In this episode of How We Made That App, host Madhukar Kumar, CMO of SingleStore, delves into the fascinating world of conversational AI with Ronald Ashri, co-founder of OpenDialog. Ronald shares his journey from studying multi-agent systems to creating OpenDialog, a platform designed to revolutionize how humans and AI collaborate.

Discover how Ronald’s expertise in autonomous agents and conversational design is shaping AI tools for regulated industries. From balancing creativity and control in conversational frameworks to leveraging knowledge graphs and retrieval-augmented generation (RAG), this episode explores the cutting-edge innovations driving enterprise AI forward.

Ronald also shares his thoughts on the challenges of building trustworthy AI, preventing hallucinations, and the future of large language models. Tune in to learn how a vision for smarter human-AI interactions is becoming a reality.

In this episode of How We Made That App, host Madhukar Kumar, CMO of SingleStore, sits down with Igor Jablokov, the pioneering mind behind Amazon Alexa and CEO of Pryon. Igor shares his journey from the early days of voice recognition technology to leading innovations in artificial intelligence. Discover the story behind Alexa’s creation, the technical challenges and breakthroughs in building a voice-powered assistant, and the forward-thinking solutions that reshaped how we interact with technology. Igor also delves into the ethical considerations and governance of AI, tackling common misconceptions and offering a glimpse into the future of intelligent systems. Tune in to hear how a vision for voice technology evolved into a transformative force in AI.

Key Takeaways:

• Igor Jablokov’s journey from Alexa co-creator to CEO of Pryon

• The inception and development of Amazon Alexa

• Technical insights into overcoming challenges in voice recognition

• Ethical considerations and governance in the field of AI

• Future perspectives on the role of AI in everyday life

Subscribe now and don’t miss an episode of How We Made That App, where we explore the stories behind the most impactful apps and the innovators who make them happen.

Links

• Connect with Igor
• Visit Pryon
• Connect with Madhukar
• Visit SingleStore

In this episode of "How We Made That App," host Madhukar Kumar, CMO of SingleStore, sits down with Marc Locchi, the CTO of Flowd, a groundbreaking water management company from Australia. Marc shares his journey from a frustrated programmer to leading the development of Flowd, an app that’s transforming how businesses detect and manage water leaks in real-time. Discover the story behind Flowd’s creation, the technical challenges faced, and the innovative solutions employed, including the use of Laravel and AWS for scalability. Marc also delves into the impact Flowd has had on saving water and reducing costs for clients, sharing fascinating success stories along the way. Tune in to learn how a chance meeting and a passion for technology led to a solution that’s making waves in water conservation.

Key Takeaways:

• Marc Locchi's journey to becoming CTO of Flowd • The inception and development of Flowd • Technical insights on building a scalable application with Laravel • Real-world impact stories of Flowd’s water management solutions • Collaboration with AWS to enhance performance and scalability

Subscribe now and don’t miss an episode of "How We Made That App," where we explore the stories behind the most innovative apps and the people who make them happen.

Links

Connect with Marc

Visit Flowd

Connect with Madhukar

Visit SingleStore

Join us on this intriguing journey where host Madhukar Kumar uncovers the story of FlowiseAI, an AI-powered chatbot tool that soared to fame in the open-source community. Henry Heng, the Founder of FlowiseAI, shares the inception of FlowiseAI was out of the need to streamline repetitive onboarding queries. Listen in as Henry shares the unexpected explosion of interest following its open-sourcing and how community engagement, spearheaded by creators like Leon, has been pivotal to its growth. The conversation takes a fascinating turn with the discussion of Flowise’s versatility, extending to AWS and single store's creative uses for product descriptions, painting a vivid picture of the tool's expansive potential.

Madhukar and Henry discuss the dynamic realm of data platforms, touching on the integration of large language models into developer workflows and the inevitable balance between commercial giants and open-source alternatives. Henry brings a personal perspective to the table, detailing his use of Fowise for managing property documentation and crafting an accompanying chatbot. Henry also addresses the critical issue of data privacy in enterprise environments, exploring how Flowwise approaches these challenges. The strategy behind monetizing Flowwise is also revealed, hinting at an upcoming cloud-hosted iteration and its future under the Y Combinator umbrella. Don't miss out on this insightful conversation on how FlowiseAI is revolutionizing GenAI!

Key Quotes:

• “What I've experienced is that first you go through the architect. So the architect of companies and the senior teams as well will decide what architecture that we want to go with. And usually, I was part of the conversation as well. We tend to decide between, NoSQL or SQL depending on the use cases that we are using. For schema that are like fast changing schema or inconsistent, not like tabular structured data, we often use NoSQL or MongoDB. And for structured data, we use MySQL from my previous company. That's how we kind of like decide based on the use cases.”
• “Judging from the interactions that I have with the community, I would say 80 percent of them are using OpenAI and OpenSource is definitely catching up but is still lagging behind OpenAI. But I do see the trend that is starting to pick up, like especially you have the MixedRoute, you have Lama2 as well. But the problem is that I think the cost is still the major factor. Like, people tend to go to which large language models has the lowest cost, right?”

Timestamps

• (00:00) Building FlowiseAI to open source
• (5:07) Innovative Use Cases of Flowwise
• (10:15) Types of users of Flowise
• (19:39) Database Architecture and Future Technology
• (32:30) Quick hits with Henry

Links

Connect with Henry

Visit FlowiseAI

Connect with Madhukar

Visit SingleStore

On this episode of How We Made That App, join host Madukar Kumar as he delves into the groundbreaking realm of AI in education with Dev Aditya, CEO and Co-Founder of the Otermans Institute. Discover the evolution from traditional teaching methods to the emergence of AI avatar educators, ushering in a new era of learning.

Dev explores how pandemic-induced innovation spurred the development of AI models, revolutionizing the educational landscape. These digital teachers aren't just transforming classrooms and corporate training. They're also reshaping refugee education in collaboration with organizations like UNICEF.

Dev’s deep dive into the creation and refinement of culturally aware and pedagogically effective AI. He shares insights into the meticulous process behind AI model development, from the MVP's inception with 13,000 lines of Q&A to developing a robust seven billion parameter model, enriched by proprietary data from thousands of learners.

We also discuss the broader implications of AI in data platforms and consumer businesses. Dev shares his journey from law to AI research, highlighting the importance of adaptability and logical thinking in this rapidly evolving field. Join us for an insightful conversation bridging the gap between inspiration and innovation in educational AI!

Key Quotes:

• “People like web only and app only, right? They like it. But in about July this year, we are launching alpha versions of our products as Edge AI. Now that's going to be a very narrowed down version of language models that we are working on right now, taking from these existing stacks. So that's going to be about 99 percent our stuff. And it's, going to be running on people's devices. It's going to help with people's privacy. Your data stays in your device. And even as a business, it actually helps a lot because I am hopefully, going to see a positive difference in our costs because a lot of that cloud costs now rests in your device.”
• “My way of dealing with AI is, is narrow intelligence, break a problem down into as many narrow points as possible, storyboard, storyboard color, as micro as possible. If you can break that down together, you can teach each agent and each model to do that phenomenally well. And then it's just an integration game. It will do better than a human being in, you know, as a full director of a movie. Also, if you know, if you, from the business logic standpoint, understand what does a director do, it is possible, theoretically. I don't think people go deep enough to understand what a teacher does, or what a doctor is just not a surgeon, right? How they are thinking, what is their mechanism? If you can break that down. You can easily make, like, probably there are 46, I'm just saying, 46 things that a doctor does, right? If you have 46 agents working together, each one knowing that,be amazing. That's a different game. I think agents are coming.”

Timestamps

• (00:00) - AI Avatar Teachers in Education
• (09:29) - AI Teaching Model Development Challenges
• (13:27) - Model Fine-Tuning for Knowledge Augmentation
• (25:22) - Evolution of Data Platforms and AI
• (32:15) - Technology Trends in Consumer Business

Links

Connect with Dev

Visit the Otermans Institute

Connect with Madhukar

Visit SingleStore

In this episode of How We Made That App, host Madhukar welcomes Jack Ellis, CTO and co-founder of Fathom Analytics, who shares the inside scoop on how their platform is revolutionizing the world of web analytics by putting user privacy at the forefront. With a privacy-first ethos that discards personal data like IP addresses post-processing, Fathom offers real-time analytics while ensuring user privacy. Breaking away from the traditional cookie-based tools like Google Analytics. Jack unpacks the technical challenges they faced in building a robust, privacy-centric analytics service, and he explains their commitment to privacy as a fundamental service feature rather than just a marketing strategy.

Jack dives into the fascinating world of web development and software engineering practices. Reflecting on Fathom's journey with MySQL and PHP, detailing the trials and tribulations of scaling in high-traffic scenarios. He contrasts the robustness of PHP and the rising popularity of frameworks like Laravel with the allure of Next.js among the younger developer community. Jack also explores the evolution from monolithic applications to serverless architecture and the implications for performance and scaling, particularly as we efficiently serve millions of data points.

Jack touches on the convergence of AI with database technology and its promising applications in healthcare, such as enhancing user insights and decision-making. Jack shares intriguing thoughts on how AI can transform societal betterment, drawing examples from SingleStore's work with Thorn. You don’t want to miss this revolutionizing episode on how the world of analytics is changing!

Key Quotes:

• “When we started selling analytics people they were a bit hesitant to pay for analytics but over time people have started valuing privacy over everything And so it's just compounded from there as people have become more aware of the issues and people absolutely still will only use Google Analytics but the segment of the market that is moving towards using solutions like us is growing.”
• “People became used to Google's opaque ways of processing data. They weren't sure what data was being stored, how long were they keeping the IP address for. All of these other personal things as well. And we came along and we basically said, we're not interested in that. tracking person A around multiple different websites. We're actually only interested in person A's experience on one website. We do not, under any circumstances, want to have a way to be able to profile an individual IP address across multiple entities. And so we invented this mechanism where the web traffic would come in and we'd process it and we'd work out whether they're unique and whatever else. And then we would discard the personal data.”
• “The bottleneck for most applications is not your web framework, it's always your database and I ran through Wikipedia's numbers, Facebook's numbers and I said it doesn't matter, we can add compute, that's easy peasy, it's always the database, every single time, so stop worrying about what framework you're using and pick the right database that has proven that it can actually scale.”
• “If you're using an exclusively OLTP database, you might think you're fine. But when you're trying to make mass modifications, mass deletions, mass moving of data, OLTP databases seem to fall over. I had RDS side by side with SingleStore, the same cost for both of them, and I was showing people how quickly SingleStore can do stuff. That makes a huge difference, and it gives you confidence, and I think that you need a database that's going to be able to do that.”

Timestamps

• (00:55) Valuing consumer’s privacy
• (06:01) Creating Fathom Analytics' architecture
• (20:48) Compounding growth to scale
• (23:08) Structuring team functions
• (25:39) Developing features and product design
• (38:42) Advice for building applications

Links

Connect with Jack

Visit Fathom Analytics

Connect with Madhukar

Visit SingleStore