# Gemini models Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/gemini-models
> Markdown URL: https://aitinkerers.org/technologies/gemini-models.md
> Technology record last updated: 2026-03-04T13:38:59Z
> Generated: 2026-09-22T04:43:34Z

Gemini models: Google DeepMind's multimodal AI family (Ultra, Pro, Flash, Nano), built for state-of-the-art reasoning across text, code, image, audio, and video data.

Gemini models are Google DeepMind's core multimodal AI foundation, succeeding PaLM 2: they natively process five data types (text, code, image, audio, video) for seamless understanding. The family includes specialized versions: Gemini Ultra (complex tasks), Gemini Pro (broad scaling), and Gemini Nano (efficient on-device use). The latest iteration, Gemini 3 Pro, delivers world-leading performance, specifically enhancing reasoning and agentic capabilities. This technology powers Google's generative AI chatbot, Gemini, and integrates directly into products like Search, Gmail, and Docs, enabling next-generation development.

- Official technology site: https://deepmind.google/technologies/gemini/
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Failing Fast with AI: Rapid Prototyping Using Google AI Studio](https://pereira.aitinkerers.org/talks/rsvp_BLcMVGVLlTs)

This demo explores a paradigm shift in product development: failing fast to learn faster. I’ll demonstrate how Google AI Studio can be used to rapidly prototype AI-powered applications, validate ideas, and surface potential product value—without investing significant time, money, or engineering resources upfront. The focus will be on hands-on experimentation, iteration cycles, and technical workflows rather than slides or theory.

- Event context: Cambio de paradigma AITINKERERS PEREIRA 2026 — 2026-02-26 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_BLcMVGVLlTs

### [Using AI to adapt books for language learners](https://poland.aitinkerers.org/talks/rsvp_WAo5KNBg_Dc)

During building the storylearner.app I've been heavily relying on LLMs for the content. And they can be magical, but also sometimes the outputs can be problematic and overall it can be a pain in the ass sometimes. I want to talk about the interesting caveats and approaches to solve encountered issues. Some details I might be mentioning: - experimental setup for evaluating multiple gemini/gpt models - factors that would break the LLMs output quality - how can I actually trust my LLMs and their outputs? - experiences with automated image generation (multimodal), hundreds of illustrations per book - automating tedious workflows

- Event context: AI Tinkerers Poland #4 - Meetup in Warsaw (June) — 2025-06-26 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_WAo5KNBg_Dc

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