markdown syntax
<details>
<summary> <code> click here to see logs 👇 </code> </summary>
```  
[INFO] [launch]: Default logging verbosity is set to INFO
...
markdown syntax
<details>
<summary> <code> click here to see logs 👇 </code> </summary>
```  
[INFO] [launch]: Default logging verbosity is set to INFO
...
| ROLE | |
| You are an expert performance review coach specializing in the STAR framework. Your primary skill is transforming raw notes and statements into compelling, concise, and impactful evidence claims suitable for a formal performance review. | |
| GOAL | |
| Your goal is to help me polish a list of my achievements into well-structured evidence claims using the STAR framework. You will guide me through the process by asking clarifying questions to fill in any missing details and will help consolidate similar points to create a strong, clear narrative for my reviewer. | |
| CONTEXT: THE STAR FRAMEWORK | 
| Distill the provided audio file into a detailed technical document for an expert audience. | |
| Meticulously extract and synthesize all significant technical concepts, arguments, evidence, and methodological details from the entire transcript. Preserve the core content, including all nuances, counterarguments, and conclusions, with complete fidelity. | |
| Ensure absolute technical accuracy. Maintain the original's expert-level detail and strictly use its specific terminology and jargon without simplification. | |
| For traceability, substantiate all key claims, evidence, and critical conclusions with brief, targeted quotes or precise references to the source material. | |
| Organize the resulting text logically, reflecting the thematic or argumentative structure of the original discussion. | 
| You are an expert software architect tasked with creating detailed technical specifications for software development projects. | |
| Your specifications will be used as direct input for planning & code generation AI systems, so they must be precise, structured, and comprehensive. | |
| First, carefully review the project request: | |
| <project_request> | |
| ROLE | |
| You are a system architect and AI engineer. Your task is to design a system for a custom, controllable, deep research process, similar in spirit to Grok's "deeper search." The final output should be a conceptual design and pseudocode for a CLI tool that orchestrates this process. | 
DistSys Interview Challenge
An infinitely fast, parallel DBMS:
Why will this horizontal DBMS not scale beyond N TPS? Solve for N.
| /** | |
| * This script automates the process of scrolling down a page, | |
| * scraping tweet text, and logging the unique results. | |
| * It's designed to be pasted directly into the browser console. | |
| */ | |
| (async () => { | |
| // --- 1. SETUP --- | |
| // A Set is used to automatically store only unique tweet texts. | |
| const scrapedText = new Set(); | |
| const maxScrolls = 100; // Safety limit to prevent an infinite loop. | 
As an expert in self-correcting code generation using large language models, your task is to analyze the following codebase. Your goal is to help me understand its iterative refinement process.
Please follow these steps:
IDENTIFY THE REFINEMENT MECHANISM Explore the codebase and pinpoint the exact functions, classes, or code blocks responsible for self-correction, iterative refinement, or any refinement loop. I need to see where this is explicitly implemented. Please highlight the specific code snippets, including file names and line numbers if possible.
EXPLAIN THE LOGIC Provide a semantic explanation of the code you identified. Infer what the developers are trying to do. Explain step-by-step how the code iterates to refine its self-correcting code generation results or enhance the LLM's output. How does the loop work? What triggers a new iteration? What is the goal of each cycle?
As an expert in web-augmented large language models, your task is to analyze the following codebase. Your goal is to help me understand its iterative search and refinement process.
Please follow these steps:
IDENTIFY THE REFINEMENT MECHANISM Explore the codebase and pinpoint the exact functions, classes, or code blocks responsible for self-correction, iterative search, or any refinement loop. I need to see where this is explicitly implemented. Please highlight the specific code snippets, including file names and line numbers if possible.
EXPLAIN THE LOGIC Provide a semantic explanation of the code you identified. Infer what the developers are trying to do. Explain step-by-step how the code iterates to refine its web search results or enhance the LLM's output. How does the loop work? What triggers a new iteration? What is the goal of each cycle?