Luca Berton
🌍 Welcome to the official channel of Luca Berton — IT Infrastructure & AI Consultant, global speaker, and open-source advocate with roots in the Ansible team and hands-on experience at the world’s leading banks.
Here you’ll find deep dives, live demos, and conference talks on infrastructure automation, DevOps, Kubernetes, cloud-native security, AI workflows, and modern data platforms. Whether you’re an engineer, architect, or tech leader, this channel helps you master Ansible, Terraform, Kubernetes, Vault, Dagster, dbt, and AI automation frameworks with real-world case studies and step-by-step tutorials.
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Built with Qwen2 backbone, future releases may improve
Книга рецептов Kubernetes!
Weak at identifying environment (Linux/Windows/web/mobile), OCR struggles
Reflections from Paris | Building a Global Tech Community Together
Show model — fast, gives coordinates/action dictionaries
Fast with defaults, but struggles with structured output & UI grounding
A popular model with 120k+ downloads — but way too prompt-sensitive
Limitations when pushing prompts beyond training scope
Intro: hacking on grounding models
Организаторы Edge Case 2025 | True Fullstack на Kubernetes, Cloud Native и инновации
Prosus’ approach: AI agents + large commerce models
dbt Fusion Engine Explained: Rust Core, Setup, Limits, Licensing & Snowflake Tie-In
Anthropic’s work on Claude and distributed training
Dev experience & hosting options (cloud, k8s, local)
Pebbling vs A2A/ACP/MCP (trust, negotiation, micropayments)
Enterprise use case: cross-site data silos in pharma
Pebbling protocol overview (DIDs, mTLS, schemas)
Vision: one-line deploy, agents speaking securely P2P
Top blockers: security, compliance, observability, eval
Importing DBT Models into Dagster | Unlock Metadata, Lineage, and Collaborative Debugging
Who’s building agents vs
Kubernetes Pod Security Deep-Dive: Deny Privilege Escalation, Drop Capabilities & Run as Non-Root
– Next release: “Intellis” coming soon
– Fun naming tradition: food-inspired releases
– Combining hacks? Still an open question
– Expert-based scheduling explained
– CPU/GPU scheduling for AI models
– Intro & performance hack overview
Get involved: GitHub org, self-service labs, docs
Looking ahead to Kubernetes 1.35