Visualizing the decision and D-day ahead of time can trigger similar responses and help you in the decision-making process. Good luck. Closing thoughts Life is a series of decisions, compromises and actions — nothing is black and white. We do the best with the information we are given and accept the consequences, that is decision making 101. I don’t know what I will do. I am very intrigued by the idea of joining a formal venture fund and bringing my skill set and deal flow to a large organization, but still undecided. You probably don’t know what you will do either. Every opportunity invites second-guessing — a derailer of success. Make a decision, push forward and re-evaluate when necessary. Detours are okay, just keep course correcting. Have you sold a company previously? What went through your mind? Any regrets? Would love to hear from others that have made hard decisions revolving around money and mission and how you evaluated opportunities. We all go through similar situations, I hope this article made you think and helps next time you are faced with a choice. Nothing is worse than blindly following the guidance of others… Learned something? Click the 👏 to say “thanks!” and help others find this article. Hold down the clap button if you liked the content! It helps me gain exposure . Clap 50 times!

Impact Multipliers and Evaluating Acquisitions

Summer 2023 was a tough time for Web3 projects. Our company was bordering on breaking even with no ETA in sight. While we were looking for new possibilities and business ideas, we worked hard on developing existing products and services. In a volatile space, we had to work in a trial and error mode, with every new project having the potential to make or break the market. When There Is No Market Quite recently, I had a short but meaningful conversation with an IT business devil. I came in with a direct approach right off the bat, stating that my company provides security services for the blockchain market. "There is no blockchain market," was his response. He even elaborated, claiming that there’s a mobile devices market and a real estate market but no "blockchain market." Bold statement, but it gave me some food for thought. The mobile development market cap was , the EV market cap was around , and the wearables market cap was . There is plenty of room to play. But what is the blockchain market? According to CoinMarketCap, the global crypto market cap was about . However, the blockchain services and products market was just . $206.85 billion in 2022 $388 billion in 2023 $71.91 billion in 2023 $1.69 trillion in January 2024 $11.4 billion in 2022 First of all, there’s a huge difference between the asset cap and services cap. Second, if your startup is aiming to take 0.01% of a $206 billion market, it is aiming at $20.6 million. But then, even 0.1% of an $11 billion market is $11 million. Consider: You’re trying to take a 10x market share but gaining half the revenue. At the end of the day, $11.4 billion doesn’t come close to a big corporation number, and here we were trying to claim the existence of a whole market. Despite working in it for years, I had no other choice but to agree with the initial claim of the IT business expert. There are companies, projects, and startups, but they haven’t yet made a market. Making A Market If the market doesn’t exist, why bother, right? There are several already successful functioning and developing markets. And if you think about it, entering them does become easier in time. It’s cheaper, too, thanks to various technological leaps and the options to start from scratch and develop an advanced product. But in the end, we always see the same image: margin drops down to zero profitability. That, however, doesn’t apply to some restricted markets that exist solely based on monopolies. Working in an officially nonexistent space has its advantages. Everything is brand new, and as a founder, you make the rules. There also is a lucky chance event that can happen to a market that doesn’t exist: It can appear. If you still have doubts, you can look back at history. Every revolution, be it industrial or technological, kickstarted the development of not one but several dedicated markets. A company might start with 3% of nothing and grow to 3% of everything in no time, surpassing huge corporations in other fields. It might be a long-lasting success or just a flash, but at the end of the day, that’s how many success stories began. How To Approach A Nonexistent Market Whether a market is new or well-developed, success is impossible unless you try to achieve it. But I’m going to take this segment to talk about chickens. It’s no secret that hens are the point of interest for poultry farms. Roosters, however, often become meat as soon as they reach a required weight. Yet, in many cases, it's only possible to tell the sex of a chicken around the fifth or sixth week of their life. Before that, hens and roosters look identical. This brings massive expenses that go into feeding and maintaining about 50% of the birds, which are virtually useless for the farm. Surprisingly, the issue could be solved: Factories and farms have trained people who can identify a chicken’s sex with and only a day after the chicken is hatched. The best can process 1,000 chickens per hour, and what’s most incredible is that it’s a teachable skill. There’s no theory to that, just intuition and practice: After every attempt, a professional confirms or denies the results, and this happens until the student achieves the required accuracy. 98% accuracy A startup in a new market is similar to a chicken. Most founders attempt to identify its potential after six to 12 months of existence, even after investing large sums in launching the project. But there is another way. Like those chicken sex experts, a founder has to practice and train their intuition so that they eventually will be able to determine the perspectives and potential success of their startup. There is no logic to this; the circumstances change every day, and hard data comes in too late. So the only real skill is your gut feeling—that’s what founders should be training and developing. This skill is the only important and viable advantage for any founder. Conclusion There are two ways to conquer a market. A startup can disrupt the existing field with an innovative product or hope for a multiplier in a market that doesn’t yet exist. Either way, success isn’t guaranteed. So, the blockchain market, whether it’s already created and developed or just exists in our imaginations, can still be a fruitful field. Follow me on . Check out my . LinkedIn website

What To Do As A Startup Without A Market

একটি বাজার ছাড়া একটি স্টার্টআপ হিসাবে কি করতে হবে

Was tun als Startup ohne Markt?

Qué hacer como startup sin mercado

Que faire en tant que startup sans marché

बिना बाज़ार के स्टार्टअप के रूप में क्या करें?

市場のないスタートアップとして何をすべきか

시장이 없는 스타트업이 해야 할 일

O que fazer como uma startup sem mercado

Что делать стартапу без рынка

Pazarı Olmayan Bir Startup Olarak Ne Yapmalı?

Phải làm gì khi khởi nghiệp không có thị trường

初创企业没有市场该怎么办

Overcoming Challenges in Web3 Gaming: Interview with RACE Protocol's Co-Founder

ওয়েব 3 গেমিং-এ চ্যালেঞ্জগুলি অতিক্রম করা: RACE প্রোটোকলের সহ-প্রতিষ্ঠাতার সাথে সাক্ষাৎকার

Herausforderungen beim Web3-Gaming meistern: Interview mit dem Mitbegründer des RACE Protocol

Superando los desafíos en los juegos Web3: entrevista con el cofundador del protocolo RACE

Surmonter les défis du jeu Web3 : entretien avec le co-fondateur de RACE Protocol

वेब3 गेमिंग में चुनौतियों पर काबू पाना: RACE प्रोटोकॉल के सह-संस्थापक के साथ साक्षात्कार

Web3 ゲームの課題を克服する: RACE Protocol の共同創設者へのインタビュー

Web3 게이밍의 과제 극복: RACE 프로토콜 공동 창립자와의 인터뷰

Superando Desafios em Jogos Web3: Entrevista com o Cofundador do Protocolo RACE

Преодоление проблем в играх Web3: интервью с соучредителем RACE Protocol

Web3 Oyunlarında Zorlukların Üstesinden Gelmek: RACE Protokolünün Kurucu Ortağı ile Röportaj

Vượt qua những thách thức trong trò chơi Web3: Phỏng vấn Người đồng sáng lập RACE Protocol

克服 Web3 游戏中的挑战：采访 RACE 协议联合创始人

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu;
(2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu;
(3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu;
(4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu;
(5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. Table of Links Abstract & Introduction
Background and Related Work
Partially Convex Potential Maps (PCP-Map) for Conditional OT
Conditional OT flow (COT-Flow)
Implementation and Experimental Setup
Numerical Experiments
Discussion and Reference 4. Conditional OT flow (COT-Flow). In this section, we extend the OT-regularized continuous normalizing flows in [36] to the conditional generative modeling problem introduced in section 2. Training Problem. Following the general approach of continuous normalizing flows [11, 19], we express the transport map that pushes forward the reference distribution ρz to the target π(x|y) via the flow map of an ODE. That is, we define g(z, y) = u(1) as the terminal state of the ODE where the evolution of u: [0, 1] → R n depends on the velocity field v : [0, 1] × R n × R m → R n and will be parameterized with a neural network below. When the velocity is trained to minimize the negative log-likelihood loss in (3.2), the resulting mapping is called a continuous normalizing flow [19]. We add y as an additional input to the velocity field to enable conditional sampling. Consequently, the resulting flow map and generator g depend on y. One advantage of defining the generator through an ODE is that the loss function can be evaluated efficiently for a wide range of velocity functions. Recall that the loss function requires the inverse of the generator and the log-determinant of its Jacobian. For sufficiently regular velocity fields, the inverse of the generator can be obtained by integrating backward in time. To be precise, we define g −1 (x, y) = p(0) where p : [0, 1] → R n satisfies (4.1) with the terminal condition p(1) = x. As derived in [58, 19, 56], constructing the generator through an ODE also simplifies computing the log-determinant of the Jacobian, i.e., Penalizing transport costs during training leads to theoretical and numerical advantages; see, for example, [58, 56, 36, 17]. Hence, we consider the OT-regularized training problem where α1 > 0 is a regularization parameter that trades off matching the distributions (for α1 ≪ 1) and minimizing the transport costs (for α1 ≫ 0) given by the dynamic transport cost penalty This penalty is stronger than the static counterpart in (3.4), in other words However, the values of both penalties agree when the velocity field in (4.1) is constant along the trajectories, which is the case for the optimal transport map. Hence, we expect the solution of the dynamic problem to be close to that of the static formulation when α1 is chosen well. To provide additional theoretical insight and motivate our numerical method, we note that (4.3) is related to the potential mean field game (MFG) Here, the terminal and running costs in the objective functional are the L2 anti-derivatives of JNLL and PDOT, respectively, and the continuity equation is used to represent the density evolution under the velocity v. To be precise, (4.3) can be seen as a discretization of (4.6) in the Lagrangian coordinates defined by the reversed ODE (4.14). Note that both formulations differ in the way the transport costs are measured: (4.4) computes the cost of pushing the conditional distribution to the Gaussian, while (4.6) penalizes the costs of pushing the Gaussian to the conditional. While these two terms do not generally agree, they coincide for the L2 optimal transport map; see [16, Corollary 2.5.13] for a proof for unconditional optimal transport. For more insights into MFGs and their relation to optimal transport and generative modeling, we refer to our prior work [43] and the more recent work [57, Sec 3.4]. The fact that the solutions of the microscopic version in (4.3) and the macroscopic version in (4.6) agree is remarkable and was first shown in the seminal work [28]. By Pontryagin’s maximum principle, the solution of (4.3) satisfies the feedback form where Φ : [0, 1] × R n × R m → R and Φ(·, ·, y) can be seen as the value function of the MFG and, alternatively, as the Lagrange multiplier in (4.6) for a fixed y. Therefore, we model the velocity as a conservative vector field as also proposed in [36, 43]. This also simplifies the computation of the log-determinant since the Jacobian of v is symmetric, and we note that Another consequence of optimal control theory is that the value function satisfies the Hamilton Jacobi Bellman (HJB) equations with the terminal condition which is only tractable if the joint density, π(x, y), is available. These n-dimensional PDEs are parameterized by y ∈ R m. Neural network approach. We parameterize the value function, Φ, with a scalar-valued neural network. In contrast to the static approach in the previous section, the choice of function approximator is more flexible in the dynamic approach. The approach can be effective as long as Φ is parameterized with any function approximation tool that is effective in high dimensions, allows efficient evaluation of its gradient and Laplacian, and the training problem can be solved sufficiently well. For our numerical experiments, we use the architecture considered in [36] and model Φ as the sum of a simple feed-forward neural network and a quadratic term. That is, As in [36], we model the neural network as a two-layer residual network of width w that reads Sample generation. After training, we draw i.i.d. samples from the approximated conditional distribution for a given y by sampling z ∼ ρz and solving the ODE (4.1). Since we train the inverse generator using a fixed number of integration steps, nt, it is interesting to investigate how close our solution is to the continuous problem. One indicator is to compare the samples obtained with different number of integration steps during sampling. Another indicator is to compute the variance of ∇Φ along the trajectories. Hyperparameters. As described in section 5, we randomly sample the width, w, of the neural network, the number of time integration steps during training, the penalty parameters α1, α2, and the hyperparameters of the Adam optimizer (batch size and initial learning rate) from the values listed in Table 2. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; (2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; (3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu; (4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; (5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zheyu Oliver Wang, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and olivrw@mit.edu; (2)  Ricardo Baptista, Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA and rsb@caltech.edu; (3) Youssef Marzouk, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA and ymarz@mit.edu; (4) Lars Ruthotto, Department of Mathematics, Emory University, Atlanta, GA and lruthotto@emory.edu; (5) Deepanshu Verma, Department of Mathematics, Emory University, Atlanta, GA and deepanshu.verma@emory.edu. Table of Links Abstract & Introduction Background and Related Work Partially Convex Potential Maps (PCP-Map) for Conditional OT Conditional OT flow (COT-Flow) Implementation and Experimental Setup Numerical Experiments Discussion and Reference Abstract & Introduction Abstract & Introduction Background and Related Work Background and Related Work Partially Convex Potential Maps (PCP-Map) for Conditional OT Partially Convex Potential Maps (PCP-Map) for Conditional OT Conditional OT flow (COT-Flow) Conditional OT flow (COT-Flow) Implementation and Experimental Setup Implementation and Experimental Setup Numerical Experiments Numerical Experiments Discussion and Reference Discussion and Reference 4. Conditional OT flow (COT-Flow). In this section, we extend the OT-regularized continuous normalizing flows in [36] to the conditional generative modeling problem introduced in section 2. Training Problem. Following the general approach of continuous normalizing flows [11, 19], we express the transport map that pushes forward the reference distribution ρz to the target π(x|y) via the flow map of an ODE. That is, we define g(z, y) = u(1) as the terminal state of the ODE where the evolution of u: [0, 1] → R n depends on the velocity field v : [0, 1] × R n × R m → R n and will be parameterized with a neural network below. When the velocity is trained to minimize the negative log-likelihood loss in (3.2), the resulting mapping is called a continuous normalizing flow [19]. We add y as an additional input to the velocity field to enable conditional sampling. Consequently, the resulting flow map and generator g depend on y. One advantage of defining the generator through an ODE is that the loss function can be evaluated efficiently for a wide range of velocity functions. Recall that the loss function requires the inverse of the generator and the log-determinant of its Jacobian. For sufficiently regular velocity fields, the inverse of the generator can be obtained by integrating backward in time. To be precise, we define g −1 (x, y) = p(0) where p : [0, 1] → R n satisfies (4.1) with the terminal condition p(1) = x. As derived in [58, 19, 56], constructing the generator through an ODE also simplifies computing the log-determinant of the Jacobian, i.e., Penalizing transport costs during training leads to theoretical and numerical advantages; see, for example, [58, 56, 36, 17]. Hence, we consider the OT-regularized training problem where α1 > 0 is a regularization parameter that trades off matching the distributions (for α1 ≪ 1) and minimizing the transport costs (for α1 ≫ 0) given by the dynamic transport cost penalty This penalty is stronger than the static counterpart in (3.4), in other words However, the values of both penalties agree when the velocity field in (4.1) is constant along the trajectories, which is the case for the optimal transport map. Hence, we expect the solution of the dynamic problem to be close to that of the static formulation when α1 is chosen well. To provide additional theoretical insight and motivate our numerical method, we note that (4.3) is related to the potential mean field game (MFG) Here, the terminal and running costs in the objective functional are the L2 anti-derivatives of JNLL and PDOT, respectively, and the continuity equation is used to represent the density evolution under the velocity v. To be precise, (4.3) can be seen as a discretization of (4.6) in the Lagrangian coordinates defined by the reversed ODE (4.14). Note that both formulations differ in the way the transport costs are measured: (4.4) computes the cost of pushing the conditional distribution to the Gaussian, while (4.6) penalizes the costs of pushing the Gaussian to the conditional. While these two terms do not generally agree, they coincide for the L2 optimal transport map; see [16, Corollary 2.5.13] for a proof for unconditional optimal transport. For more insights into MFGs and their relation to optimal transport and generative modeling, we refer to our prior work [43] and the more recent work [57, Sec 3.4]. The fact that the solutions of the microscopic version in (4.3) and the macroscopic version in (4.6) agree is remarkable and was first shown in the seminal work [28]. By Pontryagin’s maximum principle, the solution of (4.3) satisfies the feedback form where Φ : [0, 1] × R n × R m → R and Φ(·, ·, y) can be seen as the value function of the MFG and, alternatively, as the Lagrange multiplier in (4.6) for a fixed y. Therefore, we model the velocity as a conservative vector field as also proposed in [36, 43]. This also simplifies the computation of the log-determinant since the Jacobian of v is symmetric, and we note that Another consequence of optimal control theory is that the value function satisfies the Hamilton Jacobi Bellman (HJB) equations with the terminal condition which is only tractable if the joint density, π(x, y), is available. These n-dimensional PDEs are parameterized by y ∈ R m. Neural network approach. We parameterize the value function, Φ, with a scalar-valued neural network. In contrast to the static approach in the previous section, the choice of function approximator is more flexible in the dynamic approach. The approach can be effective as long as Φ is parameterized with any function approximation tool that is effective in high dimensions, allows efficient evaluation of its gradient and Laplacian, and the training problem can be solved sufficiently well. For our numerical experiments, we use the architecture considered in [36] and model Φ as the sum of a simple feed-forward neural network and a quadratic term. That is, As in [36], we model the neural network as a two-layer residual network of width w that reads Sample generation. After training, we draw i.i.d. samples from the approximated conditional distribution for a given y by sampling z ∼ ρz and solving the ODE (4.1). Since we train the inverse generator using a fixed number of integration steps, nt, it is interesting to investigate how close our solution is to the continuous problem. One indicator is to compare the samples obtained with different number of integration steps during sampling. Another indicator is to compute the variance of ∇Φ along the trajectories. Hyperparameters. As described in section 5, we randomly sample the width, w, of the neural network, the number of time integration steps during training, the penalty parameters α1, α2, and the hyperparameters of the Adam optimizer (batch size and initial learning rate) from the values listed in Table 2.

Efficient Neural Network Approaches for Conditional Optimal Transport:Conditional OT flow (COT-Flow)

The $1 trillion question: what will happen to Bitcoin’s price after halving? If history is our guide, not much. What did Bitcoin’s price do after the three previous halving events? In 2012, bitcoin’s price languished for more than a month. In 2016, its price dropped and took four months to get back to even. In 2020, its price stayed flat for two months. See for yourself: Don’t be surprised if we get a similar outcome this time. Typical operation To summarize a far more nuanced, complicated story: In 2024, we caught a massive bid. This round of speculative enthusiasm pushed Bitcoin’s price to a new all-time high. The market found itself overextended in a way we’ve never seen heading into a halving event. Today, there’s no reason to think this market can’t go a little higher for a little longer, simply on the momentum it’s built up for months. Stepping back from the day-to-day commotion, we’re still at extremes on many market metrics. I have shared the reasons in my newsletter. Crypto is Easy It all makes sense and fits historical patterns. I guess we’ll see whether these bids keep pouring in at the pace we’ve seen in the past two months. Otherwise, we could have a bumpy road ahead. But Mark, the ETFs! Those Wall Street ETFs have taken in $12 billion in new inflows. While that is a substantial accomplishment and certainly helpful, it can’t count for the $650 billion rise in Bitcoin’s market cap — even if you give the ETFs a generous multiplier effect. Anyway, how sure are you that Aunt Sally, Uncle Morton, and the pickleball crew haven’t already gotten their 1–3% allocation? If that’s the case, they have no reason to put more money into the ETFs. One less point: ETFs let people buy . So far, we’ve seen a lot of buying and not a lot of selling. and sell What happens when that changes? In recent weeks, net inflows have crashed. Some days, they’re negative. See for yourself, from : Farside Investors UK Is the trend starting to shift, or is it no big deal? We’ll see. Mark, do you mean Bitcoin’s price will never go up? On the contrary, its price will do just fine. The halving will cut new issuance in half — equivalent to roughly $11 billion per year at today’s prices. That’s almost $1 billion less selling pressure on the market each month (more as the price goes up) and as much as the Wall Street ETFs brought in this year. While $1 billion each month is not a lot for an asset as large as Bitcoin, it’s enough to make a difference. And, unlike those ETFs, those Bitcoins are off the market forever. Supply from those ETFs can hit the market at any time. We also have a strong base of advocates (that now includes major Wall Street firms), people who consistently, habitually buy Bitcoin all the time, and speculators who see financial opportunity in it. The halving helps push that process along. Sometimes, it takes longer than you expect to see the result. I would expect the same will “HODL” true this time. $150,000 is still possible by the end of 2025. If it happens, the halving will play a key role. In the near term, expect something far less spectacular. Mark Helfman publishes the newsletter. He is also the author of three books and a top Bitcoin writer on and . Learn more about him in his and connect with him on or . Crypto is Easy Medium Hacker Noon bio Superpeer Tealfeed

How Much Will Bitcoin’s Price Go Up After This Month’s Halving?

Blast is an Ethereum Layer 2 platform that leverages the technology of rollups to address the limitations of the Ethereum network, particularly in terms of scalability, transaction fees, and security. By bundling numerous transactions off-chain, processing them within its own layer, and then submitting a condensed summary of these transactions back to the ETH mainnet, Blast significantly reduces the workload on the ETH blockchain. This approach results in key benefits such as increased transaction throughput, dramatically lower transaction costs for using Blast-based decentralized applications (dApps), and the robust security guarantees of the underlying Ethereum blockchain. Let’s take a look at how Blast became one of the most discussed projects this year. As a marketing professional, I want to show you how hype around such projects is created and how they act and position themselves to become one of the current DeFi leaders. And, if you run a DeFi project and you need advice on marketing and community building - you can directly. message me This article is just a marketing case, not an investment advice. Please, be careful and do your own research. Blast’s business model Let’s start with fundamentals - what differs your project from others. One of the key elements is a business model. It’s not only a technical solution, but also one of the key things you can use in marketing campaigns. Blast's unique business model differentiates it significantly from other DeFi protocols through several innovative features. These features not only enhance the user experience, but also contribute to the platform's attractiveness for both users and developers, leading to a rapid accumulation of Total Value Locked (TVL). Here's how Blast stands out: Native Yield for ETH and Stablecoins Blast is unique in offering built-in yields for ETH and stablecoins directly on its Layer 2 platform. This eliminates the need for users to interact with separate DeFi protocols to earn yields on their assets. For example, ETH staking on Blast leverages the post-Merge Ethereum staking capabilities, offering a 3.2% yield, while its stablecoin, USDB, earns a 13% yield by integrating with MakerDAO’s on-chain T-Bill protocol. This direct yield generation is a significant departure from the norm in DeFi, where Layer 2 solutions typically do not offer native yields. Gamified Airdrop Blast has implemented a gamified approach to its airdrop, making the process of earning and distributing tokens more engaging for users. This gamification includes mechanisms like referral codes, social media interactions, and a "super spin" lottery draw, where users' "luck" can increase based on their activities and the size of their "squad" or referral network. This approach incentivizes user participation and engagement in a way that's more interactive than traditional airdrop models. Layered Staking System This system allows for more nuanced and flexible staking options, catering to different risk appetites and investment strategies. This flexibility is appealing to a broader range of users and can lead to increased TVL as users find staking options that suit their preferences. Liquid Re-Staking Tokens Blast's model includes the concept of liquid re-staking tokens, which allows users to trade yield and earn a fixed yield on their assets. Support for Diverse DeFi Projects Blast's architecture and ecosystem are designed to support a wide range of DeFi projects, including DEXs, derivatives, NFT games, and more. This broad support encourages the development and migration of diverse applications to Blast's Layer 2 platform, contributing to its TVL and overall ecosystem growth. Now let’s take a closer look at the marketing mechanism of Blast DeFi to understand how they created such a hype around. And I want to start with the airdrop campaign that generated lots of noise. Airdrop The airdrop aims to incentivize user participation and engagement on the platform by rewarding them with Blast points, which will be convertible to BLAST tokens in May. Here's a detailed look at Blast's airdrop strategy and how users can receive points. Airdrop Mechanics Blast offers a native yield of 3.2% for ETH and 13% for stablecoins, derived from ETH staking and Real-World Asset (RWA) protocols. This yield is automatically passed back to users, distinguishing Blast from other L2 solutions that typically offer a 0% default interest rate. Blast's strategy to receive points for the airdrop involves several steps: Users need to sign up on the Blast website with an invite code (you can use 25N3O), follow Blast on social media, and connect their wallet. They must bridge ETH or other supported tokens from Ethereum to Blast L2 to start earning Blast points. Users can earn additional points by inviting friends to join Blast. Those who bridged assets to Blast before the mainnet launch receive a 10x multiplier on their earned points. Interacting with dApps on Blast allows users to earn Blast Gold, which will also be convertible to BLAST tokens in May. Additional Strategies for Blast Airdrop: Developer Incentives: 50% of the airdrop pool is reserved for users, while the remaining 50% is allocated to developers, encouraging both user and developer engagement on the platform. Multiplier Campaign: A future "Multiplier" campaign is planned, offering users additional ways to increase their points by using highlighted dApps, although this feature is not yet live. Blast's airdrop strategy is multifaceted, focusing on rewarding early users, encouraging asset bridging, and fostering engagement with dApps on the platform. By offering native yields and leveraging a points system that rewards participation, Blast aims to attract and retain users, thereby growing its ecosystem. This approach not only incentivizes user activity but also supports developers, ensuring a vibrant and active community around the Blast L2 solution. With such strategy the platform has reached over $2 billion in TVL, with a significant portion of this value coming from users bridging funds to participate in the future airdrop of Blast tokens, scheduled for May. Now let’s take a look at how Blast promoted it’s airdrop and the platform itself. Social Media The Blast project's social media presence has been crucial in spreading awareness and engaging with the community, especially in the early stages of the project's launch. That’s why one of the tasks of the airdrop campaign is to follow Blast’s Twitter. Blast has 580+K followers on its official Twitter account . @Blast_L2 Partnerships Blast has established partnerships with real-world asset protocols to enhance its ecosystem's accessibility and inclusivity. These partnerships aim to integrate tokenized assets seamlessly, thereby expanding DeFi's reach and fostering a more inclusive financial ecosystem. Additionally, collaborations with web3 risk management firms like Gauntlet and DeFi money market protocols like Morpho have been announced, indicating a focus on security and sustainability within the DeFi space. Influencer Collaborations The project has strategically collaborated with influencers to leverage their reach and credibility. These collaborations have been instrumental in promoting Blast and its features to a wider audience. You can find dozens of videos on Youtube, posts on Reddit and Twitter about Blast. Most of them are educational - they show how to use the platform and maximize the points you potentially can get. Events Blast has engaged the community through events, including developer contests like the "Big Bang" contest, which attracted over 3,000 projects. These events aim to foster innovation and encourage the development of applications on the Blast platform. Such initiatives not only promote Blast but also contribute to the broader Ethereum ecosystem by supporting developers and new projects. Content Marketing Through platforms like Substack and various crypto-focused blogs, Blast has disseminated detailed information about its features, benefits, and the future vision of the platform. Articles exploring the scalability solutions offered by Blast, its unique yield mechanisms, and real-world asset integration have been pivotal in educating the community and attracting interest from developers and users alike. Additionally, the project's focus on creating a developer-friendly environment and its promise of native yields for ETH and stablecoins have been highlighted as key differentiators. Investors Investors in crypto are not just only money. Projects use it as a proof of confidence to its audience. Let’s take a brief look at how invested in Blast. : A leading crypto investment firm, Paradigm has been mentioned as a major backer of Blast. Paradigm is known for its investments in various successful crypto projects and platforms, emphasizing its confidence in Blast's potential within the Ethereum ecosystem. Paradigm Another notable investor in Blast, Standard Crypto is a venture capital firm that focuses on supporting projects at the forefront of the crypto and blockchain space. Their investment in Blast underscores the platform's promising approach to enhancing Ethereum's scalability and user experience. Standard Crypto: : This crypto-native investment firm, known for its unique approach and community-driven investment strategy, has also invested in Blast. eGirl Capital's support further validates Blast's appeal to the DeFi community and its potential to attract a diverse user base. eGirl Capital : Co-founded by Andrew Kang, Mechanism Capital is an investment firm that focuses on blockchain and DeFi projects. Their investment in Blast indicates a belief in the platform's strategy to offer native yields and improve transaction efficiency on Ethereum. Mechanism Capital : Known for his insights and contributions to the crypto space, Hasu's involvement as an investor in Blast adds a layer of credibility and expertise, especially in the context of staking solutions and yield generation. Lido Strategy Advisor Hasu Blast is currently a notable player in DeFi and Ethereum Layer 2 space. As you can see the project has employed a multifaceted marketing strategy to promote its platform and attract users. Let’s see how Blast will complete it’s airdrop campaign and what results we will see afterwards. But anyway, it’s worth it to see how the project is promoted and what mechanics it uses to build its community. P.S. Check out some of my previous articles at HackerNoon: Google Introduces Gemma: Revolutionary Access to Pre-Trained AI Models Sora's AI Innovation Set to Disrupt Video Content Creation Breaking Down the Next Big Thing in NFTs: ERC-404 Spotlight Gemini 1.5 Unleashes Unprecedented Context for AI Applications The AI Revolution in Journalism: A New Era of Enhanced Reporting DeFi Community Building: A Step-by-Step Guide for Crypto Startups

Why Everyone's Talking About Blast: The DeFi & Airdrop

**JERUSALEM, Israel, March 28th, 2024/Cyberwire/--**In 2023 alone, more than 10 customers and partners signed commercial agreements with C2A Security, including a global, long term enterprise agreement with an European Commercial Vehicle Manufacturer. C2A Security’s DevSecOps Platform, ‘EVSec’, has been gaining widespread traction as the automotive industry rushes to meet cybersecurity regulations and industry standards, such as UN Regulation No. 155, ISO/SAE 21434, Chinese GB Standards, and others. 2024 is a pivotal year for cybersecurity regulations in the automotive industry, as UN Regulation No. 155 goes into full effect. C2A Security’s EVSec risk-driven product security platform allows developers to focus on creative features and manage software and operations at scale, in an automated manner. EVSec automates archaic manual processes and enables cross-functional sharing and collaboration between teams, customers, and supply chains while offering full digital twin capabilities. EVSec applies continuous feedback from product operations and vulnerabilities to improve development and design, as part of agile software development. In 2023, C2A Security added multiple OEMs and Tier 1s to its portfolio of customers, successful evaluations, and partnerships such as Daimler Truck AG, BMW Group, Marelli, NTT Data, Siemens, and Valeo, among others. The collaboration with C2A Security supports the customers on the applicable standards and best practices for regulatory compliance. EVSec maps and automates the relevant standards and regulations, like ISO/SAE 21434 and UN Regulation No. 155, to simplify compliance efforts, which are essential to business success, as companies forge new grounds for their vehicles, develop innovative EV-powered vehicles, and plan for the EV infrastructure that supports them. “We’re thrilled that EVSec has proven so popular and effective, as companies like Daimler Truck AG choose it as its product security platform. Dealing with current and emerging regulatory demands, software development at scale and overall product security operations can be a limitation on business continuity of a company if not automated,” noted Roy Fridman, CEO, C2A Security. “At the end of 2023, we witnessed the first case of a premium car maker that stopped the sale of their most popular model in the European Union because it failed to comply with the regulation. To stay competitive companies must utilize advanced product security automation in their development and operations and we are excited to support them in achieving this goal.” Images available . here About C2A Security C2A Security is the only risk-driven DevSecOps Platform vendor that addresses the specific needs of car makers, Tier 1 suppliers, and mobility companies. Founded in 2016, C2A Security’s customers and technology partners include top-tier global players including Daimler Truck AG, BMW Group, Siemens, Valeo, ThunderSoft, Marelli, NTT Data, and Evvo Labs, among others. C2A Security transforms cybersecurity from being a company-wide limitation to a business value multiplier through advanced security automation and compliance to shorten software release times and decrease costs. Our vision is to turn product security into a seamless, automated, and transparent process, reducing time to deployment and costs of managing automotive software products and resources. C2A Security was founded by NDS/Cisco veteran Michael Dick, with its global headquarters in Jerusalem, Israel. . www.c2a-sec.com Contact Estee Yaari C2A Security estee.yaari@gmail.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

EVSec Risk Management And Automation Platform Gains Traction In Automotive Industry 

AI has taken over social media. you have a tool that can dig deep into audience insights, predict the next big content hit, craft posts and visuals on its own, spot consumer trends, and decode public sentiment. Picture this: There is no doubt to say that AI is the backbone of every social media platform out there. It doesn't only help in analyzing what content pops up in your feed, but also cleaning up comments for a better browsing experience, and suggesting new content and friends you might enjoy. But, it poses a challenge for marketers, finding that spot where content appeals to both humans and AI algorithms. When it comes to what “The Algorithm" wants, we're left guessing. And, All too often, we switch from cheap tricks to increased engagement without creating any lasting value for our audiences. Interestingly, AI can lead to more genuine marketing on social media. It helps us understand the vast amount of data from social media platforms. This way, we can create content that truly matters to our audience. AI analyzes posts, comments, and interactions to let us provide what people want and address their interests better. Plus, AI saves a lot of time and money by automating and scaling these processes. That's why top social media marketers are using AI to: Automate repetitive tasks, saving time and money. Expand social media strategies across different platforms. Create posts and campaigns based on data that actually works. You can do this too, no matter your budget or company size. The rest of this post shows you how. How AI is Being Used in Social Media So how exactly can you use artificial intelligence to take your social media marketing game to a whole new level? Let's look at some of the best ways to do this. AI-Powered Content Curation One of the most challenging aspects of social media marketing is coming up with fresh content ideas that can drive engagement for your brand. Instead of copying your competitors or throwing a bunch of content ideas at the wall and hoping one sticks, AI analyzes your data and creates targeted messages based on what your audience has liked in the past. This way, you can be sure that the content you share will resonate with your social media audience and get the reaction you want. AI also helps in improving the quality of your content in terms of tone and grammar. Note: AI-enabled tools even analyze your social media data and determine which keywords, images, hashtags, and colors have a stronger impact on your audience based on past results. In addition, AI-powered content curation tools automatically create social media posts using a URL or information you provide that suits your brand voice. These tools also share the posts on different social platforms. Did you know that Elon Musk says xAI will open-source Grok this week? Automation of Social Network Management Your business is likely on more than one social media platform. And, while consistently publishing quality content, keeping up with the latest trends/updates on every platform can be quite a hassle. However, with the help of AI-powered social media management tools, it is easy to grow your influence and connect with your audience. These tools also plan out your social media posts in advance, track all your profiles, and generate appropriate responses for consumers after analyzing their messages. In simple form, AI will not only plan your campaigns. but also fill content gaps and keep your followers engaged with your brand voice. This tool can recycle content you've previously published, such as blog posts or infographics, and turn them into upload-ready social media posts with hashtags and relevant links. Advertising Campaigns on Social Networks How to get ahead of your competitors and get the most out of advertising on social networks? Several companies are using paid advertising on social media. If you aim to stand out and increase your campaign's impact, it's time to start creating better, smarter ads. AI-powered tools like Sprinklr help you create compelling, personalized social media ads and monitor and optimize your campaigns to maximize ROAS. Such tools offer recommendations to help your campaigns perform better. From comparing ad performance to planning out strategies, AI reduces your overall advertising costs. The copywriting AI tool analyzes your brand language and creates social media ad copy that will increase engagement and conversions for your business. Ensure that your social media ad copy matches your landing page to maintain consistency and communicate with users. Personalization Using Chatbots More followers and clients mean more money, but it also means more time spent serving them. Luckily for you, highly effective customer support and personalized audience experiences are some of the benefits that help your brand grow with the help of artificial intelligence. Hiring enough customer service staff to provide 24/7 customer support may not be feasible. Investing in AI-powered chatbots will respond to audience messages immediately, regardless of the time of day. You can even combine chatbots with your existing live chat software to provide your audience with 24/7 customer service support. With the help of machine learning, your AI chatbots will be able to provide relevant answers to various problems, questions, or queries that your audience may have. Such tools allow you to deliver engaging, seamless, and personalized customer experiences using bots across a variety of social media platforms. Competitor Analysis and Social Listening It's not enough to just know who your competitors are. If you want to get an advantage over them, you need to analyze their social media strategies, find out their weaknesses, and determine what they think. The manually searching and analyzing competitor pages can take weeks or months of grueling work., And, if you succeed in your analysis, there is a high probability that it will contain errors. But with the help of AI, you can easily track your competitors and analyze their social media strategies, reach, engagement rates, conversion rates, brand sentiment, and more. Various AI-powered competitive analysis tools monitor your competitors' social marketing efforts, from user interactions to posts and campaigns. It helps in knowing which parts require a more effective marketing strategy to achieve success. Artificial intelligence tools track mentions of your business, as well as specific hashtags and keywords related to you on social media. Analyzing this information will help you determine whether the public opinion of your brand is negative, positive, or neutral. You will be able to identify potential problems before they become more serious. Emerging AI Technologies in Social Media AI is about to take social media on a futuristic ride. We're not just talking about minor upgrades here - AI is changing the way we scroll, post, and consume content online. Different technologies like synthetic media, AI-created content, and virtual influencers are expanding the limits of what we can do online. Let's take a look at what insanely innovative experiences are coming next… Deepfakes You've probably seen those mind-bending "deepfake" videos going viral where celebrities or politicians seem to be saying and doing things they never actually did. Now creators can create stunningly realistic footage that pushes the boundaries of imagination thanks to artificial intelligence. Deepfakes also open up a world of exciting opportunities for creative self-expression and next-level storytelling through synthetic media. As the technology continues evolving, we'll see deepfakes enabling Hollywood-level visual effects that bring our wildest visions to life. Virtual Influencers This one's unquestionably innovative - companies are using AI to generate incredibly human-looking virtual influencers to advertise products on social media. We're talking lifelike computer avatars with their names, personalities, and millions of followers! Now, imagine the creative potential here. Brands can craft highly engaging, endlessly customizable digital personas to connect with audiences in new ways. Virtual influencers offer limitless brand ambassadorship and storytelling opportunities. AI Content CreatorsImagine telling an AI to whip up your next social media caption, graphic, or video ad...and having it generate something cleverly crafted in seconds. Using large language models and other AI tools, brands can augment human creativity. For content creators, this AI assistance will prove to be an amazing productivity multiplier and source of innovative inspiration. While not replacing humans, AI can elevate our creative visions to new heights while saving tons of time. Feed Personalization Of course, the biggest AI impact on social is automated feed curation. It further deeply understands our preferences to serve up a perfectly personalized experience tailored to each user's interests. This allows for a more rewarding, endlessly engaging social media journey of discovery and relevance. While some worry about filtered bubbles, smarter AI will also ensure we're exposed to a balanced variety of perspectives selected just for us. Conclusion There's no doubt to say that AI is completely changing the world of social media as we know it. From automated content creation to hyper-personalized feeds to wildly realistic synthetic media, this technology is rapidly transforming how we connect, share, and experience online. Artificial intelligence (AI) is making it easier for social media platforms to sort through vast amounts of data. It helps them spot the latest trends, understand what users like, block harmful content, and do much more. AI has a promising future in this space because it makes things better for users and helps brands cater to them more effectively. In social media marketing, AI is crucial because it allows companies to track how well they're doing and pinpoint potential customers.

How AI is Shaping the Future of Social Media

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