# Svelte Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/svelte
> Markdown URL: https://aitinkerers.org/technologies/svelte.md
> Technology record last updated: 2026-09-18T15:13:42Z
> Generated: 2026-09-22T19:42:46Z

Svelte is a UI compiler: it converts declarative components into highly efficient, vanilla JavaScript, eliminating the Virtual DOM overhead.

Svelte redefines front-end development by operating as a compiler, not a traditional framework or runtime library. Unlike React or Vue, which perform bulk work in the browser (e.g., Virtual DOM diffing), Svelte shifts that load to the build step. This compile-time approach generates highly optimized, surgical code that updates the DOM directly: the result is smaller application bundles, minimal overhead (often just 2KB of runtime code), and superior performance metrics. Developers appreciate the concise component syntax, which uses standard HTML, CSS, and JavaScript, ensuring a faster, more approachable workflow.

- Official technology site: https://svelte.dev
- Public AI Tinkerers demos and talks: 5
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Being a dungeon master for agents](https://columbus.aitinkerers.org/talks/rsvp_bciO_4-2f2c)

I built a system where you can DM a contiguous story for agents, describing their light attributes and DM for them entirely via voice. Built with Svelte and node/express

- Event context: AI Tinkerers - Columbus July Meetup — 2026-07-06 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_bciO_4-2f2c

### [Pull-architecture agents: how I forced myself to do growth work](https://valencia.aitinkerers.org/talks/rsvp_-p7b50BEZW0)

A multi-agent system that manufactures pull for the growth work — writing, conference follow-up, CRM hygiene, responding to news — that I'd otherwise skip when product and ops eat my week. Seven agents across five repos, two APIs, and two websites (one fully autonomous), all driven from the only two surfaces I actually live in: my terminal and Mattermost. The demo walks a live pass through the rig: a LinkedIn contact lands in our self-hosted CRM, an article draft gets pushed to a website, a post-conference follow-up gets queued — every action is a curl command an agent emits, with circuit breakers I can tighten in the moment or across the long arc.

- Event context: AI Tinkerers Valencia May Meetup — 2026-05-26 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_-p7b50BEZW0

### [Lixpi is a visual, node-based workflow engine for building AI image and video generation pipelines](https://toronto.aitinkerers.org/talks/rsvp_SxMp4PNqTxU)

Lixpi is a visual, node-based workflow engine for AI image and video pipelines an infinite canvas where spatial arrangement is the workflow. Instead of writing a workflow DSL or fighting a linear chat prompt, you drop documents, images, and AI chat threads onto the canvas and draw edges between them. The edge graph directly drives context extraction, dependency chains, and execution order for the underlying models. In the demo I'll show live character-consistent image generation via "artifact piping" (one generated image fanned out as edge-context into multiple downstream threads), mid-conversation model switching between OpenAI / Anthropic / Google, and progressive image streaming rendered into the node in real time.

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_SxMp4PNqTxU

### [Reasoning traces in LangChainJS](https://boston.aitinkerers.org/talks/rsvp_ghXjZ5aNfAc)

I will show how to use langchainJS to interact with current reasoning models (GPT-5, Grok 4, Gemini-2.5 Pro) and how to handle their responses. Why are they different? Well, if you treat them like "normal" models you get some surprising behavior. Most obviously very slow responses. Less obvious, but also common they might stop their response midway, without obvious reason. The "trick" is to consider the reasoning trace. Both, when prompting and when when building UX. Unfortunately, langchain currently doesn't document how to do this well so, instead, I will dive into the code and show you instead ;) For this we will use a real world example: A private chat app that we use to unify conversations with major LLM providers. You can talk to multiple models in parallel within the same conversation, which is the perfect scenario to see different thinking vs non-thinking models in action.

- Event context: AI Tinkerers Boston: Langchain & Beyond Meetup — 2025-08-25 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_ghXjZ5aNfAc

### [Automatic Embeddings Using Supabase](https://dublin.aitinkerers.org/talks/rsvp_MUbrgn11KsU)

Supabase is a Backend As A Service built on top open source technologies. This talk will showcase how to automatically generate embeddings when a new row is inserted into a table. This way, we can make sure we have access to embeddings for all the rows. All of this will be implemented using open source technologies like Postgres an PgVector.

- Event context: AI Tinkerers Dublin Meetup - July 31, 2025 — 2025-07-31 — Dublin
- Public talk page: https://dublin.aitinkerers.org/talks/rsvp_MUbrgn11KsU

## Related Technologies

- [Node](https://aitinkerers.org/technologies/node) ([Markdown](https://aitinkerers.org/technologies/node.md)) — 99 public demos
- [LangChain](https://aitinkerers.org/technologies/langchain) ([Markdown](https://aitinkerers.org/technologies/langchain.md)) — 445 public demos
- [PostgreSQL](https://aitinkerers.org/technologies/postgresql) ([Markdown](https://aitinkerers.org/technologies/postgresql.md)) — 106 public demos
- [Agent](https://aitinkerers.org/technologies/agent) ([Markdown](https://aitinkerers.org/technologies/agent.md)) — 12 public demos
- [Anthropic API](https://aitinkerers.org/technologies/anthropic-api) ([Markdown](https://aitinkerers.org/technologies/anthropic-api.md)) — 66 public demos
- [APIs](https://aitinkerers.org/technologies/apis) ([Markdown](https://aitinkerers.org/technologies/apis.md)) — 19 public demos
- [Astro](https://aitinkerers.org/technologies/astro) ([Markdown](https://aitinkerers.org/technologies/astro.md)) — 3 public demos
- [AWS](https://aitinkerers.org/technologies/aws) ([Markdown](https://aitinkerers.org/technologies/aws.md)) — 38 public demos
- [Claude (Anthropic)](https://aitinkerers.org/technologies/claude-anthropic) ([Markdown](https://aitinkerers.org/technologies/claude-anthropic.md)) — 2 public demos
- [Claude Code](https://aitinkerers.org/technologies/claude-code) ([Markdown](https://aitinkerers.org/technologies/claude-code.md)) — 216 public demos
- [Docker](https://aitinkerers.org/technologies/docker) ([Markdown](https://aitinkerers.org/technologies/docker.md)) — 147 public demos
- [Express](https://aitinkerers.org/technologies/express) ([Markdown](https://aitinkerers.org/technologies/express.md)) — 18 public demos
- [Gemini API](https://aitinkerers.org/technologies/gemini-api) ([Markdown](https://aitinkerers.org/technologies/gemini-api.md)) — 20 public demos
- [LangGraph](https://aitinkerers.org/technologies/langgraph) ([Markdown](https://aitinkerers.org/technologies/langgraph.md)) — 67 public demos
- [Mattermost](https://aitinkerers.org/technologies/mattermost) ([Markdown](https://aitinkerers.org/technologies/mattermost.md)) — 1 public demo
- [NATS](https://aitinkerers.org/technologies/nats) ([Markdown](https://aitinkerers.org/technologies/nats.md)) — 2 public demos
- [OpenAI API](https://aitinkerers.org/technologies/openai-api) ([Markdown](https://aitinkerers.org/technologies/openai-api.md)) — 520 public demos
- [Python](https://aitinkerers.org/technologies/python) ([Markdown](https://aitinkerers.org/technologies/python.md)) — 662 public demos