Skip to content

AI

How to fix your application logs with AI

More than 57% of developers' time is spent in war rooms solving application performance issues rather than building new features (Cisco/Splunk Developer Survey, 2024). That's not a skills problem. It's a logging problem.

The logs your application writes today were crafted for the developer sitting at the keyboard. Someone who knows the code structure, remembers which module emits which message, and understands the internal state machine. When an incident hits at 02:00 in the morning, the person staring at those logs isn't that developer. It's an on-call engineer who doesn't know your code, or an AI agent parsing them for patterns.

  • in AI, Parser
  • 9 min read

Testing Local LLMs in Practice: Code Generation, Quality vs. Speed

Over the past few months, the landscape of open-weight large language models has changed dramatically. New models are being released at a pace that makes systematic evaluation difficult, yet increasingly necessary.

Most LLM comparisons still rely on synthetic benchmarks. While useful, they often fail to answer a more practical question:

How do these models perform in a real, production-like task?

This article presents a structured evaluation of local LLMs based on a concrete, repeatable, and measurable workload: autonomous code generation. Specifically, we focus on how well an autonomous agent, backed by these models, generates production-ready log parsers, and how that performance scales in terms of quality vs. speed.