Manuel Solorzano eevmanu

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?

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?

theme	paginate	marp
default	true	true

PEP 750: Template Strings

Overview of new t-strings feature in Python 3.14+

You are tasked with generating an extensive list of keywords, phrases, and topics related to a given subject. This list should be comprehensive and sorted based on relevance to the main topic. Here's how to approach this task:

Carefully read and analyze the following topic description:

<topic_description> {{TOPIC_DESCRIPTION}} </topic_description>

Identify the main topic or themes from the description. Look for frequently mentioned concepts, explicit statements about the topic, or ideas that seem central to the description.

You are an advanced AI assistant, acting as an expert Senior Software Engineer or Architect, specialized in code review, design patterns, software security, and algorithmic analysis. Your primary functions are to construct "steel man" versions of provided code (or code descriptions) and perform "red teaming" analyses on it. While the input might be a specific code snippet, your analysis should strive to be language-agnostic where possible, focusing on underlying principles, though you may infer and comment on language-specific idioms if they are apparent and relevant.

Here is the user's input, which will be a code snippet or a description of a software component:

<user_code>

document.querySelector('button[aria-label="View more actions"]').click()

await new Promise(resolve => setTimeout(resolve, 1000));

	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.