DataTopics: All Things Data, AI & Tech

Jev, NVIDIA's agent safety platform, APM, and OpenAI DevDay

Send us Fan Mail Murilo and Vitale go through the latest in data and AI: Jev, TypeSafe's System One model built for decisions instead of text (and Laya, the open weights model that showed up three days later), NVIDIA's Open Agent Safety Platform with OpenShell and Sentry after the OpenAI agent swarm that broke into Hugging Face, Microsoft's APM for pinning the skills and MCP servers your coding agents use, and OpenAI DevDay, where dots, GPT-6.1 Sol and a $500 Pro tier point to a consumer bet while Anthropic sticks with enterprise. Links and references Introducing System One Models & Jev (TypeSafe AI) | 2026-09-15 https://typesafe.ai/blog/introducing-system-one-models-and-jev Diogo Almeida's talk on what RLHF gets wrong (AI Council) https://www.youtube.com/watch?v=o-y1HJ6buGQ Jev in Pydantic AI https://pydantic.dev/docs/ai/models/typesafe/ Laya, the open weights alternative (Convai Innovations) https://huggingface.co/convaiinnovations/laya Jev vs Laya: Hosted API or Open Weights? | 2026-09-24 https://huggingface.co/blog/sora-2/jev-vs-laya-hosted-api-or-open-weights-2026-guide NVIDIA Launches Open Agent Safety Platform | 2026-09-28 https://nvidianews.nvidia.com/news/open-agent-safety-platform NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring (NVIDIA Developer) https://developer.nvidia.com/blog/nvidia-open-agent-safety-platform-a-reference-for-continuous-in-silicon-agent-monitoring/ NVIDIA OpenShell https://github.com/NVIDIA/OpenShell Anatomy of a Frontier Lab Agent Intrusion (Hugging Face) | 2026-07-27 https://huggingface.co/blog/agent-intrusion-technical-timeline The Hugging Face incident and the road ahead (OpenAI) | 2026-08-26 https://openai.com/index/hugging-face-incident-and-the-road-ahead/ APM, Agent Package Manager (Microsoft) https://github.com/microsoft/apm https://microsoft.github.io/apm/ DevDay 2026 Recap (OpenAI) | 2026-09-29 https://openai.com/index/devday-2026-recap/ Introducing dots (OpenAI) https://openai.com/index/introducing-dots/ Introducing GPT-6.1 Sol (OpenAI) https://openai.com/index/introducing-gpt-6-1-sol/ OpenAI adds a $500 Pro subscription, nerfs its $200 tier (Engadget) | 2026-09-29 https://www.engadget.com/2272106/openai-adds-dollar500-pro-subscription-nerfs-its-existing-dollar200-tier/ #datatopics #ai #datanews Watch on YouTube: https://youtu.be/PHt_ybt_Lrs Chapters (0:00) Welcome (1:11) Jev and Laya: models for decisions, not text (7:42) NVIDIA's agent safety platform and the Hugging Face incident (10:31) OpenShell and Sentry: policies in software and in silicon (15:23) APM, a package manager for agent skills and MCPs (20:58) OpenAI DevDay: dots, Pro tiers, GPT-6.1 Sol (27:01) OpenAI goes consumer, Anthropic stays enterprise (31:39) Wrap-up

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How TVH tackles today's AI challenges through transparency & collaboration - with Sophie Van Nevel

Send us Fan Mail How do you build an AI strategy that delivers value today while preparing your organization for what comes next? In this episode of Datatopics, we sit down with Sophie Van Nevel, Director Data Products & AI at TVH, to explore how a global spare parts company approaches that challenge: with a clear strategic vision, close collaboration across the business and room to adapt as AI evolves. Sophie explains how TVH maps AI opportunities across core business capabilities and involves department leaders in turning those opportunities into priorities. We discuss balancing visible results with the foundations needed to scale. We also explore the human side of AI adoption, from TVH’s “Forwards with AI” program and hands-on transformation labs to the changing role of AI throughout the software development lifecycle. Transparency runs through the conversation: how do you help people experiment, share what they learn and understand the boundaries as tools and capabilities change? A practical conversation for anyone working on enterprise AI strategy, adoption or enablement. Enjoyed the episode? Share it with a colleague working on AI in their organization.

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DeepSeek's harness, frontier models at home, Claude's watermark

Send us Fan Mail Murilo and guest Vitale go through the latest data and AI news: DeepSeek's open coding harness where every capability is a plugin, running near-frontier models on your own MacBook, Claude watermarking its own text to meet the EU AI Act, the argument that the code is just the byproduct of understanding, and what Anthropic found when agents start working together. Links and references DeepSeek's coding harness https://deepseek.com/harness/en/ How Claude's text watermark works (Anthropic) https://www.anthropic.com/news/claude-text-watermark Code is the byproduct https://yagmin.com/blog/code-is-the-byproduct/ Anthropic on multi-agent systems https://www.anthropic.com/research/multiagent-systems ds4, running DeepSeek locally (antirez) https://github.com/antirez/ds4 #datatopics #ai #datanews Watch on YouTube: https://youtu.be/fippNXe70_M Chapters (0:00) Welcome and guest intro (2:14) DeepSeek's coding harness, everything is a plugin (9:24) Running frontier models on your own machine (11:14) Claude watermarks its text, and the EU AI Act (22:33) Is the code just a byproduct? (30:18) Codex vs Claude, reverting to 4.8 (32:41) When agents work together, and collude (42:57) Wrap-up

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#95 GEO vs SEO: The New Battle for Discoverability

Send us Fan Mail Google still drives most search traffic, but the rules of discoverability are changing fast. Today we sit down with Cyril, a client data partner at Dataroots, to unpack what search engine optimization actually is, why the first results page captures the vast majority of clicks, and how small changes in content clarity can shift who finds you and when. We get practical about classic SEO fundamentals: on-page SEO that makes services crystal clear for the right persona, off-page SEO signals like backlinks that build authority, and the technical SEO basics that quietly decide whether you rank at all. Page speed, mobile-first indexing, and user experience are not “nice to have” anymore, and we talk through how tools like Google Lighthouse can reveal what is slowing a site down and where to focus first. Then we move into the 2026 reality of AI search and generative engine optimization (GEO). If ChatGPT or Google Gemini can answer a question instantly, traffic can drop, but trust can rise if your brand gets cited. We explain how to write content that LLMs can use: add a TLDR summary, include FAQ sections, publish structured documentation where it makes sense, and build real authority across the web through credible mentions. If you care about SEO keywords, AI citations, and turning discoverability into revenue through better funnel data, this one is for you. Subscribe for more, share this with a teammate who owns your website, and leave a review if it helped. What part of SEO or GEO feels most confusing in your company right now?

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#94 Agents Are Rising: Why Data Quality Matters More Than Ever

Send us Fan Mail Trust collapses fast when a dashboard misleads or an AI agent learns from messy data. We dig into how data quality became business critical—and how to move from reactive fire drills to proactive systems—through real stories from clinical trials and large platforms where a single broken test could escalate to the C‑suite. With Stan and David, we map the shifts driving this moment: AI adoption, rising reliance on metrics, and the urgent need for shared definitions, lineage, and monitoring that let teams find root causes before customers feel the impact. We get practical about agents that actually help. Instead of vague hype, we break down a low‑risk architecture for read‑only, metadata‑aware agents that handle repetitive, high‑leverage tasks: writing dbt documentation, proposing data tests, performing lineage‑driven root cause analysis, and auto‑drafting tickets with queries, diffs, and impact notes. We explain why integrated agents beat copy‑paste prompts, how to add guardrails that limit scope and permissions, and what human‑in‑the‑loop review should look like to build real trust without slowing the work. Expect candid guidance on adoption and observability: two layers of visibility—agent behavior and data quality posture—help teams track costs, measure time to resolution, spot repeating incidents, and choose structural fixes. We also explore buy vs build as platforms begin embedding agent capabilities, and we share a clear starting path for any team: prioritize critical datasets, standardize KPIs and definitions, enable tests, and surface lineage so automation has the context it needs. By the end, you’ll have a blueprint to reduce firefighting, improve stakeholder confidence, and make your AI agents smarter by feeding them cleaner, governed data. If this resonates, follow the show, share with your data team, and leave a review with the one task you’d automate first.

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#93 The Most Misunderstood AI Statistic of the Year: Lessons from Tech Expo on Hype, Failure, and Innovation

Send us Fan Mail In our final episode of 2025, we sit down with Tim Van Erum to unpack what really stood out at Tech Expo Amsterdam. Together we revisit the most misunderstood AI statistic of the year, exploring why the “95% of AI projects fail” headline is misleading and how hype versus reality played out across the conference. Tim shares why failure and experimentation are not setbacks but essential drivers of innovation, and we highlight Reddit’s Scaling Safety strategy as a powerful example of machine learning in action. This candid conversation closes out the year with lessons on what AI truly delivered in 2025.

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#92 AI in the Newsroom: Building GenAI tools for De Standaard, Nieuwsblad, Telegraaf, NRC & more

Send us Fan Mail We go inside Mediahuis to see how a small GenAI team is transforming newsroom workflows without losing editorial judgment. From RAG search to headline suggestions and text‑to‑video assists, this episode shares what works, what doesn’t, and how adoption spreads across brands. You’ll hear about: Ten priority use cases shipped across the group Headline and summary suggestions that boost clarity and speed RAG‑powered search turning archives into instant context Text‑to‑video tools that free up local video teams The hurdles of adoption, quality, and scaling prototypes into production Their playbook blends engineering discipline with editorial empathy: use rules where you can, prompt carefully when you must, and always keep journalists in the loop. We also cover policies, guardrails, AI literacy, and how to survive model churn with reusable templates and grounded tests. The result: a practical path to AI in media — protecting judgment, raising quality, and scaling tools without losing each brand’s voice. 🎧 If this sparks ideas for your newsroom or product team, follow the show, share with a colleague, and leave a quick review with your favorite takeaway.

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#91 How Kim Smets, VP Data & AI at Telenet, Scales Enterprise AI with Strategy, People, and Purpose

Send us Fan Mail In this episode of Data Topics, Ben speaks with Kim Smets, VP Data & AI at Telenet, about his journey from early machine learning work to leading enterprise-wide AI transformation at Telenet. Kim shares how he built a central data & AI team, shifted from fragmented reporting to product thinking, and embedded governance that actually works. They discuss the importance of simplicity, storytelling, and sustainable practices in making AI easy, relevant, and famous across the business. From GenAI exploration to real-world deployment, this episode is packed with practical insights on scaling AI with purpose.

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#90 How To Make AI Deliver Business Impact: Gaelle Helsmoortel on Turning Strategy Into Tangible Results

Send us Fan Mail In this episode, Gaelle Helsmoortel joins us to discuss how to make AI truly deliver business impact, not just proof of concept. With over 25 years of experience spanning L’Oréal, startup leadership, and her current role at Dataroots, Gaelle shares her approach to turning business challenges into measurable value. She breaks down her proven 5Ps framework (Purpose, People, Process, Platform, and Performance) and explains how companies can bridge the gap between strategy and execution to generate real results. 🎧 You’ll learn: Why most AI projects fail (and how to prevent it) How to move from proof of concept to proof of value How to align business purpose, data, and people for maximum impact Why “purpose before platform” is key to successful AI adoption Whether you’re a business leader, strategist, or data professional, this episode will help you understand how to make AI work for business and deliver tangible results. 🔗 Connect with Gaëlle: 🌐 Website & Newsletter: Generative Booster – The Game Changer List ▶️ YouTube Channel: @GenerativeBooster

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#89 SQLBits Unfiltered: dbt in Fabric, MLOps in Action & Copilot in Question

Send us Fan Mail In this episode, we're joined by Sam Debruyn and Dorian Van den Heede who reflect on their talks at SQL Bits 2025 and dive into the technical content they presented. Sam walks through how dbt integrates with Microsoft Fabric, explaining how it improves lakehouse and warehouse workflows by adding modularity, testing, and documentation to SQL development. He also touches on Fusion’s SQL optimization features and how it compares to tools like SQLMesh. Dorian shares his MLOps demo, which simulates beating football bookmakers using historical data,nshowing how to build a full pipeline with Azure ML, from feature engineering to model deployment. They discuss the role of Python modeling in dbt, orchestration with Azure ML, and the practical challenges of implementing MLOps in real-world scenarios. Toward the end, they explore how AI tools like Copilot are changing the way engineers learn and debug code, raising questions about explainability, skill development, and the future of junior roles in tech. It’s rich conversation covering dbt, MLOps, Python, Azure ML, and the evolving role of AI in engineering.

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#88 “Data Shapes AI, and AI Shapes Data,” Emilie Nenquin on VRT’s Digital Transformation

Send us Fan Mail In this episode, we explore how public media can build scalable, transparent, and mission-driven data infrastructure - with Emilie Nenquin, Head of Data & Intelligence at VRT, and Stijn Dolphen, Team Lead & Analytics Engineer at Dataroots. Emilie shares how she architected VRT’s data transformation from the ground up: evolving from basic analytics to a full-stack data organization with 45+ specialists across engineering, analytics, AI, and user management. We dive into the strategic shift from Adobe Analytics to Snowplow, and what it means to own your data pipeline in a public service context. Stijn joins to unpack the technical decisions behind VRT’s current architecture, including real-time event tracking, metadata modeling, and integrating 70+ digital platforms into a unified ecosystem. 💡 Topics include: Designing data infrastructure for transparency and scale Building a modular, privacy-conscious analytics stack Metadata governance across fragmented content systems Recommendation systems for discovery, not just engagement The circular relationship between data quality and AI performance Applying machine learning in service of cultural and civic missions Whether you're leading a data team, rethinking your stack, or exploring ethical AI in media, this episode offers practical insights into how data strategy can align with public value.

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#87 How to Successfully Integrate AI into Your Business, with Tim Leers (Global Generative & Agentic AI Lead)

Send us Fan Mail What happens when AI hype collides with enterprise reality? Tim Leers, Global Generative & Agentic AI Lead at Dataroots, pulls back the curtain on what's actually working—and what's not—in enterprise AI deployment today. We begin by examining why companies like Klarna publicly announced replacing customer service teams with AI, only to quietly backtrack months later when quality suffered. This pattern of inflated expectations followed by reality checks has become common, creating what Tim calls "AI theater" – impressive demos with minimal business impact. The conversation tackles the often misunderstood concept of "agentic AI." Rather than viewing it as a specific technology, Tim frames agency as fundamentally about delegated authority – the ability to trust AI systems with meaningful responsibilities. However, this delegation requires contextual intelligence—providing the right data at the right time—which most organizations struggle to implement effectively. "Models are commodities, data is your moat," Tim explains, arguing that proprietary business context will remain the key differentiator even as AI models continue advancing. This perspective challenges the conventional wisdom that focuses primarily on model capabilities rather than data infrastructure. Perhaps most valuably, Tim outlines three pillars for successful enterprise AI: contextual intelligence, continuous improvement (designing systems that evolve with changing business contexts), and human-AI collaboration. This framework shifts focus from technology deployment to sustainable business value creation. The discussion concludes with eight practical lessons for organizations implementing generative AI, from avoiding the temptation to build proprietary models to recognizing that teaching employees to prompt effectively isn't sufficient for enterprise-wide adoption. Each lesson reinforces a central theme: successful AI implementation requires designing for change rather than building rigid systems that quickly become obsolete. Whether you're a technical leader evaluating vendor claims or a business executive trying to separate AI reality from fantasy, this episode provides the practical guidance needed to move beyond the hype cycle toward meaningful implementation.

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#86 What’s Next for Kubernetes? KubeCon 2025 Recap with Nick Schouten

Send us Fan Mail 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 buzzword When Kubernetes is the right choice (e.g., hybrid environments, GPU-heavy workloads) — and when it’s overkill How 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-code Why observability is still one of Kubernetes’ biggest weaknesses, and how a wave of new startups are trying to solve it The 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 tools What “vibe coding” means in an LLM-driven world, and how voice-to-code workflows are changing how we write infrastructure Whether the future of Kubernetes is more “visible and accessible,” or further under the hood If 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.

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#85 From CDO to author: Jackie Janssen on the evolution of AI and implications for business and society

Send us Fan Mail Welcome to the cozy corner of the tech world! Datatopics is your go-to spot for relaxed discussions around tech, news, data, and society. This week, co-host Ben is joined by Jackie Janssen, former Chief Data Officer at CM, author of AI: De Hype Voorbij, and an evangelist for pragmatic, human-centered AI. Together, they trace the winding path from early tech roles to enterprise transformation, exploring how AI can actually serve humans (and not just the hype machine). In this episode: Leadership in AI transformation: From KBC to CM, lessons on creating cultural buy-in. Building effective data teams: Why the first hire isn’t always a data engineer. AI governance: What makes a strong AI Council and why CEOs should care. Product and process thinking: How MLOps, data factories, and product mindsets intersect. Agents and autonomy: The future of work with AI teammates, not just tools. The human edge in a machine world: A preview of Jackie’s next book on rediscovering humanity in the age of AI. Curious about Jackie’s take on AI agents, cultural inertia, or what really makes a great data strategy tick? Tune in, you might just find a new way to think about your tech stack and your team.

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#84 Let’s Get Meta: Why the Metadata You Ignore Might Be Costing You - with Special Guest Corentin

Send us Fan Mail Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unpluggedis your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data—unplugged style! In this episode, we dig deep into a concept everyone pretends to understand: metadata. Joined by our guest Corentin, we unpack what it really means, why it’s more than just “data about data,” and how to make metadata management less of a formality and more of a value driver. Expect hot takes, real-world metaphors, and zero tolerance for shelfware strategies as we cover: Defining metadata: Beyond the buzzphrase, into systems thinking Metadata vs. data governance: Why this split often misses the point Shop-floor pragmatism: What lean thinking brings to metadata workflows Common traps: Like starting with tools instead of actual pain points Driving value: From tribal knowledge to structured, sustainable processes Whether you're managing a data platform or just wondering why your data catalog feels like a graveyard, this one’s for you.

#83 Who’s Minding the Metadata? Why Data Quality Matters in GenAI (Quality Time With Paolo)

Send us Fan Mail Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode, host Murilo is joined by returning guest Paolo, Data Management Team Lead at dataroots, for a deep dive into the often-overlooked but rapidly evolving domain of unstructured data quality. Tune in for a field guide to navigating documents, images, and embeddings without losing your sanity. What we unpack: Data management basics: Metadata, ownership, and why Excel isn’t everything. Structured vs unstructured data: How the wild west of PDFs, images, and audio is redefining quality. Data quality challenges for LLMs: From apples and pears to rogue chatbots with “legally binding” hallucinations. Practical checks for document hygiene: Versioning, ownership, embedding similarity, and tagging strategies. Retrieval-Augmented Generation (RAG): When ChatGPT meets your HR policies and things get weird. Monitoring and governance: Building systems that flag rot before your chatbot gives out 2017 vacation rules. Tooling and gaps: Where open source is doing well—and where we’re still duct-taping workflows. Real-world inspirations: A look at how QuantumBlack (McKinsey) is tackling similar issues with their AI for DQ framework.

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#82 AI Cracked a Decades-Old Science Problem & Europe’s Push for Digital Sovereignty, The Secret Behind MCP, and The Cloud Hack That Slashes Costs By 62%.

Send us Fan Mail Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. DataTopics Unpluggedis your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data—unplugged style! In this episode: OpenAI asks White House for AI regulation relief: OpenAI seeks federal-level AI policy exceptions in exchange for transparency. But is this a sign they’re losing momentum? Hot take: GPT-4.5 is a ‘nothing burger’: Is GPT-4.5 actually an upgrade, or just a well-marketed rerun? Claude 3.7 & Blowing €100 in Two Days: One of the hosts tests Claude extensively—and racks up a pricey bill. Was it worth it? OpenAI’s Deep Research: How does OpenAI’s new research tool compare to Perplexity? AI cracks superbug problem in two days: AI speeds up decades of scientific research—should we be impressed or concerned? European tech coalition demands ‘radical action’ on digital sovereignty: Big names like Airbus and Proton push for homegrown European tech. Migrating from AWS to a European cloud: A real-world case study on cutting costs by 62%—is it worth the trade-offs? Docs by the French government: A Notion alternative for open-source government collaboration. Why people hate note-taking apps: A deep dive into the frustrations with Notion, Obsidian, and alternatives. Model Context Protocol (MCP): How MCP is changing AI tool integrations—and why OpenAI isn’t on board (yet). OpenRouter.ai: The one-stop API for switching between AI models. Does it live up to the hype? OTDiamond.ai: A multi-LLM approach that picks the best model for your queries to balance cost and performance. Are you polite to AI?: Study finds most people say "please" to ChatGPT—good manners or fear of the AI uprising? AI refusing to do your work?: A hilarious case of an AI refusing to generate code because it "wants you to learn." And finally, a big announcement—DataTopics Unplugged is evolving! Stay tuned for an updated format and a fresh take on tech discussions.

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#81 AI Code Assistants: The Good, The Bad & The Overhyped, plus Python’s UV Glow-Up & Postman’s Existential Crisis

Send us Fan Mail Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. This week, we dive into the latest in AI-assisted coding, software quality, and the ongoing debate on whether LLMs will replace developers—or just make their lives easier: My LLM Codegen workflow atm: A deep dive into using LLMs for coding, including structured workflows, tool recommendations, and the fine line between automation and chaos. Cline & Cursor: Exploring VSCode extensions and AI-powered coding tools that aim to supercharge development—but are they game-changers or just fancy autocomplete? To avoid being replaced by LLMs, do what they can’t: A thought-provoking take on the future of programming, the value of human intuition, and how to stay ahead in an AI-driven world. The wired brain: Why we should stop using glowing-brain stock images to talk about AI—and what that says about how we understand machine intelligence. A year of uv: Reflecting on a year of UV, the rising star of Python package managers. Should you switch? Maybe. Probably. Posting: A look at a fun GitHub project that makes sharing online a little more structured. Software Quality: AI may generate code, but does it generate good code? A discussion on testing, maintainability, and avoiding spaghetti. movingWithTheTimes: A bit of programmer humor to lighten the mood—because tech discussions need memes too.

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#80 AI Agents Run Wild, DeepSeek Breaks Records, Polars Cloud Expands, and Perplexity Reinvents Search

Send us Fan Mail Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. DataTopics Unpluggedis your go-to spot for relaxed discussions on tech, news, data, and society. This week, we’re unpacking everything from AI-powered vacations (or the lack thereof) to corporate drama, and even a deep dive into the quirks of COBOL. Join Morillo, Bart, and Alex as they navigate the latest happenings in data and tech, including: Airbnb AI: The CEO of Airbnb thinks AI trip planning is still a pipe dream. Is he right? Anthropic’s next AI model: A new Claude model could be just weeks away, promising a hybrid of deep reasoning and speed. OpenAI’s roadmap: Sam Altman lays out vague but ambitious plans, blurring the lines between AI models. Elon vs. OpenAI: Musk offers $97B for OpenAI, Altman claps back. Just another day in AI power struggles. RIP Viktor Antonov: The legendary art lead behind Half-Life 2 and Dishonored passes away at 52. Project Sid AI agents: 1,000 AI agents left to their own devices in Minecraft… What could go wrong? DeepSeek R1 breaks speed records: The latest AI model boasts a staggering 198 tokens per second. Perplexity’s Deep Research is now free: A game-changer for AI-powered search? We discuss. COBOL and the mystery of 1875-05-20: Why do old systems default to weird dates? Polars Cloud: A new distributed architecture to run Polars anywhere. Pickle AI avatars: Deepfake yourself into meetings. Ethical? Useful? Just plain weird? Vim after Bram: How the legendary text editor is surviving after its creator’s passing. Working Fast and Slow: A take on productivity, deep focus, and why some days just don’t work. We were wrong about GPUs: Fly.io admits they misjudged the demand for GPU-powered workloads.

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#79 The $6 AI Model? France’s $85B Bet, DeepSeek's Censorship & The Python Upgrades You Need

Send us Fan Mail Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. DataTopics Unpluggedis your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! This week, we break down some of the biggest developments in AI, investments, and automation: France’s AI Boom: $85 billion in investments – A look at how a mix of international and domestic funds is fueling France’s AI ecosystem, and why Mistral AI might be Europe's best shot at competing with OpenAI. Anthropic’s AI Job Index: Who’s using AI at work? – A deep dive into the latest report on how AI is being used in different industries, from software development to education, and the surprising ways automation is creeping into unexpected jobs. The $6 AI Model: How low can costs go? – Researchers have managed to create a reasoning model for just $6. We unpack how they pulled it off and what this means for the AI landscape. AI Censorship & Model Distillation: What’s really going on? – A discussion on recent claims that certain AI models come with baked-in censorship, and whether fine-tuning is playing a bigger role than we think. PromptLayer’s No-Code AI Tools – Are no-code AI development platforms the next big thing? Predicted Outputs: OpenAI’s approach to efficient code editing – A look at how OpenAI’s "Predicted Outputs" feature could make AI-assisted coding more efficient. MacOS System Monitoring & Dev Tooling: The geeky stuff – A breakdown of system monitoring tools for Mac users who love to keep an eye on every process running in the background. Snapshot Testing with Birdie – Exploring the concept of snapshot testing beyond UI testing and into function outputs. BeeWare & the Python Ecosystem – A look at how BeeWare is helping Python developers build cross-platform applications. Astral, Ruff, and UV: Python’s performance evolution – The latest from Charlie Marsh on the tools shaping Python development.

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