Harshit Yadav @HarshitStack
Building Lincom (AI for your LinkedIn inbox) 📩 | SWE @uniqode, prev @stoxhero | Agentic AI + voice AI, always shipping | https://t.co/WSKESqEzMk linkedin.com/in/harshit2123/ Indore Joined January 2022-
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Wrote 550+ Datadog synthetic tests covering critical user flows and API endpoints. Result: post-deployment issues dropped 30%. Testing infra doesn't get the credit shipping features does, but it's what lets you ship features fast without breaking things.
Designed and rolled out a Comments & Collaboration module — threaded comments, @mentions, real-time updates — that close to 20% of active users adopted within the first month of launch.
Shipped 4 production features this year (age gating, browser title control, deep links, link analytics) now reaching an estimated 15,000+ monthly active users. Small features, real reach.
Best "boring infra, real impact" story from work: architected a migration bridge moving customers off a legacy platform, handling cross-system DNS + config remapping. It directly unlocked a $20K deal within a week.
Built a Watchlist system on Redis + Celery — an async daily digest pipeline pushing link analytics and notifications to 5,000+ active users every day. Async-by-default is the only way that scales without falling over.
Architected multi-domain routing + org-level access control + custom domain verification for a 60k+ user platform (Django REST + Angular). The unglamorous infra work is usually where the actual engineering is.
Day job update: built an internal Dependabot Vulnerability Tracker at work — Python, Angular, GitHub APIs, Claude API — that automates dependency triage and safe-to-merge calls across 48 AWS Lambda functions, tested against LocalStack.
Takeaway: the interesting engineering problem was never the chatbot. It's separating the product path from the telemetry path — so an observability failure can never become an application failure.
Verification, not vibes: 98 tests, no skips. 33 real incremental SSE delta frames on a live stream. A dead ingest endpoint, event survived on the spool, replayed clean after recovery. 3 worker replicas split 12 tasks off one queue with zero API changes.
Just shipped a chatbot. But the actual product isn't the chat — it's the observability layer underneath it. Thread on the architecture 🧵
Throwback to a project that taught me more about integration than AI: a disease-detection app with Flutter + React frontends talking to a FastAPI backend running a custom CNN (95% accuracy). Cross-platform API design is its own skill, separate from the model.
Went from engineering intern to full-time Software Developer at the same company. The unglamorous version of "career growth" is just: keep showing up, keep shipping, let the work make the case for itself.
Favorite design decision in that project: every escalation a human resolves feeds back into the knowledge base. The agent doesn't stay static — it gets better from real usage instead of me hand-tuning prompts forever.
The part of the voice agent nobody asks about but that determines if it feels human: latency budget. STT (AssemblyAI) → reasoning (GPT-4.1 Mini) → voice synthesis (Cartesia), orchestrated to keep the back-and-forth feeling like a real conversation.
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