Your stack is essentially my daily toolkit. I use Supabase, PostgreSQL, n8n, and Claude Code every day to build and ship production systems. Have built complete automation platforms with these exact tools - webhook-driven workflows, database design, API integrations, and AI-powered features.
Comfortable owning a system end-to-end from infrastructure through product features. Currently building AI agent systems and automation platforms as my primary work.
Strong stack overlap here. I work daily with TypeScript, React, Node.js, and PostgreSQL. Heavy user of Claude Code and the Anthropic API for building AI-powered features - prompt engineering, structured outputs, tool use patterns.
Have built full-stack products with LLM integration end-to-end, including AI agent systems with real business logic. Comfortable owning features from design through deployment.
Based in India (UTC+5:30) but flexible on US timezone overlap. Happy to discuss further.
This is right in my wheelhouse. I build RAG systems and LLM-powered data pipelines - ingestion, embedding, retrieval, and structured output generation. Python and Node.js, PostgreSQL with pgvector, OpenAI/Anthropic APIs.
Have built end-to-end async pipelines that process unstructured text into structured insights. Comfortable owning the full stack from scraping/ingestion through to the API layer.
Available for remote work. Happy to discuss further.
India-based, interested in the backend engineer role. I work on AI document pipelines, async workflows, and API systems daily. Stack: Python, Node.js, React, PostgreSQL, Redis.
Have built production document processing and AI agent systems - LLM orchestration, webhook-driven workflows, and enterprise integrations. Comfortable with long-running async pipelines and observability concerns.
Bangalore hybrid works for me. Happy to share proof of work.
Interested in this. I build AI automation systems for professional services - intake workflows, document processing, and client-facing AI agents. Python, Node.js, REST/webhook integrations, PostgreSQL.
Have shipped production systems that handle inbound client communication (voice, chat, WhatsApp) with qualification logic and CRM sync. Comfortable working directly with end users to iterate fast.
Remote works well for me. Happy to chat about the role.
I build voice AI agents professionally - conversational systems with telephony integration, real-time STT/TTS pipelines, and LLM orchestration. Python and TypeScript daily, plus PostgreSQL and Redis.
1.5+ years shipping production voice agent systems including outbound/inbound call automation and WhatsApp integrations.
Based in India (UTC+5:30) but flexible on hours and open to discussing hybrid/remote arrangements.
This is a real operational problem when you're building client-facing automation systems on top of these APIs. I build chatbots, workflow automation, and AI agent systems for clients — and the hardest conversation is explaining that your system's uptime is fundamentally capped by your LLM provider's uptime.
Patterns that have helped in production:
1. Multi-provider fallback. For conversational systems, route to Claude by default, fall back to GPT-4 on 5xx errors. The response quality difference is usually acceptable for the 2-3% of requests that hit the fallback. This turns a hard outage into a slight quality degradation.
2. Async queuing for non-real-time workflows. If you're processing documents, generating reports, or running batch analysis — don't call the API synchronously. Queue the work, retry with exponential backoff, and let the system self-heal when the API recovers. Most of our automation pipelines run with a 15-minute SLA, not a 500ms one.
3. Graceful degradation in real-time systems. For chatbots and voice agents, have a scripted fallback path. "I'm having trouble processing that right now — let me transfer you to a human" is infinitely better than a hung connection or error message.
The broader issue: we're all building on infrastructure where "four nines" isn't even on the roadmap yet. That's fine if you architect for it — treat LLM APIs like any other unreliable external dependency, not like a database query.
The comment about having "3 to 6 hours per day" to work directly with code is the key insight here. I run a small AI consultancy and use Claude Code daily to deliver client projects — chatbots, automation pipelines, API integrations — and the spec-driven approach described in this post is what makes it actually work at scale.
The pattern I've converged on: spend the first 30 minutes writing detailed markdown specs (inputs, outputs, edge cases, integration points), then let Claude Code chew through the implementation while I review, test, and iterate. For a typical automation project — say a WhatsApp bot that handles booking flows and integrates with a client's CRM — this cuts delivery time roughly in half compared to writing everything manually.
The biggest practical lesson: the spec quality is everything. A vague spec produces code you'll spend more time debugging than you saved. A good spec with explicit error handling expectations, API response formats, and state transitions produces code that's 80-90% production-ready on the first pass.
Where I disagree slightly with the parallel agent approach: for client-facing work where correctness matters more than speed, I've found 2-3 focused agents (one on backend, one on frontend, one on tests) more reliable than 6-8 competing agents that create merge conflicts. The overhead of resolving conflicts and ensuring consistency across parallel outputs eats into the productivity gains fast.
I've recently started adding a PROJECT.md to all my own projects to keep the direction consistent.
Just something that tells the LLM (and me, as I tend to forget) what is the actual purpose of the project and what are the next features to be added.
In many cases the direction tends to get lost and the AI starts adding features like it's doing a multi-user SaaS or helfully adding things that aren't in the scope for the project because I have another project doing that already.
Having built a bunch of production AI agent systems (workflow automation, conversational agents, data pipelines with LLM-in-the-loop) in Python and Node.js — my take is that language choice is genuinely the least important decision you'll make.
The bottleneck in agent systems is almost never your language runtime. It's LLM API latency (200-2000ms per call), external service I/O, and retry/error handling across unreliable tool calls. Whether your orchestration loop runs in 2ms (Go) or 15ms (Python) is irrelevant when you're waiting 800ms for Claude to respond.
What actually matters for production agent systems: (1) state management across multi-step workflows that can fail at any point, (2) graceful degradation when one tool in a chain times out, (3) observability into what the agent decided and why. These are design problems, not language problems.
Python wins on ecosystem breadth — every LLM provider ships a Python SDK first, every embedding model has Python bindings, and the tooling around prompt engineering and evaluation is Python-native. When you're iterating on agent behavior (which is 80% of the work), that ecosystem advantage compounds fast.
That said, Go's argument is strongest for the "agent runtime" layer — the part that manages concurrency, schedules tool calls, and handles streaming. If you separate the orchestration runtime from the AI logic, Go for the former and Python for the latter isn't a bad split.
Great writeup. I've been building production voice agents and automation systems with the same stack (Twilio + Deepgram + ElevenLabs + LLM APIs) for client-facing use cases — appointment booking, lead qualification, guest concierge, and workflow orchestration.
The "turn-taking problem, not transcription problem" framing is exactly right. We burned weeks early on optimizing STT accuracy when the actual UX killer was the agent jumping in mid-sentence or waiting too long. Switching from fixed silence thresholds to semantic end-of-turn detection was night and day.
One dimension I'd add: geography matters even more when your callers are in a different region than your infrastructure. We serve callers in India connecting to US-East, and the Twilio edge hop alone adds 150-250ms depending on the carrier. Region-specific deployments with caller-based routing helped a lot.
The barge-in teardown is the part most people underestimate. It's not just canceling LLM + TTS — if you have downstream automation (updating booking state, triggering webhook workflows, writing to DB), you need to handle the race condition where the system already committed to a response path that's now invalid. We had a bug where a barged-in appointment confirmation was still triggering the downstream booking pipeline.
Comfortable owning a system end-to-end from infrastructure through product features. Currently building AI agent systems and automation platforms as my primary work.
Remote works well. Available to start quickly.
Contact: priyanshukumarmaurya2224@gmail.com
reply