This article details how to set up an email triage system using Home Assistant and a local Large Language Model (LLM) to summarize and categorize incoming emails, reducing inbox clutter and improving email management. It covers the setup of a REST command to interface with Ollama, the automation process, and the benefits of using a local LLM for privacy.
**Experiment Goal:** Determine if LLMs can autonomously perform root cause analysis (RCA) on live application
Five LLMs were given access to OpenTelemetry data from a demo application,:
* They were prompted with a naive instruction: "Identify the issue, root cause, and suggest solutions."
* Four distinct anomalies were used, each with a known root cause established through manual investigation.
* Performance was measured by: accuracy, guidance required, token usage, and investigation time.
* Models: Claude Sonnet 4, OpenAI GPT-o3, OpenAI GPT-4.1, Gemini 2.5 Pro
* **Autonomous RCA is not yet reliable.** The LLMs generally fell short of replacing SREs. Even GPT-5 (not explicitly tested, but implied as a benchmark) wouldn't outperform the others.
* **LLMs are useful as assistants.** They can help summarize findings, draft updates, and suggest next steps.
* **A fast, searchable observability stack (like ClickStack) is crucial.** LLMs need access to good data to be effective.
* **Models varied in performance:**
* Claude Sonnet 4 and OpenAI o3 were the most successful, often identifying the root cause with minimal guidance.
* GPT-4.1 and Gemini 2.5 Pro required more prompting and struggled to query data independently.
* **Models can get stuck in reasoning loops.** They may focus on one aspect of the problem and miss other important clues.
* **Token usage and cost varied significantly.**
**Specific Anomaly Results (briefly):**
* **Anomaly 1 (Payment Failure):** Claude Sonnet 4 and OpenAI o3 solved it on the first prompt. GPT-4.1 and Gemini 2.5 Pro needed guidance.
* **Anomaly 2 (Recommendation Cache Leak):** Claude Sonnet 4 identified the service restart issue but missed the cache problem initially. OpenAI o3 identified the memory leak. GPT-4.1 and Gemini 2.5 Pro struggled.
Five LLMs were given access to OpenTelemetry data from a demo application,:
* They were prompted with a naive instruction: "Identify the issue, root cause, and suggest solutions."
* Four distinct anomalies were used, each with a known root cause established through manual investigation.
* Performance was measured by: accuracy, guidance required, token usage, and investigation time.
* Models: Claude Sonnet 4, OpenAI GPT-o3, OpenAI GPT-4.1, Gemini 2.5 Pro
* **Autonomous RCA is not yet reliable.** The LLMs generally fell short of replacing SREs. Even GPT-5 (not explicitly tested, but implied as a benchmark) wouldn't outperform the others.
* **LLMs are useful as assistants.** They can help summarize findings, draft updates, and suggest next steps.
* **A fast, searchable observability stack (like ClickStack) is crucial.** LLMs need access to good data to be effective.
* **Models varied in performance:**
* Claude Sonnet 4 and OpenAI o3 were the most successful, often identifying the root cause with minimal guidance.
* GPT-4.1 and Gemini 2.5 Pro required more prompting and struggled to query data independently.
* **Models can get stuck in reasoning loops.** They may focus on one aspect of the problem and miss other important clues.
* **Token usage and cost varied significantly.**
**Specific Anomaly Results (briefly):**
* **Anomaly 1 (Payment Failure):** Claude Sonnet 4 and OpenAI o3 solved it on the first prompt. GPT-4.1 and Gemini 2.5 Pro needed guidance.
* **Anomaly 2 (Recommendation Cache Leak):** Claude Sonnet 4 identified the service restart issue but missed the cache problem initially. OpenAI o3 identified the memory leak. GPT-4.1 and Gemini 2.5 Pro struggled.
This blog post details a personal code review tool built around `llm` and `git diff`. It describes installation, how it works, how the author uses it, and its advantages over GitHub's Copilot review tool.
Augment Code joins the CLI coding agent race, positioning itself as and alternative to Claude Code with a focus on automation.
The article discusses how agentic LLMs can help users overcome the learning curve of the command line interface (CLI) by automating tasks and providing guidance. It explores tools like ShellGPT and Auto-GPT that leverage LLMs to interpret natural language instructions and execute corresponding CLI commands. The author argues that this approach can make the CLI more accessible and powerful, even for those unfamiliar with its intricacies.
