# HTML Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/html
> Markdown URL: https://aitinkerers.org/technologies/html.md
> Technology record last updated: 2026-02-25T05:31:08Z
> Generated: 2026-08-24T05:46:22Z

HTML (HyperText Markup Language) is the foundational technology: it structures all web content using a system of elements and tags for browser display.

HyperText Markup Language (HTML) is the essential, foundational technology for the World Wide Web. It provides the core structure for all documents: defining elements like headings (``), paragraphs (``), and critical hyperlinks (``). Browsers parse these tags to render a document into a complete multimedia web page. The current specification is maintained as a 'Living Standard' by the WHATWG, ensuring continuous, active development beyond the widely adopted HTML5 revision. HTML works in concert with CSS (for appearance) and JavaScript (for behavior) to deliver a complete, functional user experience across all platforms.

- Official technology site: https://html.spec.whatwg.org/multipage/
- Public AI Tinkerers demos and talks: 9
- 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

### [Building My Own Piano Teacher Because I'm Too Impatient for YouTube](https://nurnberg.aitinkerers.org/talks/rsvp_NveTwvTUV4E)

I totally vibe coded a browser-based piano learning app that uses your microphone to detect pitches and gamifies music theory with timed challenges. (I've never really seen the code.) Demo: 1. Show the app and what it can do 2. Show my workflow: Prompting in Claude Code to add another feature 3. Showing the infrastructure (hosting for free with static website on Github Pages, using localStorage as Database)

- Event context: AI Tinkerers Nürnberg: April Meetup — 2026-04-22 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_NveTwvTUV4E

### [Moonshine: distilling interactive technical explanations](https://atlanta.aitinkerers.org/talks/rsvp_VaMWMEBQbr4)

https://github.com/enjalot/moonshine moonshine is a skill for distilling interactive technical explanations from AI generated complexity. I'll show some examples of articles I've built, including documentation for my open source projects which have gotten more complex with new AI assisted additions.

- Event context: AI Tinkerers Atlanta: Community Demos & Technical Deep Dives — 2026-04-21 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_VaMWMEBQbr4

### [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

### [Perfile – Smart Profile Web App](https://mumbai.aitinkerers.org/talks/rsvp_CASvf6HxGgM)

Perfile is a modern, responsive profile page builder that lets users showcase their portfolio, skills, and contact details in one sleek link. Designed using HTML, CSS, and JavaScript, it emphasizes clean UI, easy navigation, and mobile-first design principles. Ideal for developers, freelancers, and students to maintain a personal brand.

- Event context: AI Tinkerers Mumbai Meetup - April — 2025-04-26 — Mumbai
- Public talk page: https://mumbai.aitinkerers.org/talks/rsvp_CASvf6HxGgM

### [WhimsyPaws](https://toronto.aitinkerers.org/talks/rsvp_peDGmfwfVcs)

On one side, it's a simple game made for kids between ages 6-11. It has an AI-powered animal friend that chats with the kid and can play a suite of different games with the kid. On the other hand, the data that is generated through the child's interactions with this game is gathered, then insights are extracted from it using LLMs, and then reported to their parents. For now, the insight in question is broad strokes of the child's feelings like joy, sadness, anger, etc. The main idea is this: kids spend a lot of time on their phones these days and, consequently, they spend less time talking to their parents. On the other hand, all of their data is being collected and sold for potentially shady purposes. So why not give that data to the people who actually *should* be supervising them and knowing what they do online, i.e. their parents. Hence, we're making an app that exactly does this. In future iterations, it can be more than a game; it can be an all-present agent on kids' phones that monitors all activities on the phone so there's no way for kids to uninstall it or cheat it.

- Event context: AI Tinkerers Toronto - April 2025 Meetup at Shopify — 2025-04-24 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_peDGmfwfVcs

### [Visual prompting for fun and profit](https://portland.aitinkerers.org/talks/rsvp_fGAlJQAvWUA)

Cloud hosted LLMs (such as GPT4 by OpenAI) charge per-token. Depending on the length of your input, this can become expensive, if not impossible. When building RealEstatePulse, I came across the challenge of needing to extract structured data from city zoning codes that were hundreds of pages long -- well passed the capabilities of even the largest LLMs. Moreover, there was a heavy dependence on textual layout (for example, with tables). The extra HTML markup blew up the token count and made analyzing zoning codes very expensive. However, recent research in LLMs have shown that the world model they acquire from textual learning translates well into visual question answering. Moreover, there is good reason to believe that current models are highly redundant, meaning they can theoretically process a lot more data than what the token embedding layer produces. Some cloud LLMs, such as GPT4, offer the option of visual input. Unlike text input, visual input is charged at a flat fee depending on the size of the image. I would like to present the technique I developed for analyzing extremely large zoning codes by rendering the text as an image and using this to prompt GPT. I will show that this makes the problem tractable and produces good results.

- Event context: AI Tinkerers Portland Inaugural Meetup - July — 2024-07-24 — Portland
- Public talk page: https://portland.aitinkerers.org/talks/rsvp_fGAlJQAvWUA

### [websim](https://sf.aitinkerers.org/talks/rsvp_Xnz3wl4pw2E)

explore, create, and remix your hallucinated internet with websim

- Event context: AI Tinkerers - San Francisco - Summer Edition - July 2024 — 2024-07-12 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_Xnz3wl4pw2E

### [Website to RAG](https://lausanne.aitinkerers.org/talks/rsvp_h_rL2U5w59s)

This project aims to transform any website into a knowledge base that can be searched with natural language. Upon submission of a sitemap, or synchronization with a Wordpress site, the tool indexes the content into a vector database. Then the owner can inject a script in the website that will render a search bar widget where people can ask questions. The tool answers using LLMs and the vector database while citing sources so users can manually check the information if needed. (If there is enough time I can also show a second project that create new formulas for Google Sheets that use generative AI. For instance generating text using prompt from a column, or batch generating images using prompts from a column, or transcribing audio files based of urls in a column, ...)

- Event context: AI Tinkerers Lausanne Inaugural June Meetup — 2024-06-24 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_h_rL2U5w59s

## Related Technologies

- [JavaScript](https://aitinkerers.org/technologies/javascript) ([Markdown](https://aitinkerers.org/technologies/javascript.md)) — 40 public demos
- [CSS](https://aitinkerers.org/technologies/css) ([Markdown](https://aitinkerers.org/technologies/css.md)) — 6 public demos
- [Plotly](https://aitinkerers.org/technologies/plotly) ([Markdown](https://aitinkerers.org/technologies/plotly.md)) — 7 public demos
- [Python](https://aitinkerers.org/technologies/python) ([Markdown](https://aitinkerers.org/technologies/python.md)) — 654 public demos
- [BAN](https://aitinkerers.org/technologies/ban) ([Markdown](https://aitinkerers.org/technologies/ban.md)) — 1 public demo
- [Claude](https://aitinkerers.org/technologies/claude) ([Markdown](https://aitinkerers.org/technologies/claude.md)) — 170 public demos
- [Claude-3](https://aitinkerers.org/technologies/claude-3) ([Markdown](https://aitinkerers.org/technologies/claude-3.md)) — 110 public demos
- [Claude AI](https://aitinkerers.org/technologies/claude-ai) ([Markdown](https://aitinkerers.org/technologies/claude-ai.md)) — 2 public demos
- [Claude Code](https://aitinkerers.org/technologies/claude-code) ([Markdown](https://aitinkerers.org/technologies/claude-code.md)) — 204 public demos
- [Claude Sonnet 4](https://aitinkerers.org/technologies/claude-sonnet-4) ([Markdown](https://aitinkerers.org/technologies/claude-sonnet-4.md)) — 16 public demos
- [CLIP](https://aitinkerers.org/technologies/clip) ([Markdown](https://aitinkerers.org/technologies/clip.md)) — 10 public demos
- [Flask](https://aitinkerers.org/technologies/flask) ([Markdown](https://aitinkerers.org/technologies/flask.md)) — 23 public demos
- [Gemini](https://aitinkerers.org/technologies/gemini) ([Markdown](https://aitinkerers.org/technologies/gemini.md)) — 187 public demos
- [GitHub](https://aitinkerers.org/technologies/github) ([Markdown](https://aitinkerers.org/technologies/github.md)) — 73 public demos
- [Github Pages](https://aitinkerers.org/technologies/github-pages) ([Markdown](https://aitinkerers.org/technologies/github-pages.md)) — 3 public demos
- [Google Sheets](https://aitinkerers.org/technologies/google-sheets) ([Markdown](https://aitinkerers.org/technologies/google-sheets.md)) — 10 public demos
- [GPT-4](https://aitinkerers.org/technologies/gpt-4) ([Markdown](https://aitinkerers.org/technologies/gpt-4.md)) — 529 public demos
- [LLMs](https://aitinkerers.org/technologies/llms) ([Markdown](https://aitinkerers.org/technologies/llms.md)) — 83 public demos