# OpenRouter Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/openrouter
> Markdown URL: https://aitinkerers.org/technologies/openrouter.md
> Technology record last updated: 2026-09-18T15:13:48Z
> Generated: 2026-09-22T22:37:40Z

OpenRouter: The unified API gateway for hundreds of LLMs, providing single-endpoint access, automatic fallbacks, and cost-optimized routing across all major providers (e.g., OpenAI, Anthropic, Google).

OpenRouter is your single, high-efficiency API gateway to hundreds of LLMs from over 60 providers, including OpenAI, Anthropic, and Google. We eliminate the integration complexity: one API key, one endpoint, zero code rewrites when switching between models like GPT-5 or Claude Sonnet 4.5. The platform automatically handles dynamic routing for cost-optimization, pools provider uptime for superior reliability, and consolidates all usage into a single billing dashboard. Expect minimal impact on performance: we operate at the edge, adding approximately 15ms latency, and maintain full compatibility with the OpenAI SDK.

- Official technology site: https://openrouter.ai
- Public AI Tinkerers demos and talks: 34
- Result page: 1 of 2

## Recent Public Talks and Demos

### [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

### [Create Your Own DeepSeek Moment](https://nyc.aitinkerers.org/talks/rsvp_UswbXaI7O_0)

I built an autonomous loop that tests and proves small models (finetuned in some cases) can replace large ones on real traffic before safely rerouting to them. Live I'll show the decision engine choosing which work to optimize, fine-tuning candidate models, clustering traffic to anonymize data, and if time allows examples of a small model beating a large one on specialized work, an eval that fails and keeps the large model live, and the prove-gate thresholds that make it production-safe.

- Event context: August Demo Day ft Runpod, Veris, Openrouter, — 2026-08-19 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_UswbXaI7O_0

### [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

### [Building Agentic OS for Marketing](https://denver-boulder.aitinkerers.org/talks/rsvp_BZZDcloDPXM)

I have been solo for 3 years after selling my Denver marketing agency. I was doing everything low code until early March when I discovered Claude Code and working at the terminal. I know have moved my system to a full dev stack using Neon, Claude Managed Agents, 5 repos (website, clients, agent os, reporting admin app) to run my entire AI native consultancy with 12+ clients.

- Event context: AI Tinkerers Denver - June Meetup — 2026-06-30 — Denver
- Public talk page: https://denver-boulder.aitinkerers.org/talks/rsvp_BZZDcloDPXM

### [NeverTwice: cost optimized API's for LLM inference tailored to your personal use case.](https://lausanne.aitinkerers.org/talks/rsvp_oYss-NBmvTQ)

A system capable to identify how you should optimised your queries to keep your standard of accuracy high for a fraction of the costs. The methodology rely heavily on using evals and a set of rules to only evaluate the most promising strategies until you land on the cheapest option for your use case. What I'll show is a little demo + discuss the architecture.

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

### [Podsumo - your personal podcast agent](https://nurnberg.aitinkerers.org/talks/rsvp_RZYtKUm6Y7c)

I follow a lot of podcasts but just don't have time to actually listen. I built Podsumo (podsumo.io) which scans and identifies interesting episodes from my list of podcasts and also answers questions if I have. The cool part is that it all runs within the modal labs free credit with less than 50 cents of Deepseek API credits.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_RZYtKUm6Y7c

### [Agents Building Agents: Reflective Optimization Loops](https://toronto.aitinkerers.org/talks/rsvp_4gS7qartFFk)

I'll show how an AI agent can build and optimize another AI agent, using reflective optimization to find issues, optimize evals, iterate on architecture, find the optimal prompt/model, and more. We've built a system with multiple levels of reflective optimization for agent development. - GEPA: reflective prompt optimization - Synthetic eval generation: going from a 1-off bug to an proper eval you can use in reflective optimization - Expanding reflective optimization beyond prompts: model selection, tool use, subagents -- reflective optimization can drive all levels of agent optimization.

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_4gS7qartFFk

### [\[UW Data Science Club x HFF\] ParcoursLab: A Human-Centered Approach To Course Recommendations](https://toronto.aitinkerers.org/talks/rsvp_hbsKwKccnH8)

ParcoursLab is an academic co-pilot that saves students and academic advisors time by building a course plan that respects degree requirements and pre-reqs while also taking into account your goal, desired skills, extracurricular interests, and crowd-sourced student ratings. We use AI to automate the manual bookkeeping of reading through dozens of course descriptions and checking prereqs/eligiblity. However, despite this, the platform aims to be transparent, human-centered, and hallucination-free by retrieving course skills from a human-curated skills database (ESCO), providing you with justifications for each of its selection, and allowing you to update your plan conversationally. In the linked demo, the student picks "Computer Science" as their major and "bioinformatics" as their goal. They also mention their interest in Music and Art. Our platform fetches your degree requirements and lays out your required courses (e.g. algorithms, operating systems) using ASAP/ALAP scheduling. Your goal is used to derive a set of desired skills (e.g. biochemistry, machine learning), matched against the ESCO database, which in turn guides a search for the ideal electives. We perform course recommendation using an LLM on a UWaterloo dataset deterministically distilled based on prereqs and augmented with student ratings. We also find you school clubs that match your major, goal, and extracurricular interests (e.g. Waterloo iGEM, Visual Arts club). Courses can be dragged around, and prereqs are enforced. You can chat to replace courses. Adding a desired skill manually will add a course to your schedule that fulfils that skill. Lastly, the user can generate a printable AI summary that they can take to a meeting with an academic advisor for final human guidance.

- Event context: AI Tinkerers Toronto - April 2026 - hosted by Shopify — 2026-04-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_hbsKwKccnH8

### [Bugtrace AI: Building the First Deterministic Agentic Framework for Reliable Web Pentesting](https://hong-kong.aitinkerers.org/talks/rsvp_pv9QZncWqV0)

Bugtrace AI is the first deterministic Agentic AI framework specifically engineered for automated web pentesting.

- Event context: AI Tinkerers Hong Kong at AWS: Agentic AI in Action (April) — 2026-04-29 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_pv9QZncWqV0

### [VisionClaw Agent an Agentic AI Corporation Platform](https://chicago.aitinkerers.org/talks/rsvp_pqxZHBHIMTo)

VisionClaw Agent is a fully autonomous AI corporation platform where 14 specialized AI personas — from CEO to CFO to engineers to sales — operate as a complete company, autonomously executing business operations like revenue generation, content creation, market research, and financial management with humans only in the loop for final decisions. VisionClaw Agent is a fully autonomous AI corporation platform where 14 specialized AI personas — from CEO to CFO to engineers to sales — operate as a complete company, autonomously executing business operations like revenue generation, content creation, market research, and financial management with humans only in the loop for final decisions, featuring a live activity demo that simulates the AI corporation in real-time with agent delegations, revenue tracking, memory storage, and task completions cycling across departments to showcase how the platform runs itself.

- Event context: AI Tinkerers Chicago: April Meetup ft OneTwoLoop — 2026-04-14 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_pqxZHBHIMTo

### [CoChef: Your AI enabled copilot in the kitchen](https://boston.aitinkerers.org/talks/rsvp_vcHPPp4v4BA)

I am currently working on a device that provides users real time advice on their cooking. It is run on a Raspberry PI and uses a thermal + RBG camera to see what is happening in the kitchen. We have lights and speakers to give users visual and verbal feedback. There is an option for the users to interact with the device and ask any general questions.

- Event context: AI Tinkerers Boston: March 2026 Meetup — 2026-03-30 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_vcHPPp4v4BA

### [From Abacus to AI: Small Potatoes, Big Results](https://hong-kong.aitinkerers.org/talks/rsvp_3XIaboEtPiM)

I'll share my real, month-long journey as a complete non-coder who used today's AI tools (like Grok, Copilot, Claude Haiku, and especially Claude Sonnet) to build actual working things: two World of Warcraft in-game addons for my uncles to track character stats and items automatically, a "Next Bus" app for our island's private bus routes (pulling schedules from PDFs and images), and a simple dinner-bell phone app that rings my phone when someone hits bell button their phone. The talk walks through the messy reality — starting with total confusion (SQL commas feeling like ancient abacus work, AWS looking like a blurry PS2 game, pasting code line-by-line and debugging parentheses I didn't understand), failing a lot, switching models when one got stuck, and eventually getting functional apps and addons. I'll show how I "failed faster" by iterating quickly, using my own low-tech version control (100+ numbered folders), and leaning on different AIs for different strengths. It's not about becoming a pro developer overnight — it's about an ordinary person getting useful results with zero prior experience.

- Event context: AI Tinkerers Hong Kong GBA at the Hive: Creative AI Demos & Technical Show-and-Tell — 2026-03-26 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_3XIaboEtPiM

### [Automating Community Operations](https://seattle.aitinkerers.org/talks/rsvp_ibjALFX6YKw)

Foundations runs on a custom-built internal platform designed to automate the work typically handled by operations staff or project managers. At the center is an integrated agent that interacts directly with members through Slack and email—sending weekly updates, coordinating mentor sessions, answering questions, and managing access to tools and resources. Behind the scenes, the platform tracks member activity, manages billing, runs the application process, and structures the community’s collective knowledge. This presentation explores how automation and agent-driven workflows can operate a complex community with minimal manual coordination.

- Event context: AI Tinkerers Seattle: GTM Track — March — 2026-03-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_ibjALFX6YKw

### [Semantic Data Deduplication: HNSW, MNN, and LLM](https://montreal.aitinkerers.org/talks/rsvp_3hHtv5BiXF4)

I built a semantic deduplication pipeline designed to identify, validate, and merge duplicate event records across noisy datasets. The system operates in three layers: Vector Indexing (HNSW), Mutual Nearest Neighbors, and Agentic Reconciliation (LLM) ensuring no data is lost by aggregating all unique attributes and data points across sources.

- Event context: AI Tinkerers Montreal - March Demo Night — 2026-03-24 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_3hHtv5BiXF4

### [Autonomous web browsing](https://paris.aitinkerers.org/talks/rsvp_yPQdUNm0qBI)

a basic use of OpenClaw: install and prompt it

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_yPQdUNm0qBI

### [Using AI to channel Olympic excitement into skill development of homegrown athletes](https://toronto.aitinkerers.org/talks/rsvp_pFLrl0ONOj0)

I coach at a speedskating club, and one of the most difficult aspects of training young athletes is focusing them on technical aspects, tracking progress, and communicating tips and form correction effectively. This is a mobile app designed specifically with speedskaters in mind, and it gamifies the process of holding a "basic position" i.e. the crouched-over skating position. The user begins a game and video is captured. A wireframe is overlaid on the athlete, and the knee and hip angles are calculated. When the athlete goes into basic position, the user enters a session and begins to accrue a score. Score accrual multipliers are applied the closer to "perfect form" an athlete has, which has visual indicators displayed in realtime. Feedback from an AI coach is also given in realtime, which keeps the user engaged and focused. The user finishes a session after they stand up, and a report generated by AI is shown to help them understand what could be improved, as well as their final score. AI also uses the user's home country to query a public API on speedskater results and world records (ex. a Canadian user would be shown world records and stats of famous skaters such as Laurent Dubreuil). It helps users connect more with how strong the Olympic-level athletes are, along with recording progress and showing improvement over time.

- Event context: AI Tinkerers Toronto - February 2026 @ Cohere! — 2026-02-26 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_pFLrl0ONOj0

### [Building Knobase: Personalized AI Tutoring with mem0 Memory, ZeroEntropy RAG, and Real-Time Content Safety](https://hong-kong.aitinkerers.org/talks/rsvp_cSLy04LtLvE)

Context: Knobase powers personalized AI tutoring for 1,000+ students across various schools in Asia. The core insight we learned: teachers need AI agents they can configure and trust without writing code, and students need personalization that remembers their learning style across sessions — not just generic ChatGPT wrappers that hallucinate or give direct answers when Socratic questioning is more effective. This talk is a technical deep-dive into how we orchestrate multiple AI systems to deliver that experience at scale, walking through five implementation layers: 1. Personalization with mem0 — A hybrid memory architecture combining mem0’s semantic vector search with a local confirmed-memories table in Supabase. As students chat, we extract preferences, learning styles, goals, and challenges via regex pattern matching and prompt scoring (confidence threshold ≥ 0.85). Students confirm these memories (“Yes, I’m preparing for IB exams”), and they’re injected into every subsequent chat. A 10th-grader studying physics gets reminders about their preference for step-by-step explanations; a university student preparing for finals gets context about their exam timeline. I’ll walk through  getContextualMemoriesForPrompt()  and how we merge local + mem0 results to build a per-student profile that persists across days and subjects. 2. RAG with ZeroEntropy — Teachers upload textbooks, lecture slides, problem sets, and institutional syllabi. Document ingestion pipeline: file upload → Supabase Storage → base64 encoding → ZeroEntropy with semantic chunking (chunk_size: 1800, overlap: 200). Collections are scoped per school ( school_{id} ) so students only retrieve content their teachers authorized. Retrieval uses topSnippets queries with metadata filtering by document/knowledge IDs, plus a parallel RAG agent that expands queries and aggregates deduplicated results. This means when a Harrow student asks “What’s Newton’s second law?”, the AI cites their specific uploaded physics textbook, not generic web content. I’ll show the filter-building logic and how we resolve documents through bot → knowledge → collective → document chains. 3. Real-Time Context API — Teachers can connect external data sources (Google Sheets of upcoming assignments, Notion databases of class resources, live sports scores for a journalism class analyzing data) via webhook-based context providers. On every message, we call registered providers, AI-process the response with token optimization (60-90% reduction), and inject it alongside RAG results. Example: A history teacher at ISF Academy configured a timeline of World War II events that updates the AI’s context window in real time, so students always get era-appropriate answers. I’ll trace the full flow from  chat.context_config  → provider webhook → context processing → system prompt assembly. 4. No-Code AI Chat Builder for Teachers — Educators configure role, tone, age group (elementary/middle/high school/university), complexity, subject, language, Socratic mode toggle (forces the AI to ask guiding questions instead of giving direct answers), citation preferences, and custom instructions — all stored as  custom_details  JSON. Organization-level master prompts override per-bot settings for school-wide safety policies. I’ll show how the system prompt is assembled in 10 steps: master prompt → bot intro → RAG context → memory context → custom instructions. A teacher creating a “Socratic Math Tutor” for 8th graders clicks 6 dropdowns and writes 2 sentences of instruction; the system generates a 2,000-token prompt behind the scenes that enforces age-appropriate language, refuses to solve homework directly, and cites only the uploaded textbook. 5. Education Safety Stack — Real-time prompt scoring (Clarity/Specificity/Task Definition/Context/Structure on a 0-100 scale) via a Supabase Edge Function runs on every student input. Content flagging across 7 categories (sexual content, bullying, profanity, racial bias, political sensitivity, harmful advice, PII detection) plus custom organization-defined flags (e.g., Harrow added “exam cheating detection”). A daily digest cron emails flagged messages to designated safety managers. Student interest keywords and learning purpose analysis are extracted as a side effect of scoring and fed back into mem0, creating a feedback loop where the AI becomes more personalized the more the student uses it. We’ve processed 16,000+ messages since December 2025 with this stack in production. ** Code walkthrough will focus on the chat route orchestration (~3,700 lines) that ties all five layers together in a single request lifecycle. No slides — just live code, architecture diagrams on a whiteboard, and real examples from our production deployment.

- Event context: AI Tinkerers Hong Kong & GBA: Using AI as a Superconductor for Learning — 2026-02-26 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_cSLy04LtLvE

### [Stop Wasting Time Giving AI Context It Should Already Have](https://prague.aitinkerers.org/talks/rsvp__P7J5gqyEG8)

Every time you open an AI chat, you start from zero. Re-explaining what you're working on, pasting in links, setting the scene. MemoryLane fixes that. It quietly watches your screen, understands what you're doing, and gives your AI tools that context through MCP. I'll walk through the full system live: how we capture your activity without killing your machine, how we turn pixels into searchable meaning, and how the MCP server is designed so AI assistants ask smart questions about your actual workflow. By the end, I'll ask Claude what I was doing during the talk - and it'll know.

- Event context: ❄️ AI Tinkerers Prague – February Meetup — 2026-02-19 — Prague
- Public talk page: https://prague.aitinkerers.org/talks/rsvp__P7J5gqyEG8

### [From Screenshot to Code: AI-Powered Design Cloning and Verification](https://nyc.aitinkerers.org/talks/rsvp_oSYfxCynLls)

Live demo of an AI pipeline I made that takes any website URL and recreates it in an editable sandbox. The system: 1. Captures a live site using Playwright (screenshots, DOM, styles) 2. Uses Claude to analyze the design and generate a React clone 3. Creates an instant editable sandbox with the cloned site 4. Verifies the clone matches the original using design contracts 5. Suggests and applies CSS fixes for any differences I'll show the full loop: paste a URL → AI analyzes it → generates code → live preview appears → iterate with chat to refine. No Figma access needed, no manual coding - just point at any site and get an editable clone.

- Event context: Feb Demo Day: How are you turning design into code? (with Baz) — 2026-02-04 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_oSYfxCynLls

### [Taming Newsletter Chaos](https://seattle.aitinkerers.org/talks/rsvp_tbkrTl7AZbY)

I can't keep up with my email newsletter and RSS feed subscriptions, and yet I keep signing up for more. So I built a system that summarizes, prioritizes, and deduplicates incoming news for me. The key points are presented in an iOS app and can be narrated interactively by a voice agent (e.g. while on a walk).

- Event context: AI Tinkerers Seattle: January Meetup — 2026-01-31 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_tbkrTl7AZbY

### [Who's the Imposter? 15 AIs Play a Social Deduction Game](https://nashville.aitinkerers.org/talks/rsvp_5m6nlurQ1fo)

What started as my niece's breakfast table math question became something unexpected: a live arena where 15 AI models from Google, Anthropic, OpenAI, Meta, and X.AI play social deduction against each other. You can watch their inner monologue as they deceive, accuse, and reason about who to trust. Claude hedges. GPT accuses. Gemini analyzes. Same game, very different behaviors. I'll run a live game and show the architecture that makes multi-model coordination possible.

- 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_5m6nlurQ1fo

### [Remember its just a game](https://sydney.aitinkerers.org/talks/rsvp_IzdexMsQyVU)

My 13 year old son will be presenting with me as a support. The thing we have built is a vibe coding tool whose primary interaction is voice based and is called Muse. The idea came out of a thing he was building that was a voice chat agent that was able to express emotion as a precursor to a robot he wants to build next year that was able to express emotion and put a face to the AI chats he was having. This evolved into a desire to work on something together that was a voice based coding agent that was a little bit brain rotted, showing you video, playing music or inviting you to play a game while you waited for the coding to happen.

- Event context: Trust and Security in AI - Sydney Demo Night — 2025-12-11 — Sydney
- Public talk page: https://sydney.aitinkerers.org/talks/rsvp_IzdexMsQyVU

### [Benchmarking LLMs Through Knowledge Graphs](https://paris.aitinkerers.org/talks/rsvp_F06bPn4kpgM)

I will be showing how understanding for llms is connected to their ability to produce connected graphs

- Event context: Turn Your Knowledge into an API for LLMs - Meetup — 2025-12-09 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_F06bPn4kpgM

### [AgenticInternet](https://paris.aitinkerers.org/talks/rsvp__eC_JM0n_W4)

AgenticInternet automates complex online workflows by building AI agents that autonomously browse, interact, and collaborate to boost productivity.

- Event context: Turn Your Knowledge into an API for LLMs - Meetup — 2025-12-09 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp__eC_JM0n_W4

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