Assembling My Agent Harness: Building a Conversational Airline Agent with Claude Code + Rasa

Assembling My Agent Harness: Building a Conversational Airline Agent with Claude Code + Rasa

Video by Rasa via YouTube
Assembling My Agent Harness: Building a Conversational Airline Agent with Claude Code + Rasa

We set up an agent harness — Claude Code, a Rasa MCP server, and some markdown files — and use it to build a flight upgrade assistant from scratch, live on camera.
The airline bot on WhatsApp is actually pretty slick. You’re already logged in, your bookings are there. But the conversation feels like filling in a form. Say anything off-script and it’s lost.
So we build something better: an upgrade agent that can check availability, show seats, quote prices, and handle hesitation naturally. "That’s a lot" doesn’t break it — it re-asks.
The whole thing is built in one sitting using Claude Code with Rasa’s MCP tools. No slides, no prep.

*Try it yourself:* https://gist.github.com/amn41/537a607ccbc3ca557e7f23a97233f0be

*What’s in the harness:*
– Claude Code (the base)
– Rasa MCP tools (train, test, inspect — without leaving the editor)
– CLAUDE.md (workflow instructions that make Claude Code self-validating)

*Links:*
– *Rasa Docs:* https://rasa.com/docs
– *Code from this video:* https://gist.github.com/amn41/537a607ccbc3ca557e7f23a97233f0be

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Open Source AI, RL Agents & Cloud-Native Ops Highlights

NVIDIA and OpenAI’s GPT-5.5-Codex: Engineering at Scale OpenAI’s new GPT-5.5-Codex model (backed by NVIDIA) is touted as a superpower for complex engineering tasks. This development underscores the trend of AI moving from chat to practical code generation and task automation. For open-source enthusiasts, the challenge is to keep pace with proprietary advances while leveraging community-driven … Read more

Introducing GPT-5.5 with NVIDIA

Introducing GPT-5.5 with NVIDIA

Video by OpenAI via YouTube
Introducing GPT-5.5 with NVIDIA

"GPT-5.5’s superpower is that it can just get things done.”

It’s so cool to see how masters of the AI universe like NVIDIA’s Dennis Hannusch are leveraging GPT-5.5-Codex for complex engineering tasks.

Be like Dennis.

Build with GPT-5.5: https://openai.com/index/introducing-gpt-5-5/

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LLM Instruction Tuning & DPO via H2O Enterprise LLM Studio | Part 13

LLM Instruction Tuning & DPO via H2O Enterprise LLM Studio | Part 13

Video by H2O.ai via YouTube
LLM Instruction Tuning & DPO via H2O Enterprise LLM Studio | Part 13

How to fine-tune domain-specific LLMs for tasks like text-to-SQL and multimodal QA using H2O Enterprise LLM Studio.

When prompt engineering alone is insufficient, fine-tuning a domain-specific model can reduce costs while improving accuracy. H2O Enterprise LLM Studio walks through the full instruction tuning process—leveraging LoRA adapters, built-in AutoML for hyperparameter optimization, and real-time training metrics like loss curves and validation perplexity. Models are evaluated for safety and quality, then exported directly to Hugging Face for distribution across the organization.

Technical Capabilities & Resources

➤ Multimodal Generative AI Tuning: Train models for domain-specific tasks including multi-modal causal language modeling and image/text classification.
🔗 https://docs.h2o.ai/h2o-enterprise-llm-studio/get-started/what-is-h2o-enterprise-llm-studio#use-cases

➤ Instruction Tuning & DPO Alignment: Fine-tune base models using labeled data, automated hyperparameter search, and preference optimization.
🔗 https://docs.h2o.ai/h2o-llmstudio/guide/experiments/supported-problem-types#dpo-modeling

➤ Augmentation for Fine-Tuning Datasets: Use LLM DataStudio to augment and prepare training data for downstream instruction tuning.
🔗 https://docs.h2o.ai/h2o-llm-data-studio/guide/augment/augmentation-datasets

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Managing Experiments, LLMs, and Model Serving with MLflow

Managing Experiments, LLMs, and Model Serving with MLflow

Video by MLflow via YouTube
Managing Experiments, LLMs, and Model Serving with MLflow

In this webinar, MLflow Ambassador and Machine Learning Engineer Thor Steen Larsen from DSB demonstrates how to move beyond messy local files to a structured, enterprise-ready machine learning environment using MLflow. Thor explains how MLflow provided essential tools for experiment tracking, model evaluation, and various deployment strategies suitable for both Data Scientists and ML Engineers.

Topics Covered:
🔹 Getting Started with MLflow
🔹 MLflow for AI & GenAI
🔹 Deployment Strategies
🔹 MLOps Environments
🔹 Algorithmic Prompt Optimization
🔹 Deploying to production with MLflow and DABs
🔹 New & Upcoming Features
🔹 Tracking Image Datasets with MLflow

🗣️ Hosted by Pål de Vibe, Databricks MVP
💻 Monthly Euro Databricks Community Slack Hangout

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RL for Agents Workshop – Deep Dive on Training Agents with RL and Open Source

RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source

Video by Hugging Face via YouTube
RL for Agents Workshop - Deep Dive on Training Agents with RL and Open Source

Reinforcement learning is becoming central to agentic systems, but moving from RL for LLMs to RL for agents introduces a new set of challenges: environments, rollouts, tool use, inference bottlenecks, reward design, and evaluating multi-step behavior in the real world.

In this live Hugging Face workshop, we bring together researchers and builders working on the frontier of RL for agents. The session will feature short talks followed by a discussion on what is working today, where open methods still fall short, and what comes next.

Speakers include:

– Lewis Tunstall, Hugging Face
– Will Brown, Prime Intellect
– Ofir Press, Princeton University
– Alex Zhang, MIT CSAIL
additional guests TBA

Topics include:
– training agents with open source tools
– scaling RL for language agents
– multi-step verification and reward design
– benchmarking agent capability beyond static tasks
– recursive reasoning and new agent architectures

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Cloud Native Live Fireside Chat—Powering Private AI: Customer’s View

Cloud Native Live Fireside Chat—Powering Private AI: Customer’s View

Video by CNCF [Cloud Native Computing Foundation] via YouTube
Cloud Native Live Fireside Chat—Powering Private AI: Customer’s View

As enterprises transition from AI experimentation to real-world production, Private AI is quickly becoming a strategic imperative. In this CNCF Fireside Chat, Chris Wolf, Global Head of AI and Advanced Services at VMware by Broadcom, and Pankaj Gupta, Senior Director of Modern Application Solutions at VMware by Broadcom, share firsthand perspectives from customers who are successfully building and scaling AI in secure, well-governed, and cost-efficient environments.

From real-world use cases like contact center modernization and computer vision at the edge, to the critical role of cloud-native technologies such as Kubernetes and key CNCF projects, this conversation explores what it באמת takes to operationalize AI at scale.

We’ll also dive into the biggest challenges organizations face -from Day 2 operations and data sovereignty to rising infrastructure and energy costs – and how a cloud native approach is helping solve them.

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The year of operationalizing GenAI in healthcare

The year of operationalizing GenAI in healthcare

Video by Open Data Science and AI Conference via YouTube
The year of operationalizing GenAI in healthcare

The year of operationalizing GenAI in healthcare is here

Want to learn more about AI in person? Check out ODSC AI East 2026, coming to Boston this April 28th-30th: https://hubs.li/Q041BP6P0

#DataScience #AI #ArtificialIntelligence #ODSCAI

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2.5 Admins 296: Beware of the Leopard

2.5 Admins 296: Beware of the Leopard

Video by The Late Night Linux Family via YouTube
2.5 Admins 296: Beware of the Leopard

Support us on Patreon and get an ad-free RSS feed with some early episodes. https://www.patreon.com/LateNightLinux

Microsoft locks devs out of important accounts, the foreign router ban exemptions make even less sense, Backblaze shows that "unlimited" never means that, and attempting to avoid software that’s written with AI.

https://2.5admins.com/2-5-admins-296/

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