Seamless.AI

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Optimizing workflows with AI has become a game-changer for me, especially when it comes to searching for information. For a while, I relied on the free version of Perplexity, but recently, I upgraded to Perplexity Pro. Meanwhile, as a longtime user of ChatGPT, I’ve also been exploring its new ChatGPT Search feature. In this article, I’ll share my experiences with both tools, compare their strengths and weaknesses, and help you decide which one might be the better fit for your needs. Perplexity I’d heard about Perplexity.ai many times, promised myself I’d try it, and eventually did, but it didn’t quite click. It’s hard to fully appreciate a tool until you use it regularly. I forgot about it until I got a test assignment for a job interview in a new field, requiring tons of research. Honestly, I’m not great at googling. But since I frequently use ChatGPT and have gotten pretty good at writing prompts, I thought of giving Perplexity another shot. This was before the release of ChatGPT Search, and at that point, I’d already maxed out what ChatGPT could offer. Perplexity turned out to be a lifesaver. It made long, tedious searches much quicker and was my first truly game-changing experience with AI search. At the time, I used the free version. Compared to browser searches, here’s what I found more convenient in AI search: Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own.


Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off.


Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier.


Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature.


Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. For a while, I continued using the free version, but recently I switched to Pro. The free version uses the GPT-3.5 model, whereas the Pro plan grants access to the latest models: GPT-4 Omni, Claude-3 Sonnet/Opus, and Sonar Large 32k, so all the best features are integrated into your search. Of course, the Pro version offers better, smarter, and faster search. In addition to the features mentioned above, here’s what I found most useful: Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful.


Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. ChatGPT Search This feature was launched in October 2024 and is based on GPT-4o, specifically trained for search. It’s integrated into ChatGPT, allowing you to enable it with a Search button or simply ask ChatGPT to search the web. As I mentioned, I’ve been a long-time user of ChatGPT for several years, so I seamlessly integrated the search feature into my routine. Since free Perplexity uses GPT-3.5, all its features like AI search with no ads, no endless scrolling, and results organized into threads with helpful tips are available here as well. My favorite feature here is its accessibility, offering text-to-speech, and the ability to create queries via audio, and audio dialogs. Choose your fighter Search Quality Perplexity excels at finding information, while ChatGPT Search is better at explaining it. From a user perspective, I wouldn’t say I’ve encountered any critical issues with not finding something. It’s just a matter of different approaches. Perplexity provides more an overview of sources, whereas ChatGPT tends to give more explanation than summary. Generated Answers Without Sources Both tools provide sources for their answers, but sometimes the information might be generated without a reliable link. Even when a link is provided, the relevant information might not actually be there. It’s always better to double-check and read the sources yourself. Based on my experience, ChatGPT is more prone to generating unsupported content than Perplexity, but I’ve encountered this issue in both tools. Search Speed I didn’t notice a significant difference between the two in terms of speed. However, both are slower than a standard browser search. Chrome extensions Both tools offer Chrome extensions, and I tested both. The ChatGPT extension lets you use ChatGPT as your primary search engine, replacing, for example, Google. When you enter something in the search bar, it redirects you to ChatGPT, where your query is processed. Honestly, I wasn’t a fan. It’s slower than Google, and you can feel it. I still prefer a traditional search bar for quick lookups. Perplexity’s Chrome extension works differently. Instead of replacing your search engine, it opens a small window over the current page, allowing you to googling and query Perplexity simultaneously. I really loved this one, because it’s more useful for me. The drawback is that you can only enter one query at a time, and if you want to continue the conversation, it redirects you to a separate Perplexity tab. Unique Features Since both tools use GPT models, they share many great features. Perplexity Pro stands out with its use of multiple models and built-in illustrations. On the other hand, I really appreciate ChatGPT’s accessibility and seamless integration into its apps, which I use for more than just search. Bonus: Hangman game test I saw a funny tweet claiming ChatGPT can’t play Hangman. Turns out, neither can Perplexity Pro, even with all its models. Final Take: Which Works Best for What To start with, I’m a fan of AI search. Usually, it takes longer than quick googling, so here’s how I see it: if I need a super quick answer from the first link, I’ll use a standard browser search. But if I need to find more information or conduct deeper research, AI search is the way to go. It’s the best method to uncover valuable insights more efficiently. As for ChatGPT Search and Perplexity, in my experience, they solve different problems. ChatGPT Search perfectly extends ChatGPT’s capabilities, given that ChatGPT’s knowledge base only goes up to the year 2021. It’s perfect for studying and gaining insights using up-to-date web information. Perplexity, on the other hand, is better for finding something more thoroughly. It takes more from browser search, like incognito pages, and its approach to work with your prompts. It is about searching for information first and explaining second, in contrast to ChatGPT Search. For me: Need to find something? Perplexity.
Need to understand something? ChatGPT + Search. They complement each other perfectly in work. Optimizing workflows with AI has become a game-changer for me, especially when it comes to searching for information. For a while, I relied on the free version of Perplexity , but recently, I upgraded to Perplexity Pro . Meanwhile, as a longtime user of ChatGPT , I’ve also been exploring its new ChatGPT Search feature. In this article, I’ll share my experiences with both tools, compare their strengths and weaknesses, and help you decide which one might be the better fit for your needs. Perplexity Perplexity Pro ChatGPT ChatGPT Search Perplexity I’d heard about Perplexity.ai many times, promised myself I’d try it, and eventually did, but it didn’t quite click. It’s hard to fully appreciate a tool until you use it regularly. I forgot about it until I got a test assignment for a job interview in a new field, requiring tons of research. Honestly, I’m not great at googling. But since I frequently use ChatGPT and have gotten pretty good at writing prompts, I thought of giving Perplexity another shot. This was before the release of ChatGPT Search, and at that point, I’d already maxed out what ChatGPT could offer. Perplexity turned out to be a lifesaver. It made long, tedious searches much quicker and was my first truly game-changing experience with AI search. Perplexity.ai At the time, I used the free version. Compared to browser searches, here’s what I found more convenient in AI search: Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Fewer distractions from ads: Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Threaded searches with context: Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Helpful question suggestions: Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Incognito mode: Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Language flexibility: For a while, I continued using the free version, but recently I switched to Pro . The free version uses the GPT-3.5 model, whereas the Pro plan grants access to the latest models: GPT-4 Omni, Claude-3 Sonnet/Opus, and Sonar Large 32k, so all the best features are integrated into your search. Of course, the Pro version offers better, smarter, and faster search. Pro In addition to the features mentioned above, here’s what I found most useful: In addition to the features mentioned above, here’s what I found most useful: Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Broaden your search with images: Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Files, code, and more input options: ChatGPT Search ChatGPT Search This feature was launched in October 2024 and is based on GPT-4o, specifically trained for search. It’s integrated into ChatGPT , allowing you to enable it with a Search button or simply ask ChatGPT to search the web. ChatGPT As I mentioned, I’ve been a long-time user of ChatGPT for several years, so I seamlessly integrated the search feature into my routine. Since free Perplexity uses GPT-3.5, all its features like AI search with no ads, no endless scrolling, and results organized into threads with helpful tips are available here as well. My favorite feature here is its accessibility, offering text-to-speech, and the ability to create queries via audio, and audio dialogs. Choose your fighter Search Quality Search Quality Search Quality Search Quality Perplexity excels at finding information, while ChatGPT Search is better at explaining it. From a user perspective, I wouldn’t say I’ve encountered any critical issues with not finding something. It’s just a matter of different approaches. Perplexity provides more an overview of sources, whereas ChatGPT tends to give more explanation than summary. overview explanation Generated Answers Without Sources Generated Answers Without Sources Generated Answers Without Sources Generated Answers Without Sources Both tools provide sources for their answers, but sometimes the information might be generated without a reliable link. Even when a link is provided, the relevant information might not actually be there. It’s always better to double-check and read the sources yourself. Based on my experience, ChatGPT is more prone to generating unsupported content than Perplexity, but I’ve encountered this issue in both tools. Search Speed Search Speed Search Speed Search Speed I didn’t notice a significant difference between the two in terms of speed. However, both are slower than a standard browser search. Chrome extensions Chrome extensions Chrome extensions Chrome extensions Both tools offer Chrome extensions, and I tested both. The ChatGPT extension lets you use ChatGPT as your primary search engine, replacing, for example, Google. When you enter something in the search bar, it redirects you to ChatGPT, where your query is processed. Honestly, I wasn’t a fan. It’s slower than Google, and you can feel it. I still prefer a traditional search bar for quick lookups. Perplexity’s Chrome extension works differently. Instead of replacing your search engine, it opens a small window over the current page, allowing you to googling and query Perplexity simultaneously. I really loved this one, because it’s more useful for me. The drawback is that you can only enter one query at a time, and if you want to continue the conversation, it redirects you to a separate Perplexity tab. Unique Features Unique Features Unique Features Unique Features Since both tools use GPT models, they share many great features. Perplexity Pro stands out with its use of multiple models and built-in illustrations. On the other hand, I really appreciate ChatGPT’s accessibility and seamless integration into its apps, which I use for more than just search. Bonus: Hangman game test Bonus: Hangman game test Bonus: Hangman game test Bonus: Hangman game test I saw a funny tweet claiming ChatGPT can’t play Hangman. Turns out, neither can Perplexity Pro, even with all its models. Final Take: Which Works Best for What To start with, I’m a fan of AI search. Usually, it takes longer than quick googling, so here’s how I see it: if I need a super quick answer from the first link, I’ll use a standard browser search. But if I need to find more information or conduct deeper research, AI search is the way to go. It’s the best method to uncover valuable insights more efficiently. As for ChatGPT Search and Perplexity, in my experience, they solve different problems. ChatGPT Search perfectly extends ChatGPT’s capabilities, given that ChatGPT’s knowledge base only goes up to the year 2021. It’s perfect for studying and gaining insights using up-to-date web information. Perplexity, on the other hand, is better for finding something more thoroughly. It takes more from browser search, like incognito pages, and its approach to work with your prompts. It is about searching for information first and explaining second, in contrast to ChatGPT Search. For me: For me: Need to find something? Perplexity. Need to understand something? ChatGPT + Search. Need to find something? Perplexity. Need to find something? Need to understand something? ChatGPT + Search. Need to understand something? They complement each other perfectly in work.

Perplexity Pro vs. ChatGPT Search: Which AI Tool is Best Fits Your Workflow Needs?

Perplexity супраць пошуку ChatGPT: які інструмент AI лепш за ўсё адпавядае вашым патрэбам працоўнага працэсу?

Perplexity срещу ChatGPT Search: Кой AI инструмент отговаря най-добре на вашите нужди от работен процес?

Perplexity vs. Búsqueda en ChatGPT: ¿Qué herramienta de IA se adapta mejor a sus necesidades de flujo de trabajo?

Շփոթմունքն ընդդեմ ChatGPT որոնման. Ո՞ր AI գործիքն է լավագույնս համապատասխանում ձեր աշխատանքային հոսքի կարիքներին:

Perplexity と ChatGPT Search: どちらの AI ツールがワークフローのニーズに最適ですか?

ភាពច្របូកច្របល់ធៀបនឹងការស្វែងរក ChatGPT៖ តើឧបករណ៍ AI មួយណាដែលសាកសមបំផុតនឹងតម្រូវការលំហូរការងាររបស់អ្នក?

Perplexity vs. ChatGPT paieška: kuris AI įrankis geriausiai atitinka jūsų darbo eigos poreikius?

Fikarohana ChatGPT: Inona no fitaovana AI mifanaraka amin'ny filanao?

Збунетост наспроти Пребарување ChatGPT: која алатка за вештачка интелигенција најдобро одговара на вашите потреби на работниот тек?

Urujijo na ChatGPT Gushakisha: Niki Gikoresho cya AI gihuye neza nakazi kawe gakenewe?

குழப்பம் மற்றும் ChatGPT தேடல்: எந்த AI கருவி உங்கள் பணிப்பாய்வு தேவைகளுக்கு மிகவும் பொருத்தமானது?

Perplexity กับ ChatGPT Search: เครื่องมือ AI ตัวใดที่เหมาะกับความต้องการเวิร์กโฟลว์ของคุณที่สุด

In a world moving faster than ever, the demands for intuitive, developer-centric tools are reshaping the landscape. My co-founder, Guy Ben-Aharon, and I launched ChartDB not just as another database tool, but as a leap forward in how we design and interact with data. Within just three days, ChartDB took off on GitHub, amassing over 1,500 stars-an overwhelming response that shows the hunger for accessible database visualization. Star us ⭐ Why ChartDB Stands Out in a Crowded Field Developers need tools that streamline workflow without compromising on power. ChartDB does just that. By delivering an intuitive, user-friendly interface, we’ve made database visualization and management both approachable and powerful. From newcomers to database experts, ChartDB adapts to fit every level of expertise. Our community on Discord is filled with users who’ve shared how ChartDB is transforming their work, and their feedback keeps driving us forward. Built in Three Weeks, Fueled by a Vision ChartDB’s rapid development was no accident. In just three weeks, we transformed an idea into a fully functional tool, coding late nights and early mornings. And while the journey was intense, advancements in AI tools like Code Cursor and Claude 3.5 Sonnet have paved the way for even faster development. Now, we’re in a position to accelerate future builds, equipped with these groundbreaking resources that bring ideas to life at unprecedented speeds. ChartDB’s Future: What’s Next on the Horizon Our journey with ChartDB has just begun. Here’s a glimpse into what’s coming: AI-Driven Features: We’re working on AI integrations that will supercharge ChartDB’s capabilities, making database management smarter and more intuitive.
Community-Powered Growth: We’re committed to keeping open lines of communication with our community. Your input will shape ChartDB’s evolution as we continue adding features that you want.
Expanding Functionality: User suggestions are at the core of our roadmap. We’re excited to bring new features that make database design and visualization seamless. Join the Movement 🚀 If you haven’t tried ChartDB yet, now’s the time. Explore the demo, star us on GitHub, and let us know what you think. The support we’ve received fuels our ambition to innovate and set a new standard in database visualization. In our next post, we’ll share insights from our Hacker News launch, our strategies, and what we learned through the experience. Join us as we continue pushing boundaries in database technology. Shaping the Future of Development Together AI and automation aren’t just trends; they’re transforming how we approach development. Whether you’re a seasoned developer or just starting out, the possibilities are endless. Drop a comment below with your thoughts or feedback, or just say hello. Together, let’s build something incredible. Feel free to share your thoughts, feedback, or just say hello in the comments below. Let’s connect and build something amazing. In a world moving faster than ever, the demands for intuitive, developer-centric tools are reshaping the landscape. My co-founder, Guy Ben-Aharon, and I launched ChartDB not just as another database tool, but as a leap forward in how we design and interact with data. Within just three days, ChartDB took off on GitHub, amassing over 1,500 stars-an overwhelming response that shows the hunger for accessible database visualization. ChartDB Star us ⭐ Star us ⭐ Why ChartDB Stands Out in a Crowded Field Developers need tools that streamline workflow without compromising on power. ChartDB does just that. By delivering an intuitive, user-friendly interface, we’ve made database visualization and management both approachable and powerful. From newcomers to database experts, ChartDB adapts to fit every level of expertise. Our community on Discord is filled with users who’ve shared how ChartDB is transforming their work, and their feedback keeps driving us forward. ChartDB Built in Three Weeks, Fueled by a Vision ChartDB’s rapid development was no accident. In just three weeks, we transformed an idea into a fully functional tool, coding late nights and early mornings. And while the journey was intense, advancements in AI tools like Code Cursor and Claude 3.5 Sonnet have paved the way for even faster development. Now, we’re in a position to accelerate future builds, equipped with these groundbreaking resources that bring ideas to life at unprecedented speeds. ChartDB’s Future: What’s Next on the Horizon Our journey with ChartDB has just begun. Here’s a glimpse into what’s coming: AI-Driven Features: We’re working on AI integrations that will supercharge ChartDB’s capabilities, making database management smarter and more intuitive. Community-Powered Growth: We’re committed to keeping open lines of communication with our community. Your input will shape ChartDB’s evolution as we continue adding features that you want. Expanding Functionality: User suggestions are at the core of our roadmap. We’re excited to bring new features that make database design and visualization seamless. AI-Driven Features: We’re working on AI integrations that will supercharge ChartDB’s capabilities, making database management smarter and more intuitive. AI-Driven Features: Community-Powered Growth: We’re committed to keeping open lines of communication with our community. Your input will shape ChartDB’s evolution as we continue adding features that you want. Community-Powered Growth: Expanding Functionality: User suggestions are at the core of our roadmap. We’re excited to bring new features that make database design and visualization seamless. Expanding Functionality: Join the Movement 🚀 If you haven’t tried ChartDB yet, now’s the time. Explore the demo, star us on GitHub, and let us know what you think. The support we’ve received fuels our ambition to innovate and set a new standard in database visualization. In our next post, we’ll share insights from our Hacker News launch, our strategies, and what we learned through the experience. Join us as we continue pushing boundaries in database technology. Shaping the Future of Development Together AI and automation aren’t just trends; they’re transforming how we approach development. Whether you’re a seasoned developer or just starting out, the possibilities are endless. Drop a comment below with your thoughts or feedback, or just say hello. Together, let’s build something incredible. Feel free to share your thoughts, feedback, or just say hello in the comments below. Let’s connect and build something amazing. Feel free to share your thoughts, feedback, or just say hello in the comments below. Let’s connect and build something amazing.

ChartDB: Pioneering the Future of Database Visualization ✨

How I Created AI Photocraft: An AI SaaS Tool Using Open Source Projects on GitHub

Pocket Guide: How to Create, Sell, and Buy AI NFTs

Sakgids: Hoe om KI NFT's te skep, te verkoop en te koop

Cib bələdçisi: AI NFT-ləri necə yaratmaq, satmaq və almaq olar

Οδηγός τσέπης: Πώς να δημιουργήσετε, να πουλήσετε και να αγοράσετε AI NFT

Guía de bolsillo: cómo crear, vender y comprar NFT de IA

מדריך כיס: כיצד ליצור, למכור ולקנות מכשירי AI NFT

ポケットガイド: AI NFT の作成、販売、購入方法

Pocket Guide: Tsela ya go Hlama, go Rekiša, le go Reka di-NFT tša AI

Agasanduku k'umufuka: Uburyo bwo gukora, kugurisha, no kugura AI NFTs

Mwongozo wa Mfukoni: Jinsi ya Kuunda, Kuuza, na Kununua AI NFTs

Jübü gollanmasy: AI NFT-lerini nädip döretmeli, satmaly we satyn almaly

Cep Rehberi: AI NFT'leri Nasıl Oluşturulur, Satılır ve Satın Alınır

袖珍指南：如何创建、销售和购买 AI NFT

Hi everyone, I am really excited to present some of the insights and strategies that shaped my journey in digital tech business development, AI, and global market expansion. A Bit About My Background 📝 With 14+ years of international experience in the United States, Europe, China, and Southeast Asia, I have operated in nearly all walks of businesses. The work has taken me from building AI and ML solutions for Yandex and Toloka to running international sales for large enterprises, and even launching subscription services for millions of users across Asia. I want to share some strategies and lessons learned in this post, hoping to help you navigate this complex world of business development, lead generation, and AI-driven growth. How to Build a Sales Engine Without a Massive SDR 🏆 Building global door-opener teams doesn't always have to mean pounding the pavement with dozens of SDRs. I have built completely new international business operations for 7 companies. One of the most impressive was for pro-glance.com, an expansion from zero to exporting into 35 countries within six months. This is how we did it: Data-driven targeting: By focusing on perfectly accurate Ideal Customer Profiles, we could focus our efforts where they would reap the most value.
Negotiation Mastery: I learned how to manage a deal from multiple continents, where cultural nuances and negotiation tactics really made a difference in closing key partnerships.
**Resource Efficiency:**We focused on building a system that maximized impact with minimal resources, instead of building a huge sales force. Unorthodox partnerships can magic your business overnight—and they are usually the last things you expect. While working at RPC LLC, I was leading sales for the company in international markets since the pandemic. We closed multiple deals, each above $100,000, with Chinese partners, and all that with a shoestring marketing budget due to the closed borders of these countries. Key takeaways: 💥 Cultural Competence: My fluency in Mandarin was used to leverage trust and relationships beyond belief.
With a meagre marketing budget of $1,000, we were able to win hearts and get our job done excellently. This was possible due very specific and niche outreach in Chinese social media and also through accurate lines of networking. Think first, where your leads might be, what do they use, and what they read, so you can get in front and catch them. Despite all, cold calling doesn’t work, you need to engage smartly, and find those who really might be your customers starting from SEO and social media content up to search engine ads.

Long-Term Relationships: Partnerships do not mean a short-term gain. It is the relationships that helped us to secure continuous growth even in the most adverse of situations. Build trust with your potential partner, even if he doesn’t show interest in your product right now, he might help you with networking or other means. From Content Moderation to AI-Driven Success 🏦 🎰 The rise of AI has been a game-changer. At Yandex Toloka, I led efforts from development to launch in creating AI/ML solutions that are being used in a wide variety of mission-high scenarios, from content moderation to fraud detection. Here are a few highlights: Search Optimization for E-commerce Giants: Develop and implement search relevance models for big e-commerce players, from Zalora to Tokopedia, in a way that would optimize their search engines, making them better for customers and increasing the conversion rate. Content Moderation at Scale: Development of an AI content moderation system attuned to vast amounts of text, video, and image content produced daily across multiple languages and regions in Southeast Asia.


Advanced AI Applications: Projects in this portfolio include checking weather conditions using IBM Watson to developing a stock market fraud detection model. They show the adaptability and strength of Artificial Intelligence in solving a wide range of complex problems. Whether you are trying to leverage AI in your business for success or developing a B2B SaaS in generative AI, I have designed and outlined frameworks and methodologies that take you through the process seamlessly. When you’re trying to acquire leads within AI market, everything becomes more complex and you should be super sharp with your outreach. Types of lead generation and methods: 💥 Firstly, you should understand there 2 types of lead generation: inbound and outbound. Inbound is when you use GoogleAds or SEO to acquire warm leads that already showed some interest to your topic or even to your solution personally. Outbound is when you try to outreach through cold calling, LinkedIn messaging and emails cold leads that most probably didn’t show any interest or have little interest to what you’re doing/selling/promoting. For B2B SaaS products you should have both (inbound and outbound) and it might take time to set up efficiently the sales pipeline and marketing engine, but efficiency and accuracy are the keys to the door that will boost your sales. You will have to experiment in variety of ways and methods. One of the tools that helped me and which is crucial for both outbound and inbound lead generation is Zoominfo, and particularly “Buyer Intent” feature that shows you which leads showed interest to your topic and potentially your product. You can check for alternatives - Seamless.ai, Telescope App etc. Secondly, never forget to create target lists of companies and ICP profiles, this could help not only you, but other team members to identify which companies are crucial for you to get to and they will help you acquiring leads from them. Thirdly, for cold lead generation you should post a lot and use influence marketing. Think of tools like Ivee, Lempod, chatGPT and others that might help you launch your B2B influence marketing and generate leads as well. Building a B2B Empathetic AI Business 🪄 Currently, I'm building a B2B empathetic artificial intelligence business at Ex-human that would fundamentally change how humans interact with machines. Our goal is to create naturally human-like, empathetic, multimodal conversations long before any tool can be built, such as ChatGPT or DALL-E. This is what we are doing now: Next-gen GTM Strategies: Launching new technologies in competitive markets needs a savvy go-to-market strategy. Leading with this approach, we fuse both inbound and outbound lead generation with digital performance marketing to pave the road to success.


Cross-Industry Applications: Our AI technology is versatile, as shall be the applications in a broad range: in-game NPCs to dating sites, smart gadgets, and AR/VR environments. I am quite excited to share more with you as we continue to build and refine our strategies. To Close I hope these insights shared valuable strategies and perspectives that you can replicate in your own ventures. I for sure have had my own journey full of lessons learned across industries and markets; I am always open to sharing more. Do not hesitate to contact me in case of any questions or thoughts. Let's grow and learn together! Hi everyone, I am really excited to present some of the insights and strategies that shaped my journey in digital tech business development, AI, and global market expansion. A Bit About My Background 📝 A Bit About My Background With 14+ years of international experience in the United States, Europe, China, and Southeast Asia, I have operated in nearly all walks of businesses. The work has taken me from building AI and ML solutions for Yandex and Toloka to running international sales for large enterprises, and even launching subscription services for millions of users across Asia. I want to share some strategies and lessons learned in this post, hoping to help you navigate this complex world of business development, lead generation, and AI-driven growth. How to Build a Sales Engine Without a Massive SDR 🏆 How to Build a Sales Engine Without a Massive SDR Building global door-opener teams doesn't always have to mean pounding the pavement with dozens of SDRs. I have built completely new international business operations for 7 companies. One of the most impressive was for pro-glance.com, an expansion from zero to exporting into 35 countries within six months. This is how we did it: Data-driven targeting: By focusing on perfectly accurate Ideal Customer Profiles, we could focus our efforts where they would reap the most value. Negotiation Mastery: I learned how to manage a deal from multiple continents, where cultural nuances and negotiation tactics really made a difference in closing key partnerships. **Resource Efficiency:**We focused on building a system that maximized impact with minimal resources, instead of building a huge sales force. Data-driven targeting: By focusing on perfectly accurate Ideal Customer Profiles, we could focus our efforts where they would reap the most value. Data-driven targeting: Negotiation Mastery: I learned how to manage a deal from multiple continents, where cultural nuances and negotiation tactics really made a difference in closing key partnerships. Negotiation Mastery: **Resource Efficiency:**We focused on building a system that maximized impact with minimal resources, instead of building a huge sales force. Unorthodox partnerships can magic your business overnight—and they are usually the last things you expect. While working at RPC LLC, I was leading sales for the company in international markets since the pandemic. We closed multiple deals, each above $100,000, with Chinese partners, and all that with a shoestring marketing budget due to the closed borders of these countries. Key takeaways: 💥 Key takeaways: 💥 Cultural Competence: My fluency in Mandarin was used to leverage trust and relationships beyond belief.
With a meagre marketing budget of $1,000, we were able to win hearts and get our job done excellently. This was possible due very specific and niche outreach in Chinese social media and also through accurate lines of networking. Think first, where your leads might be, what do they use, and what they read, so you can get in front and catch them. Despite all, cold calling doesn’t work, you need to engage smartly, and find those who really might be your customers starting from SEO and social media content up to search engine ads. Long-Term Relationships: Partnerships do not mean a short-term gain. It is the relationships that helped us to secure continuous growth even in the most adverse of situations. Build trust with your potential partner, even if he doesn’t show interest in your product right now, he might help you with networking or other means. Cultural Competence: My fluency in Mandarin was used to leverage trust and relationships beyond belief. With a meagre marketing budget of $1,000, we were able to win hearts and get our job done excellently. This was possible due very specific and niche outreach in Chinese social media and also through accurate lines of networking. Think first, where your leads might be, what do they use, and what they read, so you can get in front and catch them. Despite all, cold calling doesn’t work, you need to engage smartly, and find those who really might be your customers starting from SEO and social media content up to search engine ads. Cultural Competence: My fluency in Mandarin was used to leverage trust and relationships beyond belief. Cultural Competence: With a meagre marketing budget of $1,000, we were able to win hearts and get our job done excellently. This was possible due very specific and niche outreach in Chinese social media and also through accurate lines of networking. Think first, where your leads might be, what do they use, and what they read, so you can get in front and catch them. Despite all, cold calling doesn’t work, you need to engage smartly, and find those who really might be your customers starting from SEO and social media content up to search engine ads. Long-Term Relationships: Partnerships do not mean a short-term gain. It is the relationships that helped us to secure continuous growth even in the most adverse of situations. Build trust with your potential partner, even if he doesn’t show interest in your product right now, he might help you with networking or other means. Long-Term Relationships: Partnerships do not mean a short-term gain. It is the relationships that helped us to secure continuous growth even in the most adverse of situations. Build trust with your potential partner, even if he doesn’t show interest in your product right now, he might help you with networking or other means. Long-Term Relationships: From Content Moderation to AI-Driven Success 🏦 🎰 From Content Moderation to AI-Driven Success The rise of AI has been a game-changer. At Yandex Toloka, I led efforts from development to launch in creating AI/ML solutions that are being used in a wide variety of mission-high scenarios, from content moderation to fraud detection. Here are a few highlights: Here are a few highlights: Search Optimization for E-commerce Giants: Develop and implement search relevance models for big e-commerce players, from Zalora to Tokopedia, in a way that would optimize their search engines, making them better for customers and increasing the conversion rate. Content Moderation at Scale: Development of an AI content moderation system attuned to vast amounts of text, video, and image content produced daily across multiple languages and regions in Southeast Asia. Advanced AI Applications: Projects in this portfolio include checking weather conditions using IBM Watson to developing a stock market fraud detection model. They show the adaptability and strength of Artificial Intelligence in solving a wide range of complex problems. Search Optimization for E-commerce Giants: Develop and implement search relevance models for big e-commerce players, from Zalora to Tokopedia, in a way that would optimize their search engines, making them better for customers and increasing the conversion rate. Content Moderation at Scale: Development of an AI content moderation system attuned to vast amounts of text, video, and image content produced daily across multiple languages and regions in Southeast Asia. Search Optimization for E-commerce Giants: Develop and implement search relevance models for big e-commerce players, from Zalora to Tokopedia, in a way that would optimize their search engines, making them better for customers and increasing the conversion rate. Content Moderation at Scale: Development of an AI content moderation system attuned to vast amounts of text, video, and image content produced daily across multiple languages and regions in Southeast Asia. Search Optimization for E-commerce Giants: Advanced AI Applications: Projects in this portfolio include checking weather conditions using IBM Watson to developing a stock market fraud detection model. They show the adaptability and strength of Artificial Intelligence in solving a wide range of complex problems. Advanced AI Applications: Projects in this portfolio include checking weather conditions using IBM Watson to developing a stock market fraud detection model. They show the adaptability and strength of Artificial Intelligence in solving a wide range of complex problems. Advanced AI Applications: Whether you are trying to leverage AI in your business for success or developing a B2B SaaS in generative AI, I have designed and outlined frameworks and methodologies that take you through the process seamlessly. When you’re trying to acquire leads within AI market, everything becomes more complex and you should be super sharp with your outreach. Types of lead generation and methods: 💥 Types of lead generation and methods: Firstly, you should understand there 2 types of lead generation: inbound and outbound. Inbound is when you use GoogleAds or SEO to acquire warm leads that already showed some interest to your topic or even to your solution personally. Outbound is when you try to outreach through cold calling, LinkedIn messaging and emails cold leads that most probably didn’t show any interest or have little interest to what you’re doing/selling/promoting. For B2B SaaS products you should have both (inbound and outbound) and it might take time to set up efficiently the sales pipeline and marketing engine, but efficiency and accuracy are the keys to the door that will boost your sales. You will have to experiment in variety of ways and methods. One of the tools that helped me and which is crucial for both outbound and inbound lead generation is Zoominfo, and particularly “Buyer Intent” feature that shows you which leads showed interest to your topic and potentially your product. You can check for alternatives - Seamless.ai, Telescope App etc. Secondly, never forget to create target lists of companies and ICP profiles, this could help not only you, but other team members to identify which companies are crucial for you to get to and they will help you acquiring leads from them. Thirdly, for cold lead generation you should post a lot and use influence marketing. Think of tools like Ivee, Lempod, chatGPT and others that might help you launch your B2B influence marketing and generate leads as well. Building a B2B Empathetic AI Business 🪄 Building a B2B Empathetic AI Business Currently, I'm building a B2B empathetic artificial intelligence business at Ex-human that would fundamentally change how humans interact with machines. Our goal is to create naturally human-like, empathetic, multimodal conversations long before any tool can be built, such as ChatGPT or DALL-E. This is what we are doing now: This is what we are doing now: Next-gen GTM Strategies: Launching new technologies in competitive markets needs a savvy go-to-market strategy. Leading with this approach, we fuse both inbound and outbound lead generation with digital performance marketing to pave the road to success. Cross-Industry Applications: Our AI technology is versatile, as shall be the applications in a broad range: in-game NPCs to dating sites, smart gadgets, and AR/VR environments. Next-gen GTM Strategies: Launching new technologies in competitive markets needs a savvy go-to-market strategy. Leading with this approach, we fuse both inbound and outbound lead generation with digital performance marketing to pave the road to success. Next-gen GTM Strategies : Launching new technologies in competitive markets needs a savvy go-to-market strategy. Leading with this approach, we fuse both inbound and outbound lead generation with digital performance marketing to pave the road to success. Next-gen GTM Strategies Cross-Industry Applications: Our AI technology is versatile, as shall be the applications in a broad range: in-game NPCs to dating sites, smart gadgets, and AR/VR environments. Cross-Industry Applications : Our AI technology is versatile, as shall be the applications in a broad range: in-game NPCs to dating sites, smart gadgets, and AR/VR environments. Cross-Industry Applications I am quite excited to share more with you as we continue to build and refine our strategies. To Close To Close I hope these insights shared valuable strategies and perspectives that you can replicate in your own ventures. I for sure have had my own journey full of lessons learned across industries and markets; I am always open to sharing more. Do not hesitate to contact me in case of any questions or thoughts. Let's grow and learn together!

Scaling Digital Tech Businesses & AI Solutions: Lessons Learned Across Continents

“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Mark Weiser Ubiquitous Computing We begin with Mark Weiser, an American computer scientist who coined the term “ubiquitous computing” in 1988. His ideas profoundly influenced the world of computer science. Ubiquitous computing, also known as pervasive computing, envisions embedding computing power into everyday objects and environments, making technology omnipresent yet invisible to users. It aims to provide seamless interaction and services, enhancing daily activities without requiring direct engagement or conscious awareness. To achieve this vision, we need to understand some key concepts: Context-Aware Computing: Systems that adapt to the user’s environment and context (e.g., location, time, activity) to provide personalized and relevant information or services.


Direct Manipulation: Reducing the information processing distance between the user’s intentions and the machine’s capabilities by using familiar metaphors and intuitive interactions from the physical world.


Software Agents: Computer programs that act autonomously on behalf of users or other programs to perform specific tasks, often without direct user intervention. In order to achieve intuitive and seamless navigation in the digital world using metaphors and mental models from the physical world, we’ll need to find the balance between autonomous context-aware software agents and direct manipulation. Achieving this balance is key in our pursuit of the true ubiquities of computing. The Chatbot Oracle The reason for this introduction is to equip us with the tools to assess how AI fits into our daily lives by following these concepts and whether it has achieved ubiquity. In my opinion, the general public’s primary interaction with AI nowadays occurs through chatbots, engaging in a dialogue with an “oracle.” Engaging in this question-and-answer format has created, in quite a short period of time, a mental model that has shaped the public perception of AI as an omniscient oracle. This perception has influenced both how people view AI and how companies and organizations implement AI within their operations. The current trend of deploying basic chatbots reflects a lack of thoughtful design in AI integration. Businesses should move beyond this mental model and explore ways to seamlessly weave AI into their products for a ubiquitous and impactful user experience. Envisioning Seamless AI Integration The current paradigm of perceiving AI primarily through the lens of chatbots is a limited view of AI’s true potential. While chatbots serve as a mental model to demonstrate the capabilities of large language models (LLMs) like ChatGPT, it is crucial to recognize that chatbots are merely one application of AI. The true potential of AI lies in our ability to seamlessly integrate it into the background of our daily lives, working invisibly and intuitively to enhance our experiences. Imagine a world where AI isn’t confined to a chatbot window. It anticipates your needs and preferences, subtly woven into the fabric of your everyday activities. This seamless integration depends on our ability to think outside the box on how to make AI a more context-aware software agent, implementing the subtle concepts of direct manipulation. References: The Computer for the 21st Century: https://www.youtube.com/watch?v=CkHALBOqn7s&ab_channel=NicolaBrown https://www.bizjournals.com/jacksonville/news/2024/02/23/ai-chatbot-small-businesses-liability-chatgpt.html https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/ Amit Zoran’s course — “Interaction” in in the Hebrew University. “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Mark Weiser Mark Weiser Ubiquitous Computing Ubiquitous Computing We begin with Mark Weiser, an American computer scientist who coined the term “ubiquitous computing” in 1988. His ideas profoundly influenced the world of computer science. Ubiquitous computing, also known as pervasive computing, envisions embedding computing power into everyday objects and environments, making technology omnipresent yet invisible to users. It aims to provide seamless interaction and services, enhancing daily activities without requiring direct engagement or conscious awareness. To achieve this vision, we need to understand some key concepts: Context-Aware Computing: Systems that adapt to the user’s environment and context (e.g., location, time, activity) to provide personalized and relevant information or services. Direct Manipulation: Reducing the information processing distance between the user’s intentions and the machine’s capabilities by using familiar metaphors and intuitive interactions from the physical world. Software Agents: Computer programs that act autonomously on behalf of users or other programs to perform specific tasks, often without direct user intervention. Context-Aware Computing: Systems that adapt to the user’s environment and context (e.g., location, time, activity) to provide personalized and relevant information or services. Context-Aware Computing: Systems that adapt to the user’s environment and context (e.g., location, time, activity) to provide personalized and relevant information or services. Context-Aware Computing: Direct Manipulation: Reducing the information processing distance between the user’s intentions and the machine’s capabilities by using familiar metaphors and intuitive interactions from the physical world. Direct Manipulation: Reducing the information processing distance between the user’s intentions and the machine’s capabilities by using familiar metaphors and intuitive interactions from the physical world. Direct Manipulation: Software Agents: Computer programs that act autonomously on behalf of users or other programs to perform specific tasks, often without direct user intervention. Software Agents: Computer programs that act autonomously on behalf of users or other programs to perform specific tasks, often without direct user intervention. Software Agents: In order to achieve intuitive and seamless navigation in the digital world using metaphors and mental models from the physical world, we’ll need to find the balance between autonomous context-aware software agents and direct manipulation. Achieving this balance is key in our pursuit of the true ubiquities of computing. The Chatbot Oracle The Chatbot Oracle The reason for this introduction is to equip us with the tools to assess how AI fits into our daily lives by following these concepts and whether it has achieved ubiquity. In my opinion, the general public’s primary interaction with AI nowadays occurs through chatbots, engaging in a dialogue with an “oracle.” Engaging in this question-and-answer format has created, in quite a short period of time, a mental model that has shaped the public perception of AI as an omniscient oracle. This perception has influenced both how people view AI and how companies and organizations implement AI within their operations. The current trend of deploying basic chatbots reflects a lack of thoughtful design in AI integration. Businesses should move beyond this mental model and explore ways to seamlessly weave AI into their products for a ubiquitous and impactful user experience. Envisioning Seamless AI Integration Envisioning Seamless AI Integration The current paradigm of perceiving AI primarily through the lens of chatbots is a limited view of AI’s true potential. While chatbots serve as a mental model to demonstrate the capabilities of large language models (LLMs) like ChatGPT, it is crucial to recognize that chatbots are merely one application of AI. The true potential of AI lies in our ability to seamlessly integrate it into the background of our daily lives, working invisibly and intuitively to enhance our experiences. Imagine a world where AI isn’t confined to a chatbot window. It anticipates your needs and preferences, subtly woven into the fabric of your everyday activities. This seamless integration depends on our ability to think outside the box on how to make AI a more context-aware software agent, implementing the subtle concepts of direct manipulation. References: References: The Computer for the 21st Century: https://www.youtube.com/watch?v=CkHALBOqn7s&ab_channel=NicolaBrown https://www.youtube.com/watch?v=CkHALBOqn7s&ab_channel=NicolaBrown https://www.bizjournals.com/jacksonville/news/2024/02/23/ai-chatbot-small-businesses-liability-chatgpt.html https://www.bizjournals.com/jacksonville/news/2024/02/23/ai-chatbot-small-businesses-liability-chatgpt.html https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/ https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/ Amit Zoran’s course — “Interaction” in in the Hebrew University. Amit Zoran’s

Navigating the AI Landscape: Beyond the Chatbot

We live in a world where powerful technological forces such as AI and blockchain are not only shaping our digital destiny but also redefining the boundaries of innovation. A prime example on our radar is DePIN (short for Decentralized Physical Infrastructure Network), an amalgam of web3 ecosystems offering cost-effective solutions to the everyday challenges faced by humans. DePIN empowers a more democratic future for computing power through a vast global network where everyone can participate in a resource-sharing economy. As a peer-to-peer infrastructure, it unlocks efficient distribution of computing resources and allows users to earn rewards for their contributions. This innovation seeks to bridge the gap between the digital and physical worlds through blockchain technology. There have been different groundbreaking DePIN projects such as StorX Network, Drife, Helium Network, Healthblocks, Smartpoints, Airweave, etc. Drife, for example, is not only poised to take Uber and Lyft out of business but it is also set to disrupt the transportation sector. The emergence of DePIN has paved the way for innovators to create cost-effective solutions to the inefficiencies and limitations of the existing critical infrastructure such as transportation, water supply, energy, communication systems, financial services, healthcare, and defense. Most especially, it is set to eliminate centralization and encourage decentralized ownership of these critical services. As an independent researcher, I find DePIN projects fascinating enough to be studied individually. In this article, we explore Aten Krotos (aka Suraj Venkat) and Arthava’s works on  Zero-knowledge Artificial General Intelligence(ZkAGI), an open-source AI project built on DePIN. Leveraging Zero-Knowledge and DePIN, the duo proposed ZkAGI to tackle the privacy concerns in AI. What Are Zero-Knowledge Proofs(ZKPs)? Zero-Knowledge Proofs (ZkPs)are a cryptographic technique that allows one party (the prover) to convince another party (the verifier) that they possess a specific piece of information, without revealing the information itself. In a zero-knowledge system, a prover convinces a verifier they hold a secret without revealing it, using a special proof and verification key. This relatively new field  has become a cornerstone of privacy in the cryptocurrency world, reinventing how sensitive data or information is shared without it being revealed. Since the keypoint about ZK is to share data while preserving privacy, the team made a strong case for extending Zero-knowledge  use case to Artificial Intelligence(AI) within the DePIN ecosystem. As an open-source DePIN project, ZkAGI seeks to tackle the challenge of privacy in AI by combining two cutting-edge technologies: Zero Knowledge (ZK) and Decentralized Physical Infrastructure Network (DePIN). This innovative approach highlights the need for secure and private AI computation on a cost-effective platform. Using a diary analogy, Aten, the CEO and Founder, describes it as “a secret diary where you store important information but the diary not just records thoughts but understands what has to be done and gets it done”! Defining Zero knowledge Artificial General Intelligence(ZkAGI) ZkAGI (Zero-Knowledge Artificial General Intelligence) is an open-source framework that facilitates secure and private AI computation on a decentralized network (DePIN). By integrating federated learning and zero-knowledge proofs, ZkAGI incentivizes all participants – GPU providers, trainers, developers, and data owners – to contribute to a privacy-focused AI ecosystem. Aten (Suraj) the founder and CEO of ZkAGI writes that: “ZkAGI addresses the privacy issue in AI by merging Zero Knowledge (ZK) technology with decentralized GPU infrastructure (DePIN), offering secure and private AI computation on a cost-effective platform.” “Leveraging Zynapse, our ZkML Coprocessor, the API empowers seamless AI operations without exposing sensitive data, making it the prime choice for developers and enterprises seeking privacy-centric AI solutions with high processing speeds and significant cost savings.” Zynapse API As an open-source DePIN project, ZkAGI offers valuable insights into Zynapse, its proposed solution to privacy concerns in AI. According to Aten, Zynapse is a ZkML(Zero Knowledge Machine Learning) Coprocessor, which empowers seamless AI operations without compromising sensitive data. The associate API for Zynapse makes ZkAGI the ideal choice for developers and enterprises seeking privacy-centric solutions, high processing speeds, and cost-effective solutions. The choice for DePIN is justified by the fact that it offers efficient and scalable AI computation and significantly lowers the cost compared to what traditional AI platforms offer. By leveraging Zynapse, the goal is to help “ developers and enterprises unlock the full potential of AI while ensuring the privacy of their data”. Components of Zero-Knowledge Artificial General Intelligence It is crucial to briefly discuss the layers upon which Zero-Knowledge Artificial General Intelligence(ZkAGI) as a DePIN project is built. These layers are the basic building blocks for ZkAGI and they include: DePIN, Zk,Decentralized Data Ownership, Federated Learning and Incentivization. DePIN Aten defines DePIN “as a network of physical infrastructure resources (such as computing power, storage, and networking) that are distributed across various locations rather than being centralized in one data center”,highlighting the fact the this decentralized model offers benefits such as improved fault tolerance, scalability, and reduced latency. Incentivization/Resource-Sharing Experts have identified the key advantage of DePIN projects as their ability to provide token incentives, which helps to boost the supply-and-demand side of it . It is essentially the lifeblood that is sustained by participants who contribute their computing power and users who are in need of it. In the words of Aten: “ZkAGI aims to motivate various stakeholders within the ecosystem. GPU providers are incentivized to contribute their computational resources to the network, model trainers are encouraged to develop and refine AI models, and AI developers are motivated to create innovative applications. These incentives can take various forms, such as financial rewards, access to network resources, or reputation within the community”. Decentralized Data Ownership As the AI landscape continues to expand at a breakneck speed, one major concern that has recently surfaced in AI and data-driven applications is the ownership and privacy of data. According to Aten, solving this challenge through ZkAGI requires “implementing mechanisms to enable data owners retain control over their data while still allowing it to be used for training AI models. This solution may incorporate techniques such as federated learning “where models are trained collaboratively across multiple devices or servers without sharing raw data, and zero-knowledge proofs, which allow parties to prove the validity of a statement without revealing the underlying data” Federated Learning This underscores the role and importance of a machine learning model in the development of ZkAGI as a privacy-centric AI built on a Decentralized Physical Infrastructure Network. Aten describes Federated learning as “a machine learning approach where the training process is decentralized, and individual devices or servers collaboratively train a global model without sharing their raw data”. ZK Proofs As discussed earlier, Zero-knowledge proofs are cryptographic techniques that allow the sharing of sensitive data or information between one party(the prover)and another(the verifier)without revealing its content. In the context of ZkAGI, zero-knowledge proofs can be used to verify the integrity of computations or to attest to the accuracy of AI models without disclosing sensitive data. Conclusion DePIN as a web3 innovation is revolutionizing how we access and share computing power. This shift fuelled by its potential to provide cost-effective solutions has seen the rise of groundbreaking companies such as Drife, a decentralized ride-sharing platform, and StorX Network, a decentralized cloud computing platform. This decentralized approach also empowers a more democratic future for AI. However, the demands for more powerful infrastructure, incentives for stakeholders, and heightened concerns for privacy are growing. The pressing concern over privacy protection in AI gave birth to ZkAGI, an innovative open-source framework poised to reshape AI data privacy within DePIN ecosystem. We live in a world where powerful technological forces such as AI and blockchain are not only shaping our digital destiny but also redefining the boundaries of innovation. A prime example on our radar is DePIN (short for Decentralized Physical Infrastructure Network), an amalgam of web3 ecosystems offering cost-effective solutions to the everyday challenges faced by humans. DePIN DePIN empowers a more democratic future for computing power through a vast global network where everyone can participate in a resource-sharing economy. As a peer-to-peer infrastructure, it unlocks efficient distribution of computing resources and allows users to earn rewards for their contributions. This innovation seeks to bridge the gap between the digital and physical worlds through blockchain technology. There have been different groundbreaking DePIN projects such as StorX Network, Drife, Helium Network, Healthblocks, Smartpoints, Airweave, etc. Drife, for example, is not only poised to take Uber and Lyft out of business but it is also set to disrupt the transportation sector. The emergence of DePIN has paved the way for innovators to create cost-effective solutions to the inefficiencies and limitations of the existing critical infrastructure such as transportation, water supply, energy, communication systems, financial services, healthcare, and defense. Most especially, it is set to eliminate centralization and encourage decentralized ownership of these critical services. As an independent researcher, I find DePIN projects fascinating enough to be studied individually. In this article, we explore Aten Krotos (aka Suraj Venkat) and Arthava’s works on  Zero-knowledge Artificial General Intelligence(ZkAGI), an open-source AI project built on DePIN. Leveraging Zero-Knowledge and DePIN, the duo proposed ZkAGI to tackle the privacy concerns in AI. What Are Zero-Knowledge Proofs(ZKPs)? Zero-Knowledge Proofs (ZkPs)are a cryptographic technique that allows one party (the prover) to convince another party (the verifier) that they possess a specific piece of information, without revealing the information itself. In a zero-knowledge system, a prover convinces a verifier they hold a secret without revealing it, using a special proof and verification key. Zero-Knowledge Proofs This relatively new field  has become a cornerstone of privacy in the cryptocurrency world, reinventing how sensitive data or information is shared without it being revealed. Since the keypoint about ZK is to share data while preserving privacy, the team made a strong case for extending Zero-knowledge  use case to Artificial Intelligence(AI) within the DePIN ecosystem. As an open-source DePIN project, ZkAGI seeks to tackle the challenge of privacy in AI by combining two cutting-edge technologies: Zero Knowledge (ZK) and Decentralized Physical Infrastructure Network (DePIN). This innovative approach highlights the need for secure and private AI computation on a cost-effective platform. Using a diary analogy, Aten, the CEO and Founder, describes it as “a secret diary where you store important information but the diary not just records thoughts but understands what has to be done and gets it done”! Defining Zero knowledge Artificial General Intelligence(ZkAGI) ZkAGI (Zero-Knowledge Artificial General Intelligence) is an open-source framework that facilitates secure and private AI computation on a decentralized network (DePIN). By integrating federated learning and zero-knowledge proofs, ZkAGI incentivizes all participants – GPU providers, trainers, developers, and data owners – to contribute to a privacy-focused AI ecosystem. Aten (Suraj) the founder and CEO of ZkAGI writes that: “ZkAGI addresses the privacy issue in AI by merging Zero Knowledge (ZK) technology with decentralized GPU infrastructure (DePIN), offering secure and private AI computation on a cost-effective platform.” “Leveraging Zynapse, our ZkML Coprocessor, the API empowers seamless AI operations without exposing sensitive data, making it the prime choice for developers and enterprises seeking privacy-centric AI solutions with high processing speeds and significant cost savings.” Zynapse API As an open-source DePIN project, ZkAGI offers valuable insights into Zynapse, its proposed solution to privacy concerns in AI. According to Aten, Zynapse is a ZkML(Zero Knowledge Machine Learning) Coprocessor, which empowers seamless AI operations without compromising sensitive data. The associate API for Zynapse makes ZkAGI the ideal choice for developers and enterprises seeking privacy-centric solutions, high processing speeds, and cost-effective solutions. The choice for DePIN is justified by the fact that it offers efficient and scalable AI computation and significantly lowers the cost compared to what traditional AI platforms offer. By leveraging Zynapse, the goal is to help “ developers and enterprises unlock the full potential of AI while ensuring the privacy of their data”. Components of Zero-Knowledge Artificial General Intelligence It is crucial to briefly discuss the layers upon which Zero-Knowledge Artificial General Intelligence(ZkAGI) as a DePIN project is built. These layers are the basic building blocks for ZkAGI and they include: DePIN, Zk,Decentralized Data Ownership, Federated Learning and Incentivization. DePIN Aten defines DePIN “as a network of physical infrastructure resources (such as computing power, storage, and networking) that are distributed across various locations rather than being centralized in one data center”,highlighting the fact the this decentralized model offers benefits such as improved fault tolerance, scalability, and reduced latency. Incentivization/Resource-Sharing Experts have identified the key advantage of DePIN projects as their ability to provide token incentives, which helps to boost the supply-and-demand side of it . It is essentially the lifeblood that is sustained by participants who contribute their computing power and users who are in need of it. In the words of Aten: “ZkAGI aims to motivate various stakeholders within the ecosystem. GPU providers are incentivized to contribute their computational resources to the network, model trainers are encouraged to develop and refine AI models, and AI developers are motivated to create innovative applications. These incentives can take various forms, such as financial rewards, access to network resources, or reputation within the community”. Decentralized Data Ownership As the AI landscape continues to expand at a breakneck speed, one major concern that has recently surfaced in AI and data-driven applications is the ownership and privacy of data. According to Aten, solving this challenge through ZkAGI requires “implementing mechanisms to enable data owners retain control over their data while still allowing it to be used for training AI models. This solution may incorporate techniques such as federated learning “where models are trained collaboratively across multiple devices or servers without sharing raw data, and zero-knowledge proofs, which allow parties to prove the validity of a statement without revealing the underlying data” Federated Learning This underscores the role and importance of a machine learning model in the development of ZkAGI as a privacy-centric AI built on a Decentralized Physical Infrastructure Network. Aten describes Federated learning as “a machine learning approach where the training process is decentralized, and individual devices or servers collaboratively train a global model without sharing their raw data”. ZK Proofs As discussed earlier, Zero-knowledge proofs are cryptographic techniques that allow the sharing of sensitive data or information between one party(the prover)and another(the verifier)without revealing its content. In the context of ZkAGI, zero-knowledge proofs can be used to verify the integrity of computations or to attest to the accuracy of AI models without disclosing sensitive data. Conclusion DePIN as a web3 innovation is revolutionizing how we access and share computing power. This shift fuelled by its potential to provide cost-effective solutions has seen the rise of groundbreaking companies such as Drife, a decentralized ride-sharing platform, and StorX Network, a decentralized cloud computing platform. This decentralized approach also empowers a more democratic future for AI. However, the demands for more powerful infrastructure, incentives for stakeholders, and heightened concerns for privacy are growing. The pressing concern over privacy protection in AI gave birth to ZkAGI, an innovative open-source framework poised to reshape AI data privacy within DePIN ecosystem.

Exploring Zero-Knowledge Artificial General Intelligence Within The Context Of DePIN

Around this time last year, I wrote “ ”, a deep dive into why the idea of new software foundations to handle a shift in the way we interact through spatial computing was needed. It explored concepts new and old but ultimately the conclusion was that where we are heading in many respects requires a rethink in OS design from the ground up. The Metaverse Needs An Operating System We simply cannot move forward where thinking is still stuck in kernel design and operating system architecture from the mid-1980s and 90s. Now with the rise of AI and Large Language Models, data sovereignty and user control, identity, and the age-old arguments of ‘ ’ that question of needing to rethink the OS of yesterday for tomorrow rears its head again. proprietary vs open source What I wanted to do was take the exploration done last year but shift the thinking to another industry, one which has received a lot of attention and is touted to be the future of humanity — the space industry. There probably isn’t a sector where the software and hardware demands are as stringent or as secure because of the environment they operate in (save for defence and aerospace which of course are still part of the same overall sector anyway). Rather large caveats: what’s to follow is purely conceptual based on desk research in fields that I am not an expert in but with a fundamental belief (whether right or wrong) that things need to change. I stick to the core tenets of decentralisation, open source and modularity on purpose. I will attempt to avoid altogether the questions around new CPU and silicon architectures needed to really take advantage of the changes because let's face it, we’re kind of stuck with the same thinking because of OS design. It’s a two-fold problem. No More Monoliths The space industry, for all its innovation in the last decade or so thanks to SpaceX, still sits on operational software principles that hark back to the 1960s and this isn’t a foundation to build the future of space exploration said Matt Monson in a Reddit AMA back in 2020. That’s a lot of code sitting in a fragmented architecture originally conceived in the 90s. (“The [Starlink] constellation has more than 30,000 Linux nodes (and more than 6,000 microcontrollers) in space right now,” I’m a massive supporter and believer in decentralization despite the ideology being usurped by moronic web3 VCs and cryptocoin startups. Fundamentally, a distributed and decentralized architecture points the way to a new internet and the way we write software, one which would take us beyond our Earthy confines. The operating system landscape, particularly within the space sector, is characterized by a patchwork of proprietary and open-source systems, each with its own set of interfaces and protocols. . Something new would directly address these challenges by providing a cohesive platform that ensures compatibility and seamless comms between diverse hardware and software components through a unique approach — a combination of decentralized and RTOS architectures. This lack of standardization has led to inefficiencies, increased costs, and complexities in mission design The concept of a decentralized OS isn’t new, which showed the path forward for this and it's time to pick up where they left off and complete the work. Bell Labs back in the 1980s created Plan 9 Plan 9 from Bell Labs is a distributed operating system that originated from the Computing Science Research Center (CSRC) at Bell Labs in the mid-1980s and built on UNIX concepts first developed there in the late 1960s. Since 2000, Plan 9 has been free and open-source. The final official release was in early 2015. Plan 9 replaced Unix as Bell Labs’s primary platform for operating systems research. It explored several changes to the original Unix model that facilitate the use and programming of the system, notably in distributed multi-user environments. For the uninitiated, Why care about this at all, why bother with this? Well, because the concepts behind Plan 9 (and to a certain extent, GridOS too mentioned in the original article) point the way to a radical shift in how we really need to think about operating system design and kernel architecture, especially in the space industry. Metaverse OS What are the requirements as I see them? : something new should be designed to be decentralized, meaning it can operate across a distributed network, reducing single points of failure and potentially enhancing resilience and fault tolerance which is critical for space-based operations. Decentralized and Modular : thanks to a modular microkernel architecture, it should allow for greater flexibility. Modules can be added or removed as needed for different applications or missions, making it highly adaptable to various requirements. Customisability : integrating real-time processing capabilities, crucial for time-sensitive applications such as those found in space exploration and satellite operations addresses some of the immediate concerns about decentralization and node communication. Real-Time Capabilities : it has to be built on an open-source model, encouraging community contributions and making the source code available for review, which can foster innovation and trust. Community-Driven and Open Source : needs to be designed with compatibility in mind, so supports existing hardware platforms and can run legacy applications within secure modules, easing the transition from traditional operating systems. Compatibility and Transition The design of something this critical would intend to dismantle the incumbent jigsaw puzzle of Red Hat, various Linux flavors, embedded software, and Vxworks by Wind River by creating a leap in OS architecture and opening up a new and uncontested market space. By proving the design in one of the toughest markets you could then be poised to replicate and work backwards into similar industries that will be necessary for space exploration — including mining, manufacturing, IOT, and other heavy industries that all sit upon the same old software principles. What Windows is as a general purpose and productivity operating system platform would make this a highly tuned operating software platform for the future of humanity in space as its opposite. If you wanted to take this a stage further, especially where (SDA) is a critical initiative with government agencies then any new operating system made for space exploration could be a critical component for both civilian and military entities that operate assets in space also. Space Domain Awareness : a modular nature allows for the seamless integration of various sensors and data sources. This capability is crucial for SDA, where data from radar, telescopes, satellites, and other sensors must be synthesized to provide a comprehensive picture of the space environment. Enhanced Data Integration : the decentralized aspect of a new OS can facilitate distributed data processing, reducing the time it takes to analyze vast amounts of space-domain data. Faster data processing leads to more timely responses to threats such as space debris, adversarial maneuvers, or natural phenomena. Improved Data Processing and Analysis : for military operations, resilience is critical so a decentralized structure can offer greater resilience against cyber-attacks and system failures. If one node fails, others can take over, ensuring continuous SDA operations. Resilience and Redundancy : as military operations often involve coalitions, a decentralized OS can provide standardized communication protocols and interfaces, enabling interoperability between systems of different countries and services, which is essential for joint SDA efforts. Interoperability : the modular design of a decentralized OS allows for rapid adaptation to new sensors, technologies, or mission requirements. As the space domain evolves, so too can this incorporate new modules to address emerging SDA needs without overhauling the entire system. Adaptability and Scalability : with a new kernel architecture, security protocols can be tightly integrated into each module, providing robust security measures that are vital for military operations. The decentralized nature also means that an attack on one module is less likely to compromise the entire system. Security : standardizing on a modular OS can lead to cost savings by reducing the need for custom software development for each new SDA initiative. This economic efficiency can free up resources for other critical defense needs. Cost Efficiency Artificial Intelligence Points Another Way Forward Now, let’s discuss the future of operating systems like Windows and in a world of artificial intelligence. Aren’t monolithic OS redundant where we can use AI to build applications, browse the web, answer complex questions, conduct research and do a grocery shop with automated agents at our beck and call? Linux I would say so. The approach right now is just to LLMs and AI into various parts of the OS or productivity platforms rather than architect AI from the ground up to be . Subtle difference. integrate integral The integration (more like shoehorning) of AI into operating systems like Windows does indeed prompt the question of whether a complete redesign, starting from the kernel upwards, is necessary to fully harness AI’s capabilities in this new era so we need to take a look at what might be required. current operating systems could integrate AI as an additional layer, enhancing certain functionalities. However, this approach may not leverage the full potential of AI. A redesign from the kernel level could embed AI more deeply into the core functions of the OS, leading to a more integral approach. Deep Integration vs. Superficial Add-Ons: : traditional operating systems are not primarily designed for the complexities of AI workloads. Redesigning the kernel could allow for more efficient management of resources (like CPU, GPU, and memory) for AI processes, optimizing performance and energy consumption. Resource Management and Scheduling AI introduces new security and privacy challenges. A kernel redesigned with AI in mind could incorporate more advanced security protocols to handle these challenges, especially in processing large volumes of sensitive data. Security and Privacy: : AI applications, particularly those involving machine learning and real-time data processing, can benefit from low-latency and high-speed processing. A kernel-level redesign could optimize these processes, especially for edge computing scenarios. Real-Time Processing and Edge Computing : an AI-driven kernel could enable the operating system to perform autonomous optimization and self-healing tasks, predicting and preventing system failures, and optimizing performance without human intervention. Autonomous Operation and Self-Healing : modern AI applications often rely on specialized hardware like GPUs and TPUs. A kernel designed with these in mind could provide better support and optimization for such hardware, enhancing AI application performance. Much like what Graphcore set out to do with its IPU but has fallen foul of product market fit and high capital investment requirements to continue. Hardware Acceleration : a significant challenge in redesigning the kernel for AI is maintaining compatibility with existing applications and systems. This transition would require careful planning and gradual implementation. Backward Compatibility and Transition : the system could adapt its behavior based on the environment and usage patterns. For instance, it could optimize itself for energy efficiency, performance, or security, depending on the context. Adaptive Behaviour If we take a revolutionary approach to operating system design, combining AI-first architecture, kernel-level AI integration, and decentralization as core principles, a new kernel and OS architecture would differ significantly from traditional systems like Windows and Linux. Of course, such a shift would also require overcoming significant bumps in the road in terms of development, adoption, and compatibility with existing tech and infrastructure. No mean feat but if you approached this from the angle that building an OS like this was a Blue Ocean Strategy then being patient and nurturing this throughout a couple of decades and this is bigger game and prize to aim for. Let’s Go Swimming Blue ocean strategy is the simultaneous pursuit of differentiation and low cost to open up a new market space and create new demand. It is about creating and capturing uncontested market space, thereby making the competition irrelevant. It is based on the view that market boundaries and industry structure are not a given and can be reconstructed by the actions and beliefs of industry players. Red oceans are all the industries in existence today — the known market space, where industry boundaries are defined and companies try to outperform their rivals to grab a greater share of the existing market. Cutthroat competition turns the ocean bloody red. Hence, the term ‘red’ oceans. Blue oceans denote all the industries not in existence today — the unknown market space, unexplored and untainted by competition. Like the ‘blue’ ocean, it is vast, deep and powerful –in terms of opportunity and profitable growth. A perfect example of this was when Nintendo released the Wii. The Nintendo Wii launched in 2006 and at its heart is the concept of value innovation. This is a key principle of blue ocean strategy which sees low cost and differentiation being pursued simultaneously. To reduce costs, Nintendo did away with the hard disk and DVD functionality found in most game consoles and reduced the processing quality and graphics. At the same time, Nintendo introduced a wireless motion control stick to differentiate itself against the market offering. This allowed the company to offer a range of new features and benefits that hadn’t been seen in the world of gaming previously such as the ability to use a games console to get fit or to play in a larger social group. By pursuing value innovation, Nintendo could go beyond competing against the likes of PlayStation and X-Box in a crowded and fiercely competitive red ocean. Instead, it was able to open up a new market entirely. The Nintendo Wii, with its innovative, new features and affordable price point, appealed to an entirely new and expansive market — a blue ocean-spanning non-gamers, the elderly and parents with young children. By adopting the same approach with a new operating system it would obliterate the incumbent market riddled with technical debt and legacy which would be unable to respond because of the effort required to change direction. Where Do We Go From Here? It’s not a simple or small effort by any means. The reason I chose AI and Space was because they are complementary approaches to the same problem using the same answers. We’re building on concepts and ideas that have never been pulled together like this before but could lead to the building blocks of the next 50–100 years of software architecture because they have to be fit for purpose for the brave new world that’s coming at us fast. . This mission is the first of its kind to evaluate in-space use cases for decentralized storage. It will be hosted aboard Lockheed Martin’s self-funded LM 400 Technology Demonstrator — a software-defined satellite about the size of a refrigerator, designed to support a wide range of missions and customers. Once the spacecraft is in orbit, it will use its SmartSat™ software-defined satellite technology to upload and perform the IPFS demonstration. Take the current experimental deployments of IPFS (Interplanetary File System) with Lockheed Martin We’re experimenting with decentralised technologies all the time but seem hesitant to make it a core foundation of platforms going forward. These are conceptual frameworks and ideas I’ve been kicking around and god knows whether they’ll stick but if there’s anyone out there nodding in violent agreement — whether you’re a software engineer or an investor — beat down my door and lets talk because I have a desire to make this a reality. Also published here.

The World Is Ready for a New Type of Operating System

বিশ্ব একটি নতুন ধরনের অপারেটিং সিস্টেমের জন্য প্রস্তুত

Die Welt ist bereit für eine neue Art von Betriebssystem

El mundo está preparado para un nuevo tipo de sistema operativo

Le monde est prêt pour un nouveau type de système d'exploitation

दुनिया एक नए प्रकार के ऑपरेटिंग सिस्टम के लिए तैयार है

世界は新しいタイプのオペレーティング システムを受け入れる準備ができています

세상은 새로운 유형의 운영 체제를 맞이할 준비가 되어 있습니다.

O mundo está pronto para um novo tipo de sistema operacional

Мир готов к новому типу операционной системы

Dünya Yeni Bir İşletim Sistemi Türüne Hazır

Thế giới đã sẵn sàng cho một loại hệ điều hành mới

世界已准备好迎接新型操作系统

The about the is that you don’t need much to be part of it. good thing SaaS industry You can build something that provides value for a group of people, have a way to monetize it, and now you’re in business. The about the SaaS industry is that you don’t need much to be part of it. bad thing And that applies to everyone else who wants to build a product of their own. With so many products targeting a segment of the market, it can be hard to stand out from the crowd. That’s why having a useful product is not enough and you have to employ other methods to increase growth and brand awareness. In this article, we’ll explore a couple of different strategies to do just that. Let’s go for it. The Power of SaaS Content Marketing This strategy is becoming more popular and for good reason. Creating and distributing useful and relevant content for the target audience is the most cost-effective way to establish expertise and increase brand awareness. When we talk about “SaaS content marketing”, it’s not just about putting out regular blog posts on the company’s website. The same can be said about whitepapers and case studies. These types of content can (and should) be repurposed and posted in social media platforms, added to news aggregator sites, or even shared in relevant online communities. It might seem like a lot of work for not much gain, but building a content engine is one of the best investments to make so that you’re not solely relying on word of mouth or worse… ads. With the latest advancements in AI, you can create one piece of content and then repurpose it into many different formats. : a podcast episode where the founder was interviewed. Practical example The video of it can get turned into… A blog post highlighting insights/lessons of the journey. A LinkedIn post summarizing the main points linking to the blog. 5-10 pieces of short-form video content. 5+ Twitter(X) posts. Content for the newsletter. Audio for those who prefer to listen than to watch. You see that’s a lot of content created from just one episode. This helps keep your company “top of mind” and all that other stuff that marketers talk about. You know, like having great positioning, building thought leadership, becoming a trusted advisor, and so on. Leverage Good ol’ SEO for Organic Growth If you’re already aboard the SaaS content marketing train, the next step is to get a good grip on for improving organic growth. SEO At first, this doesn’t sound like something that applies that much to indie hacking or building a SaaS. It sounds more fitting for the ad revenue from blogging or affiliate marketing space. And that’s where the opportunity resides to use SEO as a key piece of your product distribution strategy. The majority of indie builders and SaaS founders don’t understand SEO well enough to leverage its power. After all, their focus is on building something and shipping it fast enough to test the demand for it. SEO is this intricate part of marketing we have all heard is important but don’t know how to go about implementing. To top it all off, most of the great resources to learn SEO from are made by companies that built keyword research tools or full-time marketers who don’t speak the indie language. Fortunately, a great indie hacker like Danny Postma wrote about how he’s using SEO to drive growth to his products without relying on his big Twitter following. 👇 SEO thread That’s the most practical SEO “course” written from the indie perspective I’ve found (and the best part is that it’s free 😄) A different kind of SaaS Email Marketing This marketing approach can be very different depending on the type of SaaS. For our purposes here, think about a small to medium B2B SaaS that is doing well but doesn’t have a strong positioning in the market. It’s different from traditional email marketing where you get product updates, special offers, and the like in your inbox. And in case you’re thinking… “Ah he’s talking about cold emailing people”. It’s not quite that either. It would more likely be called “warm emailing” prospects. Yeah, I know, let me explain. The whole gist of this strategy is to make the connections first, either through social media or sites like IndieHackers/ProductHunt/Reddit and the like. After that comes the getting the contact details part. You can start the conversation directly on the platform where you found the people or use any of the many email discovery tools out there like Hunter, Apollo, SeamlessAI, etc. Now, the key part for when you're contacting the person in question is to not sound like a salesman right off the bat. Don’t use the typical lead generation approach of “spray and pray” sending thousands of emails daily and then checking who replied. Instead, you want to use a different and innovative strategy called… Communicating like a normal human being. Seriously, you don’t need to make assumptions that are mostly wrong here. Talk like a friendly person wanting to help with whatever problem the product solves. That works for several reasons. But the main one is that you don’t sound like a desperate sales rep trying to meet the weekly outreach goals. The person who does this better than anyone I know is Ved Rasic, founder of (a LinkedIn CRM). LeadDelta Even if you don’t get customers from that process, you can get people who would recommend you to someone else, or people open to doing business with you in the future. Try SaaS Influencer Marketing While content marketing and email marketing are more well-known marketing practices, going the route of SaaS influencer marketing is one not traveled too often. And you know what they say, do what most people aren’t doing to get the results that most people aren’t getting. But I think I know why this strategy is not so popular or talked about. In a way, it sounds “icky”. Almost like you will be selling out by getting online cringy influencers making posts and hard-pushing your product in people’s faces (kinda like what some people do with YouTube ads). But for everything one can try, there are many ways to do it wrong and a few to do it right. In this case, you can do it tactfully by only working with people whose vibe aligns with your product and your content. You can do this on different platforms like , , , and such. A good deal of research will be necessary to see which platform has better chances of bringing high-quality traffic to the product and what types of influencers you want to work with. YouTube IG TikTok And if you still think that what I’m saying is too “fluffy” or too good on paper but doesn’t apply in real life… Let me give you an example of it in action. Even Pieter had not tried that strategy before (until very recently). He also mentioned in a podcast that he talked with a TikTok influencer to make posts about PhotoAI (his startup about AI pictures) and MRR went from $12k to $40k from that alone. So yeah, the possibilities are almost endless… Create an interactive product walkthrough A great product walkthrough can help turn visitors into paying users. The walkthrough can also become a marketing asset to drive conversions up and be a literal “show don’t tell” strategy. And yes, I’m talking about an interactive walkthrough of the main workflow, not just a recorded video of someone going through the process. The whole fact of people being able to click and poke around can give a potential customer a feel for how it works and what it would be like when using it as part of the daily work (or whatever other frequency). The other benefit of this strategy is that it can make it easier for people to understand how the interface is structured and how the features work. You know, stuff like “if I click on this tab or this option, I get presented with this data or these controls and then I can go back to the dashboard clicking this other tab”. Just from that you can see how people would need less of a user manual or knowledge base. But, of course, you can have one for those other things not shown in the walkthrough. And if the product walkthrough is something people have access to even before signing up, they could pretty much have done the onboarding process to the platform without realizing it. They’re one step closer to becoming a full-fledged user right there. And no, this doesn’t have to be a super complicated thing to implement for the product. There are already tools out there that make this process pretty easy. One that comes to mind now is called Arcade(dot)software. Other “Outside-the-Box” Ideas If all the other methods discussed above weren’t enough, here are some more unusual ideas to try. For example, host Twitter Spaces instead of the traditional “online webinars”.  I know Alessio Matara from LiveLogue has been killing it in that Space (get it? (s). Alright, ok. Moving on…) Space You can collaborate with other founders in your niche that provide complimentary services. For instance, if your product is a cold DM Twitter tool, you can partner up with someone who runs a Twitter analytics tool. Or maybe not Twitter but instead LinkedIn, same idea but a different platform. You can also appear on other people’s podcasts or do guest blogging to grow the brand, increase exposure, and get some mentions and also backlinks. Something else to try is to build features as if they were standalone micro products that are free to use and launch them on ProductHunt. These products can create buzz and get people to discover the main product through the use of the free ones. See if you can implement a referral program. Dropbox is the classic example here. Users were rewarded with a sizable amount of free space by referring others to the service. (I know of people who referred their classmates to get lots of free space.) Depending on your style and what you’re comfortable with you can also employ the strategy of “meme marketing”. That’s where you use the entertaining and potentially viral nature of memes to spread awareness of your product and the problems it solves for a specific audience. And hey, if you’re feeling daring enough, you can take the marketing out in the real world. Consider going to events in the niche like workshops or conferences. You can either find people who make up your target audience or potential collaborators whom to join forces with. This is just a sample of the many activities you can do to bring more awareness to your SaaS and reach a wider audience than the one captured in the first launch. Conclusion If you’ve read all the way down here, it is safe to say that you’re well-armed with new weapons for your marketing arsenal. Using a combination of SaaS content marketing, email marketing, strategic SEO, influencer marketing, and the other ideas mentioned, SaaS products can go from “just another tool” to prime market positioning. Remember to think outside the box, try unconventional stuff that most people aren’t doing, and record what works and what doesn’t from the marketing experiments. There’s something out there for everyone and not all strategies will produce the same results for those who try them. So, experiment, figure out what works for your case, and discard the rest. That’s about it for this time. Thanks a lot for reading. Hope to see you next time. Don’t forget to so you don’t miss any new articles. subscribe on Hackernoon

Hack Growth With SaaS Content Marketing and Other Unusual Methods 

Artificial Intelligence (AI) has had a huge impact on the healthcare industry. From improving patient outcomes to enhancing medical research and even streamlining healthcare operations, the potential of AI in healthcare is massive. AI-powered tools also help medical practitioners to detect diseases early and develop individualized treatment plans. Consequently, many healthcare organizations are heavily investing in AI. As per , as of 2021, around one-fifth of the surveyed healthcare organizations were in the early stage of AI adoption. It was also seen that the most common kind of AI software being used in the healthcare industry was related to natural language processing (NLP) and healthcare data integration. Statista That being said, many healthcare providers face massive challenges when it comes to seamless AI integration, such as data privacy and integration with legacy systems. To address these challenges, they must partner with AI developers who can help them successfully navigate real-world healthcare problems. It is predicted that the impending 5 to 10 years will be critical for the expansion of AI in the healthcare industry. Thus, progressive healthcare organizations need to collaborate with AI developers to make the most of this technology and deliver superlative patient outcomes. Understanding AI in Healthcare Artificial Intelligence is all set to become a transformational force in healthcare. Essentially, AI in healthcare is an umbrella term that is used to define the application of ML algorithms along with many other cognitive technologies. In other words, AI is when a machine mimics human cognition and becomes capable of learning, discerning, and making informed decisions. AI in healthcare helps to evaluate and predict patient outcomes based on past medical data gathered through electronic health records. Here are some of the most notable uses of AI in healthcare. Medical Imaging: AI algorithms can help medical providers analyze medical images, such as X-rays and MRI scans, to find signs of disease. This can help them make more accurate diagnoses and develop more effective treatment plans. Personalized Medicine: AI can help medical providers develop individualized treatment plans for patients based on their individual medical history, genetics, and lifestyle. This can lead to better outcomes for patients. Drug Discovery: AI can help researchers identify new drugs and treatments for diseases by analyzing vast amounts of data and predicting how different compounds will interact with the human body. Virtual Assistants: AI-powered virtual assistants, such as chatbots, can help patients schedule appointments, ask basic medical questions, and get information about their health without much delay. Predictive Analytics: AI can help medical providers predict which patients are at risk for certain diseases, such as diabetes or heart disease, and intervene early to prevent or treat the disease. Overall, the use of AI in healthcare can help medical providers provide better care to patients, improve medical research, and streamline healthcare operations. Importance of Collaboration between Healthcare Providers and AI Developers Collaboration between healthcare providers and AI developers is critical for improving patient outcomes, reducing healthcare costs, and driving innovation in healthcare. By collaborating with AI developers, healthcare providers can gain access to cutting-edge technology and data analysis tools that can help them make better-informed decisions. Additionally, AI developers can help healthcare providers identify gaps in data collection and develop tools to collect more accurate and complete data. This, in turn, will ensure quality data that helps in accurate diagnosis. Collaboration between healthcare providers and AI developers can also lead to the development of new and innovative technologies and approaches to healthcare. By working together, healthcare providers and AI developers can identify unmet needs and develop solutions to address them. At Algoscale, we can help you collaborate with expert AI developers who can build even highly complex yet sophisticated AI software for healthcare. As a leading , we have developed several specialized software to help healthcare providers streamline their medical processes as well as enhance patient outcomes. Artificial Intelligence Company Here is how Algoscale helped its clients in the healthcare industry. Case Study: ML-Based Analytics Solution for a Healthcare Supply Chain Platform In a highly regulated field like healthcare, transparency in the supply chain is immensely critical. Long lead periods and slow-moving products may cause a lack of visibility in the supply chain, resulting in stock shortages. It may also increase unnecessary expenditures associated with erroneous inventory counts. To resolve this, Algoscale developed a solution that generated business intelligence from data that is dispersed throughout the organization. We developed an ML-based integrated platform for advanced Spend Analytics, offered the client complete visibility on the spending process, and gathered insights to standardize their medical business processes. . Read to know more Case Study: Optimizing Costs for Healthcare Supply Chain with Predictive Analytics Business Intelligence is very important in the healthcare supply chain. Without it, healthcare organizations may face increased costs, substandard patient outcomes, and a loss of business opportunities.With this in mind, Algoscale helped its client, a provider of a cloud-based SaaS purchasing platform, to bring greater transparency to its supply chain. We built a global database to handle massive amounts of data and standardize it across all regions. We gathered info on product catalogs from all the leading healthcare equipment industries by crawling across sources. All this data was used to enrich the database.Our developers also built an end-to-end healthcare eCommerce platform for the Indian healthcare market. The platform included an AI-based recommendation engine that studied past history to provide recommendations and further simplify inventory replenishment for customers.Our solution helped the client discover new-fangled opportunities for savings and generate new revenue. It helped him check spending and supply chain analysis with a vast network of products and suppliers. . Read to know more Challenges of AI in Healthcare AI in healthcare indubitably has many benefits. However, there are also a few challenges that must be addressed. From the point of view of healthcare organizations, the most pressing challenges are as below: Data quality The quality of data is a leading challenge in integrating AI in healthcare. AI systems rely on massive amounts of data to train and enhance their accuracy. Therefore, the data provided by these systems must be complete, accurate, and reliable. Poor-quality data can result in faulty predictions and diagnoses. Privacy and security The use of personal health information raises concerns about privacy and security. AI systems must be designed to protect patient data, comply with privacy regulations, and prevent unauthorized access. Ethical considerations There are ethical considerations when developing and implementing AI in healthcare, including issues around bias, transparency, and accountability. AI models must be developed in a way that is fair and unbiased and that takes into account the potential impact on patients. Adoption and acceptance Healthcare providers may be hesitant to adopt new AI technologies, particularly if they are not familiar with them. It may take time to gain acceptance and trust in AI systems. Integration with existing systems Integrating AI systems with existing healthcare systems can be challenging, particularly if those systems are outdated or not designed for interoperability. AI must be able to work seamlessly with existing systems to be effective. To reach its full potential in the healthcare industry, AI developers can help to overcome the most daunting barriers to adoption. For instance, AI developers can improve and boost engagement with healthcare providers to support AI training which has the potential to increase the likelihood of AI adoption in the future. Similarly, close collaboration between AI developers and healthcare organizations can ensure the highest quality of data. AI developers can implement data governance to comply with the legal and ethical norms of conduct and ensure good practices for the collection, storage, and use of sensitive patient data. Finally, the developers can also streamline the process of AI integration with legacy systems, ensuring complete interoperability. How Algoscale can collaborate with Healthcare Providers to Adopt AI-powered Healthcare Solutions? At Algoscale, we can help you scale your IT team and work with certified developers who are well-versed in all AI technologies. Our specialized can meet the demands of your organization in unique ways. Whether it is a short-term project or a long-term one, our AI developers can help you run your IT operations smoothly, efficiently, and cost-effectively. staff augmentation services We follow a stringent screening process that carefully evaluates each professional in categories like mathematical reasoning, technical proficiencies, and soft skills. With our services, your organization can reduce costs and improve productivity while still maintaining quality standards. Get in touch with our reps today to schedule a free consultation.

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