Our team is working to simplify the way organizations manage document workflows

Complicore

Here we are in the middle of the AI revolution and I keep hearing the same thing: ‘AI content is bad for SEO’ - ‘Google won’t rank your content if it’s AI generated’-  ‘No-one wants to read AI generated content as it’s too generic’. Lets’s get one thing out of the way here. I’m an SEO professional and a content manager for hire, and I’m a HUGE fan of AI content. And, yes, I have been using AI generated content to create a wide range of articles across a several industries. So what have my organic results been like? Before we get onto that, I want to look at a few other things. The rise of low quality content Low quality content is widespread. In fact, there is a booming market in low quality content which probably contributes billions to the global economy. I’m talking specifically about the world of link building, guest posts and thin or re-copied content that is literally everywhere. As someone who runs a portfolio of general content magazines and blogs, I see these ‘guest posts’ every day. By and large the majority of these posts are created specifically to provide a backlink to a business website and serve little value beyond that. They usually just need to hit a specific word count, maybe have a keyword focus and hey presto, here’s your shit content. I wrote about the broader before, which contributes to other issues such as . But the other problem is that we just have a huge market for utterly hollow, badly written and generally low quality content. problems of the link building industry ad fraud Some of the sites that host this type of content are actually huge too. I’m looking at sites like ScoopEarth dot com, or TimeBusinessNews dot com. Both of these are examples of the massive rise of low quality content platforms - which begs the question - if these sites can rank content with garbage content, why shouldn’t AI be any different? Creating quality content with AI This brings me to using AI to create content. On a daily basis I create probably 5 different types of content, from scratch. High quality blog content for my clients High quality blog content for my own websites Social media content for clients and my own sites Low grade content for guest posts and backlinks Video scripts and other broader application content I use a suite of premium tools to do this. While I do love Chat GPT, I also use Copy.ai to create the bulk of my content. There are several reasons for this, which are basically that Copy AI allows you to summarise live content from the internet, add your own prompt templates and store things like brand voice. Take a of my full Copy AI review. read But generating my AI content isn’t the end of it. I then run it through Surfer SEO to ensure we’re hitting those SEO tick boxes, and usually add a human element to it as well. So how am I doing? What are the results of this human edited and optimized AI generated content? Actually, a lot of it is ranking. That might be because I’m an SEO pro, so I identify my search terms and structure the content in such a way that it is more than just machine generated fluff. One of my clients has even seen a consistent growth in traffic and search rankings as a result of my AI generated content campaign. Building a site with AI generated content But wait… I have also been creating AI content for a news magazine site I run, with very little additional editing. And guess how thats been going? Yup. This site has pretty much basic AI generated content that has been mostly untouched. I started it about a year ago (ish) and with only minimal time, it’s gone from zero to pretty much 1000 monthly views. That might not be a prolific amount of traffic, but it’s pretty solid for a site which only receives around 4 or 5 AI generated blogs per month. The trick has been, again, to focus on specific search terms and then create a good prompt for my AI tool. And because I’m an SEO pro I know about things like good headers, alt-tags and getting the all-important backlinks (from those sh*tty sites like ScoopEarth). What’s the lesson here? AI content does rank, and AI content is most definitely NOT bad for SEO. But the caveat is: You have to do it properly. Crappy content vs AI content: Is it the same thing? Perhaps you’re asking, ‘but doesn’t this rise in AI generated content just mean more spun content?’ In short, does more AI content mean more crud in the swamp of low quality search results? One result of all this AI generated content for the search results is that there is going to be more of it on page one of Google. From a user perspective does this mean that content is of lower quality than before the rise of AI tools? Actually, I’m going to say that AI content is currently producing better original content than some of the writers I’ve worked with in the past. Example: I hired a writer a year or so back to put together some lifestyle related guides for a new site. His content was pretty much the same thing you’d have found on any other site. Basically re-hashed content. A year later, I used Copy AI to come up with a bunch of content ideas based on a similar theme. Some good suggestions resulted in me using Copy AI to write the articles. And now? Those articles are actually some of my best ranking content and contain a mixture of useful original information, actionable elements and are even less full of typos and weird formatting errors than my content writer written blogs. In fact I think that AI content writers, run by people who know what they’re doing, are reducing the amount of crap out there. In Summary: Is AI content bad for SEO? While AI content might be used to generate a huge amount of generic content, which could potentially dilute the SERPs more than usual, the actual performance of SEO is, from my experience, excellent. AI content is less prone to being generic and rubbish than a cheap content writer. And with a bit of professional expertise, SEO content can actually perform really well for your site. Even relatively un-edited content can perform well if it’s crafted with the right audience in mind.

Is AI Content Bad for SEO? It's Complicated..

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn;
(2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com;
(3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn;
(4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn;
(5) Fei Wu, Zhejiang University & wufei@zju.edu.cn;
(6) Jiwei Li, Zhejiang University &  jiwei_li@zju.edu.cn;
(7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg;
(8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. Table of Links Abstract and Introduction
Related Work
LLM Negotiation for Sentiment Analysis
Experiments
Ablation Studies
Conclusion and References 2 Related Work 2.1 Sentiment Analysis Sentiment analysis (Pang and Lee, 2008; Go et al., 2009; Maas et al., 2011a; Zhang and Liu, 2012; Baccianella et al., 2010; Medhat et al., 2014; Bakshi et al., 2016; Zhang et al., 2018) is a task that aims to determine the overall sentiment polarity (e.g., positive, negative, neutral) of a given text. Earlier work often formalized the task as a two-step problem: (1) extract features using RNNs (Socher et al., 2013; Qian et al., 2016; Peled and Reichart, 2017; Wang et al., 2016b; Guggilla et al., 2016; Vo and Zhang, 2015), CNNs (Kalchbrenner et al., 2014; Wang et al., 2016a; Guan et al., 2016; Yu and Jiang, 2016; Mishra et al., 2017), pretrained language models (Lin et al., 2021; Sun et al., 2021; Phan and Ogunbona, 2020; Dai et al., 2021), etc; and (2) feed extracted features into a classifier for obtaining a pre-defined sentimental label. In recent years, in-context learning (ICL) has achieved great success and changed the paradigm of NLP tasks. Many works adapt ICL to the sentiment analysis task: Qin et al. (2023b); Sun et al.(2023a) propose a series of strategies to improve ChatGPT’s performance on the sentiment analysis task; Fei et al. (2023) propose a threehop reasoning framework, which induces the implicit aspect, opinion, and finally the sentiment polarity for the implicit sentiment analysis task;ƒ Zhang et al. (2023d) find that LLMs can achieve satisfactory performance on the binary sentiment classification task, but they underperform to the supervised baseline on more complex tasks (e.g., fine-grained sentiment analysis) that require deeper understanding or structured sentiment information. 2.2 Large Language Models and In-context Learning Large language models (LLMs) (Wang et al., 2022a; Zhang et al., 2023b) are models trained on massive unlabeled text corpora with self supervised learning techniques. Based on the model architecture, LLMs can be categorized into three types: (1) encoder-only models, which contain a text encoder and generate the input representations, such as BERT (Devlin et al., 2018) and its variants (Lan et al., 2019; Liu et al., 2019; Sun et al., 2020; Clark et al., 2020; Feng et al., 2020; Joshi et al., 2020; Sun et al., 2020, 2021); (2) decoder-only models, which have a decoder and generate text conditioned on the input text like GPT-series models (Radford et al., 2019; Brown et al., 2020; Keskar et al., 2019; Radford et al., 2019; Chowdhery et al., 2022; Ouyang et al., 2022; Zhang et al., 2022a; Scao et al., 2022; Zeng et al., 2022b; Touvron et al., 2023a; Peng et al., 2023; OpenAI, 2023); and (3) encoder-decoder models, which have a pair of encoder-decoder and generate text conditioned on the input representation, such as T5 (Raffel et al., 2020) and its variants (Lewis et al., 2019; Xue et al., 2020). Starting with GPT-3 (Brown et al., 2020), LLMs have shown emerging capabilities (Wei et al., 2022a) and completed NLP tasks through incontext learning (ICL), where LLMs generate labelintensive text conditioned on a few annotated examples without gradient updates. Many studies in the literature propose strategies for improving ICL performances on NLP tasks. Li and Liang (2021); Chevalier et al. (2023); Mu et al. (2023) optimize prompts in the continuous space. Liu et al. (2021a); Wan et al. (2023); Zhang et al. (2023a) search through the train set to retrieve k nearest neighbors of a test input as demonstrations. Zhang et al. (2022b); Sun et al. (2023b); Yao et al (2023) decompose a task into a few subtasks and solve them step-by-step towards the final answer conditioned on LLM-generated reasoning chains. Sun et al. (2023a); Wang et al. (2023) propose to verify LLMs’ results by conducting a new round of prompting; Liu et al. (2021b); Feng et al. (2023) use LLMs to generate natural language knowledge statements and integrate external knowledge statements into prompts. 2.3 The LLM collaboration The LLM collaboration involves multiple LLMs working together to solve a given task. Specifically, the task is decomposed to several intermediate tasks, and each LLM is assigned to complete one intermediate task independently. The given task is solved after integrating or summarizing these intermediate results. The LLM collaboration approach can exploit the capabilities of LLMs, improve performances on complex tasks and enable to build complicated systems. Shinn et al. (2023); Sun et al. (2023a); Gero et al. (2023); Wang and Li (2023); Chen et al. (2023b) construct auxiliary tasks (e.g., reflection, verification tasks) and revise the response to the original task referring to the result of the auxiliary task. Talebirad and Nadiri (2023); Hong et al. (2023); Qian et al. (2023) assign characterize profiles (e.g., project manager, software engineer) to LLMs and gain performance boosts on character-specific tasks through behavior animations. Li et al. (2022); Zeng et al. (2022a); Chen et al. (2023a); Du et al. (2023); Liang et al. (2023) use a debate strategy in which multiple different LLMs propose their own responses to the given task and debate over multiple turns until getting a common final answer. Besides, Shen et al. (2023); Gao et al. (2023); Ge et al. (2023); Zhang et al. (2023c); Hao et al. (2023) employone LLM as the task controller, which devises a  plan for the given task, selects expert models for implementation and summarizes the responses of intermediate planned tasks. Other LLMs serve as task executors, completing intermediate tasks in their areas of expertise. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University &  jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University &  jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. Table of Links Abstract and Introduction Related Work LLM Negotiation for Sentiment Analysis Experiments Ablation Studies Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work LLM Negotiation for Sentiment Analysis LLM Negotiation for Sentiment Analysis Experiments Experiments Ablation Studies Ablation Studies Conclusion and References Conclusion and References 2 Related Work 2.1 Sentiment Analysis Sentiment analysis (Pang and Lee, 2008; Go et al., 2009; Maas et al., 2011a; Zhang and Liu, 2012; Baccianella et al., 2010; Medhat et al., 2014; Bakshi et al., 2016; Zhang et al., 2018) is a task that aims to determine the overall sentiment polarity (e.g., positive, negative, neutral) of a given text. Earlier work often formalized the task as a two-step problem: (1) extract features using RNNs (Socher et al., 2013; Qian et al., 2016; Peled and Reichart, 2017; Wang et al., 2016b; Guggilla et al., 2016; Vo and Zhang, 2015), CNNs (Kalchbrenner et al., 2014; Wang et al., 2016a; Guan et al., 2016; Yu and Jiang, 2016; Mishra et al., 2017), pretrained language models (Lin et al., 2021; Sun et al., 2021; Phan and Ogunbona, 2020; Dai et al., 2021), etc; and (2) feed extracted features into a classifier for obtaining a pre-defined sentimental label. In recent years, in-context learning (ICL) has achieved great success and changed the paradigm of NLP tasks. Many works adapt ICL to the sentiment analysis task: Qin et al. (2023b); Sun et al.(2023a) propose a series of strategies to improve ChatGPT’s performance on the sentiment analysis task; Fei et al. (2023) propose a threehop reasoning framework, which induces the implicit aspect, opinion, and finally the sentiment polarity for the implicit sentiment analysis task;ƒ Zhang et al. (2023d) find that LLMs can achieve satisfactory performance on the binary sentiment classification task, but they underperform to the supervised baseline on more complex tasks (e.g., fine-grained sentiment analysis) that require deeper understanding or structured sentiment information. 2.2 Large Language Models and In-context Learning Large language models (LLMs) (Wang et al., 2022a; Zhang et al., 2023b) are models trained on massive unlabeled text corpora with self supervised learning techniques. Based on the model architecture, LLMs can be categorized into three types: (1) encoder-only models, which contain a text encoder and generate the input representations, such as BERT (Devlin et al., 2018) and its variants (Lan et al., 2019; Liu et al., 2019; Sun et al., 2020; Clark et al., 2020; Feng et al., 2020; Joshi et al., 2020; Sun et al., 2020, 2021); (2) decoder-only models, which have a decoder and generate text conditioned on the input text like GPT-series models (Radford et al., 2019; Brown et al., 2020; Keskar et al., 2019; Radford et al., 2019; Chowdhery et al., 2022; Ouyang et al., 2022; Zhang et al., 2022a; Scao et al., 2022; Zeng et al., 2022b; Touvron et al., 2023a; Peng et al., 2023; OpenAI, 2023); and (3) encoder-decoder models, which have a pair of encoder-decoder and generate text conditioned on the input representation, such as T5 (Raffel et al., 2020) and its variants (Lewis et al., 2019; Xue et al., 2020). Starting with GPT-3 (Brown et al., 2020), LLMs have shown emerging capabilities (Wei et al., 2022a) and completed NLP tasks through incontext learning (ICL), where LLMs generate labelintensive text conditioned on a few annotated examples without gradient updates. Many studies in the literature propose strategies for improving ICL performances on NLP tasks. Li and Liang (2021); Chevalier et al. (2023); Mu et al. (2023) optimize prompts in the continuous space. Liu et al. (2021a); Wan et al. (2023); Zhang et al. (2023a) search through the train set to retrieve k nearest neighbors of a test input as demonstrations. Zhang et al. (2022b); Sun et al. (2023b); Yao et al (2023) decompose a task into a few subtasks and solve them step-by-step towards the final answer conditioned on LLM-generated reasoning chains. Sun et al. (2023a); Wang et al. (2023) propose to verify LLMs’ results by conducting a new round of prompting; Liu et al. (2021b); Feng et al. (2023) use LLMs to generate natural language knowledge statements and integrate external knowledge statements into prompts. 2.3 The LLM collaboration The LLM collaboration involves multiple LLMs working together to solve a given task. Specifically, the task is decomposed to several intermediate tasks, and each LLM is assigned to complete one intermediate task independently. The given task is solved after integrating or summarizing these intermediate results. The LLM collaboration approach can exploit the capabilities of LLMs, improve performances on complex tasks and enable to build complicated systems. Shinn et al. (2023); Sun et al. (2023a); Gero et al. (2023); Wang and Li (2023); Chen et al. (2023b) construct auxiliary tasks (e.g., reflection, verification tasks) and revise the response to the original task referring to the result of the auxiliary task. Talebirad and Nadiri (2023); Hong et al. (2023); Qian et al. (2023) assign characterize profiles (e.g., project manager, software engineer) to LLMs and gain performance boosts on character-specific tasks through behavior animations. Li et al. (2022); Zeng et al. (2022a); Chen et al. (2023a); Du et al. (2023); Liang et al. (2023) use a debate strategy in which multiple different LLMs propose their own responses to the given task and debate over multiple turns until getting a common final answer. Besides, Shen et al. (2023); Gao et al. (2023); Ge et al. (2023); Zhang et al. (2023c); Hao et al. (2023) employone LLM as the task controller, which devises a  plan for the given task, selects expert models for implementation and summarizes the responses of intermediate planned tasks. Other LLMs serve as task executors, completing intermediate tasks in their areas of expertise.

Sentiment Analysis through LLM Negotiations: Related Work

Unlocking the Power of Advanced Data Types in Big Data

The words "venture" and "venture capital investment" are heard more and more often nowadays, but many beginner investors experience fear of uncertainty and doubt when choosing this method of financing. In order to better understand this topic, I asked Aleksandr Belov, co-founder and CEO of the Prosto VC club*.* First of all, it is important to understand that angel investors are very eager to reach out to the top startups investments in which can multiply your invested money many times over. But for a regular angel investor, this is virtually impossible. Especially remotely, it is impossible to reach the best venture deals that take place, for example, in Silicon Valley - there are thousands of funds there, claiming your place in the startup. Therefore, the only working business model for getting into such deals is the model of community and co-investment with those who already know how to get into such deals. Why do people go into venture capital? And what is it, anyway? Venture means nothing less than investment. It’s neither philanthropy, nor sponsorship of startups and not just the financing of some technologies. In abstract terms, venture is a field of financial engineering. Simply speaking, it is one of the ways to multiply your own capital. Not to save, but to multiply, because venture capital is a high-risk tool, so it is much less suitable for saving than classical banking and stock exchange options, which means that people come to venture capital to earn money. It is important for both beginner investors and startups to understand this - a venture capitalist is the same as an entrepreneur who is looking for a way to make a super profit, but in a more passive way. That is, if a start-up founder's contribution consists of 90% of his time and 10% of his personal funds, an investor, on the contrary, wants to devote 10% of his time, but is ready to compensate 90% of his "involvement" with money. This is what the investor's partnership with the startup is based on. In Kiyosaki's Cash Flow Quadrant, this type of employment is called "Investor". It turns out that those who aim at this part of the quadrant go into venture capital. They can be former entrepreneurs, top managers, celebrities who have the initial capital to make the first steps towards this role. Venture is, obviously, not quick, you have to wait several years for your share in the startup to grow in monetary terms. And during these years you have to build a whole portfolio of projects, i.e. invest in other startups to reduce the risk of losses and increase the chances that several portfolio projects will shoot up and bring super profits - 10, 100, in rare cases 1000 times more than you invested in them. This is why people come to venture capital when there is no longer a goal of making money "here and now". Why do they go into venture capital - from a purely practical point of view, to be able to earn much more than with any other tool. In real estate the average income yield is 8-10% per annum, the average annual yield of the US stock market is 15%,the investments into metals in some cases can reach 20%. In venture capital it is historically 30-40%. But it is important to realize that not every startup will show such profitability, and the portfolio as a whole - you should be prepared that out of 10 startups 5 will pass away, a few more will bring nothing, and only 1-2 will show such growth that the earnings from them will cover the losses from all the others. To sum it up - people go into venture capital with the aim to improve their standard of living; to satisfy their own motivation to provide for the future, for their tom orrow better due to strategically higher profitability than can be obtained in classical financial instruments. Is venture capital complicated, is it knowledge available only to a few? After talking to the founders of the Venture Club, I can say that, in fact, no - this opinion about venture capital is not accurate - it comes from ignorance, from the lack of completeness of information. Like when you first go to the gym and you think that losing weight and gaining muscle is some kind of rocket science. Or like when we all thought that about math in first grade. But as you dive in, the subject gets easier. Venturing is an extension of entrepreneurial or management experience. Decision making is very similar, the same questions about market, team, strategy are relevant as in any other non-venture project. Of course, there are nuances - for example, in venture capital you have to be a little more immersed in technological trends; you have to do market analytics a little differently. But there is already a lot of information on how to do this. The main rule of thumb is that if you start today, in six months you will have a good understanding of venture capital and an idea of what kind of portfolio you want to have. If you don't start - you will be six months off your own startup earning schedule. Do venture capitalists need large amounts of capital? It's no secret that previously an investor's check to a startup was several tens or hundreds of thousands of dollars. Even 5 years ago, the minimum check was $25,000-30,000. But the industry is evolving. The business model of syndicates and venture clubs has emerged - where several investors can throw together smaller checks, and eventually score one substantial one. There are already deals where the minimum check is $10,000, and there are even deals where the minimum check is $5,000. But at the same time, all club members together can raise up to $500,000, which is even more than the checks of many funds at the early stage of a startup's development. In this case, the investor has no commitments, they independently decide whether to join the deal and in what amount. To do this, the club provides all the information required to make the decision. In addition, there are online platforms where you can invest as little as $1,000 in your favorite startup according to the same principle. All this makes it possible, even with a relatively small amount of capital allocated for high-risk venture investments, to assemble a balanced portfolio of projects in different industries, in different markets and with different levels of risk. Moreover, Aleksandr told me that even those club members who have large venture budgets should not be in a hurry to put $100,000 into each project. From the point of view of statistics and mathematical expectations, it is better to distribute these $100,000 into a dozen of projects. Strategically, this will give a better return on the entire portfolio. And if you see that some startup is rapidly taking off, you will have the opportunity to put a bigger check into the startup at the next round, thus greatly reducing risks by investing substantial checks only in the most successful projects in your portfolio. So, small initial checks are a boon for the investor, it is his option to "take a place" in a promising project without risking all his capital at once. So if you are ready to allocate $5,000-10,000 several times a year to your favorite startups, join venture clubs - they give you the opportunity to get a small check into interesting and often exclusive deals that are simply impossible to discover without a club. The words "venture" and "venture capital investment" are heard more and more often nowadays, but many beginner investors experience fear of uncertainty and doubt when choosing this method of financing. The words "venture" and "venture capital investment" are heard more and more often nowadays, but many beginner investors experience fear of uncertainty and doubt when choosing this method of financing. In order to better understand this topic, I asked Aleksandr Belov, co-founder and CEO of the Prosto VC club *.* In order to better understand this topic, I asked Aleksandr Belov, co-founder and CEO of the Aleksandr Belov, co-founder and CEO of the Prosto VC club Prosto VC club First of all, it is important to understand that angel investors are very eager to reach out to the top startups investments in which can multiply your invested money many times over. But for a regular angel investor, this is virtually impossible. Especially remotely, it is impossible to reach the best venture deals that take place, for example, in Silicon Valley - there are thousands of funds there, claiming your place in the startup. Therefore, the only working business model for getting into such deals is the model of community and co-investment with those who already know how to get into such deals. Why do people go into venture capital? And what is it, anyway? Why do people go into venture capital? And what is it, anyway? Venture means nothing less than investment. It’s neither philanthropy, nor sponsorship of startups and not just the financing of some technologies. In abstract terms, venture is a field of financial engineering. Simply speaking, it is one of the ways to multiply your own capital. Not to save, but to multiply, because venture capital is a high-risk tool, so it is much less suitable for saving than classical banking and stock exchange options, which means that people come to venture capital to earn money. It is important for both beginner investors and startups to understand this - a venture capitalist is the same as an entrepreneur who is looking for a way to make a super profit, but in a more passive way. That is, if a start-up founder's contribution consists of 90% of his time and 10% of his personal funds, an investor, on the contrary, wants to devote 10% of his time, but is ready to compensate 90% of his "involvement" with money. This is what the investor's partnership with the startup is based on. In Kiyosaki's Cash Flow Quadrant , this type of employment is called "Investor". It turns out that those who aim at this part of the quadrant go into venture capital. They can be former entrepreneurs, top managers, celebrities who have the initial capital to make the first steps towards this role. Cash Flow Quadrant Venture is, obviously, not quick, you have to wait several years for your share in the startup to grow in monetary terms. And during these years you have to build a whole portfolio of projects, i.e. invest in other startups to reduce the risk of losses and increase the chances that several portfolio projects will shoot up and bring super profits - 10, 100, in rare cases 1000 times more than you invested in them. This is why people come to venture capital when there is no longer a goal of making money "here and now". Why do they go into venture capital - from a purely practical point of view, to be able to earn much more than with any other tool. In real estate the average income yield is 8-10% per annum, the average annual yield of the US stock market is 15%,the investments into metals in some cases can reach 20%. In venture capital it is historically 30-40%. But it is important to realize that not every startup will show such profitability, and the portfolio as a whole - you should be prepared that out of 10 startups 5 will pass away, a few more will bring nothing, and only 1-2 will show such growth that the earnings from them will cover the losses from all the others. Why do they go into venture capital Why do they go into venture capital To sum it up - people go into venture capital with the aim to improve their standard of living; to satisfy their own motivation to provide for the future, for their tom orrow better due to strategically higher profitability than can be obtained in classical financial instruments. Is venture capital complicated, is it knowledge available only to a few? Is venture capital complicated, is it knowledge available only to a few? After talking to the founders of the Venture Club, I can say that, in fact, no - this opinion about venture capital is not accurate - it comes from ignorance, from the lack of completeness of information. Like when you first go to the gym and you think that losing weight and gaining muscle is some kind of rocket science. Or like when we all thought that about math in first grade. But as you dive in, the subject gets easier. Venturing is an extension of entrepreneurial or management experience. Decision making is very similar, the same questions about market, team, strategy are relevant as in any other non-venture project. Of course, there are nuances - for example, in venture capital you have to be a little more immersed in technological trends; you have to do market analytics a little differently. But there is already a lot of information on how to do this. The main rule of thumb is that if you start today, in six months you will have a good understanding of venture capital and an idea of what kind of portfolio you want to have. If you don't start - you will be six months off your own startup earning schedule. Do venture capitalists need large amounts of capital? Do venture capitalists need large amounts of capital? It's no secret that previously an investor's check to a startup was several tens or hundreds of thousands of dollars. Even 5 years ago, the minimum check was $25,000-30,000. But the industry is evolving. The business model of syndicates and venture clubs has emerged - where several investors can throw together smaller checks, and eventually score one substantial one. There are already deals where the minimum check is $10,000, and there are even deals where the minimum check is $5,000. But at the same time, all club members together can raise up to $500,000, which is even more than the checks of many funds at the early stage of a startup's development. In this case, the investor has no commitments, they independently decide whether to join the deal and in what amount. To do this, the club provides all the information required to make the decision. In addition, there are online platforms where you can invest as little as $1,000 in your favorite startup according to the same principle. All this makes it possible, even with a relatively small amount of capital allocated for high-risk venture investments, to assemble a balanced portfolio of projects in different industries, in different markets and with different levels of risk. Moreover, Aleksandr told me that even those club members who have large venture budgets should not be in a hurry to put $100,000 into each project. From the point of view of statistics and mathematical expectations, it is better to distribute these $100,000 into a dozen of projects. Strategically, this will give a better return on the entire portfolio. And if you see that some startup is rapidly taking off, you will have the opportunity to put a bigger check into the startup at the next round, thus greatly reducing risks by investing substantial checks only in the most successful projects in your portfolio. So, small initial checks are a boon for the investor, it is his option to "take a place" in a promising project without risking all his capital at once. So if you are ready to allocate $5,000-10,000 several times a year to your favorite startups, join venture clubs - they give you the opportunity to get a small check into interesting and often exclusive deals that are simply impossible to discover without a club.

Breaking into Venture Capital: Overcoming Obstacles and Embracing Potential 

Cloud computing was once at the forefront of emerging data center technology. But now, it’s the backbone of digital businesses worldwide. “The cloud” is no longer just a corporate buzzword — it powers everything from mobile apps to billion-dollar enterprises. And it’s not done growing yet! Visionary leaders like Obinna Odirionye, CEO and creator of Clouddley, believe it will define the next generation of technologies like artificial intelligence, machine learning, and the Internet of Things. However, cloud computing also has inherent complexities that can hinder innovation. To combat this, Obinna is improving accessibility and simplicity in cloud technologies, making these tools available to more people than ever before. Cloud Computing: Powerful Tech with a High Barrier to Entry Setting up and maintaining cloud infrastructure can be quite a chore. You must have a firm understanding of networking, security protocols, resource optimization, and many other complicated concepts. Even feature-rich cloud platforms like AWS don’t exactly make it easy for a relative beginner to launch a cloud app. This means there’s a steep barrier for small companies and startups that could greatly benefit from cloud computing but lack the resources to hire trained professionals. But this is one area where these businesses can’t afford to fall behind. 85% of organizations are expected to take a cloud-first approach by 2025. 95% of all new digital workloads are predicted to be deployed on cloud-native platforms by then as well. This means even small businesses must leverage cloud technologies if they hope to keep up with the competition. That’s why Obinna aims to democratize the cloud, empowering organizations big and small to use the cloud in simple but powerful ways. Simplifying the Cloud — the Clouddley Way Ever since he was a teenager, Obinna was fascinated with “The Cloud.” As he developed his tech skills and learned the ins and outs of cloud computing, he only came to love it more. Now, he calls the cloud “the heart of the internet.” After starting his career as a DevOps engineer for Deimos, a cloud developer and security operations company, Obinna noticed how complicated cloud platforms have become. He realized just how many talents a developer has to have in order to create a smooth cloud experience, and he also learned how many ways cloud computing can go wrong. But rather than accept it as the status quo, Obinna decided to do something about it. Enter Clouddley. Clouddley is Obinna’s baby, the new kid on the block. It’s a reimagining of the cloud, a simple, cost-effective approach that any organization can use to develop robust cloud apps with a fraction of the complexity. All you have to do is connect Clouddley to your GitHub account, configure your app, and then deploy it. So, instead of having to worry about load balancing, IAM roles, policies, etc., you can let Clouddley handle it all, allowing you to focus on the code and shipping a successful product. The latest iteration of Clouddley includes a product called Triggr, a continuous deployment platform. Like the base Clouddley platform, it’s designed to simplify a developer’s work — by auto-detecting your programming language and framework, deploying to the server/VM of your choice without you having to lift a finger, it dramatically expedites the deployment workflow. As you can see, Clouddley vastly eliminates many of the complexities that come with cloud computing. It makes the process straightforward, allowing you to deploy an app without needing extensive cloud training. It’s the culmination of Obinna’s dream: democratizing the cloud and making the heart of the internet accessible to everyone. A Cloudy Future Never Looked So Bright Despite achieving his goal of simplifying the cloud, Obinna isn’t done. He’s already hard at work with version 2 of Clouddley, which aims to give users even more flexibility in how they deploy their apps. In this version, Triggr users will be able to easily add team members, deploy databases on the fly in seconds, and run LLMs like Llama 2 and Mixtral 8x22b on top of their virtual machines with OpenAI-compatible endpoints provided by Clouddley Triggr to chat with these models. After that, Obinna has plans for a Heroku alternative that vastly simplifies containerized app development. Like the Clouddley platform as a whole, his goal for the project is to reduce the barrier to entry when running a production-managed app. Despite Clouddley’s impressive capabilities, Obinna insists on offering these top-flight solutions at a fraction of what he could realistically sell them for. In his words, “If you can afford a Starbucks coffee, you can use the Clouddley platform.” As cloud computing continues to evolve, leaders like Obinna will be at the forefront. By making cloud development more accessible, Obinna is empowering a more diverse group of developers and entrepreneurs to create and implement innovative cloud-based solutions. He’s paving the way for this next generation of developers — innovators who will enjoy a more inclusive and user-friendly cloud computing environment. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author Cloud computing was once at the forefront of emerging data center technology. But now, it’s the backbone of digital businesses worldwide. “The cloud” is no longer just a corporate buzzword — it powers everything from mobile apps to billion-dollar enterprises. And it’s not done growing yet! Visionary leaders like Obinna Odirionye, CEO and creator of Clouddley, believe it will define the next generation of technologies like artificial intelligence, machine learning, and the Internet of Things. However, cloud computing also has inherent complexities that can hinder innovation. To combat this, Obinna is improving accessibility and simplicity in cloud technologies, making these tools available to more people than ever before. Cloud Computing: Powerful Tech with a High Barrier to Entry Setting up and maintaining cloud infrastructure can be quite a chore. You must have a firm understanding of networking, security protocols, resource optimization, and many other complicated concepts. Even feature-rich cloud platforms like AWS don’t exactly make it easy for a relative beginner to launch a cloud app. This means there’s a steep barrier for small companies and startups that could greatly benefit from cloud computing but lack the resources to hire trained professionals. But this is one area where these businesses can’t afford to fall behind. 85% of organizations are expected to take a cloud-first approach by 2025 . 95% of all new digital workloads are predicted to be deployed on cloud-native platforms by then as well. This means even small businesses must leverage cloud technologies if they hope to keep up with the competition. 85% of organizations are expected to take a cloud-first approach by 2025 85% of organizations are expected to take a cloud-first approach by 2025 That’s why Obinna aims to democratize the cloud, empowering organizations big and small to use the cloud in simple but powerful ways. Simplifying the Cloud — the Clouddley Way Ever since he was a teenager, Obinna was fascinated with “The Cloud.” As he developed his tech skills and learned the ins and outs of cloud computing, he only came to love it more. Now, he calls the cloud “the heart of the internet.” Obinna Obinna After starting his career as a DevOps engineer for Deimos, a cloud developer and security operations company, Obinna noticed how complicated cloud platforms have become. He realized just how many talents a developer has to have in order to create a smooth cloud experience, and he also learned how many ways cloud computing can go wrong. But rather than accept it as the status quo, Obinna decided to do something about it. Enter Clouddley . Clouddley Clouddley Clouddley is Obinna’s baby, the new kid on the block. It’s a reimagining of the cloud, a simple, cost-effective approach that any organization can use to develop robust cloud apps with a fraction of the complexity. All you have to do is connect Clouddley to your GitHub account, configure your app, and then deploy it. So, instead of having to worry about load balancing, IAM roles, policies, etc., you can let Clouddley handle it all, allowing you to focus on the code and shipping a successful product. The latest iteration of Clouddley includes a product called Triggr , a continuous deployment platform. Like the base Clouddley platform, it’s designed to simplify a developer’s work — by auto-detecting your programming language and framework, deploying to the server/VM of your choice without you having to lift a finger, it dramatically expedites the deployment workflow. Triggr Triggr As you can see, Clouddley vastly eliminates many of the complexities that come with cloud computing. It makes the process straightforward, allowing you to deploy an app without needing extensive cloud training. It’s the culmination of Obinna’s dream: democratizing the cloud and making the heart of the internet accessible to everyone. A Cloudy Future Never Looked So Bright A Cloudy Future Never Looked So Bright Despite achieving his goal of simplifying the cloud, Obinna isn’t done. He’s already hard at work with version 2 of Clouddley, which aims to give users even more flexibility in how they deploy their apps. In this version, Triggr users will be able to easily add team members, deploy databases on the fly in seconds, and run LLMs like Llama 2 and Mixtral 8x22b on top of their virtual machines with OpenAI-compatible endpoints provided by Clouddley Triggr to chat with these models. Clouddley Triggr Clouddley Triggr After that, Obinna has plans for a Heroku alternative that vastly simplifies containerized app development. Like the Clouddley platform as a whole, his goal for the project is to reduce the barrier to entry when running a production-managed app. Despite Clouddley’s impressive capabilities, Obinna insists on offering these top-flight solutions at a fraction of what he could realistically sell them for. In his words, “If you can afford a Starbucks coffee, you can use the Clouddley platform.” As cloud computing continues to evolve, leaders like Obinna will be at the forefront. By making cloud development more accessible, Obinna is empowering a more diverse group of developers and entrepreneurs to create and implement innovative cloud-based solutions. He’s paving the way for this next generation of developers — innovators who will enjoy a more inclusive and user-friendly cloud computing environment. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

COMPLICORE

complicore.co

-- employees

Since n.d.

COMPANY RANKING

COMPLICORE

EVERGREEN INDEX #3274

Representing this company? Click here to claim and customize this page!

Complicore WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

COMPLICORE

complicore.co

-- employees

Since n.d.

COMPANY RANKING

COMPLICORE

EVERGREEN INDEX #3274

RELATED COMPANIESRANK

STORIES ABOUT COMPLICORE

Representing this company? Click here to claim and customize this page!

Complicore WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!