Role: Full Stack Developer
Company: Subtle Reply (AI engagement for freelancers & startups)
Goal: Help businesses get in front of high-intent audiences on Reddit by automatically detecting relevant conversations and posting helpful, non-spammy replies at scale.

The challenge

Subtle Reply’s value proposition depends on three things:

• Finding the right conversations: detect posts and comments where people are actively describing problems a product/service can solve.

• Staying subtle, not spammy: generate replies that are genuinely helpful, context-aware, and aligned with subreddit culture.

• Scaling safely: automate engagement across many subreddits and projects while respecting platform limits and avoiding repetitive noise.

That meant building a system that could:

• Continuously monitor Reddit for relevant discussions.

• Score and filter those signals for quality and intent.

• Generate and dispatch AI replies in a controlled way.

• Give users visibility into what’s happening and how it’s performing.

What I did

I worked across backend, AI integration, and frontend to strengthen the funnel from signal detection → ranking → AI reply → dispatch → insights.

1) Built real-time monitoring and relevance scoring

• Implemented workers to track targeted subreddits and search terms through Reddit’s APIs.

• Standardized raw posts/comments into an internal schema (subreddit, author, content, parent context, timestamps).

• Added a relevance scoring layer combining keyword matches, semantic similarity, subreddit constraints, and historical performance.

• Introduced early filtering to drop low-quality or off-topic threads before they hit the AI layer.

Outcome: higher-quality leads and fewer wasted AI calls on low-intent or irrelevant conversations.

2) Integrated AI for context-aware, on-brand replies

• Built a reply generation service that pulls in: post content, thread context, user’s value proposition, and tone/brand settings.

• Designed prompts and templates to keep replies helpful first, promotional second, in line with “subtle reply” positioning.

• Added configurable guardrails (no hard claims, avoid sensitive topics, respect subreddit rules) to reduce the chance of harmful outputs.

• Logged all generations with metadata (input context, chosen variant, engagement outcome) for later tuning.

Outcome: replies that felt native to the conversation and subreddit, instead of generic ad-like posts.

3) Controlled dispatch & rate-limit-friendly automation

• Introduced an async job queue for scheduling and sending replies, decoupled from detection and generation.

• Implemented per-account and per-subreddit throttling, plus global rate-limit awareness for Reddit API.

• Added deduplication and cool-down logic to avoid replying multiple times in the same thread or over-engaging the same user.

• Created monitoring around dispatch success/failures and API errors for quick debugging.

Outcome: stable, predictable posting behavior that scaled across projects without tripping platform limits.

4) Shipped dashboards and workflows that made it usable

• Built UI flows for creating and managing “projects” (subreddits + keywords + product info) in one place.

• Added activity views: which posts were detected, which got replies, and what stage they’re in.

• Exposed sentiment and basic engagement signals (upvotes/replies trend) to help users gauge what’s working.

• Optimized frontend performance so users could tweak targeting and see new results quickly.

Outcome: users could actually understand and control what the automation was doing, instead of it feeling like a black box.

5) Added controls to balance automation with safety & trust

• Implemented a “review before posting” mode for new users or high-risk projects.

• Added per-project settings: auto vs manual approval, max replies per day, forbidden phrases, and tone presets.

• Logged all actions (detected post → generated reply → posted → engagement) to an auditable trail.

• Used these logs to iterate on scoring rules and prompt design based on real-world outcomes.

Outcome: safer automation and higher user trust, with a clear path to improve quality over time.

Tech stack

• Frontend: React + TypeScript, SPA dashboard

• Backend: Node.js (NestJS/Express), background workers

• Data: PostgreSQL (projects, activity, logs), caching for recent Reddit activity

• AI: LLM-based reply generator + sentiment/relevance scoring

• Infra/DevOps: Containerized services, CI/CD, metrics + logging for pipelines and dispatch

Results

• Increased share of replies hitting high-intent threads (better relevance due to scoring and filtering).

• Reduced spammy/duplicative behavior through throttling, deduplication, and guardrails.

• Improved user visibility into performance via dashboards, making it easier to iterate on targeting and messaging.

• Delivered a platform that lets small teams “be everywhere that matters” on Reddit without manual monitoring.