Yaad

Inspiration

Every AI assistant I've used has amnesia. Each conversation starts from zero — it doesn't remember my projects, my preferences, or what we discussed yesterday. Real personal assistance is impossible without memory. "Yaad" — the Hindi word for memory — is my answer: an always-on personal AI that truly remembers, learns my workflows, and gets smarter over time.

What it does

Yaad is a private personal AI with persistent memory and self-improving skills:

• Never forgets: bi-temporal memory — facts carry validFrom/validTo timestamps, so contradictions are resolved by retiring old facts, never deleting history. Hybrid RRF retrieval (dense embeddings + keyword + entity signals) scores recall@1 = 1.00 on our eval harness.
• Sees connections: automatic entity extraction builds a knowledge graph you can explore visually; background "dreaming" clusters memories into insights.
• Improves itself: when it spots a repeated workflow, it drafts a reusable SKILL.md pack — always approval-gated, never auto-applied.
• Researches deeply: Tavily-powered deep research runs planner → parallel searches → parallel page extraction → cited synthesis, plus fire-and-forget background research jobs with proactive "done" nudges.
• Acts proactively: reminders, morning briefings, and dream-insight nudges surface on their own.
• Shows its work: streaming responses, visible reasoning traces, parallel tool calls, sandboxed code execution, voice input, photo memory via vision model, PWA install, and Markdown chat export.

How we built it

• Brain: NVIDIA Nemotron models via Nebius Token Factory — Nemotron 3 Nano 30B for fast routing, Nemotron 3 Super 120B for deep reasoning, Qwen3-Embedding-8B for semantic memory, and a Nemotron vision model for photo memory.
• Stack: TypeScript throughout — Express backend, React + Tailwind frontend, JSON-based memory store with an MCP server exposing the same 7-operation interface to external agents.
• Deployed: frontend on GitHub Pages, backend on Render, with a real $0.50/day cost cap enforced server-side so the demo never burns budget.

Challenges we ran into

• The Render deploy failed in a sneaky way: a stale dashboard build command plus a stray backtick in the start command crashed every boot — fixed via the Render API.
• Our cost cap was silently dead (unmetered code paths returned null cost estimates). We revived it with conservative fallback pricing and per-turn spend reservations — now it genuinely trips before overspend.
• Free-tier reality: ephemeral disks (memory resets on redeploy) and cold starts — documented honestly and mitigated with a keepalive.

Accomplishments that we're proud of

• Memory eval: recall@1 = 1.00, MRR = 1.000 across 6 adversarial cases (contradictions, historical queries, distractors).
• A full 16-finding security audit fixed and verified — SSRF redirect-chain validation, rate limiting, sandboxed code execution.
• A real MCP server so other agents can use Yaad's memory.

What we learned

Memory isn't a feature you bolt on — it's the architecture. Bi-temporal validity, hybrid retrieval, and approval-gated self-improvement changed how we think about "personal" AI.

What's next for Yaad

Multi-user isolation with proper auth, persistent disk-backed memory, and a public skill marketplace where approved skill packs can be shared.

Built With

• embeddings
• express.js
• github-pages-nebius
• knowledge-graph
• model-context-protocol
• nebius
• node.js
• nvidia-nemotron
• pwa
• react
• render
• tailwind-css
• tavily
• typescript
• vite