Tech talk as part of the AI meetup in Paris. Topics include AI, GenAI, LLMs and Agents.
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Large Language Models (LLM)
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In this episode of Experiencing Data, I introduce part 1 of my new MIRRR UX framework for designing trustworthy agentic AI applications—you know, the kind that might actually get used and have the opportunity to create the desired business value everyone seeks! One of the biggest challenges with both traditional analytics, ML, and now, LLM-driven AI agents, is getting end users and stakeholders to trust and utilize these data products—especially if we’re asking humans in the loop to make changes to their behavior or ways of working.
In this episode, I challenge the idea that software UIs will vanish with the rise of AI-based automation. In fact, the MIRRR framework is based on the idea that AI agents should be “in the human loop,” and a control surface (user interface) may in many situations be essential to ensure any automated workers engender trust with their human overlords.
By properly considering the control and oversight that end users and stakeholders need, you can enable the business value and UX outcomes that your paying customers, stakeholders, and application users seek from agentic AI.
Using use cases from insurance claims processing, in this episode, I introduce the first two of five control points in the MIRRR framework—Monitor and Interrupt. These control points represent core actions that define how AI agents often should operate and interact within human systems:
Monitor – enabling appropriate transparency into AI agent behavior and performance Interrupt – designing both manual and automated pausing mechanisms to ensure human oversight remains possible when needed
…and in a couple weeks, stay tuned for part 2 where I’ll wrap up this first version of my MIRRR framework.
Highlights / Skip to:
00:34 Introducing the MIRRR UX Framework for designing trustworthy agentic AI Applications. 01:27 The importance of trust in AI systems and how it is linked to user adoption 03:06 Cultural shifts, AI hype, and growing AI skepticism 04:13 Human centered design practices for agentic AI 06:48 I discuss how understanding your users’ needs does not change with agentic AI, and that trust in agentic applications has direct ties to user adoption and value creation 11:32 Measuring success of agentic applications with UX outcomes 15:26 Introducing the first two of five MIRRR framework control points: 16:29 M is for Monitor; understanding the agent’s “performance,” and the right level of transparency end users need, from individual tasks to aggregate views 20:29 I is for Interrupt; when and why users may need to stop the agent—and what happens next
28:02 Conclusion and next steps
Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: https://www.datahackers.news/ Acesse os links: Inscrições do Data Hackers Challenge 2025 Live Zoho: Decisões Baseadas em Dados: Aplicando Machine Learning com o Zoho Analytics Conheça nossos comentaristas do Data Hackers News: Monique Femme Paulo Vasconcellos Matérias/assuntos comentados: Demais canais do Data Hackers: Site Linkedin Instagram Tik Tok You Tube
Send us a text Deep Diving into the future of AI:
Join Dr. Sean Falconer — AI Entrepreneur in Residence at Confluent, software engineering leader, and developer relations expert — for a deep dive into the future of AI, data streaming, and what it really means to build at the edge of innovation. From managing multiple LLMs to testing autonomous agents and sharing his bold contrarian takes, Sean helps us simplify the complexity of today's tech. 📌 Timestamps 04:38 – Meet Sean Falconer 11:11 – Lifelong Learning 12:31 – AI Entrepreneur in Residence 16:28 – Multiple LLMs in Action 21:07 – The Tech Behind Confluent 25:51 – Why Sean Chose Confluent 28:40 – Invest or Short? 36:58 – Testing Agents IRL 40:51 – The Contrarian AI Take 42:27 – Looking Ahead: The Future of AI🔗 Connect with Sean: LinkedInSubstackMedium
Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Tech talks on AI, GenAI, LLMs and Agent
Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: https://www.datahackers.news/ Conheça nossos comentaristas do Data Hackers News: Inscrições do Data Hackers Challenge 2025 Live Zoho: Decisões Baseadas em Dados: Aplicando Machine Learning com o Zoho Analytics Conheça nossos comentaristas do Data Hackers News: Monique Femme Paulo Vasconcellos Demais canais do Data Hackers: Site Linkedin Instagram Tik Tok You Tube
Send us a text She’s the legal powerhouse behind IBM’s AI ethics strategy — and she makes law fun. In this encore episode, we revisit a fan favorite: Christina Montgomery, formerly IBM’s Chief Privacy and Trust Officer, now Chief Privacy and Trust Officer, GM. From guarding the gates of generative AI risk to advising on global regulation, Christina gives us a front-row seat to what’s now, what’s next, and what needs rethinking when it comes to trust, synthetic data, and the future of AI law. 📍 Timestamps: • 01:00 Christina Montgomery! • 04:36 My Daughter and the Bar • 08:36 Chief Privacy and Trust Officer • 11:37 Keeping IBM Out of Trouble • 13:34 Client Conversations • 16:23 Where to Be Bullish and Bearish • 20:52 The Risks of LLMs • 24:21 NIST and AI Alliance • 28:26 AI Regulation • 36:13 Synthetic Data • 38:00 Misconceptions • 40:07 Worries • 41:27 The Path to AI • 43:13 Aspiring Lawyers 🔗 Christina on LinkedIn 🌐 IBM AI Ethics Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
In just a few short years, AI has transformed software development, and snazzy new tools continue to arrive, with no let-up in sight. How, as a software engineer, product builder, or CTO, do you keep up? This practical book is the result of Sergio Pereira's mission to test every AI tool he could find and provide practitioners with much-needed guidance through the commotion. Generative AI for Software Development focuses on AI tool comparisons, practical workflows, and real-world case studies, with each chapter encompassing critical evaluations of the tools, their use cases, and their limitations. While product reviews are always relevant, the book goes further and delivers to readers a coherent framework for evaluating the tools and workflows of the future, which will continue to complicate the work of software development. Learn how code generation and autocompletion assistants are reshaping the developer experience Discover a consistent method for rating code-generation tools based on real-world coding challenges Explore the GenAI tools transforming UI/UX design and frontend development Learn how AI is streamlining code reviews and bug detection Review tools that are simplifying software testing and QA Explore AI for documentation and technical writing Understand how modern LLMs have redefined what chatbots can do
Scale is one key ingredient that has enabled the success of modern LLMs. In this talk I will go over different kinds of parallelisms (model, pipeline, data, sequence, expert) and how they compose with each other to enable training of models with extremely large number of parameters.
Send us a text Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society. In this episode of Data Topics, we sit down with Nick Schouten — data engineer at dataroots — for a full recap of KubeCon Europe 2025 and a deep dive into the current and future state of Kubernetes. We talk through what’s actually happening in the Kubernetes ecosystem — from platform engineering trends to AI infra challenges — and why some teams are doubling down while others are stepping away. Here’s what we cover: What Kubernetes actually is, and how to explain it beyond the buzzwordWhen Kubernetes is the right choice (e.g., hybrid environments, GPU-heavy workloads) — and when it’s overkillHow teams are trying to host LLMs and AI models on Kubernetes, and the blockers they’re hitting (GPUs, complexity, cost)GitOps innovations spotted at KubeCon — like tools that convert UI clicks into Git commits for infrastructure-as-codeWhy observability is still one of Kubernetes’ biggest weaknesses, and how a wave of new startups are trying to solve itThe push to improve developer experience for ML and data teams (no more YAML overload)The debate around abstraction vs control — and how some teams are turning away from Kubernetes entirely in favor of simpler toolsWhat “vibe coding” means in an LLM-driven world, and how voice-to-code workflows are changing how we write infrastructureWhether the future of Kubernetes is more “visible and accessible,” or further under the hoodIf you're a data engineer, MLOps practitioner, platform lead, or simply trying to stay ahead of the curve in infrastructure and AI — this episode is packed with relevant insights from someone who's hands-on with both the tools and the teaching.
Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS. • Statsig — The unified platform for flags, analytics, experiments, and more. • Sonar — Code quality and code security for ALL code. — Steve Yegge is known for his writing and “rants”, including the famous “Google Platforms Rant” and the evergreen “Get that job at Google” post. He spent 7 years at Amazon and 13 at Google, as well as some time at Grab before briefly retiring from tech. Now out of retirement, he’s building AI developer tools at Sourcegraph—drawn back by the excitement of working with LLMs. He’s currently writing the book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond. In this episode of The Pragmatic Engineer, I sat down with Steve in Seattle to talk about why Google consistently failed at building platforms, why AI coding feels easy but is hard to master, and why a new role, the AI Fixer, is emerging. We also dig into why he’s so energized by today’s AI tools, and how they’re changing the way software gets built. We also discuss: • The “interview anti-loop” at Google and the problems with interviews • An inside look at how Amazon operated in the early days before microservices • What Steve liked about working at Grab • Reflecting on the Google platforms rant and why Steve thinks Google is still terrible at building platforms • Why Steve came out of retirement • The emerging role of the “AI Fixer” in engineering teams • How AI-assisted coding is deceptively simple, but extremely difficult to steer • Steve’s advice for using AI coding tools and overcoming common challenges • Predictions about the future of developer productivity • A case for AI creating a real meritocracy • And much more! — Timestamps (00:00) Intro (04:55) An explanation of the interview anti-loop at Google and the shortcomings of interviews (07:44) Work trials and why entry-level jobs aren’t posted for big tech companies (09:50) An overview of the difficult process of landing a job as a software engineer (15:48) Steve’s thoughts on Grab and why he loved it (20:22) Insights from the Google platforms rant that was picked up by TechCrunch (27:44) The impact of the Google platforms rant (29:40) What Steve discovered about print ads not working for Google (31:48) What went wrong with Google+ and Wave (35:04) How Amazon has changed and what Google is doing wrong (42:50) Why Steve came out of retirement (45:16) Insights from “the death of the junior developer” and the impact of AI (53:20) The new role Steve predicts will emerge (54:52) Changing business cycles (56:08) Steve’s new book about vibe coding and Gergely’s experience (59:24) Reasons people struggle with AI tools (1:02:36) What will developer productivity look like in the future (1:05:10) The cost of using coding agents (1:07:08) Steve’s advice for vibe coding (1:09:42) How Steve used AI tools to work on his game Wyvern (1:15:00) Why Steve thinks there will actually be more jobs for developers (1:18:29) A comparison between game engines and AI tools (1:21:13) Why you need to learn AI now (1:30:08) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The full circle of developer productivity with Steve Yegge • Inside Amazon’s engineering culture • Vibe coding as a software engineer • AI engineering in the real world • The AI Engineering stack • Inside Sourcegraph’s engineering culture— See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: https://www.datahackers.news/ Conheça nossos comentaristas do Data Hackers News: Inscrições do Data Hackers Challenge 2025 Live de Bain: Estratégias de GenAI para análise de dados não-estruturados Conheça nossos comentaristas do Data Hackers News: Monique Femme Paulo Vasconcellos Demais canais do Data Hackers: Site Linkedin Instagram Tik Tok You Tube
Send us a text The AI Advantage: Get Better Results from LLMs with the Perfect Prompt On this episode of Making Data Simple, we’re joined by Jonathan Mast, AI consultant and coach at Whitebeard Strategies and creator of the Perfect Prompting Framework™. Jonathan’s not just riding the AI wave—he’s teaching business leaders and everyday users how to surf it, with simple, actionable tools that unlock meaningful results from large language models. If you've ever stared at a prompt box wondering what to type—or worse, gotten garbage back from AI—this episode is for you. We talk about what works, what doesn’t, and what’s coming next (agents, anyone?). Plus, Jonathan breaks down his 4-step framework that’s helping 300K+ community members and clients scale AI with clarity and confidence. ⏱️ Episode Timestamps 01:34 Introducing Jonathan Mast04:13 Digital Agency05:29 Whitebeard Strategies08:06 ADD09:57 Back to Whitebeard14:51 The Perfect Prompting Framework21:36 The Four Step Method24:58 What if You Don't Use AI?28:37 Agents30:08 Whitebeard Engagements32:42 Getting Started36:39 What's True But Not a Consensus?37:23 For Fun🔗 Connect with Jonathan LinkedIn: https://www.linkedin.com/in/jonathanjmast/Website: https://whitebeardstrategies.com#MakingDataSimple #PerfectPromptingFramework #AIforBusiness #AIProductivity #JonathanMast #PromptEngineering #LLMs #AIAgents #WhitebeardStrategies #TechPodcast #DataSimplified Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
The data analysis landscape is changing rapidly. New AI tools are emerging every week, and it can sometimes feel overwhelming. So in this video, I compare ChatGPT and Julius AI to see how they stack up against each other. We'll use a dataset of 1,444 data job listings from FindADataJob.com to analyze trends in the 2025 data job market to answer the question: Which AI tool is best suited for your data analysis needs? Where I Go To Find Datasets (as a data analyst) 👉 https://datacareerpodcast.com/episode/131-7-resources-to-find-amazing-datasets-free 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 Introduction 00:20 Comparing ChatGPT and Julius AI 01:17 Uploading and Previewing Data 03:01 Data Analysis Suggestions 05:04 What State Has The Most Jobs Listed? 08:08 Analyzing Job Trends Over Time 10:31 Customizable Chart Themes 11:10 Analyzing Job Salary Trends 17:01 Investigating Experience Levels in Job Listings 19:44 Handling Missing Data with MissingNo 21:34 Exploring Interesting Trends in the Dataset 25:34 Conclusion: Which Tool Should You Use? 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!
To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more
If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.
👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa
Summary In this episode of the Data Engineering Podcast Kacper Łukawski from Qdrant about integrating MCP servers with vector databases to process unstructured data. Kacper shares his experience in data engineering, from building big data pipelines in the automotive industry to leveraging large language models (LLMs) for transforming unstructured datasets into valuable assets. He discusses the challenges of building data pipelines for unstructured data and how vector databases facilitate semantic search and retrieval-augmented generation (RAG) applications. Kacper delves into the intricacies of vector storage and search, including metadata and contextual elements, and explores the evolution of vector engines beyond RAG to applications like semantic search and anomaly detection. The conversation covers the role of Model Context Protocol (MCP) servers in simplifying data integration and retrieval processes, highlighting the need for experimentation and evaluation when adopting LLMs, and offering practical advice on optimizing vector search costs and fine-tuning embedding models for improved search quality.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Kacper Łukawski about how MCP servers can be paired with vector databases to streamline processing of unstructured dataInterview IntroductionHow did you get involved in the area of data management?LLMs are enabling the derivation of useful data assets from unstructured sources. What are the challenges that teams face in building the pipelines to support that work?How has the role of vector engines grown or evolved in the past ~2 years as LLMs have gained broader adoption?Beyond its role as a store of context for agents, RAG, etc. what other applications are common for vector databaes?In the ecosystem of vector engines, what are the distinctive elements of Qdrant?How has the MCP specification simplified the work of processing unstructured data?Can you describe the toolchain and workflow involved in building a data pipeline that leverages an MCP for generating embeddings?helping data engineers gain confidence in non-deterministic workflowsbringing application/ML/data teams into collaboration for determining the impact of e.g. chunking strategies, embedding model selection, etc.What are the most interesting, innovative, or unexpected ways that you have seen MCP and Qdrant used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on vector use cases?When is MCP and/or Qdrant the wrong choice?What do you have planned for the future of MCP with Qdrant?Contact Info LinkedInTwitter/XPersonal websiteParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links QdrantKafkaApache OoziNamed Entity RecognitionGraphRAGpgvectorElasticsearchApache LuceneOpenSearchBM25Semantic SearchMCP == Model Context ProtocolAnthropic Contextualized ChunkingCohereThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Info session about LLM Mini Bootcamp; attendees can ask questions and receive a discount coupon.
Join the info session, ask all your questions about LLM Mini Bootcamp and get your special coupon code for discount.
Recent breakthroughs in large language model-based artificial intelligence (AI) have captured the public’s interest in AI more broadly. With the growing adoption of these technologies in professional and educational settings, public dialog about their potential impacts on the workforce has been ubiquitous. It is, however, difficult to separate the public dialog about the potential impact of the technology from the experienced impact of the technology in the research software engineer and data science workplace. Likewise, it is challenging to separate the generalized anxiety about AI from its specific impacts on individuals working in specialized work settings.
As research software engineers (RSEs) and those in adjacent computational fields engage with AI in the workplace, the realities of the impacts of this technology are becoming clearer. However, much of the dialog has been limited to high-level discussion around general intra-institutional impacts, and lacks the nuance required to provide helpful guidance to RSE practitioners in research settings, specifically. Surprisingly, many RSEs are not involved in career discussions on what the rise of AI means for their professions.
During this BoF, we will hold a structured, interactive discussion session with the goal of identifying critical areas of engagement with AI in the workplace including: current use of AI, AI assistance and automation, AI skills and workforce development, AI and open science, and AI futures. This BoF will represent the first of a series of discussions held jointly by the Academic Data Science Alliance and the US Research Software Engineer Association over the coming year, with support from Schmidt Sciences. The insights gathered from these sessions will inform the development of guidance resources on these topic areas for the broader RSE and computational data practitioner communities.
The rapid growth of scientific data repositories demands innovative solutions for efficient metadata creation. In this talk, we present our open-source project that leverages large language models to automate the generation of standard-compliant metadata files from raw scientific datasets. Our approach harnesses the capabilities of pre-trained open source models, finetuned with domain-specific data, and integrated with Langgraph to orchestrate a modular, end-to-end pipeline capable of ingesting heterogeneous raw data files and outputting metadata conforming to specific standards.
The methodology involves a multi-stage process where raw data is first parsed and analyzed by the LLM to extract relevant scientific and contextual information. This information is then structured into metadata templates that adhere strictly to recognized standards, thereby reducing human error and accelerating the data release cycle. We demonstrate the effectiveness of our approach using the USGS ScienceBase repository, where we have successfully generated metadata for a variety of scientific datasets, including images, time series, and text data.
Beyond its immediate application to the USGS ScienceBase repository, our open-source framework is designed to be extensible, allowing adaptation to other data release processes across various scientific domains. We will discuss the technical challenges encountered, such as managing diverse data formats and ensuring metadata quality, and outline strategies for community-driven enhancements. This work not only streamlines the metadata creation workflow but also sets the stage for broader adoption of generative AI in scientific data management.
Additional Material: - Project supported by USGS and ORNL - Codebase will be available on GitHub after paper publication - Fine-tuned LLM models will be available on Hugginface after paper publication
This book provides a comprehensive and practical guide to creating cutting-edge AI agents combining advanced technologies such as LLMs, retrieval-augmented generation (RAG), and knowledge graphs. By reading this book, you'll gain a deep understanding of how to design and build AI agents capable of real-world problem solving, reasoning, and action execution. What this Book will help me do Understand the foundations of LLMs, RAG, and knowledge graphs, and how they can be combined to build effective AI agents. Learn techniques to enhance factual accuracy and grounding through RAG pipelines and knowledge graphs. Develop AI agents that integrate planning, reasoning, and live tool usage to solve complex problems. Master the use of Python and popular AI libraries to build scalable AI agent applications. Acquire strategies for deploying and monitoring AI agents in production for reliable operation. Author(s) This book is written by Salvatore Raieli and Gabriele Iuculano, accomplished experts in artificial intelligence and machine learning. Both authors bring extensive professional experience from their work in AI-related fields, particularly in applying innovative AI methods to solve challenging problems. Through their clear and approachable writing style, they aim to make advanced AI concepts accessible to readers at various levels. Who is it for? This book is ideally suited for data scientists, AI practitioners, and technology enthusiasts seeking to deepen their knowledge in building intelligent AI agents. It is perfect for those who already have a foundational understanding of Python and general artificial intelligence concepts. Experienced professionals looking to explore state-of-the-art AI solutions, as well as beginners eager to advance their technical skills, will find this book invaluable.