Technology Explorations in Data & AI

Dataminded

Deep dives and practical demos on the technologies shaping modern data and AI development. Join the Dataminded team as we explore, unbox, and critically review the latest tools, from building AI agents and RAG systems to optimizing cloud costs and accelerating data pipelines. We cut through the hype to show you what actually works in real data engineering practice, complete with demo code!

  1. 5月7日

    Snowflake Intelligence: The End of Dashboards?

    Your dashboards only answer the questions you thought of last quarter. Every new question is a ticket, a dependency, or a gut call. Snowflake Intelligence wants to fix that -- a chat interface on top of governed enterprise data that turns plain English into SQL, runs it, and gives you a chart back. No analysts involved. Jelle builds the full setup live: semantic view, verified queries, Cortex Agent, access control. They get honest about what this actually requires -- data quality, governance, and whether Snowflake is worth the cost. Resources:- Snowflake Intelligence docs: https://docs.snowflake.com/en/user-guide/snowflake-intelligence- Demo code: https://github.com/datamindedbe/demo-technology-explorationCreators & Guests Jonny Daenen - Host Jelle De Vleminck - Guest ---Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb (00:00) - Intro & meet Jelle (02:04) - What is Snowflake Intelligence? (02:54) - Demo: talking to your data (10:36) - Where it fits & how it works (15:14) - Build: the Semantic View (22:37) - Build: the Agent (29:20) - Challenges & data quality (34:26) - How ETL is evolving (40:33) - Will this replace engineers & analysts? (43:23) - Is Snowflake worth the cost? (45:14) - How do you get started? (50:06) - Summary & takeaways ---Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    53 分钟

  2. 4月23日

    AI Workflows in Agno: Building Deterministic Agents

    Enterprise data is full of sensitive information: different teams, different access rights, different rules. When you ask an AI agent a simple question and get "access denied," it's not a permissions bug. It's a design problem. Pascal has been exploring how to tackle this using Agno, an agent framework built around deterministic workflows. Instead of letting a single agent roam freely across your data, Agno lets you build specialized agents, each with its own access rules and instructions. Workflows orchestrate these agents with guardrails that keep humans in the loop when it matters. In this episode, Pascal Knapen, CTO at Dataminded, demos the full flow: from a natural language question, through an access check, to a verified answer. We explore how skills differ from workflows, how Agno handles dynamic agent creation and deployment, and how LLM-based evaluations act as a quality judge for agent responses. Additional Resources: Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/agno-workflowsIntro music by Aleksandr Karabanov from Pixabay (00:00) - Intro: AI agents and enterprise data (01:50) - Two ways to give AI access to your data (03:23) - Skills vs Workflows (04:45) - Demo: AI with controlled data access (07:45) - The workflow is deterministic - the agents aren't (09:13) - Demo: evaluations & reliability (11:00) - Code walkthrough with Agno (17:45) - Why Agno? An honest take (19:46) - Identity & exposing as an API (20:24) - Takeaways

    22 分钟

  3. 4月8日

    Managing Airflow at Scale using the Flowrs TUI

    Jan opens 12 browser tabs every morning to check overnight pipelines. Log in, check, close. Twelve times. So he built a terminal app instead. Flowrs is a TUI for Apache Airflow written in Rust. Navigate all your environments from the keyboard, drill into failed tasks, tail live logs, bulk-mark runs -- no browser, no mouse. In this episode Jan demos it live, walks through the architecture of a TUI (event loop, state, render), compares the main frameworks (Ratatui, Bubble Tea, Textual), and gives his honest take on whether agents will eventually replace tools like this. "Go forth and mulTUIply. Life is too short to click around." Resources:- Install Flowrs: brew install flowrs- GitHub: https://github.com/janbvanbuel/flowrs ---Creators & Guests Jonny Daenen - Host Jan Vanbuel - Guest Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb (00:00) - Introduction (01:17) - What is Flowrs & Airflow? (03:27) - Demo: Flowrs in action (10:18) - The evolution of CLIs and TUIs (13:07) - Why not just use agents? (14:25) - TUI frameworks: Bubble Tea, Textual, Ratatui (15:21) - What's up with the Rust hype? (16:51) - Building the Flowrs UI (19:20) - How to install Flowrs yourself (22:34) - Takeaways & what's next ---Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    24 分钟

  4. 3月10日

    Building an AI Agent with Subagents and Skills

    Every time you ask AI for help, it agrees. Fast, confident, and it never tells you your plan has holes. That's the problem Arete is built to fix. Jesus built a brainstorm agent on Claude Code skills that guides you through five phases -- Ground, Explore, Decide, Stress, Ship -- before you commit to anything. The output is an architectural decision record and an implementation plan you actually own. In this episode he demos it live, shows how parallel subagents work without polluting your main context, and answers the honest questions: tokens burned, vendor lock-in, debugging subagents, and whether this works with a team. Resources:- Install Arete: https://github.com/jesgarram/arete- Demo code: https://github.com/datamindedbe/demo-technology-exploration --- Creators & Guests Jonny Daenen - Host Jesús García Ramírez - Guest Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb (00:00) - Intro & meeting Arete (01:38) - The 5-step brainstorm workflow (04:05) - Meta: This video was made with an AI skill (06:02) - The demo: ground, explore, decide, stress, ship (14:56) - Example results: ADR and Plan (17:23) - Subagents and context engineering (21:12) - Demo: the Researcher Agent (22:48) - Practical concerns: vendor, files, big projects (25:30) - How many tokens does it burn? (26:51) - Control, agents vs skills, multi-human (29:44) - How to install Arete (30:44) - Wrap-up ---Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    32 分钟

  5. 2月24日

    From Prompts to Agents: AI Agent Skills in Claude Code

    AI agents get messy fast once you move beyond simple prompts. Context windows fill up with noise, agents start reasoning in loops, and suddenly you're dealing with brittle behavior and hallucinations. Jesus walks through how Claude Code skills fix this -- packaging repeatable workflows into modular components that load only when needed. He demos two real examples: an Explain Code skill and a PR Review skill that forks context, limits tool permissions, and uses CLI commands to analyze pull requests. Resources:- Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/agent_skills- Anthropic docs: https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview- Skills standard: https://agentskills.io- Curious about MCP? https://youtu.be/fIr55-koOJQ --- Creators & Guests Jonny Daenen - Host Jesús García Ramírez - Guest Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb (00:00) - Introduction (01:28) - Demo: Skills in Claude Code (05:57) - How agents work: from prompts to context engineering (08:19) - What are Skills? (vs MCP, RAG, Commands) (10:33) - Building your own Skill (15:20) - Skills vs MCPs (16:29) - What about hallucinations? (17:07) - Specs and Anthropic's Skill Guide (19:28) - Skillception: a skill to create skills (20:34) - Is MCP history? (22:50) - Sharing skills & wrap-up ---Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    25 分钟

  6. 2月2日

    Azure Log Analytics Costs Are Out of Control - Here's How We Cut Them by 60%

    Azure Log Analytics costs often take up 20% or more of a cloud bill, even though most teams only check logs when something breaks. Azure's default analytics logs are powerful, but they're also expensive and often unnecessary for day-to-day log inspection. Switching application logs to Basic Logs can reduce Log Analytics costs by up to 60%. In this episode, Niels walks us through a real customer case where logging costs dropped by thousands per year. They explain the difference between Analytics, Basic, and Auxiliary logs, show when Basic Logs are sufficient, and discuss practical setups using Azure Container Insights and FluentBit. This includes building a custom FluentBit plugin in Go as well as real-world gotchas like missing short-lived pods and why dynamic credentials matter. Creators & Guests Jonny Daenen - Host Niels Claeys - Guest Resources: Custom FluentBit plugin: https://github.com/nclaeys/fluent-bit-go-azureClick here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb Chapters: (00:00) - Intro: why optimize Azure log costs? (03:10) - What kind of logs are we dealing with? (06:13) - Plan types & the cost difference (09:59) - FluentBit vs Azure Container Insights (13:33) - How FluentBit works in K8S (16:41) - Can you lose log data? (17:36) - A custom plugin for Azure Workload Identity (21:05) - Why not use Azure Container Insights? (22:35) - Do all clients benefit? (23:41) - Summary & takeaways Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    26 分钟

  7. 1月21日

    AI Code Reviews with CodeRabbit and Sourcery

    Code reviews are often considered a pain, resulting in quick approvals and bugs reaching production. CodeRabbit and Sourcery aim to solve this by letting an AI agent review your changes early on. In this episode, data & platform engineer Hannes De Smet shows Jonny what he learned after exploring several AI Code Reviewers. Hannes demos both tools on a real code change, allowing a critical look at the quality of the suggestions, as well as the user experience. It turns out that, depending on the context, both could use some improvements. Resources: CodeRabbit: https://www.coderabbit.aiSourcery: https://www.sourcery.ai/Multi-workspace AI video: https://www.youtube.com/watch?v=E_kOAvmeTJ0Note: This video is not sponsored or affiliated with CodeRabbit or Sourcery. Creators & Guests Jonny Daenen - Host Hannes De Smet - Guest Resources: Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb Chapters: (00:00) - Intro: why AI code reviews? (02:53) - AI Reviewer 1: CodeRabbit (10:32) - What CodeRabbit catches (and misses) (12:18) - When AI comments become noise (80% disregard) (13:27) - Catching a PII issue (15:15) - AI Reviewer 2: Sourcery (19:14) - Cost & comparison (19:59) - What's the future of AI code reviews? (20:41) - Summary & takeaways Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    23 分钟

  8. 2025/12/23

    Cross-Project AI Code Assistance using Cursor Workspaces

    Emil shows how to let his Cursor AI access and manipulate multiple projects at the same time. He does this by leveraging the Cursor Workspace feature, which enables you to link multiple repositories together. His demo shows how to increase application resources, which also needs a Terraform change in a second project. Along the way, we discover how important human involvement still is... You'll learn how to: Set up Cursor workspaces with multiple projectsLet your agent access and manipulate multiple codebasesLeverage best practices from previous projectsBe mindful that human involvement is still importantResources: VSCode Workspace feature: https://code.visualstudio.com/docs/editing/workspaces/workspacesCursor Rules docs: https://cursor.com/docs/context/rulesMCP 101: https://www.youtube.com/watch?v=fIr55-koOJQPostgres MCP in Cursor: https://www.youtube.com/watch?v=tbrR21I3jJIClick here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnbCreators & Guests Jonny Daenen - Host Emil Krause - Guest Chapters:(00:00) - Intro & setting up a Cursor Workspace (03:21) - Demo: upgrading application memory (06:46) - Did we cheat? (08:16) - When are Workspaces most useful? (09:37) - Out with monorepos for data products? (10:15) - Best practices + read-only repos (12:17) - Learning & exploring codebases (13:24) - How Emil solved a production incident (14:40) - The future of monorepos? (15:54) - How this works with Cursor Rules (17:21) - Summary & takeaways Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype. Music by Aleksandr Karabanov from Pixabay

    20 分钟
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Deep dives and practical demos on the technologies shaping modern data and AI development. Join the Dataminded team as we explore, unbox, and critically review the latest tools, from building AI agents and RAG systems to optimizing cloud costs and accelerating data pipelines. We cut through the hype to show you what actually works in real data engineering practice, complete with demo code!

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