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Coastal Carolina University

Charleston

Nexton

It is with great pleasure that we welcome the following new agents to the Realty One Group Coastal Team. Francesca “Frankie” Wiles is a sought after realtor serving the Greater Charleston area. After ten years of salon ownership,

https://www.bing.com/th?id=OVFT.FPW7cXIMJr9Jqa1W4DGxwS&pid=News

Realty One Group Coastal Welcomes New Agents to the Team, Del Webb Charleston at Nexton Expanding

https://www.bing.com/th?id=ODF.70-LnngeSG1sW2m_LvztaA&pid=news

Post and Courier

After market highs in April 2022 and lows in January 2023, local real estate agents are saying the market is stabilizing.

https://www.bing.com/th?id=OVFT.-7xMXbM2nhUbS_4TsWxpgC&pid=News

Local real estate market stabilizes

https://www.bing.com/th?id=ODF.hTuzNY0LqWyhqIHBXt5R9g&pid=news

Skagit Valley Herald

New York-based real estate developer Rockefeller Group also announced construction has begun on its first development in the region, Nellis Logistics Center.

https://www.bing.com/th?id=OVFT.CIYBOpWyTx4kS9tcqHnqzC&pid=News

Las Vegas

Rockefeller Group

Las Vegas Review-Journal

Rockefeller Group buys second industrial plot in Las Vegas

https://www.bing.com/th?id=ODF.d-nAVodkT3w9ImFH6hGl8w&pid=news

CSRWire USA

CBRE Group

Corporate Responsibility Report highlights how CBRE is working to drive progress for a low carbon future, create opportunities so all people thrive and lead with integrity to build trust.

https://www.bing.com/th?id=OVFT.QwAvf3TJTqTLhaTfCg_YnC&pid=News

CBRE Releases 16th Annual Corporate Responsibility Report

https://www.bing.com/th?id=ODF.hEqYQrIu_oVxEcF1aiBtsQ&pid=news

CSR Wire

Share

The stock of CBRE Group Inc. (NYSE: CBRE) has increased by 0.62 when compared to last closing price of 74.51. Despite this, the company has experienced a -2.40% fall in its stock price over the last five trading sessions.

CBRE Group Inc. (CBRE) Shares Soar Above 1-Year High

newsheater

Southern California

That’s a 2.3% gain. Real estate’s share of local employment was 9.9% last month. Jonathan Lansner is the business columnist for the Southern California News Group. He can be reached at jlansner@scng.com

https://www.bing.com/th?id=OVFT.xpIJjtNpRiqZ08UC7Xpqdi&pid=News

Southern California added 10,400 real estate jobs in April

Press-Telegram

Porter

Kingwood

Private equity

Although this local real estate investment and development firm recently shifted its primary focus to multifamily development, the company continues to acquire shopping centers, its longtime bread and butter.

Dhanani Private Equity Group acquires shopping center, land for apartment complex in Montgomery County

https://www.bing.com/th?id=ODF.t3F4eXZUTDaqQci8YlUgMw&pid=news

The Business Journals

Space

Hits

Towel

Eighteen subleases totaling 2.2M SF have been taken off the market for reasons other than a new tenant occupying the space since the first quarter of last year, according to CBRE. Others, still attempting to find subletters,

https://www.bing.com/th?id=OVFT.tW1byBLWDYkDNyIW4xhYJi&pid=News

As Sublease Space Hits Record High, Some Dallas-Area Companies Are Throwing In The Towel

https://www.bing.com/th?id=ODF.Qst1N2zpqaCSmsAVG2qhnw&pid=news

Bisnow

Houston

Dallas

Interstate 610

Dallas-based ATCAP Partners bought a fully-leased industrial portfolio totaling nearly 1.2 million square feet in eight buildings near Loop 610 in northeast and southeast Houston from CenterPoint Properties.

Real estate transactions – Houston industrial portfolio near Loop 610 sells to Dallas investor

https://www.bing.com/th?id=ODF.PQRFkRxy512-QD57XWarCw&pid=news

Houston Chronicle

Property management

Investment

Dallas-based CBRE Group Inc.’s property management group has formed a global strategic partnership with Deepki that will bring Deepki Ready, one of the world’s most extensive landlord-focused real estate sustainability data-intelligence platforms,

https://www.bing.com/th?id=OVFT.8AaxKF8mvphi3JHjNiRU4y&pid=News

CBRE Property Management Makes Minority Investment in Deepki, Plans Global Partnership

https://www.bing.com/th?id=ODF.DVTCfaNC7eC8G9usVwS2nQ&pid=news

Dallas Innovates

Get the most recent info and news about CBRE Group on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

CBRE Group, Inc. operates as a commercial real estate services and investment company worldwide. It operates through three segments: Advisory Services, Global Workplace Solutions, and Real Estate Investments segments. The Advisory Services segment provides strategic advice and execution to owners, investors, and occupiers of real estate in connection with leasing; property sales and mortgage services under the CBRE Capital Markets brand; property and project management services, including construction management, marketing, building engineering, accounting, and financial services for owners of and investors in office, industrial, and retail properties; and valuation services that include market value appraisals, litigation support, discounted cash flow analyses, and feasibility studies, as well as consulting services, such as property condition reports, hotel advisory, and environmental consulting. The Global Workplace Solutions segment offers facilities management, project management, and transaction management services. The Real Estate Investments segment provides investment management services under the CBRE Investment Management brand to pension funds, insurance companies, sovereign wealth funds, foundations, endowments, and other institutional investors; development services under the Trammell Crow Company brand primarily to users of and investors in commercial real estate; and flexible-space solutions under the CBRE Hana brand. The company was founded in 1906 and is headquartered in Dallas, Texas.

Over the past decade, we have witnessed the emergence of new technologies such as blockchain technology. Blockchain can be described as a value-exchange protocol. In this regard, it is said that what the Internet did for the exchange of information, the blockchain will do for the exchange of value. Just as the Internet revolutionized the use and exchange of information, same with blockchain technology; it is set to revolutionize the recording and exchange of value. As we are making the transition to a fully digital economy, it is important to focus on how the new technologies can improve our daily lives and add value to our society and global economy. Connectivity, accessibility, and interoperability are essential as the increase of globalization provides a need to transact and connect with each other no matter where we are. Businesses and individuals increasingly expect everything in their lives to move instantly. This is mainly because of smartphones and apps where nearly anything is attainable at the press of a button, but when it comes to money and financial service providers, they are often left with bad experiences. The needs of individuals and businesses sending payments across borders have significantly evolved. They have traditionally been served by retail banks, which are not meeting their payment needs and are demanding real-time, low-cost, and fully trackable payments on a global scale. But today’s inefficient system of moving money around the world means high costs for banks and payment providers. This is because an obsolete payments infrastructure, designed almost five decades ago, leads to expensive and slow transactions that can take days to settle with little certainty as to their ultimate success. This experience runs contrary to people’s expectations for an Internet of Value, where money moves as quickly and seamlessly as information. There are more than $5 trillion sitting idle all over the world in bank accounts just to make transactions around the world. This could change if financial institutions adopted blockchain technology and crypto assets, which would lead to less costs, increased speed, better liquidity management for financial institutions and better customer experience. Blockchain has the potential to change almost every industry out there. It can create a more efficient and prosperous world. Crypto-assets have also sparked a broader debate on payments innovation and on the roles of the private and public sectors in devising new ways to make payments more affordable, efficient and inclusive. Consumerization of these solutions, and technologies including mobile wallets and blockchain, are leveling the playing field and allowing new entrants to compete with the handful of large banks that have historically dominated cross-border payments. Blockchain offers a cryptographically secure, end-to-end payment flow with transaction immutability and consistency in information sharing. The is a concept proposed by Ripple which envisions a world where value moves and is exchanged like information is exchanged today in the Internet era. With the Internet of Value, a value transaction such as a foreign currency payment can happen instantly, just as how people have been sharing messages, images, and videos online for years. And it’s not just money. Internet of Value The Internet of Value can enable the exchange of any asset that is of value to someone, including stocks, votes, frequent flyer points, securities, intellectual property, music, scientific discoveries, and more. Until now, selling, buying, or exchanging these assets has required an intermediary like a bank, credit card company, and generally third-party services. Blockchain allows any type of asset to be transferred from one party directly to another, with no middleman. The transfer is validated, permanent, and completed instantly. Blockchain technology along with crypto assets are key elements enabling this process. By creating a shared ledger where people can own their own data, it also enables a shift in the locus of value within the economy to the individual and networks. In a world of limited connectivity, limited transparency, and limited peer-to-peer trust it was necessary to have third-party institutions define, quantify and authenticate sources of value within society, and the economy. But in a world of pervasive peer-to-peer connectivity, transparency, and trusted networks, value can be defined through negotiation between peers within distributed networks. On a macro-economic level, the Internet of Value could also help drive down the cost of goods and transactions as the liquidity would increase. In today’s country-specific systems, companies and banks must tie up large sums of money in local currencies for countries in which they do business in order to make transactions. An Internet of Value that eliminates the need for these accounts suddenly frees up large sums of cash that can be more effectively put to use in the global economy. The potential use cases are plenty, from instant transactions to tokenization of assets, lending, smoother international trade, more robust digital agreements, and more. 1. Settlement of funds Blockchain technology and crypto-assets can simplify the entire process of payments and remittances, by removing unnecessary intermediaries. They can provide frictionless and instant payment and settlement solutions. Unlike traditional services and systems, a blockchain system doesn’t rely on a slow process of approving transactions, which usually goes through several mediators and requires a lot of manual work. They have the potential to disrupt not only the world’s currency market, but also the banking industry as a whole by cutting out the unnecessary intermediaries and replacing them with a trustless, borderless, and transparent system that is easy to access by anyone. The use of crypto assets as a bridge currency for cross-currency transactions can eliminate the need of banks and payment providers to hold nostro-vostro accounts with foreign currencies. This means that the trillions of dollars trapped in multiple currencies to make cross-border payments could be unlocked and used more productively to benefit the global economy. With a crypto asset as a bridge, banks only have to hold their domestic currency which minimizes the number of intermediaries involved, and their markup on spreads which can save money, time and enable instant settlement with real transfer of value. One notable example is the crypto asset XRP, which is used by Ripple and financial institutions for that reason, since it's very fast and cheap to transfer. 2. Asset tokenization Buying and selling securities and other assets, such as stocks, bonds, commodities, and derivatives requires a complex, coordinated effort between banks, brokers, clearing-houses, and exchanges. This process not only has to be efficient, but it also needs to be accurate and added complexity directly corresponds with increased time and cost. Blockchain technology simplifies this process by providing a technological base layer that enables the easy tokenization of all different types of assets. Since most financial assets are bought and sold digitally through online brokers, tokenizing them on the blockchain is a convenient solution for all involved. Some innovative blockchain companies are investigating tokenizing real-world assets, such as real estate, commodities, and art. This would make transferring ownership of assets that hold real-world value a cheap, fast and convenient process. It would also open up new avenues for investors with limited capital by enabling them to buy fractional ownership of expensive assets or investment products that might have been unavailable to them previously. 3. Lending Banks and other lending firms have monopolized the lending sector, allowing them to offer loans at relatively high-interest rates, and restrict access to capital based on credit scores and other parameters. This makes the process of borrowing money difficult and expensive. While banks have the advantage, the economy depends on banks providing the necessary funds for higher-cost items, such as houses and cars. Blockchain technology allows anyone in the world to participate in a new type of lending ecosystem, which is part of Decentralized Finance (DeFi). To create a more accessible financial system, DeFi aims to put all financial applications on top of blockchain systems. Peer-to-peer money lending allows anyone to borrow and lend money in a simple, secure, and inexpensive way with no arbitrary restrictions. With a more competitive lending landscape, banks and firms will also be forced to offer better terms to their customers. 4. Trade finance Engaging in international trade is very inconvenient due to a large number of international rules and regulations imposed on importers and exporters. Keeping track of goods and moving them through each stage still requires manual processes, which are filled with hand-written documentation and ledgers. Blockchain allows trade finance participants to provide a higher level of transparency through a shared ledger that accurately tracks goods moving around the world. By simplifying and streamlining the complex world of trade finance, blockchain can save importers, exporters, and other businesses a significant amount of time and money. 5. Safer agreements with smart contracts Contracts exist to protect individuals and businesses when they enter into agreements, but that protection comes at a high cost. Due to the complicated nature of contracts, the process of creating one requires a lot of manual work from legal experts and it takes both time and money. Smart contracts can enable the automation of agreements through tamper-proof, deterministic code that is running on the blockchain. Money or assets can safely stay in escrow and are only released when certain conditions of the agreement are fulfilled. Smart contracts reduce the element of trust needed to reach an agreement, minimizing the risks of financial agreements and the odds of ending up in court. 6. Data integrity and security Sharing data with trusted intermediaries and companies always carries a risk of the data being compromised. In addition to that, many financial institutions still use paper-based storage methods, which increases risk of loss and recordkeeping costs significantly. Blockchain enables streamlined processes that automate data verification and reporting, digitize KYC/AML data and transaction history, and allow the real-time authentication of financial documents. This reduces operational risks, risk of fraud, and decreases the cost of handling data for financial institutions. Conclusions The potential benefits of the blockchain are more than just economic. They extend into political, humanitarian, social, and scientific domains. The technological capacity of the blockchain is already being harnessed by specific groups to address real-world problems. Blockchain penetration could facilitate increased financial inclusion in emerging markets, as financial services on the blockchain gain critical mass. It could also drive the disintermediation of financial institutions, as new services and value exchanges are created directly on the blockchain. There is already an entire industry built around blockchain and crypto assets and it is held by institutions dedicated to supervising all the digital crypto-asset exchanges taking place throughout the world. The crypto asset industry is growing quickly and the economy is slowly shifting to adapt to these needs and crypto assets have a great potential in satisfying them. There are now many apps and programs that facilitate the use of crypto-assets and bring them closer to a wider audience. An added benefit of crypto assets use is that it is open and decentralized, so trading can be done freely across borders at any time. The use of this technology will facilitate a financial revolution that will leave everyone more financially connected, empowered, and enabled. Sources: 1. https://gatehub.net/blog/what-is-the-internet-of-value/ 2. https://ripple.com/insights/the-internet-of-value-what-it-means-and-how-it-benefits-everyone/ 3. https://blockkeycapital.io/blog/blockchain-dlt/ 4. https://openmarkets.cmegroup.com/10381/what-is-an-internet-of-value 5. https://www.oecd.org/finance/2019-OECD-Global-Blockchain-Policy-Forum-SummaryReport.pdf 6. https://consensys.net/blockchain-use-cases/

6 Ways The Internet of Value Can Benefit the World Economy

Top 30 Business Intelligence Consulting Companies

An espionage and hacking scandal broke out during the US presidential campaign in the summer of 2016. Quite unexpectedly, our company, , was involved. Today, I will talk about the way we managed to overcome this situation, what measures we took and what we learned. KingServers Intro Our company, KingServers, is a hosting company that provides single-unit servers rental and VPS for our customers and we also protect them from DDoS attacks. We have our equipment based in different countries: Eastern Europe, the Netherlands and the USA. We have been on the market for several years and now we’re making good progress. In addition, we have never experienced any particular problems and were not in the news headlines until the summer of 2016. We have always paid great attention to our customers’ feedback and we have constantly monitored any references to our company on different internet resources by setting-up special Google filters. Last year, on August the 29th, we learned (out of blue) that KingServers had been mentioned in a Washington Post article. This article was placed in the “national security” section and it concerned the hacking of the electoral system in one of the States. In addition, the authors of the article claimed that the attack was performed by “Russian hackers” that had used our company’s equipment. The journalists quoted data from private information security analysts. What happened later could be described in a simple word — a wave. It quickly became apparent that this news was travelling very fast and gathering a lot of interest. We didn’t expect to find our company in the middle of an international espionage and hacking scandal. We had to decide what measures to take. Attack is the Best Defense At first, this situation was not pleasant. Our services are provided to customers working in different domains, and the fact that we were suspected of dealing with hackers, controlled by the Russian special forces which then attacked the US presidential elections systems, was not very positive for our reputation. On the other hand, we realized that we had no real guilt. In addition, the US intelligence services didn’t bother us with any inquiries. Plus, the hacker attack took place in the summer, and at that time I was on holiday in Thailand, and no one tried to arrest me or even to interrogate me. Having calmed down a little bit, we made up our minds to benefit from the situation. It goes without saying that we didn’t have any experience in working with the media, so we acted on our intuition. We decided to use an equally powerful and well-known media, similar to the Washington Post, as a response. We got in touch with the editor of the NY Times and offered to tell him about our vision of the situation. Two days later, a journalist from the NY Times arrived at our town (Biysk, Russia) and interviewed me. This article added some fuel to the fire, but actually we were unable to influence the general course of the publications around the world. Journalists and bloggers did not really want to study our case closely. As a result, their articles were exaggerated and inaccurate. Sometimes they simply published their fictions. For instance, some guy studied my tattoos from the photos published by the NY Times and then made a conclusion that one of my tattoos resembled the cybergroup “Anonymous” logo and claimed that this confirms indirectly that I am the organizer of these cyberattacks. Others paid attention to where I live claiming, “he lives near the Mongolian border and obviously, he chose this place so that he could escape in case he was discovered”. Almost every journalist wrote that my company and I were accused by the US authorities of hacking. Yet we didn’t get any requests or accusations from the US authorities or special services. One of the newspapers addressed the data center where our equipment was situated. When our partners asked the journalists why they were interested in our case, they answered honestly saying, “it gives us a lot of clicks”. A hot topic means traffic and growth in advertising. Therefore, we can say that by publishing inaccurate information, newspapers and blogs solve their business problems. The situation was quite worrying because we didn’t know what reactions to expect from our customers seeing a continuous stream of negative information on our company on the web. Anything but a Necrologue The media hysteria had no impact on our company’s operations. We kept on doing our business. We did not expect that the black PR in the media would strongly influence our team, because we sincerely discussed everything and we knew perfectly well that the situation was just too exaggerated. Our company’s website traffic diagram A pleasant surprise was that this did not confuse our customers at all. OK, some of them asked questions and they were interested in the company’s perspective — and we gave them our opinion. However, there weren’t many enquiries and a lot of customers just ignored the situation. Nevertheless, we also managed to receive a lot of new customers. As more articles appeared in various media, this the increased interest in our company.We also received new customers who had learnt of us in the news (though we usually work with the B2B segment). After the hacker scandal our profits increased despite the negativity in the media A couple weeks after our involvement in the hacking scandal, it became clear that we had actually benefited from the negative publicity. This was over six months after the events, and despite sсeptics’ predictions that we would lose all our customers, we see that, on the contrary, we were getting more and more. We still haven’t had any inquiries from other countries’ authorities and security services. I have recently returned from Europe, and I was of no interest there as far as authorities and investigations are concerned. I have recently visited Italy. How to Survive a Sudden Scandal: Five Practical Tips In conclusion, I would like to share our insights on this story — perhaps, our experience will help someone to overcome a sudden scandal with dignity and without any losses. — There are many companies developing various PR regulations, including negative feedback minimization. We think that it’s impossible to be prepared for a scandal like the one described in this article. Don’t prepare — If you really did anything wrong, that’s one thing. But accidental involvement in a scandal which turns out to be beneficial (as in our case) is another thing. If a scandal arrives, you need to understand your degree of guilt — After this rain in media, there comes a great temptation to close up and to not respond to any publications in press at all. However, this would only scare off your customers. So you’ll need some feedback in any case. It’s more useful to create an open company at first — The media have their own business. Yellow headers get more clicks and they sell more advertising. This means that your involvement in a scandal will be greatly highlighted. Journalists and bloggers aren’t usually eager to figure out who is right or wrong and write the truth in their article. The media doesn’t act impartially — Our situation was not morally pleasant but it did not hurt our business financially. In fact, it even helped to attract new customers and increase our profits. So, don’t be afraid of scandals. Bad PR is not only a disadvantage

Is Black PR Really Bad? Inside a Hacker Scandal and How to Benefit From It

This is by (source: ). Barry is the founder and CEO of ConvertKit. Twitter thread Nathan Barry @nathanbarry 04-07-2023 Struggling to connect on your remote team? We’ve built a 71 person remote team that’s driving $34 million in annual revenue. Here are 10 ideas for building great culture in a distributed team: 💻 🌐 1. Create a private team stories podcast. Everyone has the same get to know you conversations starting from zero. Instead interview them about their life story for a private internal podcast. The whole team can listen and get a head start on building relationships. Build a culture of written, asynchronous communication This will save so many meetings, avoid people feeling left out if they weren't in the meeting, and protect focused work. Your team will also be forced to clearly articulate and refine their ideas. 3. Shared “no meeting” days. Everyone has the same day for focused work each week. Team members can have days that they don’t need to get camera ready (e.g. hair, make-up, etc) if they don’t want to. At we do Tuesdays and Fridays, which are wildly productive. @ConvertKit Ask What did you get into this weekend? Every Monday morning, we have a bot that posts to Slack asking people to share a photo (or a few) from the weekend. It's a great way to get to know co-workers on a personal level and see their families, interests, and lives. Create an automated email sequence for new team members Explain how you work, where to find important things (like the joke slack channels), fun facts about team members, explain inside jokes, & more. It's all automated so you can curate their first 30+ days at the company. Host unsolicited feedback sessions This is where a small team (usually 4-8 people) gathers to talk about someone in the hot seat as if they aren't there for 10 min. When it's your turn all you can do is sit & take notes, then you get 5 min to respond. Here are the prompts: a) What does this person do that you find remarkable? What do you brag about them to other people? b) If they were up for the promotion of their career in 6 mo, what would you tell them now to give them the best chance of getting it? c) Assume you're working with this person for the next 10 years. What behavior isn't a big deal now but will get really annoying or frustrating over that time? This results in the best compliments, the most constructive feedback, and a culture of direct, candid conversations. 7. Mandatory fun days With teams feeling burnt out force everyone to take the same day off. That means you don't have to come back to a mountain of slack messages and emails. Come back & share a photo. We did a 3-day weekend for the last 3 months of the year. 8. Schedule S'Ups We use a bot to pick 3 people at random each week for a 30 min catch up / get to know you call. A triad means you always get a dynamic group from a cross section of the team. This builds relationships and breaks silos across product, eng, ops, growth, etc. Host retreats 2x a year Regularly gathering your team in person is one of the most important things you can do. 2x a year ended up being the perfect cadence for us. We split our time: 33% work & strategy, 33% personal connection, and 33% downtime & fun. The biggest mistake I see companies make is trying to get too much work done on a retreat. Connections get accelerate so much through great memories and shared experiences. You can have everyone record footage for a vlog to let the memories carry through to the next retreat. Donate money together At a team retreat we divided our team into groups of 4 with one goal: give away $10,000 in $100 at a time. With 50 people on the team that meant each group had to find about 12 charities to support. Then we regrouped to share who we donated to & why. What followed were the best stories that made for connection points: Someone donated to education grants because they were first in their family to go to college. Cancer research because they'd lost a loved one. Pet rescue because that's where they'd found a best friend. ...and so many more. $100 isn't that much, so it would be fair to argue the money would be better donated to a single charity, but our main goal was life stories and points of connection. Give it a try with your team. You'll all get a peek into what your coworkers value & why. Don't let anyone tell you company culture is defined by free lunches and ping pong tables. It's a culture of trust, clear feedback, focused work, meaningful connection, and a shared mission. If you think you'll use some of these tips, share the first tweet to help more companies build intentional cultures: https://twitter.com/nathanbarry/status/1644345801487810564… Also follow me to get more threads on building great companies. @nathanbarry Feature image generated via prompt of ‘Coworker friendships’ HackerNoon Stable Diffusion

How to Connect With Your Remote Team

The Dream Censor

The year 2022 will certainly go down in history as one of the toughest years for the digital asset industry, from a hawkish Fed to idiosyncratic risks that saw crypto giants such as Three Arrows Capital and FTX bite the dust. But for those who have been around for some time, you probably understand that it is in times like these that you as an investor should regroup, digest, and position yourself for an eventual turn of the tides. Now, more than one month into the new year, the question investors should sit with is which trends will pick up in 2023? So far, the market has bounced back, with BTC over 40% since the yearly open, while some other assets have recorded even more impressive gains. Most notably, Ethereum staking derivative tokens such as $RPL and $LDO have more than doubled in price. gaining It is easy to be swallowed up by the depths of the crypto bear markets, but worth noticing is how builders are withering the ‘cold winter’ as they develop the next game-changing innovations in the crypto ecosystem. However, finding the next big trends is difficult in the noise and figuring out what themes have a tangible value proposition from those that are more speculatively based is a hard task. Builders are back at work For this reason, I have decided to outline a few sub-niches I think will thrive in 2023, and potentially be winning sectors of an emerging bull market. Let’s dive right in: DeFi: Still Knocking it out of the park As much as 2022 may have been a rollercoaster for the entire crypto market, it is hard to ignore the fundamental developments that took place in the DeFi market. For starters, the number of daily active users (DAUs) increased by 2% to hit 641,510, according to . While this growth is a drop in the ocean compared to 2020 and 2021, the growth we saw last year should be indicative of how well the sector is to thrive in improving market conditions. DApp Radar So, what’s exactly the play here? At the very least, more crypto natives will gravitate towards decentralized platforms given the events that unfolded last year; some of the most trusted custodial platforms went under, with billions of dollars worth of clients’ funds. Already, the shift is happening, with decentralized exchanges having recorded a 93% month-over-month increase in November 2022 following the collapse of FTX exchange. Besides the growth in adoption, 2023 will likely be the year that we see the integration of DeFi protocols with traditional finance instruments. MakerDAO, one of the major players in the DeFi lending space, recently passed a proposal to invest in US treasuries and corporate bonds, as well as forming partnerships with traditional banks to provide loans backed by liquidity from real-world assets (RWAs). Lastly, several traditional financial institutions are going to make a debut into the DeFi space; JP Morgan already set the pace with its first DeFi transaction in 2022. This introduces a likelihood that permissioned DeFi protocols will pop up to serve the growing demand from tradfi companies looking to leverage decentralized markets while playing by the regulators’ book. NFTs and the Metaverse: GameFi on the Rise GameFi, an intersection between finance and gaming environments, is another niche that crypto stakeholders should be on the lookout for. Last year, blockchain-oriented games accounted for 49% of the active DApp usage. There was also a growing interest from VCs, with notable players such as Andreessen Horowitz (a16z) and Sequoia Capital their footprint in the NFT and Metaverse ecosystems. expanding As 2023 unfolds, signs on the wall show that GameFi is poised for more growth, both in terms of adoption and infrastructure advancement. Innovators in this space are already taking up the challenge to develop more engaging games as opposed to only focusing on ecosystem rewards. But more importantly, traditional game publishers are integrating their established infrastructures with the metaverse; Atari recently launched a suite of experiences in The Sandbox. Well, that's only the tip of the iceberg. Gamification trends through NFT technology are being adopted by other industries as well. For example, we have emerging DApps such as Fashion League, which uses NFTs to allow players to design personalized 3D fashion NFTs and compete with other participants in the ecosystem. Although still a nascent space of innovation, the gamification of traditional industries is certainly one of the trends to watch this year. What's even more fascinating is that international companies such as Gucci, Nike, McDonald's, and Campbell's have already pioneered digital collectibles as a promotional strategy to increase interest in their offerings. Clearly, NFTs are changing the landscape of how businesses interact with consumers in the digital age and will continue to do so in the foreseeable future. Ethereum’s Shanghai Upgrade For Ethereum diehard fans, the much-anticipated Shanghai upgrade (EIP-4895) is the talk of the town. This improvement proposal will introduce a withdrawal feature, enabling ETH validators to remove their funds which are locked in the beacon chain. Notably, the event will affect the demand and supply of ETH, as stakers will be able to liquidate their locked ETH while at the same time it will be much easier for more crypto users to opt in and out of ETH staking. According to the latest update from Ethereum’s developers, the Shanghai upgrade is scheduled for March 2023, and already the narrative seems to be fueling the price action of liquid staking derivative tokens such as $LDO and $RPL. But more significantly, both these ETH staking platforms have gained massive traction, with LIDO leading the pack with a total value locked (TVL) of $8.9 billion, while RPL enjoys slightly less than $1 billion as per metrics. DeFi Llama If this upgrade is successful, it is likely that the associated tokens will experience drastic fluctuations, either up or down. All in all, it is remarkable to witness Ethereum's gradual transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus. Whether ETH's market cap will challenge BTC's dominance this year remains to be seen; it may be too ambitious for now. Asset Tokenization: A New Era of Financial Possibilities The tokenization of real-world assets is another major theme that will likely shape the digital asset industry outlook in 2023. BCG that the tokenization market will burgeon into a $16 trillion ecosystem by 2030. While this figure may seem too big, other leading investment firms are also of the opinion that asset tokenization will be an area of focus for financial institutions this year. VanEck predicts that close to $25 billion in off-chain assets will be tokenized this year. estimates So far, the central bank of Singapore has already partnered with JP Morgan to launch a blockchain pilot dubbed ‘project Guardian’, an initiative that seeks to tokenize bonds and deposits, utilizing smart contracts to facilitate trade executions. While we are still early into the year, asset tokenization is evidently becoming an important theme for institutions looking to improve the efficiency and accessibility of their market instruments. “The next generation for markets and next generation for securities will be tokenization of securities.” - Larry Fink, CEO of BlackRock — the world’s largest asset manager. Central Bank Digital Currencies (CBDCs) Over 100 central banks across the world are currently exploring the feasibility of CBDCs, some are in the research phase while others have already launched. The big question however is whether CBDCs will take off, co-exist with crypto or replace the entire ecosystem? According to a recent by the People’s Bank of China (PBoC), the digital yuan accounted for 0.13% of the total circulation of Chinese renminbi yuan in 2022. Meanwhile, Nigeria’s eNaira usage is only 0.5% of the country’s population. Still a drop in the ocean compared to the circulation of fiat currencies in both jurisdictions. report That said, the bottom line remains that we are likely to see more CBDC initiatives in 2023. The European Central Bank (ECB) is in the investigative phase of the digital euro, with an implementation bill set for proposal this year. Meanwhile, India recently launched two digital rupee pilots, one for the wholesale market and another for the retail market. Based on these trends, the CBDC theme will continue to be a major topic of discussion in 2023. It comes as no surprise that CBDCs were identified as the key tech innovation challenge in last year’s G20 TechSprint.

Top 5 Crypto Narratives to Watch in 2023

The Defenders

Our client lost only a quarter of the budget they dedicated to an AI project because they chose to start with a proof of concept. The PoC allowed them to test their idea and fail fast with limited spending. To avoid wasting time and effort, always ask your for a proof of concept — especially if your company is just testing the artificial intelligence waters. AI solutions consultant This article explains what an AI proof of concept is and elaborates on the five steps that will guide you through your first PoC, together with the challenges that you might encounter on the way. It also presents AI PoC examples from our portfolio. And you will find a happy ending of the example depicted in the opening paragraph. What is an AI proof of concept, and when is it essential for your project’s success? An artificial intelligence proof of concept (PoC) is a prototype or a demonstration of a proposed AI solution designed to test whether the solution is feasible and likely to be successful. The purpose of creating an AI PoC is to validate the concept, assess the proposed solution’s potential benefits, and identify any potential challenges or limitations. An AI PoC typically involves building a small-scale version of the proposed AI solution and testing it in a controlled environment to see how it performs and whether it meets the desired objectives. The results of an AI PoC can then be used to inform further development and implementation of the solution. Compared to ordinary software PoCs, an AI POCs may involve more complex considerations, such as the ability of the AI solution to learn and adapt over time, and the potential ethical implications of the solution, such as . AI bias The technology stack for PoC AI projects is different, too: . These algorithms allow the AI system to learn from structured data and make predictions or decisions based on that learning. There are many different , including supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms. Machine learning algorithms types of machine learning algorithms . These computational models are inspired by the structure and function of the human brain. Neural networks can process and analyze large amounts of . They can be trained to perform various tasks, such as image recognition, natural language processing, scenario modeling, and prediction. Neural networks unstructured data . This technology can be used to build physical systems capable of autonomous operation and decision-making. Robotics solutions incorporate sensors, actuators, and other hardware components that allow engineers to build a robot that can interact with its environment and perform tasks. Robotics . Cloud computing platforms like Microsoft Azure, Google Cloud, and AWS provide the computing power, storage resources, and pre-configured services needed to support the development and testing of AI POCs. These platforms can also host and deploy AI solutions once they have been developed and tested. Cloud computing Creating an AI PoC involves gathering and preparing data, building and training machine learning models, and testing and evaluating the performance of the AI system. The time it takes to create an artificial intelligence proof of concept can vary widely depending on several factors, including the complexity of the proposed AI solution, the resources and expertise available for the POC, and the specific objectives of the POC. Some AI POCs can be developed in just a few days or weeks, while others may take several months or even longer to complete. When is there absolutely no way around the AI PoC? It’s essential to start your project with a PoC in the following scenarios: Your project relies on an innovative idea that was not tested before. Something that was studied at the business level, but not attempted technically. Neither you nor your tech vendor are confident if this can be implemented at all. If you need to demonstrate to stakeholders, investors, etc. the feasibility of your idea within a limited timeframe. A PoC will do the job better than an interactive prototype or something similar. Are there situations where an AI PoC is a waste of time? Even though an AI proof of concept is beneficial in most cases, there are a few exceptions. If your project falls under the following categories, PoC can be an overkill: If your idea and approach are exceptionally well documented from the functional and technical perspectives. This is rather rare. If the solution you want to develop is standard and resembles common practices in the field. We already know that this is feasible and possible from the technical perspective. If you want to build software that your front-end and back-end developers understand and have already worked on something identical before Which benefits can you gain from AI proof of concept? Using AI proof of concept brings about the following benefits: Identifying potential challenges before committing more resources to this endeavor. PoC AI allows you to “fail fast, fail better.” If the team encounters challenges they can’t overcome, all stakeholders have time to regroup or maybe change the hypothesis to reach the same goals through other methods. Minimizing business risks, as you test innovative ideas in small steps instead of diving into a long-term project Improving data collection practices Getting investors and other stakeholders on board Saving time and resources. AI PoC might uncover business or process-related issues and give you time to fix everything before starting a full-scale project Building expertise and creating knowledge owners who will mentor other team member on similar projects in the future Testing the tech stack on a smaller scale to understand its suitability for the selected business case Examples from our portfolio where AI PoC saved the day Here are a few AI PoC examples from the ITRex portfolio that will help you appreciate the proof of concept approach even more. Realizing that ML alone is not the answer A large cargo logistics company performs 10,000–15,000 shipments per day, and every shipment is accompanied by bills of lading and invoices to cover the operations. Employees were exhausted by handling all the documentation manually. The company wanted to that would use to process scanned documents and identify different fields. build an ML-powered solution optical character recognition (OCR) The client believed that machine learning was the best choice for this case, so we proceeded with an AI PoC to test this assumption. Soon we realized that the documents were formatted differently, and the labels used for fields varied significantly. For instance, the Load ID field alone had 8 aliases. As a result, the ML model kept growing. It became slow and inefficient. Our team decided to accompany this model with a dynamic algorithm (e.g., a dictionary where different field labels are hard-coded). This modification improved the solution’s performance significantly and saved the client time and money. If the client had decided to skip the AI proof of concept, they would’ve wasted seven months just to realize that their initial idea of a pure ML-based model was not the optimal solution here. With the artificial intelligence PoC, they came to this conclusion in merely two months. Since the successful completion of the AI PoC, we built an MVP that could handle four types of documents, taking over around 25% of the manual processing load. Taken aback by Meta’s restrictions on data usage A client in the entertainment industry wanted to build an AI-driven analytical platform for independent musical performers. The solution was supposed to crawl social media, including Facebook and Instagram, to gather data. It would process all this information to gauge people’s sentiment towards the artists. Musicians could sign with the platform and receive feedback on what social media behavior is the most beneficial for their success. We proceeded with the AI proof of concept to test the idea. After just two weeks, we realized it was simply impossible to gather data from Facebook and Instagram to use it for the purpose described above. Typically, some of the data can be retrieved via Graph API. Combining this with a verified business account in Meta, we assumed we would gain access to the required information. However, the client couldn’t supply us with a verified business account, and the data from Graph API alone was not sufficient for this solution work. If the client had decided to skip the PoC, they would’ve wasted around $20,000 on the discovery project. This would include a detailed description of the solution and the estimation of the development costs. But as they chose to start with the AI PoC, they spent only around $5,000 before figuring out that this idea was impossible to execute due to data access restrictions enforced by the Meta company. A five-step guide for your first AI proof of concept Here are five steps that you can follow to successfully go through your AI PoC. In this section, we also list challenges associated with each step. Step 1: Identify which issue(s) you want to tackle with AI It is essential to specify what exactly you want to accomplish by implementing artificial intelligence PoC. The selected use case needs to be of high value and represent something that you can address best with this technology. If you have doubts, a good place to start is to look into what others in your field are using AI solutions for. Another way to go is to investigate the problems that your business is facing and compare it against the potential of AI. After you’ve accumulated a list of opportunities, you can ask the following questions to determine which ones are the best fit for your project at the moment: Is the problem you intend to solve specific enough? Can you evaluate the results to determine success? Did you already attempt to solve this problem with other technologies? Do you have the talent and the funding to support this project until the end? If there is no suitable in-house talent, can you hire an external ? dedicated team How will it impact your business? Is this effect significant enough to put in the efforts? Will you be able to sell this to the executives? Is your organization ready to take on such projects? Does your firm already have a ? How will it align with this project? data strategy What are the potential to tackle this problem? risks and limitations of using AI Associated challenge: Selecting a use case that doesn’t add much value or doesn’t use the full potential of AI. Artificial intelligence is an expensive technology, and choosing an insignificant case will mean you spend more than you will receive. Check our article on to gain a better understanding of the expenses. how much it costs to implement AI Step 2: Prepare the data Now, as you have your problem clearly defined, it’s time to aggregate and prepare the training data for the AI algorithms. You can do that by: Checking which data is available for use within your company Generating semi-synthetic data using specific ready-made applications or your own solution Purchasing datasets from reliable providers Using open source data Hiring people to scrap the data that will fit your purpose You don’t have to limit yourself to one source. You can use a combination of several options listed above. Turn to to run the initial data screening. They will perform the following tasks: data scientists Structure the data Clean it by eliminating noise Add any missing data points, in case of tabular data Perform feature engineering (i.e., adding and deleting data fields) Apply manipulations, such as combining or filtering data Data scientists can advise you on how to gather additional data or how to narrow the AI proof of concept’s scope so that you can achieve the desired results with the existing datasets. When the data is ready for usage, split it into three sets: Training set, which the model will use to learn Validation set to validate the model and iterate on training Testing set that will evaluate the algorithm’s performance Associated challenges: The training data is not representative of the entire population. In this case, algorithms might perform well on common cases, but will deliver poor results on rare occurrences. For example, a healthcare ML model that analyzes X-rays might excel at detecting common disorders, such as effusion, but will struggle to spot rare diseases, like hernia. Class imbalance, when the number of cases representing one class is significantly larger than the other, with a ratio of 99.9% to 0.1% Incorrect labeling, like mixing classes, e.g., labeling a bike as a car High noise in the training dataset Hard to achieve pure class separability. This happens when some data in the training set can’t be correctly classified under a particular class. Step 3: Architect and build or buy the solution You are probably wondering whether you should build the model yourself or you can procure an existing solution. Here’s when it makes sense to create an AI model from the ground up: Your solution is innovative and doesn’t conform to an existing standard Ready-made solutions are costly to customize The closest off-the-shelf model is an overkill, and it does much more than you actually need Consider procuring a read-made model if: The costs of buying and customizing the model are less than building it from the ground up If you decide to build the AI algorithm from scratch, it will give you more control over its accuracy. It will take longer to complete the task, but it will be tailored to your business problem and your internal processes. You will not need to make changes to your system to accommodate external software. Regarding the infrastructure for algorithm training and implementation, you can rely on the cloud instead of using the local resources. There are four parameters that you can consider deciding what suits you best: . If your data is very sensitive when it comes to security, then you better keep everything on-premises. Security . If the processing load is rather heavy, opt for the cloud. Workload . Evaluate what will cost you more — acquiring the resources locally or paying for the cloud usage over time. Costs . If you will only use the solution locally, you can depend on your in-house servers. If it needs to be accessible from different geographical locations, then it’s worth considering the cloud. Accessibility Every approach has its benefits and drawbacks. If you are operating in the healthcare sector, we have those clearly explained in the cloud computing in healthcare post on our blog. Otherwise, feel free to reach out to our AI experts to choose the best technology stack for algorithm training. Associated challenges: Lack of proper training. This will cause issues, such as poor model generalizability, which means that the model can’t make accurate predictions on data that it has not seen in training. Coming back to X-ray image analysis in the medical sector, an algorithm might successfully analyze high-quality images captured by state-of-the-art scans but still fare poorly when applied to scans generated by older machines. Integration with existing systems, some of which may be outdated or powered by proprietary technologies Failing to come up with the suitable model architecture, e.g., being unable to pick the right ML model for the problem at hand The selected architecture’s capability can’t match the model’s requirements The input data is volatile, which means the model has to be frequently retrained Using more resources than your model requires to perform its tasks. There is no need to invest in a powerful server to run a simple model. Step 4: Assess the AI proof of concept’s potential to generate value This step is about evaluating whether the AI PoC can live up to expectations. There are several ways to perform the assessment: Go back to your key performance indicators (KPIs) and test the solution against them. These factors may include accuracy, customer satisfaction, speed, flexibility, fairness, safety, etc. Collect data on how your system operated before the AI proof of concept deployment. This would include the time spent on a particular manual task, the number of errors, etc. Next, you should use the information to evaluate the impact of the PoC. Compare the solution’s performance to other products that are regarded as the benchmark for this type of problems or the industry. For instance, a benchmark for image classification-related issues would be a model that delivers accurate results on popular datasets, like ImageNet. Gather user feedback either through focus groups or online surveys to gauge the levels of satisfaction and determine what is missing Conduct cost-benefit analysis to understand the financial impact of this solution on the organization Associated challenges: Making a mistake in your assessment. It can be a simple math mistake during calculations, or an error related to estimating the business potential. Step 5: Iterate on the AI proof of concept for better results, or scale it up If the results that you received in the previous step were not up to par, you might consider modifying the solution and iterating the whole process. You can make changes to the ML algorithm and measure the performance with each adjustment. You can also experiment with different hardware components or alternative cloud service models. If you are content with the AI PoC’s performance, you can work on scaling it in different directions. Here are a few examples: . Look for other applications of this new solution within your business. For instance, if you are testing AI as one application of predictive maintenance, you can try to apply it to other related scenarios. Apply the PoC to other business cases . Review the technology used to run this software. Can you dedicate more processing power or more data storage capacity? Such modifications will enable you to use more data, decrease latency, and maybe deliver results in real time. It will also minimize the possibility of bottlenecks in the future. Scale the infrastructure . Even though you already got reasonable results in the previous step, it might be worth looking for ways to improve accuracy. You can keep training your algorithms using new data, more accurately labeled data, etc. Or you can even experiment with implementing tweaks and changes to achieve better results. Optimize the AI PoC solution If you decide to adopt AI company wide following the proof of concept phase, you can find helpful tips in our guide on . how to implement AI in your organization Associated challenges: The architecture was not carefully considered. The solution might work well with 10,000 users but crash when the audience reaches 100,000. The model contains bugs that will manifest themselves when you attempt to scale the AI solution Applying the model to other business cases, other than the ones it was intended for. For instance, a solution that is meant to assemble a garden wheelbarrow can’t be applied to assembling trucks, as it might build a large garden wheelbarrow with a motor. To conclude When it comes to implementing AI, start small and stay manageable. Make sure you have a clear business case with defined objectives and metrics to measure success. And always consider creating an AI proof of concept, except for the cases presented at the beginning of this article. This will help you identify any potential obstacles before you go fully in and make a large financial investment in a solution that will not live up to expectations. Do you want to implement AI in your organization, but aren’t sure if your business idea is feasible? ! Our team will help you conduct a PoC to test your idea on practicalities. 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How AI Proof of Concept Helps You Succeed in Your AI Endeavor

Cómo la prueba de concepto de IA lo ayuda a tener éxito en su esfuerzo de IA

Comment la preuve de concept de l'IA vous aide à réussir dans votre entreprise d'IA

कैसे एआई प्रूफ ऑफ कॉन्सेप्ट आपको अपने एआई प्रयास में सफल होने में मदद करता है

AI の概念実証が AI の取り組みの成功にどのように役立つか

Como a prova de conceito de IA ajuda você a ter sucesso em seu empreendimento de IA

Bằng chứng về khái niệm AI giúp bạn thành công như thế nào trong nỗ lực AI của mình

AI 概念验证如何帮助您在 AI 努力中取得成功

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vitíligo

Vitiligo

सफेद दाग

白斑

bệnh bạch biến

白癜风

Large language models. You must’ve heard these words before. They represent a specific type of machine learning-based algorithm that understand and can generate language, a field often called or NLP. natural language processing You’ve certainly heard of the most known and powerful language model: . GPT-3 GPT-3, as I’ve described in the video covering it is able to take language, understand it and generate language in return. But be careful here; it doesn’t really understand it. In fact, it’s far from understanding. GPT-3 and other language-based models merely use what we call dictionaries of words to represent them as numbers, remember their positions in the sentence, and that’s it. Let's dive into those powerful machine learning models and try to understand what they see instead of words, called word embeddings, and how to produce them with an example provided by Cohere. Learn more in the video... References ►Read the full article: ►BERT Word Embeddings Tutorial: ►Cohere's Notebook from the code example: ►Cohere Repos focused on embeddings: ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/text-embedding/ https://mccormickml.com/2019/05/14/BERT-word-embeddings-tutorial/#why-bert-embeddings https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/Basic_Semantic_Search.ipynb https://github.com/cohere-ai/notebooks https://www.louisbouchard.ai/newsletter/ Video Transcript 0:07 language models you must have heard 0:10 these words before they represent a 0:13 specific type of machine learning 0:14 algorithms that understand and can 0:16 generate language a field often called 0:19 natural language processing or NLP 0:22 you've certainly heard of the most known 0:24 and Powerful language models like gpt3 0:26 gpt3 as I've described in the video 0:28 covering it is able to take language 0:30 understand it and generate language in 0:33 return but be careful here it doesn't 0:35 really understand it in fact it's far 0:38 from understanding gbd3 and other 0:41 language-based models merely use what we 0:44 call dictionaries of words to represent 0:46 them as numbers remember their positions 0:49 in the sentence and that's it using a 0:52 few numbers and positional numbers 0:53 called embeddings they are able to 0:55 regroup similar sentences which also 0:58 means that they are able to kind of 1:00 understand sentences by comparing them 1:02 to known sentences like our data set 1:05 it's the same process for image sentence 1:07 models that take your sentence to 1:10 generate an image they do not really 1:11 understand it but they can compare it to 1:13 similar images producing some sort of 1:16 understanding of the concepts in your 1:18 sentence in this video we will have a 1:20 look at what those powerful machine 1:22 learning models see instead of words 1:24 called word embeddings and how to 1:27 produce them with an example provided by 1:29 the sponsor of this video a great 1:31 company in the NLP field cohere which I 1:35 will talk about at the end of the video 1:36 as they have a fantastic platform for 1:39 NLP we've talked about embeddings and 1:42 gpt3 but what's the link between the two 1:44 emittings are what is seen by the models 1:47 and how they process the words we know 1:50 and why use embeddings well because as 1:53 of now machines cannot process words and 1:56 we need numbers in order to train those 1:59 large models thanks to our carefully 2:01 built data set we can use mathematics to 2:04 measure the distance between embeddings 2:06 and correct our Network based on this 2:08 distance iteratively getting our 2:10 predictions closer to the real meaning 2:12 and improving the results and meetings 2:15 are also what the models like clip 2:17 stable diffusion or Dali used to 2:19 understand sentences and generate images 2:21 this is done by comparing both images 2:24 and text in the same embedding space 2:26 meaning that the model does not 2:28 understand either text or images but it 2:31 can understand if an image is similar to 2:33 a specific text or not so if we find 2:36 enough image caption pairs we can train 2:38 a huge and Powerful model like Dali to 2:41 take a sentence embed it find its 2:43 nearest image clone and generate it in 2:46 return so machine learning with text is 2:48 all about comparing embeddings but how 2:51 do we get those embeddings we get them 2:53 using another model trained to find the 2:56 best way to generate similar embeddings 2:58 for similar sentences while keeping the 3:01 differences in meaning for similar words 3:03 compared to using a straight one for one 3:06 dictionary the sentences are usually 3:08 represented with special tokens marking 3:10 the beginning and end of our text then 3:13 as I said we have our poses from all 3:15 embeddings which indicate the position 3:17 of each word relative to each other 3:19 often using sinusoidal functions I 3:22 linked a great article about this in the 3:25 description if you'd like to learn more 3:26 finally we have our word embeddings we 3:29 start with all our words being split 3:31 into an array just like a table of words 3:34 starting now there are no longer words 3:36 they are just tokens or numbers from the 3:40 whole English dictionary you can see 3:42 here that all the words now are 3:44 represented by a number indicating where 3:46 they are in the dictionary thus having 3:49 the same number for the word Bank even 3:51 though their meaning are different in 3:53 the sentence we have now we need to add 3:56 a little bit of intelligence to that but 3:58 not too much this is done thanks to a 4:00 model trained to take this new list of 4:03 numbers and further encode it into 4:05 another list of numbers that better 4:08 represent the sentence for example it 4:10 will no longer have the same embedding 4:13 for the two words bank here this is 4:15 possible because the model used to do 4:17 that has been trained on a lot of 4:19 annotated Text data and learned to 4:21 encode similar meaning sentences next to 4:24 each other and opposite sentences far 4:27 from each other thus allowing our 4:29 embeddings to be less biased by our 4:31 choice of words then the initial simple 4:34 one for one word embedding we initially 4:37 had here's what using imagings looks 4:39 like in a very short NLP example there 4:42 are more links below to learn more about 4:44 embeddings and how to code it yourself 4:46 here we will take some Hacker News posts 4:49 and build a model label to retrieve the 4:51 most similar post of a new input 4:53 sentence to start we need a data set in 4:56 this case it is a pre-embedded set of 4:58 3000 Hacker News posts that have already 5:01 been emitted into numbers then we build 5:04 a memory saving all those embeddings for 5:07 future comparison we basically just 5:09 saved these embeddings in an efficient 5:11 way when a new query is done for example 5:13 here asking what is your most profound 5:16 life inside you can generate its 5:18 embedding using the same embedding 5:20 Network usually it is bird or a version 5:23 of it and we compare the distance 5:25 between the embedding space to all other 5:27 Hacker News posts in our memory note 5:30 that it's really important here to 5:32 always use the same network whether for 5:34 generating your data set or for querying 5:36 it as I said there is no real 5:38 intelligence here nor that it actually 5:40 understands the words it just has been 5:42 trained to embed similar sentences 5:45 nearby in the unmanning space nothing 5:47 more if you send your sentence to a 5:50 different network to generate an 5:51 embedding and compare the embedding to 5:53 the ones you had from another Network 5:55 nothing will work it will just be like 5:58 the nice people that try to talk to me 5:59 in Hebrew at eccv last week it just 6:02 wasn't in an embedding space my brain 6:04 could understand fortunately for us our 6:06 brain can learn to transfer from one 6:08 embedding space to another as I can with 6:11 French and English but it requires a lot 6:13 of work and practice and it's the same 6:16 for machines anyways coming back to our 6:18 problem we could find the most similar 6:21 posts that's pretty cool but how could 6:23 we achieve this as I mentioned it's 6:25 because of the network birth in this 6:28 case it learns to create similar 6:30 embeddings from similar sentences we can 6:32 even visualize it in two Dimensions like 6:35 this where you can see how two similar 6:37 points represent similar subjects you 6:39 can do many other things once you have 6:41 those embeddings like extracting 6:43 keywords performing a semantic search 6:45 doing sentiment analysis or even 6:47 generating images as we said and 6:49 demonstrated in previous videos I have a 6:52 lot of videos covering those and listed 6:55 a few interesting notebooks to learn to 6:57 play with encodings thanks to the cohere 6:59 team now let me talk a little bit about 7:02 kohilu as they are highly relevant to 7:05 this video cook here provides a 7:07 everything you need if you are working 7:09 in the NLP field including a super 7:11 simple way to use embedding models in 7:14 your application literally with just an 7:16 API call you can embed the text without 7:18 knowing anything about how the embedding 7:21 models work the API does it for you in 7:23 the background here you can see the 7:25 semantic search notebook that uses 7:27 cohere API to create embeddings of an 7:30 archive of questions and question 7:32 queries to later perform search of 7:34 similar questions using cook here you 7:37 can easily do anything related to text 7:39 generate categorize and organize at 7:42 pretty much any scale you can integrate 7:44 large language models trained on 7:46 billions of words with a few lines of 7:48 code and it works in any Library you 7:51 don't even need machine learning skills 7:53 to get started they even have learning 7:55 resources like the recent cohere for 7:57 ai's colors program that I really like 8:00 this program is an incredible 8:01 opportunity for emerging talent in NLP 8:04 research around the world if selected 8:06 you will will work alongside their team 8:08 and have access to a large-scale 8:10 experimental framework and cohere 8:12 experts which is pretty cool I also 8:15 invite you to join their great Discord 8:17 Community ingeniously called Co Unity I 8:21 hope you've enjoyed this video and will 8:23 try out cohere for yourself with the 8:25 first link below I am sure you will 8:27 benefit from it thank you very much for 8:29 watching the whole video and thanks to 8:31 anyone supporting my work by leaving a 8:33 like comment or trying out our sponsors 8:36 that I carefully select for these videos

Text Embedding Explained: How AI Understands Words

Incrustación de texto explicada: cómo la IA entiende las palabras

Explication de l'intégration de texte : comment l'IA comprend les mots

पाठ एंबेडिंग समझाया: एआई शब्दों को कैसे समझता है

テキスト埋め込みの説明: AI が単語を理解する方法

Explicação da incorporação de texto: como a IA entende as palavras

Giải thích về nhúng văn bản: Cách AI hiểu các từ

文本嵌入解释：AI 如何理解单词

Saturating the Space When to Use This Method? The goal of this method is that, by sharing stories about users, team members can reconstruct a picture of events that is as robust as possible, collect the maximum number of the user's contextual and behavioral details, and thereby "saturate the space". During the sharing of stories, other participants can highlight the client's contextual and behavioral nuances and formulate new insights. Furthermore, despite the fact that all of the team's participants observed the same events, their interpretations may differ. By sharing stories, participants can put together a unified opinion about what they heard and saw. Every sticky note is a small piece of information. Saturating the space is a way to visually show all details of the user experience, arranging all our sticky notes on the board or on a sheet of flip chart paper. Thereby, we assemble all of the different pieces of information into one. Later on, we will work with this information by grouping the sticky notes and drawing out patterns. How to Use the Method? Prepare. Before you start telling a story, advise participants to prepare all materials that they will use during the story in advance: photographs, sketches, and other artifacts that will help reach the listeners. Share stories one by one. While one participant is telling his or her story, have others make notes on sticky notes. This will allow them to record interesting details and their insights; participants should always be asking, "How could this information be useful in the context of our project?" While telling stories, do not generalize to a great extent, and do not stray off-topic. Begin with a description of the events (what happened) and follow the structure of "Who, What, Where, When, and How". When telling a story, make it interesting by fully reconstructing the series of events, the context, and the character traits of individuals and objects. Interpret. While listening to other participants' stories, interpret what you hear. Always ask the questions, "why does a character behave one way and not another?" and "what significance does each detail have?" Write down on sticky notes what events you found especially surprising during the story while also formulating your insights. Saturate the space. At the end of sharing stories, all participants should have the original sticky notes they made during research and the sticky notes they made during other team members' stories. Ask the participants to "saturate the space" - to place their sticky notes on the board and eliminate duplicates. Groping Method When to Use This Method? After having recorded all interesting contextual and behavioral nuances about the client on the sticky notes and having saturated the space with them, we move on to the grouping method. In this method, the participants put sticky notes into groups based on their characteristics, which will help us highlight common topics and regularities among the received information. At the end of working with this method, we must formulate insights based on the groups we have singled out. Each insight will be dependent on one group. For instance, in a group with the general name "Security," you can find interconnections that lead to the insight: "A client feels secure when he or she feels that he or she has control over the situation". How to Use the Method? Group. Look at all the sticky notes that you have in your possession and think about how you could group them. Consider whether common themes exist or if patterns can be observed. It is quite possible that there are several ways to put the sticky notes into groups; new insights may emerge from regrouping the sticky notes according to new criteria. Analyze. Find as many interconnections as possible. After having put the sticky notes into groups, observe how the elements relate to each other internally. Pay attention not just to the interconnections but also to the differences between groups and what red flags they could signal. Formulate Insights. Do not forget that our main task is to formulate as many insights as possible into a situation after having analyzed and made conclusions about why certain elements of it are connected. 2x2 Matrix Method When to Use This Method? 2x2 Matrix is a good way to revitalize the discussion and think about the users and the problem domain that we are researching in a novel way. This method helps us find unexpected interconnections among the elements of the selected group and understand how they interplay with each other. 2x2 Matrix is often used for the analysis of the competitors' market to determine new business opportunities and open niches. How to Use the Method? Choose elements for comparison. People most often compare different products, services, types of technologies, types of users, kinds of activities, places, brands, or companies. Define what the axes designate. Look at which characteristics are inherent to the group of elements you are examining. For instance, if we are analyzing users of a particular service, then we may be confronted by such pairs of polar opposites as "experienced users vs. beginners" and "the most commonly used features vs. features that are used rarely". Discuss the results of the analysis and write down relevant insights. You will most likely have to consider different combinations of polar characteristics to discover consistencies. The discussion during the attempts to arrange sticky notes inside a certain quadrant frequently leads to more insights than the study of the whole diagram. Distribute sticky notes. After choosing two polar characteristics, arrange them on the opposite ends of the axes and place sticky notes inside the four quadrants on how they correspond to a certain characteristic. You may use the system recommended by the American design consultant Vijay Kumar in his book 101 Design Methods: Polarity. Look at which elements surfaced at the polar ends and explain why. Groups. Inside the diagram, groups of elements could form that are connected with or similar to a particular pattern. Determine the groups of elements within the diagram by drawing a circle around them. Gaps. It is just as important to analyze quadrants that are not filled in as those that are filled in. Disparities could point to unmet client needs or an open niche in the market. Migrations. Is it possible that the position of some elements in the diagram could change over time or as a result of some factor? Use arrows on the diagram to show how these elements shift. Think about how similar migrations could affect other elements. Quadrants. What unites elements that end up within a given quadrant? How do they differ from elements in other quadrants? Do the elements in one quadrant suggest any characteristics of that quadrant besides those that are defined on the axes? Venn Diagram When to Use this Method? This method is especially good for the comparison and analysis of elements that pertain to one field. For instance, you can compare different types of mobile payment systems or three different organizations offering services in one industry. A Venn Diagram is a visual depicted by intersecting circles. As a rule, three groups of elements (four maximum) are used for comparison and analysis. Otherwise, our diagram becomes too confusing and complex to read or understand. Sticky notes that characterize each of the analyzed parts are placed into the corresponding circle, and sticky notes that do not pertain to any of the groups are relegated to the outside of the circle lines. Sticky notes that pertain to two categories (on a diagram of three circles) will be placed at the circle intersections. In the center are placed those sticky notes that pertain to all groups. How to Use the Method? Determine the groups of elements you are going to compare. They can be products, services, types of technologies, types of users, places, brands, and companies. If you are starting the process using elements that you already formulated during previous analysis, find out if there are any among your groups that are connected by something or that possess any common characteristics. Merge these groups by the principles of the Venn Diagram. Study the diagram, and explain how and why the groups are interconnected. If groups are not connected, think about why that would be the case. Formulate insights starting from the items listed above.

How to Define User Behavior Using Methods Like Grouping, 2x2 Matrix, and Venn Diagram.

Interpreting Leadership: Nature Vs. Nurture Is leadership a part of DNA or an acquired feature? I think it's 50/50. Clearly, certain predispositions have a place to be. Nevertheless, a person does not become a world-class athlete, having only a penchant for sports: a champion’s life is actually hard work. I am positive that growing up matters a lot: methods of bringing up, surrounding people, places, and things, encouraged and extinguished initiatives. A background sets our threshold, where, as it seems, we should dwell humbly without looking beyond. As a child, I always looked for loopholes, invented scenarios, and organized some sort of action, which I needed badly. To pull it off, I had to find a company of like-minded people, convince them, pitch, and sell them the idea of ​​the entertainment I wanted. I simultaneously developed in several areas: I learned what to say so that people would agree with me, sorted out the logistics, and drew an action plan. And also, it was necessary to find ways of getting away from parental punishment, which is typical for children's mischief. I was driven by perseverance and invested my energy and impulse into it. Perhaps this is the essential input data defining the career path, and the external circumstances come into play afterward. Moment “X” Or Where The Leader Begins Sometimes, the growth opportunity arises when there is no position, but there is one single initiative of yours to become, for example, a lead. That's exactly my case. Over the years, I realized that I wasn’t a professional at that time, but I had a chance and will. Many have gone through a similar story at the beginning of their career, which, in my opinion, is the local specifics of the market. I became a real leader well after that when I delved into hands-on management: I provided a high level of construction and organization of processes. I needed constant feedback and devoted a lot of time to one-to-one calls and code reviews. My self-discipline helped me to single-handedly grope for different leadership aspects. At my current place of work, my growth picked up the pace and got easier. I consciously pursued the senior position because I knew what I wanted: to walk this journey with a mentor. Being dedicated to the professional development of the mentees, a mentor pulls you up on all vital topics, pays a lot of attention, and gives you the necessary boost. As you grow, finding a mentor becomes harder and harder. Even if I were offered to work under the supervision of my mentor and play a supporting role, I would agree because now I understand well what it means to be a leader and how much effort and soul the profession demands. The Main Challenge Is To Let Go The ability to loosen the grip is probably the most difficult: letting go of the processes and observing from afar takes courage. Many managers tend to think that they need to keep their eye on employees and do someone’s work because it’s faster — this applies to both coding and managerial tasks. I am often drawn to do the work for a junior, spending minutes instead of hours or even days. It’s a challenge you have to deal with. But the point is that people must learn. The more independence you provide as a leader, the more the team gives back. You can delegate more complex things that require a lot of time, moral and physical resources. Just learn to let go: instead of watching, give others the opportunity to take the initiative. Even after five years in the leading position, I continue to improve this skill to this day. There is always a temptation to show, explain, or even spell things out, but this is not always a necessity. Helping to develop means not interfering with self-comprehension of a profession, stuffing bumps, and grasping the important lessons. From a team lead’s viewpoint, there are two extremes: micromanaging and doing everything for everyone, or paying no attention to people at all and letting everything take its course. The first option is naturally closer to my heart; however, I am sure there has to be a balance here. Speaking Of Balance: Does The Leader Have One? Working day limits are necessary primarily for systematizing. Being available 24/7 means my mailbox and messengers will ding in the morning, afternoon, evening, and night. My seemingly harmless sleep will result in a bunch of unanswered letters of various criticality. By setting boundaries, we can increase the entire team's productivity and work as one. Recently, I listened to a speaker who emphasized the importance of clear boundaries and work/life balance. It was quite funny because I knew his working day was never less than 14 hours. I allow myself to work after hours, not because I take on a lot, but because I enjoy it. It is never an end in itself. I had a lot of pet projects that I ceased to update at some point. And all because I satisfied all my ambitions at work. It's not difficult for me to visit a project on Saturday and see how everything works. That gives me food for thought, which transforms into ideas and proposals for discussion. My team’s support helped bring many of those ideas to life; I can always rely on my people. Helping By Getting Out Of The Way The development of team members stems from a close connection. Everyone is special, with their own background, mindset, and mentality. There is no single path since each human being is a unique combination of preferences, propensities, and interests. Excellent performers may constantly take the initiative within one definite area. In this case, forcing them to regroup and do things that are unnatural for them would be a wrongdoing. An individual approach to everyone is the key to healthy team growth. Moreover, it is important to be able to articulate mistakes. When working with a person, the emphasis should not be on the error as such but on the solution. For instance, something went wrong, and part of the code didn't work. Instead of focusing on the wrongness of some action, I reflect on my personal experience. That is, what I did when I faced such a problem, why my solution did not lead to the desired outcome, and what I tried to do differently. I also highlight the bonus of a certain decision since we can introduce an update besides mere fixing. That is to say, I depict my path from A to Z in living colors, and afterward, a person might want to follow it, or maybe do it even better. As a leader, you can't have a problem with a teammate knowing more than you. Some managers are hostile toward superior employees. Driven by the fear of being replaced, managers tend to restrict such teammates, thus stealing a chance to prove themselves. This kind of reaction is mainly rooted in upbringing, growing up, and the process of formation as a person and a pro. In my opinion, the leader's task is to encourage and feed the teammates' sense of self-sufficiency to scale infinitely. Being a leader is not the pinnacle, there is always room for development. Seeing people evolve quickly inspires and pushes me to work on myself. Leaders always stay in tune: the growth of subordinates means you need to grow even faster to lead them. Letting Go Of People As Well Obviously, throughout my career path, I had to say goodbye a lot. The very conversation about dismissal is not as hard as deciding within yourself. Normally, the working process entails gripes and criticism since everyone is learning, and many skills and approaches need to be improved. The task of a leader is to give timely feedback, thereby fixing something and making things better. I had a case when my tireless feedback did not change a thing. Time went by, bringing zero progress. I realized giving tasks made no sense, as the results were never there. In other words, distrust in a specialist is not cool. I felt it was my duty to do everything possible and impossible to trace the weak points, level them up, and put them on the right track. Each time I gave yet another last chance (that’s just me, I sincerely believed it would work out). But the person also made the decision — not to change whatsoever. Therefore, I had no other option but to let him go. To be completely honest, my conscience did not torment me because I knew I had done everything what was in my power. But this scenario is applicable to a small team of 5-10 people. If you have a staff of 200 employees, the value of such a decision declines because you do not have constant communication with everyone. The larger the scaling, the fewer connections are personal, and it's much easier to accept less than 100% work. It's a business, and it's not worth it. You, as a leader and as a mentor, always want to reveal paths for development and tell about your own, but from a business perspective, it’s easier to just hire someone new, motivated, and inquisitive. Mentorship As A Handshake Of Two Spirits A mentor is someone easy to communicate with and interesting to be around outside of work. Mentoring is all about complete trust. You need to be able to admit a mistake, accept and own it, voice your concerns and not be afraid to sound silly. There should be no fear in expressing your thoughts — only absolute transparency. Of course, such contact presupposes mutuality. Magic will not happen if the mentee voices an opinion and the mentor sneers at it. Effective work with a mentor is based on empathy, which immediately makes it clear whether this is your mentor or someone else's. Before joining my company, I had three offers in my hands, and all three interviews were scheduled for the same day. During the first one, I felt I was in charge because I asked dozens of questions, not the other way around. The second interview had nothing memorable, it was typical. At my current place of work, my interviewer helped me answer and finished some phrases for me. I was stunned because besides asking me, they also intended to teach me a little and explain some stuff. For me, it was a pretty good sign. I thought: if they wanted to make me one iota better after barely meeting me, then what would happen if I joined them? That feeling is priceless. In ten years, I've only experienced this twice. The intent to help ascends to a certain corporate culture and the win-win philosophy established across the organization. There is a clear idea that giving a person everything and even more always yields a good result because that’s how cause and effect work in real life. I know many people who have advanced simply by being exposed to new things. Seniors became architects because they were inspired to approach the unknown. A Couple Of Tips For The Newbies First, do not be afraid of responsibility. You assume a role and are held accountable for the result, and people, by and large, do appreciate it. If everything goes well, you rise in the eyes of your teammates. And if not, your task then is to analyze, own your mistakes and draw conclusions. The second key point is leveling up communication skills. To evolve and develop others, a leader must be able to interact with people: understand the way of thinking, what plans and ambitions one has, and collect feedback. Soft skills will never lose their significance. And third, navigating the business. A technical lead knows exactly what the organization is aiming for and what its tasks are; works for the benefit of the business, and always take into account its requirements. And everything else depends on the team and the vibe of the company, which together form a leader’s home front.

Leading By Letting Go. How To Quit Disturbing Your Engineering Team From Growing

Faith, Morals, and Public Policy in The Twentieth Century

Certain Social Reactions

They say you can’t teach an old dog new tricks, but is that really true? You could learn an instrument, a language and a lot more about at any age, so what about coding? You’d be forgiven for thinking that coding is a young person’s game. In fact, they even make coding games for young people. But that doesn’t mean that your ability to code diminishes after the age of 30. So if a lot of coding education is aimed at young people, how can you learn this mysterious art? Here’s your guide to getting started with coding, no matter what your age. Why even bother learning to code? There are a lot of good reasons to learn to code as you get older, so take your pick of the best ones to convince you to go ahead: If you’re looking for a career change to the tech industry, coding can give you a step up. If your kids are learning to code, learning yourself can help them and give you something to bond over. Coding is the gateway to many . If you’re looking to make some extra money, coding could be the key to that extra income. side hustles Learning something new keeps your brain active. Teaching yourself something new can be a lot of fun, what better reason is there than that? Firstly, ignore the stereotypes It’s easy to be turned off at the thought of coding because you’re not an 8-year-old genius, like . Seth Yee But coders are made up of people of all ages and abilities. While your research might lead you to a lot of stories of child prodigies and teenage tech entrepreneurs, there are other types of coders out there. Get inspired by fellow older coders Instead of feeling defeated by the stories of younger, accomplished coders, feel inspired by those who learned a little older. The New York Times published a great article on . Reading these stories can give you a boost, especially if you feel close to giving up. people who learned to code in their 60s, 70s and 80s One of the best ways to feel inspired about coding is to connect with fellow codes. There are for all kinds of coders, so why not join one and find your people? coding communities Learning to code Now here comes the hard part - the learning. It might take some time, and some patience, but you can get there if you really want to learn. Some great tips for learning to code include: Set out the time to learn If you’re serious about learning to code, you need to make the time for it. Schedule some time each week to help you commit to it. This could be on the weekend, during your work commute, or when you’ve got some time for yourself in the morning. Get support from a platform The great thing about coding is that there is a tonne of support to help you. Platforms like can help you master coding from beginner level to advanced. It teaches different programming languages, helping you get to where you want to be. It also features a free basic plan you can sign up for while you dip your toes in. CodeAcademy Create a project for yourself One of the best ways to learn something new is to set a goal for yourself. A project you can own and work towards will help you stay motivated and give you a reason to keep learning. Don’t be afraid to ask for help Coding can be difficult, especially if you’re a complete beginner. Which is why there’s no shame in asking for some help if you need it. If you’re finding it tough, don’t give up - someone will have the answer and will be more than happy to help you work through it. Immerse yourself in the coding world When you learn a new language, they say that the best way to do it is to immerse yourself in it. So why not try the immersion technique with coding? You can really help yourself by reading coding books and , watching videos and listening to podcasts. There are a wealth of outlets that discuss nothing but coding, so you’re sure to find something that captures your interest. blogs Don’t give up When the going gets tough, that doesn’t mean you give up. , but if there’s something you’re struggling with, there is sure to be someone else who’s had the same experience. Instead of giving up when you hit a hurdle, look for a solution. Regroup and try something else, you’ll get the hang of it. Coding comes with a lot of challenges Be realistic about your expectations. If you work a full-time job and you’ve also got a family and other responsibilities, things may take a little longer than for someone who has a lot of free time and fewer commitments. Everyone learns at a different pace, and that’s ok. Where will it take you? When you get to a place where you feel confident with coding, look at where it could take you next. It may just be a hobby, or it could be something that leads to an exciting change of career. There are a lot of possibilities, and cracking the art of coding could lead you through some exciting new doors. So if you think you’re too old for coding, think again. Start making a plan and get ready to learn to code, no matter what your age is.

I'm Too Old to Learn to Code, Right?

In business, complexity is not the exception. For every facet of an organization, the increasing and infinite demands that need to be met are inevitable, especially if your company is growing at an unprecedented rate. What’s more, the factors that contribute to this zigzag road are incessant – from volume of tasks, to the relentlessly evolving market trends. And let’s face it: managing complex organizations can be challenging. For one, it’s harder to predict and more challenging to make sense of things. While the dream scenario is always going to be a simple, straightforward path, flexibility is key. An effective leader knows that complexity is an opportunity, not a threat. While its facade can be overwhelming, knowing the ins and outs of a hazy and cluttered situation is a chance to regroup, reassess, and revamp your organization. Let me explain. Think of it as being in a jungle. If you’re easily rattled by the pathless hills with sharp branches hanging left and right, and excessively thick bushes standing your way, chances are you’ll never get out of that situation. But if you compartmentalize, examine what needs to be done, craft a plan, and slowly but surely get rid of the obstacles along your way, you’ll find yourself accelerating one step at a time. Even better, you might find yourself some useful tools that may help you on your journey. If a clear path isn’t directly presented to you, then make one yourself. Here are key things to remember in turning complexities into opportunities: Accept Complexity The first step leaders should do is to accept complexity. Don’t fight it – the more you resist it, the harder it resists you. Embrace the fact that certain factors are out of your control, and you have no choice but to make lemonade out of lemons. Once you accept, you release the stress out of your system, and you’ll have a clearer mind in knowing exactly what to do and how to declutter complicated situations. The biggest obstacle in managing complexity is the tendency to be overwhelmed. By looking at it directly, with no fear, you’ll eventually adapt and find solace in knowing that it’s not a sentence; rather, it’s a challenge you need to overcome. Promote Collaboration Practicing and promoting collaboration among your employees is an effective way to simplify complex situations. Collaborating means distributing labor and creating a plan to effectively clear your path. This serves as an opportunity for your organization to truly function as a unit where every component delivers its purpose. The biggest mistake most leaders make is they try to do everything on their own. While it’s tempting to constantly have things your way, it is much more effective to give power to managers, supervisors, and other department heads and let them handle their territories. Your job is to overlook and give direction. Be hands-on, but don’t carry the burden all by yourself. Your team is your company’s most valuable asset – take advantage of that. Build Your Resilience Effective complexity management is a long-term investment. Not only will you hone your problem-solving skills, but you’ll also build your resilience. Utilize the friction of obstacles to toughen up your character and sharpen your mind, gearing you up to make impact. The takeaway here is that leaders shouldn’t rest when things get simple. Always be prepared when situations go off course. Complexity provides the growth spurts that can take your organization to the next level. Learn how to manage it and reap invaluable rewards.

Complexity is An Opportunity, Not A Threat

Computer science has become a popular discipline among students in the US and other developed nations. It is also one of the most preferred courses by international students due to its marketability in a modern job market where technological advancements are driving business. However, completing the course requires students to handle numerous lengthy programming assignments to make sure they have the skills demanded in the job market. Studying and applying different programming languages requires time, research, and resilience. Most times, students spend sleepless nights writing codes, creating Apps, and developing software. The assignments can make it difficult for students to maintain a good balance between academics and personal life, which results in burnout, anxiety, stress, and poor grades. That is where companies that help with programming assignments come in. When facing challenges with a programming assignment, do not compromise your grades. Instead, you can enlist a reputable academic writing website to help you with projects, exams, and assignments. Most programming help sites have experts in each field of computer science ready to fulfill your assignment order within the agreed deadline. Other useful sites have discussion forums where you can interact directly with experts and ask any questions related to your programming assignment. Still, with many websites to choose from, students often have difficulties identifying reliable and legitimate sites to get help. There are certain factors to consider when choosing a legit homework help service. In the following post, we have shortlisted the 7 best websites where you can get assistance with your assignment. 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All the sites shortlisted in this post are either free or affordable to students working on a budget. The Reliability of Customer Support. There is nothing as awful as paying for a programming task and not getting answers to your queries when you need them. The best sites have a support team on standby around the clock. Ability to choose your writer. You should be at liberty to choose the expert you want to handle your programming assignment. Most of the sites and forums in this post have samples written by experts and ratings by other customers to help you hire the most suited writer. That way, you can get quality online assistance. Custom Content. Plagiarism is a serious academic mistake with harsh consequences. The website you enlist to help with an assignment should be trustworthy to ensure you achieve good results in your coursework. The programming assignment websites in this list guarantee custom-written codes along with clear instructions and comments. Some of the sites give additional advice and tips that you can use in future assignments. Quality. One of the best ways to determine if a site offers good quality help with programming assignments is to check reviews on independent forums and websites. All the platforms featured in this article have top ratings on customer review platforms, such as Google, Trustpilot, and Sitejabber. The sites also have quality control teams that ensure all content adheres to high-quality standards. Guarantees. All the sites in this shortlist have policies guaranteeing customers quality services and data protection. The assurances include free revisions, confidentiality of private information, the safety of payment details, custom content, and a money-back guarantee. Customers can cancel orders and request refunds in case of late deliveries or poor quality. Detailed Overview of the Companies – One the best websites to seek high-quality professional programming assignment help. 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Students who need programming assignment help can join groups of professionals in different sections and request help with problematic programming tasks. Stackoverflow There is a high chance that your programming assignment has already been answered by an expert on the platform. If not, the group has professionals who can guide you on how to solve the programming problem. Professionals on the site also give advice, tips, and answers to help students with programming homework. – You will no longer have trouble getting programming course materials for students and educators. The free-access manuals on this site include content from undergraduate to graduate-level MIT courses, such as lecture notes, exams, problem sets, video lectures, and a reading list. The institution has published the materials in five languages. MIT OpenCourseWare MIT OpenCourseWare has a high consumer rating on Sitejabber and Google, with many students praising the quality of the video lectures. 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Maybe, you will find the answer to yours as well. What are the risks of getting online assistance? The risk is that you might stumble on an untrustworthy site. Ensure you research the company you select to confirm that it has reputable quality standards and guarantees the safety of your information and payment details. You can choose among the 7 sites listed in this review to avoid an unpleasant experience. How can these sites be legit? Yes, you can use programming assignment help sites without worry because they are legal. Students are at liberty to use the content obtained from the website in the way they desire. For example, you can choose to use the content as a reference tool or for inspiration. Who will write my programming assignment? What is my writer's degree? All experts employed by the sites have academic degrees and many years of experience in programming. The writers undergo a strict recruitment process that ensures only the most qualified experts are selected. You can check the rating and qualifications of an expert in a particular field to help you choose the best fit for your assignment. How fast can I receive my order? Several factors determine the time required to complete a paper, such as the volume and complexity of the assignment. Still, some websites, such as Domymathhomeworks.com and Stemhave.com deliver programming homework help within a deadline of at least 4 hours. However, it’s always good to order the paper early to give the writer enough time to produce a top-notch paper. Conclusion Identifying an online platform where you can get quality programming assignment help is not easy, especially if you don’t give it maximum attention. Some of the critical factors to consider include price, services offered, on-time delivery, paper quality, and writer’s qualifications. Always go for a site with positive customer reviews on independent websites. We derived the shortlist featured in this post by researching reviews from numerous American students who have used online assistance. Use this guide to make informed decisions and avoid negative experiences. Take a break sometimes by enlisting help from one of the websites so that you can regroup and start again. Looks good!

7 Best Programming Assignment Help Sites

A story about founding several startups, funding, investor relationships, writing software, and winning hackathons. Some people like Peter Levels say, ">95% of everything I ever did failed (...) ship more" while the famous Rework book provides a study-based statement that "People who failed before have the same amount of success as people who have never tried at all. (...) Already successful entrepreneurs are far more likely to succeed again". In my opinion, if you fail smart, mistakes do teach you a lesson. It's all about getting feedback and learning from it. With this in mind, some of the initiatives I'm describing were not failures at all. They either made me money or taught me a lot. This is not a story of me working hard. This is a story of how the right people around you allow you to tackle hard problems. Also, how challenging it is to create multiple complex products at the same time. I long idolized Peter Levels' legendary challenge. It seemed unreal that you can create thriving businesses by bootstrapping (starting a company with what you have without seeking external funding). While being fascinated with this notion, I also wanted to approach issues way beyond my competence, budget, and scale. To execute big ideas, I needed a team. Collaboration enables scale, and this is exactly what we did. Some of the projects got initial funding (most of which was equity-free - without giving up shares in the company). The team consisted of 5-6 people, depending on the project. "12 startups in 12 months" Sure, I could also do it the "Elon Musk way" - start something utilitarian (zip2, PayPal), make a lot of money, and use this money to invest in passion projects. I feel like I don't have the right motivation to go this route. Life feels too short to waste time on just making money. Even if this is the most efficient way. I'm sure some of you won't agree with me calling these projects startups. Some of them weren't really shipped and sold. At least they were supposed to. A lesson in all of these stories is not only that you should ship more - ship faster as well. Motivation is a finite resource, especially when a group of people is involved. Hackathons It all started with hackathons. I participated in hackathons way before I met people with whom I created the projects outlined in this article. Once, I invited a few of my friends to go to the event, create something cool, have a couple of beers, and perhaps win some money. And that's exactly what happened. No expectations, no pressure. After a few events, we had a good understanding of what hackathons were about. And how to win them. I could describe what we've learned, but it is a topic broad enough for another article. One of my friends, who was leading some projects while I led the others, created a set of guidelines for our hackathon participation: "Components of hackathon success" (Yes, it sounds ridiculous). After introducing this tongue-in-the-cheek framework, we rarely left the hackathon venue without a prize. A funny thing about hackathons is that when you win, you get all kinds of offers. Sometimes you may get a new job, and sometimes an investment or incubation offer. An off-putting thing about these events is that they aren't exactly a healthy way to spend time. Staying up for one to two nights, mixing energy drinks with alcohol, limited access to showers, and having only fast food available are not really that exciting when you do it for the tenth time. Sure, we tried to bypass those limitations, booked hotels, and introduced mandatory sleeping times for ourselves... But it didn't feel that fun anymore. It was only natural to stop going to hackathons and make the next step. We've decided to try to work on something for longer than a weekend. Startups All of the ideas are coded: 💡[when the idea came up] 🗓 [work start date] 💰[funding in $] 🛑 [date of admitting failure] You will see a lot of polish language in the screenshots as we've often started selling in our local market. Mediascribe 💡05.11.2019 🗓 19.02.2020 💰 15k 🛑 12.06.2020 Live TV and podcast analysis tool. Or bank hackathon baby. It was one of the first hackathons we took part in. The theme was focused around banks, but we didn’t feel like developing another client fingerprinting tool or insurance premium algorithm. We’ve watched all the hackathon-related webinars, met before the hackathon, brainstormed, and decided to go with something loosely related to banking. At the same time, it was an interesting, technically challenging, and exciting product. This is how mediascribe was born. Mediascribe is an audio content analysis tool. Usual brand analysis software operates on tweets, Facebook posts, Google search results, or news articles. We wanted to analyze live broadcasts, podcasts, and live TV and provide our clients with real-time information about topics of their interest, their brand, competition ad campaigns, and others. The project was awarded a distinction and a small cash prize. Two months after the hackathon, an organizer reached out to us with an investment offer. We had received a small pre-seed funding (60k PLN or 15k USD). We've worked on the product for over 3 months. We tried to go to market through an investors’ sales network (they are a software house selling a mix of products and services). Unluckily, while the product was exciting to the audience we pitched it to, decision makers (and us) had a hard time putting a dollar amount on benefits coming from using it. Podcast space that could benefit from such a tool wasn't all the rage. We also weren’t able to communicate our vision clearly. However, it was the first project with a team that later created other startups I’m describing. We also learned a heap about audio analysis. It wasn’t all for nothing. Potential clients suggested another problem we could solve using transcription and content analysis. We pivoted and created Sentimatic. “PacMan” - Pactum Manager 💡08.03.2020 🗓 08.03.2020 💰 1.2k 🛑 19.11.2020 Software for lawyers for merging original contract text and its amendments into a uniform text. Created on a hackathon with two lawyer friends. Bootstrapped, but way too much talking in comparison to doing. We had a solid prototype and the idea. We were overworked with other stuff. No one was ready to dedicate a lot of time to it. We were in advanced talks with a first client from Germany, legal tech licensing our tech. We decided that we have to let it go in order to be able to execute other projects. AI Theory 💡X 🗓 30.04.2020💰 25k 🛑 26.10.2020 Fake news detection and information spread tracking software. Covid happened and fake news flooded the internet. We had text analysis experience from previous projects. There was a very compelling acceleration programme aimed at information quality control. Our product worked, gained traction, and people spoke about it at conferences and in the news. We had conversations with potential clients. In the end, there was no real way to monetize it. Media+ 💡02.2020 🗓 09.2020💰 25k + 100k gcloud grant 🛑 10.2021 Live TV subtitling tool for television targeted at the hearing-impaired audience. Started at a hackathon that I didn't even attend! Oriented at accessibility. Our main motivation was helping people. External support included 100k PLN (25k USD) from a government incubator, 100k USD in credits from Google Cloud, and various non-monetary benefits from NGOs and companies in form of workshops, promotion, consulting, sharing questionnaires, etc. Morale was low, especially when comparing ourselves to competition and seeing gradual improvement coming from content platforms (Youtube, etc.). Overlap with another project, we simply had no time. In the end, the polish government ended up receiving IP rights to the software and continued the project. In this one, we held around ten workshops with potential users. We showed them our progress, had them interact with the prototype, and provide feedback. Sentimatic 💡X 🗓 3.09.2020💰 mid-5-figure 🛑 Ongoing AI sentiment analysis for a call center. This startup is pretty much a pivot out of Mediascribe. It is built upon our experience with voice and text processing. It was started as a result of sales conversations with one of the potential clients for Mediascribe. We knew it was a deserved set of features for at least one major company. This is by far my most successful startup. The software works, and the feedback from the market is really encouraging. The deal with the investor is prohibitive of engaging other investors without explicit permission and is a limiting factor. We are currently running multiple PoCs (Proof of Concepts) with clients and expect at least some of it to end up with yearly contracts. We are, however, low on cash at the moment. The team is highly motivated, but this year will be crucial in determining the company's future. Check out the website Temi 💡07.09.2020 🗓 21.09.2020💰 10k + 10k (self investment) 🛑 2.06.2022 A platform connecting lawyers with clients. This one is interesting and died most recently. It all started with me being a guest in a startup podcast. A host knew me from startup events and invited me for a discussion on how machine learning can improve law-related processes with a legal services provider. Later on, the host and some of his acquaintances approached me to start something in the space together. I said "yes" mostly because of their interesting backgrounds. It was the first time I was starting a business with someone more sales and management oriented. I wanted to focus only on technology and learn about sales from my partners. This one was pretty much bootstrapped. We were a part of a software incubator that helped us raise more money and provided support via business-related services. They also paid for the UI design and cloud hosting. We also invested some of our own money into hiring a developer that helped me to code the platform. The idea failed in the end. We've applied to a funding platform and spent way too much time trying to get the money when we should’ve focused more on actually developing a platform. Instead of trying to start small, we wanted to have a big launch, where everything would be perfect. I wasn't able to bring it to this level on my own, and we reached out for help way too late. With only one of four founders being an active developer, the rapid creation of a big platform was not feasible. We had an MVP and contacted lawyers that looked for new business and business users interested in using legal services. However, our projected operating costs were too big to kick off without investment. That would be fine, but in terms of funding, we put all of our eggs in one basket. When we realized it, it was too late to save our drive for the project. Summary Over the course of almost two years since the first startup on this list was founded, I gained a heap of useful skills and experience in an accelerated way. One of the startups is still alive, and I like to work on it after hours. Another "project" that I have going on is "me as a software professional" and I don't want to forgo it either. However, it is a challenge to have a happy and healthy life while being engaged in so many initiatives. At the moment, I am pursuing a full-time job as a Software Engineering Manager. I surely know, that for a while, I won't be starting anything new. I want to take a break, summarize my learnings, and internally regroup. This article is a part of this effort. It's a retrospective and a record of these two years of grinding. I've heard lessons like "don't give up more than 20% of the company at the seed stages of funding" or "launch small as fast as you can" but these resonate stronger when you live through them yourself. Remember when at the beginning of the article I said that I want to do things bigger than Peter Levels? At this moment, if my last startup fails, I feel like the next thing would be something I will prototype and ship myself. I feel that this may be more sustainable for me in the long run. Running a business with multiple people is tied to many tensions and stress. I feel that not having this burden can be empowering. At this point, I am able to invest a little money in contractor help. With that being said, I am a social person and collaborating with others gives me energy, so I'm open to whatever the future has to offer. I would appreciate it if you engaged in a discussion or checked out . my blog

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