OOm Jee Mishra /  engineer, in.
Résumé Available · 2026· IST--:--
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PostMind AI

Feb 2026 — present
PostMind AI
AI · Full Stack · Web Development · Engineer
  • —A content-intelligence platform on a 6-agent Mastra pipeline (Persona Analyst → Onboarding → Trend Research → Content Generator, plus a live Persona Chat and an AI co-writer) that turns a user's own posts into personalized, on-voice content briefs.
  • —A deterministic Writing-DNA engine extracts 16 quantitative voice metrics at zero LLM cost with 100% reproducibility, feeding a recency-decayed feedback loop that learns from ratings and real post performance.
  • —Provider-agnostic by design: every agent resolves its model through one factory, switchable between Google Gemini and Ollama Cloud by an env var, with structured JSON output enforced through four cheap-first fallback layers instead of schema-constrained decoding.
  • —Trend intelligence classifies each creator into one of 14 industry domains and fans out across Tavily, Hacker News, 66 curated RSS feeds, and Google News, with cross-source dedup, a two-tier MongoDB TTL cache, and an LLM-free fast path.
  • —Ships as a Turborepo monorepo — 11 RESTful API modules, 13 MongoDB collections, 3-tier rate limiting — alongside a Next.js 14 frontend with real-time chat and a 7-signal AI detector plus humanizer.
  • —Ships as multi-arch Docker images (amd64/arm64) on a GCP instance, with health-gated Compose startup.
Mastra AINext.js 14TypeScriptMongoDBTurborepoDockerGitHub ActionsSwaggerGoogle Cloud Platform (GCP)
◼ Challenges
  • →Generate on-voice content that doesn't read as generic LLM output
  • →Get reliable structured output from models that don't support schema-constrained decoding
  • →Stay portable across hosted and self-hosted LLMs, and ship a production monorepo with safe rollouts
◼ Solutions
  • →Deterministic Writing-DNA engine (16 voice metrics, zero LLM cost) plus a recency-decayed feedback loop weighted by real post performance
  • →Four-layer JSON enforcement — native JSON mode, prompt rule, local repair/normalize, one repair call — instead of handing the SDK a response schema
  • →Provider-agnostic model factory (Gemini ↔ Ollama by env var, zero code changes) and a domain-aware trend engine with an LLM-free fast path