The Feedback Loop: Turning Community Questions into Engineering Assets

The Signal in the Noise

In the world of "building in public," there is a misconception that volume equals value. We are told to ship daily, tweet hourly, and flood the feed. But as an engineer, I look for efficiency. I look for leverage. And the highest leverage activity for a developer building a personal brand isn't broadcasting—it's listening.

When I started releasing micro-SaaS tools and AI agents, I treated comments and DMs as distractions. I wanted to code, not do customer support. This was a mistake.

Questions are not distractions. Questions are data points. They represent friction in your UX, gaps in your documentation, or—most importantly—demand for features you haven't built yet. If one person asks a specific technical question, ten others are thinking it. If three people ask it, you have a content pillar.

This week, I shifted my focus from "What do I want to say?" to "What are they trying to ask?" I analyzed the last 90 days of interactions across my platforms to build a definitive FAQ System. Here is the breakdown of that process, the top questions I received, and how I'm turning this data into a permanent asset.

Phase 1: The Audit (Audience Research)

Before recording a single video or writing a line of copy, I treated my notification tab like a dataset. I wasn't looking for praise; I was looking for confusion. Confusion is where the value lies.

I categorized incoming queries into three buckets:

• Technical Blockers: "How do you handle context limits?"
• Stack Decisions: "Why Python over Node for this agent?"
• Career/Business: "How do you price this?"

The goal is to identify patterns. When you find a pattern, you stop answering one-on-one in DMs. You build a system to answer it at scale.

Phase 2: The Build (Answering the Top 5)

Based on the audit, I selected the top recurring questions. Instead of a quick reply, I am documenting the answers here, which will serve as the script for a short-form video series and a permanent entry in my documentation.

Q1: "How do you handle LLM hallucinations in production agents?"

The Context: This is the #1 fear for developers moving from tutorials to production. They see their bots lying and panic.

The Answer: You don't eliminate hallucinations; you bound them. In my recent scraping agent, I implemented a two-step verification loop. The first LLM call extracts the data. The second LLM call acts as an "Auditor," comparing the extracted JSON against the raw HTML snippet strictly for verification. If the Auditor flags a mismatch, the agent retries with a higher temperature setting. We don't trust; we verify.

Q2: "What is your tech stack for Micro-SaaS speed?"

The Context: People get paralyzed choosing frameworks.

The Answer: I optimize for velocity, not novelty.
Frontend: Next.js (App Router) + Tailwind CSS + ShadcnUI.
Backend: Supabase (Auth + Database). It saves me 10 hours of boilerplate per project.
AI Logic: Python (FastAPI) running on a serverless container (Railway or Fly.io). I keep the AI logic separate from the Next.js frontend to manage timeouts and heavy dependencies better.

Q3: "How do you price automation services?"

The Context: Developers undervalue their time, charging hourly rates for systems that save clients thousands.

The Answer: Never charge hourly for automation. Charge for value delivered or time saved. If my script saves a team 20 hours a month, and their hourly rate is $50, that script is worth $1,000/month in pure savings. I usually charge a setup fee (2x the estimated monthly value) plus a recurring maintenance retainer (20% of the value). This shifts the conversation from "how long did it take you?" to "how much am I saving?"

Q4: "How do you manage API costs with GPT-4?"

The Answer: Caching is non-negotiable. I use a semantic cache (like Redis or specialized vector stores). Before sending a prompt to OpenAI, I hash the query and check if I've answered a semantically similar question recently. If yes, I return the cached response. This reduces API bills by 30-40% in high-traffic chat apps. Furthermore, I route simpler logic tasks to GPT-3.5-Turbo or Haiku, reserving GPT-4 only for complex reasoning.

Q5: "Where do you find ideas for tools?"

The Answer: I look for boring, repetitive data entry tasks in non-tech industries. Real estate agents copying data from Zillow to Excel. Logistics coordinators typing emails manually. The best Micro-SaaS ideas aren't revolutionary; they are just scripts that automate boredom.

Phase 3: The Asset (The FAQ Doc)

Answering these questions in a blog post is step one. But a blog post gets buried. To make this an engineering asset, I am converting these answers into a Living FAQ Document.

I have set up a public notion page (linked in my bio) titled "The Knowledge Base." Every time I answer a new question in a DM or comment section that receives more than three likes, it gets added to the database.

Why this matters for SEO and Authority:

1. Keyword Targeting: People search for questions. By hosting the answers, I capture that intent.
2. Trust: A developer with a documented history of solving problems is more trustworthy than one who just posts "GM" every morning.
3. Time Savings: The next time someone asks about API costs, I don't type a paragraph. I send a link. This is the API-ification of my own knowledge.

Phase 4: The Feedback Loop

This process has revealed a gap in my current toolset. A significant number of questions were about "locally hosted LLMs." I haven't written much about that.

Because of this "Ask more Qs" exercise, I am pivoting my next sprint. I will be building a local RAG (Retrieval-Augmented Generation) system using Ollama and documenting the process. I wouldn't have prioritized this if I hadn't analyzed the community's confusion.

The Protocol

If you are building in public, stop broadcasting and start processing inputs.

1. Scrape your comments: Look for the questions behind the praise.
2. Identify the Top 3: What is everyone confused about?
3. Build the Artifact: Record the video, write the doc, commit the code snippet.
4. Update the Repo: Make the answer accessible permanently.

I’m updating my FAQ doc live. If you have a question about the systems I build, ask it. If it’s good, it becomes part of the documentation.