Manuel Solorzano eevmanu

markdown syntax

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<summary> <code> click here to see logs 👇 </code> </summary>

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[INFO] [launch]: Default logging verbosity is set to INFO
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DistSys Interview Challenge

An infinitely fast, parallel DBMS:

executes 2 queries in series,
per SQL transaction processed,
with 20% transactions updating the same row, 2ms RTT, and no shortcuts on ACID.

Why will this horizontal DBMS not scale beyond N TPS? Solve for N.

no retries (the baseline)
- The caller makes one attempt and propagates any error.
simple retry (fixed number of attempts)
- Retry up to N times as fast as possible.
- The most basic form of control flow is added: a loop. It introduces the concept of "more than one try" without any timing logic.
retry with fixed delay
- Same as above but waits a constant delay d between attempts.
- Adds a single, simple parameter—a static wait time. This is the first introduction of temporal decoupling but is otherwise trivial.
linear / incremental backoff
Delay grows by a fixed increment Δ: t = base + i·Δ.

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?

	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.

	/**
	* 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.

	Translate the provided Spanish text into fluent, idiomatic American English—prioritizing clarity, natural phrasing, and cultural appropriateness for native US speakers. Unless specified otherwise, default to standard American English conventions.

	If the input includes sections already in English, focus first on improving, translating, and explaining the parts written in Spanish. Only rework or refine the existing English portions if doing so clearly enhances context, cohesion, or the overall output quality—particularly in cases where mixing English and Spanish in the source text meaningfully enriches the final English result. Apply this in a nuanced way: prioritize Spanish-to-English translation, but apply careful judgment when integrating or rewriting English and Spanish segments together based on semantic intent. This approach should remain narrowly scoped to this use case to avoid reducing the output's precision or quality.

	When explaining optimizations or improvements made, provide a precise account of