# Streamlit Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/streamlit
> Markdown URL: https://aitinkerers.org/technologies/streamlit.md
> Technology record last updated: 2026-02-22T10:50:14Z
> Generated: 2026-09-22T08:35:39Z

Streamlit is the open-source Python library that transforms data scripts into interactive, shareable web applications in minutes (not weeks).

Streamlit is purpose-built for data scientists and ML engineers: It eliminates the need for front-end development knowledge (HTML, CSS, JavaScript). The core API uses pure Python to quickly build powerful data apps, dashboards, and machine learning prototypes. Users leverage simple commands (e.g., `st.slider`, `st.dataframe`) to integrate interactive widgets and display data from libraries like Pandas or Matplotlib. The platform supports rapid iteration, automatically rerunning the script on user interaction, and offers streamlined deployment via the Streamlit Community Cloud.

- Official technology site: https://streamlit.io
- Public AI Tinkerers demos and talks: 89
- Result page: 1 of 4

## Recent Public Talks and Demos

### [Teaching an LLM to be an interior designer](https://nyc.aitinkerers.org/talks/rsvp_G0dtIg_V-Dw)

A pipeline that turns a LiDAR room scan into art-placement decisions — which wall, what size, what art — by converting usdz geometry into per-wall design constraints that drive image generation, with a vision-LLM design critic whose judgments are verified, calibrated, and used to teach the deterministic scoring engine. Live, I'll walk the raw scan data (the LiDAR mesh and its JSON), the geometry visualizers that turn planes into design constraints, and the tooling we use to calibrate the critic and verify its judgments against the engine.

- Event context: August Demo Day ft Runpod, Veris, Openrouter, — 2026-08-19 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_G0dtIg_V-Dw

### [Agents Are Not Service Accounts: Post-Quantum Channels and Zero-Knowledge Delegation Between AI Agents, Live](https://dubai.aitinkerers.org/talks/rsvp__l-10tNvOo4)

Agent Trust Lab is a live simulator where two AI agents establish a quantum-resistant channel and prove delegated authority without revealing credentials. The scenario: a user delegates vacation booking up to a EUR 3,000 budget, and the agent proves that authorization to a counterparty without exposing who the user is or what else it may do. The same exchange runs side by side across three models: classical (X25519 + bearer token), PQC without ZKP (X25519 + ML-KEM-768 hybrid), and PQC + ZKP (Schnorr proof over the encrypted link), so you can watch exactly where each pattern leaks. It runs live at trustlab.mahasbini.org: the three-model exchange, a quantum-attack toggle that breaks the classical session while the hybrid one holds, and an audit-receipt tamper test where editing any bound field (responder, negotiated group, session commitment) invalidates the receipt in front of you. The demo closed with the real thing in production: a self-hosted video-conferencing stack negotiating X25519MLKEM768 on TLS 1.3 (OpenSSL 3.5), inspected live from the browser. No slides, no LLM on stage: this is the layer your agents stand on.

- Event context: AI Tinkerers Dubai — August Demo Day — 2026-08-08 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp__l-10tNvOo4

### [Agent Memory Is the Softest Attack Surface. Let Me Show You.](https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_vxTZZYnTRxQ)

A memory-enabled AI financial advisor for small businesses that remembers each customer across sessions. The app is just the testbed. The real subject is agent-memory security: what breaks when you give an agent long-lived, multi-tenant memory. It runs on a real stack, with vector-searched long-term memory, per-tenant isolation, and live similarity scores on screen. In the demo I show it working, then run two live attacks on its memory: a cross-tenant data leak on the read path, and a persistent memory-poisoning injection on the write path. Then I show the defense for each. Everything is live: the working system, the memory recall panel, the one line of code that is the entire tenant boundary, and the toggles that break and fix it. No slides. Synthetic data only.

- Event context: AI Tinkerers Minneapolis-Saint Paul — July Meetup — 2026-07-29 — Minneapolis Saint Paul
- Public talk page: https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_vxTZZYnTRxQ

### [Building an ML Decision Lab for Agriculture: Turning Predictions into Learning](https://lausanne.aitinkerers.org/talks/rsvp_4vwa4V9v2ek)

FarmBuddy is an interactive machine learning decision-support system built with Streamlit and a Random Forest regression pipeline. Users can modify agricultural inputs such as crop type, fertilizer usage, land area, and season, then observe how model predictions change in real time. Beyond prediction, the system includes decision logging, before-and-after scenario comparison, input validation, and a learning summary layer designed to help users understand how machine learning models respond to changing conditions. During the demo, I will show the live application, model inference workflow, session-state architecture, and the decision comparison engine.

- Event context: AI Tinkerers Lausanne June 2026 Meetup — 2026-06-25 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_4vwa4V9v2ek

### [Engenheiro civil + Claude Code: app de terraplenagem 100% determinístico construído por conversa](https://curitiba.aitinkerers.org/talks/rsvp_iwCDvtHEc-s)

KML Earthworks é um app Streamlit que transforma um traçado de estrada de acesso desenhado no Google Earth em estimativa de terraplenagem (perfil longitudinal, volumes de corte/aterro pela fórmula do prismatoide, balanço de massa, diagrama de Bruckner) em segundos — upload de .kml, download de Excel, zero GIS de desktop. Na demo eu vou: (1) desenhar um acesso ao vivo no Google Earth e rodar o app em produção (kml-earthworks.streamlit.app) mostrando o pipeline completo (parse → stationing a cada 20m → enriquecimento de elevação com fallback de API → otimização de grade → volumes → export); (2) abrir o repo no editor e mostrar a estrutura src/ modular, o CLAUDE.md que funciona como guideline persistente para o agente, e rodar os 58 testes ao vivo no terminal; (3) navegar pelos commits para mostrar a evolução notebook → pacote → app por desenvolvimento conversacional com Claude Code, incluindo os pontos onde eu tive que intervir manualmente.

- Event context: AI Tinkerers Curitiba: Evento Inaugural — 2026-06-10 — Curitiba
- Public talk page: https://curitiba.aitinkerers.org/talks/rsvp_iwCDvtHEc-s

### [VOXMAP - Painel de diálogo - Analisador de Sentimentos](https://saopaulo.aitinkerers.org/talks/rsvp_f2hd9JW9cho)

Desenvolvi o VOXMAP, um assistente inteligente de atendimento e conciliação que transforma conversas em insights acionáveis. A aplicação permite que equipes analisem interações com clientes (como chats ou atendimentos), gerando automaticamente resumos estruturados, identificação de sentimento, propostas de solução e próximos passos recomendados — tudo em tempo real. Além disso, o sistema oferece análises visuais complementares, como nuvem de palavras e grafos de relacionamento, ajudando a entender padrões, conflitos e oportunidades dentro das conversas. O VOXMAP foi pensado para reduzir o esforço operacional em atendimento, aumentar a clareza nas decisões e acelerar a resolução de conflitos com apoio de IA.

- Event context: AI Tinkerers SP - Meetup de Maio - Kiro & AWS — 2026-05-28 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_f2hd9JW9cho

### [911automate](https://montreal.aitinkerers.org/talks/rsvp_Fxb5kGzWsq4)

A RAG system POC for emergency protocols

- Event context: AI Tinkerers Montreal - April Demo Night — 2026-04-22 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_Fxb5kGzWsq4

### [Replacing Analysts in commodity trading](https://zurich.aitinkerers.org/talks/rsvp_i8MtV8raWAE)

Informatiom system built to deliver what traders want in LNG / Gas speculative decisions. - Visualize Fundamentals - Machine Learning models - Automate processes (anomalies alerts, analyze competitors, check auctions) - Data architecture automation - News embeddings and semantic analyzes (check what moves the market) - Monitoring users for automated feedback

- Event context: AI Tinkerers Zurich April 9th — 2026-04-09 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_i8MtV8raWAE

### [CyberSentinel: Building a Threat Detection Pipeline](https://miami.aitinkerers.org/talks/rsvp_8u_zBunWIJ0)

CyberSentinel is a multi-stage security analysis pipeline. It ingests raw logs from firewalls, SSH, web servers, and intrusion detection systems, identifies threats, maps them to MITRE ATT&CK and the Cyber Kill Chain, and generates a report with severity scores and indicators of compromise. For the live demo, I'll run it against a simulated 5-phase attack (port scanning, SSH brute force, background noise, SQL injection, data exfiltration) and show how 458 log lines become 2 correlated threat clusters and 12 IOCs in under 2 seconds.

- Event context: AI Tinkerers Miami: Spring Demos at The Lab — 2026-03-25 — Miami
- Public talk page: https://miami.aitinkerers.org/talks/rsvp_8u_zBunWIJ0

### [TDLW](https://montreal.aitinkerers.org/talks/rsvp_nT9L3-mZHQU)

a vibe-engineered personal app to help summarize long podcasts on youtube it's still WIP, but by the demo night the UI will be better organized and there will be a RAG implemented so that you could chat about the video with an agent

- Event context: AI Tinkerers Montreal - March Demo Night — 2026-03-24 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_nT9L3-mZHQU

### [Building a Stock Market Research Agent with LangGraph](https://st-louis.aitinkerers.org/talks/rsvp_a06ZanAIgII)

I’m demonstrating Infera, an app that automates stock market due diligence. Instead of manually looking up tickers, Infera takes a list of companies (e.g., UBER vs. LYFT), pulls real-time data via Yahoo Finance, profiles leadership, and generates a ranked Markdown report with comparative radar charts.

- Event context: AI Tinkerers – St. Louis Meetup: February 4, 2026 — 2026-02-04 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_a06ZanAIgII

### [Email Writer](https://nashville.aitinkerers.org/talks/rsvp_8v2jQ2SXKlk)

I’ll demo how I fine-tuned an LLM on my Gmail sent folder so it learns my writing style. The talk walks through: extracting and cleaning email data, generating usable prompts, creating JSONL training sets, running a fine-tune, and providing an interactive testing interface.

- Event context: AI Tinkerers Nashville – January 29th, 2026: Live Demos, Code, and Architecture — 2026-01-29 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_8v2jQ2SXKlk

### [Multi-Agent Fraud Detection: When LLMs Argue About Bitcoin Laundering](https://toronto.aitinkerers.org/talks/rsvp_l2U61QefZDs)

I built a real-time Bitcoin fraud detection system where three LLM agents debate whether a transaction is fraudulent: - Agent 1 (Prosecutor): Uses Graph RAG (Neo4j) to find suspicious network patterns and builds a case for fraud - Agent 2 (Defense): Searches for legitimate explanations and challenges the prosecutor's claims - Agent 3 (Judge): Reviews both arguments and makes the final verdict The Live Demo: 1. A suspicious transaction streams in via Kafka 2. Prosecutor Agent queries the Neo4j graph and constructs a fraud case using Gemini 3. Defense Agent counter-argues with alternative explanations 4. Judge Agent renders a verdict with confidence scoring 5. I pop the hood and show: the exact prompts, the Cypher graph queries, and the decision logic Technical Deep Dive: - How I structure multi-hop graph context for LLM reasoning - Prompt engineering to prevent agent "hallucination" on graph data - Latency battles: why I moved from Gemini Pro to Flash and added prompt caching - The surprising failure modes: when agents agree too quickly vs. when they hallucinate connections

- Event context: AI Tinkerers Toronto - January 2026 Meetup at Google! — 2026-01-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_l2U61QefZDs

### [Glimpse of the future: How hyperspeed inference allows for new class of ai driven apps](https://raleigh.aitinkerers.org/talks/rsvp_rjOw3LBlaI4)

I will be expanding on a project I started a few months ago using the Cerebras inference engine, which offers speeds up to 1000 tokens/s, to demonstrate how AI models running at this rate requires new ways of thinking about AI app design and UX. The demo will show how an OS might work where AI tools are generated on demand.

- Event context: Raleigh AI Tinkerers: Second Meetup — 2025-12-10 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_rjOw3LBlaI4

### [Tenderwise AI-Powered Information Extraction from Tenders](https://bremen.aitinkerers.org/talks/rsvp_8-mQk8tgQIA)

Tenderwise is a web-based Streamlit application that automates the extraction of structured information from unstructured tender documents. It is designed to support procurement, sales, and legal teams by accelerating the analysis of complex tender texts and identifying key requirements, deadlines, and decision-relevant metadata.

- Event context: AI Tinkerers Bremen — 2025-12-10 — Bremen
- Public talk page: https://bremen.aitinkerers.org/talks/rsvp_8-mQk8tgQIA

### [Automating my own Job](https://bremen.aitinkerers.org/talks/rsvp_299EfBd2mus)

how i combine llms, smolagents, chats and live rendering of ui components into a hybrid interface to collaborative work together with ai agents on critical tasks

- Event context: AI Tinkerers Bremen — 2025-12-10 — Bremen
- Public talk page: https://bremen.aitinkerers.org/talks/rsvp_299EfBd2mus

### [“Building an AI-Powered Data Insights Dashboard: From Raw Data to Intelligent Decisions”](https://boston.aitinkerers.org/talks/rsvp_mX3kaTWJ_hQ)

In this 8-minute talk, I’ll demonstrate how I built an AI-powered analytics dashboard that transforms raw business data into actionable insights using Python, OpenAI API, and Streamlit. The project automatically cleans data, generates visualizations, and uses generative AI to summarize trends and suggest data-driven recommendations in plain English. Attendees will see how simple scripts can connect LLMs to everyday analytics workflows—making complex data storytelling accessible to anyone. I’ll also share how to deploy the app and optimize it for real-time user queries.

- Event context: AI Tinkerers Boston Meetup December 2025 — 2025-12-02 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_mX3kaTWJ_hQ

### [Prompt Engineering is Dead (Long Live Prompt Optimization)](https://montreal.aitinkerers.org/talks/rsvp_qRxeXPuUSi4)

While large language models are incredibly capable, their performance can vary dramatically depending on how they’re prompted. Prompting is still mostly manual: trial and error, slow, and hard to scale. Even small differences in phrasing or the order of examples can make a big difference in results. OPRO, or Optimization by Prompting, changes that. Instead of humans manually tuning prompts, an LLM can itself iteratively propose, test, and refine new prompts in natural language, using feedback to improve performance on a target task. It treats prompt design as an optimization problem, but instead of gradient descent, it performs language-space optimization. Humans only need to define how to evaluate or score each prompt’s performance, and the model learns to improve by optimizing against that score. In short, OPRO is a simple yet powerful approach to Automatic Prompt Optimization (APO).

- Event context: AI Tinkerers Montreal: Demo Night — November 20, 2025 — 2025-11-20 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_qRxeXPuUSi4

### [Sentence Transformers to learn and understand real content](https://nurnberg.aitinkerers.org/talks/rsvp_VOKiK9VbS-U)

An AI tool made for MacOS but applicable in any other system to help users to organize content based on given categories.

- Event context: AI Tinkerers Nürnberg Meetup – Nov 20 — 2025-11-20 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_VOKiK9VbS-U

### [AI quantitative analysis education coach](https://boston.aitinkerers.org/talks/rsvp_5oUCjtPtYCo)

This is for a learning coach I made at a Hackathon last month for people learning to be quantitative traders

- Event context: AI Tinkerers Boston Meetup November 2025 — 2025-11-17 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_5oUCjtPtYCo

### [Sales Assistant Agent on Snowflake, integrating Claude 3.5 Sonnet](https://nyc.aitinkerers.org/talks/rsvp_cnnfABUQSZg)

This project develops a Sales Assistant Agent using data tools in Snowflake Cortex. The Agent is able to analyze structured and unstructured data (PDFs and images), classify data, and make data searchable through Cortex Search.

- Event context: November Demo Day ft. Google Cloud and CopilotKit — 2025-11-17 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_cnnfABUQSZg

### [Multi-tenant RAG deduplication index in Postgres](https://nyc.aitinkerers.org/talks/rsvp__3aiNrWKUNA)

When processing a lot of documents that often have similar sections into a single Postgres cluster, deduplication becomes much harder because not all users have the same view to the processed chunks. This means that you have to store all chunks in order to make sure no chunks are removed simply because there exists an inaccessible one elsewhere for the user. By using hybrid search and a MinHash + LSH clustering scheme, we can produce on-demand deduplicated search results quickly and efficiently for a user by clustering chunks when ingesting documents.

- Event context: November Demo Day ft. Google Cloud and CopilotKit — 2025-11-17 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp__3aiNrWKUNA

### [Taming Complexity: A Live Prompt Driven Development Demo from PRD to Full-Stack](https://palo-alto.aitinkerers.org/talks/rsvp__xb28xIo3j0)

This is a 100% live, "no-slides" demo. I'll start with a single Product Requirements Doc (PRD) for a complex application. First, I'll use pdd to analyze the PRD and generate a structured architecture.json file, scaffolding the entire project (modules, API endpoints, components). Next, I'll show how pdd uses that architecture file to automatically generate a complete tree of modular prompt files—one for each piece of the scaffold. Finally, I'll run pdd sync to execute that "plan" and generate the entire, complex codebase from those prompts. I'll end by making a change to the PRD and re-running the flow to show how pdd can propagate architectural changes across the entire project.

- Event context: [Cancelled] November Demo Day — 2025-11-14 — Palo Alto
- Public talk page: https://palo-alto.aitinkerers.org/talks/rsvp__xb28xIo3j0

### [Making LLMs Read Your Videos](https://berlin.aitinkerers.org/talks/rsvp_hDodwhM0FY0)

YouTube has been called the second biggest search engine. You can find videos on almost any topic you are interested to learn about. However, this same content often doesn't get used in training data of LLMs. In this talk, I will share how we build DocsForAI, that helps repurpose Videos.

- Event context: AI Tinkerers Berlin Meetup - November 12th, 2025 — 2025-11-12 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_hDodwhM0FY0

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## More Results

- Next: https://aitinkerers.org/technologies/streamlit.md?page=2