limcheekin

Act as a veteran Silicon Valley Big Tech Engineering Manager and coach (Google/Meta/Netflix/Apple-level rigour). Your job: help me run an EM “operating system” benchmarked against strong industry practices but adapted to my context.

Context:

• Role: New Engineering Manager
• Team: [number of direct reports]
• Product/Domain: [Your product or problem domain scope]
• Constraints: (fill in) direct reports = __ ; sprint/kanban = __ ; key stakeholders = __ ; major integrations = __

Deliverables (must be structured under People / Process / Product):

limcheekin

#!/usr/bin/env bash
set -euo pipefail

echo "Creating virtual sinks..."

# 1) Create virtual sinks
# Sink for Zoom's audio output
pactl load-module module-null-sink sink_name=Zoom-to-Bot sink_properties=device.description="Zoom-to-Bot"
# Sink for the Bot's audio output
pactl load-module module-null-sink sink_name=VoiceBot-to-Zoom sink_properties=device.description="VoiceBot-to-Zoom"

limcheekin

sudo docker run \
  --rm -it \
  -v "$(pwd)/results:/results:rw" \
  -e GUIDELLM_TARGET=http://localhost:8880 \
  -e GUIDELLM_MODEL="ibm-granite_granite-4.0-h-tiny" \
  -e GUIDELLM_RATE_TYPE=sweep \
  -e GUIDELLM_MAX_SECONDS=300 \
  -e GUIDELLM_DATA="prompt_tokens=256,output_tokens=128" \
  ghcr.io/vllm-project/guidellm:latest

limcheekin

services:
  clickhouse:
    image: clickhouse/clickhouse-server:25.8-alpine
    ports:
      - "8123:8123"
    environment:
      - 'CLICKHOUSE_USER=${CLICKHOUSE_USER:?Environment variable CLICKHOUSE_USER must be set.}'
      - 'CLICKHOUSE_PASSWORD=${CLICKHOUSE_PASSWORD:?Environment variable CLICKHOUSE_USER must be set.}'
  gateway:
    depends_on:

limcheekin

In Paris, the capital of France, the primary language spoken is

limcheekin

B64=$(base64 test.wav)

curl -v http://192.168.1.111:8880/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d @- <<JSON
{
  "model": "qwen2.5-omni-3b",
  "messages": [
    {
      "role": "user",

limcheekin

Carefully review and analyze the code in implementation1.txt, implementation2.txt, and implementation3.txt in detail, and think more deeply about the following items:

1. Which implementation is better for the given user story defined in <story_file> in terms of functionality, code quality, performance, modularity, test coverage, etc.? Explain your reasoning.
2. Which implementation fully complies with the user story defined in <story_file>? Identify the discrepancies in the implementation(s) that do not comply.

limcheekin

#!/usr/bin/env bash
# merge_audio_video.sh — Mux video and MP3/M4A into MP4 with AAC audio

set -euo pipefail

if [ $# -ne 3 ]; then
  echo "Usage: $0 <video.mp4> <audio.(mp3|m4a)> <output.mp4>" >&2
  exit 1
fi

limcheekin

name: snowflake-arctic-embed-l-v2.0
backend: llama-cpp
embeddings: true
parameters:
  model: snowflake-arctic-embed-l-v2.0.F16.gguf

limcheekin

name: bge-reranker-v2-m3
backend: cuda12-rerankers
parameters:
  model: BAAI/bge-reranker-v2-m3
  model_type: reranker
  pooling: rank
context_size: 2048