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# APItoolkit: Using Merkle Trees and LLMs to Detect the UnDetectable in Software Monitoring

Are missing fields during your deployments silently causing massive data disruptions? Discover how combining Merkle trees and LLMs instantly exposes undetectable software anomalies hidden inside your server logs.

- **Speakers:** [Anthony Alaribe](https://www.wearedevelopers.com/@anthony-alaribe)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 4:58
- **URL:** https://www.wearedevelopers.com/videos/1639-apitoolkit-using-merkle-trees-and-llms-to-detect-the-undetectable-in-software-monitoring

## Summary

Fast-moving engineering teams often struggle with deployment monitoring, where seemingly minor issues—such as missing fields during a migration—can lead to massive financial losses and data disruption. When incidents inevitably occur, developers are typically overwhelmed by a flood of logs, metrics, and fragmented communications across Slack channels. Monoscope (formerly APItoolkit) addresses this by functioning as an intelligent post-deployment observability platform.

The system leverages merkle trees to condense millions of server logs, requests, and errors into distinct, manageable signatures. This enables real-time anomaly detection by instantly identifying when a new or unexpected payload signature appears within the infrastructure. Building on this foundation, the platform integrates LLMs to assess whether these newly detected variations are critical breaking changes or benign updates.

By analyzing requests, traces, and logs within a unified context, engineers can chat directly with their observability data. This conversational AI approach transforms time-sensitive system diagnostics, actively streamlining incident resolution by replacing chaotic messaging channels with an efficient, context-aware troubleshooting interface.

**Keywords:** monoscope, apitoolkit, software monitoring, post-deployment observability, merkle trees, LLMs, incident management, log analysis, anomaly detection, real-time monitoring, deployment troubleshooting, breaking changes, system diagnostics, payload signatures, conversational ai

## Chapters

1. **Introducing Monoscope for intelligent post-deployment system monitoring** (00:00) — Monoscope was created to actively analyze logs, metrics, and systems to automatically assist with post-deployment issue resolution.
1. **Why minor migration errors inspire automated anomaly detection systems** (00:39) — A costly production failure involving missing application fields demonstrates why systems must actively identify unexpected behavioral shifts.
1. **Managing information overload during complex production system incidents** (02:12) — Fast-moving teams often struggle with excess diagnostic data and scattered channel communication when attempting to resolve outages.
1. **Using Merkle trees to compress logs into distinct signatures** (03:07) — Utilizing Merkle trees condenses millions of incoming payloads into unique signatures to immediately flag previously unseen anomalies.
1. **Applying large language models to evaluate real-time system changes** (03:58) — Combining language models with observability data enables context-aware impact analysis and conversational interactions during incident responses.

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