# Plotly Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/plotly
> Markdown URL: https://aitinkerers.org/technologies/plotly.md
> Technology record last updated: 2026-02-25T05:39:05Z
> Generated: 2026-09-22T16:38:12Z

The leading open-source library for creating interactive, publication-quality data visualizations and web-based analytic applications.

Plotly is a technical computing company delivering powerful, interactive data visualization tools built on its core open-source library, Plotly.js. The platform supports multiple languages (Python, R, MATLAB) through wrappers like Plotly.py and Plotly.R. Its standout product is Dash: a framework for building full-stack analytic web applications entirely in Python, R, or Julia. This allows data science teams to deploy complex visualizations—from simple scatter plots to advanced 3D charts—directly to production environments, moving beyond static reports to deliver scalable data apps for Fortune 500 clients and millions of users.

- Official technology site: https://plotly.com
- Public AI Tinkerers demos and talks: 7
- Result page: 1 of 1

## Recent Public Talks and Demos

### [From 1,157 handwritten forms to one geological fact: a 25 m pile-driving limit](https://curitiba.aitinkerers.org/talks/rsvp_nt6LgCapvyQ)

A resumable extraction pipeline that turned 1,157 handwritten pile-driving field bulletins — scanned into 1,400+ messy PDFs — into a clean, georeferenced dataset, and then into engineering knowledge. The arc is the point: reports → data → knowledge. Live, I'll show the raw handwritten forms going in (smudged, non-standard, with voided sheets and unrelated pages mixed in), parallel subagents transcribing each to JSON, and the consolidated 1,157-record CSV driving an interactive report — histograms, box plots, an E×N heatmap, and a 3D scatter of 627 georeferenced piles. Then the payoff: those production-control reports became a technical and geological analysis instrument. Aggregated, the field data no single form could show revealed a consistent practical driving limit of ~25 m for the site (mean driven depth 24 m) — a hard geological fact that makes the next foundation estimate far more assertive. The dataset is a real completed job: 1,000+ pre-cast concrete piles driven over six months for a grain terminal in southern Brazil.

- Event context: AI Tinkerers Curitiba: Encontro de Agosto (no EBANX) — 2026-08-26 — Curitiba
- Public talk page: https://curitiba.aitinkerers.org/talks/rsvp_nt6LgCapvyQ

### [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

### [Claude does queuing](https://manchester-nh.aitinkerers.org/talks/rsvp_d1ggwXXOkoE)

I asked Claude to generate a simulation of a simple deterministic queueing system in JavaScript, and to output the results as a set of three graphs using Plotly. I wrote moderate length prompt with very clear specifications for the model of the system and the desired output. I validated the results using an identical model that I created manually in the MathWorks SimEvents product. My goal was primarily experimentation, my own personal learning, and the evaluation of Claude's capabilities.

- Event context: AI Tinkerers Manchester (Bedford), NH - March 2026 Meetup — 2026-03-18 — Manchester NH
- Public talk page: https://manchester-nh.aitinkerers.org/talks/rsvp_d1ggwXXOkoE

### [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

### [Preswald: Build fast, real-time dashboards with DuckDB and Python](https://sf.aitinkerers.org/talks/rsvp_crPkMiHL2gE)

This talk explores the technical mechanics of leveraging DuckDB, an in-memory OLAP database, to build real-time dashboards using Preswald, our open-source Python framework. Preswald combines DuckDB’s high-performance query engine with a declarative API, enabling developers to create fast, lightweight, code-first dashboards with full control over their analytics stack. - In-memory query execution: DuckDB operates entirely in-memory, processing complex SQL queries directly on flat data files (CSV, Parquet, or raw streams) without the need for databases, data warehouses, or ETL pipelines. - Real-time data transformation: Learn how DuckDB enables on-the-fly data transformations with SQL-like operations that run natively on your data files, avoiding unnecessary data movement or duplication. - Design trade-offs: Preswald’s declarative Python API tightly integrates with tools like Pandas and Plotly, giving devs more control over data transformation, queries, schedules, and visualization workflows. --- From an engineering standpoint, we decided to build Preswald around DuckDB because it let us rethink how analytics pipelines should work. Traditional setups rely on data warehouses, ETL pipelines, and distributed databases, which add a lot of complexity, latency, and maintenance overhead. These systems often require moving data around constantly, which slows things down and makes them harder to manage. DuckDB stood out because of its in-memory, columnar database engine. It’s optimized for vectorized execution and can run complex SQL queries directly on flat files like CSVs and Parquet without needing a separate database server or moving data to another system. This drastically reduces I/O overhead and eliminates the need for extra infrastructure. We saw this as the foundation for building a faster, more lightweight approach to analytics. To make it easier for developers to use DuckDB, we built Preswald with a declarative Python API. It lets developers define their data workflows without having to write raw SQL, while still taking full advantage of DuckDB’s performance. We designed the API to integrate with familiar tools like Pandas for data manipulation and Plotly for visualizations. Under the hood, Preswald uses DuckDB’s vectorized query engine to handle joins, aggregations, and transformations efficiently. A big challenge was balancing performance and flexibility. DuckDB is incredibly fast in-memory, but it can also process data directly from disk for larger datasets. We built Preswald to handle both cases, allowing small, fast prototypes in memory and scaling up to larger workflows without adding extra dependencies or infrastructure.

- Event context: AI Tinkerers - San Francisco - January 2025 Meetup — 2025-01-30 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_crPkMiHL2gE

### [Traffic Congestion Predictor via Real Time Predictive Analytics](https://abu-dhabi.aitinkerers.org/talks/rsvp_fdhRli04Zag)

The project uses a machine learning algorithm (Gradient Boosting Regressor), multiple interactive visualisations, and environmental factors to predict real-time traffic congestion levels. It features an interactive dashboard, a 24-hour congestion forecast, and visual insights for current and hourly congestion trends. It also recommends alternative routes, optimal travel times, and sustainable travel options.

- Event context: AI Tinkerers - Abu Dhabi Meetup #2 (Jan 2025) — 2025-01-17 — Abu Dhabi
- Public talk page: https://abu-dhabi.aitinkerers.org/talks/rsvp_fdhRli04Zag

### [PREMIER SPORTS](https://dubai.aitinkerers.org/talks/rsvp_9275Yfq6xKc)

Title: Predicting Football Match Outcomes with AI: My Approach to Premier Sports Description: I'm excited to share my project, PREMIER SPORTS, an AI-powered application designed to predict football match outcomes. By leveraging advanced machine learning algorithms and data analytics, I aim to provide accurate predictions that enhance game analysis for fans, coaches, and analysts alike. My approach combines historical team performance data with real-time player and team statistics to deliver more insightful and actionable information. I'm particularly interested in exploring how my model can be fine-tuned using transfer learning techniques, enabling me to adapt to changing team lineups and player performances. I'll also be showcasing some key features of my tool, including: Real-time data ingestion from various sources (e.g., sports databases, social media) Advanced feature engineering techniques for handling high-dimensional data Model selection and hyperparameter tuning Technical Depth and Focus: To provide a more comprehensive understanding of my approach, I'll be focusing on the following technical aspects: Data Handling: I employ a combination of libraries such as pandas, to carry out feature engineering, for labelling and extracting as much relevant data as possible. Transfer Learning Techniques: I leverage pre-trained language models such as Llama to capture contextual relationships within predicted match data and give insights to the application user. Live Demonstration and Code Walk-through: To enhance the audience's understanding of my approach, I'll be providing a live demonstration that includes: Interactive Data Visualization: I'll use a library like Plotly to create an interactive visualization of my data, allowing the audience to explore different scenarios and see how my model generates predictions. Code Walk-through: My demo will include a code walk-through where I explain the reasoning behind specific design choices, highlighting how AI techniques can be applied to improve the accuracy and interpretability of my model. Generative AI Integration: To further enhance the interpretability of my model, I'll be incorporating generative AI techniques: Text Generation: I'll use Llama to generate text-based outputs (e.g., player profiles, and team statistics) that can provide additional context for predictions. Clarity and Engagement To ensure clarity and engagement during the presentation: Concrete Examples: I'll use concrete examples from real-world football matches together with a web UI dashboard, to illustrate how my model can be applied in practice.

- Event context: AI Tinkerers - Dubai Meetup #3 (November) — 2024-11-10 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_9275Yfq6xKc

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