ConTejas Code

Follow me on 𝕏: https://x.com/tejaskumar_This podcast episode dives deep into the world of Event Sourcing, a powerful architectural pattern used in modern software development. Beginning with a clear definition of Event Sourcing, the episode explores its roots in Domain Driven Design and its critical role in recording and storing every change made to the state of an application in an event store. I break down complex topics, such as the nature of events, the intricacies of implementing Event Sourcing in real-world applications, and the various benefits and challenges associated with this approach.Listeners will gain insights into the practical aspects of Event Sourcing, including detailed discussions on storage costs, computational expenses, and the practice of snapshotting to optimize performance. The episode also covers the concept of event ownership, Command Query Responsibility Segregation (CQRS), and ensuring data consistency using Apache Kafka, a distributed event streaming platform known for its high throughput, reliability, and scalability.Further, the episode delves into Kafka's performance mechanisms, its use as an event store, and the transition from Zookeeper to KRaft for cluster coordination. Alternatives to Kafka, such as using Postgres' Write-Ahead Logging (WAL) as an event store, are examined, providing listeners with a comprehensive view of the options available for implementing Event Sourcing.The discussion extends to real-life use cases of Event Sourcing, highlighting its application across various industries and projects. The experts also tackle some of the common problems encountered when adopting Event Sourcing, offering practical advice and solutions. Finally, the episode concludes with a thoughtful analysis on whether Event Sourcing is the right choice for your project, helping listeners to make informed decisions based on their specific needs and project requirements.This episode is a must-listen for software developers, architects, and technology leaders looking to understand Event Sourcing, its benefits, challenges, and implementation strategies. Whether you're new to Event Sourcing or looking to refine your existing knowledge, this episode provides valuable insights into making the most of this powerful architectural pattern.Chapters00:00 - Intro03:33 - Sponsor (CrabNebula.dev)04:21 - Defining Event Sourcing07:47 - What are Events? (Domain Driven Design)14:45 - Real-World Examples of Event Sourcing19:52 - Complexities of Event Sourcing21:33 - Storage Costs23:36 - Computational Costs24:10 - Snapshotting35:15 - Event Ownership36:19 - CQRS44:08 - Consistency with Kafka54:10 - Kafka Performance Mechanisms01:03:05 - Kafka as an Event Store01:04:13 - Zookeeper & KRaft01:09:47 - Postgres WAL as an Event Store?01:13:24 - Event Sourcing Use Cases01:18:50 - Event Sourcing Problems01:26:22 - Should You Event Source?01:27:44 - Conclusion

Links- Codecrafters: https://tej.as/codecrafters- Tejas on X: https://x.com/tejaskumar_- JSHeroes conference: https://jsheroes.io- Attention is All You Need Paper: https://scispace.com/pdf/attention-is-all-you-need-1hodz0wcqb.pdf- Google Agents paper: https://ppc.land/content/files/2025/01/Newwhitepaper_Agents2.pdf- Jack Herrington episode about implementing MCP server:- YouTube: https://www.youtube.com/watch?v=0zXyCQV4A84- Apple: https://podcasts.apple.com/nz/podcast/jack-herrington-model-context-protocol-mcp-growing/id1731855333?i=1000698551942- Spotify: https://open.spotify.com/episode/5u7ReU2AMnS3TOYuiSwVY1?si=HrBzavRGThOITtYdXDloTA- John McBride episode about fine-tuning Mistral 7B at OpenSauced- YouTube: https://www.youtube.com/watch?v=ipbhB3k0ik0- Apple: https://podcasts.apple.com/us/podcast/1731855333?i=1000663298584- Spotify: https://open.spotify.com/episode/77UWTis0TxCd1uPOZhGAnJ?si=CUGmHtJ2RxWhmW5MI3XYbgSummaryThis episode is a long-form lecture on AI innovation in 2025. We cover a wide range of topics. For more details, see chapters below.Chapters00:00:00 Intro00:02:31 What is AI?00:07:30 Limitations of AI00:14:29 Solving AI Problems with RAG00:22:51 Embeddings and Vector Databases Explained00:31:23 Hybrid Search: Vectors and Keywords (BM25)00:38:17 Rerankers for Maximum Accuracy00:43:51 RAG vs. Fine-Tuning00:54:29 AI Agents01:13:12 Model Context Protocol (MCP)01:26:12 How to Get Started01:34:04 Conclusion

Links- Codecrafters: https://tej.as/codecrafters- Tiny Fish: https://tinyfish.io- AgentQL: https://www.agentql.com/SummaryIn this conversation, we discuss AgentQL, a framework designed to enable AI agents to access the web using natural language. Together, we explore the technical aspects of AgentQL, its advantages over traditional web access methods, and the challenges faced in its development. The discussion also covers the role of TinyFish, the parent company of AgentQL, and the future direction of their products. Key use cases for developers are highlighted, showcasing how AgentQL can simplify web scraping and automation tasks. We deep dive into the integration of Playwright with AgentQL, the engineering decisions behind its development, and the importance of maintaining consistency across different SDKs. The conversation also touches on the challenges of remote browsing, security concerns, and the complexities of navigating data structures. Additionally, the various operating modes of AgentQL are explored, highlighting the trade-offs between speed and accuracy. Chapters03:25 Introduction to AgentQL06:33 The Technical Framework of AgentQL09:34 Challenges with Traditional Web Access12:35 The Role of TinyFish and Future Products15:25 Technical Hurdles in Building AgentQL18:26 Interacting with the DOM21:29 Use Cases for Developers24:21 Building with AgentQL27:35 Disambiguation and Query Context30:32 Balancing Precision and Flexibility33:30 Future Directions and Enhancements36:36 Integrating Playwright with AgentQL38:56 Building Infrastructure for Remote Browsing39:30 Engineering Decisions in AgentQL Development45:05 Web Test Automation and AgentQL45:55 SDK Development: Python vs JavaScript47:39 Maintaining Consistency Across Languages51:40 Cross-Browser Support with Playwright54:17 Security Concerns in Remote Browsing59:14 Navigating Complex Data Structures01:03:36 Operating Modes of AgentQL01:04:20 Understanding Browser Fingerprinting and Anti-Bot Measures01:06:31 Exploring AgentQL's Browser Toolkit for Langchain01:09:15 AgentQL's Potential in Automating Workflows01:10:17 The Future of Email Automation with AgentQL01:11:34 Navigating the Challenges of Building a Startup01:16:20 Achieving Success on Product Hunt01:19:30 Implementation Pitfalls for New AgentQL Developers01:21:37 Founder's Playbook: Lessons Learned

Links- Codecrafters (partner): https://tej.as/codecrafters- Snyk: https://snyk.io/- Liran on X: https://x.com/liran_tal- Tejas on X: https://x.com/tejaskumar_SummaryIn this conversation, we explore the complexities of software security, particularly focusing on the challenges posed by Node.js and the broader software supply chain. We discuss the evolution of security practices, the importance of awareness among developers, and the role of automation in enhancing security measures. The conversation highlights the need for a balance between automated tools and manual audits, emphasizing that human oversight remains crucial in high-risk environments. We also explore the vulnerabilities associated with open-source software and the trust developers place in third-party tools and extensions, specifically the importance of SBOMs in understanding software dependencies. We discuss the SolarWinds attack as a pivotal case in supply chain security and the role of tools like lockfile lint in enforcing security policies. Finally, we discuss AI and the role of LLMs in security, particularly regarding attack vectors and the reliability of AI-generated code.Chapters00:00 Liran Tal01:44 Introduction to Security in Software Development04:53 The Evolution of Node.js and Security Challenges07:29 Understanding Software Supply Chain Vulnerabilities10:49 The Role of Open Source in Security13:51 Exploring Security in Development Tools and Extensions16:40 The Importance of Security Awareness and Training19:40 Automating Security: Tools and Best Practices22:30 The Balance Between Automation and Manual Audits25:43 Conclusion and Future of Security in Software Development35:00 Balancing Automation and Human Intervention in Security38:08 Understanding S-BOMs and Their Importance41:14 The SolarWinds Attack: A Case Study in Supply Chain Security43:29 Lockfile Lint: Enforcing Security Policies in Code46:49 Generating SBOMs: A Practical Approach49:03 Demystifying CVSS: Understanding Vulnerability Scoring52:50 AI in Security: Attack Vectors and Defense Strategies59:52 Navigating Security in AI-Generated Code01:05:39 The Role of LLMs in Security Vulnerability Detection01:08:24 Integrating Agents for Secure Code Generation01:11:16 Challenges of LLMs in Security Validation01:14:42 The Complexity of Security in AI Systems01:20:56 Understanding Fuzzing and AI's Role01:24:08 Container Breakout Threats and Mitigation Strategies

Links- Codecrafters (sponsor): https://tej.as/codecrafters- Jack on YouTube: https://www.youtube.com/@jherr- Jack on X: https://x.com/jherr- Jack on Bluesky: https://bsky.app/profile/jherr.dev- Tejas on X: https://x.com/tejaskumar_- create-tsrouter-app: https://github.com/TanStack/create-tsrouter-appSummaryIn this discussion, Jack Harrington and I explore the transition from being a content creator to an open source contributor, discussing the challenges and rewards of both paths. Jack shares his journey from being a principal engineer to a YouTuber, and now to a key player in the open source community with TanStack. We explore the intricacies of YouTube's algorithm, the importance of marketing oneself, and the unique features of Tanstack that allow for a progressive development experience. We also touch on the future of Tanstack, its cross-platform capabilities, and the potential integration with React Native. We also discuss AI! Specifically, we discuss the Model Context Protocol (MCP) and how it provides tools and resources to AI, enabling seamless integration with applications. We explore the potential of local development with MCP, emphasizing its advantages over traditional cloud-based solutions. Chapters00:00 Jack Herrington06:11 Transitioning from Influencer to Open Source Contributor09:10 The YouTube Journey: Challenges and Growth12:13 Navigating the YouTube Algorithm and Marketing Yourself15:09 The Shift to Open Source and Community Engagement18:18 Creating Tanstack: A New Era in Development20:55 The Unique Features of Tanstack and Its Ecosystem24:09 Progressive Disclosure in Frameworks26:54 Cross-Platform Capabilities of Tanstack30:16 The Future of Tanstack and React Native Integration40:05 Navigating the Tanstack Ecosystem42:21 Understanding Model Context Protocol (MCP)54:04 Integrating MCP with AI Applications01:05:09 The Future of Local Development with MCP01:11:03 Creating a Winamp Clone with AI01:17:07 The Future of Front-End Development and AI01:24:49 Connecting Dots: The Power of MCP and AI Tools01:33:27 The Entrepreneurial Spirit: Beyond Money01:39:27 Closing Thoughts and Future Collaborations

Links- Codecrafters (sponsor): https://tej.as/codecrafters- Feedback Intelligence: https://www.feedbackintelligence.ai/- Chinar on X: https://x.com/movsisyanchinarSummaryIn this podcast episode, we talk to Chinar Movsisyan, the CEO and founder of Feedback Intelligence. They discuss Chinar's extensive background in AI, including her experience in machine learning and computer vision. We discuss the challenges faced in bridging the gap between technical and non-technical stakeholders, the practical applications of feedback intelligence in enhancing user experience, and the importance of identifying failure modes. The discussion also covers the role of LLMs in the architecture of Feedback Intelligence, the company's current stage, and how it aims to make feedback actionable for businesses. Chapters00:00 Chinar Movsisyan02:08 Introduction to Feedback Intelligence03:23 Chinar Movsisyan's Background and Expertise06:33 Understanding AI Engineer vs. GenAI Engineer09:08 The Lifecycle of Building an AI Application13:27 Data Collection and Cleaning Challenges16:20 Training the AI Model: Process and Techniques24:48 Deploying and Monitoring AI Models in Production27:55 The Birth of Feedback Intelligence31:58 Understanding Feedback Intelligence33:26 Practical Applications of Feedback Intelligence42:13 Identifying Failure Modes45:58 The Role of LLMs in Feedback Intelligence51:25 Company Stage and Future Directions57:24 Making Feedback Actionable01:01:30 Streamlining Processes with Automation01:03:18 The Journey of a First-Time Founder01:05:48 Wearing Many Hats: The Founder Experience01:08:22 Prioritizing Features in Early Startups01:13:09 Learning from Customer Interactions01:16:38 The Importance of Problem-Solving01:21:51 Handling Rejection and Staying Motivated01:27:43 Marketing Challenges for Founders01:29:23 Future Plans and Scaling Strategies