# Gemini Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/gemini?page=5
> Markdown URL: https://aitinkerers.org/technologies/gemini.md?page=5
> Technology record last updated: 2026-09-18T15:13:48Z
> Generated: 2026-09-23T01:48:09Z

Google's natively multimodal AI model: understands and operates across text, code, audio, image, and video.

Gemini is Google's most capable and general AI model, engineered from the ground up to be natively multimodal: it seamlessly understands and combines information across text, code, audio, image, and video inputs. The technology is optimized for flexibility, running efficiently on everything from data centers to mobile devices. It is deployed in three key sizes: Ultra (for highly complex tasks), Pro (for broad scaling), and Nano (for efficient on-device tasks). Developers access this power via the Gemini API to build next-generation applications.

- Official technology site: https://deepmind.google/technologies/gemini/
- Public AI Tinkerers demos and talks: 188
- Result page: 5 of 8

## Recent Public Talks and Demos

### [Copywriting y redacción creativa. No es lo que preguntas, es con quién conversas.](https://manizales.aitinkerers.org/talks/rsvp_hSaAP7Bb4fU)

Cómo uso 4 modelos de IA (Chat GPT, Gemini, Claude y Perplexity) como mis partners creativos para desarrollar mis tareas del día a día.

- Event context: 🚀 ¡Octavo Encuentro de AI Tinkerers Manizales! — 2025-09-24 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_hSaAP7Bb4fU

### [AI-B2in: AI-Powered In-Betweens for Real Animations](https://manizales.aitinkerers.org/talks/rsvp_IXAK25Tzaq8)

I will present AI-B2in, an AI tool designed to generate smooth in-betweens for real drawings and animations. Unlike generic interpolation systems, AI-B2in focuses on preserving artistic style while creating fluid motion. The demo will cover the full technical pipeline: image preprocessing, AI-driven frame interpolation, post-processing cleanup (binarization, line sharpening, blur reduction), and integration with modern web technologies. I will show live code and outputs from real animation samp

- Event context: 🚀 ¡Octavo Encuentro de AI Tinkerers Manizales! — 2025-09-24 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_IXAK25Tzaq8

### [AI-Powered Terminal Workflows: Integrating Gemini CLI with Jira MCP](https://montreal.aitinkerers.org/talks/rsvp_ftQQzJ0wD0o)

A practical demonstration of integrating Gemini CLI with Jira's Model Context Protocol to manage, plan, and execute project workflows directly from the terminal.

- Event context: AI Tinkerers Montreal – September 2025 Meetup — 2025-09-24 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_ftQQzJ0wD0o

### [How to remove yourself from the LLM design loop](https://nyc.aitinkerers.org/talks/rsvp_XfntTaAG5tQ)

I'll show the code and run through TextEvolve, which I built to automate the iteration loop that someone performs when they're working on some LLM-based applicaiton or system.

- Event context: Demopalooza @ The Javits Center with MongoDB — 2025-09-17 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_XfntTaAG5tQ

### [Citation - AI Reporting Automation](https://brisbane.aitinkerers.org/talks/rsvp_JLUvlImoyRI)

Citation is an AI-powered platform designed to automate and accelerate report writing, starting with civil engineering. It takes on the repetitive, time-consuming tasks—like data collection, formatting, image markup, and table creation—so engineers can focus on analysis and decision-making.

- Event context: AI Tinkerers – Brisbane Inaugural Meetup · 11 September 2025 — 2025-09-11 — Brisbane
- Public talk page: https://brisbane.aitinkerers.org/talks/rsvp_JLUvlImoyRI

### [LLM-assisted/automated Data Analysis](https://dublin.aitinkerers.org/talks/rsvp_eb_PEG9oPwQ)

A Proof-of-Concept showcases how LLMs can facilitate data analytics tasks.

- Event context: AI Tinkerers Dublin Anniversary Meetup — September 10, 2025 — 2025-09-10 — Dublin
- Public talk page: https://dublin.aitinkerers.org/talks/rsvp_eb_PEG9oPwQ

### [Beyond Chatbots: In-Database GenAI for Advanced Analytics with SQL](https://miami.aitinkerers.org/talks/rsvp_3GbFgHysbaw)

This session moves beyond familiar LLM applications to demonstrate a powerful, in-database approach for advanced analytics. We'll dive into BigQuery ML's AI.GENERATE_TABLE function, showcasing how you can run Gemini models directly on your data warehouse using simple SQL. We'll walk through end-to-end notebooks that tackle real-world business problems: extracting structured features like sentiment and urgency from unstructured patient surveys, performing unsupervised theme discovery on customer support chats, and generating rich customer personas from raw transaction data. The core of the demo will highlight how these newly generated features can be immediately joined with existing structured data (e.g., clinical records, sales figures) to power deeper analysis and predictive models—all within a unified, zero-ETL workflow. We will also briefly showcase how Colab's built-in Data Science Agent can be used to accelerate the development and analysis of these powerful notebooks.

- Event context: AI Tinkerers Miami Meetup — September 3, 2025 — 2025-09-03 — Miami
- Public talk page: https://miami.aitinkerers.org/talks/rsvp_3GbFgHysbaw

### [From Chaos to Concurrency: Building a Scalable Medical Document Processor with AI](https://pereira.aitinkerers.org/talks/rsvp_9bnOR7GfOXw)

This is a technical deep-dive into how we tried (and failed, repeatedly) to turn a messy pile of unstructured medical documents — scanned PDFs, DOCX files, and images — into a clean, structured clinical database, and what we learned along the way. We began with a naive approach: a single Python script (document_processor.py) that extracted raw text using PyPDF2 and python-docx, then sent it all to Gemini with a massive prompt asking for 80 fields in JSON format. That broke quickly — the model returned incomplete or hallucinated data, with under 30% success, and most scanned PDFs were unreadable by PyPDF2. In Phase 2, we split the problem: a fallback pipeline used pdf2image and Tesseract OCR for robust text extraction, we introduced specialized prompts for each section (e.g., _extract_basic_data, _extract_medical_history), and added a lightweight AI classifier to detect form type (initial vs. follow-up) and select the correct taxonomy. This improved accuracy but killed performance, with each document requiring 5–6 sequential API calls and batch processing taking hours. So we moved to Phase 3: a parallel architecture using Docker. We introduced a SQLite task queue, split the system into two containers — medical-survey-processor (a Flask API that registers documents as PENDING) and worker.py (a Python service that processes jobs and updates their status) — and scaled using deploy: replicas: in docker-compose.yml to run multiple workers in parallel. The result is a resilient, scalable system combining deterministic logic with contextual AI, capable of processing hundreds of medical documents in parallel. The live demo will show a real file upload triggering the pipeline, key pieces of the code (OCR fallback, classification, prompts, worker orchestration), and no slides — just raw code, terminal logs, and real-world pain turned into working architecture.

- Event context: AI Tinkerers - Pereira: Mentes en Beta: Prototipar, Fallar, Reinventar con IA — 2025-08-28 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_9bnOR7GfOXw

### [AI Slop Factory: One Prompt to Infinite Brainrot](https://london.aitinkerers.org/talks/rsvp_frOmFuaMHfU)

We’re living in an era where content never sleeps — and neither do the AIs cranking it out. In this demo, I’ll show how I built a fully automated AI “slop factory” that turns a single input (even a dry Wikipedia page) into a fully fledged TikTok-style short, complete with: Scriptwriting Scene planning and “direction” Character creation & visual generation Voiceovers with AI personalities Final cut and transitions All automated. I’ll run a real-time demo — starting from a random Wikipedia page and ending with a finished short-form video ready to feed the endless scroll. Along the way, I’ll dive deep into the pipeline’s architecture, my code orchestration in Python, API juggling, and the hacks I used to keep latency and unit cost low.

- Event context: AI Tinkerers London Meetup - 28th August 2025 — 2025-08-28 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_frOmFuaMHfU

### [How We Built a Browser Agent That Won't Go Rogue: A Technical Deep Dive](https://london.aitinkerers.org/talks/rsvp_MQDGdCKY6-I)

This demo is a technical show-and-tell of how we built Fabraix, a browser-native AI assistant designed from the ground up to be trustworthy. I'll walk through the architecture of our Chrome extension, focusing on the interplay between the local agent running in the browser and our core security engine. We'll dive into the code that captures page context in real-time, how we process it securely, and the design decisions we made to prevent the agent from being hijacked, even when connected to sensitive web apps. Expect a live demo of the agent completing a complex task, with a walkthrough of the specific security checks happening at each step.

- Event context: AI Tinkerers London Meetup - 28th August 2025 — 2025-08-28 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_MQDGdCKY6-I

### [From a quick prototype to a continuously learning production system: Composable agents for enterprise workflows](https://seattle.aitinkerers.org/talks/rsvp_PEpbFyNMHMg)

I'll walk through both my process -- of collaboratively prototyping AI Agents with domain experts and then building a working application by composing those agents into an agentic system with guardrails, logging, etc. Most importantly, feedback is captured to allow continuous learning.

- Event context: Summer on the Lake - August Tinkerers Meetup! (Demos, Food, Friends) — 2025-08-28 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_PEpbFyNMHMg

### [Un agente para dominarlos a todos](https://manizales.aitinkerers.org/talks/rsvp_pMixfk-2Js4)

En esta charla mostraremos cómo, a través de gemini‑cli y el módulo n8n‑mcp, y aplicando técnicas avanzadas de prompt engineering, es posible diseñar y desplegar flujos de trabajo en n8n que automatizan desde tareas sencillas hasta integraciones complejas con APIs externas, todo sin escribir código extenso. Veremos la configuración paso a paso, la creación de prompts efectivos y varios casos prácticos que demuestran el poder de esta combinación.

- Event context: 🚀 ¡Sexto Encuentro de AI Tinkerers Manizales! 🤖 — 2025-08-27 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_pMixfk-2Js4

### [ValueSpotter](https://nyc.aitinkerers.org/talks/rsvp_N_ruuSgF2LY)

We built ValueSpotter to help small businesses, and their financial partners. Insurance adjuster, bankers, lawyers, and auctioneers spend countless hours manually cataloging and valuing assets. That inefficiency drives up costs and delays recoveries. That’s why we built ValueSpotter. In just seconds, our platform uses computer vision to identify items from a simple walk through video. The items are valued using AI services with grounded results providing a low-high range for each item with primary source links.

- Event context: AI Tinkerers August Demo Day ft. Auth0 and Comet. Demo what you've built! — 2025-08-26 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_N_ruuSgF2LY

### [How to remove yourself from the LLM design loop](https://nyc.aitinkerers.org/talks/rsvp_SLNn2oQtDM0)

I'll be presenting TextEvolve, a system I've been working on for a few months. TextEvolve was created to drastically reduce the time it takes to design and refine LLM-based applications. You provide TextEvolve with some text dataset of inputs/outputs (like Q&A datasets, LLM benchmarks, real text applications) and the system uses LLMs to write programs mapping inputs to outputs, gradually learning over time through memory and LLM-based feedback what program approaches work well. For example, given some question and answer dataset that involves complex reasoning over some reference passages, the system might design a program on iteration 1 that fetches the context and makes a simple LLM call. Through LLM-based feedback, the system finds that this approach doesn't handle the complex reasoning well, so on iteration 2 it makes complex control flow to handle different types of questions. On iteration 3 it decides a better approach is to revise iteration 1 and add a ReAct-style loop, etc. I'll probably do exactly this kind of demo, and talk through how the system works and show the different approaches (simple python scripts) that it generates, show the memory files it generates, the experimental log it generates, etc. For most datasets you can review the performance history for each iteration and can see the system gradually improve over time. Concretely, the system uses LLMs to: - write a script, end-to-end, that attempts to map the dataset inputs to outputs - run the script on a batch of test data - generate feedback - write feedback and previous attempts into memory - pick a strategy for the next iteration: try something new? refine a good past attempt? combine the best performing attempts? - repeat For the sake of comparison, it's kind of like DSPy on steroids or an alternative to AlphaEvolve.

- Event context: AI Tinkerers August Demo Day ft. Auth0 and Comet. Demo what you've built! — 2025-08-26 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_SLNn2oQtDM0

### [AI in compliance](https://pune.aitinkerers.org/talks/rsvp_iBvsvJNvs6Y)

Compliance is often the slowest—and most frustrating—part of bringing a product to market. While product development has accelerated, meeting standards like ISO 27001 or SOC still takes weeks, delaying customer trust. Our solution brings this timeline down from days to hours by embedding AI into the heart of compliance readiness. In this talk, I’ll walk through the two key user flows in our system: For compliance-ready teams — Users upload their documents and select the compliance framework. Our AI agents collaboratively analyze these proofs against each requirement, reasoning over them with LLMs to identify gaps. For founders new to compliance — A chat interface guides them step-by-step to meet the chosen compliance standard, making the process as simple as onboarding to a new tool. We also address real-time compliance monitoring using MCP (Model Context Protocol) servers that connect to the user’s systems in read-only mode, collecting data, identifying risks, and suggesting fixes. We’re building an execution agent that will apply approved changes automatically. Under the hood, we use: Litellm as an LLM gateway to tap into the latest models (OpenAI GPT-5, Google Gemini 2.5 Pro, Claude, etc.) Langfuse for agent monitoring YAML-based prompt management for maintainability LlamaIndex for document indexing to create a knowledge base from uploaded proofs Our internally built compliance knowledge layer for ISO 27001 & SOC standards ECS, Vercel, Next.js, and FastAPI for serving & UI Attendees will see how LLMs, agent frameworks, and real-time integrations can remove compliance as a blocker, turning it into a continuous, automated process.

- Event context: AI Tinkerers Pune - Meetup — 2025-08-23 — Pune
- Public talk page: https://pune.aitinkerers.org/talks/rsvp_iBvsvJNvs6Y

### [SwiftAIAgent](https://sydney.aitinkerers.org/talks/rsvp_ZYfgQw-P9BM)

A swift AI agent framework. One demo website that is using this framework https://deepsearch.timwang.au

- Event context: AI Tinkerers – Sydney Inaugural Meetup · 20 August 2025 — 2025-08-20 — Sydney
- Public talk page: https://sydney.aitinkerers.org/talks/rsvp_ZYfgQw-P9BM

### [Using AI as your second brain: Methods & practices](https://sydney.aitinkerers.org/talks/rsvp_lEkoBb4Dm3Y)

I will demonstrate methods & practices I use every day as a Software Engineer showing how a small feature in a product I am building is implemented with aid of various AI tools such as Kiro Code, Claude Code, Gemini CLI and ChatGPT agents. The takeaway will be to keep the suite of tools small/thin, orchestrate them well and make sure they are directed to deliver outcomes as per Product requirements.

- Event context: AI Tinkerers – Sydney Inaugural Meetup · 20 August 2025 — 2025-08-20 — Sydney
- Public talk page: https://sydney.aitinkerers.org/talks/rsvp_lEkoBb4Dm3Y

### [AI Content Pipeline](https://sf.aitinkerers.org/talks/rsvp_1fasc8u4gw4)

we built something kinda cool and only gemini made it possible :)

- Event context: Mastering the Google AI Stack — 2025-08-15 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_1fasc8u4gw4

### [Using Gemini's Large Context and Schema-Enforced Function Calling to Generate Production Kubernetes & Sveltos YAML Configurations](https://sf.aitinkerers.org/talks/rsvp_HXnmVafjK_w)

This demo is a technical deep dive into an AI assistant that generates production-grade Kubernetes and Sveltos configurations from natural language. I'll show how I built it to tackle the complexity of Kubernetes schemas and answer advanced technical questions about Sveltos, a multi-cluster management platform that extends Kubernetes with advanced GitOps capabilities. The core technical challenge is that Kubernetes and Sveltos schemas are massive and complex, requiring large context windows and strict structural enforcement to generate valid YAML. I'll walk through the code and demonstrate how I use a multi-stage agentic workflow with Gemini 2.5 Flash. The system first classifies user intent (Q&A, new config, modification, or out-of-topic) and then routes to a specialized agent. The key innovation I'll showcase is the use of Gemini's function calling capabilities to enforce the complex, nested schemas required for valid Kubernetes resources. This unique approach, combined with Gemini's large context window, makes it possible to reliably generate intricate K8s or Sveltos configurations directly from a simple prompt. For the Q&A portion, the assistant uses a RAG pipeline with Google's embedding models and Google Search Grounding to provide accurate, cited answers from technical documentation.

- Event context: Mastering the Google AI Stack — 2025-08-15 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_HXnmVafjK_w

### [AI Insights and Applications](https://orange-county.aitinkerers.org/talks/rsvp_ZVuuYqph8yQ)

I will use PPT to Share unique prompt inquiries such as: Johari Window, Theory of Constraints, Theory of Invention, Quantum mechanics, Phase space with a variety of objects.

- Event context: AI Tinkerers - Orange County Meetup- Thursday July 31st 2025 — 2025-08-01 — Orange County
- Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_ZVuuYqph8yQ

### [Red-Team AI models before Your Users Do](https://atlanta.aitinkerers.org/talks/rsvp_WNRew-MWc8w)

Users will always find bold and innovative ways to use your product. Some of these ways could cause harm. As developers and product managers, we're responsible for ensuring that the products we make are safe. To make safe products, we need to test them. We want to present testing frameworks that the audience can use to make safer AI products.

- Event context: 1 Year Anniversary Meetup — 2025-07-31 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_WNRew-MWc8w

### [Signal from the Noise: Using Gemini AI to find great Jazz](https://boston.aitinkerers.org/talks/rsvp_nsMQhi8qh4Q)

Jazz Signal uses AI combined with human curators to find great jazz to listen to. We crawl a curated set of Jazz Blogs and review sites, and use Gemini APIs to discover the artists and music mentioned in the articles. A web app provides a way to browse the reviews and articles while listening to the music. We also use AI to summarize the articles and categorize them with short, evocative tags to prevent information overload. We will provide a walkthrough of a live site demonstrating the results, followed by a code walkthrough of our nodejs-based Gemini api usage. In particular we discuss prompt construction, Gemini Flash 2.5 schema configuration, and api calls. We will show how we use Gemini to extract structured data from highly unstructured jazz blogs and review sites. In particular, we describe how we specify the structured data we wish to extract and discuss how Gemini performs on these tasks. We'll also demonstrate how we map our findings to music databases (e.g., Spotify, YouTube, etc.), allowing for a heterogeneous listening environment.

- Event context: AI Tinkerers July Meetup @ Tech Superpowers — 2025-07-28 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_nsMQhi8qh4Q

### [State as Fixed-Size Context](https://boston.aitinkerers.org/talks/rsvp_sg_1w-rtpns)

LLM applications often rely on a long-running conversation context (including content from documents and the conversation with the user so far) to keep track of the information needed to respond to the user's next requests sensibly. This is very difficult problem when approached from the point of view of extracting the relevant information from a conversation context. One approach is RAG (retrieval augmented generation), where an embedding of the conversation context is queried for the most relevant snippets to include in the context for the next response to the user. Another approach is to maintain a rolling summary of the most important points about the conversation so far, and to include that summary into the context for the next response to the user. All of these types of approaches have two main issues: - unreliability: it's impossible to _guarantee_ that an important piece of info will be extracted from the context - non-scalability: while they improve scalability over including as much of the original conversation context as possible into the context used to generate a response, they still have worsening performance as the original conversation context scales One LLM application I've been interested in, where these problems come up, is text adventure games. A well-known example of this kind of thing is AI Dungeon, which suffered from the unreliability, non-scalability problems, as well as admitting problems of: - ephemeral state: application state becomes distorted by whatever method is used to re-include it into context when necessary (if it is retrieved at all), making particular bits of information ephemeral over time - enforced rules: since everything is in terms of natural language, it is difficult for the application designer to enforce any rules on user behavior (even careful natural-language instructions are not enough, viz Lakera's Gandalf prompting challenges) In this presentation, I demonstrate an alternative approach that addresses all of these problems in building an LLM text adventure game. The central idea is to formalize the application state (e.g. the game world state) into structured data, formalize the user's fixed-sized view of this state (e.g. their player information, inventory, and immediate surroundings), and formalize an enumerated set of actions (in the form of a DSL i.e. domain-specific language) that the user can take to affect the state (e.g. actions available to the player). All of these formalizations are in terms of Zod schemas with natural-language descriptions, especially taking advantage of schemas unions with enum tags e.g. `const BooleanOrNumber = z.union([ z.object({ type: z.enum(["boolean"]), value: z.boolean() }), z.object({ type: z.enum(["number"]), value: z.number() }) ]);`. In this way, the LLM always has access to the most important information (which is fixed-size in terms of the number of conversation turns that user has taken) for deciding how to interpret the user's prompt, the application state is maintained even when the player is only viewing a fragment of it, and the LLM can only interpret the user's prompt into a DSL for interacting with the state, which lets the application designer decide the rules for interacting with the state in a way that is transparent to the user. The presentation will be organized as follows: - Introduction of the problems: unreliability, non-scalability, ephemeral state, enforced rules - Demonstration of the text adventure game that implements some techniques that address these problems - Technical walkthrough of how the app implements these techniques, in particular the formalization of state, view, and action

- Event context: AI Tinkerers July Meetup @ Tech Superpowers — 2025-07-28 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_sg_1w-rtpns

### [Navigating Classrooms with AI: The VidyaNav-ai Sprint Story](https://munich.aitinkerers.org/talks/rsvp_YYFSk84kxpI)

In many under-resourced schools across Europe and India, one teacher must simultaneously teach multiple grade levels, each with its own syllabus, pace, and learning needs. In this talk, I will present VidyaNav-ai, an AI-powered assistant designed to support such teachers by generating differentiated worksheets in multiple languages from textbook images, creating visual aids for students from simple prompts, and answering various student questions by giving simple analogies using a retrieval-augmented generation (RAG) approach. I will present about the technologies and LLM models I have used, and why I used them. I will talk about the models like Gemini-pro and Gemini-flash. I will show how I built the frontend using the google firebase studio and share the hacks that I learnt.

- Event context: AI Tinkerers Munich - July 25 — 2025-07-25 — Munich
- Public talk page: https://munich.aitinkerers.org/talks/rsvp_YYFSk84kxpI

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