# Auth Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/auth
> Markdown URL: https://aitinkerers.org/technologies/auth.md
> Technology record last updated: 2026-03-05T19:11:01Z
> Generated: 2026-09-23T00:36:16Z

Auth.js is an open source authentication library for modern web applications that provides full control over your data and identity flow.

Auth.js (formerly NextAuth.js) is the standard for implementing secure, flexible authentication in the JavaScript ecosystem. It supports over 75 popular OAuth providers—including Google, GitHub, and Apple—while offering built-in protection against CSRF and session hijacking. By utilizing standard Web APIs, the library runs on any platform (Node.js, Serverless, or Edge) and integrates with major databases like PostgreSQL and MongoDB through dedicated adapters. It is designed for developers who need to own their authentication layer without the overhead of third-party SaaS costs or vendor lock-in.

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

## Recent Public Talks and Demos

### [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

## Related Technologies

- [Edge Functions](https://aitinkerers.org/technologies/edge-functions) ([Markdown](https://aitinkerers.org/technologies/edge-functions.md)) — 1 public demo
- [Gemini](https://aitinkerers.org/technologies/gemini) ([Markdown](https://aitinkerers.org/technologies/gemini.md)) — 188 public demos
- [KV/Redis](https://aitinkerers.org/technologies/kv-redis) ([Markdown](https://aitinkerers.org/technologies/kv-redis.md)) — 1 public demo
- [mem0](https://aitinkerers.org/technologies/mem0) ([Markdown](https://aitinkerers.org/technologies/mem0.md)) — 3 public demos
- [Next](https://aitinkerers.org/technologies/next) ([Markdown](https://aitinkerers.org/technologies/next.md)) — 186 public demos
- [OpenRouter](https://aitinkerers.org/technologies/openrouter) ([Markdown](https://aitinkerers.org/technologies/openrouter.md)) — 34 public demos
- [Pinecone](https://aitinkerers.org/technologies/pinecone) ([Markdown](https://aitinkerers.org/technologies/pinecone.md)) — 25 public demos
- [Storage](https://aitinkerers.org/technologies/storage) ([Markdown](https://aitinkerers.org/technologies/storage.md)) — 5 public demos
- [Supabase](https://aitinkerers.org/technologies/supabase) ([Markdown](https://aitinkerers.org/technologies/supabase.md)) — 90 public demos
- [TypeScript](https://aitinkerers.org/technologies/typescript) ([Markdown](https://aitinkerers.org/technologies/typescript.md)) — 205 public demos
- [Vercel](https://aitinkerers.org/technologies/vercel) ([Markdown](https://aitinkerers.org/technologies/vercel.md)) — 48 public demos
- [ZeroEntropy](https://aitinkerers.org/technologies/zeroentropy) ([Markdown](https://aitinkerers.org/technologies/zeroentropy.md)) — 1 public demo