The Power of Open Source: Building Giants in the Open
Skills:
Building Custom AI Tools70%
Explore how open-source AI powers production today: candid lessons from European founders (n8n, Black Forest Labs), a pragmatic tour of the open infra stack (Snowflake, OpenAI, NVIDIA, Supabase/Postgres), and investor playbooks for OSS licensing and sustainable monetization—plus 2026 bets on agentic apps, fine-tuning, unified data platforms, world models, and robotics demos.
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## ⏰ Timestamps
0:00 – Welcome & Event Setup
Opening remarks, why Hugging Face hosted this side event at Slush, and overview of the agenda (3 panels + demos).
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Panel 1 – Open-Source AI Founders (n8n & Black Forest Labs)
3:10 – Founder intros & origin stories
Backgrounds of Jan (n8n) and Robin (BFL), their transition from creative/academic work to founding open-source AI companies.
6:20 – Building global OSS companies from Europe
YC experiences, German GmbH setups, and lessons learned growing OSS brands globally.
10:40 – Licensing, business models & the OSS tension
Fair-code decisions, monetization transparency, and handling license shifts with community trust.
15:00 – Product philosophy & internal dogfooding
How both teams use community contributions internally and balance openness with product direction.
17:36 – What’s next for AI tools & assistants
Future vision on agents, automation systems, and world-model-inspired workflows.
18:53 – Panel 1 wrap-up
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Panel 2 – Open Infra & Developer Ecosystem (Snowflake, OpenAI, NVIDIA, Supabase)
19:23 – Intro: The rising developer stack for AI apps
Why 2025–2026 are the “agentic application” years and how infra is adapting.
21:51 – Snowflake: Open formats & lakehouse future
Iceberg, Polaris metadata catalog, ingestion tooling, and how Snowflake integrates HF models through Cortex/containers.
25:29 – OpenAI: OSS models & developer tools
GPT-OSS + GPT-5 coexistence, Safeguard policies, and OpenAI’s focus on startups building on top of ChatGPT.
28:52 – NVIDIA: Full-stack open tooling & robotics
Nemotron recipes, Cosmos world models, Groot for robotic
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The Future of Natural Language Processing
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Trends in Model Size & Computational Efficiency in NLP
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Increasing Data Usage in Natural Language Processing
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In Domain & Out of Domain Generalization in the Future of NLP
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The Limits of NLU & the Rise of NLG in the Future of NLP
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The Lack of Robustness in the Future of NLP
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Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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Train a Hugging Face Transformers Model with Amazon SageMaker
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What is Transfer Learning?
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The pipeline function
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Navigating the Model Hub
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Transformer models: Decoders
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The Transformer architecture
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Transformer models: Encoder-Decoders
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Transformer models: Encoders
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Keras introduction
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The push to hub API
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Fine-tuning with TensorFlow
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Learning rate scheduling with TensorFlow
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TensorFlow Predictions and metrics
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Welcome to the Hugging Face course
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The tokenization pipeline
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Supercharge your PyTorch training loop with Accelerate
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The Trainer API
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Batching inputs together (PyTorch)
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Batching inputs together (TensorFlow)
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Hugging Face Datasets overview (Pytorch)
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Hugging Face Datasets overview (Tensorflow)
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What is dynamic padding?
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What happens inside the pipeline function? (PyTorch)
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What happens inside the pipeline function? (TensorFlow)
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Instantiate a Transformers model (PyTorch)
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Instantiate a Transformers model (TensorFlow)
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Preprocessing sentence pairs (PyTorch)
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Preprocessing sentence pairs (TensorFlow)
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Write your training loop in PyTorch
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Managing a repo on the Model Hub
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Chapter 1 Live Session with Sylvain
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Chapter 2 Live Session with Lewis
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The push to hub API
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Chapter 2 Live Session with Sylvain
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Chapter 3 live sessions with Lewis (PyTorch)
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Day 1 Talks: JAX, Flax & Transformers 🤗
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Day 2 Talks: JAX, Flax & Transformers 🤗
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Day 3 Talks JAX, Flax, Transformers 🤗
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Chapter 4 live sessions with Omar
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Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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[Webinar] How to add machine learning capabilities with just a few lines of code
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Hugging Face + Zapier Demo Video
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Hugging Face + Google Sheets Demo
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Hugging Face Infinity Launch - 09/28
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Build and Deploy a Machine Learning App in 2 Minutes
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Hugging Face Infinity - GPU Walkthrough
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Otto - 🤗 Infinity Case Study
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Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
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Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
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🤗 Tasks: Causal Language Modeling
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🤗 Tasks: Masked Language Modeling
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Chapters (11)
Welcome & Event Setup
3:10
Founder intros & origin stories
6:20
Building global OSS companies from Europe
10:40
Licensing, business models & the OSS tension
15:00
Product philosophy & internal dogfooding
17:36
What’s next for AI tools & assistants
18:53
Panel 1 wrap-up
19:23
Intro: The rising developer stack for AI apps
21:51
Snowflake: Open formats & lakehouse future
25:29
OpenAI: OSS models & developer tools
28:52
NVIDIA: Full-stack open tooling & robotics
🎓
Tutor Explanation
DeepCamp AI