
What we’re about
We are a group of startup engineers, research scientists, computational linguists, mathematicians, philosophers, and others interested in understanding the meaning of text, reasoning, and human intent through technology. We want to apply our understanding to building new businesses and improving overall human experience in the modern connected world. The MIND Stack explained: mind.wtf.
This is a technical AI meetup: we build systems with Machine Learning on top of Data Pipelines, and concern ourselves with the stuff we can try in open source, learn, improve, and model human behavior in industry for practical results.
The advisory board for this meetup is Cicero Institute (Cicero.ai), and its conferences are AI.vision and self.driving.cars. We like specific technical problems (self-driving cars) and the way they inform better higher-level inference of the future of AI (AI.vision).
Upcoming events (1)
See all- [Cross Posting] Gemma Developer Meetup (June 2025)San Francisco, San Francisco, CA
[Cross-posting event]
Registration is REQUIRED at HERE by Monday June 23
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Join us for an evening of tech talks at the Gemma Developer Meetup in San Francisco! Whether you're a developer, researcher, or data scientist working with open LLMs, this event is a great opportunity to learn from experts and connect with the Gemma experts and developers in SF and Silicon Valley.
#### Talks & Speakers
🧠Fine-Tuning Gemma to Replicate an Individual's Personality
Speaker: Mayank Chaturvedi, Google
Explore how the Gemma model can be fine-tuned to emulate the personality and cognitive style of a specific individual. This hobby project uses Hugging Face Transformers and the TRL library to push the boundaries of personalization in AI.
🤖 Building Effective AI Agents with Reinforcement Learning and Gemma 3
Speaker: Daniel Han-Chen, unsloth
Learn how to create your own local AI agents using Gemma 3. This talk covers everything from lightweight fine-tuning to advanced reinforcement learning using GRPO. You'll also see how to train models efficiently using Unsloth — even on limited hardware or for free via Google Colab and Kaggle.
More talks to be announced soon.