Podcasts about langchain

This week we want to introduce you to a new podcast from Intercom - Fin Meetups: Conversations with AI Leaders.Explore the promise and practice of AI, from the leaders building it. Hear conversations from founders, engineering, product and customer service leaders, as they discuss the key opportunities and challenges of the AI era.Episode 1 is our Fin x LangChain meetup in Amsterdam, where Fergal Reid, Chief AI Officer, Intercom, building Fin and Marco Perrini, Deployed Engineer, LangChain, shared how Fin evolved from hand-coded experiments to a complex, multi-model system powered by LangGraph.Make sure to follow Fin Meetups: Conversations with AI Leaders:Apple Podcasts: https://podcasts.apple.com/us/podcast/fin-meetups-conversations-with-ai-leaders/id1852609029Spotify: https://open.spotify.com/show/2Ae5VqOQ0ftClgEI8ltqOqOr wherever you listen to podcasts.If you'd like to join future Fin meetups, subscribe on Luma → https://luma.com/FINSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Gemini 3 is a few days old and the massive leap in performance and model reasoning has big implications for builders: as models begin to self-heal, builders are literally tearing out the functionality they built just months ago... ripping out the defensive coding and reshipping their agent harnesses entirely. Ravin Kumar (Google DeepMind) joins Hugo to breaks down exactly why the rapid evolution of models like Gemini 3 is changing how we build software. They detail the shift from simple tool calling to building reliable "Agent Harnesses", explore the architectural tradeoffs between deterministic workflows and high-agency systems, the nuance of preventing context rot in massive windows, and why proper evaluation infrastructure is the only way to manage the chaos of autonomous loops. They talk through: - The implications of models that can "self-heal" and fix their own code - The two cultures of agents: LLM workflows with a few tools versus when you should unleash high-agency, autonomous systems. - Inside NotebookLM: moving from prototypes to viral production features like Audio Overviews - Why Needle in a Haystack benchmarks often fail to predict real-world performance - How to build agent harnesses that turn model capabilities into product velocity - The shift from measuring latency to managing time-to-compute for reasoning tasks LINKS From Context Engineering to AI Agent Harnesses: The New Software Discipline, a podcast Hugo did with Lance Martin, LangChain (https://high-signal.delphina.ai/episode/context-engineering-to-ai-agent-harnesses-the-new-software-discipline) Context Rot: How Increasing Input Tokens Impacts LLM Performance (https://research.trychroma.com/context-rot) Effective context engineering for AI agents by Anthropic (https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/CloimQsQuJM) Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

How do you build systems with AI? Not code-generating assistants, but production systems that use LLMs as part of their processing pipeline. When should you chain multiple agent calls together versus just making one LLM request? And how do you debug, test, and deploy these things? The industry is clearly in exploration mode—we're seeing good ideas implemented badly and expensive mistakes made at scale. But Google needs to get this right more than most companies, because AI is both their biggest opportunity and an existential threat to their search-based business model.Christina Lin from Google joins us to discuss Agent Development Kit (ADK), Google's open-source Python framework for building agentic pipelines. We dig into the fundamental question of when agent pipelines make sense versus traditional code, exploring concepts like separation of concerns for agents, tool calling versus MCP servers, Google's grounding feature for citation-backed responses, and agent memory management. Christina explains A2A (Agent-to-Agent), Google's protocol for distributed agent communication that could replace both LangChain and MCP. We also cover practical concerns like debugging agent workflows, evaluation strategies, and how to think about deploying agents to production.If you're trying to figure out when AI belongs in your processing pipeline, how to structure agent systems, or whether frameworks like ADK solve real problems versus creating new complexity, this episode breaks down Google's approach to making agentic systems practical for production use.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinGoogle Agent Development Kit Announcement: https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/ADK on GitHub: https://google.github.io/adk-docs/Google Gemini: https://ai.google.dev/gemini-apiGoogle Vertex AI: https://cloud.google.com/vertex-aiGoogle AI Studio: https://aistudio.google.com/Google Grounding with Google Search: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overviewModel Context Protocol (MCP): https://modelcontextprotocol.io/Anthropic MCP Servers: https://github.com/modelcontextprotocol/serversLangChain: https://www.langchain.com/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

Ev Randle is a General Partner @ Benchmark, one of the best funds in venture capital. In their latest fund, they have Mercor ($10BN valuation), Sierra ($10BN valuation), Firework ($4BN valuation), Legora ($2Bn valuation) and Langchain ($1.4Bn valuation). To put this in multiples on invested capital, that is a 60x, two 30x and two 20x. Before Benchmark, Ev was a Partner @ Kleiner Perkins and before Kleiner, Ev was an investor at Founders Fund and Bond.  AGENDA: 05:25 Biggest Investing Lessons from Peter Thiel, Mary Meeker and Mamoon Hamid 14:36 OpenAI Will Be a $TRN Company & OpenAI or Anthropic: Who Wins Coding? 22:27 Why We Should Not Focus on Margin But Gross Dollar Per Customer 30:25 Why AI Labs are the Biggest Threat to AI App Companies 44:26 Do Benchmark Fire Founders? If so… Truly the Best Partner? 54:38 People, Product, Market: Rank 1-3 and Why? 57:36 Why the Mega Funds Have Just Replaced Tiger 01:04:08 GC, Lightspeed and a16z Cannot Do 5x on Their Funds…  01:14:09 Single Biggest Threat to Benchmark  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the $1.7B acquisition of Security AI, LangChain's massive valuation, and the surprise $300M funding” round for Periodic Labs. They The post SED News: AMD's Big OpenAI Deal, Intel's Struggles, and Apple's AI Long Game appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the $1.7B acquisition of Security AI, LangChain's massive valuation, and the surprise $300M funding” round for Periodic Labs. They The post SED News: AMD's Big OpenAI Deal, Intel's Struggles, and Apple's AI Long Game appeared first on Software Engineering Daily.

In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of "dogfooding" - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification. The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications. We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the "sweet spot" by asking employees what they hate most about their jobs. The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers "cannot be trusted." Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development. What was launched at Ship AI 2025: AI SDK 6.0 & Agent Architecture Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can "define once, deploy everywhere". How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design? Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy? Type Safety Across Models: AI SDK 6 promises "end-to-end type safety across models and UI". Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral? Workflow Development Kit (WDK) Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern? Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)? Vercel Agent (beta) Code Review Validation: The Agent reviews code and proposes "validated patches". What does "validated" mean in this context? Are you running automated tests, static analysis, or something more sophisticated? AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies? Python Support (For the first time, Vercel now supports Python backends natively.) Marketplace & Agent Ecosystem Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model? "An Agent on Every Desk" Program Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agent

George Werbacher, Head of Security Operations at Live Oak Bank, reviews the practical realities of implementing AI agents in security operations, sharing his journey from exploring tools like Cursor and Claude Code to building custom agents in-house. He also reflects on the challenges of moving from local development to production-ready systems with proper durability and retry logic. The conversation explores how AI is changing the security analyst role from alert analysis to deeper investigation work, why SOAR platforms face significant disruption, and how MCP servers enable natural language interactions across security tools. George offers pragmatic advice on cutting through AI hype, emphasizing that agents augment rather than replace human expertise while dramatically lowering barriers to automation and query language mastery. Through technical insights and leadership perspective, George illuminates how security teams can embrace AI to improve operational efficiency and mean time to detect without inflating budgets, while maintaining the critical human judgment that effective security demands. Topics discussed: Understanding AI's role in augmenting security analysts rather than replacing them, shifting roles toward investigation and threat hunting. Building custom AI agents using Python and exploring frameworks like LangChain to solve specific SecOps use cases. Managing moving agents from local development to production, including retry logic, failbacks, and durability requirements. Implementing MCP servers to enable natural language interactions with security tools, eliminating the need to learn multiple query languages. Navigating AI hype by focusing on solving specific problems and understanding what agents can realistically accomplish. Predicting SOAR platform disruption as agents take over enrichment, orchestration, and response with simpler automation approaches. Removing platform barriers by enabling analysts to use natural language rather than mastering specific tools or query languages. Exploring context management, prompt engineering, and conversation history techniques essential for building effective agentic systems. Adopting tools like Cursor and Claude Code to empower technical security professionals without deep coding backgrounds.  Listen to more episodes:  Apple  Spotify  YouTube Website

Dell Technologies has announced Dell AI Data Platform advancements designed to help enterprises turn distributed, siloed data into faster, more reliable AI outcomes. Why it matters As enterprise AI adoption surges and data grows, organisations need a platform that can securely transform distributed, siloed data into actionable insights. The Dell AI Data Platform, a critical component of the Dell AI Factory, delivers an open, modular foundation to create value from scattered data silos. By decoupling data storage from processing, it eliminates bottlenecks and provides the flexibility needed for AI workloads like training, fine-tuning, retrieval-augmented generation (RAG) or inferencing. The platform, integrated with the NVIDIA AI Data Platform reference design, is powered by four core building blocks: Storage engines for smart data placement and seamless data movement Data engines to turn data into actionable insights Built-in cyber resiliency Data management services Together, they create a scalable, flexible foundation for customers to realise AI's full potential. Dell AI Data Platform storage engines deliver peak AI performance Dell PowerScale and Dell ObjectScale, the Dell AI Data Platform's storage engines, offer the performance, security and multi-protocol access essential for AI data. Dell PowerScale delivers NAS (network-attached storage) simplicity and parallel performance for AI workloads like training, fine-tuning, inferencing and retrieval-augmented generation (RAG) pipelines. With new integration of NVIDIA GB200 and GB300 NVL72 and ongoing software updates, Dell PowerScale delivers reliable performance, simplified management at scale and seamless compatibility with applications and solution stacks. PowerScale F710, which has achieved NVIDIA Cloud Partner (NCP) certification for high-performance storage, delivers 16k+ GPU-scale with up to 5X less rack space, 88% fewer network switches and up to 72% lower power consumption compared to competitors. Dell ObjectScale, the industry's highest-performing object platform, provides extremely performant, scalable S3-native object storage for massive AI workloads. ObjectScale is available as an appliance or through a new software-defined option on Dell PowerEdge servers that is up to 8 times faster than previous-generation all-flash object storage. New advancements improve ObjectScale's speed, scalability and efficiency. S3 over RDMA support will soon enter tech preview. It will offer up to 230% higher throughput, 80% lower latency and 98% lower CPU usage compared to traditional S3. Small object performance and efficiency improvements for large deployments deliver up to 19% higher throughput and up to 18% lower latency for 10KB objects. Deeper AWS S3 integration and bucket-level compression give developers and data scientists better tools to store, move and use large amounts of data. Dell AI Data Platform data engines power real-time AI Dell is also expanding its data engines, the specialised tools in the Dell AI Data Platform that organise, query and activate AI data. Dell's data engines are built in collaboration with trusted AI leaders like NVIDIA, Elastic and Starburst. The new Data Search Engine, developed in collaboration with Elastic, speeds decision-making by allowing customers to interact with data as naturally as asking a question. Designed for tasks like RAG, semantic search and generative AI pipelines, it integrates with MetadataIQ data discovery software to search billions of files on PowerScale and ObjectScale using granular metadata. Developers can build smarter RAG applications in tools like LangChain with the engine, ingesting only updated files to save compute time and keep vector databases current. The Data Analytics Engine, developed in collaboration with Starburst, enables seamless data querying across spreadsheets, databases, cloud warehouses and lakehouses. The new Data Analytics Engine Agentic Layer transforms raw data into business-ready products in...

welcome to wall-e's tech briefing for wednesday, october 22! explore today's key topics in tech: openai atlas launch: openai introduces the atlas web browser, leveraging chatgpt for conversational search, challenging google's dominance in online search and advertising. netflix & generative ai: ceo ted sarandos discusses generative ai as a tool for enhancing, not replacing, creative storytelling, already utilizing it for special effects. langchain's unicorn status: with a $1.25 billion valuation, langchain garners significant investment, cementing its role in aiding ai agent development through its open source framework. call for google regulation: cloudflare's ceo matthew prince advocates for tighter regulations on google's search and ai practices in the u.k., citing competitive concerns. aws outage resolution: amazon resolves a dns issue causing significant internet disruptions, underscoring the dependency on aws infrastructure by major websites and services. stay tuned for more tech insights tomorrow!

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Amey Desai, the Chief Technology Officer at Nexla, speaks with host Sriram Panyam about the Model Context Protocol (MCP) and its role in enabling agentic AI systems. The conversation begins with the fundamental challenge that led to MCP's creation: the proliferation of "spaghetti code" and custom integrations as developers tried to connect LLMs to various data sources and APIs. Before MCP, engineers were writing extensive scaffolding code using frameworks such as LangChain and Haystack, spending more time on integration challenges than solving actual business problems. Desai illustrates this with concrete examples, such as building GitHub analytics to track engineering team performance. Previously, this required custom code for multiple API calls, error handling, and orchestration. With MCP, these operations can be defined as simple tool calls, allowing the LLM to handle sequencing and error management in a structured, reasonable manner. The episode explores emerging patterns in MCP development, including auction bidding patterns for multi-agent coordination and orchestration strategies. Desai shares detailed examples from Nexla's work, including a PDF processing system that intelligently routes documents to appropriate tools based on content type, and a data labeling system that coordinates multiple specialized agents. The conversation also touches on Google's competing A2A (Agent-to-Agent) protocol, which Desai positions as solving horizontal agent coordination versus MCP's vertical tool integration approach. He expresses skepticism about A2A's reliability in production environments, comparing it to peer-to-peer systems where failure rates compound across distributed components. Desai concludes with practical advice for enterprises and engineers, emphasizing the importance of embracing AI experimentation while focusing on governance and security rather than getting paralyzed by concerns about hallucination. He recommends starting with simple, high-value use cases like automated deployment pipelines and gradually building expertise with MCP-based solutions. Brought to you by IEEE Computer Society and IEEE Software magazine.

Sylvain Ramousse est VP of AI chez Brevo, la licorne française qui propose une solution de marketing automation qui permet notamment d'orchestrer ses campagnes d'emailing ou de SMS. La scaleup a acquis le statut de “centaure” après avoir dépassé les 100 millions d'euros de revenus annuels.On aborde :

Alex Merced discusses the rising standards within the Agentic AI Space. Buy Alex Merced’s latest book “Architecting an Apache Iceberg Lakehouse” use discount code mercedconf25 for a 40% discount. https://www.manning.com/books/architecting-an-apache-iceberg-lakehouse Follow Alex on Social at AlexMerced.com

Lance: https://www.linkedin.com/in/lance-martin-64a33b5/ How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html How New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.html Content Engineering: https://x.com/RLanceMartin/status/1948441848978309358 https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharing Manus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Cognition Post: https://cognition.ai/blog/dont-build-multi-agents Multi-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-system Human-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch - Bitter Lesson in AI Engineering - Hyung Won Chung on the Bitter Lesson in AI Research: https://www.youtube.com/watch?v=orDKvo8h71o Bitter Lesson w/ Claude Code: https://www.youtube.com/watch?v=Lue8K2jqfKk&t=1s Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/ Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraph Scaling and building things that "don't yet work": https://www.youtube.com/watch?v=p8Jx4qvDoSo - Frameworks - Roast framework at Shopify / standardization of orchestration tools: https://www.youtube.com/watch?v=0NHCyq8bBcM MCP adoption within Anthropic / standardization of protocols: https://www.youtube.com/watch?v=xlEQ6Y3WNNI How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/ RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/ Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/

Miles Grimshaw is a Partner at Thrive Capital, an investment firm that builds and invests in internet, software, and technology-enabled companies. Thrive recently closed on $5BN in new funds and also announced Thrive Holdings, a permanent capital vehicle to invest in, acquire, and operate businesses for the long term with the strategic application of technology.During his time at Thrive, Miles has led investments in companies like Airtable, Monzo, Benchling, Lattice, and more recently Cursor, a code editor built for programming with AI, which you'll hear us chat about. That team raised a $900 million round at a $9.9B valuation in June.Prior to Thrive, Miles was a General Partner at Benchmark, where he led seed investments, most notably in LangChain.We spoke about trillion dollar companies, silicon valley as an idea, business genetics, practicing scales, and Swedish House Mafia.0:00 - Intro2:14 – “The Era of Doing”6:15 – Startup Capital Intensity in the Age of AI9:14 – The Rise of Trillion Dollar Outcomes15:11 – Silicon Valley as an Idea21:04 – Physics vs Biology-Style Investing25:41 – Business Genetics and Compounding33:04 – Dying of Indigestion and Going Multi-Product35:55 – Co-Pilots, Command Centers, and Defensibility40:07 – Investing Stage Agnostically44:29 – When is VC a Good Capital Instrument?49:18 – Thrive's Core Beliefs53:57 – A Bet vs a Commitment57:49 – The Few Ideas Miles Takes Seriously59:47 – Doing a Few Big Things vs a Million Little Things1:03:54 – Practicing Scales1:06:22 – What Should More People Be Thinking About?

Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services.   Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we'll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what's the difference between traditional AI and generative AI?  01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction.  But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you.  Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work.  Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create.   02:16 Lois: How did traditional AI progress to the generative AI we know today?  Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex.  Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc.  Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content.  Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code.  03:55 Nikita: Himanshu, what motivated this sort of progression?   Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models.  And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas.   04:25 Lois: So, what's happening behind the scenes? How is gen AI making these things happen?  Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs.  They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos.  And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities.  It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes.  05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let's talk more about them. Himanshu, what is an LLM and how does it work?  Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before.  This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories.  06:06 Nikita: But what's large about this? Why's it called a large language model?   Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning.  There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human.  06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work?  Himanshu: Diffusion models start with something that looks like pure random noise.  Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside.  And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict.   07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com.  07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I'm sure the uses go far beyond that, right? Can you walk us through some of them?  Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems.  Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents.  The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts.  The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes.  For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization.  Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output.  Text to image It's being used in e-commerce, movie concept art, and educational content creation. For text to video, this is still in early days, but imagine creating a product explainer video just by typing out the script. Marketing teams love this for quick turnarounds. And the last one is text to audio.  Tools like ElevenLabs can convert text into realistic, human like voiceovers useful in training modules, audiobooks, and accessibility apps. So generative AI is no longer just a technical tool. It's becoming a creative copilot across departments, whether it's marketing, design, product support, and even operations.  10:42 Lois: That's great! So, we've established that generative AI is pretty powerful. But what kind of risks does it pose for businesses and society in general?  Himanshu: The first one is deepfakes. Generative AI can create fake but highly realistic media, video, audios or even faces that look and sound authentic.  Imagine a fake video of a political leader announcing a policy, they never approved. This could cause mass confusion or even impact elections. In case of business, deepfakes can be also used in scams where a CEO's voice is faked to approve fraudulent transactions.  Number two, bias, if AI is trained on biased historical data, it can reinforce stereotypes even when unintended. For example, a hiring AI system that favors male candidates over equally qualified women because of historical data was biased.  And this bias can expose companies to discrimination, lawsuits, brand damage and ethical concerns. Number three is hallucinations. So sometimes AI system confidently generate information that is completely wrong without realizing it.   Sometimes you ask a chatbot for a legal case summary, and it gives you a very convincing but entirely made up court ruling. In case of business impact, sectors like health care, finance, or law hallucinations can or could have serious or even dangerous consequences if not caught.  The fourth one is copyright and IP issues, generative AI creates new content, but often, based on material it was trained on. Who owns a new work? A real life example could be where an artist finds their unique style was copied by an AI that was trained on their paintings without permission.  In case of a business impact, companies using AI-generated content for marketing, branding or product designs must watch for legal gray areas around copyright and intellectual properties. So generative AI is not just a technology conversation, it's a responsibility conversation. Businesses must innovate and protect.  Creativity and caution must go together.   12:50 Nikita: Let's move on to generative AI agents. How is a generative AI agent different from just a chatbot or a basic AI tool?  Himanshu: So think of it like a smart assistant, not just answering your questions, but also taking actions on your behalf. So you don't just ask, what's the best flight to Vegas? Instead, you tell the agent, book me a flight to Vegas and a room at the Hilton. And it goes ahead, understands that, finds the options, connects to the booking tools, and gets it done.   So act on your behalf using goals, context, and tools, often with a degree of autonomy. Goals, are user defined outcomes. Example, I want to fly to Vegas and stay at Hilton. Context, this includes preferences history, constraints like economy class only or don't book for Mondays.  Tools could be APIs, databases, or services it can call, such as a travel API or a company calendar. And together, they let the agent reason, plan, and act.   14:02 Nikita: How does a gen AI agent work under the hood?  Himanshu: So usually, they go through four stages. First, one is understands and interprets your request like natural language understanding. Second, figure out what needs to be done, in this case flight booking plus hotel search.  Third, retrieves data or connects to tools APIs if needed, such as Skyscanner, Expedia, or a Calendar. And fourth is takes action. That means confirming the booking and giving you a response like your travel is booked. Keep in mind not all gen AI agents are fully independent.  14:38 Lois: Himanshu, we've seen people use the terms generative AI agents and agentic AI interchangeably. What's the difference between the two?  Himanshu: Agentic AI is a broad umbrella. It refers to any AI system that can perceive, reason, plan, and act toward a goal and may improve and adapt over time.   Most gen AI agents are reactive, not proactive. On the other hand, agentic AI can plan ahead, anticipate problems, and can even adjust strategies.  So gen AI agents are often semi-autonomous. They act in predefined ways or with human approval. Agentic systems can range from low to full autonomy. For example, auto-GPT runs loops without user prompts and autonomous car decides routes and reactions.  Most gen AI agents can only make multiple steps if explicitly designed that way, like a step-by-step logic flows in LangChain. And in case of agentic AI, it can plan across multiple steps with evolving decisions.  On the memory and goal persistence, gen AI agents are typically stateless. That means they forget their goal unless you remind them. In case of agentic AI, these systems remember, adapt, and refine based on goal progression. For example, a warehouse robot optimizing delivery based on changing layouts.  Some generative AI agents are agentic, like auto GPT. They use LLMs to reason, plan, and act, but not all. And likewise not all agentic AIs are generative. For example, an autonomous car, which may use computer vision control systems and planning, but no generative models.  So agentic AI is a design philosophy or system behavior, which could be goal-driven, autonomous, and decision making. They can overlap, but as I said, not all generative AI agents are agentic, and not all agentic AI systems are generative.  16:39 Lois: What makes a generative AI agent actually work?  Himanshu: A gen AI agent isn't just about answering the question. It's about breaking down a user's goal, figuring out how to achieve it, and then executing that plan intelligently. These agents are built from five core components and each playing a critical role.  The first one is goal. So what is this agent trying to achieve? Think of this as the mission or intent. For example, if I tell the agent, help me organized a team meeting for Friday. So the goal in that case would be schedule a meeting.  Number 2, memory. What does it remember? So this is the agent's context awareness. Storing previous chats, preferences, or ongoing tasks. For example, if last week I said I prefer meetings in the afternoon or I have already shared my team's availability, the agent can reuse that. And without the memory, the agent behaves stateless like a typical chatbot that forgets context after every prompt.  Third is tools. What can it access? Agents aren't just smart, they are also connected. They can be given access to tools like calendars, CRMs, web APIs, spreadsheets, and so on.  The fourth one is planner. So how does it break down the goal? And this is where the reasoning happens. The planner breaks big goals into a step-by-step plans, for example checking team availability, drafting meeting invite, and then sending the invite. And then probably, will confirm the booking. Agents don't just guess. They reason and organize actions into a logical path.  And the fifth and final one is executor, who gets it done. And this is where the action takes place. The executor performs what the planner lays out. For example, calling APIs, sending message, booking reservations, and if planner is the architect, executor is the builder.   18:36 Nikita: And where are generative AI agents being used?  Himanshu: Generative AI agents aren't just abstract ideas, they are being used across business functions to eliminate repetitive work, improve consistency, and enable faster decision making. For marketing, a generative AI agent can search websites and social platforms to summarize competitor activity. They can draft content for newsletters or campaign briefs in your brand tone, and they can auto-generate email variations based on audience segment or engagement history.  For finance, a generative AI agent can auto-generate financial summaries and dashboards by pulling from ERP spreadsheets and BI tools. They can also draft variance analysis and budget reports tailored for different departments. They can scan regulations or policy documents to flag potential compliance risks or changes.  For sales, a generative AI agent can auto-draft personalized sales pitches based on customer behavior or past conversations. They can also log CRM entries automatically once submitting summary is generated. They can also generate battlecards or next-step recommendations based on the deal stage.  For human resource, a generative AI agent can pre-screen resumes based on job requirements. They can send interview invites and coordinate calendars. A common theme here is that generative AI agents help you scale your teams without scaling the headcount.   20:02 Nikita: Himanshu, let's talk about the capabilities and benefits of generative AI agents.  Himanshu: So generative AI agents are transforming how entire departments function. For example, in customer service, 24/7 AI agents handle first level queries, freeing humans for complex cases.  They also enhance the decision making. Agents can quickly analyze reports, summarize lengthy documents, or spot trends across data sets. For example, a finance agent reviewing Excel data can highlight cash flow anomalies or forecast trends faster than a team of analysts.  In case of personalization, the agents can deliver unique, tailored experiences without manual effort. For example, in marketing, agents generate personalized product emails based on each user's past behavior. For operational efficiency, they can reduce repetitive, low-value tasks. For example, an HR agent can screen hundreds of resumes, shortlist candidates, and auto-schedule interviews, saving HR team hours each week.  21:06 Lois: Ok. And what are the risks of using generative AI agents?  Himanshu: The first one is job displacement. Let's be honest, automation raises concerns. Roles involving repetitive tasks such as data entry, content sorting are at risk. In case of ethics and accountability, when an AI agent makes a mistake, who is responsible? For example, if an AI makes a biased hiring decision or gives incorrect medical guidance, businesses must ensure accountability and fairness.  For data privacy, agents often access sensitive data, for example employee records or customer history. If mishandled, it could lead to compliance violations. In case of hallucinations, agents may generate confident but incorrect outputs called hallucinations. This can often mislead users, especially in critical domains like health care, finance, or legal.  So generative AI agents aren't just tools, they are a force multiplier. But they need to be deployed thoughtfully with a human lens and strong guardrails. And that's how we ensure the benefits outweigh the risks.  22:10 Lois: Thank you so much, Himanshu, for educating us. We've had such a great time with you! If you want to learn more about the topics discussed today, head over to mylearn.oracle.com and get started on the AI for You course.  Nikita: Join us next week as we chat about AI workflows and tools. Until then, this is Nikita Abraham…  Lois: And Lois Houston signing off!  22:32 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You'll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code. Episode sponsors Posit Talk Python Courses Links from the show Matt Makai: linkedin.com Plushcap Developer Content Analytics: plushcap.com DigitalOcean Gradient AI Platform: digitalocean.com DigitalOcean YouTube Channel: youtube.com Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com AI Changes Everything: lucumr.pocoo.org Claude Code - 47 Pro Tips in 9 Minutes: youtube.com Cursor AI Code Editor: cursor.com JetBrains Junie: jetbrains.com Claude Code by Anthropic: anthropic.com Full Stack Python: fullstackpython.com Watch this episode on YouTube: youtube.com Episode #517 deep-dive: talkpython.fm/517 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

MariaDB is a name with deep roots in the open-source database world, but in 2025 it is showing the energy and ambition of a company on the rise. Taken private in 2022 and backed by K1 Investment Management, MariaDB is doubling down on innovation while positioning itself as a strong alternative to MySQL and Oracle. At a time when many organisations are frustrated with Oracle's pricing and MySQL's cloud-first pivot, MariaDB is finding new opportunities by combining open-source freedom with enterprise-grade reliability. In this conversation, I sit down with Vikas Mathur, Chief Product Officer at MariaDB, to explore how the company is capitalising on these market shifts. Vikas shares the thinking behind MariaDB's renewed focus, explains how the platform delivers similar features to Oracle at up to 80 percent lower total cost of ownership, and details how recent innovations are opening the door to new workloads and use cases. One of the most significant developments is the launch of Vector Search in January 2023. This feature is built directly into InnoDB, eliminating the need for separate vector databases and delivering two to three times the performance of PG Vector. With hardware acceleration on both x86 and IBM Power architectures, and native connectors for leading AI frameworks such as LlamaIndex, LangChain and Spring AI, MariaDB is making it easier for developers to integrate AI capabilities without complex custom work. Vikas explains how MariaDB's pluggable storage engine architecture allows users to match the right engine to the right workload. InnoDB handles balanced transactional workloads, MyRocks is optimised for heavy writes, ColumnStore supports analytical queries, and Moroonga enables text search. With native JSON support and more than forty functions for manipulating semi-structured data, MariaDB can also remove the need for separate document databases. This flexibility underpins the company's vision of one database for infinite possibilities. The discussion also examines how MariaDB manages the balance between its open-source community and enterprise customers. Community adoption provides early feedback on new features and helps drive rapid improvement, while enterprise customers benefit from production support, advanced security, high availability and disaster recovery capabilities such as Galera-based synchronous replication and the MacScale proxy. We look ahead to how MariaDB plans to expand its managed cloud services, including DBaaS and serverless options, and how the company is working on a “RAG in a box” approach to simplify retrieval-augmented generation for DBAs. Vikas also shares his perspective on market trends, from the shift away from embedded AI and traditional machine learning features toward LLM-powered applications, to the growing number of companies moving from NoSQL back to SQL for scalability and long-term maintainability. This is a deep dive into the strategy, technology and market forces shaping MariaDB's next chapter. It will be of interest to database architects, AI engineers, and technology leaders looking for insight into how an open-source veteran is reinventing itself for the AI era while challenging the biggest names in the industry.

Episode 254: What if the future of AI in customer experience is built not by giant platforms but by small, reusable, open source AI agents? Gurdeep Pall, President of AI Strategy at Qualtrics, believes open, modular AI agents will outmaneuver big tech's locked-down systems. In this conversation from the X4 Summit, Gurdeep argues that “experience agents”—task-specific bots that can plug into any stack—will give companies more control, better performance, and real freedom. Closed AI platforms promise convenience, but they trap businesses in rigid walled gardens. Gurdeep argues that modular architectures unlock something better: flexibility, reuse, and evolution. “Break down the agents to very specific functionality,” he says. “And those agents can be invoked by many different agents for different types of tasks.” This isn't just a tech choice. It's a business and philosophical stance. Qualtrics is partnering with LangChain and releasing open connectors to build an ecosystem of interoperable agents. The goal? Let companies mix, match, and scale customer-facing systems without depending on any one vendor. “This is one semantic level up,” he says, comparing today's agentic architectures to the launch of the web and mobile eras. “What agents are going to do for user experience—taking our digital game to the next level—is very exciting.” Guest: Gurdeep Pall, President of AI Strategy, Qualtrics Host: Rob Markey, Partner, Bain & Company Give Us Feedback: https://bit.ly/CCPodcastFeedback Time-Stamped Topics: (00:01) Why Qualtrics is going all-in on open agentic AI (00:04) An overview of the Qualtrics and LangChain partnership (00:06) The modular architecture of “experience agents” (00:08) Why one task might require seven agents (00:09) How specialization allows reuse and scale (00:10) Rejecting the walled garden model (00:11) Making open systems friction-free (00:12) A real-time use case from the X4 stage (00:14) Plug and play simplicity for complex integrations (00:15) Why this is a new digital paradigm Time-Stamped Quotes: [7:00]  “It's about how you break up the task. Like, when you call the human, the human didn't sit there and not do anything and the password got reset. The human went to a piece of software and they went and worked on it. So, what we are talking about here is the combination of software and the human, now organized most efficiently.” [8:00] “ If you're able to break down the agents to very specific functionality, then those agents can be invoked by many different agents for different types of tasks.”  [10:00] “ There is one example of a very small, open system called the Internet, which somehow, through open standards, became one of the most incredible innovations of human beings ever. So what we are trying to do is to take a stand and say, 'We believe in open systems and we want to let our customers know that this is a choice.'”

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Why you should listenSanjeet Mahajan shares his journey building AgentForce agents and custom AI solutions, revealing the critical prompt engineering techniques that eliminate hallucinations and deliver real ROI.Learn the decision framework for when to use AgentForce versus building custom agents with LangChain and CrewAI, plus real case studies from hospitality and real estate showing measurable results.Discover how to create your own "Content Crafter" AI agent that generates marketing ideas in 10 minutes instead of 3-4 weeks, based on your unique business journey and client data.As a Salesforce consultant, you're watching competitors struggle with AgentForce implementation while others race ahead with custom AI agents. The hallucinations, pricing concerns, and lack of clear guidance on when to build versus buy has left many of you spinning your wheels. I see this frustration constantly - talented consultants who know their platforms inside-out but feel lost in the AI maze. In this episode, I talk with Sanjeet Mahajan from Kizzy Consulting, who's spent months in the trenches building both AgentForce and custom agents. We dive deep into the prompt engineering techniques that actually work, the decision framework for choosing platforms, and proven case studies that show real ROI. If you're tired of AI hype and want practical implementation strategies that work, this conversation will give you the roadmap you need.About Sanjeet MahajanSanjeet Mahajan is the Founder & CEO of Kizzy Consulting, a Salesforce Ridge and ISV Partner helping nonprofits, real estate, and homecare teams grow with clean data, smart automation, and human-first design. A seasoned Technical Architect with a deep curiosity for AI, Sanjeet is building intelligent systems that think, act, and adapt—so businesses don't just keep up, they leap ahead.Resources and LinksKizzyconsulting.comSanjeet's LinkedIn profileLangChainCrewAIN8NNotebookLMNapkin.aiPrevious episode: 624 - How to Turn Client Cloud Platform Pain Into Profitable Migration Projects with Jon TopperCheck out more episodes of the Paul Higgins PodcastSubscribe to our YouTube channel: @PaulHigginsMentoringFree Training for AI & Tech Consultants Ready to Stop Trading Time for MoneyJoin our newsletterSuggested resource

Sherwood Callaway, tech lead at 11X, joins us to talk about building digital workers—specifically Alice (an AI sales rep) and Julian (a voice agent)—that are shaking up sales outreach by automating complex, messy tasks.He looks back on his YC days at OpKit, where he first got his hands dirty with voice AI, and compares the wild ride of building voice vs. text agents. We get into the use of Langgraph Cloud, integrating observability tools like Langsmith and Arize, and keeping hallucinations in check with regular Evals.Sherwood and Demetrios wrap up with a look ahead: will today's sprawling AI agent stacks eventually simplify? // BioSherwood Callaway is an emerging leader in the world of AI startups and AI product development. He currently serves as the first engineering manager at 11x, a series B AI startup backed by Benchmark and Andreessen Horowitz, where he oversees technical work on "Alice", an AI sales rep that outperforms top human SDRs.Alice is an advanced agentic AI working in production and at scale. Under Sherwood's leadership, the system grew from initial prototype to handling over 1 million prospect interactions per month across 300+ customers, leveraging partnerships with OpenAI, Anthropic, and LangChain while maintaining consistent performance and reliability. Alice is now generating eight figures in ARR.Sherwood joined 11x in 2024 through the acquisition of his YC-backed startup, Opkit, where he built and commercialized one of the first-ever AI phone calling solutions for a specific industry vertical (healthcare). Prior to Opkit, he was the second infrastructure engineer at Brex, where he designed, built, and scaled the production infrastructure that supported Brex's application and engineering org through hypergrowth. He currently lives in San Francisco, CA.// Related Links~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Sherwood on LinkedIn: /sherwoodcallaway/ #aiengineering Timestamps:[00:00] AI Takes Over Health Calls[05:05] What Can Agents Really Do?[08:25] Who's in Charge—User or Agent?[11:20] Why Graphs Matter in Agents[15:03] How Complex Should Agents Be?[18:33] The Hidden Cost of Model Upgrades[21:57] Inside the LLM Agent Loop[25:08] Turning Agents into APIs[29:06] Scaling Agents Without Meltdowns[30:04] The Monorepo Tangle, Explained[34:01] Building Agents the Open Source Way[38:49] What Production-Ready Agents Look Like[41:23] AI That Fixes Code on Its Own[43:26] Tracking Agent Behavior with OpenTelemetry[46:43] Running Agents Locally with Phoenix[52:55] LangGraph Meets Arise for Agent Control[53:29] Hunting Hallucinations in Agent Traces[56:45] Off-Script Insights Worth Hearing

Scott and Wes break down how to code with and for AI; perfect for skeptics, beginners, and curious devs. They cover everything from Ghost Text and CLI agents to building your own AI-powered apps with embeddings, function calling, and multi-model workflows. Show Notes 00:00 Welcome to Syntax! 03:56 How to interface with AI. 04:07 IDE Ghost Text. 05:45 IDE Chat, Agents. 08:00 CLI Agents. Claude Code. Open Code. Gemini. 11:13 MCP Servers. Context7 14:47 GUI apps. v0. Bolt.new. Lovable. Windsurf. 19:07 Existing Chat app like ChatGPT. 22:37 Building things WITH AI. 23:32 Prompting. 26:53 Streaming VS not streaming. 28:14 Embeddings and Rag. 31:09 MCP Server. CJ's MCP Deep Dive. 32:36 Brought to you by Sentry.io. 33:25 Multi-model, multi-provider. 36:27 npm libs to use to code with AI. OpenAI SDK. AI SDK. Cloudflare Agents. Langchain. Local AI Tensorflow. Transformers.js. Huggingface. 44:12 Processes and exploring. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

AI infrastructure startup LangChain is raising a new round at about $1 billion valuation led by IVP. Learn more about your ad choices. Visit podcastchoices.com/adchoices

You've probably seen the headlines about AI replacing developers, but is AI really coming for our jobs, or is there more going on?In this episode, we break down what's actually happening in the tech industry and how you can use AI to make sure you don't get left behind. We talk about the role of AI in layoffs, how it's changing software development, and why this shift is more evolution than extinction.We cover real-world examples, practical use cases for AI as a developer, and why companies are now hiring for AI skills. Plus, we explain key AI concepts like LLMs, prompts, agents, and tokenisation in classic Hot Girls Code fashion. We also share some of our hot tips on how to use AI as a tool so it can be your bestie instead of your enemy!Whether you're curious, cautious, or fully on board with AI, this episode will help you understand how to work with it—not against it!Where to Find Us:⁠Instragam⁠⁠Tik Tok⁠⁠The Hot Girls Code Website⁠Links mentioned in the episode:Check out our original AI episodes to hear about the basics and ethics behind AI: Episode 16 and Episode 17Learn more about APIs in Episode 68. What is an API?Learn more about some popular pre-trained models: Open AI's GPT, Google's Gemini, and DALL-E Learn more about popular frameworks and libraries: LangChain, LlamaIndex, and TensorFlowLearn more about AI coding assistants: GitHub Copilot, Windsurf (formerly Codeium), and ClineSponsored By: ⁠Trade Me

Pour l'épisode #307 je recevais Maxime Thoonsen. On en débrief avec Julien.🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |Distribué par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Is building the backend for your AI application slowing you down? In this episode of the MongoDB Podcast, host Jesse Hall sits down with Srikar and Jimmy, the creators of Daemo AI, a revolutionary tool designed to eliminate the tedious "plumbing" of backend development.Discover how Daemo AI is building upon deprecated MongoDB features like Realm App Services, creating a more powerful and flexible solution for developers. We dive deep into their tech stack, including Next.js, Deno, and Express , and explore why they chose MongoDB for its speed and flexibility in AI applications. Plus, you'll see a live demo of Daemo's new SDK and CLI , learn how it can generate data migrations and dummy data on the fly , and get a real answer to the big question: Is AI going to take your job? In This Episode, You Will Learn: What Daemo AI is and how it accelerates development. * How to build AI agents and integrate them with frameworks like LangChain. Why MongoDB is the ideal database for rapid-growth startups and AI. The future of developer jobs in the age of AI.

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle: linkedin.com LangGraph: github.com LangChain: langchain.com LangGraph Studio: github.com LangGraph (Web): langchain.com LangGraph Tutorials Introduction: langchain-ai.github.io How to Think About Agent Frameworks: blog.langchain.dev Human in the Loop Concept: langchain-ai.github.io GPT-4 Prompting Guide: cookbook.openai.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

In this episode, Ricardo discusses how AI Agents are transforming project management. Unlike traditional tools, these agents are autonomous, understand context, make decisions, and interact with people and systems to deliver value. With the advancement of models like ChatGPT and platforms such as LangChain, Crew AI, and Google NotebookLM, building smart agents has become much easier. They can update schedules, write meeting notes, draft emails, generate reports, and monitor risks—all integrated with tools like Notion, Slack, Trello, and Google Docs. This shift changes the project manager's role to that of an “AI orchestrator.” However, caution is needed due to potential errors, hallucinations, and data security concerns. AI isn't here to replace project managers but to empower them to focus on what truly matters. Listen to the podcast to learn more!

Neste episódio, Ricardo apresenta como agentes de inteligência artificial (AI Agents) estão revolucionando o gerenciamento de projetos. Diferentes das automações tradicionais, esses agentes são autônomos, interpretam contextos, tomam decisões e interagem com ferramentas como Notion, Slack, Trello e Google Docs. Com o avanço de modelos como ChatGPT e plataformas como LangChain, Crew AI e NotebookLM, ficou mais fácil criar agentes que entendem linguagem natural e atuam com autonomia. Eles podem atualizar cronogramas, gerar atas, escrever e-mails e sugerir ações. O papel do gerente muda de executor para orquestrador de IA. Porém, há riscos como erros e alucinações, exigindo supervisão humana. A IA não substitui o gerente de projetos, mas libera tempo para decisões mais estratégicas. Escute o podcast para saber mais!

At the close of this year's Google Cloud Next, The New Stack's Alex Williams, AI editor Frederic Lardinois, and analyst Janakiram MSV discussed the event's dominant theme: AI agents. The conversation focused heavily on agent frameworks, noting a shift from last year's third-party tools like Langchain, CrewAI, and Microsoft's Autogen, to first-party offerings from model providers themselves. Google's newly announced Agent Development Kit (ADK) highlights this trend, following closely on the heels of OpenAI's agent SDK. MSV emphasized the significance of this shift, calling it a major milestone as Google joins the race alongside Microsoft and OpenAI. Despite the buzz, Lardinois pointed out that many companies are still exploring how AI agents can fit into real-world workflows. The panel also highlighted how Google now delivers a full-stack AI development experience — from models to deployment platforms like Vertex AI. New enterprise tools like Agent Space and Agent Garden further signal Google's commitment to making agents a core part of modern software development. Learn more from The New Stack about the latest in AI agents: How AI Agents Will Change the Web for Users and Developers AI Agents: A Comprehensive Introduction for Developers AI Agents Are Coming for Your SaaS Stack Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Demetrios, Sam Partee, and Rahul Parundekar unpack the chaos of AI agent tools and the evolving world of MCP (Model Context Protocol). With sharp insights and plenty of laughs, they dig into tool permissions, security quirks, agent memory, and the messy path to making agents actually useful.// BioSam ParteeSam Partee is the CTO and Co-Founder of Arcade AI. Previously a Principal Engineer leading the Applied AI team at Redis, Sam led the effort in creating the ecosystem around Redis as a vector database. He is a contributor to multiple OSS projects including Langchain, DeterminedAI, LlamaIndex and Chapel amongst others. While at Cray/HPE he created the SmartSim AI framework which is now used at national labs around the country to integrate HPC simulations like climate models with AI. Rahul ParundekarRahul Parundekar is the founder of AI Hero. He graduated with a Master's in Computer Science from USC Los Angeles in 2010, and embarked on a career focused on Artificial Intelligence. From 2010-2017, he worked as a Senior Researcher at Toyota ITC working on agent autonomy within vehicles. His journey continued as the Director of Data Science at FigureEight (later acquired by Appen), where he and his team developed an architecture supporting over 36 ML models and managing over a million predictions daily. Since 2021, he has been working on AI Hero, aiming to democratize AI access, while also consulting on LLMOps(Large Language Model Operations), and AI system scalability. Other than his full time role as a founder, he is also passionate about community engagement, and actively organizes MLOps events in SF, and contributes educational content on RAG and LLMOps at learn.mlops.community.// Related LinksWebsites: arcade.devaihero.studio~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Rahul on LinkedIn: /rparundekarConnect with Sam on LinkedIn: /samparteeTimestamps:[00:00] Agents & Tools, Explained (Without Melting Your Brain)[09:51] MVP Servers: Why Everything's on Fire (and How to Fix It)[13:18] Can We Actually Trust the Protocol?[18:13] KYC, But Make It AI (and Less Painful)[25:25] Web Automation Tests: The Bugs Strike Back[28:18] MCP Dev: What Went Wrong (and What Saved Us)[33:53] Social Login: One Button to Rule Them All[39:33] What Even Is an AI-Native Developer?[42:21] Betting Big on Smarter Models (High Risk, High Reward)[51:40] Harrison's Bold New Tactic (With Real-Life Magic Tricks)[55:31] Async Task Handoffs: Herding Cats, But Digitally[1:00:37] Getting AI to Actually Help Your Workflow[1:03:53] The Infamous Varma System Error (And How We Dodge It)

Recorded live at Sequoia's AI Ascent 2025: LangChain CEO Harrison Chase introduces the concept of ambient agents, AI systems that operate continuously in the background responding to events rather than direct human prompts. Learn how these agents differ from traditional chatbots, why human oversight remains essential and how this approach could dramatically scale our ability to leverage AI.

How do you move from dabbling with AI and vibe coding to building real, production-grade software with it? In this episode, Austin Vance, CEO of Focused returns and we transition the conversation from building AI-enabled applications to fostering AI-native engineering teams. Austin shares how generative AI isn't just a shortcut—it's reshaping how we architect, code, and lead. We also get to hear Austin's thoughts on the leaked ‘AI Mandate' memo from Shopify's CEO, Tobi Lutke.  We cover what Austin refers to as ‘AI-driven development', how to win over the skeptics on your teams, and why traditional patterns of software engineering might not be the best fit for LLM-driven workflows.  Whether you're an engineer,product leader, or startup founder, this episode will give you a practical lens on what AI-native software development actually requires—and how to foster adoption on your teams quickly and safely to get the benefits of using AI in product delivery. Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Inside the episode... Why Shopify's leaked AI memo was a "permission slip" for your own team The three personas in AI adoption: advocates, skeptics, and holdouts How AI-driven development (AIDD) differs from “AI-assisted” workflows Tools and practices Focused uses to ship faster and cheaper with AI Pair programming vs. pairing with an LLM: similarities and mindset shifts How teams are learning to prompt effectively—without prompt engineering training Vibe coding vs. integrating with entrenched systems: what's actually feasible Scaling engineering culture around non-determinism and experimentation Practical tips for onboarding dev teams to tools like Cursor, Windsurf, and Vercel AI SDK Using LLMs for deep codebase exploration, not just code generation Mentioned in this episode Cursor Windsurf LangChain Claude GPT-4 / ChatGPT V0.dev GitHub Copilot Focused (focused.io) Shopify internal AI memo Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

What does it actually take to build agentic AI applications that hold up in the real world? In this episode, Ashok sits down with Austin, founder of Focused, to share field stories and hard-won lessons from building AI systems that go beyond flashy demos. From legal assistants to government transparency tools, Austin breaks down the concrete criteria for identifying where AI makes sense — and where it doesn't. They unpack how to find the right starting point for your first agentic app, why integration with legacy systems is the real hurdle, and the engineering must-haves that keep AI behavior safe and reliable. You'll hear practical guidance on designing eval frameworks, using abstraction layers like LangChain, and how observability can shape your development roadmap just like in traditional software. Whether you're a product leader or a CTO, this conversation will help you distinguish hype from real opportunity in AI. Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Inside the episode... A practical checklist for identifying your first AI-powered app The hidden cost of "AI for AI's sake" and where traditional software is better Why repetitive knowledge work is prime territory for automation How Focused helped Hamlet build an AI for parsing government meeting data Where read-only data access gives you a safe starting point Why integration is often more complex than the AI itself The importance of eval frameworks and test-driven LLM development How to use observability to continuously improve AI agent behavior Speed vs. believability: surprising lessons from Groq-powered inference Using multiple models in one system and LLMs to QA each other Mentioned in this episode Hamlet (government transparency startup) - https://www.myhamlet.com/?convergence  LangChain - https://www.langchain.com/?convergence  Groq - https://groq.com/?convergence  Claude (Anthropic) - https://claud.ai/?convergence  Dspy Prompting framework - https://dspy.ai/?convergence  Shopify AI memo (referenced) - https://convergence.fm/episode/shopifys-leaked-ai-mandate-explained-6-takeaways-for-your-product-team?convergence Amazon Bedrock / SageMaker - https://aws.amazon.com/bedrock/?convergence Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**In this episode of MongoDB Podcast Live, host Shane McAllister is joined by Sachin Hejip from Dataworkz. Sachin will showcase “Dataworkz Agent Builder” which is built with MongoDB Atlas Vector Search, and demonstrate how it can use Natural Language to create Agents and in turn, automate and simplify the creation of Agentic RAG applications. Sachin will demo the MongoDB Leafy Portal Chatbot Agent, which combines operational data with unstructured data for personalised customer experience and support, built using Dataworkz and MongoDB.Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #Dataworkz #Observability #Developer #BigData #RAG

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #RAG #SemanticSearch #Langchain #LlamaIndex #Developer #BigData

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

Roy Derks, Developer Experience at IBM, talks about the integration of Large Language Models (LLMs) in web development. We explore practical applications such as building agents, automating QA testing, and the evolving role of AI frameworks in software development. Links https://www.linkedin.com/in/gethackteam https://www.youtube.com/@gethackteam https://x.com/gethackteam https://hackteam.io We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Roy Derks.

In this episode: Jason sits down with Harrison Chase, CEO of LangChain, to explore how AI-powered agents are transforming the way startups operate. They discuss the shift from traditional entry-level roles to AI-driven automation, the importance of human-in-the-loop systems, and the future of AI-powered assistants in business. Harrison shares insights on how companies like Replit, Klarna, and GitLab are leveraging AI agents to streamline operations, plus a look ahead at what's next for AI-driven workflows. Brought to you in partnership with Google Cloud.*Timestamps:(0:00) Introduction to Startup Basics series & Importance of AI in startups(2:04) Partnership with Google Cloud & Introducing Harrison Chase from Langchain(4:38) Evolution of entry-level jobs & Examples of AI agents in startups(8:00) Challenges & Future of AI agents in startups(14:24) AI agents in collaborative spaces & Non-developers creating AI agents(18:40) Closing remarks and where to learn more*Uncover more valuable insights from AI leaders in Google Cloud's 'Future of AI: Perspectives for Startups' report. Discover what 23 AI industry leaders think about the future of AI—and how it impacts your business. Read their perspectives here: https://goo.gle/futureofai*Check out all of the Startup Basics episodes here: https://thisweekinstartups.com/basicsCheck out Google Cloud: https://cloud.google.com/Check out LangChain: https://www.langchain.com/*Follow Harrison:LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/X: https://x.com/hwchase17*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis*Follow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems.The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jakob Foerster's supervision, where he focused on meta-learning and multi-agent systems. Chris is the first author of the DiscoPOP paper, which demonstrates how language models can discover and design better training algorithms. Also joining is Robert Tjarko Lange, a founding member of Sakana AI who specializes in evolutionary algorithms and large language models. Robert leads research at the intersection of evolutionary computation and foundation models, and is completing his PhD at TU Berlin on evolutionary meta-learning. The discussion also features Cong Lu, currently a Research Scientist at Google DeepMind's Open-Endedness team, who previously helped develop The AI Scientist and Intelligent Go-Explore.SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/**** DiscoPOP - A framework where language models discover their own optimization algorithms* EvoLLM - Using language models as evolution strategies for optimizationThe AI Scientist - A fully automated system that conducts scientific research end-to-end* Neural Attention Memory Models (NAMMs) - Evolved memory systems that make transformers both faster and more accurateTRANSCRIPT + REFS:https://www.dropbox.com/scl/fi/gflcyvnujp8cl7zlv3v9d/Sakana.pdf?rlkey=woaoo82943170jd4yyi2he71c&dl=0Robert Tjarko Langehttps://roberttlange.com/Chris Luhttps://chrislu.page/Cong Luhttps://www.conglu.co.uk/Sakanahttps://sakana.ai/blog/TOC:1. LLMs for Algorithm Generation and Optimization [00:00:00] 1.1 LLMs generating algorithms for training other LLMs [00:04:00] 1.2 Evolutionary black-box optim using neural network loss parameterization [00:11:50] 1.3 DiscoPOP: Non-convex loss function for noisy data [00:20:45] 1.4 External entropy Injection for preventing Model collapse [00:26:25] 1.5 LLMs for black-box optimization using abstract numerical sequences2. Model Learning and Generalization [00:31:05] 2.1 Fine-tuning on teacher algorithm trajectories [00:31:30] 2.2 Transformers learning gradient descent [00:33:00] 2.3 LLM tokenization biases towards specific numbers [00:34:50] 2.4 LLMs as evolution strategies for black box optimization [00:38:05] 2.5 DiscoPOP: LLMs discovering novel optimization algorithms3. AI Agents and System Architectures [00:51:30] 3.1 ARC challenge: Induction vs. transformer approaches [00:54:35] 3.2 LangChain / modular agent components [00:57:50] 3.3 Debate improves LLM truthfulness [01:00:55] 3.4 Time limits controlling AI agent systems [01:03:00] 3.5 Gemini: Million-token context enables flatter hierarchies [01:04:05] 3.6 Agents follow own interest gradients [01:09:50] 3.7 Go-Explore algorithm: archive-based exploration [01:11:05] 3.8 Foundation models for interesting state discovery [01:13:00] 3.9 LLMs leverage prior game knowledge4. AI for Scientific Discovery and Human Alignment [01:17:45] 4.1 Encoding Alignment & Aesthetics via Reward Functions [01:20:00] 4.2 AI Scientist: Automated Open-Ended Scientific Discovery [01:24:15] 4.3 DiscoPOP: LLM for Preference Optimization Algorithms [01:28:30] 4.4 Balancing AI Knowledge with Human Understanding [01:33:55] 4.5 AI-Driven Conferences and Paper Review

MongoDB product leader Sahir Azam explains how vector databases have evolved from semantic search to become the essential memory and state layer for AI applications. He describes his view of how AI is transforming software development generally, and how combining vectors, graphs and traditional data structures enables high-quality retrieval needed for mission-critical enterprise AI use cases. Drawing from MongoDB's successful cloud transformation, Azam shares his vision for democratizing AI development by making sophisticated capabilities accessible to mainstream developers through integrated tools and abstractions. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Introducing ambient agents: Blog post by Langchain on a new UX pattern where AI agents can listen to an event stream and act on it  Google Gemini Deep Research: Sahir enjoys its amazing product experience Perplexity: AI search app that Sahir admires for its product craft Snipd: AI powered podcast app Sahir likes

Artificial intelligence agents are facing significant challenges when tasked with complex responsibilities, as highlighted by a recent study from Langchain. The research indicates that AI performance deteriorates under cognitive overload, with one model's effectiveness dropping to just 2% when managing more than seven domains. This finding emphasizes the need for businesses to design AI systems that can effectively manage complexity rather than assuming that AI can scale to handle human-like multitasking. The implications are particularly relevant for industries reliant on automation, such as customer service and IT operations.In a related development, researchers from Stanford and the University of Washington have introduced a new AI reasoning model called S1, which can be trained at a fraction of the cost of existing high-end models. This innovation raises concerns about the commoditization of AI, as smaller teams can replicate sophisticated models with minimal resources. Meanwhile, Hugging Face has quickly developed an open-source AI research agent that aims to compete with OpenAI's offerings, showcasing the rapid advancements in AI capabilities and the importance of community contributions in this space.The podcast also discusses the ongoing legal battles surrounding AI and copyright, particularly the recent ruling in favor of Thomson Reuters against Ross Intelligence. This case underscores the complexities of how AI tools are trained using copyrighted material and sets a precedent that could impact future AI developments. As AI continues to evolve, the legal landscape surrounding its use and the rights of content creators remains a critical area of concern.Finally, the episode touches on the rising unemployment rate in the IT sector, attributed to the increasing influence of AI and automation. The data reveals a significant jump in unemployment among IT workers, with many routine jobs being automated rather than replaced. This shift highlights the need for IT professionals to upskill and adapt to the changing job market, focusing on areas such as AI integration and cybersecurity, as traditional software development roles decline. Four things to know today 00:00 AI Agents Struggle Under Pressure—More Complexity Means Less Accuracy03:23 Big AI, Big Trouble? Low-Cost Models Challenge Industry Leaders07:17 Apple Faces UK Encryption Fight, CISA's Role Strengthens, and AI Copyright Battle Heats Up10:38 AI Disrupts IT Hiring: Fewer Software Jobs, More Automation, and Higher Unemployment  Supported by: https://www.huntress.com/mspradio/ Event: https://nerdiocon.com/    All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech

LangChain is a popular open-source framework to build applications that integrate LLMs with external data sources like APIs, databases, or custom knowledge bases. It's commonly used for chatbots, question-answering systems, and workflow automation. Its flexibility and extensibility have made it something of a standard for creating sophisticated AI-driven software. Erick Friis is a Founding Engineer The post LangChain and Agentic AI Engineering with Erick Friis appeared first on Software Engineering Daily.

News includes an impressive case study from Remote showing how they scaled Elixir to support nearly 300 engineers and reach unicorn status, Tailwind CSS 4.0's major release with Phoenix integration in progress, Chris McCord teasing an exciting AI code generator project on Fly.io, the release of Elixir LangChain v0.3.0 with expanded support for multiple AI providers, ElixirConfEU 2025 tickets going on sale in Kraków, and more! Show Notes online - http://podcast.thinkingelixir.com/239 (http://podcast.thinkingelixir.com/239) Elixir Community News https://elixir-lang.org/blog/2025/01/21/remote-elixir-case/ (https://elixir-lang.org/blog/2025/01/21/remote-elixir-case/?utm_source=thinkingelixir&utm_medium=shownotes) – New case study about Remote, a unicorn company using Elixir as their primary technology with nearly 300 engineers. https://github.com/sasa1977/boundary (https://github.com/sasa1977/boundary?utm_source=thinkingelixir&utm_medium=shownotes) – Remote uses Saša Jurić's Boundary library to help enforce boundaries in their monolithic codebase. https://www.reddit.com/r/elixir/comments/1i77ia9/comment/m8il2ho/ (https://www.reddit.com/r/elixir/comments/1i77ia9/comment/m8il2ho/?utm_source=thinkingelixir&utm_medium=shownotes) – Discussion about the type spec future in Elixir, with plans to replace Dialyzer typespecs in versions 1.19 and 1.20. https://bsky.app/profile/zachdaniel.dev/post/3lgqdugp7zs2b (https://bsky.app/profile/zachdaniel.dev/post/3lgqdugp7zs2b?utm_source=thinkingelixir&utm_medium=shownotes) – Ash installer now supports Oban integration via a flag option. https://tailwindcss.com/blog/tailwindcss-v4 (https://tailwindcss.com/blog/tailwindcss-v4?utm_source=thinkingelixir&utm_medium=shownotes) – Tailwind CSS 4.0 released with major changes including moving theme configuration to CSS variables. https://tailwindcss.com/docs/upgrade-guide (https://tailwindcss.com/docs/upgrade-guide?utm_source=thinkingelixir&utm_medium=shownotes) – Comprehensive upgrade guide for Tailwind CSS v4. https://github.com/phoenixframework/phoenix/pull/5990 (https://github.com/phoenixframework/phoenix/pull/5990?utm_source=thinkingelixir&utm_medium=shownotes) – WIP PR to support Tailwind v4 in Phoenix. https://bsky.app/profile/zachdaniel.dev/post/3lggmuk4dis2x (https://bsky.app/profile/zachdaniel.dev/post/3lggmuk4dis2x?utm_source=thinkingelixir&utm_medium=shownotes) – Zach Daniel shares how Tailwind v4 changes will improve igniter's utility configuration capabilities. https://github.com/brainlid/langchain (https://github.com/brainlid/langchain?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir LangChain v0.3.0 released with expanded support for OpenAI, Anthropic, Gemini, Llama, and more. https://x.com/chris_mccord/status/1880377175200669770 (https://x.com/chris_mccord/status/1880377175200669770?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord teases new Fly.io AI code generator project with IDE/terminal integration. https://x.com/chris_mccord/status/1880392153924530376 (https://x.com/chris_mccord/status/1880392153924530376?utm_source=thinkingelixir&utm_medium=shownotes) – Demo video of Chris McCord's AI-integrated editor creating a multiplayer Phoenix LiveView app. https://www.elixirconf.eu/ (https://www.elixirconf.eu/?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConfEU 2025 tickets on sale, happening May 15-16 in Kraków Poland & Virtual. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Episode 653: Shaan Puri ( https://x.com/ShaanVP ) talks to Furqan Rydhan ( https://x.com/FurqanR ) about the biggest opportunities in AI right now.  — Show Notes:  (0:00) Intro (4:42) Define the Job-to-be-done (8:20) How to build an AI Agent workflow (11:16) AI Tools break down (27:05) How Polymarket won (31:48) Why VR is a sleeping giant? (44:43) Be a lifelong player in tech (58:52) The unbeatable combination (1:02:27) Adam Foroughi's A+ execution (1:18:35) Betting on -1 to 0 — Links: • Furqan's site - https://furqan.sh/  • Founders, Inc - https://f.inc/  • Applovin - https://www.applovin.com/  • Claude - https://claude.ai/  • OpenAI - https://platform.openai.com/  • Langchain - https://www.langchain.com/  • AutoGen - https://autogenai.com/  • Crew - https://www.crewai.com/  • CloudSDK - https://cloud.google.com/sdk/  • Perplexity - https://www.perplexity.ai/  • “Attention is all you need” - https://typeset.io/papers/attention-is-all-you-need-1hodz0wcqb  • Anthropic - https://www.anthropic.com/  • Third Web - https://thirdweb.com/  • Luna's AI Brain - https://terminal.virtuals.io/  • Oasis - https://oasis.decart.ai/welcome  • Polymarket - https://polymarket.com/  • Gorilla Tag - https://www.gorillatagvr.com/  • Yeeps - https://tinyurl.com/59z2yrdu  — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano