We built AI features for users — then pointed them at ourselves. RAG engine. Memory system. Multi-agent orchestration. Voice pipeline. All self-improving.
128-dim embeddings, concept extraction, cosine similarity, context windowing, 4-weight reranking, proactive nudge system. Full pipeline.
CRUD + cloud sync + LLM-powered organization. Auto-categorize, smart tags, natural language search. Knowledge that persists.
Decompose complex requests into parallel subtasks. Researcher, Designer, Builder, Analyst, Writer agents. Synthesize results.
Describe an app. Coder agent generates the project, creates a GitHub repo, pushes code, deploys to Vercel. Returns a live URL.
Whisper transcription with voice activity detection. Auto-stop on silence. Multi-language. Upload, transcribe, and process.
Plan, review, execute from the command line. Coder and Researcher agents. Structured task graphs with agent assignment.
11 event types. Session tracking, AI request logging, code attribution, debug logs, team analytics. Full observability.
Drafts messages in your voice by analyzing contact history. Auto-respond with a learning loop that improves over time.
Record a voice memo. voice.transcribe turns it into text. fastr.plan turns it into a structured task breakdown. Zero typing, executable output.
After every session, auto-call memories.organize with a summary. Next session, query via RAG. The AI remembers what it learned yesterday.
Vercel webhook into telemetry.logEvents. The AI knows if its code works in production. Failed deploy? AI sees the error before you do.
Production errors auto-generate test cases, invoke the pipeline to write fixes, run the suite, and commit. You wake up to a changelog of self-fixed bugs.
Before shipping, embed the diff with rag.embed, search for conflicting patterns via rag.search, flag contradictions. The AI code-reviews itself.
When bugs hit production, RAG finds the nearest test file. LLM generates a reproducer. If it catches the bug, commit it. Darwin for your test suite.
Point the orchestrator at the codebase. It decomposes refactoring tasks, the coder agent executes, tests validate. The code improves itself.
Every LLM call is logged in telemetry. Cluster similar prompts, distill into reusable templates. One-off hacks become organizational knowledge.
Every CI failure teaches the pipeline. Parse logs, generate new stages that would have caught it. Your build system gets smarter every time it breaks.