monotykamary

#!/usr/bin/env bash
# Requires direnv: https://direnv.net/

# Check if this is a devcontainer project
if [[ ! -f .devcontainer/devcontainer.json ]] && [[ ! -f devcontainer.json ]]; then
  return # Not a devcontainer project, do nothing
fi

if ! command -v devpod >/dev/null 2>&1; then
  return # devpod not available, do nothing

monotykamary

defmodule EventStore do
  @moduledoc """
  - Resilient disk_log handling with automatic repair
  - Batch writing for better performance
  - Automatic log file maintenance
  - Monitoring and metrics
  - Quick recovery after crashes
  """

  use GenServer

monotykamary

Formal Definition: Hybrid Semantic-Dimensional Similarity

1. Preliminaries: The Embedding Space

Let $\mathcal{T}$ be the set of all possible text inputs. We define a pre-trained embedding function $E$ that maps text to a $d$-dimensional vector space $\mathbb{R}^d$.

$$ E: \mathcal{T} \to \mathbb{R}^d $$

For any text input $x \in \mathcal{T}$, its vector representation is $\mathbf{v}_x = E(x)$. We assume the standard measure of semantic distance in this space is Cosine Similarity.

monotykamary

Playwright MCP Context Optimization Guide

This document describes how to reduce context overflow when using Playwright MCP with LLMs. A single tool call can produce 400k+ tokens due to ads, trackers, cookie banners, and verbose page snapshots. These optimizations can reduce that by 80-90%.

Problem Statement

Playwright MCP returns full page snapshots (accessibility tree in YAML format) after every action. This causes:

• Token overflow: 400k+ tokens per tool call on complex pages
• Wasted context: Ads, trackers, cookie banners inflate content
• Unnecessary data: Most actions don't need the resulting snapshot

monotykamary

Letta Code Memory Architecture Deep Dive

A comprehensive technical breakdown of how memory works in Letta Code

Table of Contents

1. Overview
2. Philosophy: Session-Based vs Agent-Based
3. Memory Block Architecture
4. System Architecture Diagram

monotykamary

# frozen_string_literal: true

# Reward Aggregator
# Partners return inconsistent key types: some JSON APIs return strings,
# internal services return symbols. Result must unify them.

GRAB_PARTNER = {
  'reward_a' => { 'points' => 5000, 'key' => 'gr-a', 'tier' => 'silver' },
  'reward_b' => { 'points' => 7500, 'key' => 'gr-b' },

monotykamary

Given the root of a binary tree, return the level order traversal of its nodes' values (i.e., from left to right, level by level). [1, 2]

Example 1:

• Input: root = [3,9,20,null,null,15,7]
• Output: [[3],[9,20],[15,7]] [1, 3, 4, 5, 6]

Example 2:

• Input: root = [1]

monotykamary

Thread-Safe LRU Cache with TTL

Implement an in-memory LRUCache class in Ruby. It must support O(1) get and put operations. You need to handle production realities:

Requirements:

1. get(key) → Returns value or nil if key doesn't exist or is expired.
2. put(key, value, ttl_seconds) → Inserts/updates. Evicts least recently used if capacity exceeded. TTL is per-key expiration.
3. Thread-safe: Multiple threads can safely call get/put concurrently.
4. Expiration happens lazily (on access) or proactively (bonus points).

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Concurrent Data Pipeline with Backpressure

Implement a high-throughput data processor in Go that simulates the FireGroup analytics workload:

// ProcessData ingests items from a source channel, transforms them via a CPU-intensive 
// operation (simulate with time.Sleep), and sends to a sink. Requirements:
// 1. Process up to N items concurrently (worker pool), but limit total concurrent processing 
//    to prevent OOM under load (backpressure)
// 2. Implement graceful shutdown on context cancellation: finish in-flight items, 

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echo "=== WITHOUT tools ==="
curl -s --max-time 30 "https://pass.wafer.ai/v1/chat/completions" \
  -H "Authorization: Bearer $WAFER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "GLM-5.1",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Read the file /tmp/test.txt"}
    ],