The most successful internal and open-source tools aren’t born from market research; they’re forged in the fire of solving a real, immediate, and personal pain point.

Stop Building for Ghosts: Why Your Best Tools Solve Your Own Problems First

I still remember “Project Chimera.” It was maybe 2018, and management had decided we needed a unified, single-pane-of-glass deployment dashboard. It was going to slice, dice, and visualize every metric from every environment. We spent six months on it. We had planning meetings, UX design sprints, stakeholder check-ins… the works. When we finally launched it, you know who used it? Almost nobody. Why? Because while we were building the perfect tool for a hypothetical manager, the actual engineers in the trenches were still just using a handful of bookmarked Grafana links and a messy-but-functional bash script to get their jobs done. That script, which took me about 90 minutes to write to solve my own deployment headaches, ended up being passed around and used by the entire team for two years. Project Chimera was shut down in less than one.

The Root of the Problem: You Can’t Empathize with a Ghost

When you build a tool for a vague, abstract user—”the business,” “other developers,” “the market”—you are building for a ghost. You’re guessing at their problems, inventing their workflows, and prioritizing features based on assumptions. This leads directly to two project-killers:

• Feature Creep: Without a clear, personal problem to solve, every “what if” scenario seems equally important. You end up bloating the tool with features for edge cases that a real user would never encounter.
• No Feedback Loop: Your primary user is you. When you aren’t your own user, you create a massive delay between building a feature and getting feedback on its usefulness. That delay is where bad ideas fester and good projects die.

The developer in that Reddit thread didn’t set out to build the world’s next great commenting library. They had a problem: they wanted a way to leave contextual comments on a live web page for their own feedback process. By solving that specific, tangible problem for themselves, they automatically built something that had a real-world use case. That’s the secret.

Fixing Your Mindset, Not Just Your Code

This isn’t about a specific bug; it’s about your entire approach to building internal tools or new projects. Here’s how we drill this into our engineers at TechResolve.

Approach 1: The “What Hurts Right Now?” Method

This is the quick and dirty, “in the trenches” fix. Before you write a single line of a new “project,” write a script. Solve the immediate pain. Does it take you 15 manual steps to check the health of an application after a deployment to the `staging-west-2` cluster? Automate it. Now. Don’t worry if it’s perfect. Don’t worry if it’s in Python, Bash, or Powershell. Just solve the problem.

That 90-minute script I mentioned? It looked something like this. Ugly, but it saved us hours.

#!/bin/bash
# Simple deploy checker - DO NOT USE IN PROD AS-IS!
DEPLOYMENT_NAME="my-app-v2"
NAMESPACE="staging"
MAX_RETRIES=10
RETRY_COUNT=0

echo "Checking status for deployment: $DEPLOYMENT_NAME"

while [ $RETRY_COUNT -lt $MAX_RETRIES ]; do
  READY_REPLICAS=$(kubectl get deployment -n $NAMESPACE $DEPLOYMENT_NAME -o jsonpath='{.status.readyReplicas}')
  DESIRED_REPLICAS=$(kubectl get deployment -n $NAMESPACE $DEPLOYMENT_NAME -o jsonpath='{.spec.replicas}')

  if [[ ! -z "$READY_REPLICAS" && "$READY_REPLICAS" -ge "$DESIRED_REPLICAS" ]]; then
    echo "Deployment successful! $READY_REPLICAS/$DESIRED_REPLICAS pods are ready."
    exit 0
  fi

  echo "Waiting for pods... ($READY_REPLICAS/$DESIRED_REPLICAS ready) - Attempt $((RETRY_COUNT+1))"
  RETRY_COUNT=$((RETRY_COUNT+1))
  sleep 15
done

echo "Error: Deployment failed to become ready after $MAX_RETRIES attempts."
exit 1

This tiny script was V1 of what eventually became a proper, robust internal CLI tool. But it started by solving a real, painful problem.

Approach 2: The “Eat Your Own Dog Food” Mandate

Once you have that initial, ugly solution, make it your primary tool. This is non-negotiable. If you build a tool to provision infrastructure, you are forbidden from using the AWS console. You must use your tool. If you build a log aggregator, you use that, not Datadog or Splunk. This forces you to feel the pain of your own creation’s shortcomings. It’s the most powerful and immediate feedback loop in existence. You’ll fix bugs and add quality-of-life features with a fire you’ve never felt before when your own workflow is blocked.

Approach 3: The “Set It Free” Litmus Test

This is the final step, and it’s what the Reddit developer did so brilliantly. After your tool solves your problem, and after your immediate team starts using it because it solves their problem too, open source it. Put it on GitHub. Don’t make a big deal about it. Just put it out there.

This isn’t about getting famous. It’s the ultimate test. If other people, with no context from your company, find it and start using it, you’ve accidentally discovered a universal problem. If they don’t? Who cares. The tool has already paid for its development cost a hundred times over by solving a real, internal issue. You lose nothing.

Pro Tip from the Trenches: A tool that solves a hypothetical problem has a 99% chance of becoming expensive shelfware. A tool that solves your real, current problem has a 100% chance of delivering value, even if you’re the only one who ever uses it.

So next time you get an idea for a tool, don’t write a project plan. Don’t design a logo. Ask yourself one question: “What hurts right now?” Then go fix that.

Darian Vance

Lead Cloud Architect & DevOps Strategist

With over 12 years in system architecture and automation, Darian specializes in simplifying complex cloud infrastructures. An advocate for open-source solutions, he founded TechResolve to provide engineers with actionable, battle-tested troubleshooting guides and robust software alternatives.

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