British accessories brand, Kurt Geiger, announces its remarkable results for this financial year, highlighting impressive growth, international success, a

Kurt Geiger posts strong growth and international expansion in its 60th anniversary year

Carolina Place mall owner Brookfield Properties defaulted on a $149 million loan debt, according to a CMBS report provided to The Charlotte Observer by commercial real estate research group CoSta r. CMBS is a commercial mortgage-backed security. The 90-acre mall property is at N.C. Hwy. 51 and Interstate 485 in Pineville.

What’s next for Carolina Place mall after it defaulted on a $149 million loan?

On October 13, Abu Dhabi Investment Authority (ADIA) pumped in Rs 4,966.80 crore in the global conglomerate’s retail arm Reliance Retail Ventures for a 0.59 percent stake. The investment values ...

ADIA acquires stake in Reliance Retail for Rs 4,966.80 crore; RIL shares slip

Westgate Mall, built in 1975 on West Blackstock Road in Spartanburg, is for sale. The mall is the only enclosed mall in Spartanburg County. It was last renovated in 1996.

Spartanburg's WestGate Mall is up for sale

U.S. stock futures slid lower Tuesday morning after retail sales came in hotter than economists expected. The Commerce Department said U.S. retail sales rose 0.7% in September from August.

Stock Futures Slide Following Hot Retail Sales

Retail sales rose 0.7% in September, more than twice what economists had expected, and close to a revised 0.8% bump in August, the Commerce Department reported Tuesday. Retail sales in August were ...

Retail sales up 0.7% in September as American consumers defy rising prices, interest

A South Carolina shopping mall that's been open for almost 50 years is up for sale. SVN Blackstream Commercial Real Estate Advisors has been retained to "offer for sale the leasehold interest" of Spartanburg's WestGate Mall on Blackstock Road off Interstate 26,

Upstate mall goes up for sale, realtors warn of 'misinformation'

Reedy River Retail and Second Story Real Estate Management — have been retained to offer for sale the leasehold interest of WestGate Mall, an enclosed regional shopping mall in Spartanburg. The mall’s anchor tenants include Belk,

SVN Blackstream Selected to Market Sale of WestGate Mall in Spartanburg, South Carolina

Three years after the owners of WestGate Mall filed for bankruptcy, a leasehold interest in the property is listed for sale on a commercial real estate website.

Spartanburg’s WestGate Mall listed for sale

SPARTANBURG, S.C. (WSPA) – The only mall in Spartanburg is up for sale nearly three years after filing for bankruptcy. The WestGate Mall was put on the market Thursday. The 450,688 square-foot mall, located at 205 Blackstock Road, was built in 1975 and is now in receivership, according to Dustin Tenney with Reedy River Retail at SVN Blackstream.

Westgate Mall property up for sale

Dillard's

dillard's

It’s not a nice feeling to wake up one fine day and realize what used to be your HOURLY rate is now your DAILY pay rate. It makes you want to cry. Thank you, AI. Long post-ChatGPT-story short: once a freelance writer, now a carpet cleaner. That was the best result of my transition from the virtual and remote to the real working world. There was a silver lining, though, if you fancy and get Monty Python’s sense of humor the right way according to “Life of Brian.” I’m talking about a healthy lifestyle filled with various exercises and social interactions. To my huge surprise, my new colleagues accepted me without any prejudice and the Jedi initiation test. It goes without saying that no CV was required, and no one couldn’t care less about my previous work experience. The thing about carpet cleaning is that it isn’t only about carpets but also about - the furniture. For this one, I had to actually consult a dictionary and do some Google research. I’m talking about upholstery cleaning. I can tell you first-hand that it’s a completely different cleaning game. Upholstery Cleaners and Bitcoin Preachers Upholstery cleaning is more demanding and personal. If I weren’t stuck in some corporate building cleaning job, I was a sidekick carrying steam cleaners from home to home. It wasn’t all that bad. People are, in general, nice and generous to the cleaners who “facelift” their furniture. As an assistant, I really enjoyed talking to our hosts and sharing refreshments with my steam cleaning maestro, including the tips. One day, we found ourselves in a young couple’s home. It wasn’t the fact that both of them were at home during regular 9-to-5 working hours and casually dressed (not in pajamas; we all know that’s not true when it comes to remote working) that gave them away. As soon as I saw a laptop almost in every room and Linux, I knew I met my kind after a long time. The young man was a coder, and his girlfriend was a web designer. We were on the same page in the blink of my colleague’s eye, who wasn’t particularly happy with their demands. There was no point in justifying that we’d have to go the extra mile for people who were working from home, so I kept it quiet. When you’re staying at home and working all day long, you have to have furniture that looks and smells nice. That meant more work for my colleague and more time for me to reflect on the good old remote days. All good things have to end, my dear virtual friend; it was time to say goodbye and get paid. “I’ve been wondering, do you accept cards by any chance.” The young lady’s innocent question raised my colleague’s eyebrows. I wasn’t the only one to notice this “anti-plastic” sentiment. “Don’t bother, dear. What I wonder is, what would have happened if I were to mention Bitcoin?” Little did the coder know that his laughter following his hypothetical situation would help all hell break loose. North Remembers - As Do Blue Collars “I don’t prefer. I only accept cash, period. Non-negotiable. We all do.” “What do you mean ‘we?’ You may have colleagues who’re more open-minded when it comes to the future.” The young man’s was looking at his girlfriend for approval and support, but she was gone with the wind. “Let me check our wallet.” That was all we heard of her before she ran upstairs quicker than The Flash after Snyder’s cut. I was safe and sound in a neutral corner, busy packing the equipment. “We, the carpet cleaners, are the least of your worries.” Uh, that went well. For a second, I really thought my colleague would say, We The People. “You can always get new furniture, but what about plumbs and wires? Do you think that plumbers and electricians are stupid enough to get paid with air? What are you going to do, ha? Buy a new home each time something is wrong with your toilet or light?” “Hey buddy, let’s cool down. I didn’t mean to…” “I ain’t your buddy. Buddy!! And, secondly, what you do, I can, if I want to, but what I do, you can’t. Not in a million years.” My colleague was on fire, and to tell you the truth, it was too interesting to try to de-escalate the situation. Yeah, my guilty pleasure that day. “I don’t get it.” I did, but I was waiting to hear my colleague’s version. “Oh, yes, you do. You’re just playing dumb. Learn to code. I can learn to code, but you are totally useless without your computer. And, your crypto mumbo jumbo, you can stick…” “How about some additional refreshments for the excellent work you just did? We truly appreciate it. And here’s the cash, as we agreed.” If you’re asking me, the young lady ruined what could’ve been an exciting non-celebrity death match. No Drakaris?! Damn, girl, you could’ve been a minute or two late. The Future of Bitcoin is Bright, But Not Without The Fight On our way out, it wasn’t the door that hit me back but my host’s question. “Do you honestly believe that people like him,” the coder was pointing at my colleague who already started our company’s van and waited impatiently for me to hop in, “would ever accept Bitcoin or any other crypto for that matter?” “I don’t know, man. He’s a good, hard-working person, but he operates only with cash, plain and simple. Nothing personal; it’s the way this business is running. We’re paying for everything we need in cash.” “No disrespect, but I think that you, I mean, they’re the dying out species. They’re going to be left behind. It’s only a matter of time.” Well, it was time to agree to disagree. “We’re the endangered species, my friend. The AI is going to wipe us out, and it’s not a matter of time; it’s happening, one by one, first writers, then designers, and you coders are just buying time.” I didn’t let him interrupt me. “Some people will never accept Bitcoin. They would rather die on the cash hill than settle for anything else than gold or silver, even if it means going back to bartering.” “We have the advantage. We can adapt. You could write AI prompts. You don’t have to do this. It’s beneath you, man.” My colleague was Morse Coding me get-in-the-van-or-walk with the car’s horn. I realized that any further discussion and resistance is futile, Borg-style. I had to wrap up this one quickly and brutally. “Look, I have to go. Anything that can be done only using your hands on site is safe; all other things are on the AI kill list. You have to adapt and learn to do something else.” I was done talking. On my way to the van, I could only think about the tip. Did we get enough from my cyber-brother-and-sister to get something to eat and drink on our way back? If they stuck to the HackerNoon Covid 20 percent tip rule, then we were covered. “What was that all about? What took you so long? I’m tired, man. I did all the work while you were chit-chatting with your cyber-buddies.” I couldn’t tell whether my colleague was more curious or angry at me. “Nothing. Just a few nice and calming words, so they don’t leave us a bad review on Google.” “Don't bullshit a bullshitter, or you ain’t getting your share of all tips today.” He was dead serious. Surprise! Surprise! Guess Who Else Is Also In A Bitcoin Race For A Prize? “The guy is a crypto maniac. That’s all.” My mind was on burgers, not on Bitcoins. “Back in the day, I bought some Bitcoins too.” “Covid taught me an invaluable lesson. The only way to survive is to diversify.” I was left speechless. What in the world? “Don’t put all of your eggs, I mean assets, in one basket.” I knew it was a cheesy thing to say, but I had to say something. Tomorrow is a new day, and I have to play a cleaning game to survive. No more Zoom meetings, only carpets and upholstery cleaning. “Relax. I’m just tired, that’s all. You’ll get your share.” “Then, I don’t get it. Why did you have to make it so personal?” Now, when I’m sure that I’m going to get paid and fed, some things need to be asked and said. “Don’t push it on me or anyone else. That’s all, man. Do you even know how much money we are spending each month on Google ads? How do you think we got this call in the first place?” The flashing fast food sign made my colleague stop driving and pause talking. “More payment options, more work. Quicker payment, more work. It’s that simple. But, for now, let’s stick to cash.” I was still sitting in the van, processing all things said and done. “C’mon! Get out! Let’s get in line. What do you want me to order for you?” He was counting the money on our way to the burgers big as a house. “Ah, one more thing. Don’t take any advice from me. I ain’t no financial expert. What do I know? I’m just a carpet cleaner. Spicy or not?” That night was good for both of us and Bitcoin, too. The tips were more than generous, and Bitcoin hit $30K after almost a year. It’s not a nice feeling to wake up one fine day and realize what used to be your HOURLY rate is now your DAILY pay rate. It makes you want to cry. Thank you, AI. Long post-ChatGPT-story short: once a freelance writer, now a carpet cleaner. That was the best result of my transition from the virtual and remote to the real working world. There was a silver lining, though, if you fancy and get Monty Python’s sense of humor the right way according to “Life of Brian.” I’m talking about a healthy lifestyle filled with various exercises and social interactions. To my huge surprise, my new colleagues accepted me without any prejudice and the Jedi initiation test. It goes without saying that no CV was required, and no one couldn’t care less about my previous work experience. The thing about carpet cleaning is that it isn’t only about carpets but also about - the furniture. For this one, I had to actually consult a dictionary and do some Google research. I’m talking about upholstery cleaning. I can tell you first-hand that it’s a completely different cleaning game. Upholstery Cleaners and Bitcoin Preachers Upholstery cleaning is more demanding and personal. If I weren’t stuck in some corporate building cleaning job, I was a sidekick carrying steam cleaners from home to home. It wasn’t all that bad. People are, in general, nice and generous to the cleaners who “facelift” their furniture. As an assistant, I really enjoyed talking to our hosts and sharing refreshments with my steam cleaning maestro, including the tips. One day, we found ourselves in a young couple’s home. It wasn’t the fact that both of them were at home during regular 9-to-5 working hours and casually dressed (not in pajamas; we all know that’s not true when it comes to remote working) that gave them away. As soon as I saw a laptop almost in every room and Linux, I knew I met my kind after a long time. The young man was a coder, and his girlfriend was a web designer. We were on the same page in the blink of my colleague’s eye, who wasn’t particularly happy with their demands. There was no point in justifying that we’d have to go the extra mile for people who were working from home, so I kept it quiet. When you’re staying at home and working all day long, you have to have furniture that looks and smells nice. That meant more work for my colleague and more time for me to reflect on the good old remote days. All good things have to end, my dear virtual friend; it was time to say goodbye and get paid. “I’ve been wondering, do you accept cards by any chance.” The young lady’s innocent question raised my colleague’s eyebrows. I wasn’t the only one to notice this “anti-plastic” sentiment. “Don’t bother, dear. What I wonder is, what would have happened if I were to mention Bitcoin?” Little did the coder know that his laughter following his hypothetical situation would help all hell break loose. North Remembers - As Do Blue Collars North Remembers - As Do Blue Collars “I don’t prefer. I only accept cash, period. Non-negotiable. We all do.” “What do you mean ‘we?’ You may have colleagues who’re more open-minded when it comes to the future.” The young man’s was looking at his girlfriend for approval and support, but she was gone with the wind. “Let me check our wallet.” That was all we heard of her before she ran upstairs quicker than The Flash after Snyder’s cut. I was safe and sound in a neutral corner, busy packing the equipment. “We, the carpet cleaners, are the least of your worries.” Uh, that went well. For a second, I really thought my colleague would say, We The People. “You can always get new furniture, but what about plumbs and wires? Do you think that plumbers and electricians are stupid enough to get paid with air? What are you going to do, ha? Buy a new home each time something is wrong with your toilet or light?” “Hey buddy, let’s cool down. I didn’t mean to…” “I ain’t your buddy. Buddy!! And, secondly, what you do, I can, if I want to, but what I do, you can’t. Not in a million years.” My colleague was on fire, and to tell you the truth, it was too interesting to try to de-escalate the situation. Yeah, my guilty pleasure that day. “I don’t get it.” I did, but I was waiting to hear my colleague’s version. “Oh, yes, you do. You’re just playing dumb. Learn to code. I can learn to code, but you are totally useless without your computer. And, your crypto mumbo jumbo, you can stick…” “How about some additional refreshments for the excellent work you just did? We truly appreciate it. And here’s the cash, as we agreed.” If you’re asking me, the young lady ruined what could’ve been an exciting non-celebrity death match. No Drakaris?! Damn, girl, you could’ve been a minute or two late. The Future of Bitcoin is Bright, But Not Without The Fight The Future of Bitcoin is Bright, But Not Without The Fight On our way out, it wasn’t the door that hit me back but my host’s question. “Do you honestly believe that people like him,” the coder was pointing at my colleague who already started our company’s van and waited impatiently for me to hop in, “would ever accept Bitcoin or any other crypto for that matter?” “I don’t know, man. He’s a good, hard-working person, but he operates only with cash, plain and simple. Nothing personal; it’s the way this business is running. We’re paying for everything we need in cash.” “No disrespect, but I think that you, I mean, they’re the dying out species. They’re going to be left behind. It’s only a matter of time.” Well, it was time to agree to disagree. “We’re the endangered species, my friend. The AI is going to wipe us out, and it’s not a matter of time; it’s happening, one by one, first writers, then designers, and you coders are just buying time.” I didn’t let him interrupt me. “Some people will never accept Bitcoin. They would rather die on the cash hill than settle for anything else than gold or silver, even if it means going back to bartering.” “We have the advantage. We can adapt. You could write AI prompts. You don’t have to do this. It’s beneath you, man.” My colleague was Morse Coding me get-in-the-van-or-walk with the car’s horn. I realized that any further discussion and resistance is futile, Borg-style. I had to wrap up this one quickly and brutally. “Look, I have to go. Anything that can be done only using your hands on site is safe; all other things are on the AI kill list. You have to adapt and learn to do something else.” I was done talking. On my way to the van, I could only think about the tip. Did we get enough from my cyber-brother-and-sister to get something to eat and drink on our way back? If they stuck to the HackerNoon Covid 20 percent tip rule , then we were covered. HackerNoon Covid 20 percent tip rule “What was that all about? What took you so long? I’m tired, man. I did all the work while you were chit-chatting with your cyber-buddies.” I couldn’t tell whether my colleague was more curious or angry at me. “Nothing. Just a few nice and calming words, so they don’t leave us a bad review on Google.” “Don't bullshit a bullshitter, or you ain’t getting your share of all tips today.” He was dead serious. Surprise! Surprise! Guess Who Else Is Also In A Bitcoin Race For A Prize? Surprise! Surprise! Guess Who Else Is Also In A Bitcoin Race For A Prize? “The guy is a crypto maniac. That’s all.” My mind was on burgers, not on Bitcoins. “Back in the day, I bought some Bitcoins too.” “Covid taught me an invaluable lesson. The only way to survive is to diversify.” I was left speechless. What in the world? “Don’t put all of your eggs, I mean assets, in one basket.” I knew it was a cheesy thing to say, but I had to say something. Tomorrow is a new day, and I have to play a cleaning game to survive. No more Zoom meetings, only carpets and upholstery cleaning. “Relax. I’m just tired, that’s all. You’ll get your share.” “Then, I don’t get it. Why did you have to make it so personal?” Now, when I’m sure that I’m going to get paid and fed, some things need to be asked and said. “Don’t push it on me or anyone else. That’s all, man. Do you even know how much money we are spending each month on Google ads? How do you think we got this call in the first place?” The flashing fast food sign made my colleague stop driving and pause talking. “More payment options, more work. Quicker payment, more work. It’s that simple. But, for now, let’s stick to cash.” I was still sitting in the van, processing all things said and done. “C’mon! Get out! Let’s get in line. What do you want me to order for you?” He was counting the money on our way to the burgers big as a house. “Ah, one more thing. Don’t take any advice from me. I ain’t no financial expert. What do I know? I’m just a carpet cleaner. Spicy or not?” That night was good for both of us and Bitcoin, too. The tips were more than generous, and Bitcoin hit $30K after almost a year.

The Revenge of Blue-Collars: Death to Bitcoin, Long Live Dollars

انتقام العمال ذوي الياقات الزرقاء: الموت للبيتكوين، وعاش الدولار

Mavi yaxalıların qisası: Bitcoin-ə ölüm, yaşa Dollar

ব্লু-কলারের প্রতিশোধ: বিটকয়েনের মৃত্যু, দীর্ঘজীবী ডলার

La venjança dels colls blaus: mort de Bitcoin, visca els dòlars

La venganza de los obreros: muerte al bitcoin, larga vida al dólar

Tire revanj nan Blue-Collars: Lanmò Bitcoin, Viv Dola

ブルーカラーの復讐: ビットコインの死、ドルの永遠

블루칼라의 복수: 비트코인의 죽음, 달러의 만세

Zilo apkaklīšu atriebība: nāve Bitcoin, lai dzīvo dolāri

د بلیو کالر انتقام: بیټکوین ته مرګ، اوږد ژوندی ډالر

தி ரிவெஞ்ச் ஆஃப் ப்ளூ காலர்ஸ்: டெத் டு பிட்காயின், லாங் லைவ் டாலர்கள்

Mavi Yakalıların İntikamı: Bitcoin'e Ölüm, Dolarlara Uzun Yaşa

My Plan to Make a Million Dollars Writing Online

অনলাইনে লেখার জন্য আমার একটি মিলিয়ন ডলার করার পরিকল্পনা

Mein Plan, mit Online-Schreiben eine Million Dollar zu verdienen

Mi plan para ganar un millón de dólares escribiendo en línea

Mon plan pour gagner un million de dollars en écrivant en ligne

ऑनलाइन लेखन से एक मिलियन डॉलर कमाने की मेरी योजना

オンラインで執筆して100万ドル稼ぐ計画

온라인으로 백만 달러를 벌기 위한 나의 계획

Meu plano para ganhar um milhão de dólares escrevendo online

Мой план заработать миллион долларов на писательстве в Интернете

Çevrimiçi Yazarak Bir Milyon Dolar Kazanma Planım

Kế hoạch kiếm một triệu đô la của tôi bằng cách viết trực tuyến

我的在线写作计划赚百万美元

Traditional RAG systems work by indexing raw data. This data is simply chunked and stored in vector DBs. Whenever a query comes from the user, it queries the stored chunks and retrieves relevant chunks. If you wish to learn the fundamentals of RAG I have written a comprehensive intro about it here. As the retrieval step happens for every single query from the user, it is the most crucial bottleneck to speed up naive RAG systems. Would it not be logical to make the retrieval process super efficient? This is the promise of LightRAG. Why Not GraphRAG? Before we look at them, you may ask, “Wait. Do we not have GraphRAG from Microsoft?”. Yes, but GraphRAG seems to have a couple of drawbacks. Incremental knowledge update. (sec 3.1) GraphRAG first creates a reference to entities and relationships in the entire private dataset. It then does bottom-up clustering that organizes the data hierarchically into semantic clusters. An update to the dataset with new knowledge means that we have to go through the entire process of building the graph! LightRAG on the other hand addresses this by simply appending new knowledge to the existing one. More specifically, it combines new graph nodes and edges with existing ones through a simple union operation. Computational intensity. As seen from their study, LightRAG significantly reduces the cost of the retrieval phase. What takes 610,000 tokens for GraphRAG takes less than 100 tokens for LightRAG. So, without further adieu, let's dive into LightRAG. Visual Learning If you are a visual learner like me, why not check this YouTube video explaining the idea of LightRAG: https://youtu.be/5EmRZcJIfnw?si=UPhwRtxUUcZbTfRU&embedable=true LightRAG The two main selling points of LightRAG are Graph-based indexing and a dual-level retrieval framework. So, let's look into each of them. Graph-based Indexing Below are the steps LightRAG follows to incorporate graph-based indexing. Entity and Relationship (ER) extraction. ER extraction is shown by R(.) in the above figure. This step ensures that simple entities are first extracted from a given document. For example, in the above example, “bees” and “beekeeper” are two entities. And they are related by “observe” relation. As in, a beekeeper observes bees. Key-value (KV) pair generation using LLM. KV pairs are then generated using a simple LLM. The LLM profiling step gives a small note or explanation of what the entity or relation is all about. For example, the LLM explains who a “beekeeper” is in our chosen example. This step is denoted by the P(.) in the above figure. Note that this LLM is different from the general-purpose LLM used in the main RAG pipeline. Deduplication. Given that the documents have to do with bees, it is quite possible that the entity “beekeeper” could have been retrieved from several documents or chunks. So, we need a deduplication step that just keeps one and discards the rest with the same meaning. This is shown by the D(.) in the above figure. Dual-level Retrieval A query to a RAG system can be one of two types — specific or abstract. In the same bee example, a specific query could be “How many queen bees can be there in the hive?” An abstract query could be, “What are the implications of climate change on honey bees?” To address this diversity, LightRAG employs two retrieval types: Low-level retrieval. It simply extracts precise entities and their relationships like bees, observe, and beekeepers. High-level retrieval. Employing an LLM, LightRAG aggregates information and summarizes multiple sources of information. Why Bother Doing All This? Doing all this exercise and switching to LightRAG improves execution time indeed. During indexing, the LLM needs to be called just once per chunk to extract entities and their relationships. Likewise, during user query, we only retrieve entities and relationships from chunks using the same LLM we used for indexing. This is a huge saving on the retrieval overhead and hence computation. So, we have a “light” RAG at last! Integrating new knowledge into existing graphs seems to be a seamless exercise. Instead of re-indexing the whole data whenever we have new information, we can simply append new knowledge to the existing graph. Evaluation In their evaluations, they have compared against Naive RAG, RQ-RAG, HyDE, and GraphRAG. To keep the comparison fair, they have used GPT-4o-mini as the LLM across the board with a fixed chunk size of 1200 for all datasets. The answers were evaluated for comprehensiveness, diversity, and effectiveness in answering the user(a.k.a. empowerment in the paper). As we can see from the underlined results, LightRAG beats all of the state-of-the-art methods currently available. In general, they draw the following conclusions: Using graph-based methods (GraphRAG or LightRAG) improves significantly over the baseline Naive RAG. LightRAG produces quite diverse answers powered by the dual-level retrieval paradigm. LightRAG can deal with complex queries better Conclusion Though RAG is a fairly recent technique, we are seeing rapid progress in the area. Techniques like LightRAG which can take RAG pipelines to cheap commodity hardware are the most welcome. While the hardware landscape is ever-growing, there is always an increasing need to run LLMs and RAG pipelines in compute-constrained hardware in real time. Would you like to see some hands-on study of LightRAG? Please stay tuned… Traditional RAG systems work by indexing raw data. This data is simply chunked and stored in vector DBs. Whenever a query comes from the user, it queries the stored chunks and retrieves relevant chunks. If you wish to learn the fundamentals of RAG I have written a comprehensive intro about it here . retrieves here here As the retrieval step happens for every single query from the user, it is the most crucial bottleneck to speed up naive RAG systems. Would it not be logical to make the retrieval process super efficient? This is the promise of LightRAG . LightRAG Why Not GraphRAG? Before we look at them, you may ask, “Wait. Do we not have GraphRAG from Microsoft?”. Yes, but GraphRAG seems to have a couple of drawbacks. Incremental knowledge update. (sec 3.1) GraphRAG first creates a reference to entities and relationships in the entire private dataset. It then does bottom-up clustering that organizes the data hierarchically into semantic clusters. An update to the dataset with new knowledge means that we have to go through the entire process of building the graph! LightRAG on the other hand addresses this by simply appending new knowledge to the existing one. More specifically, it combines new graph nodes and edges with existing ones through a simple union operation. Incremental knowledge update. (sec 3.1) GraphRAG first creates a reference to entities and relationships in the entire private dataset. It then does bottom-up clustering that organizes the data hierarchically into semantic clusters. An update to the dataset with new knowledge means that we have to go through the entire process of building the graph! LightRAG on the other hand addresses this by simply appending new knowledge to the existing one. More specifically, it combines new graph nodes and edges with existing ones through a simple union operation. Incremental knowledge update. Computational intensity. As seen from their study, LightRAG significantly reduces the cost of the retrieval phase. What takes 610,000 tokens for GraphRAG takes less than 100 tokens for LightRAG. Computational intensity. As seen from their study, LightRAG significantly reduces the cost of the retrieval phase. What takes 610,000 tokens for GraphRAG takes less than 100 tokens for LightRAG. Computational intensity. So, without further adieu, let's dive into LightRAG. Visual Learning If you are a visual learner like me, why not check this YouTube video explaining the idea of LightRAG: https://youtu.be/5EmRZcJIfnw?si=UPhwRtxUUcZbTfRU&embedable=true https://youtu.be/5EmRZcJIfnw?si=UPhwRtxUUcZbTfRU&embedable=true LightRAG The two main selling points of LightRAG are Graph-based indexing and a dual-level retrieval framework. So, let's look into each of them. Graph-based Indexing Below are the steps LightRAG follows to incorporate graph-based indexing. Entity and Relationship (ER) extraction. ER extraction is shown by R(.) in the above figure. This step ensures that simple entities are first extracted from a given document. For example, in the above example, “bees” and “beekeeper” are two entities. And they are related by “observe” relation. As in, a beekeeper observes bees. Entity and Relationship (ER) extraction. ER extraction is shown by R(.) in the above figure. This step ensures that simple entities are first extracted from a given document. For example, in the above example, “bees” and “beekeeper” are two entities. And they are related by “observe” relation. As in, a beekeeper observes bees. Entity and Relationship (ER) extraction. Key-value (KV) pair generation using LLM. KV pairs are then generated using a simple LLM. The LLM profiling step gives a small note or explanation of what the entity or relation is all about. For example, the LLM explains who a “beekeeper” is in our chosen example. This step is denoted by the P(.) in the above figure. Note that this LLM is different from the general-purpose LLM used in the main RAG pipeline. Key-value (KV) pair generation using LLM. KV pairs are then generated using a simple LLM. The LLM profiling step gives a small note or explanation of what the entity or relation is all about. For example, the LLM explains who a “beekeeper” is in our chosen example. This step is denoted by the P(.) in the above figure. Note that this LLM is different from the general-purpose LLM used in the main RAG pipeline. Key-value (KV) pair generation using LLM. Deduplication. Given that the documents have to do with bees, it is quite possible that the entity “beekeeper” could have been retrieved from several documents or chunks. So, we need a deduplication step that just keeps one and discards the rest with the same meaning. This is shown by the D(.) in the above figure. Deduplication. Given that the documents have to do with bees, it is quite possible that the entity “beekeeper” could have been retrieved from several documents or chunks. So, we need a deduplication step that just keeps one and discards the rest with the same meaning. This is shown by the D(.) in the above figure. Deduplication. Dual-level Retrieval A query to a RAG system can be one of two types — specific or abstract. In the same bee example, a specific query could be “How many queen bees can be there in the hive?” An abstract query could be, “What are the implications of climate change on honey bees?” To address this diversity, LightRAG employs two retrieval types: Low-level retrieval. It simply extracts precise entities and their relationships like bees, observe, and beekeepers. Low-level retrieval. It simply extracts precise entities and their relationships like bees, observe, and beekeepers. Low-level retrieval. High-level retrieval. Employing an LLM, LightRAG aggregates information and summarizes multiple sources of information. High-level retrieval. Employing an LLM, LightRAG aggregates information and summarizes multiple sources of information. High-level retrieval. Why Bother Doing All This? Doing all this exercise and switching to LightRAG improves execution time indeed. During indexing, the LLM needs to be called just once per chunk to extract entities and their relationships. Likewise, during user query, we only retrieve entities and relationships from chunks using the same LLM we used for indexing. This is a huge saving on the retrieval overhead and hence computation. So, we have a “light” RAG at last! Integrating new knowledge into existing graphs seems to be a seamless exercise. Instead of re-indexing the whole data whenever we have new information, we can simply append new knowledge to the existing graph. Evaluation In their evaluations, they have compared against Naive RAG, RQ-RAG, HyDE, and GraphRAG. To keep the comparison fair, they have used GPT-4o-mini as the LLM across the board with a fixed chunk size of 1200 for all datasets. The answers were evaluated for comprehensiveness, diversity, and effectiveness in answering the user(a.k.a. empowerment in the paper). empowerment As we can see from the underlined results, LightRAG beats all of the state-of-the-art methods currently available. In general, they draw the following conclusions: Using graph-based methods (GraphRAG or LightRAG) improves significantly over the baseline Naive RAG. Using graph-based methods (GraphRAG or LightRAG) improves significantly over the baseline Naive RAG. LightRAG produces quite diverse answers powered by the dual-level retrieval paradigm. LightRAG produces quite diverse answers powered by the dual-level retrieval paradigm. LightRAG can deal with complex queries better LightRAG can deal with complex queries better Conclusion Though RAG is a fairly recent technique, we are seeing rapid progress in the area. Techniques like LightRAG which can take RAG pipelines to cheap commodity hardware are the most welcome. While the hardware landscape is ever-growing, there is always an increasing need to run LLMs and RAG pipelines in compute-constrained hardware in real time. Would you like to see some hands-on study of LightRAG? Please stay tuned…

LightRAG - Is It a Simple and Efficient Rival to GraphRAG?

In this guide, you will register a new domain name for your web application. You will then connect that domain name through the Domain Name System (DNS) to a frontend or backend application. There's an annual fee to register a domain. The fee can range from $3 to several hundred dollars depending on the top-level domain. This fee is not refundable. See the Amazon Route 53 domain pricing documentation for full details. When you register a domain, we automatically create a hosted zone that has the same name as the domain. You use the hosted zone to specify where you want Amazon Route 53 to route traffic for your domain. The fee for a hosted zone is $0.50 per month. You can delete the hosted zone if you want to avoid this charge. See the Amazon Route 53 Hosted Zone pricing documentation for full details. Step 1: Register a domain name a. Open a browser and navigate to the AWS Management Console. Once you have logged in, ensure you have selected your desired Region in the upper right hand corner based on your infrastructure requirements. b. Enter Route 53 in the search bar and select Route 53 to open the service console. c. Choose the Register domain button. d. Enter a desired domain name in the text box and choose an extension such as .com in the dropdown list and choose the Check button. Choose the Add to cart button next to the domain that you want to purchase.
Choose the Continue button at the bottom of the page.
If the domain you chose is not available, choose one of the related domain suggestions or try again with a different domain name. e. Enter the registrant contact detail for your domain. Choose the Continue button on the bottom of the page. f. Verify your contact details. Choose either Enable or Disable in the auto renewal section. Select the check box after reading and agreeing to the terms and conditions. Choose Complete Order. Step 2: Configure DNS a. After domain registration is complete, return to the AWS console. Enter Route 53 in the search bar and select Route 53 to open the service console. b. Choose Hosted zones from the left navigation pane. c. Select the hosted zone with your domain name that Route 53 created for you as part of the domain registration. d. You can now create DNS records for your domain. In this guide, we will create a simple A record type. Choose the Create record button to get started. e. Choose Switch to quick create if you are currently in the wizard view. f. To allow clients to connect to your ALB using IPv4, enter your A record information and ensure A is selected in the Record type field. For this guide, we will enter www for Record name. Turn on Alias. Select Alias to Application and Classic Load Balancer in the Choose an end point dropdown menu. In the Choose Region dropdown menu, select the Region in which your ALB is located. In the Choose load balancer dropdown menu, select the existing ALB that you want to receive traffic from this domain. Choose the Create records button once you have finished. g. To allow clients to connect to your ALB with IPv6, enter in your AAAA record information and ensure AAAA is selected in the Record type field. For this guide, we will enter www for Record name. Turn on Alias. Select Alias to Application and Classic Load Balancer in the Choose an end point dropdown menu. In the Choose Region dropdown menu, select the region in which your ALB is located. In the Choose load balancer dropdown menu, select the existing ALB that you want to receive traffic from this domain. Choose the Create records button once you have finished. In this guide, you will register a new domain name for your web application. You will then connect that domain name through the Domain Name System (DNS) to a frontend or backend application. There's an annual fee to register a domain. The fee can range from $3 to several hundred dollars depending on the top-level domain. This fee is not refundable. See the Amazon Route 53 domain pricing documentation for full details. Amazon Route 53 domain pricing Amazon Route 53 domain pricing When you register a domain, we automatically create a hosted zone that has the same name as the domain. You use the hosted zone to specify where you want Amazon Route 53 to route traffic for your domain. The fee for a hosted zone is $0.50 per month. You can delete the hosted zone if you want to avoid this charge. See the Amazon Route 53 Hosted Zone pricing documentation for full details. Amazon Route 53 Hosted Zone pricing Amazon Route 53 Hosted Zone pricing Step 1: Register a domain name a. Open a browser and navigate to the AWS Management Console . AWS Management Console AWS Management Console Once you have logged in, ensure you have selected your desired Region in the upper right hand corner based on your infrastructure requirements. Once you have logged in, ensure you have selected your desired Region in the upper right hand corner based on your infrastructure requirements. b. Enter Route 53 in the search bar and select Route 53 to open the service console. Route 53 Route 53 c. Choose the Register domain button. Register domain d. Enter a desired domain name in the text box and choose an extension such as .com in the dropdown list and choose the Check button. Check Choose the Add to cart button next to the domain that you want to purchase. Choose the Continue button at the bottom of the page. If the domain you chose is not available, choose one of the related domain suggestions or try again with a different domain name. Choose the Add to cart button next to the domain that you want to purchase. Add to cart Choose the Continue button at the bottom of the page. Continue If the domain you chose is not available, choose one of the related domain suggestions or try again with a different domain name. e. Enter the registrant contact detail for your domain. Choose the Continue button on the bottom of the page. Choose the Continue button on the bottom of the page. Continue f. Verify your contact details. Choose either Enable or Disable in the auto renewal section. Choose either Enable or Disable in the auto renewal section. Enable Disable Select the check box after reading and agreeing to the terms and conditions. Select the check box after reading and agreeing to the terms and conditions. Choose Complete Order. Choose Complete Order . Complete Order Step 2: Configure DNS a. After domain registration is complete, return to the AWS console. Enter Route 53 in the search bar and select Route 53 to open the service console. Enter Route 53 in the search bar and select Route 53 to open the service console. Route 53 Route 53 b. Choose Hosted zones from the left navigation pane. Hosted zones c. Select the hosted zone with your domain name that Route 53 created for you as part of the domain registration. d. You can now create DNS records for your domain. In this guide, we will create a simple A record type. Choose the Create record button to get started. Choose the Create record button to get started. Create record e. Choose Switch to quick create if you are currently in the wizard view. Switch to quick create f. To allow clients to connect to your ALB using IPv4, enter your A record information and ensure A is selected in the Record type field. A Record type For this guide, we will enter www for Record name. For this guide, we will enter www for Record name. www Record name. Turn on Alias. Turn on Alias . Alias Select Alias to Application and Classic Load Balancer in the Choose an end point dropdown menu. Select Alias to Application and Classic Load Balancer in the Choose an end point dropdown menu. Alias to Application and Classic Load Balancer Choose an end point In the Choose Region dropdown menu, select the Region in which your ALB is located. In the Choose Region dropdown menu, select the Region in which your ALB is located. Choose Region In the Choose load balancer dropdown menu, select the existing ALB that you want to receive traffic from this domain. In the Choose load balancer dropdown menu, select the existing ALB that you want to receive traffic from this domain. Choose load balancer Choose the Create records button once you have finished. Choose the Create records button once you have finished. Create records g. To allow clients to connect to your ALB with IPv6, enter in your AAAA record information and ensure AAAA is selected in the Record type field. For this guide, we will enter www for Record name. Turn on Alias. Turn on Alias . Alias Select Alias to Application and Classic Load Balancer in the Choose an end point dropdown menu. Select Alias to Application and Classic Load Balancer in the Choose an end point dropdown menu. Alias to Application and Classic Load Balancer Choose an end point In the Choose Region dropdown menu, select the region in which your ALB is located. In the Choose Region dropdown menu, select the region in which your ALB is located. Choose Region In the Choose load balancer dropdown menu, select the existing ALB that you want to receive traffic from this domain. In the Choose load balancer dropdown menu, select the existing ALB that you want to receive traffic from this domain. Choose load balancer Choose the Create records button once you have finished. Choose the Create records button once you have finished. Create records

Registering a Domain in AWS Cloud

21 Cranium-Catching Bitcoin Marketing Ideas For Dummies & Savvyies

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Dummies va Savvyies uchun 21 Boshsuyagi-ushlab bitcoin marketing g'oyalar

HackerNoon: What is your company in 2–5 words? Tyler Hochman: AI Workforce Productivity Analytics Why is now the time for your company to exist? AI allows workforce algorithmic scaling at a cost that is finally competitive and reasonable for businesses. What do you love about your team, and why are you the ones to solve this problem? Our company (FORE Enterprise) blends experienced professionals with young and hungry tech talent. Innovative Stanford-educated engineers work alongside professors from Stanford, Wharton, and Kellogg to find the most effective ways to improve employee retention at companies. If you weren’t building your startup, what would you be doing? I would be building another startup. At the moment, how do you measure success? What are your metrics? We measure our success by the amount of workforce retention we create, the dollars we save businesses and the productivity we increase. Our clients’ success is our success. In a few sentences, what do you offer to whom? We offer B2B workforce analytics to operation departments and mergers and acquisitions departments. We use analytics from advanced AI models to identify key trends and issues that lead to employee turnover and then provide advice from an expert advisory council on how to intervene. We’ve developed a range of creative intervention strategies that allow our clients to take steps to combat turnover with solutions tailored to their specific companies and employees. We also factor predicted employee turnover into company valuations. What’s most exciting about your traction to date? We are excited about the amount of revenue we’ve affected for clients. We have impacted billion-dollar deals as well as saved millions for our clients in operational workforce costs. Where do you think your growth will be next year? By next year, our product will be able to seamlessly generate analytics based on data from 100,000 publicly available companies within a two-day timespan. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was a major American manufacturer, and we helped them significantly decrease the cost associated with turnover, as well as evaluate the effectiveness of the interventions for their employees. By the end of this year, we’re targeting a million and a half in revenue and by the end of next year, we hope to be at $10 to 20 million in revenue. What’s your biggest threat? Corporate ignorance. Most companies don’t understand the true cost of turnover, and even when they do, they often waste millions of dollars on ineffective interventions for employees that don’t help them retain their top talent. Many don’t know how to analyze the effectiveness of their programs and improve them to save money. We hope to show them the importance of this analysis. HackerNoon : What is your company in 2–5 words? HackerNoon Tyler Hochman : AI Workforce Productivity Analytics Tyler Hochman Tyler Hochman Why is now the time for your company to exist? AI allows workforce algorithmic scaling at a cost that is finally competitive and reasonable for businesses. What do you love about your team, and why are you the ones to solve this problem? Our company ( FORE Enterprise ) blends experienced professionals with young and hungry tech talent. Innovative Stanford-educated engineers work alongside professors from Stanford, Wharton, and Kellogg to find the most effective ways to improve employee retention at companies. FORE Enterprise If you weren’t building your startup, what would you be doing? I would be building another startup. At the moment, how do you measure success? What are your metrics? We measure our success by the amount of workforce retention we create, the dollars we save businesses and the productivity we increase. Our clients’ success is our success. In a few sentences, what do you offer to whom? We offer B2B workforce analytics to operation departments and mergers and acquisitions departments. We use analytics from advanced AI models to identify key trends and issues that lead to employee turnover and then provide advice from an expert advisory council on how to intervene. We’ve developed a range of creative intervention strategies that allow our clients to take steps to combat turnover with solutions tailored to their specific companies and employees. We also factor predicted employee turnover into company valuations. What’s most exciting about your traction to date? We are excited about the amount of revenue we’ve affected for clients. We have impacted billion-dollar deals as well as saved millions for our clients in operational workforce costs. Where do you think your growth will be next year? By next year, our product will be able to seamlessly generate analytics based on data from 100,000 publicly available companies within a two-day timespan. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was a major American manufacturer, and we helped them significantly decrease the cost associated with turnover, as well as evaluate the effectiveness of the interventions for their employees. By the end of this year, we’re targeting a million and a half in revenue and by the end of next year, we hope to be at $10 to 20 million in revenue. What’s your biggest threat? Corporate ignorance. Most companies don’t understand the true cost of turnover, and even when they do, they often waste millions of dollars on ineffective interventions for employees that don’t help them retain their top talent. Many don’t know how to analyze the effectiveness of their programs and improve them to save money. We hope to show them the importance of this analysis.

"Our clients' success is our success": 10 Questions with FORE Enterprise Founder & CEO

To what extent that fintechs could possibly disrupt the market is still hard to predict. But the lending business is no doubt undergoing a paradigm shift. The old model of commercial banks was the interest rate spreads and fees backed by fairly rigid regulatory and sophisticated licensing regimes. The conventional perception of commercial banking functions consists of expanding deposits on a significant scale and then chasing after well-connected large businesses to extend credit. Hence, private small enterprises are usually underserved, suffering financial challenges at different critical moments of their life cycle. A review of the cost of lending makes such financing gaps sensible: commercial banks reported that a small business client was profitable only if it purchased more than three products. In China, lending cost to one small or medium-sized enterprise was as high as 30 percent of the country’s GDP per capita in 2013–2014. Yes, call centers and Internet banking had already been widely used, but they were often created and promoted by bureaucrats from individual banks. The technologies tend to be closed systems and information was not shared across crucial SD 4 trillion US dollars globally. Today, we have reached the digital tipping point. Cost reduction and data accessibility are playing instrumental roles in constructing a customer-centric model in the banking industry. Fifty years ago, ATMs were perceived as breakthroughs for financial inclusion, and they squeezed the cost of serving a client for physical banks to 21%. The rise of Internet banking two decades ago further cut it to 4%. When fintechs are beginning to shake up the industry, the cost finally falls to only 1~2%. Looking beyond individual digital gadgets, we can also find innovations that integrate different parts of the ecosystem into one single data analytic compass. By constantly tracking behavioral data pertaining to small retailers on its internet-based shopping platform, Alibaba is closely monitoring their transactions, constantly collecting valuable data that could be used for generating customized credit scores and risk models, which no banks were able to achieve ever before. The lending process that was built on this approach has proved reliable and timely, where oftentimes no collateral is required. Per transaction, the cost of extending credit has dropped by 1000 times to only 0.36 USD for Alibaba. It appears that traditional brick-and-mortar banks are facing steep challenges posed by fintechs. So, is this an alert that new technology entrants will ‘cherry pick’ key products and quickly eat into banks’ small business market share? Should banks be transformed into technology companies? In fact, customer management approaches targeting SME borrowers already existed even prior to this tipping point. IFC Customer Management in SME Banking report suggests that customer management is a concept explaining good practices of understanding customer needs, matching them with banks’ offers, and managing long-lasting relationships through customer life-cycle. Experiences in the EU and the US have revealed that effective customer management leads to greater profitability — ROE performance. In a study carried out by Roland Berger, a consultancy, Banks are categorized into different performance groups as in Figure 1. Group 1 employs customer management approaches. By contrast, Group 4 and 5 banks have not yet seen the value of strategies. The findings have shown a clear relationship between customer management and the ROE of different performance groups. Segmentation is one of the SME customer management strategies. And the IFC report features Garanti Bank in Turkey as a good example, which involves deciding which SMEs to target and identifying segments requiring distinct offers. At the Bank, industrial sectors were used primarily to sub-segment the customer base. Gender was also used as a parameter of sub-segmentation to a degree. Then, the Bank identifies the individual needs of each sub-segment through in-depth market research. This, in turn, facilitates the development of a sub-segment ‘proposition’ comprising around 20 product bundles. Garanti Bank has a market share of SMEs that is three times higher than in its other segments and has become the leading SME bank in Turkey. Other than segmentation, the IFC has also pointed out acquisition, cross-selling, retention, and collection as fundamental customer management phases. If, in the past, only a few banks could afford to acquire the full set of capabilities in customer management due to considerably high overheads and walled fortresses of data scattered around the world, then today’s digital innovation can make this approach much more viable and applicable for the majority. From this perspective, the customer-centric model does not require a sharp switch of industries but can actually be achieved within the banking sector itself with the help of innovative technologies. The real question now is: Will banks successfully marry digital technologies with their SME customer management approaches in this new age of ‘creative destruction’? If yes, how? If No, why not? Note: The author is the founder of Stonelake Capital (www.stonelake.capital) and wrote this article as a consultant to the World Bank Group. To what extent that fintechs could possibly disrupt the market is still hard to predict. But the lending business is no doubt undergoing a paradigm shift. The old model of commercial banks was the interest rate spreads and fees backed by fairly rigid regulatory and sophisticated licensing regimes. The conventional perception of commercial banking functions consists of expanding deposits on a significant scale and then chasing after well-connected large businesses to extend credit. Hence, private small enterprises are usually underserved, suffering financial challenges at different critical moments of their life cycle. A review of the cost of lending makes such financing gaps sensible: commercial banks reported that a small business client was profitable only if it purchased more than three products. In China, lending cost to one small or medium-sized enterprise was as high as 30 percent of the country’s GDP per capita in 2013–2014. Yes, call centers and Internet banking had already been widely used, but they were often created and promoted by bureaucrats from individual banks. The technologies tend to be closed systems and information was not shared across crucial SD 4 trillion US dollars globally. Today, we have reached the digital tipping point. Cost reduction and data accessibility are playing instrumental roles in constructing a customer-centric model in the banking industry. Fifty years ago, ATMs were perceived as breakthroughs for financial inclusion, and they squeezed the cost of serving a client for physical banks to 21%. The rise of Internet banking two decades ago further cut it to 4%. When fintechs are beginning to shake up the industry, the cost finally falls to only 1~2%. Looking beyond individual digital gadgets, we can also find innovations that integrate different parts of the ecosystem into one single data analytic compass. By constantly tracking behavioral data pertaining to small retailers on its internet-based shopping platform, Alibaba is closely monitoring their transactions, constantly collecting valuable data that could be used for generating customized credit scores and risk models, which no banks were able to achieve ever before. The lending process that was built on this approach has proved reliable and timely, where oftentimes no collateral is required. Per transaction, the cost of extending credit has dropped by 1000 times to only 0.36 USD for Alibaba. It appears that traditional brick-and-mortar banks are facing steep challenges posed by fintechs. So, is this an alert that new technology entrants will ‘cherry pick’ key products and quickly eat into banks’ small business market share? Should banks be transformed into technology companies? In fact, customer management approaches targeting SME borrowers already existed even prior to this tipping point. IFC Customer Management in SME Banking report suggests that customer management is a concept explaining good practices of understanding customer needs, matching them with banks’ offers, and managing long-lasting relationships through customer life-cycle. Experiences in the EU and the US have revealed that effective customer management leads to greater profitability — ROE performance. In a study carried out by Roland Berger, a consultancy, Banks are categorized into different performance groups as in Figure 1. Group 1 employs customer management approaches. By contrast, Group 4 and 5 banks have not yet seen the value of strategies. The findings have shown a clear relationship between customer management and the ROE of different performance groups. Segmentation is one of the SME customer management strategies. And the IFC report features Garanti Bank in Turkey as a good example, which involves deciding which SMEs to target and identifying segments requiring distinct offers. At the Bank, industrial sectors were used primarily to sub-segment the customer base. Gender was also used as a parameter of sub-segmentation to a degree. Then, the Bank identifies the individual needs of each sub-segment through in-depth market research. This, in turn, facilitates the development of a sub-segment ‘proposition’ comprising around 20 product bundles. Garanti Bank has a market share of SMEs that is three times higher than in its other segments and has become the leading SME bank in Turkey. Other than segmentation, the IFC has also pointed out acquisition, cross-selling, retention, and collection as fundamental customer management phases. If, in the past, only a few banks could afford to acquire the full set of capabilities in customer management due to considerably high overheads and walled fortresses of data scattered around the world, then today’s digital innovation can make this approach much more viable and applicable for the majority. From this perspective, the customer-centric model does not require a sharp switch of industries but can actually be achieved within the banking sector itself with the help of innovative technologies. The real question now is: Will banks successfully marry digital technologies with their SME customer management approaches in this new age of ‘creative destruction’? If yes, how? If No, why not? Note: The author is the founder of Stonelake Capital (www.stonelake.capital) and wrote this article as a consultant to the World Bank Group. Note: The author is the founder of Stonelake Capital (www.stonelake.capital) and wrote this article as a consultant to the World Bank Group. Note: The author is the founder of Stonelake Capital (www.stonelake.capital) and wrote this article as a consultant to the World Bank Group.

Do All Banks Have to Become Fintechs to Better Manage SME Customers?

There are certain times in the history of investing when trends turn into full market swings, and it’s hard to find a better example than Environmental, Social, and Governance investing strategies. The focus for ESG investing was always on sustainability, corporate responsibility, and ethical governance, and it’s really reshaped how both individuals and institutional investors have allocated capital in the last decade. In recent years, though, ESG investing has been turned on its head. There have been many critics of the strategy, especially on performance metrics, and concerns about “greenwashing” have raised massive doubts about the true impacts of these strategies as well. It’s led to the growing debate: Is ESG investing losing its appeal, or is it evolving into a more mature and transparent market? The Rise of ESG Investing In the 1960s, there was a term for investing that was similar, called Socially Responsible Investing (SRI). This was really the beginning of ESG, but SRI investing mainly avoided certain industries, like tobacco and weapons. Over the decades, SRI evolved into the broader ESG framework, which gained significant traction in the 2000s. After the 2008 financial crisis, it grew even further as investors wanted safer, more responsible investment options. And by the 2010s, ESG transformed into a mainstream investment strategy from the niche offering it started as. The COVID-19 pandemic further accelerated growth in the area, as it highlighted how corporations can be important players in addressing global challenges. Maxim Manturov, head of investment research at Freedom24, explained: “The COVID-19 pandemic has made it clear that corporations play a crucial role in solving global problems. Governments alone cannot solve problems such as disease, climate change, workplace safety, or pay inequality based on gender and race. These problems affect people around the world and require the resources and expertise of corporations to address them. So, what makes a stock a good ESG investment? A good ESG company focuses on supporting environmental programmes, caring for its employees, making a positive contribution to local communities and delivering value to shareholders. It is important to validate its reputation through ESG rating agencies such as MSCI and Sustainalytics. Financial performance remains critical to ESG investing. As an ESG investor, you can promote responsible corporate behaviour without sacrificing returns. Institutional investors use ESG ratings to support the high standards of companies, and individual investors can do the same. By investing in leading ESG companies, you support initiatives such as energy efficiency, waste reduction, safer workplaces, and ethical business practices.” Globally, ESG investments are projected to continue with expansion. According to Bloomberg, ESG assets are expected to “reach over $40 trillion by 2030 – over 25% of” global assets under management. Europe is expected to be the leader of this sector with about $18 trillion in ESG assets, while the US may have slower growth due to political and economic challenges. Challenges Facing ESG Investing Recent years, however, haven’t been so rosy in the ESG investing space. The biggest challenge has been the issue of ‘greenwashing’, where companies falsely claim or exaggerate their environmental and social credentials to attract those ESG-conscious dollars. Many calls have been made to increase regulatory scrutiny and also standardisation of ESG reporting frameworks so there’s more transparency and accountability. According to Bloomberg Intelligence, this is exactly what will be needed to give more credibility to ESG investments and should help address some concerns about false claims and inconsistent ESG practices. The next issue for ESG investing is financial performance. A report from the Financial Times noted that ESG ETFs have underperformed their traditional counterparts over the past decade annually by 0.2 percentage points. Much was made about the outperformance in 2020 - often cited as ESG’s financial success, but it was largely a statistical outlier driven by sector biases, as ESG tends to be overweight in technology and underweight in energy, which drove much of the year’s success. Investor sentiment has since been mixed. Some remain committed to ESG for ethical reasons, while others question whether these funds are really delivering long-term returns. Is ESG Dead or Evolving? Despite the challenges facing ESG investing, it is likely evolving rather than dying as it enters a phase of slower growth and consolidation. It’s natural for any growth area or investing. As the market starts to mature, it needs better regulation and transparency. Once it has that and restores investor confidence, it may continue to grow. The core principles of sustainability, corporate responsibility, and governance aren’t going anywhere, especially in this transparent and data-driven digital age. However, to regain credibility, better analysis and accountability are needed. As for performance, there is likely a reason that ESG strategies are failing to outperform, according to Scientific Beta. They found that many ESG strategies rely on superficial ratings rather than the deep, fundamental analysis of companies - reinforcing the need for better ESG strategies that go beyond simple criteria. Evolution can lead to better outcomes for everyone in this scenario. Conclusion To bring it all together, ESG has had significant growth, but now, in its mature stage, there are many challenges. None of which are bigger than greenwashing and underperformance of assets. That being said, ESG is likely not dead - it is evolving. Long-term growth projections, alongside increased regulatory scrutiny, suggest that ESG investing should remain a big part of financial markets in the future. Investors, either ones that want to invest online or offline, should stay informed, understand the limitations of ESG strategies, and be mindful of greenwashing risks. There are certain times in the history of investing when trends turn into full market swings, and it’s hard to find a better example than Environmental, Social, and Governance investing strategies. The focus for ESG investing was always on sustainability, corporate responsibility, and ethical governance, and it’s really reshaped how both individuals and institutional investors have allocated capital in the last decade. In recent years, though, ESG investing has been turned on its head. There have been many critics of the strategy, especially on performance metrics, and concerns about “greenwashing” have raised massive doubts about the true impacts of these strategies as well. It’s led to the growing debate: Is ESG investing losing its appeal, or is it evolving into a more mature and transparent market? The Rise of ESG Investing In the 1960s, there was a term for investing that was similar, called Socially Responsible Investing (SRI). This was really the beginning of ESG, but SRI investing mainly avoided certain industries, like tobacco and weapons. Over the decades, SRI evolved into the broader ESG framework, which gained significant traction in the 2000s. After the 2008 financial crisis, it grew even further as investors wanted safer, more responsible investment options. And by the 2010s, ESG transformed into a mainstream investment strategy from the niche offering it started as. The COVID-19 pandemic further accelerated growth in the area, as it highlighted how corporations can be important players in addressing global challenges. Maxim Manturov, head of investment research at Freedom24, explained: “The COVID-19 pandemic has made it clear that corporations play a crucial role in solving global problems. Governments alone cannot solve problems such as disease, climate change, workplace safety, or pay inequality based on gender and race. These problems affect people around the world and require the resources and expertise of corporations to address them. So, what makes a stock a good ESG investment? A good ESG company focuses on supporting environmental programmes, caring for its employees, making a positive contribution to local communities and delivering value to shareholders. It is important to validate its reputation through ESG rating agencies such as MSCI and Sustainalytics. Financial performance remains critical to ESG investing. As an ESG investor, you can promote responsible corporate behaviour without sacrificing returns. Institutional investors use ESG ratings to support the high standards of companies, and individual investors can do the same. By investing in leading ESG companies, you support initiatives such as energy efficiency, waste reduction, safer workplaces, and ethical business practices.” Globally, ESG investments are projected to continue with expansion. According to Bloomberg , ESG assets are expected to “reach over $40 trillion by 2030 – over 25% of” global assets under management. Europe is expected to be the leader of this sector with about $18 trillion in ESG assets, while the US may have slower growth due to political and economic challenges. Bloomberg Bloomberg Challenges Facing ESG Investing Recent years, however, haven’t been so rosy in the ESG investing space. The biggest challenge has been the issue of ‘ greenwashing ’, where companies falsely claim or exaggerate their environmental and social credentials to attract those ESG-conscious dollars. Many calls have been made to increase regulatory scrutiny and also standardisation of ESG reporting frameworks so there’s more transparency and accountability. According to Bloomberg Intelligence , this is exactly what will be needed to give more credibility to ESG investments and should help address some concerns about false claims and inconsistent ESG practices. greenwashing greenwashing Bloomberg Intelligence Bloomberg Intelligence The next issue for ESG investing is financial performance. A report from the Financial Times noted that ESG ETFs have underperformed their traditional counterparts over the past decade annually by 0.2 percentage points. Much was made about the outperformance in 2020 - often cited as ESG’s financial success, but it was largely a statistical outlier driven by sector biases, as ESG tends to be overweight in technology and underweight in energy, which drove much of the year’s success. Financial Times Financial Times Investor sentiment has since been mixed. Some remain committed to ESG for ethical reasons, while others question whether these funds are really delivering long-term returns. Is ESG Dead or Evolving? Despite the challenges facing ESG investing, it is likely evolving rather than dying as it enters a phase of slower growth and consolidation. It’s natural for any growth area or investing. As the market starts to mature, it needs better regulation and transparency. Once it has that and restores investor confidence, it may continue to grow. The core principles of sustainability , corporate responsibility, and governance aren’t going anywhere, especially in this transparent and data-driven digital age. However, to regain credibility, better analysis and accountability are needed. sustainability As for performance, there is likely a reason that ESG strategies are failing to outperform, according to Scientific Beta. They found that many ESG strategies rely on superficial ratings rather than the deep, fundamental analysis of companies - reinforcing the need for better ESG strategies that go beyond simple criteria. Evolution can lead to better outcomes for everyone in this scenario. Conclusion To bring it all together, ESG has had significant growth, but now, in its mature stage, there are many challenges. None of which are bigger than greenwashing and underperformance of assets. That being said, ESG is likely not dead - it is evolving. Long-term growth projections, alongside increased regulatory scrutiny, suggest that ESG investing should remain a big part of financial markets in the future. Investors, either ones that want to invest online or offline, should stay informed, understand the limitations of ESG strategies, and be mindful of greenwashing risks.

Is ESG Investing Dead? The Rise and Fall of Sustainable Investing

¿Ha muerto la inversión ESG? El auge y la caída de la inversión sostenible

Onko ESG Investing kuollut? Kestävän sijoittamisen nousu ja lasku

האם ESG Investing מת? עלייתה ונפילתה של השקעות בר קיימא

ESG投資は終わったのか？持続可能な投資の興亡

Maty ve ny ESG Investing? Ny fiakarana sy ny fianjeran'ny fampiasam-bola maharitra

ESG Investing үхсэн үү? Тогтвортой хөрөнгө оруулалтын өсөлт ба уналт

Investițiile ESG sunt moarte? Creșterea și scăderea investițiilor durabile

ESG ኢንቨስትመንት ሞይቱ ድዩ? ምልዓልን ምውዳቕን ዘላቒ ወፍሪ

Patay na ba ang ESG Investing? Ang Pagtaas at Pagbagsak ng Sustainable Investing

Xana ESG Ku Vekisa Swi file? Ku Tlakuka na ku Wa ka Vuvekisi lebyi nga heriki

کیا ESG سرمایہ کاری ختم ہو گئی ہے؟ پائیدار سرمایہ کاری کا عروج اور زوال

ESG 投资已死？可持续投资的兴衰

Donald Trump, $80k Bitcoin and the Strategic Bitcoin Reserve

The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion.” India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa, according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre. Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform, which is able to simulate global weather patterns in great detail. Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The "Scalable Ensemble Envelope Diffusion Sampler" (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances. In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” “Humans are a lot better at that than AI is right now,” she said. The massive, catastrophic flash flood that occurred in Spain on October 29 was so horrific and devastating that it left the world aghast at its destructive nature. Over 200 people died, dozens went missing, and whole towns were covered in mud and debris, especially in the eastern Valencia region. In the United States alone, in 2023, 25 weather or climate disasters [caused at least $1 billion in losses and the [deaths](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) of 482 people, according to the National Centers for Environmental Information (NCEI). According to the US Government Accountability Office (GAO), “Severe storms, hurricanes, floods, and wildfires kill hundreds of people and cause billions of dollars in damage in the U.S. each year.” kill hundreds of people In fact, [according](https://www.ncei.noaa.gov/access/billions/#:~:text=In%202023%20(as%20of%20December,and%201%20winter%20storm%20event) to the National Oceanic and Atmospheric Administration (NOAA), “the U.S. has sustained 400 weather and climate disasters since 1980 where overall damages/costs reached or exceeded $1 billion (including CPI adjustment to 2024). The total cost of these 400 events exceeds $2.785 trillion .” The total cost of these 400 events exceeds $2.785 trillion India suffers from a similar problem. The frequency of flooding is getting worse due to the rapid growth of cities and climate change. The frequency of flooding is getting worse Numerous other countries, especially in Africa, have seen similar catastrophes caused by unprecedented flooding. Widespread flooding in West and Central Africa , according to the World Health Organization, “has affected more than 4 million people, caused extensive damage to homes, schools, health facilities and swaths of farmland, heightening the risk of water-borne diseases, food insecurity and malnutrition.” Widespread flooding in West and Central Africa Human-induced climate change “is likely to have significantly worsened the situation”, according to scientists at the Red Cross Climate Centre . Red Cross Climate Centre Many experts now agree that with the advancement of artificial intelligence, this technology can and should play a bigger role in predicting those events and saving lives. And Northeastern University faculty experts predict it will. In an interview last year, Auroop R. Ganguly, professor of civil and environmental engineering and director of AI4CaS (AI for Climate and Sustainability) said, “In the next 12 months, we are going to see more and more efforts where data-driven systems and artificial intelligence come together.” One example is the Earth-2 platform , which is able to simulate global weather patterns in great detail. Earth-2 platform Taiwan is already using a similar tool to precisely predict the path and impact of typhoons. AI4CaS serves as a focus area within Northeastern University’s Institute for Experiential AI. The GAO found that AI weather forecasting models can help predict these catastrophic weather events. Machine learning specifically “can help improve model accuracy, increase warning times, and reduce uncertainty” and while some of these types of models are currently in use, “there are challenges to developing and adopting them more widely.” Google recently released an AI model that it claims can generate accurate weather forecasts at scale — while being cheaper than conventional physics-based forecasting. The " Scalable Ensemble Envelope Diffusion Sampler " (SEEDS) model is designed similarly to popular large language models (LLMs) like ChatGPT. Scalable Ensemble Envelope Diffusion Sampler SEEDS generates many ensembles — or multiple weather scenarios — much quicker and cheaper than traditional predicting models can. The team described its findings in a paper published March 29 in the journal Science Advances . Science Advances In yet another effort to get ahead of life-threatening weather patterns, the University of Illinois and IBM have joined forces to work together on an AI model capable of anticipating heavy rainfall and flash floods in the Appalachian Mountains. joined forces Such models can triple or quadruple the warning time currently available to meteorologists and scientists. In August, NVIDIA Research, announced that it has developed a new AI model for predicting extreme weather events, called StormCast, part of a larger effort by NVIDIA to improve climate research through AI. announced These new AI models are financially beneficial since they can process information far quicker than older models. As extreme weather events, like flooding, become more frequent and intense, it is becoming increasingly important to be able to predict, understand, and prepare for these events. AI models will assist in predicting weather catastrophes and will help reduce not just major damage, but will also save thousands of lives. Such AI technologies will help scientists make faster and more accurate predictions, which can ultimately save lives and reduce the cost of disasters. In September, Smithsonian Magazine interviewed computer scientist and meteorologist Amy McGovern. She said that while AI will not replace human meteorologists in the near future, “We’re developing things to give them more options so that they can focus on turning the forecasts into actionable information for people to make better decisions.” Smithsonian Magazine interviewed “Humans are a lot better at that than AI is right now,” she said.

Will AI Ever Be Able to Predict Disasters and Save Lives?

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