ConTejas Code

Links- Codecrafters (sponsor): https://tej.as/codecrafters- TC39 Signals Proposal: https://github.com/tc39/proposal-signalsSummaryIn this conversation, Kristen Maevyn, and Daniel Ehrenberg discuss reactivity and its importance in JavaScript. They explore the concept of reactivity, its role in keeping applications deterministic, and the challenges of achieving consistency in modern rendering frameworks. They also compare signals to observables and explain why signals are being considered as a language-level feature. The conversation touches on the API for signals, the use of classes in JavaScript, and the benefits of functional programming. Signals are an evolution of the long-standing problem of managing state in JavaScript applications. They provide a solution to the issues with classes and functions by combining the benefits of both. Signals allow for encapsulation, testing in isolation, and pure functions while still being able to handle state. Chapters00:00:00 Intro00:09:57 What are signals?00:26:37 Classes and the `new` keyword00:41:31 State Management and Signals00:49:25 Push-based vs. Pull-based Reactivity01:04:43 Language-level vs. Framework-level Signals01:14:27 Server-side Signals01:25:06 Self-referencing Signals01:36:29 Audience Q&A01:47:07 Conclusion

Links- Codecrafters (sponsor): https://tej.as/codecrafters- React Africa (partner): https://react-africa.com/- Browserbase: https://browserbase.com/- Browserbase Series A Announcement: https://www.kleinerperkins.com/perspectives/browserbase-AI-seriesa/- Paul on X: https://x.com/pk_ivSummaryIn this conversation, we talk to Paul Klein IV, the founder of BrowserBase. We discuss the concept of browser-based software and its potential to automate tasks on the web. Paul explains that the future of software is one where it does the work for us, and Browserbase is designed to power this future by providing a programmable web browser for AI applications. We also touch on the challenges of running browser automation frameworks in production and how Browserbase solves these challenges. Paul introduces Stagehand, an open-source SDK that helps developers automate web workflows, and emphasizes the importance of ethical use of browser automation tools. Customers are building various applications on Browserbase, including web scraping, testing, AI agents, B2B software, and professional services. The company has experienced rapid growth, with thousands of hours of browser sessions per week. To handle the scale, Browserbase had to make architectural changes, such as moving from Postgres to ClickHouse for better performance. They also had to address challenges like connection management, resource allocation, and debugging customer issues. The founder believes they have achieved product-market fit based on strong customer retention and repeatable growth strategies. Browserbase is a technical product, so the number one priority for hiring is that candidates are technical and understand what Browserbase does. The team is currently focused on growing the top of the funnel and the sales edge side. They have specific roles for growth engineers and customer-focused engineers. The company recently raised a Series A round of $27M, with the goal of becoming a category-defining infrastructure company. The pricing model for Browserbase includes a hobby plan, a startup plan, and a scale plan. The company offers 10 free sessions for users to try out the product.Chapters00:00:00 Paul Klein IV00:04:41 Why Browserbase?00:11:57 What is Browserbase?00:19:09 Stagehand: the open source SDK00:35:30 How People are Using Browserbase00:49:17 Creating web APIs where there are none00:59:40 Product-Market Fit01:06:16 AI Engineering at Browserbase01:08:42 Remote Work vs. Onsite01:13:36 Browserbase's Series A Round01:20:02 AI Fatigue01:28:11 Advice for Founders01:32:10 Conclusion

Links- Codecrafters (sponsor): https://tej.as/codecrafters- React Brussels: https://react.brussels- React Africa: https://react-africa.com- Kelly on X: https://x.com/kvlly- Spot AI: https://spot.ai- Tejas on X: https://x.com/tejaskumarSummaryKelly Vaughn, Director of Engineering at Spot AI, shares her unique career journey from self-taught coder to entrepreneur and now corporate leader. She discusses running a Shopify agency, co-founding a venture-backed startup, and transitioning to her current role. Key points include:- The challenges of running an agency and managing employee livelihoods- Insights on bootstrapping vs. venture funding for startups- The importance of genuine relationships and networking in career growth- Adapting to different company stages and cultures- Navigating the transition from entrepreneur to corporate employee- The value of diverse backgrounds in tech leadership rolesKelly emphasizes the importance of self-reflection, embracing change, and learning from failures throughout one's career journey.Chapters00:00:00 Intro00:05:08 Spot AI: Video Intelligence00:12:46 Dealing with Rapid Growth00:17:05 Getting into Tech00:22:25 TapRoom Consultancy/Shopify00:32:35 Running a Consultancy00:39:09 Venture Capital or no?00:40:08 Shutting Down Consultancy00:41:11 Attention, Effort, Mistakes as a Path to Growth00:47:26 The Pressure of Running a Business00:52:05 How to find a job: networking01:01:02 Being taken advantage of01:04:59 How Kelly got her audience01:08:31 Big audience problems01:12:01 Therapist and Engineering Leadership01:18:18 Boundaries01:26:06 Advice for Founders

Links- Codecrafters (sponsor): https://tej.as/codecrafters- Upvote Fashn on ProductHunt: https://www.producthunt.com/posts/fashn-virtual-try-on- Fashn: https://fashn.ai- Aya on 𝕏: https://x.com/ayaboch- React Africa (partner): https://react-africa.comSummaryAya Bochman, co-founder of Fashion AI, discusses her journey in creating a generative AI platform for virtual try-on in the fashion industry. She shares insights on building a self-funded startup, the challenges of developing AI models, and the importance of work-life balance for founders.Chapters00:00:00 Introduction to Fashion AI00:07:42 Demo and product description00:11:10 Technical challenges in virtual try-on00:17:48 Data collection and labeling process00:26:51 API development and pricing model00:34:45 Collaboration with other services (RunPod, Fal.ai)00:43:02 Dealing with content moderation00:46:46 Business model and pricing structure00:52:12 Incident with Meta and open-source code00:59:10 Future plans and potential competition01:03:13 Advice for technical founders01:20:12 Work-life balance and avoiding burnout01:29:51 Working with a spouse as a co-founder

Links- Codecrafters (sponsor): https://tej.as/codecrafters- React Brussels: https://react.brussels- React Africa: https://react-africa.com - Cal.com: https://cal.com- Peer on X: https://x.com/peer_rich- Tejas on X: https://x.com/tejaskumar_SummaryIn this discussion, Peer Richelsen, co-founder of Cal.com, shared insights into the company's open-source scheduling infrastructure. Cal.com offers customizable scheduling solutions for individuals and enterprises across various industries, with its open-source approach driving rapid development and community engagement. Richelsen highlighted key features like "instant meetings" for customer support and discussed the platform's technical architecture, primarily based on Next.js with plans to partially migrate to Nest.js.The conversation also touched on Cal.com's cautious approach to AI integration, its structure as a U.S. entity despite European roots, and commitment to transparency in operations. Richelsen expressed satisfaction with Cal.com's current trajectory, outlining plans to enhance offerings in healthcare scheduling, improve sales team routing, and expand capabilities across different sectors. Throughout the discussion, Richelsen's passion for open-source development and innovative scheduling solutions was evident.Chapters00:00:00 Intro00:03:52 Supabase hate00:04:41 What is Cal.com?00:09:12 Balancing Open Source Maintenance and New Features00:14:34 Prioritizing Customer Feedback00:18:33 Cal.com's Product-Market fit00:22:12 Cal.com's Transparency (Open Startup)00:25:02 Cal.com's Hiring Process00:29:10 Location based pay nuance00:35:55 Cal.com stats: active users and recurring revenue00:42:51 Cal.com technical deep dive00:54:06 Cal.com API Platform00:58:01 Cal.com Atoms01:06:15 Cal.ai AI-enabled scheduling agent01:18:16 What founders should know about running a startup01:27:24 Future experimental Cal.com features01:30:36 Germany's Startup Reputation (bad)01:33:57 Positioning and Marketing for Cal.com

Links- Codecrafters (sponsor): https://tej.as/codecrafters- React Africa (partner): https://react-africa.com/- Harper Carroll: https://harpercarrollai.com/- 10 Days of AI Basics Course: https://www.youtube.com/watch?v=Ie7qxG9os1U&list=PL-ocKywdn6lE9l4LIPL4gMY4nNRfjEswG&pp=iAQB- Harper on X: https://x.com/harperscarroll- Isabelle Boemeke on X: https://x.com/isabelleboemeke- Tejas on X: https://x.com/tejaskumar_SummaryIn this enlightening conversation with Harper Carroll, we dove deep into the world of AI, covering everything from the basics of neural networks to the cutting-edge concepts of AI agents. Harper's expertise, stemming from her 10 years of Stanford education, provided invaluable insights into the inner workings of machine learning models. We explored the environmental impact of AI and the potential role of nuclear energy in sustaining AI development. Harper's balanced view on AI's future was refreshing, emphasizing its potential for optimization rather than doom-and-gloom scenarios. The discussion concluded with thought-provoking reflections on AI's potential to free humanity for more creative pursuits and its possible connection to fundamental universal principles of peace and love.Chapters00:00:00 Intro00:06:09 What is AI?00:11:34 Machine Learning Deep Dive00:17:08 Impostor Syndrome00:22:11 Machine Learning Deep Dive Continued00:28:04 What are Hidden Layers?00:34:29 Model Architectures00:37:04 How do embeddings models work?00:40:23 AI Engineering Deep Dive00:48:06 Smaller specialized models vs. LLMs (Large Language Models)00:49:57 Hallucinations and RAG00:52:16 Fine-tuning a model and blends00:55:24 RAG vs. Fine-tuning01:00:31 RAG Explained01:04:15 Machine Learning Evals01:10:28 Backpropagation01:12:44 AI Agents01:16:25 Agentic RAG01:17:51 AI and Energy: The rise of nuclear power01:25:06 AI Optimism vs. Doomerism01:31:27 AGI and Superintelligence01:36:22 Hope for the future with AI01:41:03 Conclusion