Adejare Akolawole
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m-ops

Something broke. Do you know why?

Role
Founder & Lead Engineer
Year
2025 – Present
Live site
m-ops.pro
Overview

m-ops is an intelligent stack monitoring tool that watches your entire infrastructure and tells you exactly what broke, why it broke, and how to fix it — before your users even notice. Most developers find out their app is down when a user complains. m-ops changes that.

The Problem

Existing monitoring tools (Datadog, Sentry, PagerDuty) are powerful but overwhelming — they flood you with alerts and leave the diagnosis to you. Smaller teams don't have the bandwidth to parse dashboards at 3am. m-ops was built to close the gap between 'something is wrong' and 'here's what to do about it.'

What I Built
Monitoring engine

Built a lightweight agent that monitors uptime, latency, error rates, and stack-level signals across services. The engine runs on a distributed scheduler that checks services at configurable intervals with intelligent backoff on failures.

AI-powered incident analysis

When something breaks, m-ops doesn't just alert — it analyzes. By correlating error patterns, deployment history, and service dependencies, it generates a plain-English root cause analysis and a fix recommendation automatically.

Incident timeline

Every incident is tracked with a full timeline — when it started, what changed, which services were affected, and when it resolved. Teams can annotate and share incident postmortems directly from the dashboard.

Alerting and integrations

Alerts route to Slack, email, or webhooks. On-call schedules let teams define escalation paths so the right person gets paged at the right time — not everyone at once.

Tech Stack
Frontend
Next.jsTypeScriptTailwind CSS
Backend
Node.jsPostgreSQLRedis
Infrastructure
VercelCronWebhooks
Key Decisions
AI diagnosis over raw alerts

The core product bet: tell people what to do, not just what happened. This required investing heavily in prompt engineering and context-gathering to make the AI analysis genuinely useful rather than generic.

Simple pricing, no seat bloat

Monitoring tools often charge per seat, which penalises growing teams. m-ops prices by monitored services, so teams can grow without dreading the invoice.

What I Learned
  • →The hardest part of DevOps tooling is building trust — developers won't rely on a tool that's ever wrong.
  • →AI-generated explanations need to be humble. Saying 'likely caused by' is more useful than confidently wrong.
  • →Onboarding must be instant — if the first monitor isn't set up in under 2 minutes, you've already lost the user.
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