# React Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/react
> Markdown URL: https://aitinkerers.org/technologies/react.md
> Technology record last updated: 2026-09-18T15:13:52Z
> Generated: 2026-09-22T12:37:07Z

React is an open-source JavaScript library for building dynamic user interfaces (UIs).

React is a component-based JavaScript library, developed by Meta (Facebook), engineered for building fast, declarative UIs. It mandates a one-way data flow and utilizes a Virtual DOM mechanism to ensure efficient, predictable updates to the user interface. Developers construct complex UIs by composing small, encapsulated components; this architecture promotes code reusability and simplifies state management across large applications. The library employs JSX (a syntax extension) to integrate HTML-like markup directly within JavaScript logic, supporting development for both web (React DOM) and native mobile platforms (React Native).

- Official technology site: https://react.dev/
- Public AI Tinkerers demos and talks: 220
- Result page: 1 of 10

## Recent Public Talks and Demos

### [FORGE: Webcam-Native Hand Control for AI-Assisted 3D Design](https://tokyo.aitinkerers.org/talks/rsvp_07ajHMVupG0)

Still a WIP, you can see the current build in the Video Demo URL. I built FORGE, a browser-based 3D design prototype that lets people modify a procedural model with natural language and manipulate it directly using their hands through an ordinary webcam. During the demo, I’ll show the working system: issuing spoken or typed design commands, translating them into validated parameter changes with Claude, and grabbing, rotating, moving, and scaling the model with one- and two-hand gestures. I’ll also open the code and live vision debugger to explain the architecture, MediaPipe landmark pipeline, gesture state machine, frame-age and inference metrics, operation validation, and the separation between vision, interaction logic, and Three.js rendering.

- Event context: AI Tinkerers Tokyo — October 5 Meetup — 2026-10-05 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_07ajHMVupG0

### [Giving an AI Agent the Keys: Building a Safe Admin Assistant](https://lahore.aitinkerers.org/talks/rsvp_FBzr6t9dj50)

Hania is a platform for building AI agents that work across chat, phone, messaging apps and email. Every account includes Hania Assistant, an AI agent that sets up and manages the account through conversation: it creates agents, connects tools, and builds teams of agents that work together. In the live demo I'll ask the assistant to connect an integration, build a team, and make changes, showing how it asks questions, collects secrets safely, and waits for human approval before risky actions.

- Event context: AI Tinkerers Lahore: September 19, 2026 — 2026-09-19 — Lahore
- Public talk page: https://lahore.aitinkerers.org/talks/rsvp_FBzr6t9dj50

### [Building a UI that builds itself](https://copenhagen.aitinkerers.org/talks/rsvp_CD2l5EihkZY)

it's a UI layer for agent harnesses where agents (Claude/Codex/Hermes/OpenClaw) can work and build personal software by extending their own UI. I'll cover why we decided work on this idea, some existing approaches, how we organized the CLI/Skill and what design guidelines we have written.

- Event context: September Demo Night — 2026-09-16 — Copenhagen
- Public talk page: https://copenhagen.aitinkerers.org/talks/rsvp_CD2l5EihkZY

### [Your BD Pipeline Is A Good First Autonomous Agent](https://boston.aitinkerers.org/talks/rsvp_Vjy6qYY7UX0)

An end-to-end BD pipeline that runs itself on a weekly cron: it sources leads, researches them, drafts the outreach, and emails me the two or three things to do this week. Sourcing → research → draft → nudge, with a lightweight CRM to run it from. One rule holds everywhere: the system researches and drafts; a human always sends. Why this is worth five minutes of a GTM room’s time: almost every agent demo shows one primitive at a time — here’s tool use, here’s a cron, here’s memory. That leaves you knowing the parts and not the shape. BD is a workflow everyone in this room already has opinions about, which makes it a good place to see the whole surface area doing real work at once. The demo is a guided tour of that: I trace one target end to end, and name the primitive earning its keep at each step. The tour, following one company through the system: It gets found. The Opportunity Radar treats my ICP as a graph rather than a list. It starts from private-equity sponsors active in my space and walks outward — a locked sub-agent maps each fund’s portfolio in parallel threads, and qualified portfolio companies auto-feed the pipeline. The frontier of unvisited sponsors lives in a memory store, so the walk resumes each Monday instead of cold-starting. Ownership structure is what makes a buyer resemble my last buyer, so the graph edge qualifies better than a keyword match. It gets carded. The Radar pushes the target into my CRM over MCP — the CRM is itself an MCP server, so the agents and I drive the same pipeline through the same tools. It gets researched. A graded session produces a seven-section pre-call brief off the live web. The run ships with a rubric; the platform scores the deliverable and sends the agent back to revise before I see it. The verdict lands on the card. It gets formatted. Skills load the output contract on demand — including a bundled validate_blocks.py the agent runs against its own draft so the CRM can parse the people and portfolio blocks. It gets drafted. A nightly cron drafts outreach from a compiled brand pack. Secrets come from a vault injected at egress — the agents never see a key. It stops. The draft sits at the gate. Nothing sends. Pipeline status is never machine-written. What I’ll show live: the Monday-morning board with fit scores and grader verdicts that arrived without me; one target’s drawer opened — why-now, signals, key people, dossier, draft; the sweep receipt showing what the Radar covered and where it stopped; a rubric and the revision it forced; and the eval scorecard where a prompt change shows up as a delta against a pinned baseline.

- Event context: Back from Summer: AI GTM Builders — 2026-09-03 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_Vjy6qYY7UX0

### [PalliAssist: Transforming Palliative Care with Compassionate AI](https://mombasa.aitinkerers.org/talks/rsvp_lhA4geGRg08)

PalliAssist is an AI-powered palliative care companion that helps patients, caregivers, and healthcare providers manage symptoms, medications, appointments, and access trusted care guidance through compassionate, personalized support. During this demo, we'll showcase the working web application, walk through the complete patient and caregiver workflow, demonstrate how Gemma powers real-time AI conversations, explain our system architecture and RAG pipeline, highlight key sections of our codebase on GitHub, and show how the platform delivers intelligent, accessible, and privacy-conscious palliative care in low-resource settings.

- Event context: AI Tinkerers – Mombasa Chapter Launch · 22 August 2026 — 2026-08-22 — Mombasa
- Public talk page: https://mombasa.aitinkerers.org/talks/rsvp_lhA4geGRg08

### [With AI, Make Videos your Audience Can Talk To](https://nyc.aitinkerers.org/talks/rsvp_ABb3qDK2Hwk)

The project is a cloud-based platform that generates multiple consistent videos simultaneously with a voice engine included, so that viewers can talk to a video on any screen. The video can hear them and talk back in real time for personalized conversations on any screen. I would show snippets of short films and commercial uses where people are engaging with media by voice.

- Event context: August Demo Day ft Runpod, Veris, Openrouter, — 2026-08-19 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_ABb3qDK2Hwk

### [Kestrel: Building a Local-First macOS Agent with Durable Approvals](https://st-louis.aitinkerers.org/talks/rsvp_IC38fqR_MhE)

Kestrel is a local-first macOS desktop agent that can choose tools, execute work, pause before consequential actions, and verify what actually happened. I’ll demo one task end-to-end: a request enters the conversation, the model chooses a route, the isolated agent core invokes tools, and a deterministic policy layer decides whether each action can run automatically or needs explicit approval. I’ll show a consequential action pausing for a restart-safe approval, then the resulting verification and audit trail. After the live run, I’ll open the code and architecture behind it: the Electron/React renderer, separate utility-process agent core, Zod-validated IPC boundary, encrypted SQLite state, provider-neutral model/tool runtime, policy gates, idempotency controls, and verification events.

- Event context: AI Tinkerers St. Louis: August 2026 Meetup — 2026-08-19 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_IC38fqR_MhE

### [AI as your thinking partner](https://islamabad-rawalpindi.aitinkerers.org/talks/rsvp_D-baTz8uoCQ)

Chatbots are for chats, not for brainstorming or exploration. Ahmed H. & Ali Asad gave a talk on "Bonscape: AI as your thinking partner" where they presented a radically simplified yet extremely powerful way of working with AI. The whiteboard-style canvas that lets you use AI in the way its supposed to be used in future. Features: - Sticky notes, branching, merging, model agnostic, file upload, project mode, data and branch views. - Fully control your context including version control and avoid context compression. - Control which AI model gets the data Alongside the product is very light on the pocket: - All models on a single subscription. - Saves cost on tokens through branching. - Saves cost by roll-over (un-used credits don't expire at end of the month, the roll over into the next month) Upcoming features in next 2 weeks: MCP, Artifacts and more.

- Event context: AI Tinkerers Islamabad - August 15, 2026 — 2026-08-15 — Islamabad Rawalpindi
- Public talk page: https://islamabad-rawalpindi.aitinkerers.org/talks/rsvp_D-baTz8uoCQ

### [When the AI Judge Is Wrong: A Verifiable Floor for LLM Evaluations](https://dubai.aitinkerers.org/talks/rsvp_e3czbFAtNPA)

I am building Lithrim, an open-source eval harness that pairs a configurable LLM judge council with a deterministic grounding floor. The grounding floor changes an outcome only when it can prove the change against an external oracle. Live on the real UI, I'll grade a few AI-generated clinical notes (no real patient data) using a two-judge council. The judges catch the planted defects, but they also over-flag, so recall is high and precision is low. The floor then clears a provable false positive through a grounding check against a SNOMED CT terminology server. It also independently verifies a real upcoded diagnosis against the terminology graph, without another model call and with the same result on every run. The final verdict accuracy visibly improves, and the floor has never cleared a genuine defect; that counter is shown on every run. Everything shown runs against the live system with a per-run audit trail. The repo is public.

- Event context: AI Tinkerers Dubai — August Demo Day — 2026-08-08 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_e3czbFAtNPA

### [Multi-Pass Building Defect Detection: Getting a VLM to Find Facade Defects for Visual Inspections](https://dc.aitinkerers.org/talks/rsvp_3zzrUv1SHxo)

We built an AI system that turns raw building inspection photos into annotated defect reports, detecting 15 distinct pathologies (building cracks, sealant degradation, brick spalling, mortar erosion, steel corrosion, and so on) with accurate bounding boxes across facade inspections. I'll show the architecture of how we've setup the inspection image processing pipeline: the multi-pass detection architecture, the model training process, the defect annotation catalog, the eval harness we use to verify accuracy on new datasets & catch regressions. And why this was our selected way to set it up. I'll walk through the examples of real reports going from photo dump to structured outputs (where the model gets it right and where it still needs a human check).

- Event context: AI Tinkerers DC Metro - Arlington: July 23rd Meetup — 2026-07-23 — DC
- Public talk page: https://dc.aitinkerers.org/talks/rsvp_3zzrUv1SHxo

### [Don’t Just Build an App: Build a Playground - Designing flexible, high-growth ecosystems that scale seamlessly.](https://orange-county.aitinkerers.org/talks/rsvp_FR1uOvCMPCI)

Oddjob lets you spin up teams of AI agents that plan and execute multi-step work as missions, cycles, and pipelines — extracting knowledge, running research, generating documents and datasets, and completing tasks end to end. It can build and deploy working software and mini-apps on demand through App Forge, stand up org and team structures through Org Forge, and turn a document template into a live form agent that captures a structured schema automatically. It grounds every agent in your own data with retrieval-augmented generation, a knowledge graph, and reusable templates, and routes work through configurable LLM providers with automatic failover to a local model when a provider is unavailable.

- Event context: AI Tinkerers Orange County: Tuesday, July 21, 2026 at Centercode — 2026-07-22 — Orange County
- Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_FR1uOvCMPCI

### [Orchestrating attention with human-on-the-loop](https://seattle.aitinkerers.org/talks/rsvp_yjRZexQA3q8)

I built a new notification paradigm in https://comment.io, the Multiplayer Markdown Editor, that helps orchestrate not just agents but the people who work with them

- Event context: AI Dev Tools Track - Seattle - July 13 — 2026-07-14 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_yjRZexQA3q8

### [Scrape, sense, snipe: local LLMs reading Twitter to trade Polymarket](https://zurich.aitinkerers.org/talks/rsvp_jO-ELvIgcaQ)

A self-hosted pipeline that scrapes Twitter, runs local LLMs to score sentiment, cross-references on-chain Polymarket activity, and surfaces ranked trade suggestions on a dashboard. I review the suggestions and place the trade myself.

- Event context: AI Tinkerers Zürich: July Build Night with Swisscom Ventures — 2026-07-01 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_jO-ELvIgcaQ

### [Building Product Videos with Code: From React to Launch Video in 5 Minutes](https://zurich.aitinkerers.org/talks/rsvp_dcRr36x9-9s)

A programmatic video production pipeline using Remotion that lets you build product launch videos entirely in code. I'll walk through a live coding session where we build a video from scratch: composing scenes as React components, reusing existing web app UI components directly in video compositions, generating AI narration with ElevenLabs, syncing text transitions to the beat of a backing track, and rendering the final output. The demo shows how your easy it is to create launch videos for new features, based on the code of the feature.

- Event context: AI Tinkerers Zürich: July Build Night with Swisscom Ventures — 2026-07-01 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_dcRr36x9-9s

### [How We Put a Data Warehouse in the Browser and Let AI Agents Explore It](https://toronto.aitinkerers.org/talks/rsvp_j4OM0Xw7uFQ)

We built a customer-specific data warehouse that runs entirely in the browser. At Relay, every customer has a personalized financial dataset containing transactions, balances, and other banking data. Traditionally, exposing insights from that data required backend APIs, analytics services, and purpose-built dashboards. Instead, we export customer data into compressed Parquet files, load them into DuckDB-WASM, and run a full analytical SQL engine directly in the browser. Every customer effectively gets their own tiny data warehouse. The surprising part came next: once the warehouse existed locally, AI agents became dramatically simpler. Instead of building tool chains, APIs, or MCP servers, we gave agents direct SQL access to the customer's warehouse and let them investigate the data themselves. In the demo I'll show: - A customer-specific warehouse running entirely in the browser - Analytical queries executing locally with no backend round trips - AI agents exploring financial data and generating insights - The architecture powering this in production - The code behind the browser warehouse and agent workflows - The challenges we hit scaling hundreds of tiny warehouses

- Event context: AI Tinkerers Toronto - June 2026 - with Relay Financial x PostHog — 2026-06-30 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_j4OM0Xw7uFQ

### [Does your coding harness actually do anything? Measuring it, fairly.](https://boston.aitinkerers.org/talks/rsvp_bx0CiDi1_uo)

Everyone's bolting "harnesses" onto coding agents — Superpowers, GSD, Agent-Skills, Compound Engineering — but does the scaffolding actually make the model build better software, or just feel better? CodingHarness.xyz is an open eval that pits these frameworks head-to-head: same spec, same model, isolated sandboxes, and an evidence-based rubric (does it meet the PRD? is the code any good?). I'll share the surprising result — an inverse-scaling effect lifted straight from a recent Xiaomi paper and reproduced on real runs: harnesses help most exactly where the base model is weakest, and barely at all where it's already strong. Even better, it's framework-dependent — one framework tracks the curve almost perfectly, another inverts it and only helps strong models. Live demo of the eval studio: watch two frameworks build the same app, stream the agent's work turn-by-turn, and see the marginal-gain scorecard light up green/red. Plus a sneak peek at turning framework competitions into a spectator sport (think FIFA bracket, but for coding agents).

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_bx0CiDi1_uo

### [From AI Prospect Memos to a Scored Warm-Intro Queue](https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA)

I built a dogfood workflow on top of graph.one that connects AI-assisted company qualification to a relationship-graph work queue. I'm the founder of graph.one, but this demo is about the internals of the build, not a product pitch. It starts in Prospector: a Node CLI pipeline that creates frozen public-source packets, runs AI-generated prospect memos, validates structured outputs, and stores decisions in append-only JSONL ledgers. The handoff is a qualified-company record. Intro Queue then takes a relationship-graph org-path export with organization identity, relationship owner, optional connector, target person/role, and strength fields. It caches the export in SQLite, collapses duplicate and noisy paths into scored route options, groups selected routes by connector, tracks per-company outcomes, and surfaces fallback routes as a human action queue. In the demo I'll follow one fully synthetic target company from source packet to memo to graph path row to scored route option to failed route/fallback. The queue output is a person-centric ask docket for a human operator; there is no send pipeline. I'll use fully synthetic graph data and redacted schema/log excerpts only.

- Event context: AI Tinkerers Warsaw: GTM Engineering Track — 2026-06-24 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_k0DCDscxWlA

### [Smart flascard](https://nurnberg.aitinkerers.org/talks/rsvp_qt8PZZzucz8)

Smart Flashcard is a vocabulary learning app that helps users turn new words into guided practice and daily study habits. I will demonstrate the live product, including word capture, review sessions, personalized practice, and the learning workflow.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_qt8PZZzucz8

### [Personal computing 2.0](https://berlin.aitinkerers.org/talks/rsvp_ges6dwxOxkk)

Rho is a minimal from scratch implementation of OpenClaw that can build UI on the fly. In addition to being a personal assistant, it is an ai-managed personal runtime where your agent can build and reshape small apps around your own data. You can ask your computer for the exact interface you want to interact with it. Rho is inspired by the Pi coding agent and indeed builds on top of it. As with Pi, it is open-source and intentionally minimal but built for extensibility. For the demo, I'll work through a basic example of building a simple To-Do app from within the Rho mobile app. Rho comes with an in-built chat (in addition to the standard WhatsApp, Telegram etc channels) where you can ask it to edit itself. Next, I'll also show how you can share you apps as simple pi-extension like open source repos. You can point your rho agent to a existing extension on GitHub and ask it to install it for you.

- Event context: AI Tinkerers Berlin - June 17, 2026 — 2026-06-17 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_ges6dwxOxkk

### [Prism: Privacy-First Multi-Model Orchestration](https://dubai.aitinkerers.org/talks/rsvp_gu_mOBH3NEM)

Prism is a privacy-aware AI orchestration layer that decomposes any query into parallel sub-tasks, routes each to the right model (local Qwen3, Groq, or Gemini) based on sensitivity, executes them concurrently via a dependency DAG, then synthesizes a single coherent response — all in one WebSocket-driven interface. Live demo will show: a real query decomposing into 3 parallel tasks, a DAG rendering the execution graph in real-time, the privacy gate redacting secrets before they leave the machine, and a local Qwen3.6-35B running on a laptop GPU via llama.cpp with MoE CPU offload.

- Event context: AI Tinkerers Dubai - June Demo Day — 2026-06-13 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_gu_mOBH3NEM

### [Panel: Building an AI Comic Engine Around the Visual Grid](https://prague.aitinkerers.org/talks/rsvp_Jp8vWlSfz2g)

Panel turns static comics into animated stories with game mechanics. It is a grid-based and AI-assisted engine and editor. It transforms a static page into a structured system of panels, media layers, dialogue bubbles, animations, loops, and reader-triggered interactions. In the demo, I’ll show the current working prototype: the board/canvas view, panel inspector, visual grid editing, media layers, panel-level animation and interaction controls, the animated outline and dialogue system. I’ll also show the first AI layer: generating static images directly into the grid, with support for character/style consistency and reference-based generation. The important part is the UX and how the generated asset lands inside an editable structure. I’ll explain how the system is built around a DOM/React-based visual grid, how GSAP animation and interaction are layered on top of it, and how this structure can evolve into a more advanced AI-native workflow: video generation, layout/story assistance, and eventually an agentic layer that can understand user intent and orchestrate editing steps.

- Event context: AI Tinkerers Prague June Meetup — 2026-06-12 — Prague
- Public talk page: https://prague.aitinkerers.org/talks/rsvp_Jp8vWlSfz2g

### [Why I gave up on agent voting: hard escalation in a 3-reviewer LLM pipeline](https://nyc.aitinkerers.org/talks/rsvp_gIVCOg1tRfI)

Redline is a production-readiness review tool for AI agents. Three specialized LLM reviewers (Engineer, Risk, Business) read an agent transcript against a company's pasted rulebook, mark up the dossier in three colors, and a synthesizer returns a Deploy, Hold, or Kill verdict. Live demo: pasting a real agent dossier into sanjitkangovi.com/redline, watching the three reviewers annotate in parallel, then walking through the deterministic synthesis layer in the backend that decides the final verdict. I'll show the prompt structure for each reviewer role, the JSON contract between reviewer and synthesizer, and the server-side escalation logic firing when a critical Risk finding contradicts the other two reviewers.

- Event context: NY Tech Week Demo Day ft PostHog, Convex, Veris, & HPE — 2026-06-03 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_gIVCOg1tRfI

### [Roast.fm - Make Anything Funny, Get Shredded by AI](https://orange-county.aitinkerers.org/talks/rsvp_Gl9b5aQvJ0E)

Roast.fm is an AI that takes in anything boring and and makes it funny. Upload any URL, file, photo, PDF, YouTube video, or whatever -- get back a comedy roast in the form of a hilarious podcast you can share with your friends. Crank the dial from rated G to rated X. Plus, it roasts you in the process.

- Event context: AI Tinkerers Orange County: June 2nd, 2026: AI and the Business Startup Process — 2026-06-03 — Orange County
- Public talk page: https://orange-county.aitinkerers.org/talks/rsvp_Gl9b5aQvJ0E

### [Facilitating content moderation with human-AI research](https://montreal.aitinkerers.org/talks/rsvp_5HwapIfgdRU)

We sought to better understand how to build a collaborative tool that harmonizes strengths and weaknesses between human and AI in the goal to facilitate and improve content moderation for online video games. In this talk, we will demo the research conducted behind ToxiSight, an internal tool developed at Ubisoft La Forge, that leverages human nuance and AI scale to improve judgements on toxicity in online game chat. We will present a demo that showcases how a human and AI model will interact to provide all available context and knowledge gaps as well as simultaneously re-train and fine tune a model. We also introduce how we can integrate a multi-disciplinary approach in this endeavour by leaveraging key theory in psychology to learn more about the support humans need and the shortcomings of the AI model.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_5HwapIfgdRU

## Related Technologies

- [Python](https://aitinkerers.org/technologies/python) ([Markdown](https://aitinkerers.org/technologies/python.md)) — 662 public demos
- [TypeScript](https://aitinkerers.org/technologies/typescript) ([Markdown](https://aitinkerers.org/technologies/typescript.md)) — 205 public demos
- [FastAPI](https://aitinkerers.org/technologies/fastapi) ([Markdown](https://aitinkerers.org/technologies/fastapi.md)) — 181 public demos
- [Next](https://aitinkerers.org/technologies/next) ([Markdown](https://aitinkerers.org/technologies/next.md)) — 186 public demos
- [OpenAI API](https://aitinkerers.org/technologies/openai-api) ([Markdown](https://aitinkerers.org/technologies/openai-api.md)) — 520 public demos
- [Node](https://aitinkerers.org/technologies/node) ([Markdown](https://aitinkerers.org/technologies/node.md)) — 99 public demos
- [PostgreSQL](https://aitinkerers.org/technologies/postgresql) ([Markdown](https://aitinkerers.org/technologies/postgresql.md)) — 106 public demos
- [Supabase](https://aitinkerers.org/technologies/supabase) ([Markdown](https://aitinkerers.org/technologies/supabase.md)) — 90 public demos
- [Claude](https://aitinkerers.org/technologies/claude) ([Markdown](https://aitinkerers.org/technologies/claude.md)) — 174 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
- [Gemini](https://aitinkerers.org/technologies/gemini) ([Markdown](https://aitinkerers.org/technologies/gemini.md)) — 188 public demos
- [LangChain](https://aitinkerers.org/technologies/langchain) ([Markdown](https://aitinkerers.org/technologies/langchain.md)) — 445 public demos
- [GPT-4](https://aitinkerers.org/technologies/gpt-4) ([Markdown](https://aitinkerers.org/technologies/gpt-4.md)) — 529 public demos
- [Vite](https://aitinkerers.org/technologies/vite) ([Markdown](https://aitinkerers.org/technologies/vite.md)) — 17 public demos
- [MongoDB](https://aitinkerers.org/technologies/mongodb) ([Markdown](https://aitinkerers.org/technologies/mongodb.md)) — 15 public demos
- [Ollama](https://aitinkerers.org/technologies/ollama) ([Markdown](https://aitinkerers.org/technologies/ollama.md)) — 77 public demos
- [OpenAI](https://aitinkerers.org/technologies/openai) ([Markdown](https://aitinkerers.org/technologies/openai.md)) — 112 public demos

## More Results

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